Abstract: Understanding the relationship between climate and crop productivity is a key component of projections of future food production, and hence assessments of food security. Climate models and crop yield datasets have errors, but the effects of these errors on regional scale crop models is not well categorized and understood. In this study we compare the effect of synthetic errors in temperature and precipitation observations on the hindcast skill of a process-based crop model and a statistical crop model. We find that errors in temperature data have a significantly stronger influence on both models than errors in precipitation. We also identify key differences in the responses of these models to different types of input data error. Statistical and process-based model responses differ depending on whether synthetic errors are overestimates or underestimates. We also investigate the impact of crop yield calibration data on model skill for both models, using datasets of yield at three different spatial scales. Whilst important for both models, the statistical model is more strongly influenced by crop yield scale than the process-based crop model. However, our results question the value of high resolution yield data for improving the skill of crop models; we find a focus on accuracy to be more likely to be valuable. For both crop models, and for all three spatial scales of yield calibration data, we found that model skill is greatest where growing area is above 10-15 %. Thus information on area harvested would appear to be a priority for data collection efforts. These results are important for three reasons. First, understanding how different crop models rely on different characteristics of temperature, precipitation and crop yield data allows us to match the model type to the available data. Second, we can prioritize where improvements in climate and crop yield data should be directed. Third, as better climate and crop yield data becomes available, we can predict how crop model skill should improve.

Abstract: Understanding the capacity of agricultural systems to feed the world population under climate change requires projecting future food demand. This article reviews demand modeling approaches from 10 global economic models participating in the Agricultural Model Intercomparison and Improvement Project (AgMIP). We compare food demand projections in 2050 for various regions and agricultural products under harmonized scenarios of socioeconomic development, climate change, and bioenergy expansion. In the reference scenario (SSP2), food demand increases by 59-98% between 2005 and 2050, slightly higher than the most recent FAO projection of 54% from 2005/2007. The range of results is large, in particular for animal calories (between 61% and 144%), caused by differences in demand systems specifications, and in income and price elasticities. The results are more sensitive to socioeconomic assumptions than to climate change or bioenergy scenarios. When considering a world with higher population and lower economic growth (SSP3), consumption per capita drops on average by 9\% for crops and 18% for livestock. The maximum effect of climate change on calorie availability is -6% at the global level, and the effect of biofuel production on calorie availability is even smaller.

Abstract: Ultraviolet (UV) radiation, particularly in the UV-A + B range (280-400 nm) is a fraction of the solar spectrum that regulates almost every aspect of insect behaviour, including orientation towards hosts, alighting, arrestment and feeding behaviour. To study the role of UV radiation on the flight activity of five insect species of agricultural importance (pests Myzus persicae, Bemisia tabaci and Tuta absoluta, and natural enemies Aphidius colemani and Sphaerophoria rueppellii), one-chamber tunnels were covered with six cladding materials with different light transmittance properties ranging from 2% to 83% UV and 54% to 85% photosynthetically active radiation (PAR). Inside each tunnel, insects were released from tubes placed in a platform suspended from the ceiling. Specific targets varying with insect species were placed at different distances from the platform. Evaluation parameters were designed for each insect and tested separately. The ability of insects to leave the platform was assessed, as well as the number of captures, eggs or mummies in each target, either sticky traps or plants. Our results suggest differences in flight activity among insect species and UV-blocking nets. The UV-opaque film drastically prevented aphids, and whiteflies from flying outside the tubes whereas T. absoluta, syrphids and parasitoids were not affected. Aphid flight behaviour was affected by the UV-opaque film compared to the other nets, especially in the furthest target of the tunnel. Fewer aphids reached distant traps under UV-absorbing nets, and significantly more aphids could fly to the end of tunnels covered with non-UV-blocking materials. Orientation of B. tabaci and T. absoluta was also negatively affected by the UV-opaque film although in a different trend. Unlike aphids, differences in B. tabaci captures were mainly found in the closest targets. UV transmittance did not have any effects on parasitoids, and S. rueppellii, implying cues other than visual for these insects under our experimental conditions. Further effects of photoselective enclosures on greenhouse pests and their natural enemies are discussed.