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 - 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 - 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 - 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 -