TY - JOUR A1 - Webber, Heidi A1 - Lischeid, Gunnar A1 - Sommer, Michael A1 - Finger, Robert A1 - Nendel, Claas A1 - Gaiser, Thomas A1 - Ewert, Frank T1 - No perfect storm for crop yield failure in Germany JF - Environmental research letters N2 - Large-scale crop yield failures are increasingly associated with food price spikes and food insecurity and are a large source of income risk for farmers. While the evidence linking extreme weather to yield failures is clear, consensus on the broader set of weather drivers and conditions responsible for recent yield failures is lacking. We investigate this for the case of four major crops in Germany over the past 20 years using a combination of machine learning and process-based modelling. Our results confirm that years associated with widespread yield failures across crops were generally associated with severe drought, such as in 2018 and to a lesser extent 2003. However, for years with more localized yield failures and large differences in spatial patterns of yield failures between crops, no single driver or combination of drivers was identified. Relatively large residuals of unexplained variation likely indicate the importance of non-weather related factors, such as management (pest, weed and nutrient management and possible interactions with weather) explaining yield failures. Models to inform adaptation planning at farm, market or policy levels are here suggested to require consideration of cumulative resource capture and use, as well as effects of extreme events, the latter largely missing in process-based models. However, increasingly novel combinations of weather events under climate change may limit the extent to which data driven methods can replace process-based models in risk assessments. KW - crop yield failure KW - extreme events KW - support vector machine KW - process-based crop model KW - Germany Y1 - 2020 U6 - https://doi.org/10.1088/1748-9326/aba2a4 SN - 1748-9326 VL - 15 IS - 10 PB - IOP Publ. Ltd. CY - Bristol ER - TY - JOUR A1 - Lischeid, Gunnar A1 - Webber, Heidi A1 - Sommer, Michael A1 - Nendel, Claas A1 - Ewert, Frank T1 - Machine learning in crop yield modelling BT - A powerful tool, but no surrogate for science JF - Agricultural and forest meteorology N2 - Provisioning a sufficient stable source of food requires sound knowledge about current and upcoming threats to agricultural production. To that end machine learning approaches were used to identify the prevailing climatic and soil hydrological drivers of spatial and temporal yield variability of four crops, comprising 40 years yield data each from 351 counties in Germany. Effects of progress in agricultural management and breeding were subtracted from the data prior the machine learning modelling by fitting smooth non-linear trends to the 95th percentiles of observed yield data. An extensive feature selection approach was followed then to identify the most relevant predictors out of a large set of candidate predictors, comprising various soil and meteorological data. Particular emphasis was placed on studying the uniqueness of identified key predictors. Random Forest and Support Vector Machine models yielded similar although not identical results, capturing between 50% and 70% of the spatial and temporal variance of silage maize, winter barley, winter rapeseed and winter wheat yield. Equally good performance could be achieved with different sets of predictors. Thus identification of the most reliable models could not be based on the outcome of the model study only but required expert's judgement. Relationships between drivers and response often exhibited optimum curves, especially for summer air temperature and precipitation. In contrast, soil moisture clearly proved less relevant compared to meteorological drivers. In view of the expected climate change both excess precipitation and the excess heat effect deserve more attention in breeding as well as in crop modelling. KW - Crop modelling KW - Machine learning KW - Random forests KW - Support vector KW - machine KW - Feature selection KW - Equivocality Y1 - 2021 U6 - https://doi.org/10.1016/j.agrformet.2021.108698 SN - 0168-1923 SN - 1873-2240 VL - 312 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Kamali, Bahareh A1 - Lorite, Ignacio J. A1 - Webber, Heidi A. A1 - Rezaei, Ehsan Eyshi A1 - Gabaldon-Leal, Clara A1 - Nendel, Claas A1 - Siebert, Stefan A1 - Ramirez-Cuesta, Juan Miguel A1 - Ewert, Frank A1 - Ojeda, Jonathan J. T1 - Uncertainty in climate change impact studies for irrigated maize cropping systems in southern Spain JF - Scientific reports N2 - This study investigates the main drivers of uncertainties in simulated irrigated maize yield under historical conditions as well as scenarios of increased temperatures and altered irrigation water availability. Using APSIM, MONICA, and SIMPLACE crop models, we quantified the relative contributions of three irrigation water allocation strategies, three sowing dates, and three maize cultivars to the uncertainty in simulated yields. The water allocation strategies were derived from historical records of farmer's allocation patterns in drip-irrigation scheme of the Genil-Cabra region, Spain (2014-2017). By considering combinations of allocation strategies, the adjusted R-2 values (showing the degree of agreement between simulated and observed yields) increased by 29% compared to unrealistic assumptions of considering only near optimal or deficit irrigation scheduling. The factor decomposition analysis based on historic climate showed that irrigation strategies was the main driver of uncertainty in simulated yields (66%). However, under temperature increase scenarios, the contribution of crop model and cultivar choice to uncertainty in simulated yields were as important as irrigation strategy. This was partially due to different model structure in processes related to the temperature responses. Our study calls for including information on irrigation strategies conducted by farmers to reduce the uncertainty in simulated yields at field scale. Y1 - 2022 U6 - https://doi.org/10.1038/s41598-022-08056-9 SN - 2045-2322 VL - 12 IS - 1 PB - Macmillan Publishers Limited, CY - London ER - TY - JOUR A1 - Groh, Jannis A1 - Diamantopoulos, Efstathios A1 - Duan, Xiaohong A1 - Ewert, Frank A1 - Heinlein, Florian A1 - Herbst, Michael A1 - Holbak, Maja A1 - Kamali, Bahareh A1 - Kersebaum, Kurt-Christian A1 - Kuhnert, Matthias A1 - Nendel, Claas A1 - Priesack, Eckart A1 - Steidl, Jörg A1 - Sommer, Michael A1 - Pütz, Thomas A1 - Vanderborght, Jan A1 - Vereecken, Harry A1 - Wallor, Evelyn A1 - Weber, Tobias K. D. A1 - Wegehenkel, Martin A1 - Weihermüller, Lutz A1 - Gerke, Horst H. T1 - Same soil, different climate: Crop model intercomparison on translocated lysimeters JF - Vadose zone journal N2 - Crop model intercomparison studies have mostly focused on the assessment of predictive capabilities for crop development using weather and basic soil data from the same location. Still challenging is the model performance when considering complex interrelations between soil and crop dynamics under a changing climate. The objective of this study was to test the agronomic crop and environmental flux-related performance of a set of crop models. The aim was to predict weighing lysimeter-based crop (i.e., agronomic) and water-related flux or state data (i.e., environmental) obtained for the same soil monoliths that were taken from their original environment and translocated to regions with different climatic conditions, after model calibration at the original site. Eleven models were deployed in the study. The lysimeter data (2014-2018) were from the Dedelow (Dd), Bad Lauchstadt (BL), and Selhausen (Se) sites of the TERENO (TERrestrial ENvironmental Observatories) SOILCan network. Soil monoliths from Dd were transferred to the drier and warmer BL site and the wetter and warmer Se site, which allowed a comparison of similar soil and crop under varying climatic conditions. The model parameters were calibrated using an identical set of crop- and soil-related data from Dd. Environmental fluxes and crop growth of Dd soil were predicted for conditions at BL and Se sites using the calibrated models. The comparison of predicted and measured data of Dd lysimeters at BL and Se revealed differences among models. At site BL, the crop models predicted agronomic and environmental components similarly well. Model performance values indicate that the environmental components at site Se were better predicted than agronomic ones. The multi-model mean was for most observations the better predictor compared with those of individual models. For Se site conditions, crop models failed to predict site-specific crop development indicating that climatic conditions (i.e., heat stress) were outside the range of variation in the data sets considered for model calibration. For improving predictive ability of crop models (i.e., productivity and fluxes), more attention should be paid to soil-related data (i.e., water fluxes and system states) when simulating soil-crop-climate interrelations in changing climatic conditions. Y1 - 2022 U6 - https://doi.org/10.1002/vzj2.20202 SN - 1539-1663 VL - 21 IS - 4 PB - Wiley CY - Hoboken ER - TY - JOUR A1 - Wang, Enli A1 - He, Di A1 - Wang, Jing A1 - Lilley, Julianne M. A1 - Christy, Brendan A1 - Hoffmann, Munir P. A1 - O'Leary, Garry A1 - Hatfield, Jerry L. A1 - Ledda, Luigi A1 - Deligios, Paola A. A1 - Grant, Brian A1 - Jing, Qi A1 - Nendel, Claas A1 - Kage, Henning A1 - Qian, Budong A1 - Rezaei, Ehsan Eyshi A1 - Smith, Ward A1 - Weymann, Wiebke A1 - Ewert, Frank T1 - How reliable are current crop models for simulating growth and seed yield of canola across global sites and under future climate change? JF - Climatic change N2 - To better understand how climate change might influence global canola production, scientists from six countries have completed the first inter-comparison of eight crop models for simulating growth and seed yield of canola, based on experimental data from six sites across five countries. A sensitivity analysis was conducted with a combination of five levels of atmospheric CO2 concentrations, seven temperature changes, five precipitation changes, together with five nitrogen application rates. Our results were in several aspects different from those of previous model inter-comparison studies for wheat, maize, rice, and potato crops. A partial model calibration only on phenology led to very poor simulation of aboveground biomass and seed yield of canola, even from the ensemble median or mean. A full calibration with additional data of leaf area index, biomass, and yield from one treatment at each site reduced simulation error of seed yield from 43.8 to 18.0%, but the uncertainty in simulation results remained large. Such calibration (with data from one treatment) was not able to constrain model parameters to reduce simulation uncertainty across the wide range of environments. Using a multi-model ensemble mean or median reduced the uncertainty of yield simulations, but the simulation error remained much larger than observation errors, indicating no guarantee that the ensemble mean/median would predict the correct responses. Using multi-model ensemble median, canola yield was projected to decline with rising temperature (2.5-5.7% per degrees C), but to increase with increasing CO2 concentration (4.6-8.3% per 100-ppm), rainfall (2.1-6.1% per 10% increase), and nitrogen rates (1.3-6.0% per 10% increase) depending on locations. Due to the large uncertainty, these results need to be treated with caution. We further discuss the need to collect new data to improve modelling of several key physiological processes of canola for increased confidence in future climate impact assessments. KW - AgMIP KW - Brassica napus L. KW - Model calibration KW - Model improvement; KW - Multimodel ensemble KW - Sensitivity analysis Y1 - 2022 U6 - https://doi.org/10.1007/s10584-022-03375-2 SN - 0165-0009 SN - 1573-1480 VL - 172 IS - 1-2 PB - Springer Nature CY - Dordrecht ER -