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Climate change is expected to have a major effect on crop production in sub‐Saharan Africa. Crop models can help to guide crop management under future climate. The objective of the study was to investigate the possible effects of climate change on Ethiopian barley (Hordeum vulgare L.) production using the Decision Support System for Agrotechnology Transfer (DSSAT)‐Crop Environment Resource Synthesis (CERES)‐Barley model. The study included field data of two barley cultivars (Traveller and EH‐1493) and four climate study areas in Ethiopia over 5 yr. Climate change scenarios were set up over 60 yr using representative concentration pathways (RCP; RCP4.5 and RCP8.5) and five global climate models (GCM). The model results indicated that the prediction of days to anthesis and maturity, as well as final grain yield, was highly accurate for cultivar Traveller with normalized RMSE (nRMSE) of 2, 1, and 12%, respectively, and for cultivar EH‐1493 with nRMSE of 2, 4, and 11%. A consistent increase in average temperature up to 5 °C and a mixed pattern of rainfall (‐61 to +86%) were projected. Yield simulations showed a potential reduction in yield up to 98% for cultivar Traveller and 63% for cultivar EH‐1493 in the future. Within a sensitivity analysis, different sowing dates, sowing densities, and fertilizer rates were tested as potential adaptation approaches to climate change. The negative effects of climate change could be mitigated by early sowing, with an increased sowing density of 25% and fertilizer rate of 50% more than what is recommended. Overall, the results indicated the ability of the CERES‐Barley model to evaluate climate change effects and adaptation options on rainfed barley production in Ethiopia. Core Ideas Effects of climate change on Ethiopian barley production were simulated using the CERES‐Barley model. The model results indicated that the prediction of phenology and yield was highly accurate. A consistent increase in average temperature and a mixed pattern of rainfall were projected. The model estimate showed reduction of yield up to 98% for cultivar Traveller and 63% for cultivar EH‐1493. Adaptation measures are needed to mitigate the negative effect of climate change on barley.

Climate change and variability are major threats to crop productivity. Crop models are being used worldwide for decision support system for crop management under changing climatic scenarios. Two-year field experiments were conducted at the Water Management Research Center (WMRC), University of Agriculture Faisalabad, Pakistan, to evaluate the application of CERES-Maize model for climate variability assessment under semi-arid environment. Experimental treatments included four sowing dates (27 January, 16 February, 8 March, and 28 March) with three maize hybrids (Pioneer-1543, Mosanto-DK6103, Syngenta-NK8711), adopted at farmer fields in the region. Model was calibrated with each hybrid independently using data of best sowing date (27 January) during the year 2015 and then evaluated with the data of 2016 and remaining sowing dates. Performance of model was evaluated by statistical indices. Model showed reliable information with phenological stages. Model predicted days to anthesis and maturity with lower RMSE (< 2 days) during both years. Model prediction for biological yield and grain yield were reasonably good with RMSE values of 963 and 451 kg ha −1 , respectively. Model was further used to assess climate variability. Historical climate data (1980–2016) were used as input to simulate the yield for each year. Results showed that days to anthesis and maturity were negatively correlated with increase in temperature and coefficient of regression ranged from 0.63 to 0.85, while its values were 0.76 to 0.89 kg ha −1 for grain yield and biological yield, respectively. Sowing of maize hybrids (Pioneer-1543 and Mosanto-DK6103) can be recommended for the sowing on 17 January to 6 February at the farmer field for general cultivation in the region. Early sowing before 17 January should be avoided due to severe reduction in grain yield of all hybrids. A good calibrated CERES-Maize model can be used in decision-making for different management practices and assessment of climate variability in the region.

Core Ideas The CERES‐Maize model was applied to estimate the impacts of climate change under RCP scenarios and the effectiveness of three typical adaptation measures for maize production in Northeast China. Maize yield would decline under the future climatic conditions if no adaptation measures were adopted. Changing planting dates, switching to later‐maturing cultivars and breeding new cultivars could mitigate the negative impacts of climate change to varying degrees. Northeast China (NEC) is an important region for maize (Zea mays L.) production in China, and is the country's most significant commercial food base. However, NEC is also one of the areas that are most significantly affected by climate change in this country. Maize is sensitive to climatic changes, and to develop effective strategies for guaranteeing regional food security, it is essential to understand the mechanisms of the impacts of climate change and the effectiveness of adaptation measures in NEC. In this study, the Crop‐Environment Resource Synthesis (CERES)‐Maize v4.5 model, coupled with newly released data for Representative Concentration Pathway (RCP) scenarios, RCP4.5 and RCP8.5, was applied to simulate maize yields for future periods (2020s, 2050s, and 2080s) and to estimate the effect of CO2 fertilization and the effectiveness of three typical adaptation measures for maize production in NEC. The results indicated a trend of a continuing decline in maize yield for both RCP scenarios, and the decrease in maize yield under RCP8.5 was predicted to be greater than that under RCP4.5. The effect of CO2 fertilization was forecast to be too small to offset the negative impacts of climate change. Three adaptation measures—changing planting dates, switching to later‐maturing cultivars, and breeding new cultivars with high thermal time requirements—could mitigate negative climate change impacts to varying degrees; switching cultivars may exert the most significant effect on increasing maize yields.

Climate is one of the major factors affecting crop phenology and yield. In most previous studies, impacts of temperature (T) and rainfall (R) on crop development, growth, and yield were investigated, while the effect of wind speed (WS) has so far not been assessed. In this study, the influence of WS alteration on rainfed wheat production was evaluated in arid and semi-arid environments during a 25-year period in northeast Iran. In so doing, various climatic scenarios were defined using T, R, and WS changes, and then applied to the CERES-Wheat model included in DSSAT v4.7.5. The results showed that WS variation can alter total ET (planting to harvest) from −12.1 to +8.9%, aboveground biomass from −8.4 to +11.0%, water use efficiency from −13.4 to +19.7%, and grain yield from −11.2 to +15.3%. These changes were in many cases related to the climatic conditions. It was also revealed that in a greater amount of rainfall and shorter growing season (i.e., less drought stress), the WS variation had the stronger impact on total ET; while for aboveground biomass, water use efficiency, and grain yield, the greatest effect of WS variation was detected under the water scarcity conditions (i.e., low rainfall). The results demonstrate that wind speed needs to be better considered in climate change impact studies, in particular in water-scarce regions.

Meteorological variables are essential inputs for agricultural simulation models and the lack of measured data is a big challenge for the application of these models in many agricultural zones. Studies indicated that gridded meteorological datasets can be proper replacements for measured data. This paper aimed to examine a new gridded meteorological dataset namely CRU-JRA for crop modeling intents. The CRU-JRA is a 6-hourly dataset with a spatial resolution of 0.5° × 0.5° that was primarily constructed for modeling purposes. The CERES-Wheat model in the Decision Support System for Agrotechnology Transfer (DSSAT) was used for the simulation of irrigated and rainfed wheat production systems in Iran. Results showed that the CRU-JRA maximum and minimum temperature values had a relatively fine accuracy with a normalized root mean square error (NRMSE) of 14% for the simulated grain yield. The performance of the CRU-JRA solar radiation values for the simulation of grain yield was similar with a NRMSE of 14.4%. The weakest performance was found for the CRU-JRA precipitation values with a NRMSE of 18.9%. Overall, the CRU-JRA dataset performed comparatively acceptable and similar to existing gridded meteorological datasets for crop modeling purposes in the study area, however further calibrations can improve the accuracy of the next versions of this dataset. More research is necessary for the investigation of the CRU-JRA dataset for agricultural modeling purposes across diverse climates.

This study aimed to calibrate and compare the performances of the DSSAT-CERES and WOFOST models in predicting wheat phenology, growth, and grain yield in different sowing environments on the plains of northwest India. These models were calibrated and evaluated via independent field experiment datasets on wheat phenology, the leaf area index, and yield attributes recorded at Gurdaspur and Ludhiana in Punjab State, India, during the winter ( Rabi ) season of 2016–17. The factors of the experiments were two wheat cultivars (PBW 725 and PBW 677) and three seeding dates (5th November 20th November and 5th December). For the CERES model, the cultivar coefficient was derived via GENCALC along with a trial-and-error approach; for the WOFOST model, the coefficients were manually adjusted. The CERES and WOFOST models' performances were then assessed by computing several error indices that compare the observed and simulated crop parameters. The experimental values indicated that wheat cultivar, sowing time, and location significantly influenced wheat growth and yield. The results indicated that the CERES and WOFOST models could accurately predict wheat phenology, biomass, and harvest indices within 10% of the normalized root mean square error (NRMSE) while retaining the effects of different treatments. Furthermore, both models could estimate the grain yield with ≤ 6% NRMSE. According to the high correlation coefficient (r > 0.80; p < 0.001) and coefficient of determination (R 2  > 60%), the observed and simulated wheat growth and yield characteristics showed significant agreement under all testing conditions. Both models demonstrated acceptable accuracy in capturing wheat LAI temporal dynamics, although they exhibited notable variation in maximum leaf area index (LAI max ) estimation, with mean absolute percentage errors (MAPEs) ranging from 26 to 66%. Despite inherent model limitations, the demonstrated accuracy in simulating wheat phenological development and yield attributes supported the models' applicability for regional-scale wheat yield forecasting across diverse agronomic management practices and environmental conditions. A quantitative assessment of model performance metrics indicated that, compared with the CERES model, WOFOST demonstrated marginally superior accuracy in simulating wheat phenological development and yield parameters.

Climatic change effects on crop yields are expected to be crop‐ and site specific. Here, Decision Support System for Agrotechnology Transfer models were used to evaluate climatic change effects and mitigation strategies on maize (Zea mays L.) and soybean [Glycine max (L.) Merr.] yields in soils of the subtropical and semi‐arid region of Chaco. Simulations were performed for the DK747 and A8000 genotypes, calibrated for the CERES‐Maize model in a previous report and for the CROPGRO‐Soybean model in the present study, respectively. Both crops markedly differ in their response to climatic change and putative levels of atmospheric CO2 concentration. The observed significant reductions in maize yields in future climate scenarios (5–42% compared with the baseline, 1986–2010) were more associated with increased temperatures that shortened the crop cycle than with water stress. Delaying the sowing date is a feasible strategy to mitigate this effect. Projected temperature increases are expected to play a secondary role in determining soybean yields. Instead, water stress will continue to be an important constraint to soybean yield in the context of global warming, but this effect is strongly affected by rainfall regimes. Responses to raising CO2 levels were more pronounced in soybean (+10–40%) than in maize (+2–4%). Soil degradation exacerbated the negative effects of global warming on crop yields, especially on maize, which highlights the importance of soil conservation practices. The observed high interannual climatic variability and the different sensitivities of maize and soybean to climatic variables indicate that crop diversification would be the key to improve the resilience of the agrosystems under the future scenarios.

Climate change adversely affects food security all over the world, especially in developing countries where the increasing population is confronting food insecurity and malnutrition. Crop models can assist stakeholders for assessment of climate change in current and future agricultural production systems. The aim of this study was to use of system analysis approach through CSM-CERES-Millet model to quantify climate change and its impact on pearl millet under arid and semi-arid climatic conditions of Punjab, Pakistan. Calibration and evaluation of CERES-Millet were performed with the field observations for pearl millet hybrid 86M86. Mid-century (2040–2069) climate change scenarios for representative concentration pathway (RCP) 4.5 and RCP 8.5 were generated based on an ensemble of selected five general circulation models (GCMs). The model was calibrated with optimum treatment (15-cm plant spacing and 200 kg N ha −1 ) using field observations on phenology, growth and grain yield. Thereafter, pearl millet cultivar was evaluated with remaining treatments of plant spacing and nitrogen during 2015 and 2016 in Faisalabad and Layyah. The CERES-Millet model was calibrated very well and predicted the grain yield with 1.14% error. Model valuation results showed that there was a close agreement between the observed and simulated values of grain yield with RMSE ranging from 172 to 193 kg ha −1 . The results of future climate scenarios revealed that there would be an increase in T min (2.8 °C and 2.9 °C, respectively, for the semi-arid and arid environment) and T max (2.5 °C and 2.7 °C, respectively, for the semi-arid and arid environment) under RCP4.5. For RCP8.5, there would be an increase of 4 °C in T min for the semi-arid and arid environment and an increase of 3.7 °C and 3.9 °C in T max , respectively, for the semi-arid and arid environment. The impacts of climate changes showed that pearl millet yield would be reduced by 7 to 10% under RCPs 4.5 and 8.5 in Faisalabad and 10 to 13% in Layyah under RCP 4.5 and 8.5 for mid-century. So, CSM-CERES-Millet is a useful tool in assessing the climate change impacts.

The CERES-Maize model performance was investigated in simulating maize phenology, grain yield, soil–water, evapotranspiration, and water productivity under different irrigation and nitrogen (N) levels under a variable rate lateral (linear)-move sprinkler irrigation system. The irrigation levels were rainfed, full irrigation treatment (FIT) and 75% FIT. The N levels were 0, 84, 140, 196, and 252 kg/ha. The field experiment was conducted in the form of split plots with the irrigation levels as the main treatment and N levels as a sub-main treatment. The root mean squared error (RMSE), normalized RMSE (RMSEn), R2, T test, and model prediction error (Pe) statistics were used to evaluate the performance and accuracy of the model. Calibration of the model was done using the data of 2011 and 2012 and validation of the model was conducted for 2013 and 2014 by considering days after planting to flowering (DAPF), days after planting to maturity (DAPM), grain yield, crop evapotranspiration (ETc), water productivity (WP), and soil water content (SWC). The DAPF simulations based on the average values of Pe (0%), RMSE (2 days), and RMSEn (3%) and the DAPM simulation results based on the average values of Pe (2%), RMSE (4 days), and RMSEn (3%) showed that the model had an acceptable accuracy. In calibration years, RMSE, RMSEn, and R2, respectively, were 0.57 ton/ha, 5%, and 0.91; and in validation years, the same statistics, respectively, were 0.86 ton/ha, 10% and 0.94, indicating good performance of the model in estimating the grain yield. Good accuracy was observed in the estimation of ETc and WP. In most cases, the model accuracy was greatest for 75% FIT and FIT treatments than the stressed conditions in the rainfed treatment. The model accuracy can be enhanced by improving the model coefficients in response to low levels of water and N supply. R2 values obtained in rainfed (0.83), 75% FIT (0.81) and FIT (0.67) treatments in calibration years and R2 values in rainfed (0.75), 75% FIT (0.77) and FIT (0.86) treatments in validation years showed that the model predicted the SWC relatively well. The comparison of ETc values with respect to N levels showed that there was no considerable difference between levels of N applications impact(s) on the ETc magnitude in the rainfed treatment. Comparison of different levels of N in rainfed and FIT showed that the application of 252 kg/ha of N resulted in 2.37 kg/m3 and 2.56 kg/m3 of WP, respectively, which was significantly different from other levels of N fertilizer applications. In general, CERES-Maize model can be a useful tool for predicting plant phenology, grain yield, ETc, WP, and SWC for the conditions similar to those presented in this research. The CERES-Maize model can provide valuable data and information for sustainable maize production by examining the long-term grain yield and WP, which can be beneficial to growers, advisors, and stakeholders to enhance the maize production efficiency by accounting for irrigation and N management strategies.

Several drought indices are available to compute the degree of drought to which crops are exposed. They vary in complexity, generality, and the adequacy with which they represent processes in the soil, plant, and atmosphere. Agricultural Reference Index for Drought (ARID) was developed as a reference index to approximate the water stress factor that is used to affect growth and other physiological processes in crop simulation models. Using RMSE, Willmott d index, and modeling efficiency (ME) as performance measures, ARID was evaluated using soil water contents in the root zone measured daily in two grass fields in Florida. The ability of ARID was assessed through comparison with the water deficit index (WSPD) of the Decision Support System for Agrotechnology Transfer (DSSAT) CERES-Maize model. Seven other drought indices were compared with WSPD to identify the most appropriate agricultural drought index. Values of each index were computed for full canopy cover periods of maize (Zea mays L.) crops for 16 locations in the U.S. Southeast. Using periodic values, the performance of each index was assessed in terms of its correlation (r) with and departure from WSPD. The ARID reasonably predicted soil water contents (RMSE = 0.01-0.019, d index = 0.92-0.94, ME = 0.66-0.73) and adequately approximated WSPD (r = 0.90, RMSE = 0.15). Among the indices compared, ARID mimicked WSPD the most closely (RMSE smaller by 1-83%, r larger by 1-630%) and captured weather fluctuation effects the most accurately. Results indicated that ARID may be used as a simple index for quantifying drought and its effects on crop yields.