Temperature and precipitation effects on wheat yield across a European transect: a crop model ensemble analysis using impact response surfaces

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 degrees 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 developmen

Lessons from climate modeling on the design and use of ensembles for crop modeling

“…In considering the effect of changed conditions, the uncertainty information is presented as an inter-model coefficient of variation (standard deviation of simulated values/mean simulated value), the inter-quartile range (Pirttioja et al 2015) or as the percentage of models agreeing in the sign of change (Rosenzweig et al 2014). …”

Relationship between assimilable-nutrient content and physicochemical properties of topsoil

“…Such models, despite phasic development, organ formation, biomass production, yield and quality formation, can consider soil-crop water relations and the nutrient (N, P, K) balance. Frequently, the enforced system is used in crop growth simulation models to assess individual and comprehensive management patterns, with a variety of environments and totally different genotypes included Pirttioja et al, 2015;RuizRamos et al, 2017). Spatio-temporal changes in climate dynamics are also often analysed and assessed Hoffmann et al, 2017;Krzyszczak et al, 2017).…”

Calibration, uncertainties and use of soybean crop simulation models for evaluating strategies to mitigate the effects of climate change in Southern Brazil

“…Despite these differences, the models and their ensemble were able to estimate yield variability, which means that they can simulate the impact of environmental conditions on yield, as observed for by Pirttioja et al (2015). The models´ ensemble resulted in a reduction of errors when compare with the results from the single models, showing error between -500 and 500 kg ha -1 (Figure 3), which represent less than 17%.…”

“…The crop models also are being evaluated under climate change scenarios, by using systematic changes in climate variables (sensitivity analysis), which help to understand the model performance under different climate conditions and to identify the limitations of them. These type of analyses were performed for wheat MARTRE et al, 2015;PIRTTIOJA et al, 2015), maize ARAYA et al, 2015), rice (LI et al, 2015) and sugarcane (MARIN et al, 2015), while for soybean this kind of study is still restricted (WHITE et al, 2011).…”