tag:blogger.com,1999:blog-33831316002321911432024-03-23T03:14:53.897-07:00Farmland ecologyContains posts on farmland ecology research, with emphasis on farmland wildlife and practical conservation. Content features research and updates from my research, conference reports and relevant articles.Unknownnoreply@blogger.comBlogger72125tag:blogger.com,1999:blog-3383131600232191143.post-86931737276734733352023-11-09T12:40:00.000-08:002023-11-09T12:40:07.521-08:00Multi-species grasslands enhanced diversity of soil nematode community<p class="xmsonormal" style="text-align: left;"><span style="font-size: 12pt; text-align: justify;">New research from the Teagasc Environment
Research Centre, Johnstown Castle shows that higher plant diversity of
intensively managed multi-species swards enhances belowground soil biodiversity
and health. The study, which has been published in the international scientific
journal, ‘European Journal of Soil Biology’, showed that as grasslands
increased plant diversity up to six species of grasses, clovers and herbs,
soil-dwelling nematode communities also had increased diversity and improved
performance across a range of ecological soil health indices.</span></p><a name='more'></a> <o:p></o:p><p></p>
<p class="xmsonormal" style="text-align: justify;"><span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;">See the article at:</span><span style="text-align: left;"> </span><a href="https://www.sciencedirect.com/science/article/pii/S116455632300078X" style="text-align: left;"><span face="Arial, sans-serif" style="font-size: 12pt;">https://www.sciencedirect.com/science/article/pii/S116455632300078X</span></a> <span style="text-align: left;"> </span></p><p></p><p>
</p><p class="xmsonormal" style="text-align: justify;"><span face=""Arial",sans-serif" style="font-size: 12pt;">Soil nematodes are often used as indicators of
soil health and ecosystem functioning. Compared to monocultures of forage
plants, multi-species grasslands with six species had a higher abundance of
predatory nematodes, which can be beneficial for the biocontrol for plant
pests. Conversely, there was a lower abundance of herbivorous nematodes in
multi-species grasslands, the presence of which can negatively affect plant
performance. </span><o:p></o:p></p>
<p class="xmsonormal" style="text-align: justify;"><span face=""Arial",sans-serif" style="font-size: 12pt;"> </span><o:p></o:p><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg1MwhEJEs_X8pS9McJGFWc8HUI82GlvDefcWjhx43PJMjMNO5LyIwkhN-9HI4LW5jLVNRXJTdZMdJwk-SyWea43eNgM2g4cGbNHlfqZiZBlnKiZWjvRXRowBy4gv_hUZ3S4ELu5cUeiuTvsdzJnK2tN7f2hq5SlsUbnW9FaLkSSE6Fq3TfH3t1G7DW7YM/s1280/nematode_predator.jpg" imageanchor="1" style="font-size: 16px; margin-left: 1em; margin-right: 1em; text-align: center;"><img border="0" data-original-height="720" data-original-width="1280" height="225" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg1MwhEJEs_X8pS9McJGFWc8HUI82GlvDefcWjhx43PJMjMNO5LyIwkhN-9HI4LW5jLVNRXJTdZMdJwk-SyWea43eNgM2g4cGbNHlfqZiZBlnKiZWjvRXRowBy4gv_hUZ3S4ELu5cUeiuTvsdzJnK2tN7f2hq5SlsUbnW9FaLkSSE6Fq3TfH3t1G7DW7YM/w400-h225/nematode_predator.jpg" width="400" /></a></p>
<p class="xmsonormal" style="text-align: justify;"><span face=""Arial",sans-serif" style="font-size: 12pt;">Nematodes are tiny round worms that are widely
distributed and highly abundant in soils. They play important roles in the
health of soil systems, especially for nutrient and carbon cycling. They are
sensitive to changes in the environment and can give an indication of multiple
aspects of the soil food web and ecosystem health. Several nematode-based soil
quality indices are available that relate to soil function, making them
especially attractive as a soil biodiversity indicator.</span><o:p></o:p></p>
<p class="xmsonormal" style="text-align: justify;"><span face=""Arial",sans-serif" style="font-size: 12pt;"> </span><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhJHpoFGKxhtBwV1vhQlMu9nWbFtpjEpyEQaYEY00zIJ0mLV7TqOmYiWlmqYUrI6eQqjZpt2qyTOHexjOacZ3fQqB4WZGFCqysqHwYkQ39h5RYyV_BhsK3HiC0rqcTj44fZ94aw9DDdflpwrGBN6zGpgI2cShQDunugtT2RZaMKFvw2fqbl78HGMX5baBY/s1280/Nematode_fieldplots.jpg" imageanchor="1" style="font-size: 16px; margin-left: 1em; margin-right: 1em; text-align: center;"><img border="0" data-original-height="720" data-original-width="1280" height="360" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhJHpoFGKxhtBwV1vhQlMu9nWbFtpjEpyEQaYEY00zIJ0mLV7TqOmYiWlmqYUrI6eQqjZpt2qyTOHexjOacZ3fQqB4WZGFCqysqHwYkQ39h5RYyV_BhsK3HiC0rqcTj44fZ94aw9DDdflpwrGBN6zGpgI2cShQDunugtT2RZaMKFvw2fqbl78HGMX5baBY/w640-h360/Nematode_fieldplots.jpg" width="640" /></a></p><div class="separator" style="clear: both; text-align: center;"><span face=""Arial",sans-serif" style="font-size: 12pt;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEisnZ3mOiO_qMn2CWUWqIVejhyphenhyphenXjcwsL7k76sMR1TLdjLC8PSUnVSV0w0KibeB1D5HzRp9-fLyek9AJkGKKWjwV9hSOD64H5EpL025xvkeV8sboE9XCvrWmHiwbFSvzrSCggG7bibfqxezgraE6ovIHtgJeAkSnBQ_c_dRP6gGCNuvsmNsCUYsgt7FUNWQ/s1280/Nematode_barchart.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="720" data-original-width="1280" height="360" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEisnZ3mOiO_qMn2CWUWqIVejhyphenhyphenXjcwsL7k76sMR1TLdjLC8PSUnVSV0w0KibeB1D5HzRp9-fLyek9AJkGKKWjwV9hSOD64H5EpL025xvkeV8sboE9XCvrWmHiwbFSvzrSCggG7bibfqxezgraE6ovIHtgJeAkSnBQ_c_dRP6gGCNuvsmNsCUYsgt7FUNWQ/w640-h360/Nematode_barchart.jpg" width="640" /></a></span></div><span face=""Arial",sans-serif" style="font-size: 12pt;"><br /><div class="separator" style="clear: both; text-align: left;"><span face=""Arial",sans-serif" style="font-size: 12pt; text-align: justify;">Lead author on the study, </span><span face=""Arial",sans-serif" style="font-size: 12pt; text-align: justify;">Dr Israel Ikoyi, stated; “This study shows that even for
intensively managed grasslands, the use of multi-species forage mixtures that
include grasses, legumes, and herbs can have a positive effect on the soil
nematode community and nematode-based soil quality indices. This can
lead to improved nutrient cycling, decomposition, and plant growth, which can
have important implications for the overall health and function of the soil
ecosystem. This is an important finding linking aboveground plant diversity
with belowground soil biodiversity.”</span></div><div class="separator" style="clear: both; text-align: left;"><span face=""Arial",sans-serif" style="font-size: 12pt; text-align: justify;"><br /></span></div><div class="separator" style="clear: both; text-align: left;"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg5E_U4tR_LAslhwoxymMr49wfJDqNQXgIdLkXFfpVsP-gxrsdbqr6ulRbdyBgiS_YeRJxoH2PbRCjhACJBnPf7ZR_fVfOjT3_vhNqz0KzX1FobtaqMsBW2KRtjz3sUwkURqM8GixQ7AzqYBj76wzZZfWZ9eXysSaVB01BN5oq5jg-Oppo_O9jcuB_yM6U/s504/Nematode_MI.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="504" data-original-width="466" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg5E_U4tR_LAslhwoxymMr49wfJDqNQXgIdLkXFfpVsP-gxrsdbqr6ulRbdyBgiS_YeRJxoH2PbRCjhACJBnPf7ZR_fVfOjT3_vhNqz0KzX1FobtaqMsBW2KRtjz3sUwkURqM8GixQ7AzqYBj76wzZZfWZ9eXysSaVB01BN5oq5jg-Oppo_O9jcuB_yM6U/s320/Nematode_MI.jpg" width="296" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: medium;">Ternary diagram illustrating the nematode maturity index for the Grass (G), legume (L) and herb (H) functional groups, and various combinations of these three functional groups. Predictions are for two species (both from one functional group – the
vertices in each ternary diagram), for four species (with two species from each
of two functional groups – the sides of each ternary diagram) or for all six
species (three functional groups – all interior points in the ternary
diagrams).</span> </td></tr></tbody></table><span face=""Arial",sans-serif" style="font-size: 12pt; text-align: justify;"><br /></span></div></span>
<p class="xmsonormal" style="text-align: justify;"><span face=""Arial",sans-serif" style="font-size: 12pt;">Dr Fiona Brennan, Soil Microbiologist with
Teagasc said; “Plant-soil interactions are incredibly important for the
maintenance of below ground soil biodiversity, and play a key role in the
functioning and resilience of soil systems. Diversifying plant communities can
create more habitats in soil, which in turn supports more diverse soil
biological communities. These communities are essential for soil and plant
health, underpinning food production and playing a critical role in regulating
climate. As international efforts to safeguard our soils intensify, this paper
contributes to the scientific evidence available to underpin policy and
practical management advice.”</span></p>
<p class="xmsonormal" style="text-align: justify;"><span face=""Arial",sans-serif" style="font-size: 12pt;">Biodiversity and farmland ecology researcher
with Teagasc, Dr John Finn said; “We have already seen benefits of plant
diversity in multi-species swards for attributes such as forage production with
lower nitrogen, weed suppression, livestock performance and drought resilience.
This latest research reveals additional positive effects of multi-species
swards on belowground soil biodiversity.”</span></p>
<p class="xmsonormal"><span face=""Arial",sans-serif" style="font-size: 12pt;">See
the published scientific paper at:</span> <a href="https://www.sciencedirect.com/science/article/pii/S116455632300078X"><span face=""Arial",sans-serif" style="font-size: 12pt;">https://www.sciencedirect.com/science/article/pii/S116455632300078X</span></a><span face=""Arial",sans-serif" style="font-size: 12pt;"> </span><o:p></o:p></p>
<p class="xmsonormal"><span style="font-size: 12pt;">This
work was funded by the EU Horizon research and innovation programme under the
MASTER project (grant agreement No. 818368), and the Teagasc Walsh Scholarships
Programme (GG).</span></p><p class="xmsonormal"><o:p></o:p></p><span face="Arial, sans-serif" style="font-size: 16px; text-align: justify;">(This text is from a Teagasc Press Release in October 2023.</span><span face="Arial, sans-serif" style="font-size: 16px; text-align: justify;">)</span><p></p>Unknownnoreply@blogger.com1tag:blogger.com,1999:blog-3383131600232191143.post-71637315573182580162022-11-30T07:40:00.015-08:002022-11-30T08:41:25.893-08:00Plant diversity (and drought) affect legacy effects on soil fertility<p><span style="font-size: medium;">We investigated the effect of grassland diversity, drought and higher nitrogen level on legacy effects. Legacy effects (measured as yield of a follow-on crop, which reflect the influence of the preceding crop) were strongly positively affected by the proportion of legumes. Drought can impact legacy effects, but is modest relative to the effect of plant diversity. A</span><span style="font-size: medium;"><span>ggregated across both ley and follow-on crop phases</span><span>, the high-diversity, lower-nitrogen grassland community yielded more than the higher-nitrogen grass monoculture.</span><span> </span></span></p><p></p><p class="MsoNormal" style="-webkit-text-stroke-width: 0px; color: black; font-family: "Times New Roman"; font-size: medium; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;"></p><p></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="-webkit-text-stroke-width: 0px; font-family: "Times New Roman"; letter-spacing: normal; margin-left: auto; margin-right: auto; orphans: 2; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-transform: none; widows: 2; word-spacing: 0px;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhRQimJh0WQCkHM0GP0jZNOsXEHcxyp8NWsCpm08eM5xDyXfqyDXljHeF-vNeNdOs2YEoQYYolO25iTPzjTJE7hn_fimuvRcTqAJTciLbCwOTbz_VVpRD8V2Ky4EBBH1GJe3A5_OKZ4YfyZ8n_cKKsAiPFXiiSD9ZJ_d5Y9TAdTd0m0twuhBv5sKE4_/s1267/Droneshot.jpg" style="margin-left: auto; margin-right: auto; text-align: center;"><span style="font-size: medium;"><img border="0" data-original-height="744" data-original-width="1267" height="376" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhRQimJh0WQCkHM0GP0jZNOsXEHcxyp8NWsCpm08eM5xDyXfqyDXljHeF-vNeNdOs2YEoQYYolO25iTPzjTJE7hn_fimuvRcTqAJTciLbCwOTbz_VVpRD8V2Ky4EBBH1GJe3A5_OKZ4YfyZ8n_cKKsAiPFXiiSD9ZJ_d5Y9TAdTd0m0twuhBv5sKE4_/w640-h376/Droneshot.jpg" style="cursor: move;" width="640" /></span></a></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: medium;">Fig. 1. Overhead shot of the field site with the experimental design and plot management to track the effect of plant
diversity, drought and fertiliser level on the legacy effect within plots.</span></td></tr></tbody></table><p></p><span style="font-size: medium;"><b><span><a name='more'></a></span>Key messages</b></span><div><div><ul style="text-align: left;"><li><span style="font-size: medium;">The sown proportion of legume in the grassland most strongly enhanced follow-on crop yield.</span></li><li><span style="font-size: medium;">Drought during the grassland ley slightly reduced yield of the follow-on crop.</span></li><li><span style="font-size: medium;">Higher fertiliser rate in grassland ley did not enhance yield of the follow-on crop.</span></li><li><span style="font-size: medium;">Higher plant diversity in grassland increased rotation yield more than higher fertiliser use.</span></li></ul></div><div><b style="font-size: large;">Approach</b></div><div><p></p><p class="MsoNormal"><span style="font-size: medium;">In a recent field experiment (<a href="https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2664.13894" target="_blank">Grange et al., 2021</a>), we
varied the diversity of six-species grassland communities from monocultures to
6-species mixtures (systematically varying combinations of two species from
each of three functional groups of grasses, legumes and herbs). In the
grassland phase, all plots received 150 kg/ha/yr of nitrogen fertiliser (150N);
we also included a perennial ryegrass monoculture with additional nitrogen
fertiliser 300 kg/ha/yr of N (300N). An experimental drought was imposed across
all communities, and compared with the rainfed control. <o:p></o:p></span></p><p class="MsoNormal"><span style="font-size: medium;">We grew the grassland communities for two years as part of
Guylain Grange’s doctoral research (Fig. 1, left). Guylain then suggested that
we investigate the legacy effect of the preceding communities i.e. would the
yield of a subsequent crop be affected by the community that used to grow
there? To test this, we replaced the grassland communities with a monoculture
of Italian ryegrass (Fig. 1, right).<o:p></o:p></span></p><div class="separator" style="clear: both; text-align: center;"><span style="font-size: medium;"><br /></span></div><p></p><p class="MsoNormal"><span style="font-size: medium;">In this way, we investigated how factors that influence the
yield of a grassland ley (plant species composition and diversity, drought and
fertiliser level) also affect the follow-on crop in a rotation. The
specific aims of this study were to:<o:p></o:p></span></p><p class="MsoNormal"></p><ul style="text-align: left;"><li><span style="font-size: medium;">Investigate the effect of grassland diversity (from
monocultures to six-species combinations) on the performance of a follow-on
model crop of Italian ryegrass (<i>L.
multiflorum</i>).</span></li><li><span style="font-size: medium;">Quantify the effect of an extreme weather event (drought) on
legacy effects in a grassland-crop rotation.</span></li><li><span style="font-size: medium;">Compare the legacy effects of a range of grassland mixtures
at lower nitrogen fertiliser (150N) rate, with the legacy effect from a perennial
ryegrass monoculture with higher nitrogen (300N).</span></li></ul><div><span style="font-size: medium;"><b>Results</b></span></div><span style="font-size: medium;"><o:p></o:p></span><p></p><p class="MsoNormal"><span style="font-size: medium;"><o:p></o:p></span></p><p class="MsoNormal"><span style="font-size: medium;"><o:p></o:p></span></p><p class="MsoNormal"><o:p><span style="font-size: medium;"> </span></o:p></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiqeInac8fhO8m-DbIiCsow9MRzpm6eAaGsKFzRM7ZoJMVTzfR771WskW4XlY5GMJa_0mlvkwJaTMAh7PSaewJCiOY897rIVO86UuZkzDEZysLZLaOdiVVxz95-0aYhwzEskWjb8hLCDzoEvi6qz2wBc7qnOU6OFMyzsQUmv_tJbAuT_181kkcNPCkv/s1280/Legacy_rainfed.jpg" style="margin-left: auto; margin-right: auto; text-align: center;"><span style="font-size: medium;"><img border="0" data-original-height="720" data-original-width="1280" height="360" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiqeInac8fhO8m-DbIiCsow9MRzpm6eAaGsKFzRM7ZoJMVTzfR771WskW4XlY5GMJa_0mlvkwJaTMAh7PSaewJCiOY897rIVO86UuZkzDEZysLZLaOdiVVxz95-0aYhwzEskWjb8hLCDzoEvi6qz2wBc7qnOU6OFMyzsQUmv_tJbAuT_181kkcNPCkv/w640-h360/Legacy_rainfed.jpg" width="640" /></span></a></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: medium;">Fig. 2. Effect of functional group composition (relative proportion of grasses, herbs and legumes) on yield *in the rainfed control* of the Italian ryegrass follow-on crop. The legacy effects can be ascribed to the previous grassland community. Performance of the 300N <i>L.</i> <i>perenne</i> monoculture is indicated in red in the colour bar. </span></td></tr></tbody></table><p class="MsoNormal"><span style="font-size: medium;">The effect of plant communities on yield (t/ha) of the follow-on
crop was strongly related to the proportion of clover in the preceding
grassland community.</span></p><p class="MsoNormal"><span style="font-size: medium;">The ternary diagram in Fig. indicates how the legacy effect
varied across the design space with different proportions of the grass, herb
and legume functional groups. The greener the colour, the higher the yield of
the Italian ryegrass = the greater the legacy effect. <o:p></o:p></span></p><p class="MsoNormal"><span style="font-size: medium;"><u>Drought effect: </u>The experimentally imposed drought in the preceding
grassland plot had a modest effect on the legacy effect e.g. the drought effect
was smaller than the effect of changing % clover.<o:p></o:p></span></p><p class="MsoNormal"><span style="font-size: medium;"><u>Effect of high nitrogen:</u> Legacy effects were *lowest* on the perennial ryegrass with higher nitrogen. (See the inserted values in red in the graph legends of the ternary plots.)</span></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgk2EDUGILmpkekQHPMDZw-HsUPFT0p0seJSeCsVTZ64ieLdQ6gWe3MrwmmtJIlgACURfYLOMJP2mNiBfct-59K06n7N77l586YYY8vqzpxiog_EHis_KWHDSO8j1knZWiCOKnz3weCVVSCGQg7icGq9WgTXiXNlhI3-Wv_Wy9utxApGu61WcQr7rnn/s1280/Legacy_drought.jpg" style="margin-left: auto; margin-right: auto;"><span style="font-size: medium;"><img border="0" data-original-height="720" data-original-width="1280" height="360" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgk2EDUGILmpkekQHPMDZw-HsUPFT0p0seJSeCsVTZ64ieLdQ6gWe3MrwmmtJIlgACURfYLOMJP2mNiBfct-59K06n7N77l586YYY8vqzpxiog_EHis_KWHDSO8j1knZWiCOKnz3weCVVSCGQg7icGq9WgTXiXNlhI3-Wv_Wy9utxApGu61WcQr7rnn/w640-h360/Legacy_drought.jpg" width="640" /></span></a></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: medium;">Fig. 3. </span><span style="font-size: medium;">Effect of functional group composition (relative proportion of grasses, herbs and legumes) on yield *in the experimental drought* of the Italian ryegrass follow-on crop. The legacy effects can be ascribed to the previous grassland community. Performance of the 300N </span><i style="font-size: large;">L.</i><span style="font-size: large;"> </span><i style="font-size: large;">perenne</i><span style="font-size: large;"> </span><span style="font-size: medium;">monoculture is indicated in red in the colour bar.</span><span style="font-size: large;"> </span></td></tr></tbody></table><span style="font-size: medium;"><br /></span><p class="MsoNormal"><span style="font-size: large;"><b>High-diversity, lower-nitrogen grassland out-yielded
low-diversity, high-nitrogen grassland across both ley and follow-on crop
phases</b></span></p><p class="MsoNormal"><span style="font-size: medium;">Our approach allowed us to compare three different management
scenarios that vary in terms of their: reliance on legumes (only) with 150N; use of higher plant
diversity with 150N, and; reliance on a grass monoculture with 300N. We compare these three scenarios across both the grassland and legacy stages. </span><span style="font-size: medium;">We use the performance of </span><span style="font-size: medium;"><span>150N </span><i>L. perenne </i><span>as a reference point for comparison of the three scenarios.</span><i> </i></span></p><p class="MsoNormal"><span style="font-size: medium;"><span>Relative to the performance
of 150N </span><i>L. perenne</i><span>, the legume
monoculture in grassland had the greatest legacy effect; however, the
legume-only yield benefit was considerably lower in the grassland phase (Fig.
5a) than that from the equi-proportional 6-species mixture. Directly comparing
the high-diversity, lower-nitrogen plant community (blue bar) with the
low-diversity, high-nitrogen community across the combined grassland-crop
rotation (dashed bar), plant diversity delivered higher performance (Fig. 5b)
than additional fertiliser (300 N </span><i>L.
perenne</i><span>) (Fig. 5c).</span></span></p><p class="MsoNormal"><span style="font-size: medium;">Overall, the high-diversity, low-input grassland community yielded
more than low-diversity, high-input grassland (aggregated across both ley and
follow-on crop phases).</span></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjmdA3t1klp_twOYFrmgs8YLnITB32tvJ8FYc4OjfwuCuUhs8sTmxr1kFW6bYpsxwKEuEXAqWWwMJXPL3SpmiXDRadqRyoyabQTBHUWjggCg3a44VrSxFdTBP4xkkFFsoAYX0W6820XLojwD3RBAKafnsRqILwO6hhgdQzVBBxvSRml8QU9ddJwT-Br/s1280/LowHighinput.jpg" style="margin-left: auto; margin-right: auto;"><span style="font-size: medium;"><img border="0" data-original-height="720" data-original-width="1280" height="360" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjmdA3t1klp_twOYFrmgs8YLnITB32tvJ8FYc4OjfwuCuUhs8sTmxr1kFW6bYpsxwKEuEXAqWWwMJXPL3SpmiXDRadqRyoyabQTBHUWjggCg3a44VrSxFdTBP4xkkFFsoAYX0W6820XLojwD3RBAKafnsRqILwO6hhgdQzVBBxvSRml8QU9ddJwT-Br/w640-h360/LowHighinput.jpg" width="640" /></span></a></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: medium;">Fig. 4. Comparison of management scenarios on mean (s.e.) dry matter yield on both the grassland and follow-on crop. Relative to 150N <i>L. perenne</i> monoculture (low-diversity, low-input), we compare the effect on yield across a grassland-crop rotation of a) 150N T. repens clover monoculture, b) 6-species mixture receiving 150N (high-diversity, low-input) and c) 300N <i>L. perenne</i> (low-diversity, high-input). </span></td></tr></tbody></table><span style="font-size: medium;"><br /></span><p class="MsoNormal"><span style="font-size: medium;"><b>References</b><o:p></o:p></span></p><p class="MsoNormal"><span style="font-size: medium;">Grange, G., Brophy, C.
and Finn, J.A., 2022. <a href="https://www.sciencedirect.com/science/article/pii/S116103012200079X">Grassland
legacy effects on yield of a follow-on crop in rotation strongly influenced by
legume proportion and moderately by drought.</a> European Journal of Agronomy,
138, p.126531. Open Access, with data and statistical code. <o:p></o:p></span></p><p class="MsoNormal"><span style="font-size: medium;"><o:p> </o:p>Grange et al. 2021.<b> </b><a href="file://jcvx341/homeshares$/John.Finn/Research%20Documents/Web_Blog%20stuff/FarmEcol/LegacyEffect/Plant%20diversity%20enhanced%20yield%20and%20mitigated%20drought%20impacts%20in%20intensively%20managed%20grassland%20communities">Plant
diversity enhanced yield and mitigated drought impacts in intensively managed
grassland communities.</a> Journal of Applied Ecology. (with Open Access data
and code)</span></p><p class="MsoNormal"><span style="font-size: medium;">See <a href="https://twitter.com/Johnfinn310/status/1534287984631193603 " target="_blank">tweets for a shorter summary</a> of this!</span></p><p class="MsoNormal"><span style="font-size: medium;"><o:p></o:p></span></p><p>
</p><p class="MsoNormal"><o:p><span style="font-size: medium;"> </span></o:p></p><p><span style="font-size: medium;"><br /></span></p><p><br /></p></div></div>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-3383131600232191143.post-23933719818796666492022-11-04T09:14:00.002-07:002022-11-08T03:59:04.683-08:00Assessing the habitat quality of Irish field margins<p> </p><p class="MsoNormal"><span style="font-size: 13.5pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;">We developed a methodology to assess
the habitat quality of field margins in a set of more intensively managed farms
in Ireland. Overall, we found that over half of the field margins surveyed had
low or very low levels of habitat quality.<o:p></o:p></span></p><p class="MsoNormal"><span style="font-size: 13.5pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"></span></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEge_RCUc2ymMmHwxnd-AAjqS15mrbcAqnNqF8EkBkWZstZOQpSqMhZLoiowQXqbzkvsaDklQFRtwF5ztAm5M-fnOOzK418o6QEKjG1QkRVRr0Kx0pcV3K4WXmIgC6JyLtFnROK-o4qo6640486fxHeBIEqgaWBKG_R7TiTt_h2gehKIFMw09l2TmAMj/s1467/DSC_0747_weedy.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="826" data-original-width="1467" height="180" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEge_RCUc2ymMmHwxnd-AAjqS15mrbcAqnNqF8EkBkWZstZOQpSqMhZLoiowQXqbzkvsaDklQFRtwF5ztAm5M-fnOOzK418o6QEKjG1QkRVRr0Kx0pcV3K4WXmIgC6JyLtFnROK-o4qo6640486fxHeBIEqgaWBKG_R7TiTt_h2gehKIFMw09l2TmAMj/s320/DSC_0747_weedy.jpg" width="320" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Field margin with very low habitat quality (dominated by negative indicator species)<br /><br /></td></tr></tbody></table><p class="MsoNormal"><span></span></p><a name='more'></a><p></p><b><span style="font-size: 13.5pt;">Highlights</span></b><div>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; vertical-align: baseline;"></p><ul style="text-align: left;"><li><span style="color: black; font-size: 13.5pt; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;">Quality of field margins on intensively
managed farms was assessed by plant indicator species that indicated high to
low quality of plant diversity in field margins. </span></li><li><span style="color: black; font-size: 13.5pt; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;">Positive indicator species occurred in
77% of margins and had an average cover of 10%. </span></li><li><span style="color: black; font-size: 13.5pt; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;">Negative indicator species occurred in
93% of margins with 55% cover.</span></li><li><span style="color: black; font-size: 13.5pt; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;">We developed an approach to assess the
quality of field margins; over half of the margins surveyed did not attain an
‘acceptable’ quality level. </span></li><li><span style="color: black; font-size: 13.5pt; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;">There is a need to improve the habitat quality of
existing field margins.</span></li></ul><p></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><b style="mso-bidi-font-weight: normal;"><span style="color: black; font-size: 13.5pt; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;"><br /></span></b></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><b style="mso-bidi-font-weight: normal;"><span style="color: black; font-size: 13.5pt; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;">Introduction<o:p></o:p></span></b></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><span style="color: black; font-size: 13.5pt; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;">Field margins consist of strips of
herbaceous vegetation that are typically located between field boundary
features such as hedgerows and the main grassland or arable field. However,
little is known about their extent or ecological quality on intensively managed
farmland in Ireland. This lack of knowledge can only be addressed through the
application of a standardised assessment methodology, which we developed and
implemented in <a href="https://www.scienceopen.com/hosted-document?doi=10.15212/ijafr-2022-0102" target="_blank">this 2022 study by Julie Larkin and colleagues</a>. </span><span style="font-size: 13.5pt; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;"><o:p></o:p></span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-left: 18.0pt; margin-right: 0cm; margin-top: 0cm; margin: 0cm 0cm 0cm 18pt; text-align: justify;"><span style="color: black; font-size: 13.5pt; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;"><o:p> </o:p></span></p>
<p class="MsoNormal" style="line-height: normal; mso-margin-bottom-alt: auto; mso-margin-top-alt: auto;"><b style="mso-bidi-font-weight: normal;"><span style="color: black; font-size: 13.5pt; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;">Research
approach<o:p></o:p></span></b></p>
<p class="MsoNormal" style="line-height: normal; mso-margin-bottom-alt: auto; mso-margin-top-alt: auto;"><span style="color: black; font-size: 13.5pt; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;">We surveyed 92 intensively managed farms across
three farming enterprises (tillage, beef and dairy) in Ireland to estimate the
percentage of EFA and other farmland habitats occurring within these farms. The
*median* farm habitat area was 5% for tillage, 6% for intensive beef and 6.55%
for intensive dairy (see a <a href="https://farmecol.blogspot.com/2022/03/semi-natural-habitats-and-ecological.html" target="_blank">previous blog post</a>, and Fig. 1 from the supplementary information in Larkin et al. 2019). Linear
features such as hedgerows, field margins and drainage ditches accounted for
43% of the total area of wildlife habitat surveyed. Here, we look more
closely at the habitat quality of the field margins on this sample of farms.</span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><span style="color: black; font-size: 13.5pt; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;">A survey of field margins was conducted
on 92 intensively managed farms, across three enterprise types (arable, beef
and dairy farms) in Ireland. We described the botanical composition and
assessed the habitat quality of field margins based on threshold levels of the
percentage cover of positive, neutral and negative botanical indicator
species) that are predominantly informed by existing EU accepted methods for
vegetation classification. <o:p></o:p></span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><span style="color: black; font-size: 13.5pt; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;"><o:p> </o:p></span></p>
<div class="separator" style="clear: both; text-align: center;"><div class="separator" style="clear: both;"><br /></div></div><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiOq6jEBhbjUK2PT_vBC4KVXlEgG84me_bpBI0nj7Z1mQTPuuE6pSMEQ4W61sS0BrGdnsTbR0YD2YOOCO_vrRblaOjUXIoh1s675qQi-iBeUWE83uI8dZZCwIqws6nrSKDNuah12ycRnX3lYR_arLUFVMc9EtvR0M5pUQexbPIl2TF9vwTTc0WZUvHV/s3840/DSC_1253_high%20field%20margin.JPG" style="margin-left: 1em; margin-right: 1em; text-align: center;"><img border="0" data-original-height="3840" data-original-width="2160" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiOq6jEBhbjUK2PT_vBC4KVXlEgG84me_bpBI0nj7Z1mQTPuuE6pSMEQ4W61sS0BrGdnsTbR0YD2YOOCO_vrRblaOjUXIoh1s675qQi-iBeUWE83uI8dZZCwIqws6nrSKDNuah12ycRnX3lYR_arLUFVMc9EtvR0M5pUQexbPIl2TF9vwTTc0WZUvHV/s320/DSC_1253_high%20field%20margin.JPG" width="180" /></a><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj89PI8EDjJdxUr_pXO3Zo4CSuk7_ft7lr8So4leCafszB3_0HHc7cyDxGXpE0MD1lY9c7ahpPJqylcIfjaAa_raAmLdsDw9Oq0FdHFYr9p6g1MqKSTlN-O3ICpS_kbLgrwmSoH7LbyD1L31_GfwoDazSk4OHz6KYvMfVeYUVGN01Y94ClTSTufEUpt/s822/DSC_0325_sprayed.jpg" style="margin-left: 1em; margin-right: 1em; text-align: center;"><img border="0" data-original-height="822" data-original-width="457" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj89PI8EDjJdxUr_pXO3Zo4CSuk7_ft7lr8So4leCafszB3_0HHc7cyDxGXpE0MD1lY9c7ahpPJqylcIfjaAa_raAmLdsDw9Oq0FdHFYr9p6g1MqKSTlN-O3ICpS_kbLgrwmSoH7LbyD1L31_GfwoDazSk4OHz6KYvMfVeYUVGN01Y94ClTSTufEUpt/s320/DSC_0325_sprayed.jpg" width="178" /></a><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj5YFGR9fR96QxTiC_yTtkq_qyU6pf0dWyeigTcD_DqZXXJGsHkF2Pii5CxjjFAfM2kymgvolAti2QCOSiI4aBkDeA0MlF0xS33LBbVP9ROe9u9voWCHJtUoJ800ADGTvPX3BznSPSWoDDODo4xtC1cHB2ZnyCh9Aa8hmqs7kOIZmB9lgvILnWPaCTQ/s819/DSC_0523_lowqualityFM.jpg" style="margin-left: 1em; margin-right: 1em; text-align: center;"><img border="0" data-original-height="819" data-original-width="460" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj5YFGR9fR96QxTiC_yTtkq_qyU6pf0dWyeigTcD_DqZXXJGsHkF2Pii5CxjjFAfM2kymgvolAti2QCOSiI4aBkDeA0MlF0xS33LBbVP9ROe9u9voWCHJtUoJ800ADGTvPX3BznSPSWoDDODo4xtC1cHB2ZnyCh9Aa8hmqs7kOIZmB9lgvILnWPaCTQ/s320/DSC_0523_lowqualityFM.jpg" width="180" /></a></div><div><br /><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><b style="mso-bidi-font-weight: normal;"><span style="color: black; font-size: 13.5pt; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;">Results
& Discussion</span></b></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><span style="font-size: 13.5pt; text-align: left;">We developed a standardised </span><span style="font-size: 13.5pt; text-align: left;">methodology</span><span style="font-size: 13.5pt; text-align: left;">
to assess the conservation value of field margins. This methodology depended on
the indicator status of species (positive, neutral, negative), and the proportions
of these categories measure in field margins (Table 1).</span></p>
<p class="MsoPlainText"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEijelKOt5B99H3XVLqzUZHTxsfDg9pGOP5uzofZPM7QVzQQsMp7A5yIaIVU3Pd_bqMCzc2pt2kltRwHv98wB1RVd0yrWX1U2G40gMobQeOh4jRXHZyDiDKcMKaeRGORSIYDAreUNYjoeQEC6MD85t-2zbx9edSIkhnJyG2lUXgNY0qv-nkAHA1UC9eN/s1303/Table1.jpg" style="font-size: 18px; margin-left: 1em; margin-right: 1em; text-align: center;"><img border="0" data-original-height="379" data-original-width="1303" height="186" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEijelKOt5B99H3XVLqzUZHTxsfDg9pGOP5uzofZPM7QVzQQsMp7A5yIaIVU3Pd_bqMCzc2pt2kltRwHv98wB1RVd0yrWX1U2G40gMobQeOh4jRXHZyDiDKcMKaeRGORSIYDAreUNYjoeQEC6MD85t-2zbx9edSIkhnJyG2lUXgNY0qv-nkAHA1UC9eN/w640-h186/Table1.jpg" width="640" /></a></p>
<p class="MsoPlainText"><span style="color: black; font-size: 13.5pt; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;">A fair comment is that the threshold
values for percentage cover appear somewhat arbitrary. However, this is the
case for many such approaches, including habitat reporting for the Habitats
Directive and many other vegetation assessments that we model this approach on.
This specific approach is not a biodiversity inventory (although the
species-level data underpin the methodology); instead we are aiming to develop
a (rapid) assessment of overall field margin quality. When we apply these same
thresholds across field margins we get a spectrum of quality (which shows that
our method can differentiate from high to low quality), and can detect
differences among farm enterprises (Fig. 1). <o:p></o:p></span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><span style="color: black; font-size: 13.5pt; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;"><o:p> <br /></o:p></span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><span style="color: black; font-size: 13.5pt; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;">Positive indicator species occurred in
77% of margins and had an average cover of 10%. There was a high incidence of
negative indicator species, occurring in 93% of margins with an average cover
of 55%. Using our quality appraisal system, 15% of field margins were of high
or very high quality, and the majority (56%) were of low or very low quality
(Fig. 1).</span></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><span style="color: black; font-size: 13.5pt; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;"><br /></span></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg_gQh9Zz4Ct9slEkAGIE8G3tJESjnmjgvljZwJv2bEMndIow-f7MYetjr0qydWy4RnJUsyNhBqFMd7XIEHdTXfDb9iaUooIAVjGJQMX7Kmzyrne_icAxlgLgsavZxLEj2UfnNFNlSo2TQLSNOVg9TZ9vGJAD76op5IY85LGQHBtcihMMcMMNhM1abn/s1280/Larkin_average_field%20margins.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="720" data-original-width="1280" height="360" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg_gQh9Zz4Ct9slEkAGIE8G3tJESjnmjgvljZwJv2bEMndIow-f7MYetjr0qydWy4RnJUsyNhBqFMd7XIEHdTXfDb9iaUooIAVjGJQMX7Kmzyrne_icAxlgLgsavZxLEj2UfnNFNlSo2TQLSNOVg9TZ9vGJAD76op5IY85LGQHBtcihMMcMMNhM1abn/w640-h360/Larkin_average_field%20margins.jpg" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;"><span id="docs-internal-guid-3219cba4-7fff-5224-7106-dced955eb6a0"><span face="Calibri, sans-serif" style="font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; font-weight: 700; vertical-align: baseline; white-space: pre-wrap;">Fig. 1: </span><span face="Calibri, sans-serif" style="font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">The distribution of sampled field margins across each quality grade (very high, high, acceptable, low, very low) across the three enterprises (arable, beef and dairy). </span></span></td></tr></tbody></table><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><span style="color: black; font-size: 13.5pt; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;"> Compared to either arable or dairy farms, beef farms had a greater
percentage of higher quality margins, higher species richness and greater
percentage of positive indicator species (Fig. 2, Table 2). </span></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiXdxAuxYAVE0w6ZIgMP0eWdk8ms6N6mmXpHnTK-FjcEAape67JJbndhOgLg_kyeBYeKjLxEe5ouHUypTSgp90TQJ13SI8savBkfiC7vaMtkF2CAGExVme7RiKBOTkqafFmeKFgZt8GoMKgRrWM0xHQdMLDbO4aYOoqvORaapvjjLltz06LMOJ9ymG0/s1280/Larkin_threecat_field%20margins.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="720" data-original-width="1280" height="225" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiXdxAuxYAVE0w6ZIgMP0eWdk8ms6N6mmXpHnTK-FjcEAape67JJbndhOgLg_kyeBYeKjLxEe5ouHUypTSgp90TQJ13SI8savBkfiC7vaMtkF2CAGExVme7RiKBOTkqafFmeKFgZt8GoMKgRrWM0xHQdMLDbO4aYOoqvORaapvjjLltz06LMOJ9ymG0/w400-h225/Larkin_threecat_field%20margins.jpg" width="400" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;"><span face="Calibri, sans-serif" style="font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; font-weight: 700; vertical-align: baseline; white-space: pre-wrap;">Fig. 2: </span><span face="Calibri, sans-serif" style="font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">The distribution of sampled field margins across each quality grade (very high, high, acceptable, low, very low) for each of the three enterprises (arable (white), beef (light grey) and dairy (dark grey)). </span></td></tr></tbody></table><br /><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhLBNPZydu2xkfTsi82IOlyjNKDczuH5vkksSED8tNFzIqTY_4fLdiNhHl9-xexPiHCEiSyWijOC5MgUFlXSjbdw4fhh_43Q-ma7XHvdghdYlhG45929SILXzsyFu_P8eCAwlI9Y3KfBQMp2lgY6X_5p6zJKsLbgY7vrg-eFeSCTMZJyk_G4-oYr3fz/s1312/Table2.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="607" data-original-width="1312" height="296" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhLBNPZydu2xkfTsi82IOlyjNKDczuH5vkksSED8tNFzIqTY_4fLdiNhHl9-xexPiHCEiSyWijOC5MgUFlXSjbdw4fhh_43Q-ma7XHvdghdYlhG45929SILXzsyFu_P8eCAwlI9Y3KfBQMp2lgY6X_5p6zJKsLbgY7vrg-eFeSCTMZJyk_G4-oYr3fz/w640-h296/Table2.jpg" width="640" /></a></div><p></p><br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj05gEgCqvKvGeV6lZSxIh6EpvcncCuPXVY34WVjmQe_XP_4UNvARYB91bdev5zscUjlJ_HiFAf_f2NjlrK59g1cbJTquGrUZ83a83a0z6z0HNlqUzIipdf6ZIizmqgXg2kJgUW7ZQmyVQf5KgaXomW-C_Oe0MFfDfE1viqsImIPj1PZh8dgvGao7FH/s1459/DSC_0369_highquality.jpg" style="margin-left: auto; margin-right: auto; text-align: center;"><img border="0" data-original-height="823" data-original-width="1459" height="181" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj05gEgCqvKvGeV6lZSxIh6EpvcncCuPXVY34WVjmQe_XP_4UNvARYB91bdev5zscUjlJ_HiFAf_f2NjlrK59g1cbJTquGrUZ83a83a0z6z0HNlqUzIipdf6ZIizmqgXg2kJgUW7ZQmyVQf5KgaXomW-C_Oe0MFfDfE1viqsImIPj1PZh8dgvGao7FH/s320/DSC_0369_highquality.jpg" width="320" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Field margin area with very high habitat quality. </td></tr></tbody></table><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><span style="font-size: 13.5pt; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;"><o:p> </o:p></span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><span style="font-size: 13.5pt;">Retaining areas of high quality
farmland habitat, and enhancing those areas that have become ecologically
degraded, will be key to achieving the Common Agricultural Policy (CAP)
objective of protecting landscapes and biodiversity. However, the
implementation of appropriate management decisions requires effective
evaluation of the current ecological condition of these habitats. We show that
the majority of field margins in more intensively managed systems in Ireland are
in a botanically impoverished condition. Although this study demonstrated low
abundance of positive indicator species, their frequency of occurrence was
relatively high</span><w:sdt id="-1983462942" sdttag="goog_rdk_200" style="font-size: 13.5pt;"><span class="msoIns"><ins cite="mailto:Daire%20OHuallachain" datetime="2022-01-20T10:14"> (approximately 77% of </ins></span>field <span class="msoIns"><ins cite="mailto:Daire%20OHuallachain" datetime="2022-01-20T10:14">margins, and in 100% of farms)</ins></span></w:sdt><span style="font-size: 13.5pt;">.
Thus, as positive indicator species are already present in a high percentage of
margins, management options that reduce competition from negative indicator
species may be all that is needed to increase the abundance of positive
indicator species (although this is dependent on soil nutrient status and
management actions).</span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><span style="color: black; font-size: 13.5pt; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;"><o:p> </o:p></span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; text-align: justify;"><span style="color: black; font-size: 13.5pt; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;">Our standardised field margin quality
assessment technique may offer an appropriate method of tracking change in
habitat quality in response to conservation actions to improve habitat quality.
A results-based approach could be considered that would link farmers’ payments
for biodiversity objectives to the quality of habitats; in this case, the
higher the habitat quality of a field margin, the higher the payment received.
Such an approach would better incentivise the improvement of low quality
habitats, and also better reward the supply of higher quality habitats.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size: 13.5pt; line-height: 107%;"><o:p> </o:p></span><span style="font-size: 13.5pt;"> </span></p>
<p class="MsoNormal"><b style="mso-bidi-font-weight: normal;"><span style="font-size: 13.5pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;">References<o:p></o:p></span></b></p>
<p class="MsoNormal" style="line-height: normal; mso-margin-bottom-alt: auto; mso-margin-top-alt: auto;"><span style="color: black; font-size: 13.5pt; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;">Larkin, J., Sheridan, H., Finn,
J.A. and Denniston, H., 2019. </span><span style="font-size: 13.5pt; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"><a href="https://www.sciencedirect.com/science/article/pii/S0264837718318210" style="-webkit-text-stroke-width: 0px; font-variant-caps: normal; font-variant-ligatures: normal; orphans: 2; widows: 2; word-spacing: 0px;" target="_blank">Semi-natural
habitats and Ecological Focus Areas on cereal, beef and dairy farms in Ireland</a><span style="color: black;"><span style="-webkit-text-stroke-width: 0px; float: none; font-variant-caps: normal; font-variant-ligatures: normal; orphans: 2; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; widows: 2; word-spacing: 0px;">. Land Use Policy, 88: p.104096.</span><o:p></o:p></span></span></p>
<p class="MsoNormal" style="line-height: normal; mso-margin-bottom-alt: auto; mso-margin-top-alt: auto;"><span style="font-size: 13.5pt; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-language: EN-IE; mso-no-proof: yes;">Larkin et al. 2022. <span style="color: blue;"><a href="https://www.scienceopen.com/hosted-document?doi=10.15212/ijafr-2022-0102">Field
margin botanical diversity, composition and quality on intensively managed
farming systems</a>. </span>Irish Journal of Agricultural and Food Research.<span style="color: blue;"><o:p></o:p></span></span></p>
<p class="MsoNormal"><span style="font-size: 13.5pt; line-height: 107%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"><o:p> </o:p></span><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiOq6jEBhbjUK2PT_vBC4KVXlEgG84me_bpBI0nj7Z1mQTPuuE6pSMEQ4W61sS0BrGdnsTbR0YD2YOOCO_vrRblaOjUXIoh1s675qQi-iBeUWE83uI8dZZCwIqws6nrSKDNuah12ycRnX3lYR_arLUFVMc9EtvR0M5pUQexbPIl2TF9vwTTc0WZUvHV/s3840/DSC_1253_high%20field%20margin.JPG" style="margin-left: 1em; margin-right: 1em; text-align: center;"><img border="0" data-original-height="3840" data-original-width="2160" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiOq6jEBhbjUK2PT_vBC4KVXlEgG84me_bpBI0nj7Z1mQTPuuE6pSMEQ4W61sS0BrGdnsTbR0YD2YOOCO_vrRblaOjUXIoh1s675qQi-iBeUWE83uI8dZZCwIqws6nrSKDNuah12ycRnX3lYR_arLUFVMc9EtvR0M5pUQexbPIl2TF9vwTTc0WZUvHV/s320/DSC_1253_high%20field%20margin.JPG" width="180" /></a><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiqsuBVACSt1lwO3paa1AMcJRL190IbO0lqozKtNcdDowcxl7JZlecaEaYhv8vaV6tl4GN6KZ1hlITNabtHTNcLrQtFj0aftc5WR_f00UGFLXEd-3-MlZVha7kzQczE2fYL-ON2dAQzrJXYZmeoRKn2p5tesIqu4Y_NXn_MFcmMtLr-O6frq9YA-jww/s822/DSC_0767_nonexistent.jpg" style="margin-left: 1em; margin-right: 1em; text-align: center;"><img border="0" data-original-height="822" data-original-width="469" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiqsuBVACSt1lwO3paa1AMcJRL190IbO0lqozKtNcdDowcxl7JZlecaEaYhv8vaV6tl4GN6KZ1hlITNabtHTNcLrQtFj0aftc5WR_f00UGFLXEd-3-MlZVha7kzQczE2fYL-ON2dAQzrJXYZmeoRKn2p5tesIqu4Y_NXn_MFcmMtLr-O6frq9YA-jww/s320/DSC_0767_nonexistent.jpg" width="183" /></a></p><div><div class="separator" style="clear: both; text-align: center;"><div class="separator" style="clear: both;"></div></div></div></div>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-3383131600232191143.post-22505344828558478322022-03-03T06:00:00.003-08:002022-11-04T07:44:24.605-07:00Semi-natural habitats and Ecological Focus Areas on Irish farmland<p></p><p class="MsoNormal">HIighlights: We surveyed farmland habitats on tillage and more intensive beef and dairy farms. Habitat area was lower than that found in the general countryside, and was dominated by linear features (especially hedgerows). All tillage farms and the majority of pastoral farms in our sample met the current 5% EFA requirement, and the vast majority (93%) met a scenario with a 7% EFA requirement. There is a considerable amount of a broader range of wildlife habitats already present on intensively managed farms that was not included in EFA in Ireland, and is not reflected in policy or legislation. </p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEgg-8yBJJkJSKjeGA7bpgJNb8YtIhoG7bXAz4z6niZ1wr-ivEOCBCyagHu4VTQBQHrPHNOyeLo8l_Y-JM46JL46pDFlv_qB62-h4RZo2LCn3bMo8BV-tybsrvKp_i0nDVnF5LhhjocriAC-oLao5652748zFXOtoGrmTgesIEnLFyZsBokyTxau_uHP=s1280" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="960" data-original-width="1280" height="150" src="https://blogger.googleusercontent.com/img/a/AVvXsEgg-8yBJJkJSKjeGA7bpgJNb8YtIhoG7bXAz4z6niZ1wr-ivEOCBCyagHu4VTQBQHrPHNOyeLo8l_Y-JM46JL46pDFlv_qB62-h4RZo2LCn3bMo8BV-tybsrvKp_i0nDVnF5LhhjocriAC-oLao5652748zFXOtoGrmTgesIEnLFyZsBokyTxau_uHP=w200-h150" width="200" /></a><a href="https://blogger.googleusercontent.com/img/a/AVvXsEiJzjgVgMYZVKd_Of7MSNTJxr2vxdqhkIkkcWhMX1c98swHBPkYe5Yu4Ssv5yqFwwyGc_wOIjVdIpZJjN6bosa-YEn0IPSB7urJERxlXJ3D6nof_DfmwUt-D81RqCth8qhOgKzEnOAKOR7RfAZ6pL7Nc-52gEpN9YIufbyhz8L0vBLpjQvQGgYzXlPd=s3264" style="font-weight: 700; margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="2448" data-original-width="3264" height="150" src="https://blogger.googleusercontent.com/img/a/AVvXsEiJzjgVgMYZVKd_Of7MSNTJxr2vxdqhkIkkcWhMX1c98swHBPkYe5Yu4Ssv5yqFwwyGc_wOIjVdIpZJjN6bosa-YEn0IPSB7urJERxlXJ3D6nof_DfmwUt-D81RqCth8qhOgKzEnOAKOR7RfAZ6pL7Nc-52gEpN9YIufbyhz8L0vBLpjQvQGgYzXlPd=w200-h150" width="200" /></a><span><a name='more'></a></span></div><p></p><p class="MsoNormal">The Common Agricultural Policy (CAP) 2014-2020 introduced
three mandatory ‘Greening’ measures aimed at improving the environmental
performance of EU agriculture. The Ecological Focus Area (EFA) measure within
Greening was intended to help improve biodiversity associated with European
tillage farms. To improve understanding of the implementation of this measure
(and to inform the design of future measures), data are needed on the
implementation of Ecological Focus Area measure on tillage farms. In the discussions
preceding the last round of CAP reform, there was intense discussion about
whether grasslands should be subject to Ecological Focus Areas, or a similar
measure. In the end, they were not, but it was reasonable to expect this
proposal to surface again in the next CAP reform. With these issues in mind, we
conducted a study with the following objectives:</p><p></p><p class="MsoNormal"><o:p></o:p></p><p class="MsoNormal"></p><ul style="text-align: left;"><li>Measure and compare the area and types of habitats on three
intensively managed farming systems in Ireland (arable, beef and dairy).</li><li>Quantify the EFA areas on these farms in relation to current
EFA prescriptions.</li><li>Consider the implications of two scenarios in which a)
intensive pastoral systems are subject to EFA regulations and b) EFA thresholds
increase from 5% to 7% for all farms</li></ul><o:p></o:p><p></p><p class="MsoNormal"><o:p></o:p></p><p class="MsoNormal"><o:p></o:p></p><p class="MsoNormal">These data can help inform management strategies to attempt to halt the
decline of farmland biodiversity and associated ecosystem services.<o:p></o:p></p><p class="MsoNormal"><b>Results: farmland habitat areas</b></p><p class="MsoNormal">We surveyed 119 intensively managed farms across three
farming enterprises (tillage, beef and dairy) in Ireland to estimate the
percentage of EFA and other farmland habitats occurring within these farms. The
*median* farm habitat area of 5% for tillage, 6% for intensive beef and 6.55%
for intensive dairy (see Fig. 1, from the supplementary information in Larkin
et al. 2019). </p><p class="MsoNormal">Linear features such as hedgerows, buffer strips and drainage
ditches accounted for 43% of the total area of wildlife habitat surveyed. </p><p class="MsoNormal">Hedgerows were the most abundant and frequently occurring wildlife habitat,
present on 100% of farms and occupying an average of 3% of the
total area of every farm. Other semi-natural wooded habitats (semi-natural
woodland, isolated trees, field copse) accounted for a further average of 2%
area. <o:p></o:p></p><p class="MsoNormal"><span style="font-size: 12pt; line-height: 115%; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"><span face=""Calibri",sans-serif" style="font-size: 12pt; line-height: 115%; mso-ansi-language: EN-IE; mso-ascii-theme-font: minor-latin; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin;">
</span></span></p><p class="MsoNormal"> </p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEi13llcnQ2sBr8gkM35Jvtv-vQQf0GCb5AGb_MdOObsVdRBXNDHLj85vGGLQRtTPUBPL1yE9fj2pPiUqYkQL46rAwRwx9i4r56jKgFZI7iYal4NSLhuJPyZP18t4krhQZQRnLVO3TsN5rrpO276Z-pu1ey_K4p5Xm1DegLVy71Cw_3tqqkH8Jq3Q9NI=s1087" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="806" data-original-width="1087" height="422" src="https://blogger.googleusercontent.com/img/a/AVvXsEi13llcnQ2sBr8gkM35Jvtv-vQQf0GCb5AGb_MdOObsVdRBXNDHLj85vGGLQRtTPUBPL1yE9fj2pPiUqYkQL46rAwRwx9i4r56jKgFZI7iYal4NSLhuJPyZP18t4krhQZQRnLVO3TsN5rrpO276Z-pu1ey_K4p5Xm1DegLVy71Cw_3tqqkH8Jq3Q9NI=w570-h422" width="570" /></a></td></tr><tr><td class="tr-caption" style="text-align: left;">Fig. 1. Median farm habitat area on a sample of tillage, intensive beef and intensive dairy farms (from supplementary information in Larkin
et al. 2019).</td></tr></tbody></table><p></p><p class="MsoNormal" style="text-align: start;"><span style="text-align: right;"><b></b></span></p><div class="separator" style="clear: both; text-align: center;"><table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: left;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEjynF6TD7Wy_zhAN2YqHYU4J7MNeoO4AwzftkuyFSSkc3LVKRQ95IS7xmto_RuQJ_iHEmtnbB6SvnAiRlTqaAfsUrHqIKfvb5Y0yLrgz6aMH5Im2DteIeZmQL6_T79A2bwKje7Y4vteZhfHzi5vW7vFkgUsipjjMl2cQVCg_q99mbOv7M3njJGYzV_3=s3264" style="clear: left; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" data-original-height="2448" data-original-width="3264" height="240" src="https://blogger.googleusercontent.com/img/a/AVvXsEjynF6TD7Wy_zhAN2YqHYU4J7MNeoO4AwzftkuyFSSkc3LVKRQ95IS7xmto_RuQJ_iHEmtnbB6SvnAiRlTqaAfsUrHqIKfvb5Y0yLrgz6aMH5Im2DteIeZmQL6_T79A2bwKje7Y4vteZhfHzi5vW7vFkgUsipjjMl2cQVCg_q99mbOv7M3njJGYzV_3=w320-h240" width="320" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;"></td></tr></tbody></table><div class="separator" style="clear: both; text-align: center;"><br /></div><b><div class="separator" style="clear: both; text-align: center;"><br /></div></b></div><b>Results: Ecological Focus Areas</b><p></p><p class="MsoNormal" style="text-align: start;"><span style="text-align: right;">Habitats present on farms were categorised into three EFA
sub-categories:</span></p><div class="separator" style="clear: both; text-align: center;"><p class="MsoNormal" style="text-align: left;">a) Irish-eligible EFA (allowed under Irish legislation to qualify as EFA)<br />b) EU-eligible EFA (allowed under EU legislation to qualify as EFA but were not implemented as EFA in Ireland)<br />c) ‘non-eligible
habitats’ (habitats within the ‘wildlife habitats’ category described above, but which did not qualify as EFA under Irish or EU legislation). </p><p class="MsoNormal" style="text-align: left;">We measured the area of
habitats within these three categories, and applied the EFA conversion and
weighting factors. Note that these conversion factors typically result in the
area of EFA habitat being greater than the geographical area occupied by the
habitats. </p><p class="MsoNormal" style="text-align: left;"><o:p></o:p></p>
<p class="MsoNormal" style="text-align: left;">All tillage farms and almost all pastoral farms in our
sample met the current 5% EFA requirement (see Fig. 2 below) (see Fig. 3 and
Table 3 in Larkin et al. 2019). <o:p></o:p></p>
<p class="MsoNormal" style="text-align: left;">Field margins were the most frequently encountered habitat
that was ineligible under Irish EFA prescriptions, but which qualifies as EFA
under EU legislation. Additionally, a large percentage area (average of 9.5%
after application of conversion and weighting factors) of farms sampled was
covered by habitats not classed as EFA under EU legislation. </p></div><div style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEgp7XTXswXATpYT7xtbTtA-FO4jMpFdJCdPM6ELQQjcKzCXo6-kfaQm413WPN2LdIVfO65MPYpFFAm8iGsK7sp9-DlnfJrRJrkV5u86NPK7hDVaG6VwLufPdcrXQ65euAY4OwGszt5dsCEoBP0zTpEU4o1nuoJrXnLFJDqfFu2kNwvX1rYELvusAwO3=s521"><img border="0" data-original-height="265" data-original-width="521" height="312" src="https://blogger.googleusercontent.com/img/a/AVvXsEgp7XTXswXATpYT7xtbTtA-FO4jMpFdJCdPM6ELQQjcKzCXo6-kfaQm413WPN2LdIVfO65MPYpFFAm8iGsK7sp9-DlnfJrRJrkV5u86NPK7hDVaG6VwLufPdcrXQ65euAY4OwGszt5dsCEoBP0zTpEU4o1nuoJrXnLFJDqfFu2kNwvX1rYELvusAwO3=w612-h312" width="612" /></a></div><p></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td class="tr-caption" style="text-align: center;"><div style="text-align: center;"><span style="text-align: left;">Fig. 2.</span> Plots of EFA percentages calculated for all farms
(tillage n=38, beef=38, dairy =43) and for different scenarios of EFA
eligibility (5% and 7%). Lower dashed line on
each graph represents the 5% EFA threshold; upper dashed line represents the
7% EFA scenario percentage. Note different scale on the y-axis of 4c. From Larkin et al. (2019). (These data include EFA weighting and conversion factors.) </div><p class="MsoNormal"><o:p></o:p></p><span style="text-align: left;"><div style="text-align: left;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEi9_J2rw4z62C7Leg7_dKz-2JSiVHAY8n9ONmgE7aYJ3N71BLGqQoKd5rPoMOaWLo5O20lmxlqvpYACqQLsYnOFwFLOAsotu_Dy21MUa6ZdiYhYLWs0vgJnP5xTBuk6oD1l5Pxj-FnlJ7eI9pPkuh1UJfKPAbw2v4k9KEKXyZ9Z_jvSalNfKnOhlf5h=s3264" style="clear: right; font-weight: 700; margin-bottom: 1em; margin-left: 1em; text-align: center;"><img border="0" data-original-height="2448" data-original-width="3264" height="240" src="https://blogger.googleusercontent.com/img/a/AVvXsEi9_J2rw4z62C7Leg7_dKz-2JSiVHAY8n9ONmgE7aYJ3N71BLGqQoKd5rPoMOaWLo5O20lmxlqvpYACqQLsYnOFwFLOAsotu_Dy21MUa6ZdiYhYLWs0vgJnP5xTBuk6oD1l5Pxj-FnlJ7eI9pPkuh1UJfKPAbw2v4k9KEKXyZ9Z_jvSalNfKnOhlf5h=w320-h240" width="320" /></a></div></span><o:p style="text-align: left;"></o:p></td></tr></tbody></table><b><br /></b><div><b>What do we learn from this study?</b><p class="MsoNormal">There is a lower absolute area of habitats on more intensive
farms, compared to the area of habitats from studies that sample farmland from
the wider countryside (Rótches–Ribalta et al., 2020). This is probably not
surprising, but is confirmed nevertheless.<o:p></o:p></p>
<p class="MsoNormal">If the EFA target increased to
7% (but existing eligibility criteria for habitats remained), only eight farms
were below this threshold (3 tillage, 4 dairy, 1 beef; 7% of the farms
surveyed) (after application of the EFA conversion and weighing
factors).<o:p></o:p></p>
<p class="MsoNormal">A large percentage area (average of 9.5% after application
of conversion and weighting factors) of farms sampled was covered by habitats
not classed as EFA under EU legislation. Including this wider set of habitats
will help farms attain threshold levels, and will better ensure biodiversity
protection. (See also Rotchés-Ribalta et al. 2020.)<o:p></o:p></p>
<p class="MsoNormal">There is a very high dominance of farmland habitats by
hedgerows (and these data tell us nothing about hedgerow quality). More
generally, the EFA measure includes no specification regarding the quality of
habitats, or their effectiveness at supporting biodiversity or providing
ecosystem services. One method of improving Greening requirements would be the
addition of a quality parameter or some method of quantifying habitat quality
to help increase its environmental output e.g. through a results-based
approach. <o:p></o:p></p>
<p class="MsoNormal">There is a broader range of beneficial wildlife habitats
already present on intensively managed farms that is not incorporated into or
protected under policy and legislation. There is a surprisingly large area of
these ‘unprotected/unrecognised’ habitats. To protect biodiversity, these
habitats may be incorporated into future policy instruments, and are highly likely
to be of higher biodiversity value than newly created habitats. <span style="mso-spacerun: yes;"> </span>Preserving these habitats can help reduce
ecological losses, increase biodiversity, promote the sustainability of
agricultural systems, and reward/recognise the existing habitats on Irish
farmland. <o:p></o:p></p>
<p class="MsoNormal">See the Larkin et al. (2019) article for a wider discussion
of the additionality associated with the EFA measure. <o:p></o:p></p>
<p class="MsoNormal">John Finn and Daire Ó hUallacháin</p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEivKtdJALMkNiXb3wtQRBPPwNP61I-dhAa62QkPFSHzwP8C0JZgK7XtxUUBKqgNxAoNh6NtAxdrEkBbFsOSLrtwM0564Xpsob605fFBJMyIh8fTss7Art_59FUhptIFnNPZxN1cCuAKhRIXqKcrDPDJuro1NBGylDjdzTpHsAkRqattMDnEw5UQlm4A=s2048" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1536" data-original-width="2048" height="150" src="https://blogger.googleusercontent.com/img/a/AVvXsEivKtdJALMkNiXb3wtQRBPPwNP61I-dhAa62QkPFSHzwP8C0JZgK7XtxUUBKqgNxAoNh6NtAxdrEkBbFsOSLrtwM0564Xpsob605fFBJMyIh8fTss7Art_59FUhptIFnNPZxN1cCuAKhRIXqKcrDPDJuro1NBGylDjdzTpHsAkRqattMDnEw5UQlm4A=w200-h150" width="200" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;"></td></tr></tbody></table><p class="MsoNormal"><b>Reference</b><o:p></o:p></p><p class="MsoNormal">Larkin, J., Sheridan, H., Finn, J.A. and Denniston, H.,
2019. <a href="https://www.sciencedirect.com/science/article/pii/S0264837718318210" target="_blank">Semi-natural habitats and Ecological Focus Areas on cereal, beef and dairy farms in Ireland</a>. Land Use Policy, 88: p.104096.<o:p></o:p></p><p class="MsoNormal">Rotchés-Ribalta R, Ruas S, Ahmed KD, Gormally M, Moran J,
Stout J, White B, Ó hUallacháin D. 2021. <a href="https://link.springer.com/article/10.1007/s13280-020-01344-6" target="_blank">Assessment of semi-natural habitatsand landscape features on Irish farmland: New insights to inform EU CommonAgricultural Policy implementation</a>. Ambio 50: 346-. </p><p class="MsoNormal"><o:p>
</o:p></p><p class="MsoNormal"><b>Acknowledgement</b></p><p class="MsoNormal">This research was funded by the Teagasc Walsh Scholarship
Programme. <o:p></o:p></p><br /><p></p></div>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-3383131600232191143.post-90726712893437933002022-03-03T02:33:00.004-08:002022-03-03T02:33:48.364-08:00Open Access data: landscape classification of Ireland<p><span style="font-size: 12pt;">The <a href="https://hnvfarmforbio.ie/about/" target="_blank">FarmForBio project</a> has developed a landscape classification map of the Republic of Ireland. </span></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt;"><span style="font-size: 12pt;">The map and the GIS data are now available on Teagasc’s TStor repository<a href=" https://t-stor.teagasc.ie/handle/11019/2790" target="_blank"> https://t-stor.teagasc.ie/handle/11019/2790</a></span></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEgRUy7-e1v7TT38fqKwpIewVtd1JmMOuPDcMFq0VZSRh4ALb2IGiZuOAdVP88NMkS4d5RFe8msa4HKinFLyKvNDMp3uFegbMbYeH8H_IXxS2Bxi44TqqdYiS0uquMMJHVjiV4bDHQqLl_bwSeIvM5flZ4hfZqkL7Rx7HM_gkyjuRAQAlfzsQupcZM_S=s936" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="936" data-original-width="660" height="200" src="https://blogger.googleusercontent.com/img/a/AVvXsEgRUy7-e1v7TT38fqKwpIewVtd1JmMOuPDcMFq0VZSRh4ALb2IGiZuOAdVP88NMkS4d5RFe8msa4HKinFLyKvNDMp3uFegbMbYeH8H_IXxS2Bxi44TqqdYiS0uquMMJHVjiV4bDHQqLl_bwSeIvM5flZ4hfZqkL7Rx7HM_gkyjuRAQAlfzsQupcZM_S=w141-h200" width="141" /></a></div><span></span><p class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0cm;"><span style="font-size: 12pt;"><span></span></span></p><a name='more'></a><span style="font-size: 16px;">In a collaboration of GMIT, Teagasc and UCD, we identified nine major landscape classes across Ireland (Fig. 1). Each landscape class depicts unique spatial and composition characteristics, from coastal, lowland and elevated, to distinct and dominating land cover types. In the article, we describe and discuss each of the nine classes and as well as the wider applicability of the map. For example, we discuss the use of this classification for improved environmental and biodiversity monitoring.</span><p></p><p class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0cm;"><br /></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEjTEyPm0N9UyHQfNhYYA6w_aJjqy7aVXd5MwGv9jg0gT1JGo1k2Xt25zQXOn6edPhheoQXA8gbM2TcMWzvAxHwk4W13sUGt_vI6Fm4NrG-OHQFiPgVemd0NZSucCKEDL-FOaBIfYDPupj5baVHb4LGTMbRu_Jb7EkbkgU0C5M_mL-EvQbyXtGQ7bZXT=s936" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="936" data-original-width="660" height="640" src="https://blogger.googleusercontent.com/img/a/AVvXsEjTEyPm0N9UyHQfNhYYA6w_aJjqy7aVXd5MwGv9jg0gT1JGo1k2Xt25zQXOn6edPhheoQXA8gbM2TcMWzvAxHwk4W13sUGt_vI6Fm4NrG-OHQFiPgVemd0NZSucCKEDL-FOaBIfYDPupj5baVHb4LGTMbRu_Jb7EkbkgU0C5M_mL-EvQbyXtGQ7bZXT=w452-h640" width="452" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Fig. 1. Landscape classification of Ireland. <br />Article and data are available via Open Access. </td></tr></tbody></table><div class="separator" style="clear: both; text-align: center;"><br /></div><div class="separator" style="clear: both; text-align: center;"><br /></div><div class="separator" style="clear: both; text-align: center;"><br /></div><div class="separator" style="clear: both; text-align: center;"><br /></div><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEgjmIKyfEOVcN0u11NgsSSDMArcLByzR7GLGJNK3lJlEmglCJC3lsYMk1LWATQFVAOQjFVdXP2oxgFdgXokaGlAP5s66ODYr1iGjACsDih9pPQYMHattbek0HdDQQ_nbnauFSzM5lTBNDIoCC8wFtm6EErbDryoqCM9UHnJjnCPAlx5y3Uq9ERuXwLz=s839" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="839" data-original-width="437" height="400" src="https://blogger.googleusercontent.com/img/a/AVvXsEgjmIKyfEOVcN0u11NgsSSDMArcLByzR7GLGJNK3lJlEmglCJC3lsYMk1LWATQFVAOQjFVdXP2oxgFdgXokaGlAP5s66ODYr1iGjACsDih9pPQYMHattbek0HdDQQ_nbnauFSzM5lTBNDIoCC8wFtm6EErbDryoqCM9UHnJjnCPAlx5y3Uq9ERuXwLz=w209-h400" width="209" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Fig. 2. An example of two of the 9 classes; 'Semi-intensified elevated' and Intensified lowlands'. <br />Full descriptions of all nine classes are provided in Carlier et al. (2021). </td></tr></tbody></table><br /><div class="separator" style="clear: both; text-align: center;"><br /></div><p class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt;"><b><span style="font-size: 12pt;">Acknowledgements<o:p></o:p></span></b></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt;"><span style="font-size: 12pt;">This research was conducted as part of the High Nature Value Farmland and Forestry Systems for Biodiversity (FarmForBio) project funded by the Research Stimulus Fund (2019R425) of the Department of Agriculture, Food and the Marine.<o:p></o:p></span></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt;"><span style="font-size: 12pt;"> </span></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt;"><b><span style="font-size: 12pt;">Reference</span></b></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt;"><span style="font-size: 12pt;"><a href="https://t-stor.teagasc.ie/handle/11019/2790" target="_blank">Data file: Alandscape classification map of Ireland and its potential use in national landuse monitoring.</a> </span><span style="font-size: 12pt;"> </span><span style="font-size: 12pt;">https://t-stor.teagasc.ie/handle/11019/2790</span></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt;"><span style="font-size: 12pt;">Carlier, J., Doyle, M., Finn, J. A., Ó hUallacháin, D., & Moran, J. 2021. </span><a href="https://www.sciencedirect.com/science/article/pii/S0301479721005600" style="font-size: 12pt;" target="_blank">A landscape classification map of Ireland and its potential use in national land use monitoring</a><span style="font-size: 12pt;">. Journal of Environmental Management 289, 112498. (Open Access)</span></p><p></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt;"><span style="font-size: 12pt;">https://www.sciencedirect.com/science/article/pii/S0301479721005600</span></p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-3383131600232191143.post-62488990916278819312022-02-23T06:14:00.003-08:002022-02-23T09:07:13.127-08:00Lower nitrous oxide emissions intensity from multi-species swards<p></p><p class="MsoNormal" style="text-align: justify;"><span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;">Recent Teagasc research
shows that multispecies grasslands can potentially reduce both nitrous oxide (N</span><span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;"><sub><span face=""Arial",sans-serif" lang="EN-GB" style="font-size: 9pt; line-height: 107%; mso-ansi-language: EN-GB; mso-bidi-language: AR-SA; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">2</span></sub>O)
emissions and emissions intensities, and can contribute to more sustainable
grassland production. Greenhouse gas measurements carried out by Saoirse
Cummins showed that the annual greenhouse gas emissions per unit output (DM yield
or biomass N) from the multi-species mixture were lower than those from perennial
ryegrass receiving either 150 or 300 kg ha</span><sup style="font-family: Arial, sans-serif; text-align: left;">-1</sup><span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;"> yr</span><sup style="font-family: Arial, sans-serif; text-align: left;">-1</sup><span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;"> of inorganic
nitrogen (N).</span></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEgQDNb47bdIz5XzIP_M_NXC0pk4oA8CBbhgyu11kOTRUaSD3P9zpjRaLAKZPLDlMQeK7QI0rrp36EBvZHhTzcMrJiCGl9cD4I6lCTPIAlS_BNV-h5JWCbBWsiJ-nNn3CCQjpW84YAhbCtVgGQfZb91Gt88WOd2UXNjAPCDSInvtL-19xG9xBqDaF_06=s3264" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="2448" data-original-width="3264" height="150" src="https://blogger.googleusercontent.com/img/a/AVvXsEgQDNb47bdIz5XzIP_M_NXC0pk4oA8CBbhgyu11kOTRUaSD3P9zpjRaLAKZPLDlMQeK7QI0rrp36EBvZHhTzcMrJiCGl9cD4I6lCTPIAlS_BNV-h5JWCbBWsiJ-nNn3CCQjpW84YAhbCtVgGQfZb91Gt88WOd2UXNjAPCDSInvtL-19xG9xBqDaF_06=w200-h150" width="200" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;"></td></tr></tbody></table><br /><p></p><div class="separator" style="clear: both; text-align: center;"></div><p></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; mso-layout-grid-align: none; text-autospace: none;"><span face="Arial, sans-serif" style="font-size: 12pt;"><span></span></span></p><a name='more'></a><span face="Arial, sans-serif" style="font-size: 12pt;">Nitrous oxide (N<sub><span face=""Arial",sans-serif" lang="EN-GB" style="font-size: 9pt; line-height: 107%; mso-ansi-language: EN-GB; mso-bidi-font-size: 12.0pt; mso-bidi-language: AR-SA; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">2</span></sub>O) is a
potent greenhouse gas and is strongly associated with losses from inorganic nitrogen (N) fertiliser applied to grassland. Lower-nitrogen grassland
systems can curtail <span face="Arial, sans-serif" style="font-size: 12pt;">N</span><span face="Arial, sans-serif" style="font-size: 12pt;"><sub><span face=""Arial",sans-serif" lang="EN-GB" style="font-size: 9pt; line-height: 12.84px; mso-ansi-language: EN-GB; mso-bidi-language: AR-SA; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">2</span></sub>O</span> losses through the reduced application of inorganic N
fertiliser; this may be replaced and yields sustained by symbiotically-produced
N from clovers. Empirical measurement of the effect of legume-based
multi-species mixtures on nitrous oxide is lacking, and the specific role of
herbs is unknown.</span><p></p>
<p class="MsoNormal" style="text-align: justify;"><span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;"></span></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEiUVcxY8gXqe4wWkRU2wYI1PTDLTMZJEyX5KPINP7J01C4W6DpyHT96J9G4k3tmC2SrK1dELsoSKVrBLB-x8XroGh3rt_22olILoug73Lt6smk5xNim2ppOwZ_5kMKl3YRnDz_OM4YdC2rPaX6r0wZlvMlAqEe9tJ5kPgAh-xBOZgVPC0ss4J9LE4zK=s3264" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" data-original-height="3264" data-original-width="2448" height="200" src="https://blogger.googleusercontent.com/img/a/AVvXsEiUVcxY8gXqe4wWkRU2wYI1PTDLTMZJEyX5KPINP7J01C4W6DpyHT96J9G4k3tmC2SrK1dELsoSKVrBLB-x8XroGh3rt_22olILoug73Lt6smk5xNim2ppOwZ_5kMKl3YRnDz_OM4YdC2rPaX6r0wZlvMlAqEe9tJ5kPgAh-xBOZgVPC0ss4J9LE4zK=w150-h200" width="150" /></a></div><span face=""Arial",sans-serif" lang="EN-GB" style="font-size: 12pt; line-height: 107%; mso-ansi-language: EN-GB; mso-bidi-language: AR-SA; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">We conducted a year-long experiment to measure nitrous oxide emissions
from a variety of multi-species grassland communities that included the
monocultures of 6 species from three functional groups; two grasses </span><span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;">(</span><i style="font-family: Arial, sans-serif; font-size: 12pt; text-align: left;">Lolium
perenne, Phleum pratense</i><span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;">), two legumes (</span><i style="font-family: Arial, sans-serif; font-size: 12pt; text-align: left;">Trifolium
pratense, Trifolium repens</i><span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;">) and two herbs (</span><i style="font-family: Arial, sans-serif; font-size: 12pt; text-align: left;">Cichorium intybus, Plantago lanceolata</i><span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;">), and systematically varying
multi-species communities comprising different proportions of the six species. Plots
were harvested multiple times per year (and were not grazed). Nitrous oxide emissions
from the different plant communities were measured using static chambers over one
year. Details are in Cummins et al. (2021). Plots received 150 kg ha</span><sup style="font-family: Arial, sans-serif; text-align: left;">−1</sup><span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;">
year</span><sup style="font-family: Arial, sans-serif; text-align: left;">−1</sup><span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;"> </span><span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;">of nitrogen (N) (150N), except </span><i style="font-family: Arial, sans-serif; font-size: 12pt; text-align: left;">L.
perenne</i><span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;"> monocultures which received two N levels: 150N and 300N.</span><p></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; mso-layout-grid-align: none; text-autospace: none;"><span face="Arial, sans-serif" style="font-size: 12pt;"><i>Effects on absolute N</i><sub><span face=""Arial",sans-serif" lang="EN-GB" style="font-size: 9pt; line-height: 107%; mso-ansi-language: EN-GB; mso-bidi-font-size: 12.0pt; mso-bidi-language: AR-SA; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;"><i>2</i></span></sub><i>O
emissions</i></span></p>
<p class="MsoNormal" style="text-align: justify;"><span face=""Arial",sans-serif" lang="EN-GB" style="font-size: 12pt; line-height: 107%;">Overall,
the effect of plant diversity on <span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;">N</span><span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;"><sub><span face=""Arial",sans-serif" lang="EN-GB" style="font-size: 9pt; line-height: 12.84px; mso-ansi-language: EN-GB; mso-bidi-language: AR-SA; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">2</span></sub>O</span> emissions was derived from linear
combinations of species performances' in monoculture (species identity); there
were no net synergistic or antagonistic effects on <span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;">N</span><span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;"><sub><span face=""Arial",sans-serif" lang="EN-GB" style="font-size: 9pt; line-height: 12.84px; mso-ansi-language: EN-GB; mso-bidi-language: AR-SA; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">2</span></sub>O</span> due to mixing the
species. Increasing from 150N to 300N in <i style="mso-bidi-font-style: normal;">L.
perenne</i> resulted in a highly significant increase in cumulative <span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;">N</span><span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;"><sub><span face=""Arial",sans-serif" lang="EN-GB" style="font-size: 9pt; line-height: 12.84px; mso-ansi-language: EN-GB; mso-bidi-language: AR-SA; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">2</span></sub>O</span> emissions from 1.39 to 3.18 kg <span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;">N</span><span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;"><sub><span face=""Arial",sans-serif" lang="EN-GB" style="font-size: 9pt; line-height: 12.84px; mso-ansi-language: EN-GB; mso-bidi-language: AR-SA; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">2</span></sub>O</span>-N ha<sup style="text-align: left;">−1</sup><span style="font-size: 12pt; text-align: left;"> </span>year<sup style="text-align: left;">−1</sup>. Higher <span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;">N</span><span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;"><sub><span face=""Arial",sans-serif" lang="EN-GB" style="font-size: 9pt; line-height: 12.84px; mso-ansi-language: EN-GB; mso-bidi-language: AR-SA; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">2</span></sub>O</span> emissions were
also associated with the legume functional group. <o:p></o:p></span></p>
<p class="MsoNormal" style="text-align: justify;"><i style="font-family: Arial, sans-serif; font-size: 12pt;">Effects
on <i style="font-family: "Times New Roman"; text-align: left;">N</i><sub style="font-family: "Times New Roman"; font-style: normal; text-align: left;"><span face=""Arial",sans-serif" lang="EN-GB" style="font-size: 9pt; line-height: 12.84px; mso-ansi-language: EN-GB; mso-bidi-font-size: 12.0pt; mso-bidi-language: AR-SA; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;"><i>2</i></span></sub><i style="font-family: "Times New Roman"; text-align: left;">O</i> emissions intensity</i></p>
<p class="MsoNormal" style="text-align: justify;"><span face=""Arial",sans-serif" lang="EN-GB" style="font-size: 12pt; line-height: 107%;">We
also calculated the <span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;">N</span><span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;"><sub><span face=""Arial",sans-serif" lang="EN-GB" style="font-size: 9pt; line-height: 12.84px; mso-ansi-language: EN-GB; mso-bidi-language: AR-SA; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">2</span></sub>O</span> emissions intensity (the total annual <span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;">N</span><span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;"><sub><span face=""Arial",sans-serif" lang="EN-GB" style="font-size: 9pt; line-height: 12.84px; mso-ansi-language: EN-GB; mso-bidi-language: AR-SA; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">2</span></sub>O</span> emissions (g
ha-1 year-1) produced per kg biomass N or tonnes DM yield):<o:p></o:p></span></p>
<p class="MsoNormal" style="margin-left: 108pt; text-align: justify; text-indent: 36pt;"><span face=""Arial",sans-serif" lang="EN-GB" style="font-size: 12pt; line-height: 107%;">emissions intensity = <span face="Arial, sans-serif" style="font-size: 12pt; text-align: left; text-indent: 0px;">N</span><span face="Arial, sans-serif" style="font-size: 12pt; text-align: left; text-indent: 0px;"><sub><span face=""Arial",sans-serif" lang="EN-GB" style="font-size: 9pt; line-height: 12.84px; mso-ansi-language: EN-GB; mso-bidi-language: AR-SA; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">2</span></sub>O</span> emissions / yield <o:p></o:p></span></p>
<p class="MsoNormal" style="text-align: justify;"><span face=""Arial",sans-serif" lang="EN-GB" style="font-size: 12pt; line-height: 107%;">Emissions
intensities (yield-scaled <span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;">N</span><span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;"><sub><span face=""Arial",sans-serif" lang="EN-GB" style="font-size: 9pt; line-height: 12.84px; mso-ansi-language: EN-GB; mso-bidi-language: AR-SA; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">2</span></sub>O</span> emissions) from multi-species communities around
the equi-proportional mixture were lowered due to interactions among species;
thus, there was an antagonistic effect of mixing species that resulted in lower <span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;">N</span><span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;"><sub><span face=""Arial",sans-serif" lang="EN-GB" style="font-size: 9pt; line-height: 12.84px; mso-ansi-language: EN-GB; mso-bidi-language: AR-SA; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">2</span></sub>O</span> emission intensities in mixtures, compared to the expected <span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;">N</span><span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;"><sub><span face=""Arial",sans-serif" lang="EN-GB" style="font-size: 9pt; line-height: 12.84px; mso-ansi-language: EN-GB; mso-bidi-language: AR-SA; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">2</span></sub>O</span> emissions
intensity based on monoculture performances. This was largely caused by the strong
synergistic effects of plant diversity on biomass yield and nitrogen yield (Grange et al. 2021; see related blog post). Looking at <span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;">N</span><span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;"><sub><span face=""Arial",sans-serif" lang="EN-GB" style="font-size: 9pt; line-height: 12.84px; mso-ansi-language: EN-GB; mso-bidi-language: AR-SA; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">2</span></sub>O</span> emissions intensity scaled by nitrogen yield, the 6-species mixture was significantly
lower than that of <i style="mso-bidi-font-style: normal;">L. perenne</i> at both
300N and 150N. <o:p></o:p></span></p>
<p class="MsoNormal" style="text-align: justify;"><span face=""Arial",sans-serif" lang="EN-GB" style="font-size: 12pt; line-height: 107%;">In
comparison to 300N <i style="mso-bidi-font-style: normal;">L. perenne</i>, the
same N yield or DM yield could have been produced with the equi-proportional
6-species mixture (150 N) while reducing <span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;">N</span><span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;"><sub><span face=""Arial",sans-serif" lang="EN-GB" style="font-size: 9pt; line-height: 12.84px; mso-ansi-language: EN-GB; mso-bidi-language: AR-SA; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">2</span></sub>O</span> losses by 63% and 58%
respectively. Compared to 150N <i>L. perenne</i>, the same N yield or DM yield could
have been produced with the 6-species mixture while reducing <span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;">N</span><span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;"><sub><span face=""Arial",sans-serif" lang="EN-GB" style="font-size: 9pt; line-height: 12.84px; mso-ansi-language: EN-GB; mso-bidi-language: AR-SA; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">2</span></sub>O</span> losses by 41%
and 24% respectively. <o:p></o:p></span></p><p class="MsoNormal" style="text-align: justify;"></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEg19_LfDJN9F9ViSsphp5d8g-8d1XSotucF-Zg-0UJ7tnPMS1aSMebwIPRXAKmuBV2y1rdWvkqsfYWr7tasryI3RF54tgf5_81lTJD85rOvC13RFTwVJ5pvtvRngjpCq2YNqLryeAR45Msa6NaZVoM5yJWTteWsM5oWcelc86wv_1s29p7e4uDzCQXe=s896" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="413" data-original-width="896" height="296" src="https://blogger.googleusercontent.com/img/a/AVvXsEg19_LfDJN9F9ViSsphp5d8g-8d1XSotucF-Zg-0UJ7tnPMS1aSMebwIPRXAKmuBV2y1rdWvkqsfYWr7tasryI3RF54tgf5_81lTJD85rOvC13RFTwVJ5pvtvRngjpCq2YNqLryeAR45Msa6NaZVoM5yJWTteWsM5oWcelc86wv_1s29p7e4uDzCQXe=w640-h296" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;"><b style="font-family: "Times New Roman"; text-align: justify;"><span face="Arial, sans-serif" lang="EN-GB" style="font-size: 12pt; line-height: 17.12px;">Fig. 2. </span></b><span lang="EN-GB" style="font-size: 12pt; line-height: 17.12px; text-align: justify;"><span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;">N</span><span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;"><sub><span face=""Arial",sans-serif" lang="EN-GB" style="font-size: 9pt; line-height: 12.84px; mso-ansi-language: EN-GB; mso-bidi-language: AR-SA; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">2</span></sub>O </span>emissions per unit yield produced calculated (left) per unit yield of forage and (right) per unit yield of N in the forage.</span></td></tr></tbody></table><p></p><p class="MsoNormal" style="text-align: justify;"><span face="Arial, sans-serif" style="font-size: 12pt;">Figure 2 (above) looks at three specific communities from our overall
design, and ignores most of the combinations of species that are possible with
the three functional groups of grasses, herbs, and legumes. The emissions
intensity of <span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;">N</span><span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;"><sub><span face=""Arial",sans-serif" lang="EN-GB" style="font-size: 9pt; line-height: 12.84px; mso-ansi-language: EN-GB; mso-bidi-language: AR-SA; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">2</span></sub>O</span> is presented as a continuous response surface across the
systematic variation in sown proportions of grasses, herbs, and legumes. This
clearly shows the reduction in emissions intensity in the community sown with
equal proportions of all three functional groups, and increases in grass- and
legume- dominated communities.</span></p>
<p class="MsoNormal" style="text-align: justify;"><span face=""Arial",sans-serif" lang="EN-GB" style="font-size: 12pt; line-height: 107%;"></span></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEg5uIYmY3vHq89vQp5qFWQTlT6tOcbANyd6zmE-G7wdJysP-34PTvE1kT8wgxGJOQgwdn8_GVxPYENIC10Y0OG8b1xV51ctXNl886cKflOzXF4_p1i7cKKhA-JVTqTI74Qt9tD4yG7345okFma9Al2I7EtLJBATo20wmeghfvY_SqNbhdkR_1tS4J5S=s916" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="664" data-original-width="916" height="464" src="https://blogger.googleusercontent.com/img/a/AVvXsEg5uIYmY3vHq89vQp5qFWQTlT6tOcbANyd6zmE-G7wdJysP-34PTvE1kT8wgxGJOQgwdn8_GVxPYENIC10Y0OG8b1xV51ctXNl886cKflOzXF4_p1i7cKKhA-JVTqTI74Qt9tD4yG7345okFma9Al2I7EtLJBATo20wmeghfvY_SqNbhdkR_1tS4J5S=w640-h464" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;"><b style="font-size: 16px; text-align: justify;">Fig. 3.</b><span style="font-size: 16px; text-align: justify;"> Estimated emission intensity of <span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;">N</span><span face="Arial, sans-serif" style="font-size: 12pt; text-align: left;"><sub><span face=""Arial",sans-serif" lang="EN-GB" style="font-size: 9pt; line-height: 12.84px; mso-ansi-language: EN-GB; mso-bidi-language: AR-SA; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">2</span></sub>O</span> (scaled by total N yield in forage) in response to variation in the sown proportion of grass (G), herb (H) and legume (L) functional groups (FG) within grassland communities. The communities represented in this ternary diagram are based on an equal proportional contribution of each of the two species within a FG. Thus, each vertex indicates the average of the two component species in the respective FG; the sides represent communities with varying proportions of two FGs (comprising four species), and the interior points represent varying proportions of three FGs (comprising six species).</span></td></tr></tbody></table><span face="Arial, sans-serif" style="font-size: 12pt;"><br /></span><p></p><p></p><p class="MsoNormal" style="text-align: justify;"><span face=""Arial",sans-serif" lang="EN-GB" style="font-size: 12pt; line-height: 107%;">Overall,
this research shows the potential for multi-species grassland mixtures to
contribute to sustainable agriculture.</span></p><p>
</p><p class="MsoNormal" style="text-align: start;"><span face="Arial, sans-serif" style="font-size: 12pt; text-align: justify;">John
Finn and Saoirse Cummins, Teagasc,</span><span face="Arial, sans-serif" style="font-size: 12pt; text-align: justify;"> Johnstown Castle, Environment Research Centre</span></p><p></p><p>
</p><p class="MsoNormal" style="text-align: justify;"><b style="font-family: Arial, sans-serif; font-size: 12pt;">Reference</b></p>
<p class="MsoNormal" style="text-autospace: none;"><span face=""Arial",sans-serif" lang="EN-GB" style="font-size: 12pt; line-height: 107%; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE; mso-fareast-theme-font: minor-fareast; mso-no-proof: yes;">Cummins et al. 2021. <a href="https://www.sciencedirect.com/science/article/pii/S0048969721032344">Beneficial
effects of multi-species mixtures on N2O emissions from intensively managed
grassland swards.</a> Science of the Total Environment. (with Open Access data
and code) <o:p></o:p></span></p><br /><p></p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-3383131600232191143.post-90801329703763540202022-02-23T02:34:00.003-08:002022-02-23T02:34:55.193-08:00Webinar: piloting a biodiversity indicator in the Teagasc National Farm Survey<p class="MsoNormal"><span style="font-family: "Arial",sans-serif; font-size: 12.0pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;">As part
of the EU <span class="MsoHyperlink"><a href="https://www.smartagrihubs.eu/">SmartAgriHubs</a></span>
project, Teagasc is investigating how to incorporate biodiversity into the
Teagasc National Farm Survey. In a recent <a href="https://www.teagasc.ie/publications/2021/national-farm-survey---2020-sustainability-report.php" target="_blank">webinar</a>, I presented the first results from the application of a habitat index to a sample of 300 farms in the NFS. <o:p></o:p></span></p><p class="MsoNormal"></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEig_uQnTAU36jdu1JhAXTsO8KN400REKibobQHm7459TWtJzKi5JGSIWCoK2pw058uHNzVL2xRYzScBag_FUMd_DYZmHDR9bfO-0lWIySo4cAOavR-iFZeGaEhc1RDgQhftiXxmh0ZBYLq__N_ATrboCmR3fubmg8UdENaoWcbvBIFzOTHqJxKGrd7U=s3264" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="2448" data-original-width="3264" height="150" src="https://blogger.googleusercontent.com/img/a/AVvXsEig_uQnTAU36jdu1JhAXTsO8KN400REKibobQHm7459TWtJzKi5JGSIWCoK2pw058uHNzVL2xRYzScBag_FUMd_DYZmHDR9bfO-0lWIySo4cAOavR-iFZeGaEhc1RDgQhftiXxmh0ZBYLq__N_ATrboCmR3fubmg8UdENaoWcbvBIFzOTHqJxKGrd7U=w200-h150" width="200" /></a></div><br /><span style="font-family: "Arial",sans-serif; font-size: 12.0pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;"><br /></span><p></p>
<p class="MsoNormal"><span style="font-family: Arial, sans-serif; font-size: 12pt;"><span></span></span></p><a name='more'></a><span style="font-family: "Arial",sans-serif; font-size: 12.0pt; mso-ansi-language: EN-IE; mso-bidi-language: AR-SA; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;">In a previous post, I described the general
approach that we are taking to pilot the recording of habitat information on
300 farms that participate in the Teagasc National Farm Survey.</span><br /><p></p>
<p class="MsoNormal"><span style="font-family: Arial, sans-serif; font-size: 12pt;">A 10-minute summary of our recent work </span><b style="font-family: Arial, sans-serif; font-size: 12pt;"><span style="color: #1f497d;">‘</span><a href="https://www.teagasc.ie/publications/2021/national-farm-survey---2020-sustainability-report.php" style="color: #1f497d;">Biodiversity
measurement on NFS farms</a>’</b><span style="font-family: Arial, sans-serif; font-size: 12pt;"> is</span><b style="font-family: Arial, sans-serif; font-size: 12pt;"> </b><span style="font-family: Arial, sans-serif; font-size: 12pt;">available as a webinar. Scroll to the end of
that page, and click on the image for a YouTube video. </span></p><p class="MsoNormal"><span style="font-family: "Arial",sans-serif; font-size: 12.0pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;">This
presentation includes a description of farm habit index that weights the
habitats of a farm on a 5-point scale (5 represents higher conservation value),
and produces an index value for the farm. The more area of the farm occupied by
habitats with higher values, the higher the overall index value. A
*preliminary* calculation of the farm habitat index, grouped across the different
farm categories, is presented in Fig. 1.<o:p></o:p></span></p><p class="MsoNormal"><br /></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEi89SgCQC2Jt1tWhIt7YxKKsdA8ii8bmnCurf2o-2FjX0fTSDzEIxtD1pkG3Pd0CEAlUuQj3waZX4MfycOgVADJoGdou3YYHqY4HTcks32ygGYZa6CocKVdnTR3QVzUVYoIk89MTKHXVZVRjCuxMA6LLImdD64BmLnhmdi4XuVbcxksA0vZ9vaNdbvu=s925" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="858" data-original-width="925" height="371" src="https://blogger.googleusercontent.com/img/a/AVvXsEi89SgCQC2Jt1tWhIt7YxKKsdA8ii8bmnCurf2o-2FjX0fTSDzEIxtD1pkG3Pd0CEAlUuQj3waZX4MfycOgVADJoGdou3YYHqY4HTcks32ygGYZa6CocKVdnTR3QVzUVYoIk89MTKHXVZVRjCuxMA6LLImdD64BmLnhmdi4XuVbcxksA0vZ9vaNdbvu=w400-h371" width="400" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Fig. 1. Distribution of farm index values (1-5 scale) across the farm categories in our selection of 300 farms in the Teagasc National Farm Survey. (based on preliminary analysis) </td></tr></tbody></table><p class="MsoNormal"><span style="font-family: Arial, sans-serif; font-size: 12pt;">The
innovation in this new pilot project is delivered through:</span></p>
<p class="MsoListParagraphCxSpFirst" style="mso-list: l0 level1 lfo1; text-indent: -18.0pt;"></p><ul style="text-align: left;"><li><span style="font-family: "Arial",sans-serif; font-size: 12.0pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;">reliance
on satellite imagery to identify broad classes of farmland habitats e.g.
ploughed fields, intensively managed grasslands, arable crops, heathlands,
peatlands, lakes, ponds, woodlands and so on. (There is also ground-truthing on
10% of the farms.)<br /><br /><o:p></o:p></span></li><li><span style="font-family: "Arial",sans-serif; font-size: 12.0pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;">incorporation
of farm-specific photos via a web-based application for uploading and
transferring farm photos<br /><br /><o:p></o:p></span></li><li><span style="font-family: "Arial",sans-serif; font-size: 12.0pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;">collation
and combination of these data through an automated reporting process that can
create customisable farm-specific habitat maps and reports. An example is
provided below (Fig. 2), and see a <a href="https://farmecol.blogspot.com/2020/11/smartagrihubs-pilot-incorporation-of.html" target="_blank">previous post</a> for more detail.<br /><br /></span></li><li><span style="font-family: "Arial",sans-serif; font-size: 12.0pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;">provision
of the farm habitat report and information to the participant farmers. This can
help to better guide and inform their management of farm biodiversity<br /><br /></span></li><li><span face="Calibri, sans-serif" style="font-size: 12pt; text-indent: -18pt;"><span style="font-family: Arial, sans-serif; font-size: 12pt; text-indent: -18pt;">inclusion
of a biodiversity assessment in the Teagasc National Farm Survey (NFS). The
great benefit of conducting the biodiversity assessment on NFS farms is the
ability to link with the time series of the suite of other agronomic, economic,
environmental and social data collected by the Teagasc NFS.</span></span></li></ul><div style="text-indent: -24px;"><span style="font-family: Arial, sans-serif;"><br /></span></div><!--[if !supportLists]--><p></p>
<div><p class="MsoNormal"><span style="font-family: "Arial",sans-serif; font-size: 12.0pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;"><br /><b>Looking to the future</b></span></p><p class="MsoNormal"><span style="font-family: Arial, sans-serif; font-size: 16px;">The incorporation of a biodiversity metric into NFS would also help develop the inclusion of biodiversity in the planned transformation of the FADN into the FSDN (Farm Sustainability Data Network) (Kelly et al., 2018). </span></p><p class="MsoNormal"><span style="font-family: Arial, sans-serif; font-size: 16px;">Including such an approach on all NFS farms, as the first biodiversity metric to be included in the NFS, would provide an initial baseline assessment of habitat quantity and diversity on different types of Irish farming systems in a way that is nationally representative. Repeated assessment (over time) would also show whether and how habitat biodiversity on different types of Irish farms is changing through time and in response to national and EU policy objectives. </span><span style="font-family: Arial, sans-serif; font-size: 16px;">A distinct advantage of using the NFS set of farms is that the habitat data can also be investigated in tandem with other financial, environmental and social and economic data collected as part of the FADN. </span></p><p class="MsoNormal"><span style="font-family: Arial, sans-serif; font-size: 16px;">With the planned release of the OSI/EPA National Habitat Map in mid-2022, the cost-effectiveness of farm-scale habitat reporting of habitat quantity will greatly increase. In addition, the use of larger drones allows more rapid and affordable collection of data to assess habitat quality. These developments will allow a significant increase in capacity and cost-effectiveness for collection of data on both habitat quantity and quality.</span></p><p class="MsoNormal"><span style="font-family: Arial, sans-serif; font-size: 12pt;"> </span></p><p class="MsoNormal" style="margin-right: -9.95pt;"><span style="font-family: Arial, sans-serif; font-size: 12pt;"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEgMu7fD_ZpSHStInYtndSKMpLubsl-YgBuIX2ZNgDVD4_jKFXGJ24HGxN_MBXsftwmHh7O04og179VvjMidUjvXB29EVoAu1B2Fv3yBNTnkD753u7ris92IQn_UJw6shJ2XS3IJK4aHsvwMUny_D_SY6wPSH_aj1C7loFqy2jKmnd9sPmDoTEkCN1DE=s1139" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="786" data-original-width="1139" height="442" src="https://blogger.googleusercontent.com/img/a/AVvXsEgMu7fD_ZpSHStInYtndSKMpLubsl-YgBuIX2ZNgDVD4_jKFXGJ24HGxN_MBXsftwmHh7O04og179VvjMidUjvXB29EVoAu1B2Fv3yBNTnkD753u7ris92IQn_UJw6shJ2XS3IJK4aHsvwMUny_D_SY6wPSH_aj1C7loFqy2jKmnd9sPmDoTEkCN1DE=w640-h442" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Fig. 2. Example of the kind of information included in each farm habitat report. </td></tr></tbody></table></span><span style="font-family: Arial, sans-serif;"><br /></span></p><p class="MsoNormal" style="margin-right: -9.95pt;"><b><span style="font-family: Arial, sans-serif; font-size: 12pt;">Further information</span></b></p><p class="MsoNormal" style="margin-right: -9.95pt;"><span style="font-family: Arial, sans-serif; font-size: 12pt;">J.A. Finn and P. Moran. A pilot study of methodology for the development of farmland habitat reports for sustainability assessments. Irish Journal of Agricultural and Food Research. <a href="https://doi.org/10.15212/ijafr-2020-0103" target="_blank">DOI: 10.15212/ijafr-2020-0103</a>.<o:p></o:p></span></p><p class="MsoNormal" style="margin-right: -9.95pt;"><span style="font-family: arial;">Finn, J.A. 2020.<a href="https://farmecol.blogspot.com/2020/11/smartagrihubs-pilot-incorporation-of.html" target="_blank">SmartAgriHubs: pilot incorporation of farmland habitats in Teagasc National Farm Survey</a>. Blog
post.</span></p><p class="MsoNormal"><span style="font-family: "Arial",sans-serif; font-size: 12.0pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;">Kelly,
E., Latruffe, L., Desjeux, Y., Ryan, M., Uthes, S., Diazabakana, A., Dillon, E.
and Finn, J.A. 2018. <a href="https://www.sciencedirect.com/science/article/pii/S1470160X17308439" target="_blank">Sustainability indicators for improved assessment of the effects of agricultural policy across the EU: Is FADN the answer?</a> Ecological
Indicators. 89: 903-911.<o:p></o:p></span></p><p class="MsoNormal"><span style="font-family: "Arial",sans-serif; font-size: 12.0pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;">This
research was supported by the SmartAgriHubs project, which received funding from
the European Union’s Horizon 2020 Research and Innovation Programme under grant
agreement no. 818182.<o:p></o:p></span></p><p class="MsoNormal"><br /></p></div>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-3383131600232191143.post-55990663438102544172021-08-20T08:10:00.004-07:002022-03-03T02:39:15.791-08:00Multi-species swards: more forage with less fertiliser, and more resilient to drought<p><span face="Arial, sans-serif"><i><span style="font-size: x-small;">Based on a Teagasc press release from August 2021</span></i></span></p><p><span face="Arial, sans-serif">Six-species swards outperformed perennial ryegrass
monocultures and were considerably more resistant to drought. New research from
Teagasc, Johnstown Castle and Trinity College Dublin shows that multi-species mixtures
receiving 150 kg/ha/</span><span face="Arial, sans-serif">year of
nitrogen fertiliser, out-yielded perennial ryegrass monocultures receiving double
that amount of fertiliser (300 </span><span face="Arial, sans-serif">kg/ha/</span><span face="Arial, sans-serif">year</span><span face="Arial, sans-serif">). Increases in plant
diversity up to six species in intensively managed grasslands reduced the
impact of drought, and produced more yield with less fertiliser.</span></p><div class="separator" style="clear: both; text-align: center;"><span face="Arial, sans-serif"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhCfZyeM0rnJsE9tZoYM-saEAQPrtsIbzJqPfK9V2fh5udO4ako9mXMxE7pr0gwsSzr_dlkI1cwmXPYivfimLg4UQC6mIytPAvq5RlJWwcKmNUN9ROyIDCJGBZYa9eHMAOpzrJi9p6IyfY/s2048/IMG_7949%255B1%255D.JPG" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1536" data-original-width="2048" height="240" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhCfZyeM0rnJsE9tZoYM-saEAQPrtsIbzJqPfK9V2fh5udO4ako9mXMxE7pr0gwsSzr_dlkI1cwmXPYivfimLg4UQC6mIytPAvq5RlJWwcKmNUN9ROyIDCJGBZYa9eHMAOpzrJi9p6IyfY/s320/IMG_7949%255B1%255D.JPG" width="320" /></a></span></div><div class="separator" style="clear: both; text-align: center;"><span face="Arial, sans-serif">6-species swards from dairy grazing system at Teagasc, Johnstown Castle. </span></div><span face="Arial, sans-serif"><span><a name='more'></a></span></span><p></p>
<p class="MsoNormal"><b style="mso-bidi-font-weight: normal;"><i style="mso-bidi-font-style: normal;"><span face=""Arial",sans-serif">Higher-diversity
lower-input mixtures produced higher yields than low-diversity high-input
monoculture</span></i></b><b style="mso-bidi-font-weight: normal;"><span face=""Arial",sans-serif"> <o:p></o:p></span></b></p>
<p class="MsoNormal"><span face=""Arial",sans-serif">Averaged across
the two years of the trial, mixtures with all six species produced the highest
yields, and yielded more than the best of the six monocultures. Under rainfed
conditions, the mixture with equal proportions of all six species at sowing (produced
11.8 tonnes/ha/year) outperformed the best-performing monoculture (produced 10.5
tonnes/ha/year).<o:p></o:p></span></p>
<p class="MsoNormal"><span face=""Arial",sans-serif">Even after
increasing fertiliser rates on perennial ryegrass in monoculture, it did not yield as
much as the highest diversity mixture.</span></p><p class="MsoNormal"><i></i></p><div class="separator" style="clear: both; text-align: center;"><i><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj98KT35SKFBMXAVcAOb8hVtsMw-fRXuHZHZLOjORfLCKLydli03rGRqaVme2ah45U_VA-0s52ThVmc46ej4D_tPBsTIKdcY7FH-lUKp7ksWx9HgeITRPeb6_oRw_9nuNpFU7iQVU0tqUo/s2030/Drought%252Brainfed.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1136" data-original-width="2030" height="358" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj98KT35SKFBMXAVcAOb8hVtsMw-fRXuHZHZLOjORfLCKLydli03rGRqaVme2ah45U_VA-0s52ThVmc46ej4D_tPBsTIKdcY7FH-lUKp7ksWx9HgeITRPeb6_oRw_9nuNpFU7iQVU0tqUo/w640-h358/Drought%252Brainfed.png" width="640" /></a></i></div><i><span face=""Arial",sans-serif">Getting more from less: the annual yield
of six-species mixtures (6-species) of grasses, legumes and herbs out-performed
the perennial ryegrass monoculture and was not lower than the perennial
ryegrass monoculture with a high level of nitrogen fertiliser.</span></i><p></p>
<p class="MsoNormal"><i style="mso-bidi-font-style: normal;"><span face=""Arial",sans-serif"><o:p> </o:p></span></i></p>
<p class="MsoNormal"><b style="mso-bidi-font-weight: normal;"><i style="mso-bidi-font-style: normal;"><span face=""Arial",sans-serif">Higher diversity mixtures
produced similar yields under drought as other comparisons under rainfed
conditions</span></i></b><b style="mso-bidi-font-weight: normal;"><span face=""Arial",sans-serif"> <o:p></o:p></span></b></p>
<p class="MsoNormal"><span face=""Arial",sans-serif">As expected, total
annual yields were generally reduced by the experimental drought. Nevertheless,
the higher-diversity lower-input mixtures under unfavourable drought conditions
achieved similar yields to those from the low-diversity, high-input comparison
under favourable rainfed conditions. This indicates the potential for
multi-species mixtures to mitigate the effect of more variable weather
conditions on grassland yields across the whole year.<b style="mso-bidi-font-weight: normal;"> <o:p></o:p></b></span></p>
<p class="MsoNormal"><span face=""Arial",sans-serif">Dr John Finn, a
senior researcher at Teagasc’s Environment Research Centre at Johnstown Castle
said; “The need for research on drought in Irish grasslands has become all too
obvious in recent years, and that need will only increase in the coming
decades. The use of multi-species is one strategy to improve the resilience of
grassland production.”<o:p></o:p></span></p>
<p class="MsoNormal"><span face=""Arial",sans-serif">Dr Caroline
Brophy, Associate Professor at Trinity College Dublin, said; “It is crucial
that we reduce nitrogen fertiliser inputs to improve the sustainability of Irish
agricultural grasslands. Using innovative statistical approaches, we show that
multi-species mixtures provide a solution to reduce fertiliser usage, without
compromising on grassland production.”<o:p></o:p></span></p>
<p class="MsoNormal"><b style="mso-bidi-font-weight: normal;"><i style="mso-bidi-font-style: normal;"><span face=""Arial",sans-serif">What proportions of
grasses, legumes and herbs are the best?<o:p></o:p></span></i></b></p>
<p class="MsoNormal"><span face=""Arial",sans-serif">The most
productive swards were a combination of species from the three functional
groups of grasses, legumes and herbs. With legume proportion between 30 and
70%, yields were better than the best monoculture. <o:p></o:p></span></p>
<br />
<p class="MsoNormal"><i style="mso-bidi-font-style: normal;"><span face=""Arial",sans-serif" style="color: black; mso-themecolor: text1;"></span></i></p><div class="separator" style="clear: both; text-align: center;"><i style="mso-bidi-font-style: normal;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjpuVVJSV2qw1yHvU6dfqQQr-FnG-mKy7q2n83o3zIFnVhNQftjkJzHhTNqDTCQDVCyc6Rm_Hu20WUDv63X8bJnpXLIUMBH9HOyF_9BaI0gy5dGyWZr0PFdDzYO_DtCkK70alu-qmxLENw/s1672/ternray.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="745" data-original-width="1672" height="286" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjpuVVJSV2qw1yHvU6dfqQQr-FnG-mKy7q2n83o3zIFnVhNQftjkJzHhTNqDTCQDVCyc6Rm_Hu20WUDv63X8bJnpXLIUMBH9HOyF_9BaI0gy5dGyWZr0PFdDzYO_DtCkK70alu-qmxLENw/w640-h286/ternray.png" width="640" /></a></i></div><i style="mso-bidi-font-style: normal;">More
diverse mixtures of grasses, herbs and legumes had higher (darker green) annual
yields (tonnes/ha/year<sup>-</sup>). Results averaged across two years under
rainfed conditions (left) with a 9-week summer drought applied (right). Yields
from plots with only herbs (H), legumes (L) and grasses (G) monocultures are
indicated in the corner points. The red line on the yield scale indicates yields
from perennial ryegrass with 300 kg/ha/year of nitrogen. As one moves from the
centre toward G, L, and H, the change in colour indicates the changes in yields
with increases in the proportion of grass, legume and herb, respectively. <o:p></o:p></i><p></p>
<p class="MsoNormal"><span face=""Arial",sans-serif">Teagasc Walsh
Scholar Guylain Grange, who conducted the experiment said; “Multi-species
mixtures can be a practical, farm-scale solution for intensive grassland production
with lower nitrogen fertiliser input, and can mitigate the risk associated with
summer droughts due to climate change. Further research at Teagasc is investigating
the use of multi-species mixtures under grazed conditions.”<o:p></o:p></span></p>
<p class="MsoNormal"><span face=""Arial",sans-serif"><o:p> </o:p></span></p>
<p class="MsoNormal"><b style="mso-bidi-font-weight: normal;"><span face=""Arial",sans-serif">Further information: <o:p></o:p></span></b></p>
<p class="MsoNormal"><span face=""Arial",sans-serif">This research
is reported in the Journal of Applied Ecology and available online as an Open
Access article: <span class="MsoHyperlink"><a href="https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/1365-2664.13894">https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/1365-2664.13894</a><o:p></o:p></span></span></p>
<p class="MsoNormal"><span face=""Arial",sans-serif">This research
was made possible by a Walsh Scholarship award by Teagasc. Guylain Grange
conducted the experiment at Teagasc’s Environment Research Centre at Johnstown
Castle, Wexford, and is a PhD student at Trinity College Dublin. Dr Caroline Brophy (Trinity College Dublin) and Dr John Finn (Teagasc) supervised
the research. <o:p></o:p></span></p><br />Unknownnoreply@blogger.com1tag:blogger.com,1999:blog-3383131600232191143.post-8116643715900885852021-03-03T03:05:00.007-08:002021-03-09T06:17:07.295-08:00Our research on multi-species mixtures at British Grassland Society<p></p><div class="separator" style="clear: both; text-align: center;"><span style="text-align: left;">The British Grassland Society's 13th Research Conference addressed '<a href="https://www.britishgrassland.com/research-conference-2021/" target="_blank">Multi-species Swards'</a> on 2-4 March 2021. Here are links to some of our contributions to the conference, including an invited presentation, a research presentation and a poster. <div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEioAxA0VB0RsVJ6euZGV_C7CVY27MTDjjuPMlRyb6OVPaCoWmR2C0HnXe3yJ0ud7lztcpnjycT2vSN-Ve2ggeFL1fx_oAxC_7oj5PLr5somTHIRKyFu2SJInAob0ktlFE9hmU2UJkZCh1c/s2048/IMG_4258.JPG" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="2048" data-original-width="1536" height="200" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEioAxA0VB0RsVJ6euZGV_C7CVY27MTDjjuPMlRyb6OVPaCoWmR2C0HnXe3yJ0ud7lztcpnjycT2vSN-Ve2ggeFL1fx_oAxC_7oj5PLr5somTHIRKyFu2SJInAob0ktlFE9hmU2UJkZCh1c/w150-h200/IMG_4258.JPG" width="150" /></a></div></span></div><div class="separator" style="clear: both; text-align: center;"></div><p></p><p><a href="https://www.youtube.com/watch?v=EA3wl0C-ICg" target="_blank"><span></span></a></p><a name='more'></a>Prof Caroline Brophy of Trinity College Dublin presented our collaborative work in an invited presentation. see the YouTube video of her presentation '<a href="https://www.youtube.com/watch?v=fJafwVYgyRI&feature=youtu.be" target="_blank">Species diversity in intensively managed grasslands can promote ecosystem multifunctionality and help protect against the impact of extreme weather events</a>'. (24 min YouTube video)<br /><div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjm2SRt1E3sxkhZ3Oy-JqW0i1g8B_LPt5B3XrX0Z7s-r8NQDl1vCcmUcORsKWwN1Mlh3aqFwNxEvaPZj-NiTWxsj0V5equAO38LDAsgUUcDTvXAFEXeO8SUzjCZhUnrgGg6CfLn-Ve7z6c/" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="638" data-original-width="1206" height="169" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjm2SRt1E3sxkhZ3Oy-JqW0i1g8B_LPt5B3XrX0Z7s-r8NQDl1vCcmUcORsKWwN1Mlh3aqFwNxEvaPZj-NiTWxsj0V5equAO38LDAsgUUcDTvXAFEXeO8SUzjCZhUnrgGg6CfLn-Ve7z6c/" width="320" /></a></div><br /><br /></div><div><br /></div><div>Saoirse Cummins made a presentation '<a href="https://www.youtube.com/watch?v=EA3wl0C-ICg" target="_blank">An annual assessment of N2O emissions from multi-species grasslands</a>', available as a 10-minute YouTube video. Key points are:</div><div><ul style="text-align: left;"><li>multi-species mixtures can act to reduce nitrous oxide emissions </li><li>multi-species mixtures had a strong effect on emissions intensity of nitrous oxide</li><li>much lower nitrous oxide emissions from mixtures (150N) when compared against a higher-nitrogen perennial ryegrass monoculture (300N). </li></ul></div><div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi26OWfShINKAX7uNsQTLSlUziqz7Io9166soaTgRMnaxAQGX4Z99yrh4a35-0xo1XTp-X7m3pcH0Zt4uJ2bKCip_TQc5W-bp3DgQc9KdDSThd7h_KXWhky2yqCy_lMPlru8zY2bsp1T4g/s1129/Screenshot_SaoirseC_BGS.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="719" data-original-width="1129" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi26OWfShINKAX7uNsQTLSlUziqz7Io9166soaTgRMnaxAQGX4Z99yrh4a35-0xo1XTp-X7m3pcH0Zt4uJ2bKCip_TQc5W-bp3DgQc9KdDSThd7h_KXWhky2yqCy_lMPlru8zY2bsp1T4g/s320/Screenshot_SaoirseC_BGS.jpg" width="320" /></a></div><br /><div class="separator" style="clear: both; text-align: center;"><br /></div><br /><br /><p></p><p><a href="https://t.co/AoVG7yZqqu?amp=1" target="_blank">Effects of plant diversity on yi</a><a href="https://t.co/AoVG7yZqqu?amp=1" target="_blank">eld and nitrogen use efficiency in intensively managed grasslands</a> by Guylain Grange. This is available as a pdf poster presentation on the BGS website. Alternatively, it can be found <a href="https://drive.google.com/file/d/1d4gGtq6j8YCPI4HcAHt73Q_I-bSZzkVS/view?usp=sharing" target="_blank">here</a>. Key points are: </p><ul style="text-align: left;"><li>Grassland diversity strongly influenced forage yield. </li><li>Nitrogen use efficiency was driven by legume content. </li><li>Diverse mixtures at 150N consistently out-yielded ryegrass monocultures at 300N.</li></ul><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjSboxek4PatdO3FAMNrmfg3pxBWMm0ZeyWBAW_kOUn_9Dhsn27KARW684Wj8J0QrCMCMBNxvGCxBSgie0xv1x16y-GLgdh39PABcMtBBosepBKV5HgCfblJA1RI67di-DCZgixf2ft3wA/s1100/Screenshot_GuylainG_BGS.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="825" data-original-width="1100" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjSboxek4PatdO3FAMNrmfg3pxBWMm0ZeyWBAW_kOUn_9Dhsn27KARW684Wj8J0QrCMCMBNxvGCxBSgie0xv1x16y-GLgdh39PABcMtBBosepBKV5HgCfblJA1RI67di-DCZgixf2ft3wA/s320/Screenshot_GuylainG_BGS.jpg" width="320" /></a></div><br /><div><br /></div><div><br /></div><div>Updated 8th March 2021. </div><p></p></div>Unknownnoreply@blogger.com3tag:blogger.com,1999:blog-3383131600232191143.post-42255268392176114112021-02-13T08:55:00.054-08:002021-03-11T05:08:11.521-08:00CAP4Nature Report<p> <br /></p><p class="MsoNormal" style="text-align: justify; text-justify: inter-ideograph;"><span style="font-family: "Times New Roman",serif; font-size: 12pt; line-height: 107%;">This <a href="https://drive.google.com/file/d/1VVxmSKvCYy_A93Qq5srt_Oo6ZqPpi1hD/view?usp=sharing" target="_blank">link opens a report</a> from an Irish </span><span style="line-height: 17.12px;"><span style="font-family: "Times New Roman", serif; font-size: 12pt;">workshop to solicit biodiversity scientists' opinions as part of an EU series of workshops requested to address the topic '</span></span><span style="font-family: "Times New Roman", serif; font-size: 12pt;">The Common Agricultural Policy post-2020: A new Green Architecture, Novel Eco-schemes and biodiversity indicators. </span><span style="font-family: "Times New Roman", serif; font-size: 12pt;">How can scientists and science help to make it work?' </span></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEig8h9kKnfea8Caed3N6LYCDKGPYAfL3y73ceA99SciOhA6DLuC_sw7DdjJPjvEfw98QRsEXU-xEoP9tc9JkuUfaVEkQttRQMlFbBPB_4suCB89rEUATMYTOGE1SQ174-XokvqTP2uvL38/s2048/IMG_0964.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1536" data-original-width="2048" height="150" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEig8h9kKnfea8Caed3N6LYCDKGPYAfL3y73ceA99SciOhA6DLuC_sw7DdjJPjvEfw98QRsEXU-xEoP9tc9JkuUfaVEkQttRQMlFbBPB_4suCB89rEUATMYTOGE1SQ174-XokvqTP2uvL38/w200-h150/IMG_0964.jpg" width="200" /></a></div><p></p><p class="MsoNormal" style="text-align: justify; text-justify: inter-ideograph;"><span style="font-family: "Times New Roman", serif; font-size: 12pt;"><span></span></span></p><a name='more'></a>The report follows a template prepared by the coordinating
group for this project consisting of iDiv Helmholtz Centre for Environmental
Research, the Thünen Institute Federal Research Institute for. Rural Areas,
Forestry and Fisheries, and the Universität Rostock (following a request for
such workshops from the European Commission).<p></p><p>
</p><p class="MsoNormal" style="direction: rtl; text-align: left; text-justify: inter-ideograph;"><span style="font-family: "Times New Roman",serif; font-size: 12pt; line-height: 107%;">This
workshop report provides input from an <i>ad
hoc</i> expert group in Ireland brought together in November 2020 by the <span class="MsoHyperlink"><a href="https://www.cap4nature.com/">CAP4Nature network</a></span>
seeking to provide advice, based on relevant scientific research, on how EU
Member States in general, and Ireland in particular, could best make use of the
proposed ‘Green Architecture’ in the new CAP framework to achieve Union and
national biodiversity targets. (Updated) It also draws on a working paper '<a href="https://www.heritagecouncil.ie/content/files/Proposals-for-the-CAP-Green-Architecture-and-Implementation-in-Ireland-Farming-for-nature-task-group.pdf" target="_blank">Proposals for the CAP Green Architecture and Implementation in Ireland</a>' undertaken by the
Technical Group of the <span class="MsoHyperlink"><a href="https://www.heritagecouncil.ie/projects/farming-for-nature-technical-group" target="_blank">Farming for Nature</a></span> project,
an independent, not-for-profit initiative which aims to support High Nature Value
farming in Ireland that was established in 2018.</span></p><p class="MsoNormal" style="text-align: justify; text-justify: inter-ideograph;"><span style="font-family: "Times New Roman",serif; font-size: 12pt; line-height: 107%;"><br /></span></p><p class="MsoNormal" style="text-align: justify; text-justify: inter-ideograph;"><span style="font-family: "Times New Roman",serif; font-size: 12pt; line-height: 107%;"></span></p><div class="separator" style="clear: both; text-align: center;"><a href="https://www.blogger.com/blog/post/edit/3383131600232191143/4225526839217611411#" style="margin-left: 1em; margin-right: 1em;" target="_blank"><img alt="" data-original-height="556" data-original-width="904" height="246" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhif6zXHfZlfFx9uLv4A5kunX0z25u4qCxxvmVZPZi3BG5mBYWRzWETFf6a_FWNjP8_f6YS0Cig9dCnNySBC14QbelCkhfHRhlBqK693SYzG-bENa_Be-AIBMUz8YzkmPSXjUTOp3tunG4/w400-h246/image.png" width="400" /></a></div><br /><br /><p></p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-3383131600232191143.post-82158155549075090852021-02-03T13:23:00.011-08:002021-05-20T13:29:34.435-07:00Multi-species swards: links to webinars<p><span style="font-family: arial;"> </span></p><p><span style="font-family: arial;">This post lists some webinars and videos on multi-species swards that our research group has contributed to. <span></span></span></p><a name='more'></a>The Agricultural Science Association in Ireland hosted a webinar on multi-species mixtures on 20th May 2021. The link to the webinar is <a href="https://www.asaireland.ie/multispecies-swards-webinar/" target="_blank">here</a>. I gave the first presentation, followed by Dr Helen Sheridan, and then Prof. Tommy Boland. Each is about 15 mins, and the recording also includes the Q&A session. <div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiCZzB133YQw6u3SLMP7YITtl9dN4yFxlSE_TX1HSND7jesXKDIseAUNIQ_k24D4F4a-n8uDQX7_iwkWw7dTRbPXQQtj-h3bjIRAYHPUQfYYv9331C062ZjMepi-vzdYSafFHFkpFfSBkA/s1042/ASAwebinar.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;" target="_blank"><img border="0" data-original-height="747" data-original-width="1042" height="286" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiCZzB133YQw6u3SLMP7YITtl9dN4yFxlSE_TX1HSND7jesXKDIseAUNIQ_k24D4F4a-n8uDQX7_iwkWw7dTRbPXQQtj-h3bjIRAYHPUQfYYv9331C062ZjMepi-vzdYSafFHFkpFfSBkA/w400-h286/ASAwebinar.jpg" width="400" /></a></div><br /><div><br /></div><div><br /></div><div>John Finn gave an overview of his collaborative research in a <a href="https://youtu.be/V5CjNej3d8Q " target="_blank">webinar </a>on multi-species mixtures as part of the Teagasc Signpost Series (from March 2021). A pdf of the presentation can be viewed <a href="https://www.teagasc.ie/publications/2021/the-signpost-series---multi-species-mixtures.php" target="_blank">here</a>. <div><div class="separator" style="clear: both; text-align: center;"><a href="https://youtu.be/V5CjNej3d8Q" style="margin-left: 1em; margin-right: 1em;" target="_blank"><img border="0" data-original-height="484" data-original-width="847" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgblymt3Oie0syKTacN_9EgP0-N7JnkwRjcKzykle7EC6wgcVnj34Eej-hDb7ELXsEj8-Yr3rzxQk0FZlWqWjaEkuHBz8W-ueXjLyTTL9LRhf9lwYv8TnMm5LGS9_8l2BsM8PLDiiFGpmE/s320/Screenshot_Jfinn.jpg" width="320" /></a></div><br /><div class="separator" style="clear: both; text-align: center;"><br /></div><br /><div><br /></div><div><br /></div><div><a href="https://www.youtube.com/watch?v=u9sGhlXoHTM" target="_blank">In this video</a>, Guylain Grange and Aidan Lawless show some of our agronomic plots on multi-species mixtures, and our newest systems experiment that implements multi-species swards in the dairy system at Johnstown Castle (from July 2020)<div><a href="https://www.youtube.com/watch?v=u9sGhlXoHTM" style="margin-left: 1em; margin-right: 1em; text-align: center;" target="_blank"><img border="0" data-original-height="549" data-original-width="967" height="182" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg12rwynbk0xXi_Nm4h2KdsJ8swqpcQD87FolgmFXXjWzZZt5zrqG7-XT1-wvMQjEMz-mc2Em9gqQaAYtJd-fV785_Bh5j10gtEpBA5tFOC7hy5nveGVPHPC8Mjit1XK5QAP-xxbxQaodw/w320-h182/Guylain_MSSvideo.jpg" width="320" /></a><br /><div><div class="separator" style="clear: both; text-align: center;"><br /></div><br />DLF (Seed Company) produced a series of videos as part of a week-long focus on multi-species swards (Jan 2021)<br /><div><a href="https://www.youtube.com/watch?v=hWnyAQj-sVA&list=UUA9iXmQPnsBkTY9rSv1w1Vw&index=5" style="font-family: arial;" target="_blank">Day 1 An Introduction to Multi Species swards with DLF</a></div><div><h1 class="title style-scope ytd-video-primary-info-renderer" style="background: rgb(249, 249, 249); border: 0px; color: var(--ytd-video-primary-info-renderer-title-color, var(--yt-spec-text-primary)); font-size: var(--ytd-video-primary-info-renderer-title-font-size, var(--yt-navbar-title-font-size, inherit)); font-weight: 400; line-height: var(--yt-navbar-title-line-height, 2.4rem); margin: 0px; max-height: calc(2 * var(--yt-navbar-title-line-height, 2.4rem)); overflow: hidden; padding: 0px; text-shadow: var(--ytd-video-primary-info-renderer-title-text-shadow, none); transform: var(--ytd-video-primary-info-renderer-title-transform, none);"><span color="var(--ytd-video-primary-info-renderer-title-color, var(--yt-spec-text-primary))" style="font-size: var(--ytd-video-primary-info-renderer-title-font-size, var(--yt-navbar-title-font-size, inherit));"><a href="https://www.youtube.com/watch?v=hWnyAQj-sVA&list=UUA9iXmQPnsBkTY9rSv1w1Vw&index=5" target="_blank"><span style="font-family: arial; font-size: small;">Day 2 Choosing the right Multi-species mixture for your farm</span></a></span></h1><h1 class="title style-scope ytd-video-primary-info-renderer" style="background: rgb(249, 249, 249); border: 0px; color: var(--ytd-video-primary-info-renderer-title-color, var(--yt-spec-text-primary)); font-size: var(--ytd-video-primary-info-renderer-title-font-size, var(--yt-navbar-title-font-size, inherit)); font-weight: 400; line-height: var(--yt-navbar-title-line-height, 2.4rem); margin: 0px; max-height: calc(2 * var(--yt-navbar-title-line-height, 2.4rem)); overflow: hidden; padding: 0px; text-shadow: var(--ytd-video-primary-info-renderer-title-text-shadow, none); transform: var(--ytd-video-primary-info-renderer-title-transform, none);"><span style="font-size: small;"><yt-formatted-string class="style-scope ytd-video-primary-info-renderer" force-default-style="" style="word-break: break-word;"><span style="font-family: arial;"><a href="https://www.youtube.com/watch?v=osEUMXkavjA&list=UUA9iXmQPnsBkTY9rSv1w1Vw&index=4" target="_blank">Day 3 Multi Species Swards Research in Ireland</a> (</span></yt-formatted-string><span style="font-family: arial;">see Guylain Grange and Saoirse Cummins talk about their research (from 4.09 minutes), amid related research ongoing at UCD and WIT.) </span></span></h1><h1 class="title style-scope ytd-video-primary-info-renderer" style="background: rgb(249, 249, 249); border: 0px; color: var(--ytd-video-primary-info-renderer-title-color, var(--yt-spec-text-primary)); font-size: var(--ytd-video-primary-info-renderer-title-font-size, var(--yt-navbar-title-font-size, inherit)); font-weight: 400; line-height: var(--yt-navbar-title-line-height, 2.4rem); margin: 0px; max-height: calc(2 * var(--yt-navbar-title-line-height, 2.4rem)); overflow: hidden; padding: 0px; text-shadow: var(--ytd-video-primary-info-renderer-title-text-shadow, none); transform: var(--ytd-video-primary-info-renderer-title-transform, none);"><a href="https://www.youtube.com/watch?v=z22XsD_tGqY&list=UUA9iXmQPnsBkTY9rSv1w1Vw&index=2" target="_blank"><span style="font-family: arial; font-size: small;">Day 4 Managing a multi species sward</span></a></h1><div><h1 class="title style-scope ytd-video-primary-info-renderer" style="background: rgb(249, 249, 249); border: 0px; color: var(--ytd-video-primary-info-renderer-title-color, var(--yt-spec-text-primary)); font-size: var(--ytd-video-primary-info-renderer-title-font-size, var(--yt-navbar-title-font-size, inherit)); font-weight: 400; line-height: var(--yt-navbar-title-line-height, 2.4rem); margin: 0px; max-height: calc(2 * var(--yt-navbar-title-line-height, 2.4rem)); overflow: hidden; padding: 0px; text-shadow: var(--ytd-video-primary-info-renderer-title-text-shadow, none); transform: var(--ytd-video-primary-info-renderer-title-transform, none);"><yt-formatted-string class="style-scope ytd-video-primary-info-renderer" force-default-style="" style="word-break: break-word;"><a href="https://www.youtube.com/watch?v=zGUdCnb7v_Q&list=UUA9iXmQPnsBkTY9rSv1w1Vw&index=1" target="_blank"><span style="font-family: arial; font-size: small;">Day 5 The benefits of Multi-Species to your farm</span></a></yt-formatted-string></h1></div><p><span style="font-family: arial;"><br /></span></p><p><span style="font-family: arial;">Cotswolds Seeds also have a webinar on the use of deep-rooting herbal leys:</span></p><h1 class="title style-scope ytd-video-primary-info-renderer" style="background: rgb(249, 249, 249); border: 0px; color: var(--ytd-video-primary-info-renderer-title-color, var(--yt-spec-text-primary)); font-size: var(--ytd-video-primary-info-renderer-title-font-size, var(--yt-navbar-title-font-size, inherit)); font-weight: 400; line-height: var(--yt-navbar-title-line-height, 2.4rem); margin: 0px; max-height: calc(2 * var(--yt-navbar-title-line-height, 2.4rem)); overflow: hidden; padding: 0px; text-shadow: var(--ytd-video-primary-info-renderer-title-text-shadow, none); transform: var(--ytd-video-primary-info-renderer-title-transform, none);"><yt-formatted-string class="style-scope ytd-video-primary-info-renderer" force-default-style="" style="word-break: break-word;"><a href="https://www.youtube.com/watch?v=kwjfVsm0o8c" target="_blank"><span style="font-family: arial; font-size: small;">Making the Most of Herbal Leys - A Webinar for Farmers</span></a></yt-formatted-string></h1><p><span style="font-family: arial;"><br /></span></p><p><span style="font-family: arial;">The British Grassland Society / Agriculture and Horticulture Development Board have jointly organised the following webinars available on multi-species swards:</span></p><p class="MsoNormal" style="line-height: 150%; margin-bottom: 12.0pt; margin-left: 0cm; margin-right: 0cm; margin-top: 12.0pt; margin: 12pt 0cm;"><strong><span face="Helvetica, sans-serif" style="font-size: 10.5pt; line-height: 150%;">Webinar 1: Delivering
livestock performance and environmental improvements</span></strong><span face="Helvetica, sans-serif" style="font-size: 10.5pt; line-height: 150%;"> <br />
A number of items were discussed, and the full webinar can be viewed</span><strong><span face=""Helvetica",sans-serif" style="color: darkgreen; font-size: 10.5pt; line-height: 150%; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin;"> </span></strong><strong><span face="Helvetica, sans-serif" style="font-size: 10.5pt; line-height: 150%;"><a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fbritishgrassland.us11.list-manage.com%2Ftrack%2Fclick%3Fu%3Dd06117e46dd3a1bdc84cc86bd%26id%3D5d0848d7bf%26e%3D8035bd3921&data=04%7C01%7Cc.k.reynolds%40reading.ac.uk%7C19fa6661e6ff417bd6db08d8c9025bc9%7C4ffa3bc4ecfc48c09080f5e43ff90e5f%7C0%7C0%7C637480359257740594%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=AsMQX%2BNdcqXLFeZOqaKFwzb8GePQ6Y4TVvZVt4ak5rA%3D&reserved=0" target="_blank"><span style="color: darkgreen; font-weight: normal;">here</span></a></span></strong><strong><span face=""Helvetica",sans-serif" style="color: darkgreen; font-size: 10.5pt; line-height: 150%; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin;">.</span></strong><span style="font-size: 11pt; line-height: 150%; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin;"><u5:p></u5:p><o:p></o:p></span></p><p class="MsoNormal" style="-ms-text-size-adjust: 100%; -webkit-text-size-adjust: 100%; line-height: 150%; margin-bottom: 12.0pt; margin-left: 0cm; margin-right: 0cm; margin-top: 12.0pt; margin: 12pt 0cm; text-size-adjust: 100%;"><strong><span face="Helvetica, sans-serif" style="font-size: 10.5pt; line-height: 150%;">Webinar
2: Establishment – looking forward to 2021 </span></strong><span face="Helvetica, sans-serif" style="font-size: 10.5pt; line-height: 150%;"><br />
To view the webinar, click <strong><a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fbritishgrassland.us11.list-manage.com%2Ftrack%2Fclick%3Fu%3Dd06117e46dd3a1bdc84cc86bd%26id%3D89a6a1999f%26e%3D8035bd3921&data=04%7C01%7Cc.k.reynolds%40reading.ac.uk%7C19fa6661e6ff417bd6db08d8c9025bc9%7C4ffa3bc4ecfc48c09080f5e43ff90e5f%7C0%7C0%7C637480359257750585%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=F9%2FMgDFJjlu7655Av1lAHJa2JcPthd4uMLSOk2fFDoA%3D&reserved=0" target="_blank"><span style="color: darkgreen; font-weight: normal;">here</span></a></strong>.<u5:p></u5:p></span><span style="font-size: 11pt; line-height: 150%; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin;"><o:p></o:p></span></p><p style="background: rgb(255, 255, 255); border: 0px; box-sizing: border-box; margin: 0px; outline: 0px; padding: 0px 0px 1em; text-size-adjust: 100%; vertical-align: baseline;">
<strong><span face="Helvetica, sans-serif" style="font-size: 10.5pt;">Webinar
3: Considerations beyond the price tag</span></strong><span face="Helvetica, sans-serif" style="font-size: 10.5pt;"><br />
This webinar covered the environmental and financial aspects to consider
when using herbal leys.<br />
To view the webinar, click <strong><a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fbritishgrassland.us11.list-manage.com%2Ftrack%2Fclick%3Fu%3Dd06117e46dd3a1bdc84cc86bd%26id%3Ddf1529a341%26e%3D8035bd3921&data=04%7C01%7Cc.k.reynolds%40reading.ac.uk%7C19fa6661e6ff417bd6db08d8c9025bc9%7C4ffa3bc4ecfc48c09080f5e43ff90e5f%7C0%7C0%7C637480359257750585%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=dLKe3iKNylpqfLWjvp3IS4%2F1cI7VcVlc45qNLd%2FowK8%3D&reserved=0" target="_blank"><span style="color: darkgreen; font-weight: normal;">here</span></a></strong>.<u5:p></u5:p></span></p><p style="background: rgb(255, 255, 255); border: 0px; box-sizing: border-box; margin: 0px; outline: 0px; padding: 0px; text-size-adjust: 100%; vertical-align: baseline;"><span style="font-family: arial;"><br /></span></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgbRfgUj3XeLh60EyHUG_YJ_GCvhRtPr5laQamhX_y-xRY1FAK85FCHJIDXJqGFHYpUGROmpUB0-RsJGfMhyphenhyphen3Kp0gHqSQaLJ9pwDPvMKhT7z5IXLMH_8Ei8kI4pqS5YAWtTl26dSUWlGvY/s2048/IMG_4258.JPG" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="2048" data-original-width="1536" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgbRfgUj3XeLh60EyHUG_YJ_GCvhRtPr5laQamhX_y-xRY1FAK85FCHJIDXJqGFHYpUGROmpUB0-RsJGfMhyphenhyphen3Kp0gHqSQaLJ9pwDPvMKhT7z5IXLMH_8Ei8kI4pqS5YAWtTl26dSUWlGvY/s320/IMG_4258.JPG" /></a></div><div class="separator" style="clear: both; text-align: center;"><br /></div><div class="separator" style="clear: both; text-align: center;">Here, Caroline Brophy discusses a new R package for the analysis of data from multi-species experiments. <a href="https://scanner.topsec.com/?t=460b92e311f15da4b883a6c91e00375c573148c3&u=https%3A%2F%2Fplayer.vimeo.com%2Fvideo%2F484544377&d=1452">https://player.vimeo.com/video/484544377</a></div><div class="separator" style="clear: both; text-align: center;"><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjgrxXYwyia2HvanZhCDCmNdR6XnB7Po1mXav-JO0McQ3FnhpDJ-xhgJ_vpES_OFt7c2Km3ApNV-g2-cOP95TGAzYpEi43p19HFKurHJvHbMdpM3bez75Hko1V-XcUvWf2ptfUX6x1aZpY/" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="1080" data-original-width="1920" height="180" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjgrxXYwyia2HvanZhCDCmNdR6XnB7Po1mXav-JO0McQ3FnhpDJ-xhgJ_vpES_OFt7c2Km3ApNV-g2-cOP95TGAzYpEi43p19HFKurHJvHbMdpM3bez75Hko1V-XcUvWf2ptfUX6x1aZpY/" width="320" /></a></div><div class="separator" style="clear: both; text-align: center;"><br /></div><div class="separator" style="clear: both; text-align: center;"><br /></div><br /><br /></div><div class="separator" style="clear: both; text-align: center;"><p class="MsoNormal"><o:p></o:p></p></div><p></p></div></div><div class="separator" style="clear: both; text-align: center;"><br /></div><br /><div class="separator" style="clear: both; text-align: center;"><br /></div><br /></div></div></div></div>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-3383131600232191143.post-32883507819839687982021-01-31T09:06:00.006-08:002021-02-02T11:08:57.781-08:00Multi-species grassland mixtures: some recent Irish research<p><span style="font-family: arial;">Multi-species mixtures are expected to benefit grassland
production because of the yield advantage from nitrogen fixed by clover, the variety
of rooting structures allows them to access a wider range of soil water and
nutrients, and their canopy structure that allows them to intercept more
sunlight. In addition, they are associated with lower parasite loads in
livestock, reduced greenhouse emissions, and increased carbon
sequestration. Here, we give a brief overview of some of our current (2020) research
on mixtures at Teagasc, Johnstown Castle.</span></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiX5J0t2AujDmJUrQwkszrEqq1kzFr8AxCuqlxIJUjaI7VKuTU8B5CzoZSZH7EvLSSxyC3N_avYDTNiUfwsThYYEUH4iPapl-ffYC1SGU6Bstec8qurEDARQBgYtWGNmS9PyWwRLE3r-24/s2048/IMG_4258.JPG" style="margin-left: 1em; margin-right: 1em; text-align: center;"><span style="font-family: arial;"><img border="0" data-original-height="2048" data-original-width="1536" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiX5J0t2AujDmJUrQwkszrEqq1kzFr8AxCuqlxIJUjaI7VKuTU8B5CzoZSZH7EvLSSxyC3N_avYDTNiUfwsThYYEUH4iPapl-ffYC1SGU6Bstec8qurEDARQBgYtWGNmS9PyWwRLE3r-24/s320/IMG_4258.JPG" /></span></a></div><p></p><p class="MsoNormal"><span lang="EN-GB"><o:p><span style="font-family: arial;"></span></o:p></span></p><a name='more'></a><p></p>
<p class="MsoNormal"><b style="mso-bidi-font-weight: normal;"><span lang="EN-GB" style="font-family: arial;">How
many species?<o:p></o:p></span></b></p>
<p class="MsoNormal"><span lang="EN-GB" style="font-family: arial;">The question we are most often asked is: how
many species should be in a multi-species grassland mixture? The answer to this
depends on what you want from the grassland, and the growing conditions. We
have largely focused on forage yield and quality, but are also interested in
gaseous emissions intensity (how much GHGs are lost per unit yield), carbon sequestration potential and weed
suppression. In general, a modest number of about 3 to 6 species seems to be
optimal in intensively managed grasslands. Any fewer and you may not maximise
the benefits; any more, and it becomes difficult for some of the species to
persist, especially in regularly grazed or harvested swards. What is probably
most important is the type of species, and the inclusion of legumes is
fundamental for the incorporation of symbiotically fixed nitrogen.</span></p><p class="MsoNormal"></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiu4F9B0uiJYE_mfjIVAIh4hXHaizQPpDMYBMUa72SolvSTMICXZ97vsFiN5Ha74mLvCZOKQZ2Vd75oYXL7yj1iczjw3CGE4TuVQBWW5VHVelp5vvOoqsA73lpj1aGQnkauDen2GYx3gvY/s1502/overheadplots.jpg" style="margin-left: auto; margin-right: auto;"><span style="font-family: arial;"><img border="0" data-original-height="1126" data-original-width="1502" height="300" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiu4F9B0uiJYE_mfjIVAIh4hXHaizQPpDMYBMUa72SolvSTMICXZ97vsFiN5Ha74mLvCZOKQZ2Vd75oYXL7yj1iczjw3CGE4TuVQBWW5VHVelp5vvOoqsA73lpj1aGQnkauDen2GYx3gvY/w400-h300/overheadplots.jpg" width="400" /></span></a></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-family: arial;">Overhead drone photo of the field trial testing the performance of 6-species mixtures at Johnstown Castle. The frames for the rainout shelters are on the edge of the field, waiting to be placed on one half of the plot for a 9-week duration to create an experimental drought. Chambers for measuring greenhouse gas emissions from each plot are just visible on the top left corner of each plot. </span></td></tr></tbody></table><span lang="EN-GB" style="font-family: arial;"><br /><br /></span><p></p>
<p class="MsoNormal"><b style="mso-bidi-font-weight: normal;"><span lang="EN-GB" style="font-family: arial;">Yield
benefits from mixtures: more from less</span></b></p><p class="MsoNormal"><span style="font-family: arial;">Multi-species mixtures offer an opportunity
to reduce the use of inorganic fertiliser, without compromising forage yield.
With the emphasis on reductions in greenhouse gas emissions for grassland
systems, there is a clear focus on reduced use of inorganic nitrogen
fertiliser, and its embedded emissions. In a common experiment across 31
international sites (from Canada, to Italy and Iceland), the total annual yield
of four-species mixtures (two grasses and two legumes) consistently yielded
better than the average of the four monocultures, and even yielded more than
the best-performing monoculture in a majority of cases. </span></p><p class="MsoNormal"><span style="font-family: arial;">Multiple plot experiments
at Teagasc, Johnstown Castle have shown the same: multi-species mixtures consistently
outyielded monocultures (including perennial ryegrass) when fertiliser
application is about 150 kg ha<sup>-1</sup> yr<sup>-1</sup> of inorganic
nitrogen. Current research by Guylain Grange (a Teagasc Walsh Scholar) shows that
combining two grasses, two clovers and two herbs with 150 kg ha<sup>-1</sup> yr<sup>-1</sup>
of nitrogen out-yielded a perennial ryegrass monoculture with 300 kg ha<sup>-1</sup>
yr<sup>-1</sup> of nitrogen (Fig. 1, averaged across 2018 and 2019). Under
these conditions, we achieved higher yields from less inputs. </span></p><p class="MsoNormal"><span style="font-family: arial;">Similar positive
effects of mixtures arose from the SmartGrass experiment.</span></p><span style="font-family: arial;"><o:p></o:p></span><p></p>
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi9ENgPQWJeKbs96zwHHBJvB19uqFQOxuV4VngNc64aUJiONGtfRYZWE0YzXoXpYjcD4P2LUAiEZYeW1wiLtm7Jk2silxtexCti8QTemqlOiW7Ujrv0K-BuCh3O94LJofzQmKSpPYwobys/s1392/drought+results.jpg" style="margin-left: auto; margin-right: auto;"><span style="font-family: arial;"><img border="0" data-original-height="643" data-original-width="1392" height="296" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi9ENgPQWJeKbs96zwHHBJvB19uqFQOxuV4VngNc64aUJiONGtfRYZWE0YzXoXpYjcD4P2LUAiEZYeW1wiLtm7Jk2silxtexCti8QTemqlOiW7Ujrv0K-BuCh3O94LJofzQmKSpPYwobys/w640-h296/drought+results.jpg" width="640" /></span></a></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-family: arial;"><span style="text-align: left;"><b>Fig. 1</b>. Getting more from less. <br />Total annual yields of perennial ryegrass (150 and 300 kg ha</span><sup style="text-align: left;">-1</sup><span style="text-align: left;"> yr</span><sup style="text-align: left;">-1</sup><span style="text-align: left;"> of nitrogen (PRG 150N and PRG 300N)), and a six species mixture (150 kg ha</span><sup style="text-align: left;">-1</sup><span style="text-align: left;"> yr</span><sup style="text-align: left;">-1</sup><span style="text-align: left;"> ,(6-sp 150N)) These represent the total yield across seven harvests in both 2018 and 2019 (averaged across the two years). Although short-term effects of drought were severe during the drought period, grasslands show very good recovery once soil moisture is restored, and there were only marginal differences in total annual yield due to drought. The six species mixture under drought yielded more than the perennial ryegrass monocultures under rainfed conditions.</span></span></td></tr></tbody></table><p class="MsoNormal"><b><span lang="EN-GB" style="font-family: arial;">Environmental benefits</span></b></p>
<p class="MsoNormal" style="text-align: justify;"><span style="font-family: arial;"><span lang="EN-GB">As indicated
above, the inclusion of clover in swards can displace the use of mineral nitrogen
fertiliser. In addition to saving costs and labour, legume-based mixtures can
reduce greenhouse gas emissions, particularly nitrous oxide (N<sub>2</sub>O)
which is a by-product of N fertiliser land application. Results from the
greenhouse gas measurements carried out by Saoirse Cummins showed that the annual greenhouse gas emissions per
unit output (DM yield or biomass N) from the multi-species mixture were lower
than those from either the high input perennial ryegrass or perennial ryegrass receiving 150N (Fig.
2). Chicory or plantain inclusion in swards can also help lower nitrate
leaching. <o:p></o:p></span>The N<sub>2</sub>O
emissions intensity of perennial ryegrass receiving both 150 and 300 kg ha<sup>-1</sup>
yr<sup>-1</sup> nitrogen fertiliser, and a six species mixture (150 kg ha<sup>-1</sup>
yr<sup>-1</sup>). These represent the total annual N<sub>2</sub>O emissions (g
ha<sup>-1</sup> year<sup>-1</sup>) produced per kg biomass N or tonnes DM
yield.</span></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhPaG-doTHpY5e8ixtzRMR1KBTB5vFXB0VrI-r4ujR7xGnOkxzHiFj970mu5ul1nqTZY922aoEcvwFMcRQ1fidf_ly8O2nWDTBA6xE01TeW4b9C50z5W9HbSLg0WoglX0jtvE15kLBK3Qs/s896/emissions+intensity.jpg" style="margin-left: auto; margin-right: auto;"><span style="font-family: arial;"><img border="0" data-original-height="413" data-original-width="896" height="296" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhPaG-doTHpY5e8ixtzRMR1KBTB5vFXB0VrI-r4ujR7xGnOkxzHiFj970mu5ul1nqTZY922aoEcvwFMcRQ1fidf_ly8O2nWDTBA6xE01TeW4b9C50z5W9HbSLg0WoglX0jtvE15kLBK3Qs/w640-h296/emissions+intensity.jpg" width="640" /></span></a></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-family: arial;"><b style="text-align: justify;"><span lang="EN-GB">Fig. 2. </span></b><span lang="EN-GB" style="text-align: justify;">GHG emissions per unit yield produced calculated (left) per unit yield of forage and (right) per unit yield of N in the forage. </span>Add caption<br /></span></td></tr></tbody></table><span style="font-family: arial;"><br />In a recent <a href="https://www.youtube.com/watch?v=osEUMXkavjA&list=UUA9iXmQPnsBkTY9rSv1w1Vw&index=4" target="_blank">youtube video about mixtures by DLF</a>, see Guylain and Saoirse talk about their research (from 4.09 minutes), amid related research ongoing at UCD and WIT. <br /></span><p class="MsoNormal" style="text-align: justify;"><br /></p>
<p class="MsoNormal"><b><span lang="EN-GB" style="font-family: arial;">Mixture
experiments under grazing</span></b></p>
<p class="MsoNormal"><span lang="EN-GB" style="font-family: arial;">Most mixtures experiments have been
conducted under cutting conditions (simulated grazing), and there is a need to
investigate and demonstrate mixtures under grazed conditions in Ireland. At
Johnstown Castle, we are sowing and comparing lower-nitrogen (100 kg ha<sup>-1</sup>
yr<sup>-1</sup>) multi-species mixtures and higher-nitrogen (200 kg ha<sup>-1</sup>
yr<sup>-1</sup>) ryegrass-clover swards. These are currently being implemented
in our dairy and beef farm at Johnstown (and at Curtin’s, at Moorepark). To
date, the dairy herd at Johnstown is grazing happily on the mixtures, and 2020
will be the first year in which we can compare milk quantity and quality. In the
meantime, we are keen to learn how best to manage these swards. <o:p></o:p></span></p><p class="MsoNormal"></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjtiZjPw0paK0nXdZeRuSCq15afkFok9w_0F5FSPsW2WIPEMJqeArikD1pb9_UCcOFiTPXihz_Qfj0ssipQQJFmDA9WlMloMvGsnXpb0T7zNq-hhkFypY6DSOAQ1SpbFXXoaIgWsrxy0gs/s1462/CowsonMSS.jpg" style="margin-left: 1em; margin-right: 1em;"><span style="font-family: arial;"><img border="0" data-original-height="1102" data-original-width="1462" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjtiZjPw0paK0nXdZeRuSCq15afkFok9w_0F5FSPsW2WIPEMJqeArikD1pb9_UCcOFiTPXihz_Qfj0ssipQQJFmDA9WlMloMvGsnXpb0T7zNq-hhkFypY6DSOAQ1SpbFXXoaIgWsrxy0gs/s320/CowsonMSS.jpg" width="320" /></span></a></div><span style="font-family: arial;"><br /><span lang="EN-GB"><br /></span></span><p></p>
<p class="MsoNormal"><span style="font-family: arial;">There is no reason to believe that mixtures
cannot be beneficial under grazing. In a two-year grazing experiment (with 75
kg ha-1 yr-1 of nitrogen fertiliser) in France, an increase of mixture
diversity from one to five species (two grasses, two clovers and chicory)
resulted in positive effects on animal performance. Compared to ryegrass-clover
swards, multi-species swards improved pasture intake (+ 1.5 kg DM/day), production
of milk (+0.8 kg/day) and milk solids (+0.04 kg/day).</span></p>
<p class="MsoNormal"><span style="font-family: arial;">Research by Thomas Moloney (at Teagasc, Grange,
as part of the SmartGrass project) showed that under favourable ensiling
conditions, two-species and multi-species mixtures preserved comparably to
perennial ryegrass. Note that they had a greater
requirement for rapid wilting and/or adequate effective additive.</span></p>
<p class="MsoNormal"><b style="mso-bidi-font-weight: normal;"><span lang="EN-GB" style="font-family: arial;">Mixtures
can help mitigate effects of drought<o:p></o:p></span></b></p>
<p class="MsoNormal"><span lang="EN-GB" style="font-family: arial;">Climate models indicate that summer
droughts will become more frequent and more severe. In Europe, this means that
grasslands will be increasingly subjected to intense winter rain precipitation
and summer droughts. In a two-year experiment in and another in 2018, we have
found a positive relationship between plant diversity and yield stability in
intensely managed grassland, even under experimental drought conditions. The
benefits of multispecies mixtures were so strong that yields from a mixture of
species under drought conditions matched or exceeded yields for monocultures
under normal rainfall conditions (Fig. 1, above, and also <a href="https://farmecol.blogspot.com/2018/12/multi-species-mixtures-promote-yield.html" target="_blank">work on mixtures and drought by Eamon Haughey and collaborators</a>).</span></p><p class="MsoNormal"></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjrfzo1GHg_k3UGdcpCz1_NlurdWC5SnghBHwTgdbjEbbLhL_vpEd0KQKM9rCZCiwWqWlJHimVkeCqNmylVevVYxUQttpsHWHRIZGeNW8M1DBTE7hLzK8PFxsRFAFawY7rtZuE8QX5UkZo/s1379/shelters.jpg" style="margin-left: 1em; margin-right: 1em;"><span style="font-family: arial;"><img border="0" data-original-height="1034" data-original-width="1379" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjrfzo1GHg_k3UGdcpCz1_NlurdWC5SnghBHwTgdbjEbbLhL_vpEd0KQKM9rCZCiwWqWlJHimVkeCqNmylVevVYxUQttpsHWHRIZGeNW8M1DBTE7hLzK8PFxsRFAFawY7rtZuE8QX5UkZo/s320/shelters.jpg" width="320" /></span></a></div><span style="font-family: arial;"><br /><span lang="EN-GB"><br /></span></span><p></p>
<p class="MsoNormal"><b style="mso-bidi-font-weight: normal;"><span lang="EN-GB" style="font-family: arial;">Strong weed suppression, and persistence
of species over time<o:p></o:p></span></b></p>
<p class="MsoNormal"><span lang="EN-GB" style="font-family: arial;">In general, multi-species mixtures are
excellent at suppressing weeds. This is important because a post-emergent spray
is not possible. Over time, clover content will reduce if large quantities of
mineral nitrogen are spread. However, lowering fertiliser quantities in summer
will favour clover growth. Ensuring clover (and herb) persistence over time
(more than 4 years) is likely to the biggest issue. This is possible with
appropriate management. However, this topic requires more research on optimal
management of mixtures under grazing and soil types, improved breeding for
persistence in mixtures, and mitigation actions such as oversowing of seed to establish and rejuvenated swards, where necessary.</span></p>
<p class="MsoNormal"><span lang="EN-GB" style="font-family: arial;">Multi-species mixtures can be an effective,
farm-scale practical action to maintain or increase forage from less inputs.
They can also mitigate the effects of severe weather events such as drought.
Combined with their other benefits, mixtures offer a strong potential to
improve the sustainability of livestock production. <o:p></o:p></span></p><p class="MsoNormal"><span lang="EN-GB" style="font-family: arial;"><br /></span></p><p class="MsoNormal"><span lang="EN-GB" style="font-family: arial;">For further reading, see an <a href="https://www.farmersjournal.ie/multi-species-swards-offer-diversity-448666" target="_blank">introduction to multi-species swards</a> in a Farmers Journal article (for subscribers), and in an open article from Teagasc, Guylain Grange describes benefits of and <a href="https://www.teagasc.ie/news--events/daily/grassland/grassland-re-seeding-how-to-establish-multi-species-swards.php" target="_blank">advice on establishing a multi-species mixture</a>. </span></p>
<p class="MsoNormal"><span lang="EN-GB" style="font-family: arial;"><o:p><span style="font-size: 11pt;"><br /></span></o:p></span></p><p class="MsoNormal"><span lang="EN-GB" style="font-family: arial;"><o:p><span style="font-size: 11pt;">John
Finn, Guylain Grange and Saoirse Cummins</span></o:p></span><span style="font-family: arial;"> </span></p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-3383131600232191143.post-6304043442495903672021-01-07T02:54:00.006-08:002021-02-02T11:09:47.589-08:00Our recent online webinars on biodiversity<div><span face="soleil, sans-serif" style="color: #505050;"><br /></span></div><div class="separator" style="clear: both; text-align: center;">As part of Virtual Beef Week (July 8th), there is an <a href="https://youtu.be/jqaF_WE27BM" target="_blank">online presentation</a> and interview with the Farmer's Journal on biodiversity policy, videos of on-farm practices to improve farmland biodiversity, and interviews with farmers implementing these practices as part of the BRIDE EIP. Also discussion of habitat quantity, quality and results-based payments. With myself, Catherine Keena and Daire Ó hUallacháin of Teagasc. (From 8.17 to 30 mins in the video.)</div><div class="separator" style="clear: both; text-align: center;"><a href="https://youtu.be/jqaF_WE27BM" style="margin-left: 1em; margin-right: 1em;" target="_blank"><img border="0" data-original-height="682" data-original-width="1753" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi12Fcfy_nAIc3wliBsQuFhU6HM7CBtrmjMNj4-47Nb_KEmnhMFXympkbJC4V6y96131_VBcffrvkYufH2kWqL_fhCHJliry4oGIH_IrMRDoYfHallwOskCI4sRFnSLpDohrDTT2a0Y7uc/s320/Screenshot_Beef.jpg" width="320" /></a></div><br /><div class="separator" style="clear: both; text-align: center;"><br /></div><div class="separator" style="clear: both; text-align: center;"><span style="color: #19988b; font-family: "Times New Roman",serif; font-size: 12pt; mso-ansi-language: EN-IE; mso-bidi-language: AR-SA; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;"><a href="https://www.youtube.com/watch?v=IpPEyrECPz0&feature=youtu.be " target="_blank">Teagasc Research Insights
Webinars Dec 16th, 2020</a>. </span><span style="font-family: "Times New Roman", serif; font-size: 12pt;">In this talk, I give an overview of some of our research on the extent and distribution of farmland habitats at a national and farm scale. I also introduce results-based payments for delivery of biodiversity improvements. (From 1.30 to 19 mins in the video). </span></div><div class="separator" style="clear: both; text-align: center;"><a href="https://www.youtube.com/watch?v=IpPEyrECPz0&feature=youtu.be" style="margin-left: 1em; margin-right: 1em;" target="_blank"><img border="0" data-original-height="427" data-original-width="648" height="264" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj1A_4pSuWcBi5R3si1FzHe2iyvKOgF10YBDM2HKrHHzQSIyFnC8UBGUsRbo9QAZVHnmr8Vf-RmtTAg_A1cLrMB1yrw-Y59JEfbAU3b56LtkPcu90QhN2DwGzhXOv9hdcKDWOEvVH5V-s4/w400-h264/Screenshot+_ResearchInsights.jpg" width="400" /></a></div><div class="separator" style="clear: both; text-align: center;"><br /></div><div class="separator" style="clear: both; text-align: center;"><br /></div><div class="separator" style="clear: both; text-align: center;"><br /></div><div class="separator" style="clear: both; text-align: center;">In this webinar as part of the Burren Winterage School (Oct 2020), I discuss some of the <a href="https://www.youtube.com/watch?v=GkEfyi3VxG0" target="_blank">principles of results-based approaches for biodiversity conservation</a>. This is illustrated with examples from Irish projects, programmes and case studies that have been applying results-based approaches. It's only fitting that we presented our work back to the Winterage School, as this is where we first committed to publish the book of case studies on results-based payments! (From 30.50 to 54 min). </div><div class="separator" style="clear: both; text-align: center;"><br /></div><div class="separator" style="clear: both; text-align: center;"><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhXheSC1Zziop4H6IgbgD4edR9X2qgXR7EQM7R_MRQE86l3cCla84I_weNgK6Aq_Cv856IvH3PVKu_EtkQLpt9qHtIPwLPn5rDZ0XZbj06hbgcroSWcpYpr6gdB0nLX5KI9wAfWQGbpdkw/" style="margin-left: 1em; margin-right: 1em;" target="_blank"><img alt="" data-original-height="1080" data-original-width="1920" height="225" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhXheSC1Zziop4H6IgbgD4edR9X2qgXR7EQM7R_MRQE86l3cCla84I_weNgK6Aq_Cv856IvH3PVKu_EtkQLpt9qHtIPwLPn5rDZ0XZbj06hbgcroSWcpYpr6gdB0nLX5KI9wAfWQGbpdkw/w400-h225/image.png" width="400" /></a></div><br /><br /></div><div class="separator" style="clear: both; text-align: center;"><br /></div><div class="separator" style="clear: both; text-align: center;"><span style="font-family: arial; text-align: left;">In a recent </span><a href="https://www.youtube.com/watch?v=osEUMXkavjA&list=UUA9iXmQPnsBkTY9rSv1w1Vw&index=4" style="font-family: arial; text-align: left;" target="_blank">youtube video about multi-species grassland mixtures by DLF</a><span style="font-family: arial; text-align: left;">, see Guylain Grange and Saoirse Cummins (Teagasc, Johnstown Castle) talk about their research (from 4.09 minutes), amid related research ongoing at UCD and WIT. </span></div><br /><p></p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-3383131600232191143.post-4645094443496735662020-11-25T11:48:00.005-08:002020-11-26T02:22:05.736-08:00Grassland conservation options in AEOS from a results-based perspective<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm;"><span lang="EN"><span style="font-size: medium;">In a 2017 post, we outlined
the main results from a publication (Ó hUallacháin et al., 2016) that compared
the vegetation in three different options for grassland conservation under the Irish
agri-environment scheme (Agri-Environment Option Scheme, AEOS). Here, we outline
the main results of that study, and develop it further with a
more detailed interpretation of that work from the perspective of results-based approaches. Across the three grassland options in that study, the options had the effect of preferentially enrolling and financially rewarding lower-quality vegetation. We show how a results-based approach could better target, incentivise and reward the provision of higher-quality vegetation, and make a greater contribution to biodiversity conservation.</span><span></span></span></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm;"></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj1bP_Hl6-3BMMi8wtwO0T_ygBDnoMvdnjqa-dejvVXZGSrMgJWtGmoX2orRreL4dsRAcLBiD5syr8j7OxcxqnvBXErGUWT7q_M1z5qX1OdQMQL-hgO88kxWon0dPcq2GQbbs77srDOYvg/s2048/SNV38118.JPG" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1536" data-original-width="2048" height="240" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj1bP_Hl6-3BMMi8wtwO0T_ygBDnoMvdnjqa-dejvVXZGSrMgJWtGmoX2orRreL4dsRAcLBiD5syr8j7OxcxqnvBXErGUWT7q_M1z5qX1OdQMQL-hgO88kxWon0dPcq2GQbbs77srDOYvg/w320-h240/SNV38118.JPG" width="320" /></a></div><div class="separator" style="clear: both; text-align: center;"><br /></div><p></p><a name='more'></a><p></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm;"><b><span lang="EN" style="font-size: medium;">What did we study?<o:p></o:p></span></b></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm;"><span style="font-size: medium;"><span lang="EN">We conducted a snapshot survey
of the plant community found in three different options for grassland
conservation under the Irish agri-environment scheme, the Agri-Environmental
Options Scheme (AEOS). We sampled the vegetation in 20 fields in each of three
different grassland options (Traditional Hay Meadow (THM), Species Rich
Grassland (SRG), and Natura) in AEOS (a total of 60 fields across 60 different farms). This gives us a
good indication of the type of vegetation in the fields enrolled in these AEOS
options at the time of our survey. </span><br />
<!--[if !supportLineBreakNewLine]--><br />
<!--[endif]--><o:p></o:p></span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm;"><b><span style="font-size: medium;">Why is this research of interest?<o:p></o:p></span></b></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm;"><span lang="EN" style="font-size: medium;">To our knowledge, this was the
first published survey of grassland quality in different Irish agri-environment
options. There is a widespread lack of measurement of the environmental impact
of agri-environment schemes, despite the large amounts of budget allocated to
such schemes. As an example of the prominence of such measures, the AE
grassland measures were the three most popular measures in AEOS in 2013 (i.e.
51% of participants undertook SRG, 27% undertook THM and 19% undertook Natura
measures on their farms) (cited in Ó hUallacháin et al. 2016). </span></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm;"><span lang="EN" style="font-size: medium;">High
participation rates, coupled with payment rates of €314/ ha for SRG and THM and
€75/ha for Natura, resulted in €19.25 million, €6.31 million and €3.03 million,
respectively, being spent on these three measures in 2013. More recently, these
measures (incorporating slight changes in eligibility and management criteria)
have been included in the Green Low-Carbon Agri-environment Scheme (GLAS, the
Irish agri-environment scheme that succeeded AEOS).</span></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm;"><span lang="EN" style="font-size: medium;"><br /></span></p>
<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiX6vHkXH1hsZ1MIH1Va2KAx4xd61uXI6fOX7sAHCIRgRxXmt7Mr1tzAwNqK1f-FdLRzIg6068VPPi85VHwEJiCl3_cQlDJ1L7YYu_1R1gxnaXVInLfo4djTAs_0b47j7tRIIO4D6cWNII/s1147/AEOS_Grassland_options+-+Copy.jpg" style="margin-left: 1em; margin-right: 1em;"><span style="font-size: medium;"><img border="0" data-original-height="516" data-original-width="1147" height="288" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiX6vHkXH1hsZ1MIH1Va2KAx4xd61uXI6fOX7sAHCIRgRxXmt7Mr1tzAwNqK1f-FdLRzIg6068VPPi85VHwEJiCl3_cQlDJ1L7YYu_1R1gxnaXVInLfo4djTAs_0b47j7tRIIO4D6cWNII/w640-h288/AEOS_Grassland_options+-+Copy.jpg" width="640" /></span></a></div><div class="separator" style="clear: both; text-align: center;"><b style="text-align: left;"><span lang="EN" style="font-size: medium;"><br /></span></b></div><div class="separator" style="clear: both; text-align: center;"><b style="text-align: left;"><span lang="EN" style="font-size: medium;"><br /></span></b></div><div class="separator" style="clear: both; text-align: justify;"><b style="text-align: left;"><span lang="EN" style="font-size: medium;">What were the main
results?</span></b></div>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt; text-align: left;"><span lang="EN" style="font-size: medium;">All of the sites met the eligibility criteria and management actions. </span></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0.0001pt; text-align: left;"><span lang="EN" style="font-size: medium;">Sites enrolled in the THM
option had quite variable vegetation richness ranging from 17 to 47 plant
species per site, with an average of 29 plant species per site (including 14
negative species, and 10 positive species). Thus, compared to a recently
reseeded or highly fertilised grassland with low plant diversity, these sites certainly had more plant
species. Sampled sites enrolled in the SRG option had an average of 34 species,
14 negative species, and 12 positive species. Sites in the Natura option had an
average of 40 species, 5 negative species, and 17 positive species.</span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm;"><span style="font-size: medium;"><span lang="EN">Our results showed that fields
enrolled in the Natura option had highest vegetation quality, those in the
Traditional Hay Meadow option had lowest vegetation quality, and those in the
Species Rich Grassland option were intermediate (although closer in quality to
THM than Natura). </span>Vascular plants associated
with THM and SRG were: <i>Agrostis spp</i>.,
<i>Ranunculus repens</i>, <i>Poa trivialis</i>, <i>Lolium perenne</i>, <i>Trifolium
repens</i>, and <i>Cynosurus cristatus</i>.
Species associated with Natura were, for example, <i>Carex spp</i>., <i>Filipendula
ulmaria</i>, <i>Galium palustre</i>, <i>Cardamina pratensis</i>, <i>Hydrocotyle vulgaris</i>, <i>Succisa pratensis</i>.</span></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm;"><b><span lang="EN" style="font-size: medium;"><br /></span></b></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm;"><b><span lang="EN" style="font-size: medium;">What did we learn?</span></b></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm;"><span lang="EN" style="font-size: medium;">Although the various sites
fulfilled their eligibility and management prescriptions, the lack of clarity
in relation to specific, measurable objectives for each grassland measure made
it difficult to determine whether objectives for these measures were being
achieved.</span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm;"><span style="font-size: medium;"><span lang="EN">This research provided a
useful insight into the range of vegetation quality that one finds within and across
the different grassland options. As a snapshot survey, this research did not
allow us to track trends over time. Thus, we do not know whether the vegetation
quality in sites selecting these options is increasing or decreasing over time
– because of this, we simply do not know whether these options are helping to
halt species decline in the sampled fields. If the fields retained the same
management over a 5-year period, then we would be able to re-survey them and
measure changes in vegetation quality over time, and we would be able know
their effects on species (increasing or decreasing). On the basis of other research, however,
where sites are relatively species-poor, we would not expect the management specified
under the Traditional Hay Meadow and Species Rich Grassland options to *<b>increase</b>*
species richness (or they would do so only very slowly).</span><o:p></o:p></span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm;"><span style="font-size: medium;">For popular and
over-subscribed agri-environment measures such as the grassland options, a method
of targeting to prioritise the inclusion of higher quality vegetation would increase the cost-effectiveness *within* an option.</span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm;"><b><span lang="EN" style="font-size: medium;">Re-interpreting
these results from a results-based perspective</span></b></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; mso-layout-grid-align: none; text-autospace: none;"><span style="font-size: medium;"><span lang="EN">In the original article by Ó hUallacháin et al. (2016), there was a
brief discussion of how a results-based approach could provide a financial
incentive for improved environmental benefits – for example, linking more
positive species indicators to higher payment. </span>Here, we develop and discuss this perspective in more detail.</span></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; mso-layout-grid-align: none; text-autospace: none;"><span style="font-size: medium;">The original Fig. 3 in Ó
hUallacháin et al. (2016) is presented below (Fig 1). In a non-metric multidimensional
scaling plot of grassland composition, each of the symbols represents the
vegetation (species and % cover) across the 60 sites. In general (as indicated
above) the vegetation quality increased from left to right.</span></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm;"><span lang="EN"><br /></span></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiEwje5e-HftTaqFEX1oP5_SFVNtZNkQHXqLFxg5houUUVaIJ9Vmu1ulg8T70X9AU_oBMUE6kyKjr08WyBrGBl0JIB67WqiVZzrIQ26R22fzvxEjRNTDbLPz7iS0taSnbBhDrnqklaNXpk/s1662/ordination.png" style="margin-left: auto; margin-right: auto; text-align: center;"><img border="0" data-original-height="801" data-original-width="1662" height="309" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiEwje5e-HftTaqFEX1oP5_SFVNtZNkQHXqLFxg5houUUVaIJ9Vmu1ulg8T70X9AU_oBMUE6kyKjr08WyBrGBl0JIB67WqiVZzrIQ26R22fzvxEjRNTDbLPz7iS0taSnbBhDrnqklaNXpk/w640-h309/ordination.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: medium;">Fig. 1. Each point in this ordination diagram corresponds to the plant community in one of the 60 fields in our survey. The three AEOS grassland measures are colour-coded. Points that are located more to the right hand side are associated with plant species of higher nature conservation value. The THM points are furthest to the left, the Natura points are more associated with the right hand side, and the SRG points are intermediate.</span><br /></td></tr></tbody></table><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm;"><span lang="EN"></span></p>
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><div style="text-align: left;"><span style="font-size: medium;">Fitting an ellipsis (by eye) to cover the majority (ignoring outliers) of the sites within each of the groups resulted in the groups below (Fig. 2). Taking a vertical distribution of the extent of the ellipsis along axis 1 (shown for THM) resulted in the three horizontal bars at the bottom of the figure. The further to the right that these extend, the more they are associated with plant species of higher nature conservation value. While we accept that this may not be 100% accurate (fitting by eye), it is certainly sufficient to represent the primary pattern in all analyses of these data: sampled fields enrolled in the Natura option had highest vegetation quality, those in the THM had lowest vegetation quality, and those in SRG were intermediate.</span></div><br /><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjGjWW9ITVTH6gSot8SSY49BhZNPvA3o2JJ89PF-zjmonf3ITGkaF71o2zvPwIYxLw4oWRfqevtuiCg4NT_0mOC87YAbFF6QYkSIAnKVVT2z9VxlcQesvy0unMRkOOnvRrEni_lzSxrnA8/s1326/ordinationwithcluster.png" style="margin-left: auto; margin-right: auto; text-align: center;"><img border="0" data-original-height="884" data-original-width="1326" height="426" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjGjWW9ITVTH6gSot8SSY49BhZNPvA3o2JJ89PF-zjmonf3ITGkaF71o2zvPwIYxLw4oWRfqevtuiCg4NT_0mOC87YAbFF6QYkSIAnKVVT2z9VxlcQesvy0unMRkOOnvRrEni_lzSxrnA8/w640-h426/ordinationwithcluster.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: medium;">Fig. 2. The three horizontal lines at the bottom of the diagram are used to represent the relative vegetation condition of the three AEOS grassland measures, with higher vegetation quality being represented by a distribution toward the right hand side of the diagram (which is associated with plant species of higher nature conservation value). </span><br /></td></tr></tbody></table>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm;"><span style="font-size: medium;"><br />Plotting
the per hectare payment rates against the range of ecological condition of the
sampled sites in each of the three options (Fig. 3) suggests a highly negative
relationship: the grassland options with lowest quality provided the highest
payment rates. In principle, the relative distribution of budgets across the
three options could still have resulted in the total budget allocation across
the three options showing a positive relationship with ecological condition. In
practice, however, this did not occur (Fig. 4, 2013 data). In effect, across the three grassland
measures, they seemed to preferentially enroll and financially reward lower-quality vegetation. </span></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm;"><br /></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiwNPvVlBIpQ5unqAyg4-T4j7aiDF98lDz-b4X6sVcEmySroPGcQiDrK7g0b3xGMKfQ8wVt_lothrpOrNw359G4VnrsOdEDAtyMEIaCVBq6HBN-2XZZo1P_hPEugUw0ix-lmmRo_LtVdGg/s1499/score_by_payrate.png" style="margin-left: auto; margin-right: auto; text-align: center;"><img border="0" data-original-height="1032" data-original-width="1499" height="275" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiwNPvVlBIpQ5unqAyg4-T4j7aiDF98lDz-b4X6sVcEmySroPGcQiDrK7g0b3xGMKfQ8wVt_lothrpOrNw359G4VnrsOdEDAtyMEIaCVBq6HBN-2XZZo1P_hPEugUw0ix-lmmRo_LtVdGg/w400-h275/score_by_payrate.png" width="400" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: medium;">Fig. 3. Relationship between the payment rates (per hectare) and ecological condition of each of the THM, SRG and Natura measures in AEOS. Ecological condition is estimated from the relative horizontal distribution of the horizontal bars that represent the three grassland measures, which is the same as in Fig. 2, but vertically displaced in relation to the payment rate.</span><br /><br /><br /><br /></td></tr></tbody></table>
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiy6X_jgw9dzHUQRRpNg3vemA3JjAiGYqrxk0p5VvC2XCKaereF4hyphenhyphenTiPexxVW6vTjRtxJpT-xjTfP9jqh7GgfY_1bS66lXqmOKHwMyzWx4OOGWiWFys3ZPHxNrRy2RlavqsyAIqIrNUME/s1276/score_by_totalbudget+-+Copy.png" style="margin-left: auto; margin-right: auto; text-align: center;"><img border="0" data-original-height="972" data-original-width="1276" height="305" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiy6X_jgw9dzHUQRRpNg3vemA3JjAiGYqrxk0p5VvC2XCKaereF4hyphenhyphenTiPexxVW6vTjRtxJpT-xjTfP9jqh7GgfY_1bS66lXqmOKHwMyzWx4OOGWiWFys3ZPHxNrRy2RlavqsyAIqIrNUME/w400-h305/score_by_totalbudget+-+Copy.png" width="400" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: medium;">Fig. 4. Relationship between the 2013 budgets and ecological condition of each of the THM, SRG and Natura measures in AEOS. </span></td></tr></tbody></table><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm;"><o:p> </o:p><span style="font-size: medium;">Under a
results-based approach, and in alignment with the ‘Provider Gets’ principle, one
would expect a general relationship more like that below (Fig. 5), in which
greater provision of an environmental benefit is associated with a higher
payment. Added benefits of this approach include:</span></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm;"><span style="font-size: medium;"><o:p></o:p></span></p>
<p class="MsoListParagraphCxSpFirst" style="line-height: normal; margin-bottom: 0cm; mso-add-space: auto; mso-list: l1 level1 lfo1; text-indent: -18pt;"></p><ul style="text-align: left;"><li><span style="font-size: medium;"><span style="font-family: Symbol; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol; mso-fareast-language: EN-IE;"><span style="font-family: "Times New Roman"; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal;"> </span></span><!--[endif]-->a justifiable payment to farmers who have already being farming in a way
that delivers the highest vegetation quality. They would not have to do
anything extra, but would be paid for their efforts in actively farming in an
appropriate way to deliver vegetation quality of high nature conservation
value. </span></li><li><span style="font-size: medium;"><span style="font-family: Symbol; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol; mso-fareast-language: EN-IE;"><span style="font-family: "Times New Roman"; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal;"> </span></span><!--[endif]-->a unification of standards and approaches across three different
grassland measures. Thus, for example, a single grassland measure could be designed
to encompass the range of grassland quality encompassed by the THM, SRG and
Natura measures, and a related payment structure that rewards and incentivises the attainment of higher levels of vegetation quality.</span></li><li><span style="font-size: medium;"><span style="font-family: Symbol; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol; mso-fareast-language: EN-IE;"><span style="font-family: "Times New Roman"; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal;"> </span></span><!--[endif]-->Sites with lower vegetation quality would be incentivised to improve the
vegetation quality of their selected grassland sites.</span></li><li><span style="font-size: medium;"><span style="font-family: Symbol; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol; mso-fareast-language: EN-IE;"><span style="font-family: "Times New Roman"; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal;"> </span></span><!--[endif]-->There would be a very strong selection pressure that would result in
both targeting of participation, and targeting of budget toward sites with a)
high levels of vegetation quality, and b) higher potential for restoration from
low to medium vegetation quality, and from medium to high vegetation quality. </span></li><li><span style="font-size: medium;"><span style="font-family: Symbol; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol; mso-fareast-language: EN-IE;"><span style="font-family: "Times New Roman"; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal;"> </span></span><!--[endif]-->Relatively rapid assessment of the state of vegetation and demonstration
of payment for higher environmental standards. In addition, surveys over time
can help detect trends in improvement, decline or lack of change over time. </span></li><li><span style="font-size: medium;"><span style="font-family: Symbol; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol; mso-fareast-language: EN-IE;"><span style="font-family: "Times New Roman"; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal;"> </span></span><!--[endif]-->More widespread understanding and appreciation of the biodiversity value
of farmland (see examples in O’Rourke and Finn, 2020). </span></li></ul><span style="font-size: medium;"><!--[if !supportLists]--><o:p></o:p></span><p></p>
<p class="MsoListParagraphCxSpMiddle" style="line-height: normal; margin-bottom: 0cm; mso-add-space: auto; mso-list: l1 level1 lfo1; text-indent: -18pt;"><span style="font-size: medium;"><o:p></o:p></span></p>
<p class="MsoListParagraphCxSpMiddle" style="line-height: normal; margin-bottom: 0cm; mso-add-space: auto; mso-list: l1 level1 lfo1; text-indent: -18pt;"><span style="font-size: medium;"><o:p></o:p></span></p>
<p class="MsoListParagraphCxSpMiddle" style="line-height: normal; margin-bottom: 0cm; mso-add-space: auto; mso-list: l1 level1 lfo1; text-indent: -18pt;"><span style="font-size: medium;"><o:p></o:p></span></p>
<p class="MsoListParagraphCxSpMiddle" style="line-height: normal; margin-bottom: 0cm; mso-add-space: auto; mso-list: l1 level1 lfo1; text-indent: -18pt;"><span style="font-size: medium;"><o:p></o:p></span></p>
<p class="MsoListParagraphCxSpMiddle" style="line-height: normal; margin-bottom: 0cm; mso-add-space: auto; mso-list: l1 level1 lfo1; text-indent: -18pt;"><span style="font-size: medium;"><o:p></o:p></span></p>
<p class="MsoListParagraphCxSpLast" style="line-height: normal; margin-bottom: 0cm; mso-add-space: auto;"><o:p><span style="font-size: medium;"> </span></o:p></p><p class="MsoListParagraphCxSpLast" style="line-height: normal; margin-bottom: 0cm; mso-add-space: auto;"><br /></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjZr2jBfSTwu1LOLewW9Ed1cK_MBaWspxsTXShVosSFTJV4yCoFbZSO2Hcgv4NeCZWvV4oR4wnkyluF0vHtsHazozIargD_-H9Za5E2CMWVGwsPYZgtcdkjfzQzop5SiIKGF0Gv9pYQKWM/s1230/idealisedsocre_by_rate.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="718" data-original-width="1230" height="234" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjZr2jBfSTwu1LOLewW9Ed1cK_MBaWspxsTXShVosSFTJV4yCoFbZSO2Hcgv4NeCZWvV4oR4wnkyluF0vHtsHazozIargD_-H9Za5E2CMWVGwsPYZgtcdkjfzQzop5SiIKGF0Gv9pYQKWM/w400-h234/idealisedsocre_by_rate.png" width="400" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: medium;">Fig. 5. Illustration of the possible relationship between payments and ecological condition under a results-based approach. We show the relative ecological condition of each of the THM, SRG and Natura measures in AEOS (jittered for clarity).<br /></span><br /></td></tr></tbody></table><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm;"><span style="font-size: medium;">A
results-based approach would, however, generate other requirements that include:</span></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm;"><span style="font-size: medium;"><o:p></o:p></span></p>
<p class="MsoListParagraphCxSpFirst" style="line-height: normal; margin-bottom: 0cm; mso-add-space: auto; mso-list: l0 level1 lfo2; text-indent: -18pt;"></p><ul style="text-align: left;"><li><span style="font-size: medium; text-indent: -18pt;">A need for improved clarity about the objectives and specific
biodiversity indicators of vegetation quality. (This is very possible, with several
example approaches already being developed e.g. the Burren Programme, AranLIFE,
Hen Harrier Project.)</span></li><li><span style="font-size: medium;">The development of indicators that reflect a scoring scheme, and the
development of score-dependent payment rates. (See examples in O’Rourke and
Finn, 2020).</span></li><li><span style="font-size: medium;"><span style="font-family: Symbol; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol; mso-fareast-language: EN-IE;"><span style="font-family: "Times New Roman"; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal;"> </span></span><!--[endif]-->Improved availability of advisors and staff with ecological expertise to
advise and assess the performance, and assist in scoring to determine the
results-based payments. (See examples in O’Rourke and Finn, 2020).</span></li></ul><span style="font-size: medium;"><!--[if !supportLists]--><o:p></o:p></span><p></p>
<p class="MsoListParagraphCxSpMiddle" style="line-height: normal; margin-bottom: 0cm; mso-add-space: auto; mso-list: l0 level1 lfo2; text-indent: -18pt;"><span style="font-size: medium;"><o:p></o:p></span></p>
<p class="MsoListParagraphCxSpLast" style="line-height: normal; margin-bottom: 0cm; mso-add-space: auto; mso-list: l0 level1 lfo2; text-indent: -18pt;"><span style="font-size: medium;"><o:p></o:p></span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm;"><span style="font-size: medium;">Agri-environment schemes are
complex, and difficult to design, to implement and to measure their
environmental effectiveness. Across Europe, there has been a general lack of
research on the effectiveness of these schemes (compared to the number of
schemes, and scale of budget). It is clear that positive effects on
biodiversity can be achieved, but require a high degree of careful design, implementation,
and monitoring. It is only by doing this kind of research that we can learn to
improve agri-environment schemes so that they deliver for farmers, and deliver
for the environment. </span></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm;"><span style="font-size: medium;"><br /></span></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm;"><span style="font-size: medium;">John Finn, Daire Ó hUallacháin, and Helen Sheridan</span></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm;"><br /></p><p><b>References</b></p><div class="MsoNormal" style="margin: 0cm 0cm 10pt;"><span style="font-family: "calibri";">Ó hUallacháin, D., Finn, J.A., Keogh, B., Fritch, R., Sheridan, H. 2016. <a href="https://www.degruyter.com/view/j/ijafr.2016.55.issue-2/ijafr-2016-0018/ijafr-2016-0018.xml?format=INT" target="_blank">A comparison of grassland vegetation from three agri-environment conservation measures</a>. Irish Journal of Agricultural and Food Research 55(2): 176-191.</span></div><div class="MsoNormal" style="margin: 0cm 0cm 10pt;"><span style="font-family: "calibri";">O'Rourke, E and Finn, J.A.. 2020. <a href="www.teagasc.ie/farmingfornature ">Farming for nature: the role of results-based payments.</a> Teagasc and NPWS. </span></div><div class="MsoNormal" style="margin: 0cm 0cm 10pt;"><em style="font-family: calibri;"><br /></em></div><div class="MsoNormal" style="margin: 0cm 0cm 10pt;"><em style="font-family: calibri;">Some of our other related research related to agri-environment schemes</em></div><div class="MsoNormal" style="margin: 0cm 0cm 10pt;"><span style="font-family: verdana; font-size: x-small;"><span>Finn, J.A. and Ó hUallacháin, D. 2012. <a href="http://www.jstor.org/stable/23188060?Search=yes&resultItemClick=true&searchText=biology&searchText=and&searchText=environment&searchText=finn&searchUri=%2Faction%2FdoBasicSearch%3FQuery%3Dbiology%2Band%2Benvironment%2Bfinn%26hp%3D25%26so%3Drel%26acc%3Don%26prq%3Dbiology%2Band%2Benvironment%26amp%3D%26amp%3D%26amp%3D%26amp%3D%26amp%3D%26amp%3D%26fc%3Doff%26wc%3Don&seq=1#page_scan_tab_contents" target="_blank">A review of evidence for the environmental effectiveness of Ireland’s Rural Environmental Protection Scheme</a>. Biology and Environment 112B: 1-24. </span><span><span lang="EN-IE" style="mso-ansi-language: EN-IE; mso-no-proof: yes;">An Open Access version of this paper is available from the Teagasc repository, T-Stór. <a href="http://hdl.handle.net/11019/400"><span style="color: #5421bb;">Click here</span></a> for access to this article (requires sign-in).</span><o:p></o:p></span></span></div><div class="MsoNormal" style="margin: 0cm 0cm 10pt;"><span><span lang="EN-IE" style="font-family: verdana; font-size: x-small; mso-ansi-language: EN-IE; mso-no-proof: yes;"><p class="MsoNormal"><span lang="EN-US">Matin et al. 2020. <a href="http://scholar.google.com/scholar?oi=bibs&cluster=13244465716235164676&btnI=1&nossl=1&hl=en"><span style="color: blue;">Assessing </span></a><a href="http://scholar.google.com/scholar?oi=bibs&cluster=13244465716235164676&btnI=1&nossl=1&hl=en"><span style="color: blue;">the distribution and extent of High Nature Value farmland in
the Republic of </span></a><a href="http://scholar.google.com/scholar?oi=bibs&cluster=13244465716235164676&btnI=1&nossl=1&hl=en"><span style="color: blue;">Ireland</span></a>. Ecological Indicators, in press.</span></p>
<p class="MsoNormal">Larkin
et al. 2019. <a href="http://scholar.google.com/scholar?oi=bibs&cluster=13134616816573704083&btnI=1&nossl=1&hl=en"><span style="color: blue;">Semi-natural habitats and Ecological Focus Areas on cereal,
beef and dairy </span></a><a href="http://scholar.google.com/scholar?oi=bibs&cluster=13134616816573704083&btnI=1&nossl=1&hl=en"><span style="color: blue;">farms </span></a><a href="http://scholar.google.com/scholar?oi=bibs&cluster=13134616816573704083&btnI=1&nossl=1&hl=en"><span style="color: blue;">in Ireland</span></a>. Land Use Policy, 104096.<o:p></o:p></p></span></span></div><div class="MsoNormal" style="margin: 0cm 0cm 0pt;"><span style="font-family: verdana; font-size: x-small;">Matin, S., Sullivan, C.A., Ó hUallacháin, D., Meredith, D., Moran, J., Finn, J.A., Green, S. (2016) Predicted distribution of High Nature Value farmland in the Republic of Ireland. <a href="http://www.tandfonline.com/doi/abs/10.1080/17445647.2016.1223761" target="_blank">Journal of Maps</a></span></div><div class="MsoNormal" style="margin: 0cm 0cm 0pt;"><span style="font-family: verdana; font-size: x-small;"><span><o:p></o:p></span> </span></div><div class="MsoNormal" style="margin: 0cm 0cm 0pt;"><span style="font-family: verdana; font-size: x-small;"><span style="line-height: 18.4px; mso-bidi-font-size: 11.0pt;">Primdahl, J., Vesterager, J.P., Finn, J.A., Vlahos, G. Kristensen, L. and Vejre, H. 2010. Current use of impact models for agri-environment schemes and potential for improvements of policy design and assessment. <i style="mso-bidi-font-style: normal;"><a href="https://www.blogger.com/goog_2144095685">Journal of Environmental Management </a></i></span><a href="https://www.blogger.com/goog_2144095685">91: 1245-1254</a><span style="line-height: 18.4px; mso-bidi-font-size: 11.0pt;"><a href="http://./">.</a></span></span></div><div class="MsoNormal" style="margin: 0cm 0cm 0pt;"><span style="font-family: verdana; font-size: x-small;"><br /></span></div><div class="MsoNormal" style="margin: 0cm 0cm 0pt;"></div><div class="MsoNormal" style="margin: 0cm 0cm 10pt;"><span style="font-family: verdana; font-size: x-small;">Finn, J.A., Bartolini, F., Kurz, I., Bourke, D. and Viaggi D. 2009. Ex post environmental evaluation of agri-environmental schemes using experts’ judgements and multicriteria analysis. <a href="http://www.tandfonline.com/doi/full/10.1080/09640560902958438" target="_blank">Journal of Environmental Planning and Management, 52: 717-737.</a></span></div>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-3383131600232191143.post-5299094619498703062020-11-17T09:14:00.007-08:002022-03-03T01:47:56.770-08:00SmartAgriHubs: pilot incorporation of farmland habitats in Teagasc National Farm Survey<p><span style="font-size: 12pt;">Teagasc is
investigating how to incorporate biodiversity into </span><span style="font-size: 16px;">~300 participant farms</span><span style="font-size: 16px;"> in</span><span style="font-size: 12pt;"> the Teagasc National Farm
Survey. This is being piloted as part of the EU </span><span class="MsoHyperlink" style="font-size: 12pt;"><a href="https://www.smartagrihubs.eu/">SmartAgriHubs</a></span><span style="font-size: 12pt;"> project, which
focuses on digital innovation in the agrifood sector. Here, I briefly describe why </span><span style="font-size: 12pt;">biodiversity should </span><span style="font-size: 12pt;">be included in sustainability assessment, the aims of the pilot project and our methodology, and its potential contribution to improving biodiversity monitoring in farmland.</span></p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhuN4yaYsymkndmzYxVnCGFWbSFIjIKU3mFGlxbKNW4OV41YaVXVXHztQTZcSJ18yGKyM4ZtSCQ70j4QP7NeuWYfhsVIbE69fpde3ANzQLmW1sXxymCLUimD0GygNtePQjZpntnT7eN0QQ/s900/wildlife+pond.JPG" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="593" data-original-width="900" height="211" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhuN4yaYsymkndmzYxVnCGFWbSFIjIKU3mFGlxbKNW4OV41YaVXVXHztQTZcSJ18yGKyM4ZtSCQ70j4QP7NeuWYfhsVIbE69fpde3ANzQLmW1sXxymCLUimD0GygNtePQjZpntnT7eN0QQ/w320-h211/wildlife+pond.JPG" width="320" /></a></div><br /><span style="font-size: 12pt;"><br /></span><p></p><a name='more'></a><o:p></o:p><p></p><p class="MsoNormal"><b><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin;">Why include
biodiversity in assessment of agricultural sustainability?<o:p></o:p></span></b></p><p class="MsoNormal"><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin;">Despite a large
increase in the number of assurance and accreditation schemes for sustainable
agriculture, biodiversity and farmland wildlife are often omitted, or included
in only a trivial manner. (There are some notable exceptions, of course.) If we
truly want to represent sustainability, however, biodiversity needs to be
incorporated into assessments of farm-scale sustainability. <o:p></o:p></span></p><p class="MsoNormal"><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin;">Some
reasons to include biodiversity in sustainability assessments include:<o:p></o:p></span></p><p class="MsoNormal"></p><ul style="text-align: left;"><li><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin;">Biodiversity
is an important component of environmental sustainability that supplies a range
of positive ecosystem goods and services that can underpin stable and resilient
agricultural production.</span></li><li><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin;">Many
farms already support considerable areas of farmland habitat and associated
biodiversity, but receive no recognition or credit for this in sustainability assessments. </span></li><li><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin;">Natural
ecosystems are negatively impacted by agricultural management, resulting in
biodiversity loss. Many natural ecosystems are highly threatened. </span></li><li><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin;">Higher
performance in one environmental dimension can be at the expense of reduced
biodiversity, and this will not be known unless biodiversity is measured and included
in formal analyses. </span></li><li><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin;">Most agri-food
companies now include biodiversity as one of their stated environmental objectives for
sustainable practice.</span></li><li><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin;">Destruction
of wildlife habitats by agriculture can generate very negative publicity and
erode public trust in responsible governance of agri-food systems.</span></li></ul><p></p><p class="MsoNormal">
</p><p class="MsoNormal"><b><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin;">How to incorporate
biodiversity into the Teagasc National Farm Survey?<o:p></o:p></span></b></p><p class="MsoNormal"><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin;">Teagasc is
investigating how to incorporate a critical measure of biodiversity into ~300 participant farms in the
Teagasc National Farm Survey. This is being piloted as part of the EU <span class="MsoHyperlink"><a href="https://www.smartagrihubs.eu/">SmartAgriHubs</a></span>
project, which focuses on digital innovation in the agrifood sector. The
project is about halfway through its duration, and we expect to report the
outcomes in early 2022. <o:p></o:p></span></p><p class="MsoNormal"><span style="font-size: 12pt;">There are
several alternative approaches to measure biodiversity and how to incorporate
it into sustainability assessments. Any specific approach taken will depend on
the objectives of the assessment.</span></p><p>
</p><p class="MsoNormal"><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin;">We are
building on a previous approach that was summarised in another post ‘<a href="https://farmecol.blogspot.com/2017/03/inclusion-of-farmland-habitats-in.html" target="_blank">Methodology to include farmland habitats in sustainability assessments</a>’, and is described in detail in Finn and Moran (in press). This methodology focuses
on the inclusion of farmland habitats in sustainability assessments. As such,
it is a surrogate for direct quantification of biodiversity that would require
detailed measurement of individual species (plants, spiders, beetles, birds
etc.). </span></p><p class="MsoNormal"><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin;">The
innovation in this new pilot project is delivered through:<o:p></o:p></span></p><p class="MsoListParagraphCxSpFirst" style="mso-list: l0 level1 lfo1; text-indent: -18pt;"></p><ul style="text-align: left;"><li><span style="font-family: "Times New Roman",serif; font-size: 12pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;"><span style="font-family: "Times New Roman"; font-size: 7pt; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal;"> </span></span><!--[endif]--><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin;">reliance on
satellite imagery to identify broad classes of farmland habitats e.g. ploughed
fields, intensively managed grasslands, arable crops, heathlands, peatlands,
lakes, ponds, woodlands and so on. (There is also ground-truthing on 10% of the
farms.)<o:p></o:p></span></li><li><span style="font-family: "Times New Roman",serif; font-size: 12pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;"><span style="font-family: "Times New Roman"; font-size: 7pt; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal;"> </span></span><!--[endif]--><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin;">incorporation of
farm-specific photos via a web-based application for uploading and transferring
farm photos<o:p></o:p></span></li><li><span style="font-family: "Times New Roman",serif; font-size: 12pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;"><span style="font-family: "Times New Roman"; font-size: 7pt; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal;"> </span></span><!--[endif]--><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin;">collation and
combination of these data through an automated reporting process that can
create customisable farm-specific habitat maps and reports. An example is
provided below (Fig 1 and Fig 2), and see a <a href="https://farmecol.blogspot.com/2017/03/inclusion-of-farmland-habitats-in.html" target="_blank">previous post</a> for more.<o:p></o:p></span></li><li><span style="font-family: "Times New Roman",serif; font-size: 12pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;"><span style="font-family: "Times New Roman"; font-size: 7pt; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal;"> </span></span><!--[endif]--><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin;">provision of the
farm habitat report and information to the participant farmers. This can help
to better guide and inform their management of farm biodiversity. <o:p></o:p></span></li><li><span style="font-family: "Times New Roman",serif; font-size: 12pt; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE;"><span style="font-family: "Times New Roman"; font-size: 7pt; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal;"> </span></span><!--[endif]--><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin;">inclusion of a
biodiversity assessment in the Teagasc National Farm Survey (NFS). The great
benefit of conducting the biodiversity assessment on NFS farms is the ability
to link with the time series of the suite of other agronomic, economic,
environmental and social data collected by the Teagasc NFS. <o:p></o:p></span></li></ul><!--[if !supportLists]--><p></p><p class="MsoNormal">
</p><p class="MsoNormal"><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin;"> </span></p><table cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiVn6pr4PxOxijIBdcjB7SEQB76pha4a6cjVbdfh8lRNMDYIJQ6Xmb442vDAndNZx0XRAVPIFWdEzVg7ww6M7ROEgJf175plDK8wjqB9SjvTXyhyJCXYLrqwu_-IGG0n92vG888q5UP9_s/s1139/Farm_habitat_map.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="786" data-original-width="1139" height="442" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiVn6pr4PxOxijIBdcjB7SEQB76pha4a6cjVbdfh8lRNMDYIJQ6Xmb442vDAndNZx0XRAVPIFWdEzVg7ww6M7ROEgJf175plDK8wjqB9SjvTXyhyJCXYLrqwu_-IGG0n92vG888q5UP9_s/w640-h442/Farm_habitat_map.jpg" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: medium;">Fig. 1. Example of a farm habitat map and table with habitat types and sizes, and an estimate of the relative wildlife value of the different habitats. </span><br /><div style="clear: both; text-align: center;"><br /></div><div style="clear: both; text-align: center;"><br /></div><div class="separator" style="clear: both; text-align: center;"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhA65VTxlfHFAvG7nmVfvlD-NThXa0606AgRCXFbjgOdH9BdFiYNnfmynGJzSQv-uov6TqLpswt6rsBmwoX2vgHpvHNNG7hc1YRMNSPBPSp0sRBgIVNdFQH0B5arVJpqwgaZpqqlEBiW8o/s860/HNV_Habitats.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="860" data-original-width="602" height="640" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhA65VTxlfHFAvG7nmVfvlD-NThXa0606AgRCXFbjgOdH9BdFiYNnfmynGJzSQv-uov6TqLpswt6rsBmwoX2vgHpvHNNG7hc1YRMNSPBPSp0sRBgIVNdFQH0B5arVJpqwgaZpqqlEBiW8o/w448-h640/HNV_Habitats.jpg" title="Fig. 1. Example of a farm habitat map and table with habitat types and sizes, and an estimate of the relative wildlife value of the different habitats." width="448" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-size: medium;">Fig. 2. Example of a farm habitat map and table with habitat types and sizes, and an estimate of the relative wildlife value of the different habitats. </span><br /><br /></td></tr></tbody></table><p><br style="text-align: left;" /></p></div><div class="separator" style="clear: both; text-align: center;"><span style="color: #505050; font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin; text-align: left;">The </span><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin; text-align: left;">Teagasc
National Farm Survey includes several sustainability indicators (economic,
environmental, social, and innovation), and is exploring how to expand its
environmental indicators, including biodiversity. The following text is from
the 2019 Sustainability Report from the Teagasc NFS:</span></div><div class="separator" style="clear: both; text-align: center;">
<p class="MsoNormal" style="margin-left: 36pt; text-align: left;"><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin;">“Indicators that are currently under
development include, metrics relating to biodiversity and these will be
included in future Teagasc sustainability reports once the relevant scientific
work needed to establish indicators and consistently collect the related data
has concluded. (p.5)</span></p><p class="MsoNormal" style="margin-left: 36pt; text-align: left;"><span style="font-size: 12pt;">...</span><span style="font-size: 12pt;">However, one of the global concerns associated
with the intensification of agricultural production is that wildlife and native
flora may be negatively impacted, resulting in irrevocable or difficult to
reverse biodiversity loss. Biodiversity is therefore an important component of
farm performance, but can usually only reliably be assessed by detailed on-farm
surveys. Typically, such measurement is resource intensive and represents a
long term commitment, which would ordinarily be beyond the current scope and
resources of the Teagasc NFS.</span></p>
<p class="MsoNormal" style="margin-left: 36pt; text-align: left;"><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin;">However, competitive research funding has now
allowed ecologists to use remote mapping to identify farmland habitat
biodiversity on close to 300 NFS farms. If further funding for this initiative
could be secured this would allow the habitat biodiversity assessment to be
undertaken on all NFS farms. It could also allow this assessment to be repeated
on a periodic basis to track changes in habitat biodiversity over time. In turn
this would allow the inclusion of habitat biodiversity [in] the indicator set
of the Teagasc Sustainability Report (p.65, </span><span style="font-size: 16px;">2019 Sustainability Report</span><span style="font-size: 12pt;">).” </span></p></div><div class="separator" style="clear: both; text-align: left;"><p class="MsoNormal"><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin;">The
competitive funding mentioned in the report refers to the EU SmartAgriHubs
project that is funding the current research project. The inclusion of farmland
habitats (and other environmental indicators) in the NFS would be possible if
funding was available to do so; however, the inclusion of environmental
indicators may not be as expensive as is often thought. For example, the inclusion
of a remote-assessment methodology for habitat estimation in the NFS would be
considerably less expensive that an approach involving individual visits to
each of the NFS farms to conduct detailed wildlife surveys. (Note that such an
approach is not mutually exclusive and can provide more detail for validation
of the remote, habitat-scale approach; see Sheridan et al., (2011) for a
detailed farm-scale habitat survey of 50 farms in the NFS.) Given the
relatively slow pace of change of land use at the landscape scale, an assessment
of farmland habitats on NFS farms might only need to be conducted every four or
five years to get an adequate temporal representation, rather than on an annual
basis (as for other NFS data). This less frequent sampling would also be more
cost-effective. <o:p></o:p></span></p><p class="MsoNormal"></p><div class="separator" style="clear: both; text-align: center;"><br /></div><br /><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgOKqn9tAP2TGQcd0WJYwfQWF8oQy_AyDvrp94_NEaqRH-cTY_Ah9NkSO8kH08CR-v_737tGI6PVcPk3J75Mp3CC7arUTvbhgIQeV7fPnQz5-zSD70XCPMOrChW1sg_sPDw7YJVNKWtU54/s645/Picture1.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="485" data-original-width="645" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgOKqn9tAP2TGQcd0WJYwfQWF8oQy_AyDvrp94_NEaqRH-cTY_Ah9NkSO8kH08CR-v_737tGI6PVcPk3J75Mp3CC7arUTvbhgIQeV7fPnQz5-zSD70XCPMOrChW1sg_sPDw7YJVNKWtU54/s320/Picture1.jpg" width="320" /></a></div><br /><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin;"><br /></span><p></p><p class="MsoNormal"><b><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin;">To what extent
could the inclusion of biodiversity in the Teagasc NFS comprise a national
indicator of biodiversity trends?</span></b></p><p class="MsoNormal"><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin;">One would need
to be cautious about correctly interpreting the results from a farmland habitat
assessment based on the NFS alone (and assuming an approach similar to that above was applied across all the participant farms). It would be a very good approach to compare the
‘space for nature’ across the farming systems that are well represented by the
NFS, and would be very well able to discriminate quite broad differences in farm
habitat types and areas e.g. as in Figures 1 & 2.</span></p><p class="MsoNormal"><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin;">The time
series associated with the NFS is one of its strengths, facilitating tracking
of change over time. Given the stated aim to support biodiversity of the CAP
and the EU Farm to Fork and Biodiversity strategies, our proposed assessment should
be able to detect the relative impact on biodiversity of land use change over
time, especially for habitats that are associated with farmland biodiversity.</span></p><p class="MsoNormal"><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin;">The
Teagasc NFS captures close to 100% representation of the types of Irish grassland and
cropland agriculture operating at an intensity most likely to
have highest pressures on and/or lower levels of biodiversity. </span><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin;">In terms of careful interpretation, the results of a biodiversity assessment
from the NFS would not be nationally representative of habitats on farmland; it
would be restricted to being representative of the farming systems represented
by NFS. The NFS comprises a random, nationally representative sample of
between 1,000 and 1,200 farms per year. The survey is operated as part of the
Farm Accountancy Data Network of the EU and fulfils Ireland’s statutory
obligation to provide data on farm output, costs and income to the European
Commission. Only farms with greater than €8,000 of Standard Output (SO) are
included. Excluded farms represent about ~15% of the total farm population. The excluded farms; however,
are most likely to be those with a higher likelihood of being High Nature
Value, and with a greater provision of the environmental public goods that are
being increasingly targeted and supported by EU policies. This is further
discussed in Kelly et al. (2018). Teagasc has conducted and continues to
conduct dedicated supplementary surveys that are intended to improve the
representation of farmland types that are not well represented by the sampling
design of the FADN.</span></p><p class="MsoNormal"><span style="font-size: 12pt;">Our
assessment method measures the type of farmland habitat, which can be broadly
associated with low, medium and high levels of biodiversity (see the comments
in the final column in the tables within Figs 1 & 2). For example, we know
that there are large differences in biodiversity and relative conservation
value between an arable field, and a species-rich grassland or heathland.
However, an approach based on habitat type only would not be able to measure
habitat quality, which would need some more detailed form of assessment. The
use of improved technological solutions might be able to improve the ease with
which one could either include existing data on habitat quality or collect new
data on habitat quality. Note that a combination of the logistically less demanding approach based on categorisation of habitats in the Teagasc NFS (described above) coupled with a subsampling of NFS farms for more detailed and logistically demanding biodiversity </span><span style="font-size: 12pt;">assessments could be a powerful combination to infer the </span>trajectory<span style="font-size: 12pt;"> of both habitat quantity and quality across the NFS farming systems. This would offer improved potential to track patterns of change in biodiversity </span><span style="font-size: 12pt;">in the wider countryside, which has been a blind spot to date for farmland outside of Natura 2000 and selected priority habitats. </span></p><p class="MsoNormal">
</p><p class="MsoNormal"><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin;">As part of the EU Farm to Fork Strategy, there is a proposal for the FADN to be transformed
into a Farm Sustainability Data Network (FSDN) that would have an enhanced focus on
sustainable farming practices and their environmental impact. The current pilot
project on incorporation of farmland habitats into the Teagasc NFS has strong
potential application in future, and may also help inform methodological
approaches for FADN/FSDN in other EU Member States. </span></p><p class="MsoNormal"><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin;"><br /></span></p></div><div class="separator" style="clear: both; text-align: left;"><p class="MsoNormal" style="margin-right: -9.95pt;">UPDATE March 2022: <a href="https://farmecol.blogspot.com/2022/02/webinar-piloting-biodiversity-indicator.html" target="_blank">blog post with link to 10-minute webinar and results</a></p><p class="MsoNormal" style="margin-right: -9.95pt;"><span style="font-size: 12pt;"><b><br /></b></span></p><p class="MsoNormal" style="margin-right: -9.95pt;"><span style="font-size: 12pt;"><b>References</b></span></p><p class="MsoNormal" style="margin-right: -9.95pt;"><span style="font-size: 12pt;">J.A. Finn and P. Moran. A pilot
study of methodology for the development of farmland habitat reports for
sustainability assessments. Irish Journal of Agricultural and Food Research.
DOI: 10.15212/ijafr-2020-0103 (in press).</span></p>
<p class="MsoNormal"><span style="color: #222222; font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-language: EN-IE; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin;">Kelly, E., Latruffe, L., Desjeux, Y.,
Ryan, M., Uthes, S., Diazabakana, A., Dillon, E. and Finn, J.A., 2018. Sustainability
indicators for improved assessment of the effects of agricultural policy across
the EU: Is FADN the answer? <i>Ecological Indicators</i>. </span><span lang="EN" style="color: #111111; font-size: 12pt; mso-ansi-language: EN; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin;">89: 903-911</span><span lang="EN" style="font-size: 12pt;"><span style="color: #222222;">. </span></span><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-language: EN-IE; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin; mso-no-proof: yes;"> </span><a href="https://www.sciencedirect.com/science/article/pii/S1470160X17308439" style="font-size: 12pt;">https://www.sciencedirect.com/science/article/pii/S1470160X17308439</a></p>
<p class="MsoNormal" style="margin-right: -9.95pt;"><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin;">Sheridan, H., McMahon, B.J., Carnus, T., Finn,
J.A., Kinsella, A., Purvis, G. (2011) Pastoral farmland habitat diversity in
south-east Ireland. Agriculture, Ecosystems & Environment 144: 130-135. </span><a href="http://www.sciencedirect.com/science/article/pii/S0167880911002507" style="font-size: 12pt;">http://www.sciencedirect.com/science/article/pii/S0167880911002507</a></p>
<p class="MsoNormal"><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman"; mso-fareast-language: EN-IE; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin;">Finn, J.A.
2020. <span class="MsoHyperlink"><a href="https://farmecol.blogspot.com/2017/03/inclusion-of-farmland-habitats-in.html">Methodology
to include farmland habitats in sustainability assessments</a></span>. Blog
post. <o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size: 12pt;">T</span><span style="font-size: 12pt;">eagasc.
2019. Sustainability Report </span><span class="MsoHyperlink"><span style="font-size: 12pt; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin;"><a href="https://www.teagasc.ie/rural-economy/rural-economy/national-farm-survey/sustainability-reports/">https://www.teagasc.ie/rural-economy/rural-economy/national-farm-survey/sustainability-reports/</a></span></span></p></div><div style="text-align: left;"><br /></div><div style="text-align: left;"><br /></div><div style="text-align: left;"><br /></div></td></tr></tbody></table>Unknownnoreply@blogger.com1tag:blogger.com,1999:blog-3383131600232191143.post-46357057444157152402020-11-06T02:15:00.003-08:002020-11-18T09:27:14.273-08:00Report: Independent evaluation of environmental effects of GLAS<p>The Green Low-Carbon Agri-Environment Scheme (GLAS) has been the Agri-Environment Scheme in Ireland over the RDP period 2014-2020. GLAS. A report from summer 2020 provides an evaluation of GLAS by an independent environmental consultancy. The report is available on the DAFM website here: <a href="https://www.agriculture.gov.ie/media/migration/ruralenvironment/ruraldevelopment/ruraldevelopmentprogramme2014-2020/monitoringandevaluation/Annex1GLASSynthesisReport141020.pdf">Evaluation of the Green Low-Carbon Agri-Environment Scheme (GLAS): Synthesis of evidence</a>, and includes an Executive Summary and list of recommendations in addition to the main report. <span></span></p><a name='more'></a><p></p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://www.agriculture.gov.ie/media/migration/ruralenvironment/ruraldevelopment/ruraldevelopmentprogramme2014-2020/monitoringandevaluation/Annex1GLASSynthesisReport141020.pdf" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="399" data-original-width="1032" height="125" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEghoK9Y8ISKqWwKi2kkv6-0iLSvYQHdBVupXHmp-wYID7ZFx0KMOz_4baxTk9u1mmNP1PqUUeach1YRpEFk5mHxFIGjWLZ4LpJ8uI5IkfA8HxSj1AXBOku0BQnrGdK9fa1maRA3zGi5Tm0/w320-h125/image.png" width="320" /></a></div>The total allocation of funding for GLAS is €920m, of which €529 million (57%) was spent to the end of 2018, with an expectation that the budget will be exceeded by the end of the programme (p.1), and a measure-by-measure breakdown of intended GLAS expenditure is provided on pages 3 and 4. Some of the measures associated with the biggest items of planned expenditure are: €343m on Breeding Waders and Curlews, €233m on the Low Input Permanent Pasture measure, €175m on the Traditional Hay Meadow measure, and €158m on Commonages. <p></p><p>(In a recent article, (<span style="background-color: white; font-family: calibri;">Ó hUallacháin et al. 2016), </span>we compared the botanical composition of three grassland conservation measures in AEOS, which includes measures that are reasonably similar to the Low Input Permanent Pasture and Traditional Hay Meadow measures in GLAS.)</p><p>At a time of intense discussion about CAP reform, the report also considered "the role of the new CAP Pillar 1 eco-scheme and/or conditionality as a mechanism for delivering GLAS actions". The report recommended wider adoption of results-based payments, and the discussion on eco-schemes was summarised as follows:</p><p></p><blockquote><p>Under an eco-scheme, actions can be renewed on an annual basis and this has the potential to incentivise wide-scale uptake of sustainable practices, with an element of targeting to national or regional conditions. In this case, the criteria used were short-lag, flexibility and low administrative burden. Water and landscape actions tend to have a short time-lag between implementation and outcome, can be placed throughout the landscape and can be readily evidenced. Nesting/Roosting Features and Rare breeds also have a good fit with an eco-scheme (see Recommendation 14). Placing these actions in an eco-scheme offers an opportunity to increase uptake by making them readily accessible to all Pillar 1 applicants with low risks in terms of effective implementation. Supporting these with advice would help improve aftercare and mitigate risks to value for money.</p><p>Finally, actions were reviewed in relation to their overall suitability for inclusion in a CAP scheme, either a Pillar 1 eco-scheme or a future AECM. Actions were assessed for their effectiveness at a high level, based on early evidence from the monitoring and evaluation work and as such, our recommendations are provisional (see Recommendation 15) . </p></blockquote><p>See the main report for the findings. </p><p></p><p><br /></p><div class="separator" style="clear: both; text-align: center;"><br /></div><div class="separator" style="clear: both; text-align: center;"><br /></div><div class="separator" style="clear: both; text-align: center;"><div class="MsoNormal" style="background-color: white; font-family: "Trebuchet MS", Trebuchet, Verdana, sans-serif; margin: 0cm 0cm 10pt; text-align: start;"><em style="font-family: calibri;">S</em><em style="font-family: calibri;">ome of our other related research on agri-environment schemes</em></div><div class="MsoNormal" style="background-color: white; font-family: "Trebuchet MS", Trebuchet, Verdana, sans-serif; margin: 0cm 0cm 10pt; text-align: start;"><span style="font-family: calibri;">Ó hUallacháin, D., Finn, J.A., Keogh, B., Fritch, R., Sheridan, H. 2016. <a href="https://www.degruyter.com/view/j/ijafr.2016.55.issue-2/ijafr-2016-0018/ijafr-2016-0018.xml?format=INT" style="color: #bb2188; text-decoration-line: none;" target="_blank">A comparison of grassland vegetation from three agri-environment conservation measures</a>. Irish Journal of Agricultural and Food Research 55(2): 176-191.<o:p></o:p></span></div><div class="MsoNormal" style="background-color: white; font-family: "Trebuchet MS", Trebuchet, Verdana, sans-serif; margin: 0cm 0cm 10pt; text-align: start;"><span style="font-family: calibri;">A blog post related to this article is provided <a href="https://farmecol.blogspot.com/2017/01/our-evidence-does-not-support.html">here</a>. </span></div><div class="MsoNormal" style="background-color: white; font-family: "Trebuchet MS", Trebuchet, Verdana, sans-serif; margin: 0cm 0cm 10pt; text-align: start;"><span style="font-family: calibri;"><br /></span></div><div class="MsoNormal" style="background-color: white; font-family: "Trebuchet MS", Trebuchet, Verdana, sans-serif; margin: 0cm 0cm 0pt; text-align: start;"><span style="font-family: calibri;">Finn, J.A. and Ó hUallacháin, D. 2012. <a href="http://www.jstor.org/stable/23188060?Search=yes&resultItemClick=true&searchText=biology&searchText=and&searchText=environment&searchText=finn&searchUri=%2Faction%2FdoBasicSearch%3FQuery%3Dbiology%2Band%2Benvironment%2Bfinn%26hp%3D25%26so%3Drel%26acc%3Don%26prq%3Dbiology%2Band%2Benvironment%26amp%3D%26amp%3D%26amp%3D%26amp%3D%26amp%3D%26amp%3D%26fc%3Doff%26wc%3Don&seq=1#page_scan_tab_contents" style="color: #bb2188; text-decoration-line: none;" target="_blank">A review of evidence for the environmental effectiveness of Ireland’s Rural Environmental Protection Scheme</a>. Biology and Environment 112B: 1-24. </span><span style="font-family: calibri;"><span lang="EN-IE">An Open Access version of this paper is available from the Teagasc repository, T-Stór. <a href="http://hdl.handle.net/11019/400" style="color: #bb2188; text-decoration-line: none;"><span style="color: #5421bb;">Click here</span></a> for access to this article (my require sign-in).</span></span></div></div><p></p><p><br /><br /></p><p><br /></p><p><br /></p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-3383131600232191143.post-75205138198602865172020-10-29T03:22:00.008-07:002021-07-19T06:49:18.904-07:00Methodology to include farmland habitats in sustainability assessments<div>A new Teagasc publication to be published in the Irish Journal of Agricultural and Food Research outlines the development of scalable methods to include farmland habitats in sustainability assessments. Here, I give a brief 'taster' of the content of that paper, and an overview of the approach that is based on categorising farmland habitats using satellite imagery. </div><div><br /></div><em><div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhfm0O3l8ZAmNk9Wzfk1sQg_UZMNPuwABkS0n4UnmNThjxlDN3d8yqwh5ssZ9o75cX8IPMRF_ewBZQZf8P_PP43NH6jB6gp7jd62Rw2Kio5V_rOd9lIUI3i0Z8PEGzoeAe2w_lNAUBSb6k/s1280/Fig+2+2004_0713fieries0023.JPG" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="960" data-original-width="1280" height="240" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhfm0O3l8ZAmNk9Wzfk1sQg_UZMNPuwABkS0n4UnmNThjxlDN3d8yqwh5ssZ9o75cX8IPMRF_ewBZQZf8P_PP43NH6jB6gp7jd62Rw2Kio5V_rOd9lIUI3i0Z8PEGzoeAe2w_lNAUBSb6k/w320-h240/Fig+2+2004_0713fieries0023.JPG" width="320" /></a></div><div class="separator" style="clear: both; text-align: center;"><br /></div></div><span><a name='more'></a></span>Sustainability claims need to be verified</em><br />In response to consumer pressure and policy targets, there is a greater requirement to measure and verify the level of delivery of key sustainability indicators. A common approach involves individual assessment of farm performance according to criteria in a sustainability scheme. <div><br /></div><div><div><em>Sustainability assessments need to include farmland habitats</em></div><div>There has been a global increase in the number of industry-led sustainability assessments. However, biodiversity and farmland wildlife are often omitted, or included in only a trivial manner. (There are exceptions, of course.) To properly represent sustainability, a measure of biodiversity needs to be incorporated into assessments of farm-scale sustainability. </div><div><br /></div><div>Doing this is not without challenges. Biodiversity is multi-faceted, requires specific expertise, and can be highly location-specific, requiring site visits and assessments that can add considerably to labour costs. These costs can be a major impediment to the inclusion of farmland wildlife in large-scale implementation of sustainability assessments. (As a contrasting example, verification that farm chemicals are stored safely is relatively straightforward.)</div></div><div><br /><div><div>
<br />
<em>Pilot project on farmland habitats</em><br />
We're investigating how to include farmland habitats in assessments of agricultural sustainability. In addition, we are aiming to do so without the high costs associated with individual farm visits. Instead, we aim to see whether we can use aerial imagery and ICT (file-sharing of habitat photos) to make farmland habitat surveys a low-cost action. The key aim was: how to get from satellite imagery (as in Fig. 1 below), to a farm habitat map (see Fig. 2)?</div><div><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody><tr><td><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgR-Tke2JpOH45asMqGJw5wUD1dPC-H9VSGf81lFad3FQrdS6jvgpvL8LgjPhQXIWD75jfNS27q6vVsAS3XEi4pjEoMpC0IkvwMraWBNztdznhPhW11xqN-FZ35FEX1LiFUk-tk3b1mOwY/s1600/Blog1_LPISfarmoutline.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" height="226" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgR-Tke2JpOH45asMqGJw5wUD1dPC-H9VSGf81lFad3FQrdS6jvgpvL8LgjPhQXIWD75jfNS27q6vVsAS3XEi4pjEoMpC0IkvwMraWBNztdznhPhW11xqN-FZ35FEX1LiFUk-tk3b1mOwY/s320/Blog1_LPISfarmoutline.jpg" width="320" /></a></td></tr><tr><td class="tr-caption"><span style="font-size: small;">Fig. 1, Aerial photography provides high quality imagery, and </span><span style="font-size: small;">can be an </span><br /><span style="font-size: small;">excellent starting point </span><span style="font-size: small;">for disentangling </span><span style="font-size: small;">semi-natural </span><br /><span style="font-size: small;">wildlife habitats from intensively managed farmland. The first challenge</span><br /><span style="font-size: small;">is to identify the boundary of individual farms that are undertaking an assessment.<br /><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody><tr><td><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiMgNDmISP5mDHu9SM0ymF97ZsS7CO7RJlVEroaT8Rslk5gh8Cojxde72gzAvblEjQAnqKvkAhQ9wI6Zisi705TgPGw2uLx35yurVl3FklC9rZbC8UcQFXVVC79OjbhIi-i6o0s9xF6yT0/s1600/Blog2_LPISfarmoutline.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" height="226" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiMgNDmISP5mDHu9SM0ymF97ZsS7CO7RJlVEroaT8Rslk5gh8Cojxde72gzAvblEjQAnqKvkAhQ9wI6Zisi705TgPGw2uLx35yurVl3FklC9rZbC8UcQFXVVC79OjbhIi-i6o0s9xF6yT0/s320/Blog2_LPISfarmoutline.jpg" width="320" /></a></td></tr><tr><td class="tr-caption"><span style="font-size: small;">Fig. 2. Photos can help improve identification of farmland habitats, </span><br /><span style="font-size: small;">especially </span><span style="font-size: small;">in combination with aerial photography. We are developing</span><br /><span style="font-size: small;">ICT systems to supply an ecologist with farm-level photos of habitats. </span></td></tr></tbody></table><em style="text-align: left;"></em><br /></span></td></tr></tbody></table>
<br />
In a Teagasc-funded project in partnership with Bord Bia, we collaborated on a pilot project to implement farmland habitat surveys on about 200 Irish farms that agreed to participate. The real challenge is to develop approaches and systems that could be scaled up to many more farmers if required. Thus, we are developing data management approaches that combine aerial photography, farm boundary data, and file-sharing of farm photos. We are also automating the preparation of farm habitat plans that include: a farm habitat map, habitat types and areas, a basic habitat descriptions and information on good management practice for wildlife, and photos of wildlife habitats on the farm. An example of some of the final output is provided in Fig. 3. This would satisfy the requirements of sustainability assessments.<br />
<br /><br /></div><div><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiVn6pr4PxOxijIBdcjB7SEQB76pha4a6cjVbdfh8lRNMDYIJQ6Xmb442vDAndNZx0XRAVPIFWdEzVg7ww6M7ROEgJf175plDK8wjqB9SjvTXyhyJCXYLrqwu_-IGG0n92vG888q5UP9_s/s1139/Farm_habitat_map.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="786" data-original-width="1139" height="442" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiVn6pr4PxOxijIBdcjB7SEQB76pha4a6cjVbdfh8lRNMDYIJQ6Xmb442vDAndNZx0XRAVPIFWdEzVg7ww6M7ROEgJf175plDK8wjqB9SjvTXyhyJCXYLrqwu_-IGG0n92vG888q5UP9_s/w640-h442/Farm_habitat_map.jpg" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Fig. 3. Example of the final farm habitat map and table with habitat types and sizes, and an estimate of the relative wildlife value of the different habitats. <br /></td></tr></tbody></table><br /><div class="separator" style="clear: both; text-align: center;"><br /></div><br /></div></div><br /><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgOGoqUF1yN5VYyeVxiZeg2orSZtCT79Gl3GeXtr6x9kv12G5fLM_Z8ZSpSSOkr_UYm5P9HMX3-XW6lhpzXHaQQG-Mha8RPY3Ga4kNpY-5oiaXL7i4RRbo9l0k0FcOxoNcFPO5oFB93-II/s1280/Fig+6+2004_0901sligo0079.JPG" style="font-style: italic; margin-left: 1em; margin-right: 1em; text-align: center;"><img border="0" data-original-height="960" data-original-width="1280" height="240" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgOGoqUF1yN5VYyeVxiZeg2orSZtCT79Gl3GeXtr6x9kv12G5fLM_Z8ZSpSSOkr_UYm5P9HMX3-XW6lhpzXHaQQG-Mha8RPY3Ga4kNpY-5oiaXL7i4RRbo9l0k0FcOxoNcFPO5oFB93-II/w320-h240/Fig+6+2004_0901sligo0079.JPG" width="320" /></a></div><div><br />
<br />
<p class="MsoNormal"><span style="font-family: "Times New Roman", serif; font-size: 13.5pt;">Update 17 Nov 2020: see a more recent post ‘<a href="www.https://farmecol.blogspot.com/2020/11/smartagrihubs-pilot-incorporation-of.htmlw.blogger.com/blog/post/edit/3383131600232191143/529909461949870306" target="_blank">SmartAgriHubs:pilot incorporation of farmland habitats in Teagasc National Farm Survey</a>’ that
describes how we have used this method to pilot the incorporation of
biodiversity into the Teagasc National Farm Survey.<o:p></o:p></span></p><br /><b>Further reading:</b></div></div><div><span style="-webkit-text-size-adjust: auto; background-color: rgba(255, 255, 255, 0); text-size-adjust: auto;"><br /></span></div><div><span style="-webkit-text-size-adjust: auto; background-color: rgba(255, 255, 255, 0); text-size-adjust: auto;">
<span face=""arial" , "sans-serif"" style="font-size: 11pt;"><span style="font-family: "times" , "times new roman" , serif;"></span></span><div>J.A. Finn and P. Moran. A pilot study of methodology for the development <span style="background-color: rgba(255, 255, 255, 0);">of farmland habitat reports for sustainability </span><span style="background-color: rgba(255, 255, 255, 0);">assessments. </span><span style="background-color: rgba(255, 255, 255, 0);">Irish Journal of Agricultural and Food Research. </span><span style="background-color: rgba(255, 255, 255, 0);">DOI: 10.15212/ijafr-2020-0103. (in press)</span></div>
<span face=""arial" , "sans-serif"" style="font-size: 11pt;"><span style="font-family: "times" , "times new roman" , serif;"></span></span><div><span style="-webkit-text-size-adjust: auto; background-color: rgba(255, 255, 255, 0); text-size-adjust: auto;"><br /></span></div><span style="font-family: "Times New Roman", serif;">Larkin et al. 2019. <a href="http://scholar.google.com/scholar?oi=bibs&cluster=13134616816573704083&btnI=1&nossl=1&hl=en"><span style="color: blue;">Semi-natural habitats and Ecological Focus Areas on cereal,
beef and dairy </span></a><a href="http://scholar.google.com/scholar?oi=bibs&cluster=13134616816573704083&btnI=1&nossl=1&hl=en"><span style="color: blue;">farms </span></a><a href="http://scholar.google.com/scholar?oi=bibs&cluster=13134616816573704083&btnI=1&nossl=1&hl=en"><span style="color: blue;">in Ireland</span></a>. Land Use Policy, 104096</span></span></div><div><span style="-webkit-text-size-adjust: auto; background-color: rgba(255, 255, 255, 0); text-size-adjust: auto;"><span style="font-family: Times New Roman, serif;"><br /></span></span></div><div><span style="-webkit-text-size-adjust: auto; background-color: rgba(255, 255, 255, 0); text-size-adjust: auto;"><p class="EndNoteBibliography" style="margin-bottom: 0cm; margin-left: 36.0pt; margin-right: 0cm; margin-top: 0cm; margin: 0cm 0cm 0cm 36pt; text-indent: -36pt;"><a name="_ENREF_47"><span lang="EN-US">Sheridan, H., J. A. Finn, N. Culleton, and G.
O'Donovan. 2008. Plant and invertebrate diversity in grassland field margins.
Agriculture Ecosystems & Environment <b>123</b>:225-232.</span></a><span lang="EN-US"><o:p></o:p></span></p><p class="EndNoteBibliography" style="margin-bottom: 0cm; margin-left: 36.0pt; margin-right: 0cm; margin-top: 0cm; margin: 0cm 0cm 0cm 36pt; text-indent: -36pt;"><a name="_ENREF_47"><span lang="EN-US"><br /></span></a></p>
<p class="EndNoteBibliography" style="margin-bottom: 0cm; margin-left: 36.0pt; margin-right: 0cm; margin-top: 0cm; margin: 0cm 0cm 0cm 36pt; text-indent: -36pt;"><a name="_ENREF_48"></a><a name="_ENREF_49"><span lang="EN-US">Sheridan, H., B. J. McMahon, T. Carnus, J. A. Finn, A. Anderson, A.
J. Helden, A. Kinsella, and G. Purvis. 2011. Pastoral farmland habitat
diversity in south-east Ireland. Agriculture Ecosystems & Environment <b>144</b>:130-135.</span></a><span lang="EN-US"><o:p></o:p></span></p><p class="EndNoteBibliography" style="margin-bottom: 0cm; margin-left: 36.0pt; margin-right: 0cm; margin-top: 0cm; margin: 0cm 0cm 0cm 36pt; text-indent: -36pt;"><a name="_ENREF_49"><span lang="EN-US"><br /></span></a></p>
<p class="EndNoteBibliography" style="margin-bottom: 0cm; margin-left: 36.0pt; margin-right: 0cm; margin-top: 0cm; margin: 0cm 0cm 0cm 36pt; text-indent: -36pt;"><span lang="EN-US">Sheridan, H., B. Keogh, A.
Anderson, T. Carnus, B. J. McMahon, S. Green, and G. Purvis. 2017. Farmland
habitat diversity in Ireland. Land Use Policy <b>63</b>:206-213.</span><span lang="EN-US"><o:p></o:p></span></p><p class="EndNoteBibliography" style="margin-bottom: 0cm; margin-left: 36.0pt; margin-right: 0cm; margin-top: 0cm; margin: 0cm 0cm 0cm 36pt; text-indent: -36pt;"><br /></p>
<p class="EndNoteBibliography" style="margin-bottom: 0cm; margin-left: 36.0pt; margin-right: 0cm; margin-top: 0cm; margin: 0cm 0cm 0cm 36pt; text-indent: -36pt;"><a name="_ENREF_51"><span lang="EN-US">Sullivan, C. A., D. Bourke, M. S.
Skeffington, J. A. Finn, S. Green, S. Kelly, and M. J. Gormally. 2011.
Modelling semi-natural habitat area on lowland farms in western Ireland.
Biological Conservation <b>144</b>:1089-1099.</span></a><span lang="EN-US"><o:p></o:p></span></p><p class="EndNoteBibliography" style="margin-bottom: 0cm; margin-left: 36.0pt; margin-right: 0cm; margin-top: 0cm; margin: 0cm 0cm 0cm 36pt; text-indent: -36pt;"><a name="_ENREF_51"><span lang="EN-US"><br /></span></a></p>
<p class="EndNoteBibliography" style="margin-bottom: 0cm; margin-left: 36.0pt; margin-right: 0cm; margin-top: 0cm; margin: 0cm 0cm 0cm 36pt; text-indent: -36pt;"><a name="_ENREF_52"><span lang="EN-US">Sullivan, C. A., J. A. Finn, M. J. Gormally,
and M. S. Skeffington. 2013. Field boundary habitats and their contribution to
the area of semi-natural habitats on lowland farms in east Galway, western
Ireland. Biology and Environment: Proceedings of the Royal Irish Academy
113B(2):1-13.</span></a><span lang="EN-US"> <o:p></o:p></span></p>
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</div>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-3383131600232191143.post-90865616398963875072020-10-12T07:10:00.001-07:002020-10-12T07:10:04.438-07:00<p> </p><p class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0cm;"><o:p><b>Farming for Nature: the role of Results-Based Payments </b></o:p></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0cm;">A new book published by Teagasc and the National Parks and Wildlife
Service (NPWS) (<a href="http://www.teagasc.ie/farmingfornature">www.teagasc.ie/farmingfornature</a>) highlights several Irish case
studies of results-based payments for biodiversity conservation.</p><div class="separator" style="clear: both; text-align: center;"><br /></div><br /><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhhaTE1CyDpBLS0K4C4wyKQPvblKu3k20B9-KyvCRI3G0Ehi1O_oJuK2GD5BuH8LLTPrA_YsipHdmpVpgi6oHeN4flvXuT0qsiI7E28k-og8RQ_B4F9iRoklC8_-uXvHt9l3S1duE1toTU/s860/CoverImage.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="860" data-original-width="623" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhhaTE1CyDpBLS0K4C4wyKQPvblKu3k20B9-KyvCRI3G0Ehi1O_oJuK2GD5BuH8LLTPrA_YsipHdmpVpgi6oHeN4flvXuT0qsiI7E28k-og8RQ_B4F9iRoklC8_-uXvHt9l3S1duE1toTU/s320/CoverImage.jpg" /></a></div><br /><p class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0cm;"><br /> <span></span></p><a name='more'></a>Results-based
payments provide farmers with performance-related payments for delivering
agreed environmental objectives. For example, higher payments are made for
protection of indicator species, and for improved habitat quality, soil health
and water quality. <o:p></o:p><p></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0cm;"><o:p> </o:p>Teagasc has been involved in multiple projects that have trialled the
implementation of results-based payments: BurrenLIFE, which led to the Burren
Programme; KerryLIFE; AranLIFE; and, the RBAPS project (Developing Results
Based Agri-environmental Payment Schemes in Ireland and Spain). Across Europe,
policymakers and practitioners are looking to Ireland as a leader in this area,
and at these projects and programmes as examples of how results-based
approaches can be developed and implemented.</p><p class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0cm;"><o:p></o:p></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0cm;"><o:p> </o:p>Lessons from these projects have informed the design of ambitious
results-based European Innovation Partnerships (EIPs) that include the Hen
Harrier Project, the Pearl Mussel Project, the BRIDE project, and others. These
lessons centred on:</p><p class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0cm;"></p><ul style="text-align: left;"><li><span style="font-family: Symbol; text-indent: -18pt;"><span style="font-family: "Times New Roman"; font-size: 7pt; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal;"> </span></span><span style="text-indent: -18pt;">the necessity for combining the specialist
skills of farmers, ecologists, advisors and project managers</span><span style="font-family: Symbol; text-indent: -18pt;"><span style="font-family: "Times New Roman"; font-size: 7pt; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal;"> </span></span></li><li><span style="text-indent: -18pt;">the need to identify relevant specific
objectives and indicators that help to best target efforts;</span></li><li><span style="font-family: Symbol; text-indent: -18pt;"><span style="font-family: "Times New Roman"; font-size: 7pt; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal;"> </span></span><span style="text-indent: -18pt;">the important role of advisory support with
biodiversity expertise to engage with participant farmers;</span></li><li><span style="text-indent: -18pt;">good practice in the development of scoring
schemes;</span></li><li><span style="text-indent: -18pt;">innovative approaches to link scores to
payments;</span></li><li><span style="text-indent: -18pt;">different hybrid approaches that combine
action-based payments, results-based payments and non-productive investments;
and,</span></li><li><span style="text-indent: -18pt;">an ability to rapidly measure progress towards
the biodiversity targets, and either confirm progress or learn how to improve.</span></li></ul><p></p><p class="MsoListParagraphCxSpFirst" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0cm; mso-add-space: auto; mso-list: l0 level1 lfo1; text-indent: -18.0pt;"><!--[if !supportLists]--><o:p></o:p></p>
<p class="MsoListParagraphCxSpMiddle" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0cm; mso-add-space: auto; mso-list: l0 level1 lfo1; text-indent: -18.0pt;"><o:p></o:p></p>
<p class="MsoListParagraphCxSpMiddle" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0cm; mso-add-space: auto; mso-list: l0 level1 lfo1; text-indent: -18.0pt;"><o:p></o:p></p>
<p class="MsoListParagraphCxSpMiddle" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0cm; mso-add-space: auto; mso-list: l0 level1 lfo1; text-indent: -18.0pt;"><o:p></o:p></p>
<p class="MsoListParagraphCxSpMiddle" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0cm; mso-add-space: auto; mso-list: l0 level1 lfo1; text-indent: -18.0pt;"><o:p></o:p></p>
<p class="MsoListParagraphCxSpMiddle" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0cm; mso-add-space: auto; mso-list: l0 level1 lfo1; text-indent: -18.0pt;"><o:p></o:p></p>
<p class="MsoListParagraphCxSpLast" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0cm; mso-add-space: auto; mso-list: l0 level1 lfo1; text-indent: -18.0pt;"><o:p></o:p></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0cm;"><o:p> </o:p>The book published by Teagasc and the National Parks and Wildlife
Service (NPWS) (<a href="http://www.teagasc.ie/farmingfornature">www.teagasc.ie/farmingfornature</a>) provides details on farm
plans, scoring sheets, governance mechanisms, the role of advisory services,
the choice of indicators, monitoring details, and explores the relationship
between results and payment.</p><p class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0cm;"><o:p></o:p></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0cm;"><b>Other contributors and collaborators</b>: The Burren Programme, AranLIFE,
KerryLIFE, the NPWS, the Department of Culture, Heritage and the Gaeltacht, the
RBAPS project, Galway-Mayo Institute of Technology, and University College
Cork.</p><p class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0cm;"><o:p></o:p></p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-3383131600232191143.post-64778339139827173132020-10-12T02:11:00.004-07:002020-10-12T02:11:43.626-07:00<p> </p><p class="MsoNormal"><b>The EU Biodiversity Strategy: on the path to biodiversity
recovery by 2030</b><o:p></o:p></p>
<p class="MsoNormal">John Finn and Daire Ó hUallacháin<o:p></o:p></p>
<p class="MsoNormal">The EU Biodiversity Strategy for 2030 has proposed a number
of challenging targets that will inform policy, practice and research for the
coming decades. Here, John Finn and Daire Ó hUallacháin highlight the main
features of the Biodiversity Strategy, and focus on those that are most
relevant to agriculture.<span></span></p><a name='more'></a><p></p><p class="MsoNormal"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEimShzFNMbTuArReZo-9ZX_dMm_IG6o5VQT-rv73O__rB9zS0hvwYYsbZS49UtkEK_IkkZWEouQWFGcFqzKPqtoYohFV6xLBN-0bPuYKaUbCuIldL8Hh6sqaIt4i3iARNBNglw_xxY8gGA/s2048/IMG_1185.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em; text-align: center;"><img border="0" data-original-height="1536" data-original-width="2048" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEimShzFNMbTuArReZo-9ZX_dMm_IG6o5VQT-rv73O__rB9zS0hvwYYsbZS49UtkEK_IkkZWEouQWFGcFqzKPqtoYohFV6xLBN-0bPuYKaUbCuIldL8Hh6sqaIt4i3iARNBNglw_xxY8gGA/s320/IMG_1185.jpg" width="320" /></a></p><p class="MsoNormal"><br /></p>
<p class="MsoNormal">The EU Biodiversity Strategy for 2030 has set ambitious
targets that will require transformative change if they are to be achieved. In
an unprecedented move, the Biodiversity Strategy was announced in tandem with
the EU Farm to Fork Strategy, signalling the co-dependency of the two
strategies in supporting human health, economic recovery and food security.
Recognising the current global threat to biodiversity, the EU has committed to
the Convention on Biological Diversity’s aim to “ensure that by 2050 all of the
world’s ecosystems are restored, resilient, and adequately protected”. In the
meantime, the EU “aims to ensure that Europe's biodiversity will be on the path
to recovery by 2030.” <o:p></o:p></p>
<p class="MsoNormal">The first strategic action of the EU Biodiversity Strategy
aims to widen the network of protected areas and establish “a coherent network
of protected areas”. The aim is that at least 30% of the land (requiring an
increase of 4%) and 30% of the sea (requiring an increase of 19%) will be
protected and connected through ecological corridors as part of a European-wide
network. It remains to be seen how this EU-level target will translate to
targets for individual Member States. <o:p></o:p></p>
<p class="MsoNormal">The second strategic action of the EU Biodiversity
explicitly recognises that significant restoration actions will be required to
reverse decades of biodiversity loss. This restoration will include, for
example: strengthening the EU legal framework for nature restoration; bringing
nature back to agricultural land; addressing land take and restoring soil
ecosystems; increasing the quantity of forests and improving their health and
resilience; restoring freshwater ecosystems, reducing pollution and; addressing
invasive alien species. <o:p></o:p></p>
<p class="MsoNormal">There are key commitments associated with the above
restoration themes, and following are the ones with strongest interaction with
land use, and agricultural practices:<o:p></o:p></p>
<blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px; text-align: left;"><p class="MsoNormal">1. Legally binding EU nature restoration targets to be
proposed in 2021, subject to an impact assessment. By 2030, significant areas
of degraded and carbon-rich ecosystems are restored; habitats and species show
no deterioration in conservation trends and status; and at least 30% reach
favourable conservation status or at least show a positive trend.</p><p class="MsoNormal">2. The decline in pollinators is reversed.</p><p class="MsoNormal">3. The risk and use of chemical pesticides is reduced by 50%
and the use of more hazardous pesticides is reduced by 50%.</p><p class="MsoNormal">4. At least 10% of agricultural area is under high-diversity
landscape features.</p><p class="MsoNormal">5. At least 25% of agricultural land is under organic
farming management, and the uptake of agro-ecological practices is
significantly increased.</p><p class="MsoNormal">6. Three billion new trees are planted in the EU, in full
respect of ecological principles.</p><p class="MsoNormal">7. Significant progress has been made in the remediation of
contaminated soil sites.</p><p class="MsoNormal">8. At least 25,000 km of free-flowing rivers are restored. </p><p class="MsoNormal">9. There is a 50% reduction in the number of Red List
species threatened by invasive alien species.</p><p class="MsoNormal"></p><div class="separator" style="clear: both; text-align: center;"><br /></div><p></p><p class="MsoNormal">10. The losses of nutrients from fertilisers are reduced by
50%, resulting in the reduction of the use of fertilisers by at least 20%.</p></blockquote><p class="MsoNormal"><o:p></o:p></p>
<p class="MsoNormal"><o:p></o:p></p>
<p class="MsoNormal"><o:p></o:p></p>
<p class="MsoNormal"><o:p></o:p></p>
<p class="MsoNormal"><o:p></o:p></p>
<p class="MsoNormal"><o:p></o:p></p>
<p class="MsoNormal"><o:p></o:p></p>
<p class="MsoNormal"><o:p></o:p></p>
<p class="MsoNormal"><o:p></o:p></p>
<p class="MsoNormal"><o:p></o:p></p>
<p class="MsoNormal"><o:p> <br /></o:p>Clearly, alignment with these key commitments can be
expected to have significant impacts on the management practices of
agricultural systems for example through reduced use of pesticides and
fertilisers. As mentioned above, the Farm to Fork Strategy contains and
supports several of these same commitments, and can be expected to strongly
influence CAP reform and choices about direct payments and rural development
actions, especially the objectives and design of eco-schemes, agri-environment
schemes and results-based payments. The aim for 10% of agricultural area to be
high-diversity landscape features will incorporate e.g. buffer strips (image
1), rotational or non-rotational fallow land, hedges, non-productive trees,
terrace walls, and ponds. The inclusion of habitats currently considered ‘ineligible’
under existing Cross Compliance (e.g. ponds, scrub, wetlands) will likely be a
strong discussion point. The target of three billion new trees represents a
major intervention in land use, and the Commission will develop guidelines on
biodiversity-friendly afforestation and reforestation and closer-to-nature
forestry practices (in parallel with the new EU Forest Strategy).</p>
<p class="MsoNormal">The third strategic action aims to “Enable transformative
change”, and support “An Ambitious Global Biodiversity Agenda”. This proposes
new and improved governance frameworks that facilitate a system with effective
indicators, progress assessment and corrective actions. This aims to
significantly increase the implementation and enforcement of EU environmental
legislation. In an innovative approach, the Commission envisages improved use
of both public and private investments to financially incentivise nature-based
solutions for biodiversity conservation and climate action. The fourth
strategic action supports “An Ambitious Global Biodiversity Agenda” to improve
international governance for the protection and restoration of biodiversity. <o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p><p class="MsoNormal"><o:p>(This text appeared in TResearch, Autumn edition, 2020)</o:p></p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-3383131600232191143.post-58542514115981300492019-09-10T13:50:00.005-07:002021-01-28T02:05:13.109-08:00Plant diversity and yield in agricultural grasslands<span face=""calibri" , sans-serif" style="font-size: large;">There is a lot of discussion about the use of multi-species mixtures within
grazed grassland systems. Here, I take a look at *some* of the evidence - but note that this is definitely NOT intended to be a literature review. <div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEirvSDI1sP_FFTIV4UMet-9vfviHP-cfRjdu5RN6NhdkdkE6bEZxKi1x4BC04HC7BtuLN-fLgANfz_midk65aRbeNJCZ8ALOMMLZNojF5EhS29gS5Y8YMp4Ei32QOPN3Oz21odPg85EL2c/s2048/IMG_1050.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1536" data-original-width="2048" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEirvSDI1sP_FFTIV4UMet-9vfviHP-cfRjdu5RN6NhdkdkE6bEZxKi1x4BC04HC7BtuLN-fLgANfz_midk65aRbeNJCZ8ALOMMLZNojF5EhS29gS5Y8YMp4Ei32QOPN3Oz21odPg85EL2c/s320/IMG_1050.jpg" width="320" /></a></div><br /></span><br />
<a name='more'></a><br />
<span face=""calibri" , sans-serif" style="font-size: large;"><br /></span>
<span face=""calibri" , sans-serif" style="font-size: large;">In assessing
the benefits associated with multi-species mixtures, it’s important to disentangle the different and potentially
confounding effects of plant richness, supply of nitrogen, and whether research
has been conducted on grazed or mechanically harvested plots. Therefore, I tend to clarify these points about the different studies involved. </span><br />
<div class="MsoNormal">
<span style="font-size: large;"><br /></span></div>
<div class="MsoNormal" style="text-align: justify;">
<b><span face=""calibri" , "sans-serif"" lang="EN-GB" style="font-size: large;">Diversity promotes yield in semi-natural grasslands<o:p></o:p></span></b></div>
<div class="MsoNormal" style="text-align: justify;">
<span face=""calibri" , "sans-serif"" lang="EN-GB" style="font-size: large;">Ecological research conducted in relatively species-rich and nutrient-poor
systems very clearly shows that grassland yield is enhanced by species
diversity (e.g. reviewed in Hooper at al. 2005, Cardinale et al. 2007), and
that multiple ecological processes may be more effective when species diversity
is higher. In many studies (e.g. Hector et al 1999) one sees a very rapid
response (ascending limb) from 1-5 species, and a smaller but consistent
response as richness increases further (see Fig. 1 below). <o:p></o:p></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="font-size: large;"><br /></span></div>
<div class="MsoNormal" style="text-align: justify;">
</div>
<div class="MsoNormal" style="text-align: justify;">
<span face=""calibri" , "sans-serif"" lang="EN-GB" style="font-size: large;">Note,
however, that the level of yield from the more species-rich mixtures is not as
high as would be obtained if other (agronomic) species were used and with added
inorganic fertiliser. However, the purpose of these experiments was to
demonstrate the effect of biodiversity loss within natural ecosystems i.e the
consequences as one goes from higher (right hand side of Fig. 1) to lower
diversity (left hand side of Fig. 1). In addition, adding even modest amounts
of nitrogen fertiliser to species-rich grasslands results in a very strong reduction in plant diversity (Kleijn <i>et al</i>. 2009). <o:p></o:p></span></div>
<span style="font-size: large;"><span style="font-family: "times new roman" , serif;"><br /></span>
</span><br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj0G5V9px3TXEqxJ6gHH5mXjCdDM5IVzeXBQACf2edgOH2daSgqg8lbvINuk6fNKt4x_4EGyH_G-N-eQe2d8E6kl20TgDycLHxOpzSIGkA7ft0uw0cQHKt5MvItFEtKycq7Op1WveDmxwk/s1600/Biodepth.png" style="margin-left: auto; margin-right: auto;"><span style="font-size: large;"><img border="0" data-original-height="870" data-original-width="1538" height="361" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj0G5V9px3TXEqxJ6gHH5mXjCdDM5IVzeXBQACf2edgOH2daSgqg8lbvINuk6fNKt4x_4EGyH_G-N-eQe2d8E6kl20TgDycLHxOpzSIGkA7ft0uw0cQHKt5MvItFEtKycq7Op1WveDmxwk/s640/Biodepth.png" width="640" /></span></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><span style="font-size: small;">Fig. 1 Aboveground biomass harvested from plots sown with known numbers of species in a common experiment across multiple EU sites as part of EU BIODEPTH experiment. Adapted from Hector et al. 1999. </span></td></tr>
</tbody></table>
<div class="separator" style="clear: both; text-align: center;">
<span style="font-size: large;"><br /></span></div>
<div class="MsoNormal" style="text-align: justify;">
<b><span face=""calibri" , "sans-serif"" lang="EN-GB" style="font-size: large;">Relevance to intensively managed agricultural grasslands…<o:p></o:p></span></b></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="font-size: large;"><span face=""calibri" , "sans-serif"" lang="EN-GB">The
ability of more diverse plant communities to acquire available resources and
convert them into above-ground biomass has obvious relevance for agricultural
systems, although the results from extensively-managed (low-nutrient)
semi-natural grasslands do not necessarily extrapolate to intensively-managed
grasslands. </span><span face=""calibri" , "sans-serif"" lang="EN-GB">Although ecological principles from diversity-function research
therefore suggest that increasing plant species diversity in agronomic systems
may improve biomass production, this has not been extensively tested. The
potential multiple benefits of diverse agronomic crops with more than two
species have been under-researched, but could have important implications for
more sustainable agricultural practices by providing sufficient crop yield
while minimising environmental impacts.<o:p></o:p></span></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="font-size: large;"><br /></span></div>
<div class="MsoNormal">
</div>
<div class="MsoNormal" style="text-align: justify;">
<span face=""calibri" , "sans-serif"" lang="EN-GB" style="font-size: large;">More
recently, a number of studies have been investigating multi-species mixtures. I
discuss some of these here. <o:p></o:p></span></div>
<div class="separator" style="clear: both; text-align: center;">
<span style="font-size: large;"><br /></span></div>
<div class="MsoNormal">
<b><span face=""calibri" , "sans-serif"" lang="EN-GB" style="font-size: large;">Four-species
grass/legume mixtures generally outyielded best-performing monoculture<o:p></o:p></span></b></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="font-size: large;"><span face=""calibri" , "sans-serif"" lang="EN-GB">The
Agrodiversity experiment was conducted at each of 31 sites (17 European
countries and Canada) (Fig. 2). F</span><span face=""calibri" , "sans-serif"" lang="EN-GB">ifteen grassland communities comprising four monocultures and
eleven mixtures of four functional types of species (each represented by one
species) (see Finn <i>et al</i>., 2013 for details). The choice of species for
use in multi-species mixtures can be strategically designed to include traits
that maximise complementarity and interspecific interactions to improve
resource utilisation and yield of above-ground biomass. Thus, we selected
functional types that consisted of a fast-establishing grass, a
fast-establishing legume, a temporally persistent grass and a temporally
persistent legume. The selection of species varied across sites, but the most
commonly used species were <i>Lolium perenne
</i>L., <i>Trifolium pratense </i>L.<i>,</i> <i>Dactylis
glomerata </i>L.<i>, </i>and <i>Trifolium repens </i>L. All plots were
mechanically harvested. <o:p></o:p></span></span></div>
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh2jZ26VbiCauqrOFexiol1Lh-ZiPhakyWX-c41Da3cy0-RP1BMW5l5H_0rI0TJ7pK5gWKOduGUUiYe80ZqjceE9F_x__faf5Erax0YsLBoe9W9CxpaukmD_zR8nbxckTFV223tfsA3ns4/s1600/COST_sites.png" style="margin-left: auto; margin-right: auto;"><span style="font-size: large;"><img border="0" data-original-height="952" data-original-width="698" height="400" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh2jZ26VbiCauqrOFexiol1Lh-ZiPhakyWX-c41Da3cy0-RP1BMW5l5H_0rI0TJ7pK5gWKOduGUUiYe80ZqjceE9F_x__faf5Erax0YsLBoe9W9CxpaukmD_zR8nbxckTFV223tfsA3ns4/s400/COST_sites.png" width="292" /></span></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><span style="font-size: small;">Fig. 2. Distribution of the European sites that participated in the Agrodiversity experiment.</span><br />
<span style="font-size: small;">(Canadian site not shown.)</span></td></tr>
</tbody></table>
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<span face=""calibri" , "sans-serif"" lang="EN-GB" style="font-size: large; text-align: justify;">There
was a considerable range in site productivity, reflecting the different
geographical and climatic regions across the study sites. Annual averages of
total yield (dry matter) per site ranged from about 18 t ha<sup>-1</sup> year<sup>-1</sup>
to about 3 t ha<sup>-1</sup> year<sup>-1</sup>. Across all sites, yield of sown
species (not including weed biomass, and averaged across years and seed density) of mixtures exceeded that of
the mean monoculture in 99.7% of mixture communities with an average (across
all mixtures and sites) ratio of mixture/monoculture yield of 1.77 (Fig. 3).
Transgressive overyielding (better yields than in the best monoculture)
occurred in 79% of mixture communities and was significant at 71% of sites. </span><span face=""calibri" , "sans-serif"" lang="EN-GB" style="font-size: large; text-align: justify;">At sixteen
sites, all of the mixture communities yielded more than the best monoculture
community. The yield benefit of
mixtures was already evident in year 1, and persisted for the three years of
the experiment. </span></div>
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<span style="font-size: large;"><span face=""calibri" , "sans-serif"" lang="EN-GB">Across all sites, monocultures displayed much higher levels
(and variability) of weed invasion than mixtures. </span><span face=""calibri" , sans-serif">The median percentage of weed
biomass in the total yield of monocultures increased over time (15% in year 1,
20% in year 2 and 32% in year 3); in contrast, the median percentage of weed
biomass in the mixtures remained consistently low (4% in year 1, 3% in year 2
and 3% in year 3). </span></span></div>
<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhLB3MH9CP_C6pJxZlqaBlNhzvfoseaQ6UQRFf0tpsQPGz_AFwpPLgrBnZI8TQ98lFDwvPYyy2oL9OdpJDGun48WZPg_7mNfpjb6fz2SdxLuYodscbfgCoHfHUVwvZUbUyPPmNPWg4nc0I/s1600/COST_yield.png" style="margin-left: auto; margin-right: auto;"><span style="font-size: large;"><img border="0" data-original-height="836" data-original-width="1540" height="346" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhLB3MH9CP_C6pJxZlqaBlNhzvfoseaQ6UQRFf0tpsQPGz_AFwpPLgrBnZI8TQ98lFDwvPYyy2oL9OdpJDGun48WZPg_7mNfpjb6fz2SdxLuYodscbfgCoHfHUVwvZUbUyPPmNPWg4nc0I/s640/COST_yield.png" width="640" /></span></a></td></tr>
<tr><td class="tr-caption" style="text-align: left;"><span style="font-size: small;"><span face=""calibri" , sans-serif" style="text-align: justify;">Fig. 3. Mixtures generally yielded more than the best monoculture across 31 international sites with a common field experiment comparing four-species mixtures with the four component monocultures. Average annual yield over the whole experimental duration of yield of sown agronomic species (excludes weeds) at each of 31 sites. Each open circle represents the average aboveground biomass for a specific mixture community across multiple years; horizontal lines represent the yield of the best-performing monoculture; shaded boxes represent the mean monoculture performance. Significant transgressive overyielding is indicated by an asterisk over a site at the top of the panel. From Finn </span><i style="font-family: calibri, sans-serif; text-align: justify;">et al</i><span face=""calibri" , sans-serif" style="text-align: justify;">. (2013). The site numbers correspond to individual experiments. Data available from Kirwan </span><i style="font-family: calibri, sans-serif; text-align: justify;">et al.</i><span face=""calibri" , sans-serif" style="text-align: justify;"> (2014).</span></span></td></tr>
</tbody></table>
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<span style="font-size: large;"><span face=""calibri" , "sans-serif"" lang="EN-GB"> </span><span face=""calibri" , sans-serif" lang="EN-GB" style="text-align: justify;">The
Agrodiversity experiment varied considerably in the use of inorganic nitrogen,
with several sites operating under organic status, and the low-fertility sites
only applied low or no inorganic nitrogen. However, the more productive sites
did apply nitrogen at intermediate rates e.g. between 100 and 200 kg N per ha
per year. What was absent from the experiment was a comparison with a
monoculture with a high level of nitrogen that would act as a positive control
i.e. to test whether the mixtures could outperform a monoculture with high
N. </span><span face=""calibri" , sans-serif" lang="EN-GB" style="text-align: justify;">Nevertheless,
what was clear was that the magnitude of mixture benefits was sufficient for
mixtures to regularly yield more than the best-performing agronomic monoculture
(transgressive overyielding). This is striking for three main reasons: first,
the rapidity and frequency of transgressive overyielding; second, the
occurrence of transgressive overyielding across such a wide range of mixtures,
and; third its occurrence across such a wide range of sites that differed in
soil type, productivity and climate. </span></span></div>
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<span face=""calibri" , "sans-serif"" lang="EN-GB" style="font-size: large;"><o:p></o:p></span></div>
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<span style="font-size: large;"><br /></span></div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhrAE-Yvd85hh19mmSE2kHPr41-CUck2Lfl71pIcnVqLxTRGy-2sIxRhlZ4NIYdr3dNIRGsuNcdEwqrerLwcyqG7M8b8e-OlkWRwsdOymJccB1oLZFssHF4TC_ThJokj8oTgnNKFqVEJEs/s1600/Harvester3.JPG" style="margin-left: 1em; margin-right: 1em;"><span style="font-size: large;"><img border="0" data-original-height="1200" data-original-width="1600" height="240" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhrAE-Yvd85hh19mmSE2kHPr41-CUck2Lfl71pIcnVqLxTRGy-2sIxRhlZ4NIYdr3dNIRGsuNcdEwqrerLwcyqG7M8b8e-OlkWRwsdOymJccB1oLZFssHF4TC_ThJokj8oTgnNKFqVEJEs/s320/Harvester3.JPG" width="320" /></span></a></div>
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<span style="font-size: large;"><br /></span>
<span face=""calibri" , sans-serif" style="font-size: large;">Having demonstrated strong benefits of four-species mixtures, we are now looking at the benefits of six species mixtures with two grasses, two legumes and two herbs. The 2018 (with natural drought) total yields in plots that were mechanically harvested were as follows:</span><br />
<span face=""open sans" , "arial" , sans-serif" style="background-color: #eaeaea; color: #333333; font-size: 18.6667px;"></span><br />
<ul style="background-attachment: initial; background-clip: initial; background-image: initial; background-origin: initial; background-position: 0px 0px; background-repeat: initial; background-size: initial; border: 0px; color: #333333; font-family: "open sans", Arial, sans-serif; font-size: 18.6667px; margin: 0px 0px 20px 20px; outline: 0px; padding: 0px; text-align: start; vertical-align: baseline;">
<li style="background-attachment: initial; background-clip: initial; background-image: initial; background-origin: initial; background-position: 0px 0px; background-repeat: initial; background-size: initial; border: 0px; font-size: 18.6667px; list-style: disc; margin: 5px 20px; outline: 0px; padding: 0px; vertical-align: baseline;">Six-species mix <span style="font-size: 18.6667px;">150 kg N </span>– 12.5 t/ha</li>
<li style="background-attachment: initial; background-clip: initial; background-image: initial; background-origin: initial; background-position: 0px 0px; background-repeat: initial; background-size: initial; border: 0px; font-size: 18.6667px; list-style: disc; margin: 5px 20px; outline: 0px; padding: 0px; vertical-align: baseline;">Ryegrass 300 kg N – 11.2 t/ha</li>
<li style="background-attachment: initial; background-clip: initial; background-image: initial; background-origin: initial; background-position: 0px 0px; background-repeat: initial; background-size: initial; border: 0px; font-size: 18.6667px; list-style: disc; margin: 5px 20px; outline: 0px; padding: 0px; vertical-align: baseline;">Ryegrass 150 kg N – 9.4 t/ha</li>
</ul>
<span face=""calibri" , sans-serif" style="font-size: large;"><a href="https://www.agriland.ie/farming-news/multi-specie-swards-outperform-ryegrass-monocultures/" target="_blank">This research is summarised </a>in a recent popular article by Guylain Grange and Saoirse Cummins (<a href="https://www.agriland.ie/farming-news/multi-specie-swards-outperform-ryegrass-monocultures/" target="_blank">Agriland article online</a>). </span></div>
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<b><span face=""calibri" , "sans-serif"" lang="EN-GB" style="font-size: large;">Grass-legume
mixtures maintained yield despite substantial reductions in N fertiliser <o:p></o:p></span></b></div>
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<span face=""calibri" , "sans-serif"" lang="EN-GB" style="font-size: large;">Grass-legume mixtures in
grassland forage systems can benefit from symbiotic N<sub>2</sub> fixation of
legumes, thereby increasing total harvest yield, total N yield, and forage quality. Because legumes have access to atmospheric N<sub>2</sub>
for their N requirements, the relative availability of soil N increases for
grasses in mixtures due to<i> ‘</i>N
sparing’ (increased availability of soil N because legumes rely on symbiotic N<sub>2</sub>
fixation). Therefore, the use of grass-legume mixtures could allow substantial
reductions in amounts of industrial N fertiliser in agricultural grassland
systems without a compromise in yield. At the Swiss site of the Agrodiversity
experiment, Nyfeler <i>et al.</i> (2009)
compared monoculture and mixture yields across three levels of nitrogen (50,
150 and 450 kg N ha<sup>-1</sup> year<sup>-1</sup>) (Fig. 4). Their results indicated a
high potential for N-fertilizer replacement: grass–clover mixtures containing
40–60% clover and receiving 50 or 150 kg N ha<sup>-1</sup> year<sup>-1</sup>
achieved the same yield as grass monocultures fertilized with 450 kg N ha<sup>-1</sup>
year<sup>-1</sup> (Nyfeler <i>et al</i>.,
2009). Diversity–productivity effects were reduced at the highest level of N
fertilization and at 450 kg N ha<sup>-1</sup> year<sup>-1</sup>, they virtually
disappeared in the third year.<o:p></o:p></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span face=""calibri" , sans-serif"><span style="font-size: large;">These results illustrate an example of sustainable intensification and 'more from less'; it is possible to get more yield from less input by using legumes to replace the input of nitrogen fertiliser. </span></span></div>
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiMfhyphenhyphenNIVZlEichdWyAjHhGu4kO3y8XxXta5aFZoi5kOresZ3DMBxTI8bPHaaiojbTpTHZEqEuxMWSkKE4uor452GOihB7HNLIOhcFCCk5E8PURelxq8lq6lD5kdoRW5ZvGfkOAyRfykCM/s1600/Nyfeler.png" style="margin-left: auto; margin-right: auto;"><span style="font-size: large;"><img border="0" data-original-height="929" data-original-width="1521" height="388" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiMfhyphenhyphenNIVZlEichdWyAjHhGu4kO3y8XxXta5aFZoi5kOresZ3DMBxTI8bPHaaiojbTpTHZEqEuxMWSkKE4uor452GOihB7HNLIOhcFCCk5E8PURelxq8lq6lD5kdoRW5ZvGfkOAyRfykCM/s640/Nyfeler.png" width="640" /></span></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><span style="font-family: inherit; font-size: small;">Fig. 4. Monoculture and mixture yields across three levels of nitrogen (50, 150 and 450 kg N ha<sup style="text-align: justify;">-1</sup><span style="text-align: justify;"> year</span><sup style="text-align: justify;">-1</sup><span style="text-align: justify;">), and presented in relation to the proportion of legume in the sward. Thus, the left hand side represents 100% grass and 0% legume; the mid-point represents yield with 50% grass and 50% legume, and; the right hand side represents yield with 0% grass and 100 legume. The red line represents the performance of the 100% grass sward with 450 kg/ha of nitrogen added as a reference point. From Nyfeler et al. (2009). </span></span></td></tr>
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<b><span face=""calibri" , "sans-serif"" lang="EN-GB" style="font-size: large;">Yield benefits of mixtures persist under grazing<o:p></o:p></span></b></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="font-size: large;"><span face=""calibri" , "sans-serif"" lang="EN-GB">Many experiments on multi-species
mixtures have been conducted under conditions where the forage has been
harvested by mowing (although variety testing for grasses and clovers in pastures is harvested in the same way). Do the benefits of mixtures
that are observed under mowing also prevail under grazing? There are many
experiments with grass-clover (2-species) mixtures under grazing; however, comparisons of
more species-rich combinations are less common. </span></span><br />
<span style="font-size: large;"><span face=""calibri" , "sans-serif"" lang="EN-GB">A recent study investigated
whether grazing modifies the benefits of mixtures on total N yield compared to
mowing (Huguenin-Elie <i>et al</i>., 2016).
The design included N<sub>2-</sub>fixing and non-fixing species, as well as
shallow- and deep-rooting species. <i>Lolium
perenne</i> (Lp) monoculture and mixtures with <i>Cichorium intybus</i> (Ci), or/and <i>Trifolium
repens</i> (Tr) and <i>Trifolium pratense</i>
(Tp) were compared under grazing or mowing for their N yield and capture of
fertilizer and atmospheric N<sub>2</sub>. Mixtures of the N<sub>2</sub> fixing
and the non-fixing species with 145 kg N </span><span face=""calibri" , "sans-serif"" lang="EN-GB">ha<sup>-1</sup>
yr<sup>-1</sup></span><span face=""calibri" , "sans-serif"" lang="EN-GB"> yielded as much N as the <i>L.
perenne</i> monoculture fertilized with 350 kg N </span><span face=""calibri" , "sans-serif"" lang="EN-GB">ha<sup>-1</sup>
yr<sup>-1</sup></span><span face=""calibri" , "sans-serif"" lang="EN-GB">, showing the considerable benefit of mixtures for N efficiency. The
benefits of the mixtures on N yield were similar under grazing and mowing.
Grazing did not modify the proportion of N derived from fertilizer and
symbiotic N<sub>2</sub> fixation in the plants </span></span><span face=""calibri" , sans-serif" style="font-size: large;">(Huguenin-Elie </span><i style="font-family: calibri, sans-serif; font-size: x-large;">et al</i><span face=""calibri" , sans-serif" style="font-size: large;">., 2016)</span><span face=""calibri" , sans-serif" style="font-size: large;">.</span></div>
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<span style="font-size: large;"><br /></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span face=""calibri" , "sans-serif"" lang="EN-GB" style="font-size: large;">In a two-year grazing experiment
(with 75 kg N ha<sup>-1</sup> yr<sup>-1</sup>) in France, an increase of
botanical complexity from one to five species (two grasses, two clovers and
chicory) resulted in positive effects on animal performance (Roca-Fernández <i>et al</i>., 2016). They distinguished
between monocultures of perennial ryegrass, ‘mixed swards’ of grass and clover,
and ‘multi-species swards’ of grasses, clovers and chicory. Compared to mixed
swards, multi-species swards improved production of milk (+0.8 kg/day) and milk
solids (+0.04 kg/day), which was attributed to enhanced sward quality and
increased dry matter intake (+1.5 kg DM/day). <o:p></o:p></span></div>
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<span style="font-size: large;"><br /></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span face=""calibri" , "sans-serif"" lang="EN-GB" style="font-size: large;">In a two-year grazing experiment
in the northeastern USA (no nitrogen fertiliser was applied), four mixture
communities were compared: two species (one grass, one legume), three species
(one grass, one legume, and chicory), six species (three grasses, two legumes
and chicory), and nine species (four grasses, four legumes and chicory). In a
dry year, the two-species mixture (4800 kg ha<sup>-1</sup> dry matter) yielded
less than the other mixtures (7600 kg ha<sup>-1</sup> dry matter); there was no
difference in dry matter yields (9800 kg ha<sup>-1</sup> dry matter) in the
year with plentiful rainfall (Sanderson <i>et
al</i>. 2005).<o:p></o:p></span></div>
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<span style="font-size: large;"><br /></span></div>
<div class="MsoNormal">
<span face=""calibri" , "sans-serif"" lang="EN-GB" style="font-size: large;">More
recently in Ireland, the Smartgrass project established farmlets with perennial
ryegrass (<i>Lolium perenne</i>) only, receiving
163 kg N ha<sup>−1</sup> year<sup>−1</sup> (PRG); a perennial ryegrass and white
clover (<i>Trifolium repens</i>) sward (PRGWC);
a six species sward containing two grasses, two legumes and two herbs (6S); and
a nine species sward containing three grasses, three legumes and three herbs (9S),
and each of the latter three treatments receiving 90 kg N ha<sup>−1</sup> year<sup>−1</sup>.
Lambs grazing the multispecies 6S and 9S swards had greater liveweight gain and
body condition score than those in the PRG sward, and required fewer
anthelmintic treatment than lambs on the PRG or PRGWC swards (Grace <i>et al</i>. 2019b). Grace et al. (2018)
concluded that “Multispecies swards did not differ in terms of annual DM
production despite lower N inputs compared to PRG swards over a two year study.
Sward chemical composition was largely similar between sward types. However,
herb content of the swards decreased over the duration of this study.” <o:p></o:p></span></div>
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<span style="font-size: large;"><br /></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span face=""calibri" , sans-serif" lang="EN-GB" style="font-size: large;">Overall, these experiments point to the ability of
more complex (>2 species) mixtures to improve yields for livestock
production, and with less input of inorganic nitrogen. Several studies have reviewed
the contribution of grass-legume mixtures to the nutrition and production of
livestock (e.g. Lüscher <i>et al</i>. 2014, Dewhurst et al. 2009).<o:p></o:p></span></div>
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<span style="font-size: large;"><br /></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span face=""calibri" , sans-serif" lang="EN-GB" style="font-size: large;">As have many other authors, Grace et al. (2019a)
stated that “Multispecies swards show potential for higher DM production from lower
N inputs compared with PRG‐only swards. However, maintaining the proportions of
key species may affect the productivity of such swards." This is an important
point: to achieve the benefits of multispecies mixtures under intensive management while maintaining the multiple species that provide
the benefits. This is where systems-level research can help to investigate management
to promote species’ persistence (duration of grazing rotation), and implement effective techniques to re-introduce
species (e.g. oversowing) that may be declining in the sward. </span><br />
<span face=""calibri" , sans-serif" lang="EN-GB" style="font-size: large;"><br /></span></div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi4BivMQg-QIqnWYsE2oinzo_huJHS33W_YzSEe5-CMEX0XqiIOJIOvlf4ezblqxRy20Q3ewKVfxItg-StxkuIiajZ_dm66XiSmCwalzjHShOgvrVBFUkE6wkcCIUCe9qlRGNuOBkXUY6s/s1600/IMG_7549.jpg" style="margin-left: 1em; margin-right: 1em;"><span style="font-size: large;"><img border="0" data-original-height="1200" data-original-width="1600" height="240" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi4BivMQg-QIqnWYsE2oinzo_huJHS33W_YzSEe5-CMEX0XqiIOJIOvlf4ezblqxRy20Q3ewKVfxItg-StxkuIiajZ_dm66XiSmCwalzjHShOgvrVBFUkE6wkcCIUCe9qlRGNuOBkXUY6s/s320/IMG_7549.jpg" width="320" /></span></a></div>
<br />
Other links:<br />
<span face=""trebuchet ms" , "trebuchet" , "verdana" , sans-serif" style="background-color: white;"><a href="https://farmecol.blogspot.com/2018/12/multi-species-mixtures-promote-yield.html" target="_blank">Mul</a></span><span face=""trebuchet ms" , "trebuchet" , "verdana" , sans-serif" style="background-color: white;"><a href="https://farmecol.blogspot.com/2018/12/multi-species-mixtures-promote-yield.html" target="_blank">ti-species mixtures promote yield stability</a> (under drought conditions)</span><br />
<h3 class="post-title entry-title" itemprop="name" style="background-color: white; font-family: "Trebuchet MS", Trebuchet, Verdana, sans-serif; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; font-weight: normal; line-height: normal; margin: 0.75em 0px 0px; position: relative;">
<a href="https://farmecol.blogspot.com/2017/11/four-species-mixtures-increased-weed.html" target="_blank"><span style="font-size: small;">Four-species mixtures increased weed suppression in intensively managed grasslands</span></a></h3>
<div>
<br /></div>
<b style="text-align: justify;"><span face=""calibri" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;">Acknowledgements</span></b><br />
<div class="MsoNormal" style="text-align: justify;">
<span face=""calibri" , "sans-serif"" lang="EN-GB">Much
of the above content has been taken from a conference paper by Finn <i>et al</i>. and submitted in 2017 to the 54<sup>th</sup>
Annual Meeting of the Brazilian Society of Animal Science. <o:p></o:p></span></div>
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<br /></div>
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<span face=""calibri" , "sans-serif"" lang="EN-GB"><b> References</b></span></div>
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Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-3383131600232191143.post-16249677151760845812019-07-25T02:21:00.001-07:002019-07-25T02:21:19.170-07:00OA data on predicted distribution of Irish High Nature Value farmland<br />
<span style="font-family: "calibri";"></span><span style="font-family: "calibri";"><span style="font-family: "times new roman";">
<span style="font-family: "calibri"; font-size: large;">High Nature Value (HNV) farmland has been defined as “those
areas in Europe where agriculture is a major (usually the dominant) land use
and where agriculture sustains or is associated with either a high species and
habitat diversity, or the presence of species of European conservation concern,
or both”. Maintaining both the nature value of this farmland and the
livelihoods of farmers in these areas is a key policy challenge</span></span></span><span style="font-family: "calibri";"><span style="font-family: "times new roman";"><span style="font-family: "calibri";"><span style="font-family: "times new roman"; font-size: large;">
</span></span></span></span><br />
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<span style="font-family: "calibri";"><span style="font-family: "times new roman";"><span style="font-family: "calibri";"><span style="font-size: large;"><em>Predicting the distribution of HNV farmland in Ireland</em><br />
The IDEAL-HNV project develop (GIS) methods to improve prediction of the likely
distribution of HNV farmland in Ireland. The resulting data are available from
Teagasc 's Open Access repository TStor via the following links:</span></span></span></span></div>
<span style="font-family: "calibri";"><span style="font-family: "times new roman";"><span style="font-family: "calibri";"><span style="font-family: "times new roman"; font-size: large;">
</span></span></span></span><br />
<div style="margin: 0cm 0cm 10pt;">
<span style="font-family: "calibri";"><span style="font-family: "times new roman";"><span style="font-family: "calibri";"><span style="font-size: large;">•Predicted distribution of High Nature Value farmland in the
Republic of Ireland (electoral </span><a href="https://t-stor.teagasc.ie/handle/11019/1658" target="_blank"><span style="font-size: large;">district scale</span></a><span style="font-size: large;">)<br />
•Distribution and extent of High Nature Value farmland in the Republic of
Ireland (</span><a href="https://t-stor.teagasc.ie/handle/11019/1659" target="_blank"><span style="font-size: large;">tetrad scale</span></a><span style="font-size: large;">)</span></span></span></span><br />
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<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjH4G95G66pjjtTLjbMjVVDUCwyxw53QuAmvP0-vbAsTHw42VXxLxPO_sTozi9yeteSMo4Hl9Iq6-px4IqPEmD5QlKyN5zrjty03I4b1qjRkr2rvQGfI-l9itwxnUIX5juZVcBfv3V7EZ8/s1600/HNV_ED.PNG" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><span style="font-size: large;"><img border="0" data-original-height="876" data-original-width="623" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjH4G95G66pjjtTLjbMjVVDUCwyxw53QuAmvP0-vbAsTHw42VXxLxPO_sTozi9yeteSMo4Hl9Iq6-px4IqPEmD5QlKyN5zrjty03I4b1qjRkr2rvQGfI-l9itwxnUIX5juZVcBfv3V7EZ8/s320/HNV_ED.PNG" width="227" /></span></a></div>
<span style="font-family: Calibri; font-size: large;"><br /></span></div>
<span style="font-family: "calibri";"><span style="font-family: "times new roman";"><span style="font-family: "calibri";"><span style="font-family: "times new roman"; font-size: large;">
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<div style="margin: 0cm 0cm 10pt;">
<span style="font-family: "calibri";"><span style="font-family: "times new roman";"><span style="font-family: "calibri"; font-size: large;">We mapped the likely distribution of HNV farmland based on
established European indicators adapted for Ireland using the following
indicators:</span></span></span></div>
<span style="font-family: "calibri";"><span style="font-family: "times new roman";"><span style="font-family: "calibri";"><span style="font-family: "times new roman"; font-size: large;">
</span></span></span></span><br />
<div style="margin: 0cm 0cm 10pt;">
<span style="font-family: "calibri";"><span style="font-family: "times new roman";"><span style="font-family: "calibri"; font-size: large;">•Semi-natural land cover classes from Corine 2012 </span></span></span></div>
<span style="font-family: "calibri";"><span style="font-family: "times new roman";"><span style="font-family: "calibri";"><span style="font-family: "times new roman"; font-size: large;">
</span></span></span></span><br />
<div style="margin: 0cm 0cm 10pt;">
<span style="font-family: "calibri";"><span style="font-family: "times new roman";"><span style="font-family: "calibri"; font-size: large;">•Stocking density from DAFM </span></span></span></div>
<span style="font-family: "calibri";"><span style="font-family: "times new roman";"><span style="font-family: "calibri";"><span style="font-family: "times new roman"; font-size: large;">
</span></span></span></span><br />
<div style="margin: 0cm 0cm 10pt;">
<span style="font-family: "calibri";"><span style="font-family: "times new roman";"><span style="font-family: "calibri"; font-size: large;">•% hedgerow cover from Teagasc National Hedgerow cover </span></span></span></div>
<span style="font-family: "calibri";"><span style="font-family: "times new roman";"><span style="font-family: "calibri";"><span style="font-family: "times new roman"; font-size: large;">
</span></span></span></span><br />
<div style="margin: 0cm 0cm 10pt;">
<span style="font-family: "calibri";"><span style="font-family: "times new roman";"><span style="font-family: "calibri"; font-size: large;">•Length of river and stream from OSI river-stream map </span></span></span></div>
<span style="font-family: "calibri";"><span style="font-family: "times new roman";"><span style="font-family: "calibri";"><span style="font-family: "times new roman"; font-size: large;">
</span></span></span></span><br />
<div style="margin: 0cm 0cm 10pt;">
<span style="font-family: "calibri";"><span style="font-family: "times new roman";"><span style="font-family: "calibri"; font-size: large;">•Soil diversity calculated using the Teagasc map of soil
associations </span></span></span></div>
<span style="font-family: "calibri";"><span style="font-family: "times new roman";"><span style="font-family: "calibri";"><span style="font-family: "times new roman"; font-size: large;">
</span></span></span></span><br />
<div style="margin: 0cm 0cm 10pt;">
<span style="font-family: "calibri";"><span style="font-family: "times new roman";"><span style="font-family: "calibri"; font-size: large;">Data were modelled at the tetrad scale (2km x 2km), and
presented here at the scale of Electoral Divisions. The resulting map (from
Matin et al., in review) indicates the likely occurrence and distribution of
HNV farmland in each Electoral Division, based on a scale ranging from very low
(blue colour) to intermediate (yellow) to very high (green) (Fig. 3). Not
surprisingly, western counties such as Kerry, Clare, Mayo, Galway, Leitrim,
Donegal and Cavan exhibited greatest likelihood of containing HNV farmland
while Dublin, Meath and Kilkenny had lowest likelihood. Nevertheless, there is
considerable variation within counties. <br />
To our knowledge, this is the first Irish national-scale map that has used
objective agri-environmental criteria to predict the likely distribution of HNV
farmland. This provides a reference point for the future monitoring of the
distribution of HNV farmland in Ireland. It can also assist in policy planning
and development for the rural environment. For example, comparisons of the
spatial distribution of HNV areas and the spatial distribution of
agri-environmental and other payments can assess the degree to which payments
are targeted toward HNV farming systems. In addition, these data can be used to
incorporate impacts on farmland biodiversity of, for example, land use change
and climate change in national-scale models or scenarios.<br />
As an indicator-based prediction, such maps should be interpreted within the
limitations of the data used. The spatial scale of the map is restricted by the
coarse scale of data at national level. Given the predictive and aggregated
nature of the outputs, it is important to note that non-HNV farmland may still
occur in areas with high likelihood of HNV farmland, and vice versa. We also
know that some very specific types of HNV farmland are not well-represented by
this approach. For this reason, this output is not suitable for strictly
deciding whether farmers in certain areas should be eligible or not for
agri-environmental measures aimed at HNV farming systems. Instead, there is a
requirement for a farm-scale assessment to confirm the high nature value of
individual farms. As part of the IDEAL-HNV project, we also examine the
farm-scale characteristics of HNV farmland. See the project website
(www.high-nature-value-farmland.ie) for further details. </span></span></span></div>
<span style="font-family: "calibri";"><span style="font-family: "times new roman";"><span style="font-family: "calibri";"><span style="font-family: "times new roman"; font-size: large;">
</span></span></span></span><br />
<div style="margin: 0cm 0cm 10pt;">
<span style="font-family: "calibri";"><span style="font-family: "times new roman";"><span style="font-family: "calibri";"><span style="font-size: large;"><strong>Acknowledgements</strong><br />
This research was funded by the Stimulus Research Fund (award 11/S/108) of the
Department of Agriculture, Fisheries and the Marine through the National
Development Plan.</span></span></span></span></div>
<span style="font-family: "calibri";"><span style="font-family: "times new roman";"><span style="font-family: "calibri";"><span style="font-family: "times new roman"; font-size: large;">
</span></span></span></span><br />
<span style="font-size: large;"></span><br />
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<b style="mso-bidi-font-weight: normal;"><span style="font-family: "calibri"; font-size: large;">Publications</span></b></div>
<span style="font-family: "calibri";"><span style="font-size: large;">Matin, S., Sullivan,
C.A., Ó hUallacháin, D., Meredith, D., Moran J., Finn, J.A. and Green, S. ‘</span><a href="http://www.tandfonline.com/doi/abs/10.1080/17445647.2016.1223761" target="_blank"><span style="font-size: large;">Map of High Nature Value farmland in the Republic of Ireland</span></a><span style="font-size: large;">.’ <i style="mso-bidi-font-style: normal;">Journal of Maps.</i></span> </span><br />
<span style="font-family: "calibri";"><br /></span><br />
<span style="font-family: "calibri"; font-size: large;">Matin et al. Assessing the distribution and extent of HNV farmland in the Republic Ireland. Ecological Indicators. in review, July 2019. </span><br />
<span style="font-family: Calibri;"><br /></span><br />
<span style="font-family: "calibri";"><span style="font-family: "times new roman";">
</span></span><br />
<div style="margin: 0cm 35.5pt 0pt 0cm; mso-pagination: none; tab-stops: 57.6pt 93.6pt 129.6pt 201.6pt 237.6pt 273.6pt 309.6pt 345.6pt 381.6pt 417.6pt;">
<span style="font-family: "calibri";"><span style="font-size: 12pt; mso-ansi-language: EN-IE; mso-bidi-font-size: 10.0pt;"><span style="font-family: "times new roman";">Sullivan,
C.A., Finn, J.A., Ó hUallacháin, D., Green, S., Matin, S., Meredith, D., Clifford,
B., and Moran, J. 2017. </span><a href="http://www.sciencedirect.com/science/article/pii/S0264837716305245"><span style="color: blue; font-family: "times new roman";">The
development of a national typology for High Nature Value farmland in Ireland
based on farm-scale characteristics</span></a><span style="font-family: "times new roman";">.</span></span><span style="font-family: "times new roman";"><span style="font-size: x-small;"> </span><i style="mso-bidi-font-style: normal;"><span style="font-size: 12pt; mso-ansi-language: EN-IE; mso-bidi-font-size: 10.0pt;">Land Use Policy</span></i><span style="font-size: 12pt; mso-ansi-language: EN-IE; mso-bidi-font-size: 10.0pt;"> 67: 401–414.</span></span></span></div>
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<br />Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-3383131600232191143.post-31698379290446184982018-12-08T09:11:00.001-08:002021-01-28T02:18:05.153-08:00Multi-species mixtures promote yield stability under rainfed AND drought conditions<em>Overview</em><br />
Higher plant diversity in intensively managed agricultural grasslands resulted in higher yields AND less variation in yield. This represents gold-standard yield stability. We found this under undisturbed (rainfed) and disturbed conditions (experimental drought for 9 weeks). <a name='more'></a><br />
<br /><br />
<span lang="DE-CH" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 115%; mso-ansi-language: DE-CH; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">Climate change is expected to cause an increase in the frequency and intensity of drought events. Intensively managed agricultural grasslands are economically important and contribute to food security. Little is known, however, about their response to extreme weather events.</span><br />
<span lang="DE-CH" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 115%; mso-ansi-language: DE-CH; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;"><br /></span><br />
<span lang="DE-CH" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 115%; mso-ansi-language: DE-CH; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">Our research shows that a modest increase in the number of agronomic species in agricultural swards is a practical, farm-scale adaptation measure to extreme weather events. </span><br />
<span lang="DE-CH" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 115%; mso-ansi-language: DE-CH; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;"><br /></span><br />
<span lang="DE-CH" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 115%; mso-ansi-language: DE-CH; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">From an ecological perspective, this research demonstrates a positive relationship between diversity and ecosystem function observed under regular conditions being maintained under disturbed conditions.</span><br />
<br /><br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjKukHQuESwnfFj6GMnW9uITRZ8tli2CR8-S5StCoseq11LLpyIi9Ups5I8wAUr07lQRlQvBekWTDbnxK30OjgtMCDhi4F83jEC5MGvMiRI0WBJcQ6pVo93M9M5dzYRquhDPNGfq1dXlwQ/s1600/Droughtshelters.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1200" data-original-width="1600" height="240" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjKukHQuESwnfFj6GMnW9uITRZ8tli2CR8-S5StCoseq11LLpyIi9Ups5I8wAUr07lQRlQvBekWTDbnxK30OjgtMCDhi4F83jEC5MGvMiRI0WBJcQ6pVo93M9M5dzYRquhDPNGfq1dXlwQ/s320/Droughtshelters.jpg" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">3m x 5m rain shelters were used to simulate drought. <br />
The shelters excluded rainfall, but were open at the top, <br />
sides and bottom to prevent large increase in temperature. </td></tr>
</tbody></table>
<br /><br />
<em>What we did...</em><br />
<span lang="DE-CH" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 115%; mso-ansi-language: DE-CH; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">Over two years we investigated the effects of experimentally imposed drought on
intensively managed grassland communities (5 m x 6 m plots) of varying richness
(1, 2 and 4 species), and comprising four species (<i style="mso-bidi-font-style: normal;">Lolium perenne</i> L., <i style="mso-bidi-font-style: normal;">Cichorium
intybus</i> L., <i style="mso-bidi-font-style: normal;">Trifolium repens</i> L., <i style="mso-bidi-font-style: normal;">Trifolium pratense</i> L.). </span><span lang="DE-CH" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 115%; mso-ansi-language: DE-CH; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">In each year a
summer drought period of nine weeks with complete exclusion of precipitation
was simulated, inducing severe drought stress at Reckenholz (Zürich,
Switzerland), and extreme drought stress at Wexford (Ireland). </span><br />
<br /><br />
<span lang="DE-CH" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 115%; mso-ansi-language: DE-CH; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;"><em>Main result: species richness = higher yield, and lower variation in yield</em></span><br />
<span lang="DE-CH" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 115%; mso-ansi-language: DE-CH; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">Mean yield and
plot-to-plot variance of yield were measured across harvests during drought and
after a subsequent post-drought recovery period. At both sites, there was a
positive relationship between species richness and yield under both the rainfed
control conditions and under drought. <span lang="DE-CH" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 115%; mso-ansi-language: DE-CH; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">Under rainfed control
conditions, mean yields of four-species communities were 32% (Wexford, Ireland)
and 51% (Zürich, Switzerland) higher than the average of the four monocultures.
This positive relationship was also evident under drought, despite significant
average yield reductions due to drought (-27% at Wexford; -21% at Zürich). </span></span><br />
<span lang="DE-CH" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 115%; mso-ansi-language: DE-CH; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">At both sites, four-species communities
had lower plot-to-plot variance of yield compared to monoculture or two-species
communities under both rainfed (-49% smaller standard deviation) and drought
conditions</span><span lang="EN-US" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 115%; mso-ansi-language: EN-US; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;"> (-24%)</span><span lang="DE-CH" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 115%; mso-ansi-language: DE-CH; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">, which demonstrates higher yield stability in four-species communities.
</span><br />
<span lang="DE-CH" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 115%; mso-ansi-language: DE-CH; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">
</span><br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiI5GUj_Ju2XSBwJFCXG2gReoZfmkmn2H5diq5MYSaG9AMfOMZVtIefkM2aCaXfwIg7it4UpsjzBXbGXcQlrGeFWCAD5LZev1CgRSCOkljUdoIQF3lSBKQWdoKbEU_ZVOD6SggSJv7mmhs/s960/Mixtures_NWyke_Feb2018.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="720" data-original-width="960" height="480" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiI5GUj_Ju2XSBwJFCXG2gReoZfmkmn2H5diq5MYSaG9AMfOMZVtIefkM2aCaXfwIg7it4UpsjzBXbGXcQlrGeFWCAD5LZev1CgRSCOkljUdoIQF3lSBKQWdoKbEU_ZVOD6SggSJv7mmhs/w640-h480/Mixtures_NWyke_Feb2018.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="font-family: "Times New Roman", serif; font-size: 16px;">Effects of species richness and drought on yield mean and standard deviation (SD) across harvests under rainfed control and drought conditions at Wexford (A) and Zürich (B). Different letters indicate a difference at P < 0.05 based on regression analysis, except SD under drought at Zürich, which is at P < 0.1 (means: inference in black upper-case letters; SD: inference in grey lower case letters). See Haughey et al. 2018 for details.</span></td></tr></tbody></table><br /><span lang="DE-CH" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 115%; mso-ansi-language: DE-CH; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;"><br /></span><br /><div style="border-image: none 100% / 1 / 0 stretch;">REFERENCES</div><span lang="DE-CH" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 115%; mso-ansi-language: DE-CH; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">
<div style="margin: 1em 0px 1em 36pt; mso-add-space: auto; text-indent: -36pt;">
<span lang="EN-US">Haughey,
E., Suter, M., Hofer, D., Hoekstra, N.J., McElwain, J.C., Lüscher, A and Finn,
J.A. 2018. <a href="https://www.nature.com/articles/s41598-018-33262-9" target="_blank">Higher species richness enhances yield stability in intensively managed grasslands with experimental disturbance. Nature Scientific Reports.8:15047.</a> Open Access paper with R code in supplement, and data available in
Dryad.</span></div>
<div style="margin: 1em 0px 1em 36pt; mso-add-space: auto; text-indent: -36pt;">
<span lang="EN-US">H</span><span lang="EN-US">ofer, D.,
M. Suter, E. Haughey, J. A. Finn, N. J. Hoekstra, N. Buchmann, and A. Lüscher. 2016.
Yield of temperate forage grassland species is either largely resistant or
resilient to experimental summer drought. Journal of Applied Ecology
53:1023-1034.</span></div>
<div style="margin: 0cm 0cm 0pt;">
<span lang="EN-GB" style="font-family: "Times New Roman","serif";"><em>Acknowledgements</em><br /> A contribution to the research leading to these results was conducted as part of the AnimalChange project (FP7/2007-2013), grant agreement 266018. E.H was supported by the Teagasc Walsh Fellowship programme. </span></div>
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<span lang="DE-CH" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 115%; mso-ansi-language: DE-CH; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;"><br /></span></div>
<span lang="DE-CH" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 115%; mso-ansi-language: DE-CH; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;"><br /></span><br />
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<span lang="DE-CH" style="font-family: "Times New Roman","serif"; font-size: 12pt; line-height: 115%; mso-ansi-language: DE-CH; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;"><br /></span></div>
<div class="separator" style="clear: both; text-align: center;"><br /></div><br />Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-3383131600232191143.post-19848533785860080552018-10-16T01:09:00.006-07:002021-02-10T07:26:28.912-08:00Multi-species mixtures: our publications<br />
<div style="margin: 0cm 0cm 0pt;">
<span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;">I
regularly get asked for references to our work on multi-species mixtures. Here’s
an overview of the research that we have published over the last 15 years or so. </span><br />
<a name='more'></a><span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;">I have grouped
articles with the following broad themes. </span></div>
<ul>
<li><div style="margin: 0cm 0cm 0pt;">
<span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;">Diversity and ecosystem function in intensively managed grassland systems</span></div>
</li>
<li><div style="margin: 0cm 0cm 0pt;">
<span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;">Experimental drought in intensively managed grassland system </span></div>
</li>
<li><div style="margin: 0cm 0cm 0pt;">
<span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;">Methodology for analysis of biodiversity and ecosystem function</span></div>
</li>
<li><div style="margin: 0cm 0cm 0pt;">
<span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;">Soil tracer experiments in multi-species mixtures</span></div>
</li>
<li><div style="margin: 0cm 0cm 0pt;">
<span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;">Associated
research on diversity and ecosystem function</span><span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;"><br /></span></div>
</li>
</ul>
<div style="margin: 0cm 0cm 0pt;">
<span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;"><strong>Diversity
and ecosystem function in intensively managed grassland system systems</strong></span></div>
<br />
<div style="margin: 0cm 0cm 0pt;">
<span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;">The
Kirwan et al. (2014) paper contains the yield data from the AgroDiversity experiment,
from which many of these articles derive.</span><span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;"><br /></span></div>
<br />
<div style="margin: 0cm 0cm 0pt; mso-layout-grid-align: none;">
<span face=""arial" , "sans-serif"" style="font-size: 11pt;">Kirwan et al. 2014.
<a href="https://esajournals.onlinelibrary.wiley.com/doi/10.1890/14-0170.1"><span style="color: blue;">The
Agrodiversity Experiment: three years of data from a multi-site plant diversity
experiment in intensively managed grasslands</span></a>. <i style="mso-bidi-font-style: normal;">Ecological Archives</i>, 95: 2680.</span><span face=""arial" , "sans-serif"" lang="EN" style="font-size: 11pt;"><br /></span></div>
<br />
<div style="margin: 0cm 0cm 0pt;">
<span face=""arial" , "sans-serif"" lang="EN" style="font-size: 11pt;">Finn et al. 2013. <a href="http://onlinelibrary.wiley.com/doi/10.1111/1365-2664.12041/abstract"><span style="color: blue;">Ecosystem
function enhanced by combining four functional types of plant species in
intensively managed grassland mixtures: a 3-year continental-scale field
experiment</span></a>. Journal of Applied Ecology, 50: 365–375. </span><span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;"><br /></span></div>
<br />
<div style="margin: 0cm 0cm 0pt; mso-layout-grid-align: none;">
<span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;">Kirwan et al.
2007. <a href="https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/j.1365-2745.2007.01225.x" target="_blank">Evenness drives consistent diversity effects in intensive grassland systems across 28 European sites</a>. Journal of Ecology 95: 530-539.</span><span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;"><br /></span></div>
<br />
<div style="margin: 0cm 0cm 0pt;">
<span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;">Suter et al. 2015. <a href="http://onlinelibrary.wiley.com/doi/10.1111/gcb.12880/abstract"><span style="color: blue;">Nitrogen
yield advantage from grass-legume mixtures is robust over a wide range of
legume proportions and environmental conditions</span></a>. Global Change Biology, 21:
2424-2438.</span></div>
<br />
<div style="margin: 0cm 0cm 0pt;">
<span face=""arial" , "sans-serif"" lang="EN-GB" style="color: black; font-size: 11pt;">Connolly et al. 2018. <a href="http://onlinelibrary.wiley.com/doi/10.1111/1365-2664.12991/full"><span style="color: blue;">Weed
suppression greatly increased by plant diversity in intensively managed
grasslands: A continental-scale experiment.</span></a> Journal of Applied Ecology. 55:
852-862. Open Access.</span><span face=""arial" , "sans-serif"" style="font-size: 11pt;"><br /></span></div>
<br />
<div style="margin: 0cm 35.5pt 0pt 0cm; mso-pagination: none; tab-stops: 57.6pt 93.6pt 129.6pt 201.6pt 237.6pt 273.6pt 309.6pt 345.6pt 381.6pt 417.6pt;">
<span face=""arial" , "sans-serif"" style="font-size: 11pt;">Brophy et al. (2017), Major shifts in species’ relative abundance in
grassland mixtures alongside positive effects of species diversity in yield: a
continental-scale experiment. J Ecol. 105: 1210-1222.<span style="mso-spacerun: yes;"> </span>doi:10.1111/1365-2745.12754 <span style="mso-spacerun: yes;"> </span><span style="mso-spacerun: yes;"> </span>(<a href="http://onlinelibrary.wiley.com/doi/10.1111/1365-2745.12754/full"><span style="color: blue;">http://onlinelibrary.wiley.com/doi/10.1111/1365-2745.12754/full</span></a></span></div>
<br />
<div style="margin: 0cm 0cm 0pt;">
<span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;"> </span></div>
<br />
<div style="margin: 0cm 0cm 0pt;">
<span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;"><br /></span></div>
<br />
<div style="margin: 0cm 0cm 0pt;">
<span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;"><strong>More
detailed information on the yield data from Irish sites of the AgroDiversity
experiment:</strong></span></div>
<br />
<div style="margin: 0cm 0cm 0pt; mso-layout-grid-align: none;">
<span face=""arial" , "sans-serif"" lang="EN-US" style="font-size: 11pt;">Finn, J.A., Carnus, T., Kirwan, L., Cuddihy, A. and Connolly, J. 2009. Four-species
mixtures enhanced yield at low and high levels of nitrogen addition over two
years. <i style="mso-bidi-font-style: normal;">Irish Journal of Agricultural and
Food Research</i> 48: 272.</span></div>
<br />
<div style="margin: 0cm 0cm 0pt;">
<span face=""arial" , "sans-serif"" lang="EN-US" style="font-size: 11pt;">Connolly, J., Finn, J.A., Black, A.D., Kirwan, L., Brophy, C. and Lüscher,
A. 2009. Effects of multi-species swards on dry matter production and the
incidence of unsown species at three Irish sites. <i style="mso-bidi-font-style: normal;">Irish Journal of Agricultural and Food Research </i>48: 243–260.</span><br />
<span style="font-family: "arial";"><br /></span></div>
<br />
<div style="margin: 0cm 0cm 0pt;">
<span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;"><strong>Experimental
drought in intensively managed grassland system </strong></span></div>
<br />
<div style="margin: 0cm 35.5pt 0pt 0cm; mso-pagination: none; tab-stops: 57.6pt 93.6pt 129.6pt 201.6pt 237.6pt 273.6pt 309.6pt 345.6pt 381.6pt 417.6pt; text-align: justify;">
<span face=""arial" , "sans-serif"" lang="EN" style="font-size: 11pt;">Haughey et al. 2018 <a href="http://www.nature.com/articles/s41598-018-33262-9"><span style="color: blue;">Higher species
richness enhances yield stability in intensively managed grasslands with
experimental disturbance</span></a>. Scientific Reports 8(1): 15047. Open Access.</span></div>
<br />
<div style="margin: 0cm 35.5pt 0pt 0cm; mso-pagination: none; tab-stops: 57.6pt 93.6pt 129.6pt 201.6pt 237.6pt 273.6pt 309.6pt 345.6pt 381.6pt 417.6pt; text-align: justify;">
<span face=""arial" , "sans-serif"" lang="EN" style="font-size: 11pt;">The <a href="https://static-content.springer.com/esm/art%3A10.1038%2Fs41598-018-33262-9/MediaObjects/41598_2018_33262_MOESM1_ESM.pdf"><span style="color: blue;">supplementary
information</span></a> contains R code for the analysis, and the <a href="https://datadryad.org/resource/doi:10.5061/dryad.cq5h55f"><span style="color: blue;">yield data</span></a>
are available on Dryad. </span></div>
<br />
<div style="margin: 0cm 0cm 0pt; mso-layout-grid-align: none;">
<span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;">Finn et al.
2018. <a href="https://www.sciencedirect.com/science/article/pii/S0167880918300811"><span style="color: blue;">Greater
gains in annual yields from increased plant diversity than losses from
experimental drought in two temperate grasslands</span></a>. Agriculture, Ecosystems
and Environment 258: 149-153. </span></div>
<br />
<div style="margin: 0cm 0cm 0pt; mso-layout-grid-align: none;">
<span face=""arial" , "sans-serif"" style="font-size: 11pt;">Hofer et al. 2016. <a href="https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/1365-2664.12694"><span style="color: blue;">Yield
of temperate forage grassland species is either largely resistant or resilient
to experimental summer drought</span></a>. Journal of Applied Ecology, 53:
1023-1034<span style="mso-spacerun: yes;"> </span><span style="mso-spacerun: yes;"> </span>doi: 10.1111/1365-2664.12694</span></div>
<br />
<div style="margin: 0cm 0cm 0pt;">
<span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;"> </span></div>
<br />
<div style="margin: 0cm 0cm 0pt;">
<span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;"><strong>Methodology
for analysis of biodiversity and ecosystem function</strong></span></div>
<br />
<div style="margin: 0cm 0cm 0pt;">
<span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;">The
Kirwan et al. (2014) paper contains the data from the AgroDiversity experiment,
from which many of these articles derive:</span></div>
<br />
<div style="margin: 0cm 0cm 0pt; mso-layout-grid-align: none;">
<span face=""arial" , "sans-serif"" style="font-size: 11pt;">Kirwan et al. 2014.
<a href="https://esajournals.onlinelibrary.wiley.com/doi/10.1890/14-0170.1"><span style="color: blue;">The
Agrodiversity Experiment: three years of data from a multi-site plant diversity
experiment in intensively managed grasslands</span></a>. <i style="mso-bidi-font-style: normal;">Ecological Archives</i>, 95: 2680.</span></div>
<br />
<div style="margin: 0cm 0cm 0pt;">
<span face=""arial" , "sans-serif"" style="font-size: 11pt;">The following Kirwan et al 2009 article describes the
modelling approach that we use, and provides case studies, as well as the data
and code for worked examples:</span></div>
<br />
<div style="margin: 0cm 0cm 0pt;">
<span face=""arial" , "sans-serif"" style="font-size: 11pt;">Kirwan, L., Connolly, J., Finn, J.A., Brophy, C.,
Lüscher, A. Nyfeler, D. and Sebastia, T. 2009. <a href="http://www.esajournals.org/doi/pdf/10.1890/08-1684.1"><span style="color: blue;">Diversity-interaction
modelling - estimating contributions of species identities and interactions to
ecosystem function.</span></a> <i style="mso-bidi-font-style: normal;">Ecology</i>, 90:
2032-2038</span><span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;">.
</span></div>
<br />
<div style="margin: 0cm -9.95pt 0pt 0cm;">
<span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;">Brophy et al. 2017. <a href="https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecy.1872"><span style="color: blue;">Biodiversity
and ecosystem function: making sense of numerous species interactions in multi<span style="font-family: "cambria math" , "serif"; mso-bidi-font-family: "Cambria Math";">‐</span></span><span style="color: blue;">species
communities</span></a>. Ecology 98: 1771-1778</span></div>
<br />
<div style="margin: 0cm 0cm 0pt; mso-layout-grid-align: none;">
<span face=""arial" , "sans-serif"" lang="EN" style="font-size: 11pt;">Dooley, Á., Isbell, F., Kirwan,
L., Connolly, J., Finn, J. A., Brophy, C. (2015), </span><span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;"><a href="http://onlinelibrary.wiley.com/doi/10.1111/ele.12504/abstract"><span lang="EN" style="mso-ansi-language: EN;"><span style="color: blue;">Testing the effects of diversity on
ecosystem multifunctionality using a multivariate model</span></span></a></span><span face=""arial" , "sans-serif"" lang="EN" style="font-size: 11pt;">. Ecology Letters. doi: 10.1111/ele.12504</span></div>
<br />
<div style="margin: 0cm -9.95pt 0pt 0cm;">
<span face=""arial" , "sans-serif"" style="font-size: 11pt;">Connolly, J.,
Cadotte, M.W., Brophy, C., Dooley, Á., Finn, J.A., Kirwan, L., Roscher, C.,
Weigelt, A. 2011. <a href="https://esajournals.onlinelibrary.wiley.com/doi/full/10.1890/10-2270.1"><span style="color: blue;">Phylogenetically
diverse grassland communities are associated with pairwise interspecific
processes that capture greater resources</span></a>. <i style="mso-bidi-font-style: normal;">Ecology</i> 92: 1385-1392.</span></div>
<br />
<div style="margin: 0cm 0cm 0pt;">
<span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;">Hector, A., Bell, T., Connolly, J., Finn, J.A., Fox,
J., Kirwan, L., Loreau, M., McLaren, J., Schmid, B., Weigelt, W</span><span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;">. 2009. <i style="mso-bidi-font-style: normal;">The Analysis of Biodiversity Experiments:
From pattern toward mechanism</i>. pp. 94-104. In: Naeem, S., Bunker, D.E., Hector,
A., Loreau, M. and Perrings, C. (Eds.) <i style="mso-bidi-font-style: normal;">Biodiversity,
Ecosystem Functioning, and Human Wellbeing: An Ecological and Economic
Perspective</i>. ISBN-13: 978-0-19-954796-8 </span></div>
<br />
<div style="margin: 0cm 35.5pt 0pt 0cm;">
<span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;">Hector <i style="mso-bidi-font-style: normal;">et al</i>. 2002. Biodiversity manipulation
experiments: studies replicated at multiple sites. <i style="mso-bidi-font-style: normal;">Biodiversity and Ecosystem Functioning: synthesis and perspective. </i>(eds
M. Loreau, S. Naeem & P. Inchausti), Oxford University Press, Chapter 4.</span></div>
<br />
<div style="margin: 0cm 35.5pt 0pt 0cm;">
<span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;">Hector <i style="mso-bidi-font-style: normal;">et al</i>. 2002. Biodiversity and the
functioning of grassland ecosystems: multisite studies. <i style="mso-bidi-font-style: normal;">Functional Consequences of Biodiversity: Experimental Progress and
Theoretical Extensions</i> (eds A. Kinzig, D. Tilman & S. Pacala), Princeton
University Press, Princeton, Chapter 4.</span></div>
<br />
<div style="margin: 0cm 0cm 0pt;">
<span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;"> </span></div>
<br />
<div style="margin: 0cm 0cm 0pt;">
<span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;"><strong>Soil
tracer experiments in multi-species mixtures</strong></span></div>
<br />
<div style="margin: 0cm 0cm 0pt;">
<span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;">Hoekstra et
al. 2014.<span style="mso-spacerun: yes;"> </span><a href="https://link.springer.com/article/10.1007%2Fs11104-014-2048-2"><span style="color: blue;">Methodological
tests of the use of trace elements as tracers to assess root activity</span></a>. Plant
and Soil, 380: 265-283. </span></div>
<br />
<div style="margin: 0cm 0cm 0pt; mso-layout-grid-align: none;">
<span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;">Hoekstra
et al. A. 2015. Do belowground vertical niche differences between deep and shallow-rooted
species enhance resource uptake and drought resistance in grassland mixtures?
Plant and Soil, 394: 21-34. </span></div>
<br />
<div style="margin: 0cm 0cm 0pt; mso-layout-grid-align: none;">
<span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;"><a href="http://rd.springer.com/article/10.1007/s11104-014-2352-x?sa_campaign=email/event/articleAuthor/onlineFirst"><span style="color: blue;">http://rd.springer.com/article/10.1007/s11104-014-2352-x?sa_campaign=email/event/articleAuthor/onlineFirst</span></a></span></div>
<br />
<div style="margin: 0cm 0cm 0pt; mso-layout-grid-align: none;">
<span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;">Hoekstra
et al. 2014.<span style="mso-spacerun: yes;"> </span>The effect of drought and
interspecific interactions on the depth of water uptake in deep- and
shallow-rooting grassland species as determined by δ18O natural abundance. Biogeosciences,
11 (16), 4493-4506.</span></div>
<br />
<div style="margin: 0cm 0cm 0pt; mso-layout-grid-align: none;">
<span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;"><a href="https://www.biogeosciences.net/11/4493/2014/"><span style="color: blue;">https://www.biogeosciences.net/11/4493/2014/</span></a></span></div>
<br />
<div style="margin: 0cm 0cm 0pt; mso-layout-grid-align: none;">
<span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;"> </span></div>
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<div style="margin: 0cm 0cm 0pt;">
<span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;"><strong>Associated
research on diversity and ecosystem function</strong></span></div>
<br />
<div style="margin: 0cm 0cm 0pt;">
<span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;">O’Hea,
N., Kirwan, L. and<sup> </sup>Finn, J.A. 2010. <a href="https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1600-0706.2009.18116.x" target="_blank">Experimental mixtures of dung fauna affect dung decomposition through complex effects of species interactions</a>. Oikos 119: 1081–1088. </span><span style="font-family: "arial";"><span lang="EN-GB" style="font-size: 11pt;"><br /></span></span></div><div style="margin: 0cm 0cm 0pt;"><span face=""arial" , "sans-serif"" lang="EN-GB" style="font-size: 11pt;"><br /></span></div>
<span style="font-family: "arial";"><span lang="EN-GB" style="font-size: 11pt;">Piotrowska, K., Connolly, J., Finn, J., Black, A., Bolger, T. 2013. Evenness
and plant species identity affect earthworm diversity and community structure
in grassland soils. <i style="mso-bidi-font-style: normal;">Soil Biology and
Biochemistry</i> <b style="mso-bidi-font-weight: normal;">57</b>: 713–719. </span><span lang="EN-GB" style="border-image: none; border: 1pt currentcolor; color: #5c5c5c; font-size: 11pt; padding: 0cm;"><a href="http://dx.doi.org/10.1016/j.soilbio.2012.06.016" target="doilink"><span style="color: blue;">http://dx.doi.org/10.1016/j.soilbio.2012.06.016</span></a><a href="http://www.sciencedirect.com/science/help/doi.htm" target="sdhelp" title="Click to see how to cite or link using DOI (Opens new window)"></a><span style="mso-spacerun: yes;"> </span></span><span lang="EN-GB" style="font-size: 11pt;">http://www.sciencedirect.com/science/article/pii/S0038071712002647</span></span><br />
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Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-3383131600232191143.post-79582693872747525582018-04-24T03:36:00.002-07:002018-04-24T03:40:05.927-07:00The BRIDE project kicks off: results-based payments for farmland wildlife <br />
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<strong>The BRIDE project kicks off</strong></div>
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A new project in the Bride Valley in east
Cork will reward participating farmers for wildlife on their farms. The ‘Biodiversity
Regeneration In a Dairying Environment’ (BRIDE) project will provide participating
farmers with farm habitat plans that identify the most appropriate and
effective wildlife management options for individual farms. Farmers will be
paid for their conservation actions. </div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiCcIzrNkZGPIkq759mjI-GRGtdyAwScdpJOvB2VnUCGJSP-EO86k5AJ1q_ldieauHx5t3lwUsr5OX-nZf3ElwfD0PTQvrmlJq7GOOOIWhn7WeFUYOuMUkWqm5Wyrca7QdDYK4E7LBPtSQ/s1600/BrideValleyoverview.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="990" data-original-width="1581" height="250" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiCcIzrNkZGPIkq759mjI-GRGtdyAwScdpJOvB2VnUCGJSP-EO86k5AJ1q_ldieauHx5t3lwUsr5OX-nZf3ElwfD0PTQvrmlJq7GOOOIWhn7WeFUYOuMUkWqm5Wyrca7QdDYK4E7LBPtSQ/s400/BrideValleyoverview.jpg" width="400" /></a></div>
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<span style="font-family: "times new roman" , "serif"; font-size: 12pt;">The Bride Valley contains a rich mosaic of farmland habitats alongside more intensively managed farmland, and supports a range of farmland wildlife. The River Bride is designated as a Special Area of Conservation</span><br />
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<b>Performance-related payments for farmland wildlife </b></div>
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The BRIDE project was one of eleven projects
selected from over 100 applications by DAFM
and the European Union, under the European Innovation Partnership (EIP) funding
programme. An innovative element of the project is its higher payments for
higher wildlife gains (a results-based approach). Thus, the more flowers in a
hedgerow or field margin, the higher the payment. The greater reward for a higher
quality product is very familiar to farmers, and the BRIDE project applies this
principle to the management of wildlife habitats. This also means that farmers
will be paid for the ongoing management of selected existing wildlife habitats,
which is an important feature of the project. </div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh5_OAdFdPpW4h8B-w63PjH8w8yGyvG1c9TfCyVH685kcQ1guYYGanlR15r-eK_fkMieZ-4GEEd_Sx-qWreK-qyYiRkFqC7Y6OTffF8ubHKpuEW-CukdWDTfiT_6vwWIeqDFQZiaarGiCU/s1600/Fieldmargin.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1200" data-original-width="1600" height="300" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh5_OAdFdPpW4h8B-w63PjH8w8yGyvG1c9TfCyVH685kcQ1guYYGanlR15r-eK_fkMieZ-4GEEd_Sx-qWreK-qyYiRkFqC7Y6OTffF8ubHKpuEW-CukdWDTfiT_6vwWIeqDFQZiaarGiCU/s400/Fieldmargin.jpg" width="400" /></a></div>
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<span style="font-size: small;">Field margins that flower and form a dense vegetation provide a habitat for flowering plants, as well as cover and food for farmland wildlife such as birds and bumblebees. Farmers participating in the BRIDE project will be paid for their existing field margins, and can be paid to create new field margins. The more flowers in a hedgerow or field margin, the higher the payment.</span></div>
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<strong>Farm plans will use an ecologist to identify best options, and agree these with participating farmers</strong></div>
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An ecologist will work with participating farmers to develop a farm plan and advise on how to maximise the wildlife on their farm, and will focus on important habitats such as hedgerows, bogs, woodland, ponds, derelict buildings etc. Wild birds and other animals don’t respect farm boundaries, and the BRIDE Project is also designed to work at a landscape scale. It will involve several clusters of neighbouring farms to collectively enhance biodiversity on a much larger scale than would be possible on an individual farm basis. </div>
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The Project will run for five years and is
designed to increase and maintain biodiversity on intensively managed farms in
the area through simple, innovative measures. The effects on wildlife will be
monitored through the project, which aims to create suitable habitats for local
important populations of wildlife include skylarks, yellowhammers, bumblebees
and frogs and newts. The BRIDE Project differs from traditional
agri-environment schemes through its use of a results-based payment system i.e.
more farmland habitats will result in higher financial payments. </div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg9bZvId50DQCRjODMWAHujk-Zsdn1AkD3rTKf-Ug72zSk8EWxljifiygkDXO_ANfXyO8qY00neSm_4Pmut__NZAU5oycs5bzLJKGK2m5oRQZ_6MRI2e-jdayf3gPcFs_hivY55ezzMA-k/s1600/hedgerow.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1200" data-original-width="1599" height="300" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg9bZvId50DQCRjODMWAHujk-Zsdn1AkD3rTKf-Ug72zSk8EWxljifiygkDXO_ANfXyO8qY00neSm_4Pmut__NZAU5oycs5bzLJKGK2m5oRQZ_6MRI2e-jdayf3gPcFs_hivY55ezzMA-k/s400/hedgerow.jpg" width="400" /></a></div>
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</span><br />
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<span style="font-size: small;">Hedgerows are an important source
of food and habitat for wildlife in the Irish countryside. Their management has
a large impact on wildlife; here, the flowering hedge is a wonderful resource
for bees and butterflies, and the berries will be major food item for birds
later in the season. The taller emerging trees are perfect for breeding birds. </span></div>
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<span style="font-size: 14pt; line-height: 115%; mso-bidi-font-size: 12.0pt;"><strong>Background
information</strong></span></div>
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<b style="mso-bidi-font-weight: normal;">Contact
details</b></div>
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Donal Sheehan (Project Manager)</div>
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Landline 025 36202</div>
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Mobile: 087 2292880</div>
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Email: enquiries@thebrideproject.ie</div>
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Website: <a href="http://www.thebrideproject.ie/">www.thebrideproject.ie</a></div>
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The BRIDE Project: Farming with Nature was
designed by two farmers from east Cork, Paul Moore, a tillage farmer from
Midleton and Donal Sheehan, a dairy farmer from Castlelyons, along with Tony
Nagle, an ecologist from Minane Bridge. Additional support was given by John
Finn and Daire Ó hUallacháin (both Teagasc), Alex Copland, (Birdwatch Ireland)
and industry partners Glanbia, Kepak, Cork Co. Council, The National Biodiversity
Data Centre and Bord Bia’s Origin Green Programme. </div>
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Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-3383131600232191143.post-28171896833101688192017-11-21T02:09:00.002-08:002021-01-31T09:13:54.421-08:00Farmland Biodiversity: links to online booklet on farmland habitats<br />
In a blast from the past, I re-discovered our booklet on 'Farmland
Biodiversity: measures to create and enhance farmed habitats'. <br />
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This is in two parts<br />
Part 1: <a class="twitter-timeline-link" data-expanded-url="http://www.nuigalway.ie/applied_ecology_unit/documents/farmland_habitats_guide_part_1.pdf" dir="ltr" href="http://www.nuigalway.ie/applied_ecology_unit/documents/farmland_habitats_guide_part_1.pdf" rel="nofollow noopener" target="_blank" title="http://www.nuigalway.ie/applied_ecology_unit/documents/farmland_habitats_guide_part_1.pdf"><span class="tco-ellipsis"></span><span style="color: #1da1f2;"><span class="invisible">http://www.</span><span class="js-display-url">nuigalway.ie/applied_ecolog</span><span class="invisible">y_unit/documents/farmland_habitats_guide_part_1.pdf</span></span></a><br />
Part 2: <a class="twitter-timeline-link" data-expanded-url="http://www.nuigalway.ie/applied_ecology_unit/documents/farmland_habitats_guide_part2.pdf" dir="ltr" href="http://www.nuigalway.ie/applied_ecology_unit/documents/farmland_habitats_guide_part2.pdf …" rel="nofollow noopener" target="_blank" title="http://www.nuigalway.ie/applied_ecology_unit/documents/farmland_habitats_guide_part2.pdf"><span class="tco-ellipsis"></span><span style="color: #1da1f2;"><span class="invisible">http://www.</span><span class="js-display-url">nuigalway.ie/applied_ecolog</span><span class="invisible">y_unit/documents/farmland_habitats_guide_part2.pdf</span></span><span class="tco-ellipsis"><span class="invisible"><span style="color: #1da1f2;"> </span></span></span></a><br />
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<img height="400" 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" 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