Abstract: Field-scale crop models are often applied at spatial resolutions coarser than that of the arable field. However, little is known about the response of the models to spatially aggregated climate input data and why these responses can differ across models. Depending on the model, regional yield estimates from large-scale simulations may be biased, compared to simulations with high-resolution input data. We evaluated this so-called aggregation effect for 13 crop models for the region of North Rhine-Westphalia in Germany. The models were supplied with climate data of 1 km resolution and spatial aggregates of up to 100 km resolution raster. The models were used with 2 crops (winter wheat and silage maize) and 3 production situations (potential, water-limited and nitrogen-water-limited growth) to improve the understanding of errors in model simulations related to data aggregation and possible interactions with the model structure. The most important climate variables identified in determining the model-specific input data aggregation on simulated yields were mainly related to changes in radiation (wheat) and temperature (maize). Additionally, aggregation effects were systematic, regardless of the extent of the effect. Climate input data aggregation changed the mean simulated regional yield by up to 0.2 t ha(-1), whereas simulated yields from single years and models differed considerably, depending on the data aggregation. This implies that large-scale crop yield simulations are robust against climate data aggregation. However, large-scale simulations can be systematically biased when being evaluated at higher temporal or spatial resolution depending on the model and its parameterization.

Abstract: In this study, the WOFOST crop simulation model was used together with comprehensive empirical databases on barley Hordeum vulgare L. to study the contributions of different yield-determining and -limiting factors to observed trends of barley yield in Finland from 1988 to 2008. Simulations were performed at 3 study sites representing different agro-ecological zones, and compared with the data from experimental sites and that reported by local farmers. Yield gaps between simulated potential yields and farmers’ yields and their trends were assessed. Positive observed yield trends of Finnish barley mostly resulted from the development and usage of new, high-yielding cultivars. Simulated trends in climatic potential and water-limited potential yields of individual cultivars showed a slight declining trend. Yield gaps showed an increasing trend in 2 out of 3 study areas. Since the mid-1990s, a major reason for this has been the lack of market and policy incentives favouring crop management decisions, i.e. annual fertilisation, soil maintenance, drainage and crop rotation decisions, aiming for higher yields. The study indicates potential options for increasing or maintaining barley yields in the future. The breeding of new climate-resilient cultivars is the primary option. However, this needs to work alongside overall adjustments to farm management and must be supported by financial incentives for farmers to increase yields.

Abstract: This study explored the utility of the impact response surface (IRS) approach for investigating model ensemble crop yield responses under a large range of changes in climate. IRSs of spring and winter wheat Triticum aestivum yields were constructed from a 26-member ensemble of process-based crop simulation models for sites in Finland, Germany and Spain across a latitudinal transect. The sensitivity of modelled yield to systematic increments of changes in temperature (-2 to +9°C) and precipitation (-50 to +50%) was tested by modifying values of baseline (1981 to 2010) daily weather, with CO2 concentration fixed at 360 ppm. The IRS approach offers an effective method of portraying model behaviour under changing climate as well as advantages for analysing, comparing and presenting results from multi-model ensemble simulations. Though individual model behaviour occasionally departed markedly from the average, ensemble median responses across sites and crop varieties indicated that yields decline with higher temperatures and decreased precipitation and increase with higher precipitation. Across the uncertainty ranges defined for the IRSs, yields were more sensitive to temperature than precipitation changes at the Finnish site while sensitivities were mixed at the German and Spanish sites. Precipitation effects diminished under higher temperature changes. While the bivariate and multi-model characteristics of the analysis impose some limits to interpretation, the IRS approach nonetheless provides additional insights into sensitivities to inter-model and inter-annual variability. Taken together, these sensitivities may help to pinpoint processes such as heat stress, vernalisation or drought effects requiring refinement in future model development.