Sensitivities of crop models to extreme weather conditions during flowering period demonstrated for maize and winter wheat in Austria

Abstract

SUMMARYThe objective of the present study was to compare the performance of seven different, widely applied crop models in predicting heat and drought stress effects. The study was part of a recent suite of model inter-comparisons initiated at European level and constitutes a component that has been lacking in the analysis of sources of uncertainties in crop models used to study the impacts of climate change. There was a specific focus on the sensitivity of models for winter wheat and maize to extreme weather conditions (heat and drought) during the short but critical period of 2 weeks after the start of flowering. Two locations in Austria, representing different agro-climatic zones and soil conditions, were included in the simulations over 2 years, 2003 and 2004, exhibiting contrasting weather conditions. In addition, soil management was modified at both sites by following either ploughing or minimum tillage. Since no comprehensive field experimental data sets were available, a relative comparison of simulated grain yields and soil moisture contents under defined weather scenarios with modified temperatures and precipitation was performed for a 2-week period after flowering. The results may help to reduce the uncertainty of simulated crop yields to extreme weather conditions through better understanding of the models’ behaviour. Although the crop models considered (DSSAT, EPIC, WOFOST, AQUACROP, FASSET, HERMES and CROPSYST) mostly showed similar trends in simulated grain yields for the different weather scenarios, it was obvious that heat and drought stress caused by changes in temperature and/or precipitation for a short period of 2 weeks resulted in different grain yields simulated by different models. The present study also revealed that the models responded differently to changes in soil tillage practices, which affected soil water storage capacity.

A framework for testing the ability of models to project climate change and its impacts

“…Years with extreme or contrasting weather conditions have recently been used to assess the ability of crop models in simulating yield responses to variability in the weather (Eitzinger et al 2012). The results showed that even a short period of extreme hot weather may cause considerable differences among models.…”

Traditional agriculture: a climate-smart approach for sustainable food production

“…It refers to both a shift in mean climatic conditions (e.g. temperature and precipitation) and an increase in the frequency and severity of weather extremes (Tebaldi et al 2006;Eitzinger et al 2013;Porter et al 2014;Mandryk et al 2017). Economic activities prompted by industrial revolution have been contributed to climate change through increasing GHGs emission (IPCC 2007a).…”

Can Agrometeorological Indices of Adverse Weather Conditions Help to Improve Yield Prediction by Crop Models?

“…The effects of adverse weather events on crops and agricultural crop production are still poorly understood and their prediction with crop models is currently very difficult [6,41]. The conclusion of our study, based on the applied DSSAT models, is that the simulated yields show less sensitivity to AWCs, as represented by the selected AMI, than the correlation with actual yields.…”

“…Soils 3 and 4 are Chernozems and Fluvisols, respectively, with an available water capacity of >140 mm for a soil depth of 100 cm (sandy loam and loamy silt, respectively). DSSAT was applied at all locations of this study with calibration on Austrian WW and MZ cultivars [41]. All simulations specified a rain-fed water supply, automatic planting, N-fertilizer auto application, and an atmospheric CO2 concentration of 390 ppm, which reflects the mean value for the period of interest.…”

“…At present, a high level of agreement among the scientific community has been achieved in terms of the development and application of crop models [39]. However, due to the complexity of the soil-plant-atmosphere system, many of the relationships incorporated in the models are partially or entirely unable to reflect extreme weather impacts properly, e.g., heat or drought [40,41]. It is extremely difficult to analyze the uncertainties in a process-based model [42], and this difficulty is not simply a consequence of multiple sources of uncertainty.…”