Abstract: Intensified climate and market turbulence requires resilience to a multitude of changes. Diversity reduces the sensitivity to disturbance and fosters the capacity to adapt to various future scenarios. What really matters is diversity of responses. Despite appeals to manage resilience, conceptual developments have not yet yielded a break-through in empirical applications. Here, we present an approach to empirically reveal the ‘response diversity’: the factors of change that are critical to a system are identified, and the response diversity is determined based on the documented component responses to these factors. We illustrate this approach and its added value using an example of securing food supply in the face of climate variability and change. This example demonstrates that quantifying response diversity allows for a new perspective: despite continued increase in cultivar diversity of barley, the diversity in responses to weather declined during the last decade in the regions where most of the barley is grown in Finland. This was due to greater homogeneity in responses among new cultivars than among older ones. Such a decline in the response diversity indicates increased vulnerability and reduced resilience. The assessment serves adaptive management in the face of both ecological and socioeconomic drivers. Supplier diversity in the food retail industry in order to secure affordable food in spite of global price volatility could represent another application. The approach is, indeed, applicable to any system for which it is possible to adopt empirical information regarding the response by its components to the critical factors of variability and change. Targeting diversification in response to critical change brings efficiency into diversity. We propose the generic procedure that is demonstrated in this study as a means to efficiently enhance resilience at multiple levels of agrifood systems and beyond. (C) 2014 The Authors. Published by Elsevier Ltd.

Abstract: The available evidence suggests that the current increasing trend in global surface temperatures will continue during this century, which will be accompanied by a greater frequency of extreme events. The IPCC has projected that higher temperatures may outscore the known optimal and maximum temperatures for maize. The purpose of this study was to improve the ability of the maize model CSM-IXIM to simulate crop development, growth, and yield under hot conditions, especially with regards to the impact of above-optimal temperatures around anthesis. Field and greenhouse experiments that were performed over three years (2014-2016) using the same short-season hybrid, PR37N01 (FAO 300), provided the data for this work. Maize was sown at a target population density of 5 plants M-2 on two sowing dates in 2014 and 2015 and on one in 2016 at three locations in Spain (northern, central, and southern Spain) with a well-defined thermal gradient. The same hybrid was also sown in two greenhouse chambers with daytime target temperatures of approximately 25 and above 35 degrees C. During the nighttime, the temperature in both chambers was allowed to equilibrate with the outside temperature. The greenhouse treatments consisted of moving 18 plants at selected phenological stages (V4, V9, anthesis, lag phase, early grain filling) from the cool chamber to the hot chamber over a week and then returning the plants back to the cool chamber. An additional control treatment remained in the cool chamber all season, and in 2015 and 2016, one treatment remained permanently in the hot chamber. Two maize models in the Decision Support System for Agrotechnology Transfer (DSSAT) V4.6 were compared, namely CERES and IXIM. The HUM version included additional components that were previously developed to improve the crop N simulation and to incorporate the anthesis-silking interval (ASI). A new thermal time calculation, a heat stress index, the impact of pollen-sterilizing temperatures, and the explicit simulation of male and female flowering as affected by the daily heat conditions were added to IXIM. The phenology simulation in field experiments by IXIM improved substantially. The RMSE for silking and maturity in CERES were 7.9 and 13.7 days, decreasing in DCIM to 2.8 and 7.3 days, respectively. Similarly, the estimated kernel numbers, kernel weight, grain yield and final biomass were always closer to the measurements in HUM than in CERES. The worst simulations were for kernel weight, and for that reason, the differences in grain yield between the models were small (the RMSE in CERES was 1219 kg ha(-1) vs. 1082 kg ha(-1) in IXIM). The greenhouse results also supported the improved estimations of crop development by IXIM (RMSE of 2.6 days) relative to CERES (7.4 days). The impact of the heat treatments on grain yield was consistently overestimated by CERES, while HUM captured the general trend. The new HUM model improved the CERES simulations when elevated temperatures were included in the evaluation data. Additional model testing with measurements from a wider latitudinal range and relevant heat conditions are required.

Abstract: Highlights • We analyze the economic effects of plant breeding research in Germany. • Effects of reduced CO2 emissions due to productivity increases are being quantified. • Expansion of global agricultural area has been reduced by 1–1.5 million ha. • CO2 emissions have been reduced by 160–235 million tons. • German plant breeding research has an economic value of 10.8–15.6 billion EUR. Abstract We analyze the economic effects of plant breeding research in Germany. In addition to market effects, for the first time also effects of reduced CO2 emissions due to productivity increases are being quantified. The analysis shows that investments in German plant breeding research in the period 1991–2010 have reduced the global expansion of agricultural area by 1–1.5 million hectares. This has led to reduced CO2 emissions of 160–235 million tons. The economic value generated by plant breeding research, through increased production and reduced greenhouse gas emissions, is estimated at 10.8–15.6 billion EUR in the same period. This can be translated into a social rate of return on research investment in the range of 40–80% per year. Projections for the period 2011–2030 generate a return rate in the range of 65–140% per year. Investments into plant breeding research in Germany are highly profitable from a societal point of view. At the same time, our results show significant under-investments in agricultural research in Germany. These results provide a good justification for policy-makers to reverse funding cuts for public agricultural research over the last decades and to improve institutional conditions for private research, e.g. through better protection of intellectual property rights.