• Energy Research

Abstract Climate change is expected to impact global food supply and food security by affecting growing conditions for agricultural production. Process-based dynamic growth models are important tools to estimate crop yields based on minimum inputs of climate, soil, crop management, and crop cultivar parameters. Using region-specific cultivar parameters is critical when applying crop models at a global scale because cultivars vary in response to climate conditions, soils, and crop management. In this study, parameters were developed for modern cultivars representing all 17 CIMMYT wheat Mega Environments (MEs) using field experimental data and genetic cultivar relationships for the CROPSIM-CERES model in DSSAT v 4.5 (Decision-Support System for Agrotechnology Transfer). Cultivar performance was tested with independent CIMMYT breeding trial field experiments across several locations. Then crop simulations were carried out at 0.5 × 0.5 ° pixels for global wheat-growing areas, using cultivars representing MEs, soil information, region-specific crop management, and initial soil conditions. Aggregated simulated wheat yields and production were compared to reported country yields and production from Food and Agriculture Organization (FAO) statistics, resulting in a Root Mean Square Error (RMSE) of 1.3 t/ha for yield and 2.2 M t/country for country production. Some of the simulated errors are relatively large at country level because of uncertainties in pixel information for climate, soil, and crop management input and partly because of crop model uncertainties. In addition, FAO yield statistics have uncertainties because of incomplete farm reports or poor estimates. Nevertheless, this new cultivar-specific, partially-validated global baseline simulation enables new studies on issues of food security, agricultural technology, and breeding advancement impacts combined with climate change at a global scale.

Many crop models have been developed for potato, and a few for sweet potato, and yam. More than 30 potato models, two sweet potato models, and three yam models are described in the literature, and each differ in model structure. Some potato models have been applied to studies of nitrogen fertilizer, irrigation management, and climate change impact, but most of these models have never been validated with field measurements. The nitrogen dynamics of potato models CROPSYSTVB-CSPOTATO, EXpert-N-SPASS, and LINTUL-NPOTATO have been tested with some field data. LPOTCO and AQUACROP are two potato models that have been tested under elevated atmospheric CO2 conditions. None of the models have ever been tested with high temperature or heat stress data. The most tested and applied potato models include versions of LINTUL and SUBSTOR-Potato. Two sweet potato models, MADHURAM and SPOTCOMS, and two yam models, CROPSYSTVB-Yam and EPIC-Yam had limited field-testing under current climate conditions; however, these sweet potato and yam models are not ready for climate change impact assessments. To prepare potato, sweet potato, and yam models for climate change impact assessments, they need to be (i) calibrated with modern cultivars across agro-climatic zones; (ii) tested and improved with crop physiology and dynamic measurements of phenology, growth, partitioning, and water and nitrogen uptake under different crop management and environments; and (iii) tested and improved with field studies of crop responses to climate factors, including elevated CO2, water stress, increased temperature, heat stress, and combinations of these. Such extensive model testing and improvement with field experiments require a coordinated international effort and long-term commitment to potato, sweet potato, and yam research.

AbstractClimate change affects global agricultural production and threatens food security. Faster phenological development of crops due to climate warming is one of the main drivers for potential future yield reductions. To counter the effect of faster maturity, adapted varieties would require more heat units to regain the previous growing period length. In this study, we investigate the effects of variety adaptation on global caloric production under four different future climate change scenarios for maize, rice, soybean, and wheat. Thereby, we empirically identify areas that could require new varieties and areas where variety adaptation could be achieved by shifting existing varieties into new regions. The study uses an ensemble of seven global gridded crop models and five CMIP6 climate models. We found that 39% (SSP5‐8.5) of global cropland could require new crop varieties to avoid yield loss from climate change by the end of the century. At low levels of warming (SSP1‐2.6), 85% of currently cultivated land can draw from existing varieties to shift within an agro‐ecological zone for adaptation. The assumptions on available varieties for adaptation have major impacts on the effectiveness of variety adaptation, which could more than half in SSP5‐8.5. The results highlight that region‐specific breeding efforts are required to allow for a successful adaptation to climate change.

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.

Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections.

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.

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.