TY - GEN A1 - Tougma, Inès Astrid A1 - Van de Broek, Marijn A1 - Six, Johan A1 - Gaiser, Thomas A1 - Holz, Maire A1 - Zentgraf, Isabel A1 - Webber, Heidi T1 - AMPSOM: A measureable pool soil organic carbon and nitrogen model for arable cropping systems T2 - Environmental Modelling & Software N2 - 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. KW - Modelling KW - Soil organic matter KW - Measurable pools KW - Particulate organic matter KW - Mineral-associated organic matter KW - Radiocarbon isotope Y1 - 2025 U6 - https://doi.org/10.1016/j.envsoft.2024.106291 SN - 1364-8152 VL - 185 PB - Elsevier BV ER - TY - GEN A1 - Nóia-Júnior, Rogério de S. A1 - Martre, Pierre A1 - Deswarte, Jean-Charles A1 - Cohan, Jean-Pierre A1 - Van der Velde, Marijn A1 - Webber, Heidi A1 - Ewert, Frank A1 - Ruane, Alex C. A1 - Ben-Ari, Tamara A1 - Asseng, Senthold T1 - Past and future wheat yield losses in France’s breadbasket T2 - Field Crops Research N2 - 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. KW - Compounding factors KW - Extreme weather KW - Machine learning KW - Plant diseases KW - Wheat KW - Yield failure Y1 - 2025 U6 - https://doi.org/10.1016/j.fcr.2024.109703 SN - 0378-4290 VL - 322 PB - Elsevier BV ER - TY - GEN A1 - Bagagnan, Abdoul Rasmane A1 - Berre, David A1 - Webber, Heidi A1 - Lairez, Juliette A1 - Sawadogo, Hamado A1 - Descheemaeker, Katrien T1 - From typology to criteria considered by farmers: what explains agroecological practice implementation in North-Sudanian Burkina Faso? T2 - Frontiers in Sustainable Food Systems N2 - 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. KW - Agroecological practices KW - Farm type diversity KW - Farmers’ criteria KW - Practice implementation KW - Sub-Saharan Africa Y1 - 2024 U6 - https://doi.org/10.3389/fsufs.2024.1386143 SN - 2571-581X VL - 8 PB - Frontiers Media SA ER - TY - GEN A1 - Schaller, Jörg A1 - Webber, Heidi A1 - Ewert, Frank A1 - Stein, Mathias A1 - Puppe, Daniel T1 - The transformation of agriculture towards a silicon improved sustainable and resilient crop production T2 - npj Sustainable Agriculture N2 - 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. Y1 - 2024 U6 - https://doi.org/10.1038/s44264-024-00035-z SN - 2731-9202 VL - 2 IS - 1 PB - Springer Science and Business Media LLC ER - TY - GEN A1 - Kim, Yean-Uk A1 - Webber, Heidi T1 - Contrasting responses of spring and aummer potato to climate change in South Korea T2 - Potato Research N2 - 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. Y1 - 2024 U6 - https://doi.org/10.1007/s11540-024-09691-7 SN - 0014-3065 VL - 67 IS - 4 SP - 1265 EP - 1286 PB - Springer Science and Business Media LLC ER - TY - GEN A1 - Nóia Júnior, Rogério de S. A1 - Deswarte, Jean-Charles A1 - Cohan, Jean‐Pierre A1 - Martre, Pierre A1 - van der Velde, Marijn A1 - Lecerf, Remi A1 - Webber, Heidi A1 - Ewert, Frank A1 - Ruane, Alex C. A1 - Slafer, Gustavo A. A1 - Asseng, Senthold T1 - The extreme 2016 wheat yield failure in France T2 - Global Change Biology N2 - 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. KW - compounding factors KW - extreme weather KW - food security KW - grain number KW - grain size KW - temporally and multivariate events Y1 - 2023 U6 - https://doi.org/10.1111/gcb.16662 SN - 1354-1013 VL - 29 IS - 11 SP - 3130 EP - 3146 PB - Wiley ER - TY - GEN A1 - Diancoumba, M. A1 - MacCarthy, Dilys Sefakor A1 - Webber, Heidi A1 - Akinseye, Folorunso M. A1 - Faye, Babacar A1 - Noulèkoun, Florent A1 - Whitbread, Anthony Michael A1 - Corbeels, Marc A1 - Worou, Nadine O. T1 - Scientific agenda for climate risk and impact assessment of West African cropping systems T2 - Global Food Security N2 - 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. Y1 - 2023 U6 - https://doi.org/10.1016/j.gfs.2023.100710 SN - 2211-9124 VL - 38 PB - Elsevier BV ER - TY - GEN A1 - Rezaei, Ehsan Eyshi A1 - Faye, Babacar A1 - Ewert, Frank A1 - Asseng, Senthold A1 - Martre, Pierre A1 - Webber, Heidi T1 - Impact of coupled input data source-resolution and aggregation on contributions of high-yielding traits to simulated wheat yield T2 - Scientific Reports N2 - 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. Y1 - 2024 U6 - https://doi.org/10.1038/s41598-024-74309-4 SN - 2045-2322 VL - 14 IS - 1 PB - Springer Science and Business Media LLC ER - TY - GEN A1 - Martre, Pierre A1 - Dueri, Sibylle A1 - Guarin, Jose Rafael A1 - Ewert, Frank A1 - Webber, Heidi A1 - Calderini, Daniel A1 - Molero, Gemma A1 - Reynolds, Matthew A1 - Miralles, Daniel A1 - Garcia, Guillermo A1 - Brown, Hamish A1 - George, Mike A1 - Craigie, Rob A1 - Cohan, Jean‐Pierre A1 - Deswarte, Jean-Charles A1 - Slafer, Gustavo A. A1 - Giunta, Francesco A1 - Cammarano, Davide A1 - Ferrise, Roberto A1 - Gaiser, Thomas A1 - Gao, Yujing A1 - Hochman, Zvi A1 - Hoogenboom, Gerrit A1 - Hunt, Leslie A. A1 - Kersebaum, Kurt C. A1 - Nendel, Claas A1 - Padovan, Gloria A1 - Ruane, Alex C. A1 - Srivastava, Amit Kumar A1 - Stella, Tommaso A1 - Supit, Iwan A1 - Thorburn, Peter A1 - Wang, Enli A1 - Wolf, Joost A1 - Zhao, Chuang A1 - Zhao, Zhigan A1 - Asseng, Senthold T1 - Global needs for nitrogen fertilizer to improve wheat yield under climate change T2 - Nature Plants Y1 - 2024 U6 - https://doi.org/10.1038/s41477-024-01739-3 SN - 2055-0278 VL - 10 IS - 7 SP - 1081 EP - 1090 PB - Springer Science and Business Media LLC ER - TY - GEN A1 - Kim, Yean-Uk A1 - Webber, Heidi A1 - Adiku, Samuel G.K. A1 - Nóia Júnior, Rogério de S. A1 - Deswarte, Jean-Charles A1 - Asseng, Senthold A1 - Ewert, Frank T1 - Mechanisms and modelling approaches for excessive rainfall stress on cereals: waterlogging, submergence, lodging, pests and diseases T2 - Agricultural and Forest Meteorology N2 - 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. KW - Excess rain KW - Yield loss mechanisms KW - Process-based crop model KW - Model improvement Y1 - 2024 U6 - https://doi.org/10.1016/j.agrformet.2023.109819 SN - 0168-1923 VL - 344 PB - Elsevier BV ER -