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Lehtonen, H. (2016). Evaluating competitiveness of clover-grass as a resilient feed production option in Finland (Vol. 9 C6 -).
Abstract: 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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Rötter, L. R. (2016). Introduction to MACSUR — methodology for integrated assessment.. Rotterdam (Netherlands).
Abstract: Presentation SC 2.10 Farming systems. Introduction to MACSUR – methodology for integrated assessment, Reimund R�tter, Natural Resources Institute Finland (LUKE), Finland (2016). Presented at the international conference Adaptation Futures 2016, Rotterdam, the Netherlands. No Label
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Marton, T. (2016). Assessing the impact of agro-climatic factors and farm characteristics on the yield variation of the Norwegian fruit sector (Vol. 9 C6 -).
Abstract: 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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Hempel, S., Janke, D., König, M., Menz, C., Englisch, A., Pinto, S., et al. (2016). Integrated modelling to assess optimisation potentials for cattle housing climate. Advances in Animal Biosciences, 7(03), 261–262.
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Mas, K., Pardo, G., Galán, E., & del Prado, A. (2016). Assessing dairy farm sustainability using whole-farm modelling and life cycle analysis. Advances in Animal Biosciences, 7(03), 259–260.
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