Abstract: Highlights • The larger simulated attainable yield for a specific crop season, the larger the yield gap. • Average size of the yield gap is not affected by the inter-annual variability of attainable yield. • Technology levels (resource input and accessibility) determine average yield gap. • To reduce yield gaps in rainfed environments, farmers need to improve season-specific crop management. Abstract Provision of food security in the face of increasing global food demand requires narrowing of the gap between actual farmer’s yield and maximum attainable yield. So far, assessments of yield gaps have focused on average yield over 5–10 years, but yield gaps can vary substantially between crop seasons. In this study we hypothesized that climate-induced inter-annual yield variability and associated risk is a major barrier for farmers to invest, i.e. increase inputs to narrow the yield gap. We evaluated the importance of inter-annual attainable yield variability for the magnitude of the yield gap by utilizing data for wheat and maize at ten sites representing some major food production systems and a large range of climate and soil conditions across the world. Yield gaps were derived from the difference of simulated attainable yields and regional recorded farmer yields for 1981 to 2010. The size of the yield gap did not correlate with the amplitude of attainable yield variability at a site, but was rather associated with the level of available resources such as labor, fertilizer and plant protection inputs. For the sites in Africa, recorded yield reached only 20% of the attainable yield, while for European, Asian and North American sites it was 56–84%. Most sites showed that the higher the attainable yield of a specific season the larger was the yield gap. This significant relationship indicated that farmers were not able to take advantage of favorable seasonal weather conditions. To reduce yield gaps in the different environments, reliable seasonal weather forecasts would be required to allow farmers to manage each seasonal potential, i.e. overcoming season-specific yield limitations.

Abstract: We compare 27 wheat models’ yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981-2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models’ climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R-2 <= 0.24) 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 and motivating continuing analysis and model development efforts. Published by Elsevier Ltd.

Abstract: Climate sensitivity of GCMs was used to select 5 GCMs from the CMIP5 ensemble for impact studies in MACSUR2. Selected GCMs for MACSUR2 are EC-EARTH (7), GFDL-CM3 (8) HadGEM2-ES (10), MIROC5 (13), and MPI-ESM-MR (15). These GCMs are evenly distributed among CMIP5 (Fig 1) and should capture, in principal, climate uncertainty of the CMIP5 ensemble. Using 5 GCMs will enable us to assess uncertainties in impacts related to uncertainty in climate projections. The selection of GCMs in MACSUR2 has a good overlap with selections of GCMs used in CORDEX and AgMIP projects.  We used the LARS-WG generator to construct local-scale CMIP5-based climate scenarios for Europe (Semenov & Stratonovitch, 2015). Fifteen sites were selected in Europe for MACSUR2. For each site and each selected GCM, 100 yrs climate daily data were generated by LARS-WG for RCP4.5 and RCP8.5 emission scenarios and for baseline and 3 future periods: near-term (2021-2040), mid-term (2041-2060) and long-term (2081-2100).