• Energy Research

AbstractThis study investigates the main drivers of uncertainties in simulated irrigated maize yield under historical conditions as well as scenarios of increased temperatures and altered irrigation water availability. Using APSIM, MONICA, and SIMPLACE crop models, we quantified the relative contributions of three irrigation water allocation strategies, three sowing dates, and three maize cultivars to the uncertainty in simulated yields. The water allocation strategies were derived from historical records of farmer’s allocation patterns in drip-irrigation scheme of the Genil-Cabra region, Spain (2014–2017). By considering combinations of allocation strategies, the adjusted R2 values (showing the degree of agreement between simulated and observed yields) increased by 29% compared to unrealistic assumptions of considering only near optimal or deficit irrigation scheduling. The factor decomposition analysis based on historic climate showed that irrigation strategies was the main driver of uncertainty in simulated yields (66%). However, under temperature increase scenarios, the contribution of crop model and cultivar choice to uncertainty in simulated yields were as important as irrigation strategy. This was partially due to different model structure in processes related to the temperature responses. Our study calls for including information on irrigation strategies conducted by farmers to reduce the uncertainty in simulated yields at field scale.

Abstract Increasing global food demand will require more food production without further exceeding the planetary boundaries, while at the same time adapting to climate change. We used an ensemble of wheat simulation models, with sink-source improved traits from the highest-yielding wheat genotypes to quantify potential yield gains and associated N requirements. This was explored for current and climate change scenarios across representative sites of major world wheat producing regions. The sink-source traits emerged as climate neutral with 16% yield increase with current N fertilizer applications under both current climate and mid-century climate change scenarios. To achieve the full yield potential, a 52% increase in global average yield under a mid-century RCP8.5 climate scenario, fertilizer use would need to increase fourfold over current use, which would unavoidably lead to higher environmental impacts from wheat production. Our results show the need to improve soil N availability and N use efficiency, along with yield potential.

Potential climate-related impacts on future crop yield are a major societal concern. Previous projections of the Agricultural Model Intercomparison and Improvement Project's Global Gridded Crop Model Intercomparison based on the Coupled Model Intercomparison Project Phase 5 identified substantial climate impacts on all major crops, but associated uncertainties were substantial. Here we report new twenty-first-century projections using ensembles of latest-generation crop and climate models. Results suggest markedly more pessimistic yield responses for maize, soybean and rice compared to the original ensemble. Mean end-of-century maize productivity is shifted from +5% to -6% (SSP126) and from +1% to -24% (SSP585)-explained by warmer climate projections and improved crop model sensitivities. In contrast, wheat shows stronger gains (+9% shifted to +18%, SSP585), linked to higher CO2 concentrations and expanded high-latitude gains. The 'emergence' of climate impacts consistently occurs earlier in the new projections-before 2040 for several main producing regions. While future yield estimates remain uncertain, these results suggest that major breadbasket regions will face distinct anthropogenic climatic risks sooner than previously anticipated.

Smallholder farmers in Northern Ghana face challenges due to weather variability and market volatility, hindering their ability to invest in sustainable intensification options. Modeling can help understand the relationships between productivity, environmental, and economical aspects, but few models have explored the effects of weather variability on crop management and resource allocation. This study introduces an integrated modeling approach to optimize resource allocation for smallholder mixed crop and livestock farming systems in Northern Ghana. The model combines a process-based crop model, farm simulation model, and annual optimization model. Crop model simulations are driven by a large ensemble of weather time series for two scenarios: good and bad weather. The model accounts for the effects of climate risks on farm management decisions, which can help in supporting investments in sustainable intensification practices, thereby bringing smallholder farmers out of poverty traps. The model was simulated for three different farm types represented in the region. The results suggest that farmers could increase their income by allocating more than 80% of their land to cash crops such as rice, groundnut, and soybeans. The optimized cropping patterns have an over 50% probability of increasing farm income, particularly under bad weather scenarios, compared with current cropping systems.

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.

Comme l'intensité et la fréquence des phénomènes météorologiques extrêmes devraient augmenter sous l'effet du changement climatique, il est de plus en plus essentiel d'évaluer leur impact sur les systèmes de culture et d'explorer les options d'adaptation possibles. Les modèles de culture basés sur les processus (PBCM), qui sont largement utilisés dans les évaluations d'impact du changement climatique, ont amélioré la simulation des impacts des événements météorologiques extrêmes majeurs tels que les vagues de chaleur et les sécheresses, mais ne parviennent toujours pas à reproduire de faibles rendements agricoles dans des conditions humides. Ici, nous donnons un aperçu des mécanismes de perte de rendement des précipitations excessives dans les céréales (c'est-à-dire l'engorgement, la submersion, l'hébergement, les ravageurs et les maladies) et des approches de modélisation associées dans le but de guider les améliorations de la PBCM. Certains PBCM simulent des environnements d'engorgement et d'étang, mais peu capturent les contraintes d'aération sur la croissance des cultures. L'hébergement est souvent négligé par les PBCM ; cependant, certains modèles d'hébergement mécanistes autonomes existent, qui peuvent potentiellement être incorporés dans les PBCM. Certains cadres relient les modèles d'épidémie et de culture basés sur les processus à la prise en compte de différents mécanismes de dommages. Cependant, le manque de données pour calibrer et évaluer ces fonctions de modèle limite l'utilisation de tels cadres. Afin de générer des données pour l'amélioration du modèle et combler les lacunes dans les connaissances, des expériences ciblées sur les mécanismes de dommages de l'engorgement, de la submersion, des ravageurs et des maladies sont nécessaires. Cependant, la prise en compte de tous les mécanismes de dommage dans le PBCM peut entraîner des modèles excessivement complexes avec un grand nombre de paramètres, augmentant l'incertitude du modèle. Des cadres modulaires pourraient aider à sélectionner les mécanismes nécessaires et conduire à des structures de modèle et à une complexité appropriées qui correspondent à une question de recherche spécifique. Enfin, il existe des synergies potentielles entre les PBCM, les modèles statistiques et les données de télédétection qui pourraient améliorer l'exactitude des prédictions et la compréhension des lacunes actuelles des PBCM. A medida que se proyecta que la intensidad y la frecuencia de los fenómenos meteorológicos extremos aumentarán bajo el cambio climático, evaluar su impacto en los sistemas de cultivo y explorar opciones de adaptación factibles es cada vez más crítico. Los modelos de cultivos basados en procesos (PBCM), que se utilizan ampliamente en las evaluaciones de impacto del cambio climático, han mejorado en la simulación de los impactos de los principales fenómenos meteorológicos extremos, como las olas de calor y las sequías, pero aún no logran reproducir los bajos rendimientos de los cultivos en condiciones húmedas. Aquí, proporcionamos una descripción general de los mecanismos de pérdida de rendimiento de la lluvia excesiva en los cereales (es decir, anegamiento, inmersión, alojamiento, plagas y enfermedades) y los enfoques de modelado asociados con el objetivo de guiar las mejoras de PBCM. Algunos PBCM simulan ambientes de anegamiento y estanque, pero pocos capturan las tensiones de aireación en el crecimiento de los cultivos. Los PBCM a menudo descuidan el alojamiento; sin embargo, existen algunos modelos de alojamiento mecanicistas independientes, que potencialmente pueden incorporarse a los PBCM. Algunos marcos vinculan los modelos de epidemias y cultivos basados en procesos con la consideración de diferentes mecanismos de daño. Sin embargo, la falta de datos para calibrar y evaluar estas funciones del modelo limita el uso de dichos marcos. Con el fin de generar datos para la mejora del modelo y cerrar las brechas de conocimiento, se requieren experimentos específicos sobre los mecanismos de daño de anegamiento, inmersión, plagas y enfermedades. Sin embargo, la consideración de todos los mecanismos de daño en PBCM puede resultar en modelos excesivamente complejos con una gran cantidad de parámetros, lo que aumenta la incertidumbre del modelo. Los marcos modulares podrían ayudar a seleccionar los mecanismos necesarios y conducir a estructuras modelo y complejidad apropiadas que se ajusten a una pregunta de investigación específica. Por último, existen posibles sinergias entre los PBCM, los modelos estadísticos y los datos de teledetección que podrían mejorar la precisión de la predicción y la comprensión de las deficiencias actuales de los PBCM. 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. نظرًا لأنه من المتوقع أن تزداد شدة وتواتر الظواهر الجوية القاسية في ظل تغير المناخ، فإن تقييم تأثيرها على أنظمة المحاصيل واستكشاف خيارات التكيف الممكنة أمر بالغ الأهمية بشكل متزايد. تحسنت نماذج المحاصيل القائمة على العمليات (PBCMs)، والتي تستخدم على نطاق واسع في تقييمات تأثير تغير المناخ، في محاكاة آثار الظواهر الجوية القاسية الرئيسية مثل موجات الحر والجفاف ولكنها لا تزال تفشل في إعادة إنتاج غلة محاصيل منخفضة في ظل الظروف الرطبة. هنا، نقدم لمحة عامة عن آليات فقدان الغلة للأمطار المفرطة في الحبوب (أي التشبع بالمياه، والغمر، والسكن، والآفات والأمراض) ونهج النمذجة المرتبطة بها بهدف توجيه تحسينات PBCM. تحاكي بعض PBCMs بيئات التشبع بالمياه والبرك، لكن القليل منها يلتقط ضغوط التهوية على نمو المحاصيل. غالبًا ما يتم إهمال السكن من قبل PBCMs ؛ ومع ذلك، توجد بعض نماذج السكن الميكانيكية المستقلة، والتي يمكن دمجها في PBCMs. تربط بعض الأطر نماذج الأوبئة والمحاصيل القائمة على العمليات مع مراعاة آليات الضرر المختلفة. ومع ذلك، فإن نقص البيانات لمعايرة وتقييم هذه الوظائف النموذجية يحد من استخدام هذه الأطر. من أجل توليد بيانات لتحسين النموذج وسد الفجوات المعرفية، يلزم إجراء تجارب مستهدفة على آليات الأضرار الناجمة عن التشبع بالمياه والغمر والآفات والأمراض. ومع ذلك، قد يؤدي النظر في جميع آليات الضرر في PBCM إلى نماذج معقدة للغاية مع عدد كبير من المعلمات، مما يزيد من عدم اليقين في النموذج. يمكن أن تساعد الأطر المعيارية في اختيار الآليات اللازمة وتؤدي إلى هياكل نموذجية مناسبة وتعقيد يناسب سؤالًا بحثيًا محددًا. أخيرًا، هناك أوجه تآزر محتملة بين PBCMs والنماذج الإحصائية والبيانات المستشعرة عن بُعد والتي يمكن أن تحسن دقة التنبؤ وفهم أوجه القصور الحالية في PBCMs.

Abstract Crop multi-model ensembles (MME) have proven to be effective in increasing the accuracy of simulations in modelling experiments. However, the ability of MME to capture crop responses to changes in sowing dates and densities has not yet been investigated. These management interventions are some of the main levers for adapting cropping systems to climate change. Here, we explore the performance of a MME of 29 wheat crop models to predict the effect of changing sowing dates and rates on yield and yield components, on two sites located in a high-yielding environment in New Zealand. The experiment was conducted for 6 years and provided 50 combinations of sowing date, sowing density and growing season. We show that the MME simulates seasonal growth of wheat well under standard sowing conditions, but fails under early sowing and high sowing rates. The comparison between observed and simulated in-season fraction of intercepted photosynthetically active radiation (FIPAR) for early sown wheat shows that the MME does not capture the decrease of crop above ground biomass during winter months due to senescence. Models need to better account for tiller competition for light, nutrients, and water during vegetative growth, and early tiller senescence and tiller mortality, which are exacerbated by early sowing, high sowing densities, and warmer winter temperatures.