@article{KamaliStellaBergMohnickeetal.2022, author = {Kamali, Bahareh and Stella, Tommaso and Berg-Mohnicke, Michael and Pickert, J{\"u}rgen and Groh, Jannis and Nendel, Claas}, title = {Improving the simulation of permanent grasslands across Germany by using multi-objective uncertainty-based calibration of plant-water dynamics}, series = {European journal of agronomy}, volume = {134}, journal = {European journal of agronomy}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1161-0301}, doi = {10.1016/j.eja.2022.126464}, pages = {17}, year = {2022}, abstract = {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.}, language = {en} } @article{PohankovaHlavinkaKersebaumetal.2022, author = {Pohankov{\´a}, Eva and Hlavinka, Petr and Kersebaum, Kurt-Christian and Rodr{\´i}guez, Alfredo and Balek, Jan and Bednař{\´i}k, Martin and Dubrovsk{\´y}, Martin and Gobin, Anne and Hoogenboom, Gerrit and Moriondo, Marco and Nendel, Claas and Olesen, J{\o}rgen E. E. and R{\"o}tter, Reimund Paul and Ruiz-Ramos, Margarita and Shelia, Vakhtang and Stella, Tommaso and Hoffmann, Munir Paul and Tak{\´a}č, Jozef and Eitzinger, Josef and Dibari, Camilla and Ferrise, Roberto and Bl{\´a}hov{\´a}, Monika and Trnka, Miroslav}, title = {Expected effects of climate change on the production and water use of crop rotation management reproduced by crop model ensemble for Czech Republic sites}, series = {European journal of agronomy}, volume = {134}, journal = {European journal of agronomy}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1161-0301}, doi = {10.1016/j.eja.2021.126446}, pages = {27}, year = {2022}, abstract = {Crop rotation, fertilization and residue management affect the water balance and crop production and can lead to different sensitivities to climate change. To assess the impacts of climate change on crop rotations (CRs), the crop model ensemble (APSIM,AQUACROP, CROPSYST, DAISY, DSSAT, HERMES, MONICA) was used. The yields and water balance of two CRs with the same set of crops (winter wheat, silage maize, spring barley and winter rape) in a continuous transient run from 1961 to 2080 were simulated. CR1 was without cover crops and without manure application. Straw after the harvest was exported from the fields. CR2 included cover crops, manure application and crop residue retention left on field. Simulations were performed using two soil types (Chernozem, Cambisol) within three sites in the Czech Republic, which represent temperature and precipitation gradients for crops in Central Europe. For the description of future climatic conditions, seven climate scenarios were used. Six of them had increasing CO \& nbsp;concentrations according RCP 8.5, one had no CO2 increase in the future. The output of an ensemble expected higher productivity by 0.82 t/ha/year and 2.04 t/ha/year for yields and aboveground biomass in the future (2051-2080). However, if the direct effect of a CO2 increase is not considered, the average yields for lowlands will be lower. Compared to CR1, CR2 showed higher average yields of 1.26 t/ha/year for current climatic conditions and 1.41 t/ha/year for future climatic conditions. For the majority of climate change scenarios, the crop model ensemble agrees on the projected yield increase in C3 crops in the future for CR2 but not for CR1. Higher agreement for future yield increases was found for Chernozem, while for Cambisol, lower yields under dry climate scenarios are expected. For silage maize, changes in simulated yields depend on locality. If the same hybrid will be used in the future, then yield reductions should be expected within lower altitudes. The results indicate the potential for higher biomass production from cover crops, but CR2 is associated with almost 120 mm higher evapotranspiration compared to that of CR1 over a 5-year cycle for lowland stations in the future, which in the case of the rainfed agriculture could affect the long-term soil water balance. This could affect groundwater replenishment, especially for locations with fine textured soils, although the findings of this study highlight the potential for the soil water-holding capacity to buffer against the adverse weather conditions.}, language = {en} } @article{NendelRecklingDebaekeetal.2023, author = {Nendel, Claas and Reckling, Moritz and Debaeke, Philippe and Schulz, Susanne and Berg-Mohnicke, Michael and Constantin, Julie and Fronzek, Stefan and Hoffmann, Munir and Jakšić, Snežana and Kersebaum, Kurt-Christian and Klimek-Kopyra, Agnieszka and Raynal, H{\´e}l{\`e}ne and Schoving, C{\´e}line and Stella, Tommaso and Battisti, Rafael}, title = {Future area expansion outweighs increasing drought risk for soybean in Europe}, series = {Global change biology}, volume = {29}, journal = {Global change biology}, number = {5}, publisher = {Wiley-Blackwell}, address = {Ocford [u.a]}, issn = {1354-1013}, doi = {10.1111/gcb.16562}, pages = {1340 -- 1358}, year = {2023}, abstract = {The European Union is highly dependent on soybean imports from overseas to meet its protein demands. Individual Member States have been quick to declare self-sufficiency targets for plant-based proteins, but detailed strategies are still lacking. Rising global temperatures have painted an image of a bright future for soybean production in Europe, but emerging climatic risks such as drought have so far not been included in any of those outlooks. Here, we present simulations of future soybean production and the most prominent risk factors across Europe using an ensemble of climate and soybean growth models. Projections suggest a substantial increase in potential soybean production area and productivity in Central Europe, while southern European production would become increasingly dependent on supplementary irrigation. Average productivity would rise by 8.3\% (RCP 4.5) to 8.7\% (RCP 8.5) as a result of improved growing conditions (plant physiology benefiting from rising temperature and CO2 levels) and farmers adapting to them by using cultivars with longer phenological cycles. Suitable production area would rise by 31.4\% (RCP 4.5) to 37.7\% (RCP 8.5) by the mid-century, contributing considerably more than productivity increase to the production potential for closing the protein gap in Europe. While wet conditions at harvest and incidental cold spells are the current key challenges for extending soybean production, the models and climate data analysis anticipate that drought and heat will become the dominant limitations in the future. Breeding for heat-tolerant and water-efficient genotypes is needed to further improve soybean adaptation to changing climatic conditions.}, language = {en} }