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Lehtonen, H. |
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Title |
Evaluating competitiveness of clover-grass as a resilient feed production option in Finland |
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2016 |
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FACCE MACSUR Reports |
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9 C6 - |
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Sp9-4 |
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Clover-grasses address the following objectives:– Decreased input use (N-fertilization), reduced dependency ofinorganic N => reduced GHG emissions– Possibility for increased protein content of silage, reduceddependency on purchased protein feed supplement (homegrown proteins, resilience)© Natural Resources Institute Finland– Better utilisation of farmland in the context of climate changein the north: Higher T – improved N fixation– Compatible with sustainable agriculture and sustainableintensification: more output with the same inputs / the sameoutput with reduced (non-renewable) inputs• In contrast: Shifting to silage maize increases N fertilisation– Major shift from grasslands to silage maize in e.g. Denmark 1. Small cost reductions in clover-grass cultivation, or clover-grasspremiums, may or may not increase clover cultivation- Their effectiveness is uncertain and subject to prices2. N tax is effective, but is not a suitable policy action in currentfinancial situation of farms (milk crisis 2015-2016)3. However, the results suggest that a 25% higher N price lead to© Natural Resources Institute Finlandsignificantly higher clover grass area and a small reduction ínmilk output – with no cost reductions or extra premiums!4. To increase clover cultivation, price ratios should be adjusted!5. If increasing clover -grass yield, a robust increase in clovergrass areas may realise, with small benefits for farm economyand overall production – How much more clover grass yieldcould be attained at low costs? A topic for further discussionand analysis |
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MA @ admin @ |
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4853 |
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Author |
Marton, T. |
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Assessing the impact of agro-climatic factors and farm characteristics on the yield variation of the Norwegian fruit sector |
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2016 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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9 C6 - |
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Sp9-5 |
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Main drivers of ag. yields:–Technology–R&D (new hybrids etc.)–Weather–Etc.•Common sense and anecdotal observations (remember the Tromsø presentation) revealed extreme events tended to impact wide geographic areas•This was called the «systemic» nature of agriculture No semi-aggregation farm-level•Not the boring corn, maize, wheat fruits•No OLS-like Pearson correlation or functional form approach for conditioning spatial correlations on weather SDM•Finally, if we are smart enough to set the explanatory proxies in a meaningful way presumably we can make the distinction between the effects of, say draught and extreme heat.•And much more in policy relevance |
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4857 |
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Eory et al. |
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Title |
Economic assessment of greenhouse gas mitigation on livestock farms |
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2016 |
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FACCE MACSUR Reports |
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8 |
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SP8-6 |
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LiveM2016: International livestock modelling conference – Modelling grassland-livestock systems under climate change |
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MA @ admin @ |
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4854 |
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Schönhart et al. |
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Integrated Assessment of Climate Change Impacts on Farms and Ecosystems in a Grassland Dominated Austrian Landscape |
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2016 |
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FACCE MACSUR Reports |
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8 |
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SP8-16 |
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LiveM2016: International livestock modelling conference – Modelling grassland-livestock systems under climate change |
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no |
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MA @ admin @ |
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4844 |
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Menardo et al. |
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Title |
INNO Mil-CH4 GHG Emissions from Milk Production |
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2016 |
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FACCE MACSUR Reports |
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8 |
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SP8-4 |
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LiveM2016: International livestock modelling conference – Modelling grassland-livestock systems under climate change |
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MA @ admin @ |
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4845 |
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