Southeast Asia is a major contributor to global rice production. Yet, this production is increasingly threatened by climate change, particularly by changes in the rainy season. In this study, We examine the impact of shifts in seasonal cumulative rainfall and characteristics of the rainy season on rice production across Southeast Asia. To achieve this, we utilize the framework of the High-Resolution Model Intercomparison Project (HighResMIP) in conjunction with rice production estimates from the World Food Studies (WOFOST) crop model. Our findings reveal a trend towards drier conditions during the dry season across the region. A decrease in cumulative rainfall is observed during December-January-February in mainland Southeast Asia and the Philippines, with rainfall reductions reaching up to 33

Climate change is expected to influence crop production within the coastal regions of Bangladesh. This research aims to assess the climate change and salinity impacts on boro rice and potato yields in Bangladesh's southcentral coastal region, using the SWAP-WOFOST (Soil-Water-Atmosphere-Plant-WOrld FOod STudies) model. The study area is divided into three salinity zones. Future weather data for the 2050 s (2035-2064) and the 2080 s (2065-2094) for one location from each zone were generated from three Global Circulation Models (GCMs) and adopted the SSP585 scenario. Elevated [CO2] levels for 2050 and 2080 were utilized for the respective periods. Based on the literature review, it was assumed that climate change may increase salinity levels by 10-30% above current levels. Accordingly, crop yields were simulated under elevated soil and water salinity and compared with the historical period (2001-2021). The simulation outcomes indicate a decrease in yield with heightened salinity levels, especially in the high-salinity area. Moreover, simulated yields also decreased with increasing temperature, primarily due to a reduction in growth duration. Notably, potatoes, being susceptible to temperature, exhibited decreased yields in response to rising temperatures in the future. Although elevated [CO2] levels may reduce the adverse impacts of increasing temperatures, potato crops will not benefit significantly from increased [CO2]. Conversely, rice productivity is projected to be higher, with elevated [CO2] and early sowing/planting proving advantageous. Elevated [CO2] is expected to partially offset the adverse effects of salinity. The findings from this study are valuable for future crop-level adaptation planning.

This study examines the projected change in rainfall and temperature anomalies across Mainland Southeast Asia, focusing on the teleconnection of El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). Five GCM’s from the CMIP6 project (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL) are used to investigate the historical (1985-2014) and future periods based on two Shared Socioeconomic Pathways (SSPs), SSP3-7.0 and SSP5-8.5, divided into three periods: near-future (2015-2044), mid-future (2041-2070), and far-future (2071-2100). The impact of ENSO and IOD on climate anomalies is analyzed using partial correlation coefficient (PCOR) calculated between Niño 3.4 and DMI index. PCOR allows us to examine the influence of ENSO while excluding the effect of IOD, and vice versa. We divided the study into three seasons: March-April-May (MAM), June-July-August-September (JJAS), and October-November-December (OND). Generally, ENSO and IOD show positive correlations with temperature, which means the positive phase of each results in higher temperature, whereas their correlations with rainfall can be positive as well as negative. Negative correlations between ENSO and rainfall predominate in most MSEA areas leading to drier conditions during El Niño events, except during June-July-August-September during which ambiguous patterns occur with both negative and positive influences from ENSO. Meanwhile, IOD presents significant positive influences on rainfall over large areas. Future correlations are generally higher than historic ones, suggesting a potential for better predictability of seasonal forecasts.

This study examines the impacts of climate change on potato production in East Africa. To assess these impacts, we utilised the WOFOST crop model to simulate both potential yield (Yp) and water-limited yield (Yw) for the present-day (1981-2010), near-future (2036-2065), and far-future (2066-2100) under two climate scenarios (SSP3.7 and SSP5-8.5), using a five-member General Circulation Model (GCM) ensemble from the ISIMIP project. The simulations consistently reveal a substantial decline in both Yp and Yw across all future periods. Specifically, without CO2 fertilisation, potential yields are projected to decrease by 37-71 %, and water-limited yields by 25-57 % during the Long Rain season (LRS), while during the Short Rain Season(SRS), these declines range from 39-75 % for potential yields and 32-60 % for water-limited yields, with variations depending on elevation and scenario. Even when accounting for elevated CO2 levels, Yp still decline by 23-57 %, and Yw by 20-49 % in LRS, and by 21-60 % and 20-48 % in SRS. Furthermore, the projected decline in land suitability for potato cultivation is stark, with 82 % of land becoming unsuitable by 2050 and 89 % by 2080, particularly during the LRS. Although elevated CO2 and slight increases in rainfall may provide some limited benefits, these are insufficient to counteract the detrimental effects of rising temperatures, which remain the primary constraint on potato productivity. Consequently, these findings suggest that conventional potato cultivation may become unsustainable by the end of the century due to climate change. The study underscores the pressing need for effective adaptation strategies, including the implementation of Climate Smart Agriculture (CSA) practices, to sustain potato production in the medium term. It further highlights the potential necessity of transitioning to alternative crops in regions that may become unsuitable for potatoes under future climate conditions. By offering region-specific insights based on relatively high-resolution CMIP6 data and the WOFOST crop model, this research provides actionable guidance for the development of adaptation strategies, reinforcing the importance of integrating climate change mitigation and adaptation into agricultural planning to ensure food security and protect rural livelihoods in East Africa.

Seasonal forecast can be part of an early warning system that contributes to anticipatory management in climate-impacted sectors by providing spatiotemporal information of climate-related anomalies in the near future. This study first examined the European Centre for Medium-Range Weather Forecasts (ECMWF) Seasonal Forecasting System, version 5 (SEAS5), seasonal forecast skill over mainland Southeast Asia (MSEA). We evaluated the SEAS5 skill of temperature and precipitation for 30 years (1985-2014) against two reference model datasets, WATCH Forcing Data ERA5 (WFDE5) and Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), using probabilistic forecast verification skill metrics at grid cells for each month. Subsequently, SEAS5 was used to force the Variable Infiltration Capacity (VIC) hydrological model to predict stream-flow. These hydrological results were compared against the WFDE5-driven streamflow reanalysis and observed station data, using the same probabilistic skill statistics. The results show a prediction potential for temperature beyond 2 months in advance. The skills of precipitation and streamflow forecasting are limited to the first month, with a strong seasonal and regional dependence. The model chain exhibits strong forecast skill during the premonsoon (April-May) and postmonsoon (October-November), which are arguably the greatest practical utility period. Conversely, low skill is observed during the monsoon season (June-August). In eastern and southern MSEA, i.e., in eastern Thailand, Cambodia, Vietnam, and Malaysia, considerable skill levels are found. Year-to-year precipitation tercile plots demonstrate skill in predicting anomalous seasonal conditions associated with El Ni & ntilde;o-Southern Oscillation (ENSO). Overall, SEAS5 and derived hydrological forecasts show useful skill that can potentially be used for hydrological and agricultural anticipatory management. SIGNIFICANT STATEMENT: This study aims to evaluate the ensemble seasonal forecasting [European Centre for Medium-Range Weather Forecasts (ECMWF) Seasonal Forecasting System, version 5 (SEAS5)] performance in mainland Southeast Asia. We analyzed monthly precipitation and temperature forecasts and assessed how their skill integrates into hydrological anomalies, as simulated by the Variable Infiltration Capacity (VIC) model. Our findings present that SEAS5 forecasting performance is regionally and temporally dependent. SEAS5 has the potential to apply temperature forecasting for more than 2-month lead time for anticipatory strategic planning. Skillful 1-month lead time of precipitation and hydrology during key months offers valuable guidance for short-term operational planning. Additionally, the analysis presents a good skill for detecting anomalous precipitation during El Ni & ntilde;o-Southern Oscillation (ENSO) periods. The study highlights the potential of VIC-SEAS5 for the development of operational early warning systems.

The potential use of European Centre for Medium-Range Weather Forecast (ECMWF) ensemble prediction system SEAS5 over Mainland Southeast Asia was evaluated. The evaluation spans 30 years (1985–2014), examining SEAS5's skill in predicting temperature and precipitation. Subsequently, SEAS5 data was used to force the Variable Infiltration Capacity (VIC) hydrological model for runoff and streamflow forecasts, as well as the WOrld FOod Studies (WOFOST) crop model for rice production forecasts. These hydrological and agricultural results were compared against the WFDE5-driven reanalysis using verification skill metrics at grid cells for each month. Furthermore, the hydrological results were compared against observed station data. The reanalysis of rice yield was also compared against FAO observations, but proved inconclusive. The findings reveal promising predictive capabilities for temperature beyond a 2-month forecast, while the skill of precipitation and streamflow forecasts extend to a 1-month. Noteworthy, strong seasonal and regional dependence occurs, with high forecast skills during the pre-monsoon (April–May) and post-monsoon (October–November). Year–to–year precipitation tercile plots highlight skill in predicting the anomalous seasonal conditions associated with ENSO. The significant streamflow skill at each initiation month and lead time corresponds to the forecasting skill of meteorological variables. Nevertheless, it is important to note that the skill level of discharge and runoff forecasts is generally lower compared to the skill in temperature and precipitation. For the rice prediction, SEAS5 exhibits high performance at the beginning of the rainy season, where strong seasonal climate predictions are observed. The model shows the ability to capture anomalous rice yields and consistent accuracy throughout a 1-month to 3-month forecast. However, limitations in skill are evident when rice planting times are delayed by one or two months during the rainy season, as well as when planting in the dry season. SEAS5 shows useful skills that can potentially be used for hydrological and agricultural anticipatory management. The results could already support an initial step to come to potential anticipatory (agro-)hydrological management and could be utilised as an input for an early warning system in various sectors.

Most existed crop modelling studies are mainly cereal crops. Vegetables, the most economical and nutrient-dense crops, recieves insufficient attention, particularly on nutrient-uptake predictions. In open-field vegetable systems with shallower roots, shorter lifespan, and higher nutrient requirements, it is even more challenge to minize water pollution from fertilizers. To ensure both food and environment security, there is an urgent need of precise vegetable models to optimize productivity against fertilizer usage.We adapted the WOrld FOod STudies (WOFOST) crop growth simulation model for chili pepper (Capsicum annuum L.) and Chinese cabbage (Brassica rapa L.) to support better fertilizer management under various climate and soil conditions. We conducted field experiments with six various fertilizer strategies (etc., mixed synthetic and organic fertilizers, denitrification products, and slow-control-release fertilizers) in southwestern China from 2019 to 2021. In total about 20 parameters relevant to physiological development, dry matter accumulation, photosynthesis, and nutrient uptake were measured and used in model adaptation.Our study shows that it is possible to model chili pepper’s growth without changing much from the WOFOST-generic model structure. We provide solutions by adapting user-defined developmental stages to mimic the growth from transplanting to fruiting and subsequently ripeness. As for WOFOST-Chinese cabbage, we further modify the phenological module to mimic the special vernalization habits of Chinese cabbage. Additionally, we design a new data re-analyzation method for accurate biomass partitioning predictions. Overall, both WOFOST-Chili and WOFOST-Chinese cabbage models show good model performance on biomass assimilation (rRMSE = 0.23/0.17 for chili/cabbage leaf dry weight; rRMSE = 0.06/0.17 for chili/cabbage storage organ dry weight) and nutrient uptake (rRMSE = 0.46/0.29 for chili/cabbage leaf N amount; rRMSE = 0.12/0.41 for chili/cabbage storage organ N amount). Besides, an improved leaf area index (LAI) simulation is found in WOFOST-Chinese cabbage (rRMSE = 0.11) than WOFOST-Chili (rRMSE = 0.76).These findings improve our understanding of yield-nutrient interactions within crop models, provide insights on expanding application of original-designed-for-field crop models to different vegetable versions, also call for a refined dynamic nutrient simulation flow within soil module to evaluate mitigation effect of expanded fertilizer strategies under climate change.

Weather and Climate Information Services developed for agriculture often only provide scientific weather and climate forecasts on various timescales. Yet, local forecasts derived from indigenous knowledge and soil moisture information are still missing. In this study, we evaluate the implementation of the DROP app, a hydroclimate information service, offering both local (LF) and scientific rainfall forecasts (SF) and soil moisture forecasts, that was designed with and for smallholder farmers working on rainfed agriculture in northern Ghana. Results of the forecast assessment show that the LF generates a high probability of rain detection (POD), with a minimum value of 0.7. The hybrid forecast (HF) that integrates the SF and LF yields the highest POD value of 0.9 compared to others. However, the hybrid system also has a high number of false alarms which results in an overall lower forecast performance of HF compared to SF. Using forecasts obtained from the app, farmers adjusted their farming activities, such as time of sowing, planting and weeding dates, fertilizer and herbicide application, and harvesting. Although some limitations exist, the DROP app has potential to deliver actionable knowledge for climate-smart farm decision-making and thus, facilitate effective agriculture management.

CONTEXT: Chinese cabbage (Brassica rapa L. ssp. Pekinensis) is a leading open-field leafy vegetable crop in China, with known shallow roots, short lifespan and high nutritional content. To ensure its precise field management in a changing world, an accurate leafy vegetable model is urgently needed to enhance decision-making. OBJECTIVE: We modified the WOrld FOod STudies (WOFOST) crop model to create a version specifically for Chinese cabbage (WOFOST-Chinese cabbage) that quantifies its daily dynamic growth based on fertilizer management, climate, and soil conditions. METHODS: We conducted field experiments in southwestern China from 2019 to 2021 to collect extensive site-specific soil data and specific crop data relevant to eco-physiological processes of Chinese cabbage. We used 2021 field trail observations under optimal growing conditions to parameterize and calibrate the model with integrated image data in a stepwise procedure. The datasets from 2019 and 2020 were used for model validation, while the no-fertilizer dataset was used to test model performance under nutrient-limited conditions. RESULTS AND CONCLUSIONS: Overall, the developed WOFOST-Chinese cabbage is reliable in simulating biomass (rRMSE = 0.13 for total aboveground production), leaf area index (rRMSE = 0.34), and nutrient uptake (rRMSE = 0.15). Additionally, model robustness is increased by the sensitivity analysis of biomass-relevant and nutrient-uptake-relevant parameters. However, this good performance is constrained by a less effective differentiation between functional and non-functional leaves. Besides, model validation provides further improvement directions of model structure under nutrient-limited conditions. SIGNIFICANCE: This study provides the first set of comprehensively calibrated parameters for applying WOFOST to Chinese cabbage in open cropland. The outcome of this study will aid in fertilizer management for open-field Chinese cabbage production across diverse climate and soil conditions. Additionally, it will contribute a new model for comparative multi-model studies of leafy vegetables, addressing climate change impacts at regional, national, and global scales.

Rainfed agriculture constitutes the backbone of the economy in many regions of the Global South. Historically, smallholder farmers used their local knowledge to forecast the weather. However, with the increase in climatic variability, they can no longer solely rely on their experience to accurately forecast the weather. DROP App is a hydro-climate information service developed through a co-production approach to address the weather and climate information needs of farmers. The app gathers weather forecast from both local farmers and scientific sources, and presents this information to users to enable them to make informed decisions regarding agriculture. To test its proof-of-concept, the DROP app was implemented in five rice communities in northern Ghana. The app was introduced to farmers, who received training on it use, as well as built their capacity on weather and climate-related phenomena and the use of Information and Communication Technologies (ICT). Following the end of the cropping season, farmers evaluated the app and the results revealed that co-production of information played a crucial role to its adoption in relation to other similar platforms. Farmers consider the app as a relatively accurate and reliable source of information for planning agricultural activities. Using forecasts obtained from the app, farmers adjusted their farming activities, such as time of sowing, planting and weeding dates, fertilizer and herbicide application, and harvesting. They additionally demonstrated a significant level of knowledge about weather phenomena as a result to their engagement and capacity building. Although some limitations exist, the DROP app has potential to deliver actionable knowledge for climate-smart farm decision-making and thus, facilitate effective agriculture management.

Climate change contributes to a rise in salinity levels in the coastal regions of Bangladesh, notably impacting agricultural productivity. Therefore, crop-level adaptation strategies against salinity are crucial to increase productivity. In this study, our objective is to explore farm-level adaptation to climate change-induced salinity in the south-central coastal area of Bangladesh, considering the farmers' perception of climate change and salinity ingress as well as their adaptation strategies. Subsequently, we compare our findings with climatic and salinity data acquired from secondary sources. The study area was partitioned into three distinct zones delineated by proximity to the coastline, and primary data was collected from 475 households within these salinity zones using a multistage random sampling technique. Data collection was carried out using semi-structured questionnaires, which had been pretested on the respondents' perceptions for validity and reliability. The results indicate that while farmers possess an awareness of long-term alterations in climatic conditions, such as changes in temperature and precipitation, they often fail to attribute these changes to climate change explicitly. They could perceive changes in salinity over time but had difficulty perceiving cyclonic events. Farmers realize the risks posed by hydroclimatic variability and extreme weather events. Interestingly, while farmers may not be taking explicit measures to address perceived climatic changes, we discern that they are indeed modifying their agricultural and farming practices, such as fertilizer application, land leveling, and freshwater application. Traditional farming systems increase vulnerability and reduce persistence. In pursuit of enhanced resilience, households must implement various adaptation strategies for resilient farming practices. Moreover, our findings indicate that farmers are interested in adopting diverse adaptation strategies that require technical and financial support, particularly for the smallholders. In conclusion, this research provides valuable information for formulating climate change adaptation policies in the context of coastal agriculture in Bangladesh.

Increasing global food demand will require more food production1 without further exceeding the planetary boundaries2 while simultaneously adapting to climate change3. We used an ensemble of wheat simulation models with improved sink and source traits from the highest-yielding wheat genotypes4 to quantify potential yield gains and associated nitrogen requirements. This was explored for current and climate change scenarios across representative sites of major world wheat producing regions. The improved sink and source traits increased yield by 16% with current nitrogen 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 high warming climate scenario (RCP8.5), 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 nitrogen availability and nitrogen use efficiency, along with yield potential. Martre et al. found that to achieve the full yield potential of improved wheat varieties, nitrogen fertilizer use would need to increase fourfold over current use, which would unavoidably increase the environmental impacts of wheat production.

Abstract The impact of droughts and heatwaves on agriculture losses has been exacerbated by the occurrence of compound and cascading events. Here we present a study that evaluates the impact of these events both as singly and as compound and cascading on maize yield in Sinaloa Mexico from 1990 to 2022, using the WOFOST crop model. Drought and heatwave events were identified using the Standardized Precipitation Index and threshold method, respectively. Results show that yield reduction (25%) is found during extreme drought events, emphasizing the vulnerability of maize farming to unfavorable drought conditions. While heatwaves alone did not show a significant impact on maize yields, the compound and cascading droughts and heatwaves amplified the loss of maize yields by up to 44% compared to normal conditions. This study highlights the need for adaptive strategies in agriculture to sustain food security during extreme events, especially in the context of multi hazard framework.

This study evaluates the potential use of European Centre for Medium-Range Weather Forecast (ECMWF) ensemble prediction system-5 (SEAS5) to force the WOrld FOod Studies crop model (WOFOST) for predicting rice production in Mainland Southeast Asia (MSEA). The assessment covers a 30-year period (1985–2014) by comparing yield using the SEAS5 weather data with benchmark yield simulation based on reference climate data from WATCH Forcing Data ERA-5 (WFDE5). Two cultivation simulations were used: a water- and nutrient-limited (WN-limited) simulation representing cultivation in the rainfed area, and a nutrient-limited (N-limited) simulation representing cultivation in the irrigation area. SEAS5 shows consistent yield prediction skills between the two simulations, suggesting that water availability is not the primary factor influencing yield forecasting performance. Therefore, rainfall forecasting skill is not the main source of yield prediction skill. However, other variables, especially temperature, influence the yield prediction skill. SEAS5 exhibits high performance in predicting rice yield from early planting in the main season, with the ability to capture anomalous rice yields and consistent accuracy with lead times of one to three months . SEAS5 skills are limited when the rice planting times are delayed by one or two months during the main season. Similarly, limited skill is observed in the dry season. SEAS5 demonstrate reliable performance for crop yield prediction at the beginning of the main season, which is potentially valuable for national-level strategies and planning.

The potential use of European Centre for Medium-Range Weather Forecast (ECMWF) ensemble prediction system SEAS5 over Mainland Southeast Asia was evaluated. The evaluation spans 30 years (1985–2014), examining SEAS5's skill in predicting temperature and precipitation. Subsequently, SEAS5 data was used to force the Variable Infiltration Capacity (VIC) hydrological model for runoff and streamflow forecasts, as well as the WOrld FOod Studies (WOFOST) crop model for rice production forecasts. These hydrological and agricultural results were compared against the WFDE5-driven reanalysis using verification skill metrics at grid cells for each month. Furthermore, the hydrological results were compared against observed station data. The reanalysis of rice yield was also compared against FAO observations, but proved inconclusive. The findings reveal promising predictive capabilities for temperature beyond a 2-month forecast, while the skill of precipitation and streamflow forecasts extend to a 1-month. Noteworthy, strong seasonal and regional dependence occurs, with high forecast skills during the pre-monsoon (April–May) and post-monsoon (October–November). Year–to–year precipitation tercile plots highlight skill in predicting the anomalous seasonal conditions associated with ENSO. The significant streamflow skill at each initiation month and lead time corresponds to the forecasting skill of meteorological variables. Nevertheless, it is important to note that the skill level of discharge and runoff forecasts is generally lower compared to the skill in temperature and precipitation. For the rice prediction, SEAS5 exhibits high performance at the beginning of the rainy season, where strong seasonal climate predictions are observed. The model shows the ability to capture anomalous rice yields and consistent accuracy throughout a 1-month to 3-month forecast. However, limitations in skill are evident when rice planting times are delayed by one or two months during the rainy season, as well as when planting in the dry season. SEAS5 shows useful skills that can potentially be used for hydrological and agricultural anticipatory management. The results could already support an initial step to come to potential anticipatory (agro-)hydrological management and could be utilised as an input for an early warning system in various sectors.

CONTEXT: Crop models are essential tools for assessing the impact of climate change on national or regional agricultural production. Starting from meteorology, soil and crop management, fertilization and irrigation practices, they predict the yield of specific crop varieties. For long term assessments, climate models are the source of primary information. To make climate model results usable in a specific time frame context, bias adjustment (BA) is required. In fact, climate models tend to deviate from day-to-day values of the physical parameters while conserving the climate variability signal. BA brings the climatic signal to the actual values observed in a specific location and period, and to be representative of a specific period in absolute terms. BA techniques come in different flavours. The broadest categorization is univariate and multivariate methods. Multivariate methods adjust the variables considering possible cross -correlations while univariate methods treat the variables one by one without accounting for possible dependence on one another. OBJECTIVE: The hypothesis tested in this paper is that since crop models require as input climate variables that are in most of the cases cross -correlated, the multi-variate bias adjustment of the latter is likely to improve performance compared to univariate bias adjusted climate model results. METHODS: To verify this hypothesis, 14 BA methods were applied to 9 variables from 8 climate models at 21 locations across Europe and Northern Africa for a period of 5 years. Twelve crop models, from the AgMIP Wheat community, were run using the climate model results. All crop models, except one, were restarted at every growing season. The crop models were also run using the AgMERRA re -analysis. The latter were used as reference to compare the results when using the other climate models treated with the various sets of biasadjustment methods. RESULTS AND CONCLUSIONS: The results show that multivariate BA treatment should be preferred to univariate ones. The error obtained by comparing crop simulation obtained with AgMERRA with those obtained with multivariate bias -adjusted climate prediction is systematically lower. The error reduction varies as a function of the variable, the location, the crop model, and the climate model though the tendency is for smaller errors when multivariate methods are used to treat the latter. The results are attributed to the nature of crop models and the fact that multivariate methods consider more adequately the correlation existing between the meteorological variables. SIGNIFICANCE: The study shows the importance of considering the nature of a model and the selection of input data that best suited to the former. In this case the improvements produced when using multivariate data appears to be significant especially in the light of the variety of crop models used and the similar response obtained and it is therefore recommended.

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.

CONTEXT: Chili pepper (Capsicum annuum L.) is one of the most economically and agriculturally important, and relatively nutrient-dense, vegetables that has, to date, received little attention in model studies relevant to dry matter production and nutrient-uptake predictions. There is an urgent need for models to analyse the potential impacts of climate change, as well as responsive adaptation options, while simultaneously optimising productivity against fertilizer use to reduce nutrient pollution.OBJECTIVE: We adapted the WOrld FOod STudies (WOFOST) crop growth simulation model for chili pepper (WOFOST-Chili) to quantify dry matter production as a function of fertilizer management, climate, and soil conditions.METHODS: We used 2021 field trial data under optimal growing conditions in southwestern China to parameterise and calibrate WOFOST-Chili. The model was tested under no-fertilizer conditions and further validated with data from 2019 and 2020. In addition, a sensitivity analysis over the three consecutive years was performed. RESULTS AND CONCLUSIONS: Overall, the developed WOFOST-Chili model shows good simulations of chili growth dynamics in response to nitrogen (N) fertilization, both on biomass assimilation (rRMSE = 0.07 for total aboveground production; rRMSE = 0.06 for fruit dry weight) and nutrient uptake (rRMSE = 0.46 for leaf N amount; rRMSE = 0.29 for fruit N amount). Additionally, model robustness is increased by the sensitivity analysis of crop initialisation (e.g., biomass and leaf area index at transplanting) and climate-dependent parameters (e.g., temperature sums determining development rate and light use efficiency determining productivity), with the resulting wider simulation range covering more observations. This good performance is only limited by a weaker leaf area index (LAI) simulation (rRMSE = 0.76), which is partially attributed measurement limitations (e.g., equipment, weather conditions and labour/time constraints). Model validation confirms good performance under potential conditions, which is slightly reduced under nutrient-limited conditions.SIGNIFICANCE: These findings improve our understanding of yield-nutrient interactions of chili pepper. They provide insight on expanding the application of crop models originally designed for cereals to non-Gramineae vegetables, while calling for future improvement of model accuracy under different fertilizer application strategies.

The data set contains a portion of the International Heat Stress Genotype Experiment (IHSGE) data used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat crop models and quantify the impact of heat on global wheat yield productivity. It includes two spring wheat cultivars grown during two consecutive winter cropping cycles at hot, irrigated, and low latitude sites in Mexico (Ciudad Obregon and Tlaltizapan), Egypt (Aswan), India (Dharwar), the Sudan (Wad Medani), and Bangladesh (Dinajpur). Experiments in Mexico included normal (November-December) and late (January-March) sowing dates. Data include local daily weather data, soil characteristics and initial soil conditions, crop measurements (anthesis and maturity dates, anthesis and final total above ground biomass, final grain yields and yields components), and cultivar information. Simulations include both daily in-season and end-of-season results from 30 wheat models. All data are available via DOI 10.7910/DVN/CJJBSR.

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.