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Matthews, A. (2016). Is agriculture off the hook in the EU’s 2030 Climate Policy (Vol. 9 C6 -).
Abstract: EU climate policy and AFOLU•Overall 2030 level of ambition agreed by European Council October 2014•Commission ESR proposal July 2016 – sharing of effort in NETS across MS plus trading mechanisms•Commission LULUCF proposal – integration of LULUCF into climate policy•AFOLU mitigation pursued through CAP as well as flanking environmental policies•No specific EU targets for agricultural mitigation in NETS•Ultimately, how AFOLU mitigation is pursued will depend on MS decisions2Implications of EU bubble•Commission has put in place trading mechanisms in NETS sectors to ensure least-cost fulfilment of overall EU targets•Challenge of MS ESR targets also depends on use MS make of trading mechanisms•MS have not to date made use of these mechanisms and prefer to meet targets domestically•A number of MS have domestic targets in addition to EU targets•ESR IA looked at adding central information site, central market place for AEA transfers or mandatory auctioning•Links with annual monitoring and 5-year legal compliance checks (2027 and 2032)
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Fulu, T. (2016). Case 5: Design future climate-resilient barley cultivars using crop model ensembles.. Rotterdam (Netherlands).
Abstract: Presentation SC 2.10 Farming systems. Case 5: Design future climate-resilient barley cultivars using crop model ensembles, Tao Fulu, Natural Resources Institute Finland (LUKE), Finland (2016). Presented at the international conference Adaptation Futures 2016, Rotterdam, the Netherlands. No Label
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Ewert, F., Boote, K. J., Rötter, R. P., Thorburn, P., & Nendel, C. (Eds.). (2016). Crop modelling for agriculture and food security under global change. Abstracts. International Crop Modelling Symposium iCROPM2016, 15-17 March 2016, Berlin, Germany. Berlin.
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Niemi, J. (2016). Framework of stochastic gross margin volatility modeling of crop rotation with farm management practices (Vol. 9 C6 -).
Abstract: DP models with risk aversion through meanvariancespecification is already implemented inLuke and applied in North Savo regionHOWEVER climate change, e.g. changes in mean andvariance of crop yiels, still not yet taken into account– Recently, such crop modelling results have becomeavailble for wheat as well, not only for barley– Still CC impact available for 2 cereals crops only, whilemost farms cultivate more than 2 crops Some early conclusions• The suggested approach is consistent in terms of DPprinciples and mean-variance approach and can provideconsistent results for farm scale risk analysis• It is however hard to utilise the approach except assuming afarm with only few crops (those with crop modelling / otherresults of climate change effects on mean and (co-variance)© Natural Resources Institute Finland• Assuming no change in price (co)variability is a majorsimplification results show farm level (or local) effects ofchanges in mean yields and yield (co)variability only
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Carabano, M. J., Logar, B., Bormann, J., Minet, J., Vanrobays, M. L., Diaz, C., et al. (2016). Modeling heat stress under different environmental conditions. J. Dairy Sci., 99(5), 3798–3814.
Abstract: Renewed interest in heat stress effects on livestock productivity derives from climate change, which is expected to increase temperatures and the frequency of extreme weather events. This study aimed at evaluating the effect of temperature and humidity on milk production in highly selected dairy cattle populations across 3 European regions differing in climate and production systems to detect differences and similarities that can be used to optimize heat stress (HS) effect modeling. Milk, fat, and protein test day data from official milk recording for 1999 to 2010 in 4 Holstein populations located in the Walloon Region of Belgium (BEL), Luxembourg (LUX), Slovenia (SLO), and southern Spain (SPA) were merged with temperature and humidity data provided by the state meteorological agencies. After merging, the number of test day records/cows per trait ranged from 686,726/49,655 in SLO to 1,982,047/136,746 in BEL. Values for the daily average and maximum temperature-humidity index (THIavg and THImax) ranges for THIavg/THImax were largest in SLO (22-74/28-84) and shortest in SPA (39-76/46-83). Change point techniques were used to determine comfort thresholds, which differed across traits and climatic regions. Milk yield showed an inverted U-shaped pattern of response across the THI scale with a HS threshold around 73 THImax units. For fat and protein, thresholds were lower than for milk yield and were shifted around 6 THI units toward larger values in SPA compared with the other countries. Fat showed lower HS thresholds than protein traits in all countries. The traditional broken line model was compared with quadratic and cubic fits of the pattern of response in production to increasing heat loads. A cubic polynomial model allowing for individual variation in patterns of response and THIavg as heat load measure showed the best statistical features. Higher/lower producing animals showed less/more persistent production (quantity and quality) across the THI scale. The estimated correlations between comfort and THIavg values of 70 (which represents the upper end of the THIavg scale in BEL-LUX) were lower for BEL-LUX (0.70-0.80) than for SPA (0.83-0.85). Overall, animals producing in the more temperate climates and semi-extensive grazing systems of BEL and LUX showed HS at lower heat loads and more re-ranking across the THI scale than animals producing in the warmer climate and intensive indoor system of SPA.
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