TY - JOUR A1 - Kamali, Bahareh A1 - Stella, Tommaso A1 - Berg-Mohnicke, Michael A1 - Pickert, Jürgen A1 - Groh, Jannis A1 - Nendel, Claas T1 - Improving the simulation of permanent grasslands across Germany by using multi-objective uncertainty-based calibration of plant-water dynamics JF - European journal of agronomy N2 - The dynamics of grassland ecosystems are highly complex due to multifaceted interactions among their soil, water, and vegetation components. Precise simulations of grassland productivity therefore rely on accurately estimating a variety of parameters that characterize different processes of these systems. This study applied three calibration schemes - a Single-Objective (SO-SUFI2), a Multi-Objective Pareto (MO-Pareto), and, a novel Uncertainty-Based Multi-Objective (MO-SUFI2) - to estimate the parameters of MONICA (Model for Nitrogen and Carbon Simulation) agro-ecosystem model in grassland ecosystems across Germany. The MO-Pareto model is based on a traditional Pareto optimality concept, while the MO-SUFI2 optimizes multiple target variables considering their level of prediction uncertainty. We used measurements of leaf area index, aboveground biomass, and soil moisture from experimental data at five sites with different intensities of cutting regimes (from two to five cutting events per season) to evaluate model performance. Both MO-Pareto and MO-SUFI2 outperformed SO-SUFI2 during calibration and validation. The comparison of the two MO approaches shows that they do not necessarily conflict with each other, but MO-SUFI2 provides complementary information for better estimations of model parameter uncertainty. We used the obtained parameter ranges to simulate grassland productivity across Germany under different cutting regimes and quantified the uncertainty associated with estimated productivity across regions. The results showed higher uncertainty in intensively managed grasslands compared to extensively managed grasslands, partially due to a lack of high-resolution input information concerning cutting dates. Furthermore, the additional information on the quantified uncertainty provided by our proposed MO-SUFI2 method adds deeper insights on confidence levels of estimated productivity. Benefiting from additional management data collected at high resolution and ground measurements on the composition of grassland species mixtures appear to be promising solutions to reduce uncertainty and increase model reliability. KW - intensively managed grasslands KW - extensively managed grasslands KW - grassland productivity KW - pareto optimality Y1 - 2022 U6 - https://doi.org/10.1016/j.eja.2022.126464 SN - 1161-0301 SN - 1873-7331 VL - 134 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 - Kamali, Bahareh A1 - Jahanbakhshi, Farshid A1 - Dogaru, Diana A1 - Dietrich, Jörg A1 - Nendel, Claas A1 - AghaKouchak, Amir T1 - Probabilistic modeling of crop-yield loss risk under drought: a spatial showcase for sub-Saharan Africa JF - Environmental research letters N2 - Assessing the risk of yield loss in African drought-affected regions is key to identify feasible solutions for stable crop production. Recent studies have demonstrated that Copula-based probabilistic methods are well suited for such assessment owing to reasonably inferring important properties in terms of exceedance probability and joint dependence of different characterization. However, insufficient attention has been given to quantifying the probability of yield loss and determining the contribution of climatic factors. This study applies the Copula theory to describe the dependence between drought and crop yield anomalies for rainfed maize, millet, and sorghum crops in sub-Saharan Africa (SSA). The environmental policy integrated climate model, calibrated with Food and Agriculture Organization country-level yield data, was used to simulate yields across SSA (1980-2012). The results showed that the severity of yield loss due to drought had a higher magnitude than the severity of drought itself. Sensitivity analysis to identify factors contributing to drought and high-temperature stresses for all crops showed that the amount of precipitation during vegetation and grain filling was the main driver of crop yield loss, and the effect of temperature was stronger for sorghum than for maize and millet. The results demonstrate the added value of probabilistic methods for drought-impact assessment. For future studies, we recommend looking into factors influencing drought and high-temperature stresses as individual/concurrent climatic extremes. KW - Copula theory KW - crop model KW - drought stress KW - joint probability KW - risk Y1 - 2022 U6 - https://doi.org/10.1088/1748-9326/ac4ec1 SN - 1748-9326 VL - 17 IS - 2 PB - IOP Publishing CY - Bristol 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 -