TY - GEN A1 - Couëdel, Antoine A1 - Falconnier, Gatien N. A1 - Adam, Myriam A1 - Cardinael, Rémi A1 - Boote, Kenneth A1 - Justes, Eric A1 - Smith, Ward N. A1 - Whitbread, Anthony Michael A1 - Affholder, François A1 - Balkovic, Juraj A1 - Basso, Bruno A1 - Bhatia, Arti A1 - Chakrabarti, Bidisha A1 - Chikowo, Regis A1 - Christina, Mathias A1 - Faye, Babacar A1 - Ferchaud, Fabien A1 - Folberth, Christian A1 - Akinseye, Folorunso M. A1 - Gaiser, Thomas A1 - Galdos, Marcelo V. A1 - Gayler, Sebastian A1 - Gorooei, Aram A1 - Grant, Brian A1 - Guibert, Hervé A1 - Hoogenboom, Gerrit A1 - Kamali, Bahareh A1 - Laub, Moritz A1 - Maureira, Fidel A1 - Mequanint, Fasil A1 - Nendel, Claas A1 - Porter, Cheryl H. A1 - Ripoche, Dominique A1 - Ruane, Alex C. A1 - Rusinamhodzi, Leonard A1 - Sharma, Shikha A1 - Singh, Upendra A1 - Six, Johan A1 - Srivastava, Amit Kumar A1 - Vanlauwe, Bernard A1 - Versini, Antoine A1 - Vianna, Murilo A1 - Webber, Heidi A1 - Weber, Tobias K. D. A1 - Zhang, Congmu A1 - Corbeels, Marc T1 - Long-term soil organic carbon and crop yield feedbacks differ between 16 soil-crop models in sub-Saharan Africa T2 - European Journal of Agronomy N2 - Food insecurity in sub-Saharan Africa is partly due to low staple crop yields, resulting from poor soil fertility and low nutrient inputs. Integrated soil fertility management (ISFM), which includes the combined use of mineral and organic fertilizers, can contribute to increasing yields and sustaining soil organic carbon (SOC) in the long term. Soil-crop simulation models can help assess the performance and trade-offs of a range of crop management practices including ISFM, under current and future climate. Yet, uncertainty in model simulations can be high, resulting from poor model calibration and/or inadequate model structure. Multi-model simulations have been shown to be more robust than those with single models and help understand and reduce modelling uncertainty. In this study, we aim to perform the first multi-model comparison for long-term simulations of crop yield and SOC and their feedbacks in SSA. We evaluated the performance of 16 soil-crop models using data from four long-term maize experiments at sites in SSA with contrasting climates and soils. Each experiment had four treatments: i) no exogenous inputs, ii) addition of mineral nitrogen (N) fertilizer, iii) use of organic amendments, and iv) combined use of mineral and organic inputs. We assessed model performance in two steps: through blind calibration involving a minimum level of experimental data provided to the modeling teams, and subsequently through full calibration, which included a more extensive set of observational data. Model ensemble accuracy was greater with full calibration than blind calibration. Improvement in model accuracy was larger for maize yields (nRMSE 48 vs 18%) than for topsoil SOC (nRMSE 22 vs 14%). Model ensemble uncertainty (defined as the coefficient of variation across the 16 models) increased over the duration of the long-term experiments. Uncertainty of SOC simulations increased when organic amendments were used, whilst uncertainty of yield predictions was largest when no inputs were applied. Our study revealed large discrepancies among the models in simulating i) crop-to-soil feedbacks due to uncertainties in simulated carbon coming from roots, and ii) soil-to-crop feedbacks due to large uncertainties in simulated crop N supply from soil organic matter decomposition. These discrepancies were largest when organic amendments were applied. The results highlight the need for long-term experiments in which root and soil N dynamics are monitored. This will provide the corresponding data to improve and calibrate soil-crop models, which will lead to more robust and reliable simulations of SOC and crop productivity, and their interactions. KW - Soil-crop simulation KW - Soil organic matter KW - Soil-crop feedback KW - Ensemble modelling KW - Model intercomparison KW - Long-term experiments Y1 - 2024 U6 - https://doi.org/10.1016/j.eja.2024.127109 SN - 1161-0301 VL - 155 PB - Elsevier BV ER - TY - GEN A1 - Albasha, Rami A1 - Manceau, Loïc A1 - Webber, Heidi A1 - Chelle, Michaël A1 - Kimball, Bruce A1 - Martre, Pierre T1 - MONTPEL: a multi-component Penman-Monteith energy balance model T2 - Agricultural and Forest Meteorology KW - Atmospheric stability correction KW - Canopy temperature KW - Crop model KW - Energy balance KW - Wheat KW - Python package Y1 - 2024 U6 - https://doi.org/10.1016/j.agrformet.2024.110221 SN - 0168-1923 VL - 358 PB - Elsevier BV ER - TY - GEN A1 - Ahrends, Hella Ellen A1 - Piepho, Hans-Peter A1 - Sommer, Michael A1 - Ewert, Frank A1 - Webber, Heidi T1 - Is the volatility of yields for major crops grown in Germany related to spatial diversification at county level? T2 - Environmental Research Letters N2 - Recent evidence suggests a stabilizing effect of crop diversity on agricultural production. However, different methods are used for assessing these effects and there is little systematic quantitative evidence on diversification benefits. The aim of this study was to assess the relationship between volatility of combined crop yields (denoted as standard deviation) and diversity (denoted as Shannon’s Evenness Index SEI) for standardized yield data of major crop species grown in Germany between 1977 and 2018 (winter wheat, winter barley, silage maize and winter rapeseed) at the county level. Portfolio theory was used to estimate the optimal crop area share for minimizing yield volatility. On average, results indicated a weak negative relationship between volatility and the SEI during the past decades for the case of Germany. Optimizing crop area shares for minimizing volatility reduced yield variance on average by 24% but was associated with a decrease in SEI for most counties. This was related to the finding that the stability of individual species, i.e., barley and wheat, was more effective in reducing the volatility of combined yields than the asynchronous variation in annual yields among crops. Future studies might include an increased number of crop species and consider temporal diversification effects for a more realistic assessment of the relation between yield volatility and crop diversity and test the relationship in other regions and production conditions. KW - portfolio theory KW - optimization KW - crop yield KW - NUTS 3 Y1 - 2024 U6 - https://doi.org/10.1088/1748-9326/ad7613 SN - 1748-9326 VL - 19 IS - 10 PB - IOP Publishing ER - TY - GEN A1 - Adelesi, Opeyemi Obafemi A1 - Kim, Yean-Uk A1 - Schuler, Johannes A1 - Zander, Peter A1 - Njoroge, Michael Murithi A1 - Waithaka, Lilian A1 - Abdulai, Alhassan Lansah A1 - MacCarthy, Dilys Sefakor A1 - Webber, Heidi T1 - The potential for index-based crop insurance to stabilize smallholder farmers' gross margins in Northern Ghana T2 - Agricultural Systems N2 - Context Smallholder farmers in semi-arid West Africa face challenges such as weather variability, soil infertility, and inadequate market infrastructure, hindering their adoption of improved farming practices. Economic risks associated with uncertain weather, production and market conditions often result in measures such as selling assets and withdrawing children from school, resulting in long-term impoverishment. To break these poverty traps, there is a need for affordable and sustainable risk management approaches at the farm level. Proposed strategies include risk reduction through stress-resistant crop varieties and diversification, additional investments transfer options like crop insurance and contract farming. Despite experimentation with insurance products in sub-Saharan Africa, low adoption persists due to many factors including high premiums, imperfect indices, and cognitive factors. Objective The objective of this study is to assess the probability of two different index-based insurance products to stabilize smallholder farmers' income and limit asset losses in Northern Ghana using an integrated bio-economic modelling approach. Method We adapted an existing integrated bio-economic model comprising a process-based crop model, farm simulation model, and annual optimization model by including insurance contracts to assess their impacts on farmers' income and assets. We collaborated with an insurance service provider in sub–Saharan Africa to design and compare two weather index-based insurance contracts—one covering seeding costs and another addressing full input costs. Additionally, we considered the impact of management adaptations, such as replanting after crop establishment failure. Results The result from the study suggests that except for the most resource constrained, farmers would be better off purchasing seed insurance and replanting in the event of weather shocks, stabilizing their incomes and reducing the sale of their assets. These insurance options are less expensive than full weather index insurance for the resource-constrained farmers considering that extreme weather conditions do not occur regularly. Significance This study is significant for smallholder farmers in semi-arid West Africa, who are faced with economic and environmental challenges, challenging efforts to improve livelihoods. Focusing on Northern Ghana, the research assesses the viability of two index-based insurance products using an integrated bio-economic modelling approach. By presenting the probability of outcomes for income and farm assets, particularly through seed insurance incentivizing replanting after extreme weather shocks, the study offers a cost-effective solution for resource-constrained farmers. The results suggest the potential for weather-index insurance contracts to help smallholder farmers avoid bankruptcy or fall into poverty traps, especially after shock years. KW - Weather index-based insurance KW - Seed insurance KW - Bio-economic farm model KW - Integrated model KW - Weather risk KW - Northern Ghana Y1 - 2024 U6 - https://doi.org/10.1016/j.agsy.2024.104130 SN - 0308-521X VL - 221 PB - Elsevier BV ER - TY - GEN A1 - Stella, Tommaso A1 - Webber, Heidi A1 - Rezaei, Ehsan Eyshi A1 - Asseng, Senthold A1 - Martre, Pierre A1 - Dueri, Sibylle A1 - Guarin, Jose Rafael A1 - Pequeno, Diego A1 - Calderini, Daniel A1 - Reynolds, Matthew A1 - Molero, Gemma A1 - Miralles, Daniel A1 - Garcia, Guillermo A1 - Slafer, Gustavo A. A1 - Giunta, Francesco A1 - Kim, Yean-Uk A1 - Wang, Chenzhi A1 - Ruane, Alex C. A1 - Ewert, Frank T1 - Wheat crop traits conferring high yield potential may also improve yield stability under climate change T2 - in silico Plants N2 - Increasing genetic wheat yield potential is considered by many as critical to increasing global wheat yields and production, baring major changes in consumption patterns. Climate change challenges breeding by making target environments less predictable, altering regional productivity and potentially increasing yield variability. Here we used a crop simulation model solution in the SIMPLACE framework to explore yield sensitivity to select trait characteristics (radiation use efficiency [RUE], fruiting efficiency and light extinction coefficient) across 34 locations representing the world’s wheat-producing environments, determining their relationship to increasing yields, yield variability and cultivar performance. The magnitude of the yield increase was trait-dependent and differed between irrigated and rainfed environments. RUE had the most prominent marginal effect on yield, which increased by about 45 % and 33 % in irrigated and rainfed sites, respectively, between the minimum and maximum value of the trait. Altered values of light extinction coefficient had the least effect on yield levels. Higher yields from improved traits were generally associated with increased inter-annual yield variability (measured by standard deviation), but the relative yield variability (as coefficient of variation) remained largely unchanged between base and improved genotypes. This was true under both current and future climate scenarios. In this context, our study suggests higher wheat yields from these traits would not increase climate risk for farmers and the adoption of cultivars with these traits would not be associated with increased yield variability. KW - climate change KW - climate risk KW - genetic yield potential KW - wheat KW - yield variability Y1 - 2023 U6 - https://doi.org/10.1093/insilicoplants/diad013 SN - 2517-5025 VL - 5 IS - 2 PB - Oxford University Press (OUP) ER - TY - GEN A1 - Rezaei, Ehsan Eyshi A1 - Webber, Heidi A1 - Asseng, Senthold A1 - Boote, Kenneth A1 - Durand, Jean Louis A1 - Ewert, Frank A1 - Martre, Pierre A1 - MacCarthy, Dilys Sefakor T1 - Climate change impacts on crop yields T2 - Nature Reviews Earth & Environment Y1 - 2023 U6 - https://doi.org/10.1038/s43017-023-00491-0 SN - 2662-138X VL - 4 IS - 12 SP - 831 EP - 846 PB - Springer Science and Business Media LLC ER - TY - GEN A1 - Kim, Yean-Uk A1 - Asseng, Senthold A1 - Webber, Heidi T1 - Spring frost risk assessment on winter wheat in South Korea T2 - Agricultural and Forest Meteorology N2 - Spring frost remains a major climatic risk for winter wheat production. However, frost risk is often overlooked in climate change studies, especially those that rely on process-based crop models. This study assesses the spring frost risk for winter wheat in South Korea using observed trial data, a process-based crop model, and a large ensemble of climate data. Trial data from seven sites across South Korea suggest that the extreme yield loss in the 2019/20 season resulted from a combination of a warm winter, which accelerated phenology, and a cool April, which led to several frost events around heading. Projections with a calibrated DSSAT-Nwheat model and a large ensemble of climate data (HAPPI) suggest that the risk of yield loss due to spring frost will increase in the southern region of South Korea. However, this risk can be reduced by switching to later-maturing cultivars to avoid spring frost. In contrast, while the risk of yield loss due to spring frost in the central and northern regions is not expected to increase significantly, it will persist and can only be reduced by introducing frost-tolerant cultivars. Extending this analysis to include losses from other major stressors and linking it to socio-economic analyses will be needed for developing long-term strategies to boost wheat production, enhance self-sufficiency, and ensure food security. KW - Winter wheat KW - Frost damage KW - Phenology KW - Crop model KW - Robust risk assessment Y1 - 2025 U6 - https://doi.org/10.1016/j.agrformet.2025.110484 SN - 0168-1923 VL - 366 PB - Elsevier BV CY - Amsterdam ER - TY - GEN A1 - Nóia-Júnior, Rogério de S. A1 - Stocca, Valentina A1 - Martre, Pierre A1 - Shelia, Vakhtang A1 - Deswarte, Jean-Charles A1 - Cohan, Jean-Pierre A1 - Piquemal, Benoît A1 - Dutertre, Alain A1 - Slafer, Gustavo A. A1 - Zhang, Zhentao A1 - Van Der Velde, Marijn A1 - Kim, Yean-Uk A1 - Webber, Heidi A1 - Ewert, Frank A1 - Palosuo, Taru A1 - Liu, Ke A1 - Harrison, Matthew Tom A1 - Hoogenboom, Gerrit A1 - Asseng, Senthold T1 - Enabling modeling of waterlogging impact on wheat T2 - Field crops research N2 - Most crop simulation models do not consider the effect of waterlogging despite its importance for crop performance. Here, we reviewed the impact of waterlogging during different wheat phenological stages on grain number per unit area, average grain size, and grain yield. Episodes of waterlogging from the onset of tillering to anthesis result in fewer, and during grain filling in lighter grains. To simulate such impacts, we implemented a new waterlogging module into the wheat crop simulation model DSSAT-NWheat, accounting for the effects of waterlogging on wheat root growth, biomass growth, and potential average grain size. The model incorporating the new waterlogging routine was tested using data from a controlled experiment, and it reasonably reproduced wheat yield responses to pre-anthesis waterlogging. A sensitivity analysis showed that the simulated impact of waterlogging on above ground biomass and roots, as well as leaf area index, grain number, and grain yield varied with phenological stages. The simulated crop was most sensitive to pre-anthesis waterlogging, consistent with experimental studies. The new waterlogging-enabled crop model is an initial attempt to consider the impact of excess rainfall and waterlogging on crop growth and final grain yield to reduce model uncertainties when projecting climate change impacts with increasing rainfall intensity. KW - NWheat KW - DSSAT KW - Excess of water KW - Grain number KW - Grain size KW - Wheat crop simulation model Y1 - 2025 U6 - https://doi.org/10.1016/j.fcr.2025.110090 SN - 0378-4290 VL - 333 SP - 1 EP - 13 PB - Elsevier BV CY - Amsterdam ER - TY - GEN A1 - Kim, Yean-Uk A1 - Ruane, Alex C. A1 - Finger, Robert A1 - Webber, Heidi T1 - Robust assessment of climatic risks to crop production T2 - Nature food Y1 - 2025 U6 - https://doi.org/10.1038/s43016-025-01168-1 SN - 2662-1355 VL - 6 IS - 5 SP - 415 EP - 416 PB - Springer Science and Business Media LLC CY - Berlin ; Heidelberg ER - TY - GEN A1 - Nóia-Júnior, Rogério de S. A1 - Ruane, Alex C. A1 - Athanasiadis, Ioannis N. A1 - Ewert, Frank A1 - Harrison, Matthew Tom A1 - Jägermeyr, Jonas A1 - Martre, Pierre A1 - Müller, Christoph A1 - Palosuo, Taru A1 - Salmerón, Montserrat A1 - Webber, Heidi A1 - Maccarthy, Dilys Sefakor A1 - Asseng, Senthold T1 - Crop models for future food systems T2 - One earth N2 - Global food systems face intensifying pressure from climate change, resource scarcity, and rising demand, making their transformation toward resilience and sustainability urgent. Process-based crop growth models (CMs) are critical for understanding cropping system dynamics and supporting decisions from crop breeding to adaptive management across diverse environments. Yet, current CMs struggle to capture extreme events, novel production systems, and rapidly evolving data streams, limiting their ability to inform robust and timely decisions. Here, we outline CM structure, identify key knowledge gaps, and propose six priorities for next-generation CMs: (1) expand applications to extremes and to diverse systems; (2) support climate-resilient breeding; (3) integrate with machine learning for better inputs and forecasts; (4) link with standardized sensor and database networks; (5) promote modular, open-source architectures; and (6) build capacity in under-resourced regions. These priorities will substantially enhance CM robustness, comparability, and usability, reinforcing their role in guiding sustainable food system transformation. Y1 - 2025 U6 - https://doi.org/10.1016/j.oneear.2025.101487 SN - 2590-3322 VL - 8 IS - 10 SP - 1 EP - 7 PB - Elsevier BV CY - Amsterdam ER - TY - GEN A1 - Faye, Babacar A1 - Mbaye, Mamadou Lamine A1 - Webber, Heidi A1 - Dieye, Bounama A1 - Diouf, Diégane A1 - Gaye, Amadou Thierno T1 - Adaptation potential of alternate varieties and fertilization strategies for peanut and maize in Senegal under climate change T2 - Regional environmental change N2 - In Senegal, rising temperatures are projected to reduce maize yields due to a shortened growth duration, while elevated CO2 fertilization may increase peanut yields under climate change. However, there is limited evidence on climate change impacts if crop cultivars change and systems intensify, which is expected to occur in parallel with climate change. For climate-adapted agriculture, the performance of improved agronomy and varieties should be evaluated under current and future climate scenarios. This study assesses the impact of climate change on crop yields of two varieties of peanut and maize at each under current and intensified fertilization. Simulations were performed for mid-century (2045–2074) and end-century (2070–2099) relative to a baseline (1981–2010) using the SIMPLACE modeling framework at 0.5° resolution. Climate projections from nine global climate models (GCMs) were used under SSP2-4.5 and SSP5-8.5 scenarios. Soil data was derived from the Harmonized World Soil Database. The results indicate that the impacts of climate change on crop yields differed by crop. Peanut showed an increase in yield of up to 45% and a decrease for maize of up to 25% by the end of the century. Peanut yield gains were higher under the intensification fertilization case compared to the current fertilization case, whereas for maize, losses were high in the intensification case. Furthermore, yield losses are more substantial in the southern and western parts of the country for both crops. Additionally, for maize, yield losses were higher for the short cycle variety than the long cycle variety; there was little difference between varieties for peanut. KW - Crop yields KW - Intensification KW - Climate change KW - SIMPLACE KW - Senegal Y1 - 2025 U6 - https://doi.org/10.1007/s10113-025-02491-w SN - 1436-3798 SN - 1436-378X VL - 25 IS - 4 SP - 1 EP - 15 PB - Springer Science and Business Media LLC CY - Berlin ; Heidelberg ; New York, NY ER -