@misc{FayeMbayeWebberetal., author = {Faye, Babacar and Mbaye, Mamadou Lamine and Webber, Heidi and Dieye, Bounama and Diouf, Di{\´e}gane and Gaye, Amadou Thierno}, title = {Adaptation potential of alternate varieties and fertilization strategies for peanut and maize in Senegal under climate change}, series = {Regional environmental change}, volume = {25}, journal = {Regional environmental change}, number = {4}, publisher = {Springer Science and Business Media LLC}, address = {Berlin ; Heidelberg ; New York, NY}, issn = {1436-3798}, doi = {10.1007/s10113-025-02491-w}, pages = {1 -- 15}, abstract = {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.}, language = {en} } @misc{NoiaJuniorRuaneAthanasiadisetal., author = {N{\´o}ia-J{\´u}nior, Rog{\´e}rio de S. and Ruane, Alex C. and Athanasiadis, Ioannis N. and Ewert, Frank and Harrison, Matthew Tom and J{\"a}germeyr, Jonas and Martre, Pierre and M{\"u}ller, Christoph and Palosuo, Taru and Salmer{\´o}n, Montserrat and Webber, Heidi and Maccarthy, Dilys Sefakor and Asseng, Senthold}, title = {Crop models for future food systems}, series = {One earth}, volume = {8}, journal = {One earth}, number = {10}, publisher = {Elsevier BV}, address = {Amsterdam}, issn = {2590-3322}, doi = {10.1016/j.oneear.2025.101487}, pages = {1 -- 7}, abstract = {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.}, language = {en} } @misc{WebberCookeWangetal., author = {Webber, H. and Cooke, D. and Wang, C. and Asseng, S. and Martre, P. and Ewert, F. and Kimball, B. and Hoogenboom, G. and Evett, S. and Chanzy, A. and Garrigues, S. and Olioso, A. and Copeland, K.S. and Steiner, J.L. and Cammarano, D. and Chen, Y. and Cr{\´e}peau, M. and Diamantopoulos, E. and Ferrise, R. and Manceau, L. and Gaiser, T. and Gao, Y. and Gayler, S. and Guarin, J.R. and Hunt, T. and J{\´e}go, G. and Padovan, G. and Pattey, E. and Ripoche, D. and Rodr{\´i}guez, A. and Ruiz-Ramos, M. and Shelia, V. and Srivastava, A.K. and Supit, I. and Tao, F. and Thorp, K. and Viswanathan, M. and Weber, T. and White, J.}, title = {Wheat crop models underestimate drought stress in semi-arid and Mediterranean environments}, series = {Field crops research}, volume = {332}, journal = {Field crops research}, publisher = {Elsevier BV}, address = {Amsterdam}, issn = {0378-4290}, doi = {10.1016/j.fcr.2025.110032}, pages = {1 -- 18}, abstract = {Under climate change and increasingly extreme weather, projections of water demand and drought stress from process-based crop models can inform risk management and adaptation strategies. Previous studies investigating maize crop models demonstrated considerable error in the simulation of water use, and no similar evaluation of wheat crop models exists. The aims of this study were to (1) evaluate wheat crop models' performance in reproducing observed daily evapotranspiration (ET) for Mediterranean and semi-arid environments, and (2) identify factors and processes associated with model error and uncertainty. These were assessed with an ensemble of wheat crop models for two experiments, one conducted in Bushland, Texas, USA (three seasons, deficit and full irrigation) and another in Avignon, France (four rainfed seasons) with winter bread and durum wheat, respectively. Models were calibrated with all observed data for crop growth. The model ensemble median underestimated water use in all environments evaluated, suggesting a systematic bias. The relative error in underestimating daily ET was constant across levels of atmospheric evaporative demand; therefore, the absolute error was greater for days with larger evaporative demand. This implies errors in the soil water balance increase more rapidly under high evaporative demand conditions. Using a potential versus reference crop evapotranspiration approach did not explain relative model performance. However, the sensitivity analysis indicated that simulation of atmospheric evaporative demand terms explained much more uncertainty in seasonal water use than terms related to soil depth or root growth. Errors in simulated leaf area index were associated with errors in daily simulated ET, but the relationship varied with the growth stage. Collectively, the results suggest the need to improve simulation of atmospheric ET demand to avoid underestimating projected impacts of drought or required water resource availability for viable production systems.}, language = {en} } @misc{NoiaJuniorStoccaMartreetal., author = {N{\´o}ia-J{\´u}nior, Rog{\´e}rio de S. and Stocca, Valentina and Martre, Pierre and Shelia, Vakhtang and Deswarte, Jean-Charles and Cohan, Jean-Pierre and Piquemal, Beno{\^i}t and Dutertre, Alain and Slafer, Gustavo A. and Zhang, Zhentao and Van Der Velde, Marijn and Kim, Yean-Uk and Webber, Heidi and Ewert, Frank and Palosuo, Taru and Liu, Ke and Harrison, Matthew Tom and Hoogenboom, Gerrit and Asseng, Senthold}, title = {Enabling modeling of waterlogging impact on wheat}, series = {Field crops research}, volume = {333}, journal = {Field crops research}, publisher = {Elsevier BV}, address = {Amsterdam}, issn = {0378-4290}, doi = {10.1016/j.fcr.2025.110090}, pages = {1 -- 13}, abstract = {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.}, language = {en} } @misc{KimRuaneFingeretal., author = {Kim, Yean-Uk and Ruane, Alex C. and Finger, Robert and Webber, Heidi}, title = {Robust assessment of climatic risks to crop production}, series = {Nature food}, volume = {6}, journal = {Nature food}, number = {5}, publisher = {Springer Science and Business Media LLC}, address = {Berlin ; Heidelberg}, issn = {2662-1355}, doi = {10.1038/s43016-025-01168-1}, pages = {415 -- 416}, language = {en} } @misc{KimAssengWebber, author = {Kim, Yean-Uk and Asseng, Senthold and Webber, Heidi}, title = {Spring frost risk assessment on winter wheat in South Korea}, series = {Agricultural and Forest Meteorology}, volume = {366}, journal = {Agricultural and Forest Meteorology}, publisher = {Elsevier BV}, address = {Amsterdam}, issn = {0168-1923}, doi = {10.1016/j.agrformet.2025.110484}, pages = {10}, abstract = {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.}, language = {en} } @misc{BagagnanBerreWebberetal., author = {Bagagnan, Abdoul Rasmane and Berre, David and Webber, Heidi and Lairez, Juliette and Sawadogo, Hamado and Descheemaeker, Katrien}, title = {From typology to criteria considered by farmers: what explains agroecological practice implementation in North-Sudanian Burkina Faso?}, series = {Frontiers in Sustainable Food Systems}, volume = {8}, journal = {Frontiers in Sustainable Food Systems}, publisher = {Frontiers Media SA}, issn = {2571-581X}, doi = {10.3389/fsufs.2024.1386143}, pages = {15}, abstract = {Cropping systems in the North-Sudanian zone of Burkina Faso face significant challenges related to poor yields, declining soil fertility and harsh climatic conditions. Together these necessitate a shift toward more sustainable farming practices. Agroecology aims to enhance yields while minimizing environmental harm through the use of ecological functions and has been promoted by researchers and farmers' organizations as a solution. However, its implementation remains limited. This study investigated the criteria farmers consider when implementing agroecological practices at the farm level and how these criteria and their implementation are influenced by farm characteristics. Data collection methods included the serious game TAKIT, together with baseline and complementary household surveys (108 farmers each). Farm diversity was analyzed using a statistical typology. The influence of farm types, farm structural variables and the village location on (1) whether or not agroecological practices were implemented and (2) the criteria considered by farmers was explored. Four distinct farm types were identified: low resource endowed farms relying on off-farm income, low resource endowed farms relying on livestock income, medium resource endowed farms relying on agricultural and livestock income, and high resource endowed farms with diverse sources of income. There were no significant differences in the implementation of agroecological practices across farm types. Crop rotations were the most frequently implemented practice (by 91\% of the study farmers), while the 2-by-2 line intercropping of sorghum-cowpea was the least implemented (9\% of farmers). Implementation of zai pits varied significantly between villages, with farmers in Nagreonkoudogo more likely to use them than those in Tanvousse, due to differing soil characteristics. Farmers considered several criteria when deciding whether to implement agroecological practices, including the ability to improve yield and preserve soil. Constraints to their implementation included a lack of knowledge and their high labor requirements. These criteria did not differ across farm types, likely because they stem from shared environmental constraints or conditions. The study highlights the complexity of agroecological transitions in sub-Saharan Africa, and illustrates the need to adequately consider contextual conditions. The co-design of new practices, and the redesign of existing ones, should align with criteria considered by farmers.}, language = {en} } @misc{SchallerWebberEwertetal., author = {Schaller, J{\"o}rg and Webber, Heidi and Ewert, Frank and Stein, Mathias and Puppe, Daniel}, title = {The transformation of agriculture towards a silicon improved sustainable and resilient crop production}, series = {npj Sustainable Agriculture}, volume = {2}, journal = {npj Sustainable Agriculture}, number = {1}, publisher = {Springer Science and Business Media LLC}, issn = {2731-9202}, doi = {10.1038/s44264-024-00035-z}, pages = {9}, abstract = {Sustainable and resilient crop production is facing many challenges. The restoration of natural reactive silicon cycles offers an opportunity to improve sustainability through reducing phosphorus fertilizer use and to increase crops' resilience to drought stress and pests. We therefore call upon farmers, agri-food-researchers, and policymakers to pave the road for transforming agriculture to a silicon-improved sustainable crop production, which represents a promising approach to achieve food security under global change.}, language = {en} } @misc{KimWebber, author = {Kim, Yean-Uk and Webber, Heidi}, title = {Contrasting responses of spring and aummer potato to climate change in South Korea}, series = {Potato Research}, volume = {67}, journal = {Potato Research}, number = {4}, publisher = {Springer Science and Business Media LLC}, issn = {0014-3065}, doi = {10.1007/s11540-024-09691-7}, pages = {1265 -- 1286}, abstract = {This paper assessed the effects of climate change and planting date adjustment on spring and summer potato in South Korea for the period 2061-2090. The study applied the SUBSTOR-Potato model and outputs of 24 general circulation models to capture future variability in climate conditions for four shared socioeconomic pathway-representative concentration pathway scenarios. Without planting date adjustment, tuber yield was projected to increase by approximately 20\% for spring and summer potato, indicating that the CO2 fertilization effect would offset the adverse effect of rising temperature. The effect of planting date adjustment was significant only for spring potato, where overall climate change impact with the optimized planting date was approximately  +60\%. For spring potato, the effects of rising temperature were bidirectional: temperature increases early in the year extended the growing season, whereas the higher temperature increases in June under the most severe climate change condition accelerated leaf senescence and reduced tuber bulking rate. Based on these results, different adaptation strategies could be established for spring potato for different climate change conditions. For example, developing frost-tolerant cultivars would continue to be recommended to plant earlier under the mild climate change conditions, whereas breeding mid-late maturity cultivars with high-temperature tolerance would be needed to delay senescence and enhance late tuber growth under the severe climate change conditions. Unlike spring potato, the breeding goal for summer potato of increasing high-temperature tolerance holds across all climate change conditions. Finally, these optimistic results should be interpreted with caution as the current model does not fully capture the effect of high-temperature episodes and the interactive effect between CO2 and temperature, which may reduce beneficial projected climate change impacts.}, language = {en} } @misc{TougmaVandeBroekSixetal., author = {Tougma, In{\`e}s Astrid and Van de Broek, Marijn and Six, Johan and Gaiser, Thomas and Holz, Maire and Zentgraf, Isabel and Webber, Heidi}, title = {AMPSOM: A measureable pool soil organic carbon and nitrogen model for arable cropping systems}, series = {Environmental Modelling \& Software}, volume = {185}, journal = {Environmental Modelling \& Software}, publisher = {Elsevier BV}, issn = {1364-8152}, doi = {10.1016/j.envsoft.2024.106291}, pages = {13}, abstract = {Most cropping system models simulate conceptual soil organic matter (SOM) pools, such as active, passive and slow pools that cannot be measured, complicating model calibration. In reality, SOM can be described in terms of quantifiable pools of particulate organic matter (POM) and mineral-associated organic matter (MAOM) which respond differently to management and climate. We present the AMPSOM model, integrated in a cropping system modelling framework (SIMPLACE). AMPSOM simulates carbon and nitrogen dynamics in MAOM and POM in response to crop growth and management, as well as soil texture, water and nitrogen content and temperature. It also simulates the radiocarbon isotope (14C) of soil organic carbon (SOC) to constrain the turnover time of slowly cycling SOC pools. Model calibration and evaluation were performed for thirty six sandy and loamy arable soils in Brandenburg, Germany. Results show that AMPSOM can reproduce observed patterns of SOC and nitrogen stocks in POM and MAOM along depth profiles across different soil types.}, language = {en} } @misc{NoiaJuniorMartreDeswarteetal., author = {N{\´o}ia-J{\´u}nior, Rog{\´e}rio de S. and Martre, Pierre and Deswarte, Jean-Charles and Cohan, Jean-Pierre and Van der Velde, Marijn and Webber, Heidi and Ewert, Frank and Ruane, Alex C. and Ben-Ari, Tamara and Asseng, Senthold}, title = {Past and future wheat yield losses in France's breadbasket}, series = {Field Crops Research}, volume = {322}, journal = {Field Crops Research}, publisher = {Elsevier BV}, issn = {0378-4290}, doi = {10.1016/j.fcr.2024.109703}, pages = {11}, abstract = {Context or problem: In recent decades, compounding weather extremes and plant diseases have increased wheat yield variability in France, the largest wheat producer in the European Union. Objective or research question: How these extremes might affect future wheat production remains unclear. Methods: Based on department level wheat yields, disease, and climate indices from 1980 to 2019 in France, we combined an existing disease model with machine learning algorithms to estimate future grain yields. Results: This approach explains about 59\% of historical yield variability. Projections from five CMIP6 climate models suggest that extreme low wheat yields, which used to occur once every 20 years, could occur every decade by the end of this century, but elevated CO2 levels might lessen these events. Conclusions: Heatwave-related yield losses are expected to double, while flooding-related yield losses will potentially decline by one third, depending on the representative concentration pathway. Ear blight disease is projected to contribute to 20\% of the expected 400 kg ha-1 average yield losses by the end of the century, compared with 12\% in the historical baseline period. These projections depend on the timing of anthesis, currently between late May and early June in most departments. Anthesis advancing to early May would shift losses primarily to heavy rainfall and low solar radiation. Implications or significance: French wheat production must adapt to these emerging threats, such as heat stress, which until recently had little impact but may become the primary cause of future yield losses.}, language = {en} } @misc{AdelesiKimSchuleretal., author = {Adelesi, Opeyemi Obafemi and Kim, Yean-Uk and Schuler, Johannes and Zander, Peter and Njoroge, Michael Murithi and Waithaka, Lilian and Abdulai, Alhassan Lansah and MacCarthy, Dilys Sefakor and Webber, Heidi}, title = {The potential for index-based crop insurance to stabilize smallholder farmers' gross margins in Northern Ghana}, series = {Agricultural Systems}, volume = {221}, journal = {Agricultural Systems}, publisher = {Elsevier BV}, issn = {0308-521X}, doi = {10.1016/j.agsy.2024.104130}, pages = {18}, abstract = {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.}, language = {en} } @misc{AhrendsPiephoSommeretal., author = {Ahrends, Hella Ellen and Piepho, Hans-Peter and Sommer, Michael and Ewert, Frank and Webber, Heidi}, title = {Is the volatility of yields for major crops grown in Germany related to spatial diversification at county level?}, series = {Environmental Research Letters}, volume = {19}, journal = {Environmental Research Letters}, number = {10}, publisher = {IOP Publishing}, issn = {1748-9326}, doi = {10.1088/1748-9326/ad7613}, pages = {13}, abstract = {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.}, language = {en} } @misc{AlbashaManceauWebberetal., author = {Albasha, Rami and Manceau, Lo{\"i}c and Webber, Heidi and Chelle, Micha{\"e}l and Kimball, Bruce and Martre, Pierre}, title = {MONTPEL: a multi-component Penman-Monteith energy balance model}, series = {Agricultural and Forest Meteorology}, volume = {358}, journal = {Agricultural and Forest Meteorology}, publisher = {Elsevier BV}, issn = {0168-1923}, doi = {10.1016/j.agrformet.2024.110221}, pages = {26}, language = {en} } @misc{CouedelFalconnierAdametal., author = {Cou{\"e}del, Antoine and Falconnier, Gatien N. and Adam, Myriam and Cardinael, R{\´e}mi and Boote, Kenneth and Justes, Eric and Smith, Ward N. and Whitbread, Anthony Michael and Affholder, Fran{\c{c}}ois and Balkovic, Juraj and Basso, Bruno and Bhatia, Arti and Chakrabarti, Bidisha and Chikowo, Regis and Christina, Mathias and Faye, Babacar and Ferchaud, Fabien and Folberth, Christian and Akinseye, Folorunso M. and Gaiser, Thomas and Galdos, Marcelo V. and Gayler, Sebastian and Gorooei, Aram and Grant, Brian and Guibert, Herv{\´e} and Hoogenboom, Gerrit and Kamali, Bahareh and Laub, Moritz and Maureira, Fidel and Mequanint, Fasil and Nendel, Claas and Porter, Cheryl H. and Ripoche, Dominique and Ruane, Alex C. and Rusinamhodzi, Leonard and Sharma, Shikha and Singh, Upendra and Six, Johan and Srivastava, Amit Kumar and Vanlauwe, Bernard and Versini, Antoine and Vianna, Murilo and Webber, Heidi and Weber, Tobias K. D. and Zhang, Congmu and Corbeels, Marc}, title = {Long-term soil organic carbon and crop yield feedbacks differ between 16 soil-crop models in sub-Saharan Africa}, series = {European Journal of Agronomy}, volume = {155}, journal = {European Journal of Agronomy}, publisher = {Elsevier BV}, issn = {1161-0301}, doi = {10.1016/j.eja.2024.127109}, pages = {16}, abstract = {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.}, language = {en} } @misc{DonmezSahingozPauletal., author = {Donmez, Cenk and Sahingoz, Merve and Paul, Carsten and Cilek, Ahmet and Hoffmann, Carsten and Berberoglu, Suha and Webber, Heidi and Helming, Katharina}, title = {Climate change causes spatial shifts in the productivity of agricultural long-term field experiments}, series = {European Journal of Agronomy}, volume = {155}, journal = {European Journal of Agronomy}, publisher = {Elsevier BV}, issn = {1161-0301}, doi = {10.1016/j.eja.2024.127121}, pages = {16}, abstract = {Long-term field experiments (LTE) are highly valuable infrastructures in agricultural- and soil sciences for understanding the long-term impacts of climate and management practices. While they are designed to run under constant conditions, climate change is expected to affect site conditions considerably. This needs to be quantified when interpreting experimental results and when redesigning the experimental setup. One way to achieve this is by utilizing vegetation growth and carbon dynamics, specifically the Net Primary Productivity (NPP), as a spatially explicit indicator. NPP facilitates the assessment and interpretation of yield performance in LTEs under future climatic conditions. Our study estimated the changes in NPP for 271 LTE sites in Germany, comparing a baseline (2000-2020) with two scenarios (2081-2100) that were based on the Shared Socioeconomic Pathways (SSPs) (SSP245) and SSP585) by the Intergovernmental Panel on Climate Change (IPCC). We used the NASA-CASA biogeochemical model to calculate NPP in baseline and IPCC scenarios using Germany as a test case. LTEs were grouped by land use (crop types) and soil information (soil type, texture), drawing on the geodata infrastructure "BonaRes Repository". The total annual terrestrial NPP for the baseline was calculated as 202.4 Mt C (sum of forests, grasslands, and arable lands) in Germany, while total NPP was up to 56.0 Mt C for different land use types. For both scenarios, NPP was projected to increase in LTEs located in southern Germany, indicating increased crop productivity, while a decrease was projected for the central Germany. The decrease in NPP of numerous LTEs in central Germany was estimated to extend to the LTEs in the eastern part corresponding to the worst-case scenario SSP585. Explicitly, the use of the multi-model ensemble mean as the climate driver in modelling may overestimate projected NPP by reducing inter-annual variability, highlighting the importance of methodological choices for accurate future projections. Besides, the results indicated that poor soils are projected to experience a further decline in productivity, primarily attributed to escalating water scarcity. Conversely, soils with high quality are likely to witness enhanced productivity, largely driven by the extension of the growing seasons. The outcomes of this study provide a basis for considering the future conditions of German LTEs and facilitate distinguishing between the effects of climate change and the impact of agricultural management on productivity at the regional level. These outputs enable planning and developing research strategies for selecting future LTE sites and redesigning existing or newly planned experiments. Moreover, the integrated modelling framework presented here highlights the potential of LTE data for large-scale modelling studies of ecosystem functions.}, language = {en} } @misc{MartreDueriBrownetal., author = {Martre, Pierre and Dueri, Sibylle and Brown, Hamish and Asseng, Senthold and Ewert, Frank and Webber, Heidi and George, Mike and Craigie, Rob and Guarin, Jose Rafael and Pequeno, Diego and Stella, Tommaso and Ahmed, Mukhtar and Alderman, Phillip and Basso, Bruno and Berger, Andres and Bracho Mujica, Gennady and Cammarano, Davide and Chen, Yi and Dumont, Benjamin and Rezaei, Ehsan Eyshi and Fereres, Elias and Ferrise, Roberto and Gaiser, Thomas and Gao, Yujing and Garcia-Vila, Margarita and Gayler, Sebastian and Hochman, Zvi and Hoogenboom, Gerrit and Kersebaum, Kurt C. and Nendel, Claas and Olesen, J{\o}rgen and Padovan, Gloria and Palosuo, Taru and Priesack, Eckart and Pullens, Johannes and Rodr{\´i}guez, Alfredo and R{\"o}tter, Reimund P. and Ruiz Ramos, Margarita and Semenov, Mikhail and Senapati, Nimai and Siebert, Stefan and Srivastava, Amit Kumar and St{\"o}ckle, Claudio and Supit, Iwan and Tao, Fulu and Thorburn, Peter and Wang, Enli and Weber, Tobias and Xiao, Liujun and Zhao, Chuang and Zhao, Jin and Zhao, Zhigan and Zhu, Yan}, title = {Winter wheat experiments to optimize sowing dates and densities in a high-yielding environment in New Zealand: field experiments and AgMIP-Wheat multi-model simulations}, series = {Open Data Journal for Agricultural Research}, volume = {10}, journal = {Open Data Journal for Agricultural Research}, publisher = {Wageningen University and Research}, issn = {2352-6378}, doi = {10.18174/odjar.v10i0.18442}, pages = {14 -- 21}, abstract = {This paper describes the data set that was used to test the accuracy of twenty-nine crop models in simulating the effect of changing sowing dates and sowing densities on wheat productivity for a high-yielding environment in New Zealand. The data includes one winter wheat cultivar (Wakanui) grown during six consecutive years, from 2012-2013 to 2017-2018, at two farms located in Leeston and Wakanui in Canterbury, New Zealand. The simulations were carried out in the framework of the Agricultural Model Intercomparison and Improvement Project for wheat (AgMIP-Wheat). Data include local daily weather data, soil profile characteristics and initial conditions, crop measurements at maturity (grain, stem, chaff and leaf dry weight, ear number and grain number, grain unit dry weight), and at stem elongation and anthesis (total above ground dry biomass, leaf number per stem and leaf area index). Several in-season measurements of the normalized difference vegetation index (NDVI) and the fraction of intercepted photosynthetically active radiation (FIPAR) are also available. The crop model simulations include both daily in-season and end-of-season results from twenty-nine wheat models.}, language = {en} } @misc{KimWebberAdikuetal., author = {Kim, Yean-Uk and Webber, Heidi and Adiku, Samuel G.K. and N{\´o}ia J{\´u}nior, Rog{\´e}rio de S. and Deswarte, Jean-Charles and Asseng, Senthold and Ewert, Frank}, title = {Mechanisms and modelling approaches for excessive rainfall stress on cereals: waterlogging, submergence, lodging, pests and diseases}, series = {Agricultural and Forest Meteorology}, volume = {344}, journal = {Agricultural and Forest Meteorology}, publisher = {Elsevier BV}, issn = {0168-1923}, doi = {10.1016/j.agrformet.2023.109819}, pages = {13}, abstract = {As the intensity and frequency of extreme weather events are projected to increase under climate change, assessing their impact on cropping systems and exploring feasible adaptation options is increasingly critical. Process-based crop models (PBCMs), which are widely used in climate change impact assessments, have improved in simulating the impacts of major extreme weather events such as heatwaves and droughts but still fail to reproduce low crop yields under wet conditions. Here, we provide an overview of yield-loss mechanisms of excessive rainfall in cereals (i.e., waterlogging, submergence, lodging, pests and diseases) and associated modelling approaches with the aim of guiding PBCM improvements. Some PBCMs simulate waterlogging and ponding environments, but few capture aeration stresses on crop growth. Lodging is often neglected by PBCMs; however, some stand-alone mechanistic lodging models exist, which can potentially be incorporated into PBCMs. Some frameworks link process-based epidemic and crop models with consideration of different damage mechanisms. However, the lack of data to calibrate and evaluate these model functions limit the use of such frameworks. In order to generate data for model improvement and close knowledge gaps, targeted experiments on damage mechanisms of waterlogging, submergence, pests and diseases are required. However, consideration of all damage mechanisms in PBCM may result in excessively complex models with a large number of parameters, increasing model uncertainty. Modular frameworks could assist in selecting necessary mechanisms and lead to appropriate model structures and complexity that fit a specific research question. Lastly, there are potential synergies between PBCMs, statistical models, and remotely sensed data that could improve the prediction accuracy and understanding of current PBCMs' shortcomings.}, language = {en} } @misc{MartreDueriGuarinetal., author = {Martre, Pierre and Dueri, Sibylle and Guarin, Jose Rafael and Ewert, Frank and Webber, Heidi and Calderini, Daniel and Molero, Gemma and Reynolds, Matthew and Miralles, Daniel and Garcia, Guillermo and Brown, Hamish and George, Mike and Craigie, Rob and Cohan, Jean-Pierre and Deswarte, Jean-Charles and Slafer, Gustavo A. and Giunta, Francesco and Cammarano, Davide and Ferrise, Roberto and Gaiser, Thomas and Gao, Yujing and Hochman, Zvi and Hoogenboom, Gerrit and Hunt, Leslie A. and Kersebaum, Kurt C. and Nendel, Claas and Padovan, Gloria and Ruane, Alex C. and Srivastava, Amit Kumar and Stella, Tommaso and Supit, Iwan and Thorburn, Peter and Wang, Enli and Wolf, Joost and Zhao, Chuang and Zhao, Zhigan and Asseng, Senthold}, title = {Global needs for nitrogen fertilizer to improve wheat yield under climate change}, series = {Nature Plants}, volume = {10}, journal = {Nature Plants}, number = {7}, publisher = {Springer Science and Business Media LLC}, issn = {2055-0278}, doi = {10.1038/s41477-024-01739-3}, pages = {1081 -- 1090}, language = {en} } @misc{RezaeiFayeEwertetal., author = {Rezaei, Ehsan Eyshi and Faye, Babacar and Ewert, Frank and Asseng, Senthold and Martre, Pierre and Webber, Heidi}, title = {Impact of coupled input data source-resolution and aggregation on contributions of high-yielding traits to simulated wheat yield}, series = {Scientific Reports}, volume = {14}, journal = {Scientific Reports}, number = {1}, publisher = {Springer Science and Business Media LLC}, issn = {2045-2322}, doi = {10.1038/s41598-024-74309-4}, pages = {11}, abstract = {High-yielding traits can potentially improve yield performance under climate change. However, data for these traits are limited to specific field sites. Despite this limitation, field-scale calibrated crop models for high-yielding traits are being applied over large scales using gridded weather and soil datasets. This study investigates the implications of this practice. The SIMPLACE modeling platform was applied using field, 1 km, 25 km, and 50 km input data resolution and sources, with 1881 combinations of three traits [radiation use efficiency (RUE), light extinction coefficient (K), and fruiting efficiency (FE)] for the period 2001-2010 across Germany. Simulations at the grid level were aggregated to the administrative units, enabling the quantification of the aggregation effect. The simulated yield increased by between 1.4 and 3.1 t ha- 1 with a maximum RUE trait value, compared to a control cultivar. No significant yield improvement (\< 0.4 t ha- 1) was observed with increases in K and FE alone. Utilizing field-scale input data showed the greatest yield improvement per unit increment in RUE. Resolution of water related inputs (soil characteristics and precipitation) had a notably higher impact on simulated yield than of temperature. However, it did not alter the effects of high-yielding traits on yield. Simulated yields were only slightly affected by data aggregation for the different trait combinations. Warm-dry conditions diminished the benefits of high-yielding traits, suggesting that benefits from high-yielding traits depend on environments. The current findings emphasize the critical role of input data resolution and source in quantifying a large-scale impact of high-yielding traits.}, language = {en} } @misc{DiancoumbaMacCarthyWebberetal., author = {Diancoumba, M. and MacCarthy, Dilys Sefakor and Webber, Heidi and Akinseye, Folorunso M. and Faye, Babacar and Noul{\`e}koun, Florent and Whitbread, Anthony Michael and Corbeels, Marc and Worou, Nadine O.}, title = {Scientific agenda for climate risk and impact assessment of West African cropping systems}, series = {Global Food Security}, volume = {38}, journal = {Global Food Security}, publisher = {Elsevier BV}, issn = {2211-9124}, doi = {10.1016/j.gfs.2023.100710}, pages = {4}, abstract = {Rainfed agriculture is at the centre of many West African economies and a key livelihood strategy in the region. Highly variable rainfall patterns lead to a situation in which farmers' investments to increase productivity are very risky and will become more risky with climate change. Process-based cropping system models are a key tool to assess the impact of weather variability and climate change, as well as the effect of crop management options on crop yields, soil fertility and farming system resilience and widely used by the West African scientific community. Challenges to use are related to their consideration of the prevailing systems and conditions of West African farms, as well as limited data availability for calibration. We outline here a number of factors need to be considered if they are to contribute to the scientific basis underlying transformation of farming systems towards sustainability. These include: capacity building, improved models, FAIR data, research partnerships and using models in co-development settings.}, language = {en} } @misc{Noia JuniorDeswarteCohanetal., author = {N{\´o}ia J{\´u}nior, Rog{\´e}rio de S. and Deswarte, Jean-Charles and Cohan, Jean-Pierre and Martre, Pierre and van der Velde, Marijn and Lecerf, Remi and Webber, Heidi and Ewert, Frank and Ruane, Alex C. and Slafer, Gustavo A. and Asseng, Senthold}, title = {The extreme 2016 wheat yield failure in France}, series = {Global Change Biology}, volume = {29}, journal = {Global Change Biology}, number = {11}, publisher = {Wiley}, issn = {1354-1013}, doi = {10.1111/gcb.16662}, pages = {3130 -- 3146}, abstract = {France suffered, in 2016, the most extreme wheat yield decline in recent history, with some districts losing 55\% yield. To attribute causes, we combined the largest coherent detailed wheat field experimental dataset with statistical and crop model techniques, climate information, and yield physiology. The 2016 yield was composed of up to 40\% fewer grains that were up to 30\% lighter than expected across eight research stations in France. The flowering stage was affected by prolonged cloud cover and heavy rainfall when 31\% of the loss in grain yield was incurred from reduced solar radiation and 19\% from floret damage. Grain filling was also affected as 26\% of grain yield loss was caused by soil anoxia, 11\% by fungal foliar diseases, and 10\% by ear blight. Compounding climate effects caused the extreme yield decline. The likelihood of these compound factors recurring under future climate change is estimated to change with a higher frequency of extremely low wheat yields.}, language = {en} } @misc{RezaeiWebberAssengetal., author = {Rezaei, Ehsan Eyshi and Webber, Heidi and Asseng, Senthold and Boote, Kenneth and Durand, Jean Louis and Ewert, Frank and Martre, Pierre and MacCarthy, Dilys Sefakor}, title = {Climate change impacts on crop yields}, series = {Nature Reviews Earth \& Environment}, volume = {4}, journal = {Nature Reviews Earth \& Environment}, number = {12}, publisher = {Springer Science and Business Media LLC}, issn = {2662-138X}, doi = {10.1038/s43017-023-00491-0}, pages = {831 -- 846}, language = {en} } @misc{StellaWebberRezaeietal., author = {Stella, Tommaso and Webber, Heidi and Rezaei, Ehsan Eyshi and Asseng, Senthold and Martre, Pierre and Dueri, Sibylle and Guarin, Jose Rafael and Pequeno, Diego and Calderini, Daniel and Reynolds, Matthew and Molero, Gemma and Miralles, Daniel and Garcia, Guillermo and Slafer, Gustavo A. and Giunta, Francesco and Kim, Yean-Uk and Wang, Chenzhi and Ruane, Alex C. and Ewert, Frank}, title = {Wheat crop traits conferring high yield potential may also improve yield stability under climate change}, series = {in silico Plants}, volume = {5}, journal = {in silico Plants}, number = {2}, publisher = {Oxford University Press (OUP)}, issn = {2517-5025}, doi = {10.1093/insilicoplants/diad013}, pages = {16}, abstract = {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.}, language = {en} }