Abstract: Projections of climate change impacts on crop performances are inherently uncertain. However, multimodel uncertainty analysis of crop responses is rare because systematic and objective comparisons among process-based crop simulation models are difficult. Here we report on the Agricultural Model Intercomparison and Improvement Project ensemble of 30 wheat models tested using both crop and climate observed data in diverse environments, including infra-red heating field experiments, for their accuracy in simulating multiple crop growth, N economy and yield variables. The relative error averaged over models in reproducing observations was 24-38% for the different end-of-season variables. Clusters of wheat models organized by their correlations with temperature, precipitation, and solar radiation revealed common characteristics of climatic responses; however, models are rarely in the same cluster when comparing across sites. We also found that the amount of information used for calibration has only a minor effect on model ensemble climatic responses, but can be large for any single model. When simulating impacts assuming a mid-century A2 emissions scenario for climate projections from 16 downscaled general circulation models and 26 wheat models, a greater proportion of the uncertainty in climate change impact projections was due to variations among wheat models rather than to variations among climate models. Uncertainties in simulated impacts increased with atmospheric [CO2] and associated warming. Extrapolating the model ensemble temperature response (at current atmospheric [CO2]) indicated that warming is already reducing yields at a majority of wheat-growing locations. Finally, only a very weak relationship was found between the models’ sensitivities to interannual temperature variability and their response to long-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs. In conclusion, uncertainties in prediction of climate change impacts on crop performance can be reduced by improving temperature and CO2 relationships in models and are better quantified through use of impact ensembles. No Label

Abstract: In Europe, analysis of meteorological data shows that the average temperature has increased by ~1°C over the past hundred years (IPCC, 2013). Heat stress periods are thus expected to be more frequent even in temperate areas.  The use of an automatic milking system (AMS) implies the need to stimulate cows’ traffic to the robot, especially with grazing cows.  Describing how heat stress influenced cows’ traffic to the robot is the aim of this study.Grazing dairy cows milked by an automatic system (AMS) experienced heat stress (HS) periods, twice during the summer 2013 in July (J) and August (A). The daily temperature humidity index (THI) during these periods were higher than 75. Each HS period was compared with a “normal period”(N), presenting the same number of cows, similar lactation number, days in milk, distance to come back to the robot and an equal access to water. The first HS period of 5 days with a mean THI of 78.4 was chosen in J, and a second that lasted for 6 days in A with a THI value of 77.3.  Heat stress periods were cut off with the same duration of days with no stress (N) and mean THI <70.  Milk production, milkings and returns to the robot during HS were compared with N periods.Milkings and visits to AMS were significantly more numerous in HS periods in July (HS: 2.44 vs N: 2.23, 3.97 vs 3.03) but milk production dropped from 20.3 kg to 19.3 kg milk per cow and per day. In August, MY increased slightly during HS.  This could be explained by less high ambient temperatures and decreased distance to walk inducing less energy expenditure.  The increase in milkings and visits to the robot during HS could be linked to water availability nearby the robot and confirmed previous findings (Lessire et al., 2014). No Label

Abstract: To comply with complexity in farming and social demands with respect to farming practices, to remain competitive in and resilient to an ever changing decision environment, today’s farm managers need to develop an extensive portfolio of activities, made coherent by an overall strategic vision. This paper focusses on dairy farming, which shows complexity by integrating crop and livestock processes and faces nowadays important challenges from its social and market environment. The aim is twofold: first, what does strategic thinking mean in dairy farming and what kind of strategic decisions are eligible for a sustainable development, second, what kind of methodological framework can be built to support the farmer’s strategic thinking and decision making. The novel strategy exploration implies not only the mere crop-livestock organization alternatives, but also creatively looks for resilience increasing activities that allow for flexible food nonfood substitutions, multiple valorization trajectories and alternative multi-agents arrangements. Concrete examples include agroforestry, alternative nutrient throughputs or composting. The methodological support focusses on four principles: (i) integrative, considering the whole-farm scale, (iii) normative, leading to improved decision making, (iii)participatory, compiling transdisciplinary knowledge and (iv) communicative, using typical farm benchmarking. Findings are brought together from  literature, own research experiences on dairy farm management and interaction with stakeholders, amongst other the technical sciences researchers in the MACSUR knowledge hub. No Label