Abstract: The increasing global demand for food requires a sustainable intensification of crop production in low-yielding areas. Actions to improve crop production in these regions call for accurate spatially explicit identification of yield gaps, i.e. the difference between potential or water-limited yield and actual yield. The Global Yield Gap Atlas (GYGA) project proposes a consistent bottom-up approach to estimate yield gaps. For each country, a climate zonation is overlaid with a crop area map. Within climate zones with important crop areas, weather stations are selected with at least 10 years of daily data. For each of the 3 dominant soil types within a 100 km zone around the weather stations, the potential and water-limited yields are simulated with the WOFOST crop model, using location-specific knowledge on crop systems. Data from variety trials or other experiments, approaching potential or water-limited yields, are used for validation and calibration of the model. Actual yields are taken from sub-national statistics. Yields and yield gaps are scaled up to climate zones and subsequently to countries. The average national simulated wheat yields under rainfed conditions varied from around 5 to 6 t/ha/year in the Mediterranean to nearly 12 t/ha/year on the British Isles and in the Low Countries. The average actual wheat yield varied from around 2 to 3 t/ha/year in the Mediterranean and some countries in East Europe to nearly 9 t/ha/year on the British Isles and in the Low Countries. The average relative yield gaps varied from around 10% to 30% in many countries in Northwest Europe to around 50% to 70% in some countries in the Mediterranean and East Europe. The paper will elaborate on results per climate zone and soil type, and will also include barley and maize. Furthermore we will relate yield gaps to nitrogen use.

Abstract: The changing climate and environmental conditions play a key role on plant physiology. In this context, crop simulation models represent a useful tool for investigating the main plant processes and provide a reliable estimation of crop productivity and quality. However, the most common crop models showed many limitations, with particular concern on the effect of some meteorological variables on plant processes during sensitive stages of development. Improving models by implementing the effect of such variables on crop processes may help to improve the accuracy of models, thus their usefulness. Here we focus on the analysis of the effect of high and low temperatures during flowering in grapevine. To this, the fruit-set index, developed for taking into account for the effect of temperature on setting the number of berries per cluster and the fruit-set percentage, was applied in a preliminary explorative study to assess the impact of different conditions during flowering at European scales. The sensitivity of the index allowed to identify the differential impact of temperature around flowering in different environment and for different varieties. Once meteorological variables are available at field or sub-field scale, the index can be used to provide information about the spatial variability of crop growth, thus allowing to identify the most appropriate interventions to improve productivity.