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Brassica carinata (carinata), a non‐food oilseed feedstock mainly used for biofuel, is a relatively new alternative winter crop in the southeastern (SE) United States (US). However, there are limited N rate and N application timing data available at the regional scale. These data are needed to expand production in the SE US. An N rate study was conducted during the winter–spring growing seasons during 2017–2018 and 2018–2019 in Florida, US, and at three locations during 2018–2019 in Georgia, US, to quantify the effects of N rate (0, 45, 90, 134, and 179 kg N ha−1) on carinata nutrient uptake, biomass, seed yield, and seed chemical composition. Seed yield showed a linear response up to 134 kg N ha−1. Seed protein and glucosinolate concentrations decreased from 0 to 90 kg N ha−1, then increased from 90 to 179 kg N ha−1. Seed oil concentration was inversely related to seed protein concentration. A two‐factor N application timing study (4 N application timing: at‐plant, pre‐bolting, at‐plant + pre‐bolting, at‐plant + pre‐bolting + bolting × 4 N rates: 0, 45, 90, and 134 kg N ha−1) was conducted in Georgia, US, over three site‐years to quantify the effect of N application timing on yield and agronomic and economic optimum N rates (AONR and EONR, respectively). All split applications increased AONR by at least 10 kg N ha−1 compared to a single at‐plant application. A two‐split N application was more profitable than either a single N application or a three‐split N application based on marginal return. A two‐way split application (at‐plant + pre‐bolting) at 134 kg N ha−1 is recommended to optimize yield and economical production. Based on uncertainty analyses, the 50% credible interval of EONR occurred between 116 and 152 kg N ha−1, with a median estimate at 130 kg N ha−1.

Carinata (Brassica carinata) is an oilseed crop which, because of its non‐edible oil composition and favorable fatty acid profile, is proposed as a “green” sustainable aviation fuel. It can be grown as a winter crop in the southeastern USA or as a summer annual crop in northern latitudes. No crop models exist for carinata because it is a relatively new crop. The CROPGRO model is a mechanistic crop simulation of daily crop growth and development as a function of daily weather, soil properties, crop management, and species parameters. We adapted the CROPGRO model to simulate carinata based on growth analysis data collected over two seasons at three sites: Quincy, FL, Jay, FL, and Shorter, AL. The adaptation process required literature knowledge as well as optimization against field observations. The parameterization of model sensitivities to climatic factors is presented. The adapted model gave good simulations of carinata growth dynamics compared to observed growth during different seasons and locations and in response to N fertilization. While additional testing is appropriate, the model is sufficiently ready to be used for various applications. An example application is presented for the effect of sowing date on carinata yield and maturity over long‐term weather in the Southeastern USA.

The environmental consequences of using nonrenewable fossil fuels have motivated a global quest for sustainable alternatives from renewable sources. Carinata has been developed as a low carbon intensity, non‐food oilseed biomolecular platform to produce advanced drop‐in renewable fuels, meal, and co‐products. The crop is widely adaptable to grow in the humid subtropical and humid continental climatic regions of Asia, Africa, North America, South America, Europe, and Australia as a spring or winter crop. Carinata is heat tolerant, resistant to diseases and seed shattering with lower water‐use requirements than other oilseed brassicas. Adopting carinata in double‐cropping systems would require continuing research to integrate crop biology with agronomy, to understand growth and development and its interaction with agricultural inputs and management. Site‐specific best management agronomic practices and crop improvement research to develop frost‐tolerant, early‐maturing, nutrient use‐efficient, and high yielding varieties with desirable oil content and fatty acid profile will enhance the crop's adaptability and economic viability. The exploitation of intra‐ and interspecific and intra‐ and intergeneric diversity will further enhance carinata productivity and resistance to biotic and abiotic stresses. This review attempts to present a comprehensive description of carinata's biology, beginning with its origin and current state of distribution, availability of genetic and genomic resources, and a discussion of its morphology, phenology, and reproduction. A detailed analysis of the agronomy of the crop, including planting and germination and management practices, is presented in the context of crop growth and development. This will facilitate global adoption, sustainable production, and commercialization of carinata as a dedicated biofuel oilseed crop in diverse cropping systems and growing regions of the world, including the Southeast United States.

Aflatoxin in peanut (Arachis hypogaea L.) and other crops can negatively affect human health, especially in countries where regulatory agencies do not have limits on aflatoxin entering the food supply chain. While considerable research has been conducted addressing aflatoxin contamination in peanut at individual steps in the supply chain, studies that quantify aflatoxin contamination following combinations of interventions to crop management, drying, and storage are limited. Research was conducted during 2016 and 2017 in two villages in southern Ghana to follow aflatoxin contamination along the supply chain and to compare improved practices with traditional farmer practices used by smallholders. The farmer practice of only a single weeding was compared with improved practices during the growing season up to harvest that included applying local soaps to suppress aphids (Aphis gossypii Golver) that transmit peanut rosette virus disease (Umbravirus: Tombusviridaee), one additional weeding, and calcium applied at pegging. The improved practice for drying included placing pods removed from plants onto tarps compared with the traditional practice of drying on the ground. Storing peanut for four months in hermetically-sealed bags was the improved practice compared with storing in traditional poly bags. All improved practices individually resulted in lower aflatoxin contamination as compared to the farmer practices. While aflatoxin levels were very low (

Continuous monitoring of soil water content is a crucial element for sustainable agricultural water management. The goal of this study was to use the Cropping System Model (CSM)-CROPGRO-Soybean model in conjunction with field data to determine the impact of different irrigation regimes, soil texture, and tillage practices on soybean [Glycine max (L.) Merr.] growth, development, and yield for tropical conditions. Field experiments were conducted at two sites: (i) Piracicaba with conventional tillage (PI-1, season 2016–2017), and no-tillage practices (PI-2, season 2017–2018), where the experiments were irrigated with full water requirements; and (ii) Teresina under conventional tillage (season 2019) with two irrigation treatments of full (TE-1) and 50% (TE-2) water requirements. Soil water content was measured for all experiments using an electromagnetic probe installed at several depths. The results showed that the model was able to simulate soybean growth and development for the different sites, with a very good agreement ( D -statistic > 0.8) between the simulated and observed data. In addition, the soil water content was simulated with satisfactory accuracy ( D -statistic > 0.5). Following model evaluation, long-term hypothetical scenarios for different soil tillage practices and water management regimes were simulated for Piracicaba and Teresina sites. The results showed that the use of no-tillage could reduce the average amount of irrigation in Piracicaba by 30% and in Teresina by 17%, achieving the same yield level as conventional tillage. Thus, the CSM-CROPGRO-Soybean can be used as a tool for determining optimum water management practices for tropical environments.

International audience; Smallholder farmers in sub-Saharan Africa (SSA) currently grow rainfed maize with limited inputs including fertilizer. Climate change may exacerbate current production constraints. Crop models can help quantify the potential impact of climate change on maize yields, but a comprehensive multimodel assessment of simulation accuracy and uncertainty in these low-input systems is currently lacking. We evaluated the impact of varying [CO2], temperature and rainfall conditions on maize yield, for different nitrogen (N) inputs (0, 80, 160 kg N/ha) for five environments in SSA, including cool subhumid Ethiopia, cool semi-arid Rwanda, hot subhumid Ghana and hot semi-arid Mali and Benin using an ensemble of 25 maize models. Models were calibrated with measured grain yield, plant biomass, plant N, leaf area index, harvest index and in-season soil water content from 2-year experiments in each country to assess their ability to simulate observed yield. Simulated responses to climate change factors were explored and compared between models. Calibrated models reproduced measured grain yield variations well with average relative root mean square error of 26%, although uncertainty in model prediction was substantial (CV = 28%). Model ensembles gave greater accuracy than any model taken at random. Nitrogen fertilization controlled the response to variations in [CO2], temperature and rainfall. Without N fertilizer input, maize (a) benefited less from an increase in atmospheric [CO2]; (b) was less affected by higher temperature or decreasing rainfall; and (c) was more affected by increased rainfall because N leaching was more critical. The model intercomparison revealed that simulation of daily soil N supply and N leaching plays a crucial role in simulating climate change impacts for low-input systems. Climate change and N input interactions have strong implications for the design of robust adaptation approaches across SSA, because the impact of climate change in low input systems will be modified if farmers intensify maize production with balanced nutrient management.

Multi-genotype multi-environment trials, associated with characterization of the environment, marker information for the genotypes and measurements of the phenotypic traits of interest can potentially provide the basis for models to predict the behavior of untested genotypes in new environments. However, there is as yet no clear indication of the best form of such models, nor how to parameterize them. The purpose of this study was to propose and test an approach to crop-QTL modeling, applied to prediction of time to flowering in common bean (Phaseolus vulgaris), which avoids the pitfall of estimating separately the parameters for each genotype. The environmental model is a dynamic model with development rates that depend on daily temperature and day length. Three of the model parameters are expressed as linear functions of the QTLs for time to flowering, resulting in a model that combines environmental variables and QTLs. An innovative approach to parameter estimation is proposed, based on least squares, which makes it quite easy to estimate all the parameters of this model simultaneously, using all the data. The parameterized model explains most of the genotypic and environmental variability in the data, and 47% of the genotype by environment (GxE) interaction. Cross validation shows that the model extrapolates well to new genotypes in the same environments as those of the data, and also to new environments if they are similar in terms of temperature and photoperiod to those in the training data.

Crop models are increasingly being used as tools to simulate climate change effects or effects of virtual heat-tolerant cultivars; therefore it is important that upper temperature thresholds for seed-set, seed growth, phenology, and other processes affecting yield be developed and parameterized from elevated temperature experiments whether field or controlled-environment chambers. In this paper, we describe the status of crop models for dry bean (Phaseolus vulgaris L.), peanut (Arachis hypogaea L.), soybean (Glycine max L.), chickpea (Cicer arietinum L.), sorghum (Sorghum bicolor (L.) Moench), and millet (Pennisetum glaucum L. (R.) Br) in the Decision Support System for Agrotechnology Transfer (DSSAT) for response to elevated temperature by comparison to observed data, and we review where changes have been made or where needed changes remain. Temperature functions for phenology and photosynthesis of the CROPGRO-Dry Bean model were modified in 2006 for DSSAT V4.5, based on observed growth and yield of Montcalm cultivar grown in sunlit, controlled-environment chambers. Temperature functions for soybean and peanut models were evaluated against growth and yield data in the same chambers and found to adequately predict growth and yield, thus have not been modified since 1998 release of V3.5. The temperature functions for the chickpea model were substantially modified for many processes, and are updated for V4.6. The millet model was re-coded and modified for its temperature sensitivities, with a new function to allow the 8–10 day period prior to anthesis to affect grain set, as parameterized from field observations. For the sorghum model, the temperature effect on grain growth rate was modified to improve yield and grain size response to elevated temperature by comparison to data in controlled-environment chambers. For reliable assessments of climate change impact, it is critically important to gather additional temperature response data and to update parameterization and code of all crop models including DSSAT.

Crop production is greatly impacted by growing season duration, which is driven by prevailing environmental conditions (mainly temperature) and agronomic management practices (particularly changes in cultivars and shifts in sowing dates). It is imperative to evaluate the impact of climate change and crop husbandry practices on phenology to devise future management strategies to prepare for climate change. Historical changes in spring and autumn maize phenology were observed in Punjab, Pakistan during 1980–2014. Sowing (S) of spring maize was earlier by an average of 4.6 days decade−1, while autumn maize ‘S’ and emergence (E) were delayed on average 3.0and 1.9 days decade−1. Observed anthesis (A) plus maturity (M) dates were earlier by 7.1 and 9.2 days decade−1 and 2.8 and 4.4 days decade−1for spring and autumn maize, respectively. Similarly, S-A, S-M and A-Mphases were shortened on average by 2.4, 4.6 and 1.9 days decade−1 and 5.5, 7.8 and 2.2 days decade−1 for spring and autumn maize, respectively. The variability in phenological phases of spring and autumn maize had significant correlation,with the increase in temperature during 1980–2014. Employing the CSM-CERES-Maize model using standard hybrid for all locations and years illustrated that model-predicted phenology has accelerated with climate change more than infield-observed phenology. These findings suggest that earlier late sowing and shifts of cultivars requiring high total growing degree day during 1980–2014, have partially mitigated the negative impact of climate change on phenology of both spring and autumn grown maize.

Brachiaria and Cynodon are two of the most important pasture grasses worldwide. Computer model simulations can be used to study pasture species growth and physiological aspects to identify gaps of knowledge for genetic improvement and management strategies. The objective of this research was to compare the performance relative to calibrated parameters of the CROPGRO‐Perennial Forage Model (CROPGRO‐PFM) for simulating three different species (“Marandu” palisadegrass, “Convert HD 364®” brachiariagrass and “Tifton 85” bermudagrass) grown under similar management. The field experiment consisted of two harvest frequencies, 28 and 42 days, under irrigated and rainfed conditions. Data used to calibrate the model included regular forage harvests, plant‐part composition, leaf photosynthesis, leaf area index, light interception and plant nitrogen concentration. The simulation of biomass production of the three grasses presented d‐statistic values higher than 0.80, RMSE ranging from 313 to 619 kg/ha and ratio observed/simulated ranging 0.968 to 1.027. Harvest frequency treatments of 28 and 42 days were well simulated by the model. A sensitivity analysis was conducted to evaluate the most influential parameters needed for model calibration and to contrast the grasses, showing that the differences among the three grasses are mostly driven by plant‐part composition and assimilate partitioning among plant organs.

Weeds and diseases can reduce peanut (Arachis hypogaea L.) yield or increase cost of production to maintain acceptable yield. While herbicides and fungicides have limited availability in many areas...

The Decision Support System for Agrotechnology Transfer (DSSAT) currently provides a safflower model based on CROPGRO. The model was calibrated with the field data of one cultivar grown in New Mexico in 2013 and 2014. As it is rather new and has not yet been tested with other field data, it is important to evaluate the model in different environments. This study evaluated the CROPGRO safflower model for two different cultivars grown under field conditions in southwestern Germany. In addition, a new approach was added, enabling it to predict the yield of florets, which is of special interest, as these are used as a food colorant in Europe. The default model was evaluated with data from 2017 and 2018, obtained in a field trial in southwestern Germany with two cultivars, with row spacing of 12 and 33 cm and sowing densities of 40 and 75 plants m&minus, 2. As the default model was not well adapted to European conditions, model modifications were implemented in the species, ecotype, and cultivar files. With these modifications, observed variables such as leaf appearance over time were well predicted (RMSE: 4.76, d-index: 0.88), and simulations of the specific leaf area and leaf area index were greatly improved (RMSE: 24.14 and 0.82, d-index: 0.78 and 0.73). Simulations of the original New Mexico data set were also improved. The newly-added approach to predict floret yield was successfully integrated into the model. Over two years and two cultivars, floret yield was simulated with a RMSE of 97.24 and a d-index of 0.79. Overall, the extended model proved to be useful for simulating growth, floret yield, and yield of safflower in southwestern Germany.

In recent years, the intra-annual yield variability of traditional food crops grown in Europe increased due to extreme weather events driven by climate change. The Andean crop quinoa (Chenopodium quinoa Willd.), being well adapted to drought, salinity, and frost, is considered to be a promising new crop for Europe to cope with unfavorable environmental conditions. However, cultivation guidelines and cropping experiences are missing on a long-term scale. The adaptation of a mechanistic crop growth model will support the long-term evaluation of quinoa if grown under the diverse environmental conditions of Europe. The objective of this study was to adapt the process-based cropping system model (CSM) CROPGRO, which is included in the Decision Support System for Agrotechnology Transfer (DSSAT). Therefore, species and genetic coefficients were calibrated using literature values and growth analysis data, including crop life cycle, leaf area index (LAI), specific leaf area (SLA), dry matter partitioning and nitrogen concentrations in different plant tissues, aboveground biomass, and yield components, of a sowing date experiment (covering two cultivars and four sowing dates) conducted in southwestern Germany in 2016. Model evaluation was performed on the crop life cycle, final aboveground biomass, and final grain yield for different sowing dates using an independent data set collected at the same site in 2017. The resulting base temperatures regarding photosynthetic, vegetative, and reproductive processes ranged between 1 and 10 &deg, C, while the corresponding optimum temperatures were between 15 and 36 &deg, C. On average, the crop life cycle was predicted with a root mean square error (RMSE) of 4.7 and 3.0 days in 2016 and 2017, respectively. In 2016, the mean predicted aboveground biomass during the growth cycle showed a d-index of 0.98 (RMSE = 858 kg ha&minus, 1). Furthermore, the LAI, SLA, and leaf nitrogen concentrations were simulated with a high accuracy, showing a mean RMSE of 0.29 (d-index = 0.94), 25 cm2 g&minus, 1 (d-index = 0.88), and 0.51% (d-index = 0.95). Evaluations on the grain yield and aboveground biomass across four sowing dates in 2017 suggested a good robustness of the new quinoa model. The mean predicted aboveground biomass and grain yield at harvest maturity were 6479 kg ha&minus, 1 (RMSE = 898.9 kg ha&minus, 1) and 3843 kg ha&minus, 1 (RMSE = 450.3 kg ha&minus, 1), respectively. Thus, the CSM-CROPGRO model can be used to evaluate the long-term suitability, as well as different management strategies of quinoa under European conditions. However, further development on the simulation of small seed sizes and under water or nitrogen-limited environments are needed.

Crop simulation models can help scientists, government agencies and growers to evaluate the best strategies to manage their crops in the field, according to the climate conditions. Currently, there are many crop models available to simulate soybean growth, development, and yield, with different levels of complexity and performance. Based on that, the aim of this study was to assess five soybean crop models and their ensemble in Southern Brazil. The following crop models were assessed: FAO – Agroecological Zone; AQUACROP; DSSAT CSM–CROPGRO–Soybean; APSIM Soybean; and MONICA. These crop models were calibrated using experimental data obtained during 2013/2014 growing season in different sites, sowing dates and crop conditions (rainfed and irrigated) for cultivar BRS 284, totaling 17 treatments. The crop variables assessed were: grain yield; crop phases; harvest index; total above-ground biomass; and leaf area index. The calibration was made in three phases: using original coefficients from modelś default (no calibration); calibrating the coefficients related only with crop life cycle phases; and calibrating all set of coefficients (below and above the soil). The results from the models were analyzed individually and in an ensemble of them. The crop models showed an improvement of performance from no calibration to complete calibration. Crop phases were estimated efficiently, although different approaches were used by the models. The estimated yield had RMSE of 650, 536, 548, 550 and 535 kg ha −1 , respectively, for FAO, AQUACROP, DSSAT, APSIM and MONICA, with d indices always higher than 0.90 for all of them. The best performance was obtained when an ensemble of all models was considered, reducing yield RMSE to 262 kg ha −1 . The same tendency for ensemble being best was observed for leaf area index. The harvest index was the crop variable with the poorest performance. In general, the results showed that an ensemble of completely calibrated models were more efficient to simulate soybean yield than any single one, which was also observed when testing this procedure with independent data.

International audience; Crop models are important tools for assessing the impact of climate change on crop production. While multiple models have been developed over the past decades for the major food and fiber crops such as wheat, maize, soybean, rice, and cotton, there are few or none for many other crops. The goal of this study was to develop a simple generic crop model (SIMPLE) that could be easily modified for any crop to simulate development, crop growth and yield. The crop model SIMPLE includes 13 parameters to specify a crop type, with four of these for cultivar characteristics. Commonly available inputs that are required for the crop model SIMPLE include daily weather data, crop management, and soil water holding parameters. The initial SIMPLE model was calibrated and evaluated for 14 different annual crops using observations for biomass growth, solar radiation interception, and yield from 25 detailed field experiments for a total of 70 treatments from 17 sites, resulting in a RRMSE of 25.4% for final yield. A sensitivity analysis comparing a C3, C4 and a legume crop showed an expected response to a gradual increase in temperature and atmospheric CO2 concentrations. A regional gridded simulation for US potatoes reproduced the general observed patterns of spatial yield variability. Because the model is simple, it has several limitations, including the lack of response to vernalization and photoperiod effect on phenology. The model includes water, but no nutrient dynamics. However, an advantage of the model simplicity is that it can be easily adapted and evaluated for any new crop, based on literature data and field experiments, or general crop data such as sowing and harvest dates and yield statistics. The model is available in several simulation frame-works including a stand-alone version in R, Excel and as part of DSSAT.

Maize is the third most cultivated food crop in the world. Therefore, the impact of climate change and the development of adaptation strategies for maize are crucial to agricultural production and food security. The current study was undertaken to evaluate the impact of climate change and the development of adaptations strategies for maize in semi-arid environments using the Cropping System Model (CSM)-CERES-Maize of the Decision Support System for Agrotechnology Transfer (DSSAT). The model was calibrated and evaluated with an experimental data set and compared to on-farm data. The sensitivity of the model was evaluated against Carbon, Temperature, Water and Nitrogen (CTWN) analysis for the same environments. Survey data for maize were collected from 64 farms in the Faisalabad district of Pakistan using a stratified random sampling technique. Initial crop conditions and management practices were used as input data for CSM-CERES-Maize. Current climate data from 1980 to 2010 were obtained from the nearest weather station and future climate projections for 2040–2069 were obtained from Global Climate Models (GCMs) under Representative Concentration Pathway (RCP) 8.5. Representative Agricultural Pathways (RAPs) were designed to represent the future autonomous production system. The GCM results showed an increase of 3.4 °C in maximum and 3.8 °C in minimum temperature for hot/dry conditions. The projected increase in temperatures for the hot/dry GCM would result in a 28 % reduction for the current production system and a 29 % reduction for the future maize production system by the middle of the century. The impact of climate adaption options on current production systems was evaluated and the results showed that yield increased by 21 %. Results of climate adaptation for the future production system indicated that yield would increase by 12–17 % for all GCMs. Both the current and future production systems were negatively affected by climate change. However, improved management as adaptation strategies can offset the potential decrease in yield.

Alfalfa (Medicago sativa L.) is the predominant forage legume species in Canada and is considered a prioritized option for sustainable cropping under climate change. Crop growth models provide an opportunity to explore the potential impacts of climate change on alfalfa and for evaluating potential adaptation options. For this study, six experimental datasets in eastern Canada were used to parameterize the newly adapted CSM-CROPGRO-Perennial Forage Model (CSM-CROGRO-PFM) in simulating alfalfa regrowth and to identify areas for further model improvement needed for climate change assessments in the northern agricultural regions of North America. Estimated air temperatures under snow cover were used successfully to drive the CSM-CROPGRO-PFM model for simulating alfalfa regrowth in eastern Canada. The simulated values of aboveground biomass across all sites and years were acceptable with a root mean square error (RMSE) of 936 kg dry matter (DM) ha−1 and a normalized RMSE of 24%. A sensitivity analysis of the model revealed that with no change in the number of harvests per year, the simulated annual herbage yield (harvestable biomass) declined with increasing temperature, increased with elevated atmospheric CO2 concentration, and changed little with increased precipitation. However, the increase in the number of harvests made possible by warmer temperatures may increase the simulated annual herbage yield. Although most alfalfa physiological processes were successfully simulated, some additional model functions may be required to further improve the simulation of alfalfa regrowth for climate change studies conducted in Canada. These functions include quantifying plant density decline and its relationship with biomass in post-seeding years, estimating temperatures surrounding alfalfa crowns during the overwintering period, and simulating herbage nutritive attributes.

Resource scarcity (labor, water, and energy) and high production costs are challenging the sustainability of conventional methods for rice and wheat establishment in Central Asia. Water saving irrigation and conservation agriculture (CA) practices (e.g., dry seeded rice, zero tillage wheat, residue retention) are potential alternative, resource-saving establishment methods. The Decision Support System for Agrotechnology Transfer (DSSAT) Cropping System Model (CSM) can both be a valuable ex-ante and ex-post tool to evaluate the effects of water saving irrigation and resource saving CA-practices. The CSM-CERES-Rice and CSM-CERES-Wheat models of DSSAT were evaluated using experimental data from the 2008 to 2010 rice and wheat seasons as monitored in Urgench, the Khorezm region of Uzbekistan for growth, development of these crops, as well as soil mineral nitrogen (N) and volumetric soil moisture content in these cropping systems. Thereafter, the models were used to explore the long-term impact of water saving irrigation and CA-practices on grain yield, soil organic carbon (SOC) dynamics, N dynamics, and water balance in a rice–wheat rotation for 39 years starting from 1971. The simulation results showed that the simulated yield of water-seeded rice without residue retention and flood irrigation (WSRF-R0-FI) is likely to remain the highest and constant over 39 years. The simulated yield of dry seeded rice (DSR) with alternate wet and dry (AWD) irrigation and varying levels of residue retention was penalized for the initial years. However, the simulated rice yield increased after 13 years of CA-practices and continued to increase for the remaining years. Wheat did not experience a yield penalty for any of the treatments and simulated yield increased over time across all CA-practices based treatments. In the long-term, the effect of tillage methods and different residue levels for both rice and wheat were apparent in terms of grain yield and SOC build up. The results of the sensitivity analysis showed that WSR using AWD irrigation with puddling (WSRF-R0-AWD-Puddled) could give equivalent yield with that of WSRF-R0-FI and that irrigation water for rice could be reduced from 5435 mm to 2161 mm (or by 60%). Deep placement of urea in DSR (CT-DSR-AWD-DPUS) has the potential to increase yields of DSR by about 0.5 t ha −1 . Despite the huge water saving potential through the adoption of water saving AWD irrigation in DSR, a major challenge will be to prevent N losses. Substantial amounts of N losses through leaching, immobilization by residue mulch, combined with gaseous losses through volatilization and denitrification are the major causes for the lower simulated yield of rice for the AWD treatments. During the rice season, the implementation of water saving irrigation can improve water use efficiency by reducing percolation and seepage losses, which is an option in particular for WSRF-R0-FI. For both crops, the water use efficiency can be improved by lowering evaporation losses e.g. through residue retention on the soil surface. The creation of a sub-surface hard pan (puddling) and deep placement of urea super granules/pellet (DPUS) fertilizer could be the key for water saving and better yields of rice. Because CA-practices require almost three times less irrigation water than conventional method, and provide a long-term positive impact on grain yields of both crops, the CA-practices should be considered for double, no-till, rice–wheat cropping systems in the irrigated semi-arid drylands of Central Asia.

Phosphorus (P) deficiency is a major constraint to crop production in many agricultural systems globally. Application of phosphorus fertilizer is essential for optimal crop yields when soils are P limiting. Crop simulation models can provide an alternative, less time consuming and inexpensive means of determining the optimum crop P requirements under varied soil and climatic conditions. The CROPGRO-peanut model is capable of simulating the growth and yield of peanut in response to weather, soil, water, nitrogen and management practices but its capability in predicting crop responses to soil and fertilizer P needs to be established. Our objective was to adapt and evaluate the CROPGRO-peanut model within the DSSAT system to simulate the growth and yield of peanut (Arachis hypogaea) in response to soil and fertilizer P. Data from four P fertilizer treatments (0, 13, 26 and 39 kg P ha−1) and two cropping seasons (2002 and 2003) experiments on an Alfisol were used to calibrate the model. The model was tested using two data sets from a P fertilizer × cultivar trial conducted on-station in 1997 and 1998 and P fertilizer × fungicide trials on-farm in 2002. The limited testing showed that the P module accurately simulated the seasonal patterns of aboveground biomass and pod yield in the on-farm trials. Averaged across sampling dates RMSEs in the on-farm trials ranged from 100 to 398 kg ha−1 (d ≥ 0.99) for total biomass and from 97 to 263 kg ha−1 (d values ≥ 0.98) for pod yield. At final harvest, the variability of simulated biomass and pod yield was about 5.4 and 10.0% of the observed biomass and pod yield respectively. In the P fertilizer × cultivar trial, the variability of simulated biomass and pod yield were 22 and 13% for cv. F-Mix and 19 and 5% for cv. Chinese respectively. The model simulated the seasonal patterns of vegetative P content in the four farmers’ fields fairly well with RMSEs ranging from 0.28 to 1.29 kg ha−1 when averaged across measurements dates. The model outputs were sensitive to soil P test values, the method of P fertilizer application and to plant P uptake factors, such as root P extraction radius and root length density. There were differences in the sensitivity of the simulations to changes in target tissue P concentrations. Increasing or decreasing the optimum P concentration of seed and minimum P concentrations of leaf and stem had the greatest effects on pod yield compared to other plant parts. We conclude that the generic soil and plant P model in DSSAT 4.5 is capable of simulating peanut growth and yield in response to soil P levels or fertilizer application on an Alfisol.

Occurrence of staple crops' yield gaps is object of study worldwide. A theoretical approach, model and statistical-based, was carried out to assess the climate-induced variability of rainfed maize yields and yield gaps in different regions in Central-Southern Brazil in both main growing seasons. A crop simulation model was used to estimate potential (Yp) and water-limited (Yw) yields through thirty crop seasons. Based on observed local farmers' averages and simulated yields, yield gaps related to water deficit (WYg) and crop management (MYg) were determined for first (sowing starting in September) and second (sowing starting in January) typical maize growing seasons. Overall higher average values of Yp and Yw (15.3 and 13.1 t ha−1, respectively) were obtained in the first when compared to second growing season (10.3 and 9.2 t ha−1, respectively). Statistical approaches pointed to similar importance between water and temperature on local biophysical limits in the scenarios. Assessed regions showed greater gaps due to crop management, with absolute averages of 5.7 and 3.2 t ha−1 in the first and second growing seasons, than gaps due to water deficit, with 2.1 and 1.2 t ha−1 in the first and second growing seasons, respectively. Opportunities for increasing average yields by closing the gaps were found to be predominantly through crop management improvements, in higher and more variable absolute levels on first than on second growing season. However, this management must be aligned with local climate, since its variability can determine relatively large gaps, even at intensively managed cropping systems. This study was able to highlight the importance of combining management, climatic and regional characteristics to provide a full perspective on main constraints of maize production increases.

14 Pags.- 6 Figs.- 6 Tabls. Available freely online through the author-supported open access option. Copyright © 2018 by the American Society of Agronomy. This is an open access article distributed under the CC BY-NC-ND license., Despite alfalfa’s global importance, there is a dearth of crop simulation models available for predicting alfalfa growth and yield with its associated composition. The objectives of this research were to adapt the CSM-CROPGRO Perennial Forage Model for simulating alfalfa growth and yield and to describe model adaptation for this species. Data from six experimental plots grown under sprinkler irrigation in the Ebro valley (Northeast Spain) were used for model adaptation. Starting with parameters for Bracharia brizantha, the model adaptation was based on values and relationships reported from the literature for cardinal temperatures and dry matter partitioning. A Bayesian optimizer was used to optimize temperature effects on photosynthesis and daylength effects on partitioning and an inverse modeling technique was employed for nitrogen fixation rate and nodule growth. The calibration of alfalfa tissue composition was initiated from soybean composition analogy but was improved with values from alfalfa literature. There was considerable iteration in optimizing parameters for the processes outlined above where comparisons were made to measured data. After adaptation, the Root Mean Square Error and d-statistic of harvested herbage averaged across 58 harvests (yield range: 990–4617 kg ha–1) were 760 kg ha–1 and 0.75, respectively. In addition, good agreement was observed for Leaf Area Index (LAI) (LAI range: 0.1–6.7) with d-statistic of 0.71. Simulated belowground mass was within the range of literature values. The results of this study showed that CROPGRO-PFM-Alfalfa can be used to simulate alfalfa growth and development. Further testing with more extensive datasets is needed to improve model robustness., This work was funded by the Ministry of Economy and Competitiveness of the Spanish Government through the research grants (AGL2013-48728-C2-2-R). We thank this Ministry for awarding Wafa Malik a predoctoral fellowship and financial support for research abroad internship at the University of Florida.

Sustainable management practices are needed to enhance soil organic carbon (SOC) in degraded soils in semi-arid West Africa. We studied the effects of three amounts of nitrogen (N) (0, 60 and 120 kg N ha−1) and three amounts of phosphorus (P) fertilizer (0, 26 and 39 kg P ha−1) application over four seasons on maize residue production, residue C, N, and P concentrations, and their impacts on SOC, total soil nitrogen (TSN), and total soil phosphorus (TSP) in the 0–20 cm soil layer. Combined application of N and P fertilizers substantially increased maize grain yield on average by 294 % and biomass produced and returned to the soil by about 60–70 % compared with no fertilization. Annual C, N, and P inputs from crop residue were significantly higher with combined application of N and P fertilizer. The increased amount of crop residue and consequent increased residue C, N and P returned to the soil significantly increased SOC, TSN and TSP in the 0–20 cm soil layer after four seasons. There was a significant correlation between the amount of crop residues returned to the soil over four seasons and SOC (r = 0.82; P = 0.007), TSN (r = 0.75; P = 0.020) and TSP (r = 0.69; P = 0.039). We concluded from these experiments that returning crop residues, application of inorganic fertilizer improves SOC, TSN and TSP concentrations and enhances crop productivity. The farmers who traditionally remove crop residues for fodder and fuel will require demonstration of the relative benefits of residues return to soil for sustainable crop productivity.

Development of management tools is essential to explore the potential of grassland systems and such tools include simulation models used for management, planning and research purposes. The simulation models account for temperature effects on forage growth in various ways, and most of them use degree-day-based sub-models to simulate plant growth. Little or no growth is expected for tropical grasses when temperatures are between 10 and 15 °C; thus, the assumption of 15 °C as the base temperature for growth of these plants is not uncommon. The objective of this paper is to test an approach, commonly used for row crops, for determination of pasture grasses base temperature using a Panicum spp. dataset, and to compare different methods of calculation. Data was collected from well-established plots (4 m × 10 m) of five Panicum spp. cultivars (Atlas, Massai, Mombaca, Tanzânia and Tobiata), arranged in four randomized complete blocks, sampled from December 2002 to April 2004 in Piracicaba, SP, Brazil. Light interception measurements from three summer and one winter growth cycles were used to determine thermal time to reach 95% canopy light interception. Base-temperature was calculated using iteration method, the b-coefficient method, minimum coefficient of variation of accumulated degree-days, and minimum standard deviation in degree-days and in days. The order of best methods was iteration, coefficient of variation of accumulated degree-days and b-coefficient method, respectively. The standard deviation method in degree-days and in days resulted in high base temperatures and was not able to detect differences among cultivars. Overall base temperatures were different among cultivars: Massai: 16 °C, Atlas: 15 °C, Mombaca: 11 °C, Tobiata: 10 °C and Tanzânia: 7 °C.

Maintaining high levels of productivity under climate change will require developing cultivars that are able to perform under varying drought and heat stresses and with maturities that match water availability. The CSM-CERES-Sorghum model was used to quantify the potential benefits of altering crop life cycle, enhancing yield potential traits, and incorporating drought and heat tolerance in the commonly grown cultivar types at two sites each in India (cv. CSV 15 at both Akola and Indore) and Mali (cv. CSM 335 at Samanko and cv. CSM 63E at Cinzana), West Africa. Under current climate CSV 15 on average matured in 108 days and produced 3790 kg ha−1 grain yield at Akola; whereas at Indore it matured in 115 days and produced 3540 kg ha−1 grain yield. Similarly under current climate, CSM 335 matured in 120 days and produced 2700 kg ha−1 grain yield at Samanko; whereas CSM 63E matured in 85 days at Cinzana and produced 2210 kg ha−1 grain yield. Decreasing crop life cycle duration of cultivars by 10% decreased yields at all the sites under both current and future climates. In contrast, increasing crop life cycle by 10% increased yields up to 12% at Akola, 9% at Indore, 8% at Samanko and 33% at Cinzana. Enhancing yield potential traits (radiation use efficiency, relative leaf size and partitioning of assimilates to the panicle each increased by 10%) in the longer cycle cultivars increased the yields by 11–18% at Akola, 17–19% at Indore, 10–12% at Samanko and 14–25% at Cinzana under current and future climates of the sites. Except for the Samanko site, yield gains were larger by incorporating drought tolerance than heat tolerance under the current climate. However, under future climates yield gains were higher by incorporating heat tolerance at Akola, Samanko and Cinzana, but not at Indore. Net benefits of incorporating both drought and heat tolerance increased yield up to 17% at Akola, 9% at Indore, 7% at Samanko and 16% at Cinzana under climate change. It is concluded that different combinations of traits will be needed to increase and sustain productivity of sorghum in current and future climates at these target sites and that the CSM-CERES-Sorghum model can be used to quantify benefits of incorporating certain traits.

Common bean production in Goias, Brazil is concentrated in the same geographic area, but spread across three distinct growing seasons, namely, wet, dry and winter. In the wet and dry seasons, common beans are grown under rainfed conditions, whereas the winter sowing is fully irrigated. The conventional breeding program performs all varietal selection stages solely in the winter season, with rainfed environments being incorporated in the breeding scheme only through the multi environment trials (METs) where basically only yield is recorded. As yield is the result of many interacting processes, it is challenging to determine the events (abiotic or biotic) associated with yield reduction in the rainfed environments (wet and dry seasons). To improve our understanding of rainfed dry bean production so as to produce information that can assist breeders in their efforts to develop stress-tolerant, high-yielding germplasm, we characterized environments by integrating weather, soil, crop and management factors using crop simulation models. Crop simulations based on two commonly grown cultivars (Perola and BRS Radiante) and statistical analyses of simulated yield suggest that both rainfed seasons, wet and dry, can be divided in two groups of environments: highly favorable environment and favorable environment. For the wet and dry seasons, the highly favorable environment represents 44% and 58% of production area, respectively. Across all rainfed environment groups, terminal and/or reproductive drought stress occurs in roughly one fourth of the seasons (23.9% for Perola and 24.7% for Radiante), with drought being most limiting in the favorable environment group in the dry TPE. Based on our results, we argue that even though drought-tailoring might not be warranted, the common bean breeding program should adapt their selection practices to the range of stresses occurring in the rainfed TPEs to select genotypes more suitable for these environments.

Using CROPGRO-Chickpea model (revised version), we investigated the impacts of climate change on the productivity of chickpea (Cicer arietinum L.) at selected sites in South Asia (Hisar, Indore and Nandhyal in India and Zaloke in Myanmar) and East Africa (Debre Zeit in Ethiopia, Kabete in Kenya and Ukiriguru in Tanzania). We also investigated the potential benefits of incorporating drought and heat tolerance traits in chickpea using the chickpea model and the virtual cultivars approach. As compared to the baseline climate, the climate change by 2050 (including CO2) increased the yield of chickpea by 17% both at Hisar and Indore, 18% at Zaloke, 25% at Debre Zeit and 18% at Kabete; whereas the yields decreased by 16% at Nandhyal and 7% at Ukiriguru. The yield benefit due to increased CO2 by 2050 ranged from 7 to 20% across sites as compared to the yields under current atmospheric CO2 concentration; while the changes in temperature and rainfall had either positive or negative impact on yield at the sites. Yield potential traits (maximum leaf photosynthesis rate, partitioning of daily growth to pods and seed-filling duration each increased by 10%) increased the yield of virtual cultivars up to 12%. Yield benefit due to drought tolerance across sites was up to 22% under both baseline and climate change scenarios. Heat tolerance increased the yield of chickpea up to 9% at Hisar and Indore under baseline climate, and up to 13% at Hisar, Indore, Nandhyal and Ukiriguru under climate change. At other sites (Zaloke, Debre Zeit and Kabete) the incorporation of heat tolerance under climate change had no beneficial effect on yield. Considering varied crop responses to each plant trait across sites, this study was useful in prioritizing the plant traits for location-specific breeding of chickpea cultivars for higher yields under climate change at the selected sites in South Asia and East Africa.

Late leaf spot (LLS) caused by Cercosporidium personatum (Berk. and Curt.) Deighton leads to significant reductions in peanut (Arachis hypogaea L.) yield worldwide. This study was conducted to improve the mechanisms and methods by which LLS effects on defoliation and photosynthesis are linked to the CSM-CROPGRO-Peanut model for simulating growth and yield reductions in peanut cultivars. Field experiments were conducted in 2008 and 2009 to collect data on the effects of LLS on biomass accumulation and partitioning, leaf necrosis and defoliation, and total canopy photosynthesis (TCP) in peanut cultivars with more (York) and less (Carver) quantitative resistance to LLS. After incorporating LLS damage as defoliation percentage and necrotic area, the model accurately simulated crop growth and development for both cultivars despite different disease dynamics. Simulated TCP and leaf, total crop, and pod yield values were in good agreement with measured data. A modification in the model code to directly reduce leaf photosynthesis and quantum efficiency according to empirical observations resulted in improved simulations of LLS effects on growth and yield. Correlations among measured defoliation and necrotic area with disease ratings indicated that visual disease ratings could be successfully used to estimate necrosis and defoliation for model inputs. Results indicated that the CSM-CROPGRO-Peanut model has adequate capability to simulate LLS effects on growth and yield in peanut cultivars with differing levels of resistance to LLS when inputs on canopy necrotic area and defoliation are provided.

Crop growth models dynamically simulate processes of C, N and water balance on daily or hourly time-steps to predict crop growth and development and at season-end, final yield. Their ability to integrate effects of genetics, environment and crop management have led to applications ranging from understanding gene function to predicting potential impacts of climate change. The history of crop models is reviewed briefly, and their level of mechanistic detail for assimilation and respiration, ranging from hourly leaf-to-canopy assimilation to daily radiation-use efficiency is discussed. Crop models have improved steadily over the past 30-40 years, but much work remains. Improvements are needed for the prediction of transpiration response to elevated CO₂ and high temperature effects on phenology and reproductive fertility, and simulation of root growth and nutrient uptake under stressful edaphic conditions. Mechanistic improvements are needed to better connect crop growth to genetics and to soil fertility, soil waterlogging and pest damage. Because crop models integrate multiple processes and consider impacts of environment and management, they have excellent potential for linking research from genomics and allied disciplines to crop responses at the field scale, thus providing a valuable tool for deciphering genotype by environment by management effects.

A better understanding of the mechanisms of peanut adaptation to pre-flowering drought is important for improving pod yield productivity. Nevertheless, the mechanisms of drought tolerance are under different genetic controls, and pod yield is a complex trait. Therefore, the aim of this study was to investigate the mechanism for drought tolerance of peanut genotypes with different pod yield responses under pre-flowering drought conditions. Field experiments were conducted during February to July, 2007 and during February to July, 2009. A split-plot experiment in a randomized complete block design was used. Two water management treatments were assigned as the main plots, i.e. field capacity (F.C.) and pre-flowering stress (PFD), and six peanut genotypes as the sub-plots. Relative water content (RWC) and stomatal conductance were recorded at 5, 10, 15, 20, 25, 30, 35 and 40 days after emergence (DAE). Leaf area index was measured at 25 DAE, R5 and R7. Total dry matter samples, including shoots, roots and pods, were obtained at 25 DAE, R5, R7 and harvest. Shoot growth rate, root growth rate and pod growth rate were then calculated. Major finding, the first mechanism is explained by high water uptake of the root systems that provide sufficient water for normal transpiration, as the response of ICGV 98305 to PFD. It may induce the improvement of peanut pod growth rate in pod filling stage due to the change of assimilate proportion, resulting in increasing pod yield to PDF comparing with adequate water conditions. In contrast, such as the response of ICGV 98330, pre-flowering drought can increase the ability of peanut to save more water by reduction of transpiration, but rooting traits are not changed. This could conserve more water by reducing transpiration to maintain high RWC. Nevertheless, the ability to reduce transpiration did not support the improvement of peanut pod yield under these conditions. The increasing peanut productivity to pre-flowering drought was contributed by the improvement of assimilate proportion to economic part in reproductive phase. This knowledge will be useful for breeding of peanut for pre-flowering drought environment.

Water deficit is a major factor responsible for soybean yield gap in Southern Brazil and tends to increase under climate change. An alternative to reduce such gap is to identify soybean cultivars with traits associated to drought tolerance. Thus, the aim of this study was to assess soybean adaptive traits to water deficit that can improve yield under current and future climates, providing guidelines for soybean cultivar breeding in Southern Brazil. The following soybean traits were manipulated in the CSM-CROPGRO-Soybean crop model: deeper root depth in the soil profile; maximum fraction of shoot dry matter diverted to root growth under water stress; early reduction of transpiration under mild stress; transpiration limited as a function of vapor pressure deficit; N2 fixation drought tolerance; and sensitivity of grain filling period to water deficit. The yields were predicted for standard and altered traits using climate data for the current (1961–2014) and future (middle-century) scenarios. The traits with greater improvement in soybean yield were deeper rooting profile, with yield gains of ≈300 kg ha−1, followed by transpiration limited as a function of vapor pressure deficit and less drought-induced shortening of the grain filling period. The maximum fraction of shoot dry matter diverted to root and N2 fixation drought tolerance increased yield by less than 75 kg ha−1, while early reduction of transpiration resulted in a small area of country showing gains. When these traits were combined, the simulations resulted in higher yield gains than using any single trait. These results show that traits associated with deeper and greater root profile in the soil, reducing transpiration under water deficit more than photosynthesis, creating tolerance of nitrogen fixation to drought, and reducing sensitivity of grain filling period to water deficit should be included in new soybean cultivars to improve soybean drought tolerance in Southern Brazil.

Conference: International Crop Modelling Symposium on Crop Modelling for Agriculture and Food Security under Global Change (iCropM) - Proceedings Paper Berlin, GERMANY 2016; This study assesses the ability of 21 crop models to capture the impact of elevated CO2 concentration ([CO2]) on maize yield and water use as measured in a 2-year Free Air Carbon dioxide Enrichment experiment conducted at the Thünen Institute in Braunschweig, Germany (Manderscheid et al., 2014). Data for ambient [CO2] and irrigated treatments were provided to the 21 models for calibrating plant traits, including weather, soil and management data as well as yield, grain number, above ground biomass, leaf area index, nitrogen concentration in biomass and grain, water use and soil water content. Models differed in their representation of carbon assimilation and evapotranspiration processes. The models reproduced the absence of yield response to elevated [CO2] under well-watered conditions, as well as the impact of water deficit at ambient [CO2], with 50% of models within a range of +/−1 Mg ha−1 around the mean. The bias of the median of the 21 models was less than 1 Mg ha−1. However under water deficit in one of the two years, the models captured only 30% of the exceptionally high [CO2] enhancement on yield observed. Furthermore the ensemble of models was unable to simulate the very low soil water content at anthesis and the increase of soil water and grain number brought about by the elevated [CO2] under dry conditions. Overall, we found models with explicit stomatal control on transpiration tended to perform better. Our results highlight the need for model improvement with respect to simulating transpirational water use and its impact on water status during the kernel-set phase.

Climate change is projected to intensify drought and heat stress in groundnut (Arachis hypogaea L.) crop in rainfed regions. This will require developing high yielding groundnut cultivars that are both drought and heat tolerant. The crop growth simulation model for groundnut (CROPGRO-Groundnut model) was used to quantify the potential benefits of incorporating drought and heat tolerance and yield-enhancing traits into the commonly grown cultivar types at two sites each in India (Anantapur and Junagadh) and West Africa (Samanko, Mali and Sadore, Niger). Increasing crop maturity by 10 % increased yields up to 14 % at Anantapur, 19 % at Samanko and sustained the yields at Sadore. However at Junagadh, the current maturity of the cultivar holds well under future climate. Increasing yield potential of the crop by increasing leaf photosynthesis rate, partitioning to pods and seed-filling duration each by 10 % increased pod yield by 9 to 14 % over the baseline yields across the four sites. Under current climates of Anantapur, Junagadh and Sadore, the yield gains were larger by incorporating drought tolerance than heat tolerance. Under climate change the yield gains from incorporating both drought and heat tolerance increased to 13 % at Anantapur, 12 % at Junagadh and 31 % at Sadore. At the Samanko site, the yield gains from drought or heat tolerance were negligible. It is concluded that different combination of traits will be needed to increase and sustain the productivity of groundnut under climate change at the target sites and the CROPGRO-Groundnut model can be used for evaluating such traits.

Parameterizing crop models for more accurate response to climate factors such as temperature is important considering potential temperature increases associated with climate change, particularly for tomato (Lycopersicon esculentum Mill.), which is a heat-sensitive crop. The objective of this work was to update the cardinal temperature parameters of the CROPGRO-Tomato model affecting the simulation of crop development, daily dry matter (DM) production, fruit set, and DM partitioning of field-grown tomato from transplanting to harvest. The main adaptation relied on new literature values for cardinal temperature parameters that affect tomato crop phenology, fruit set, and fruit growth. The new cardinal temperature values are considered reliable because they come from recent published experiments conducted in controlled-temperature environments. Use of the new cardinal temperatures in the CROPGRO-Tomato model affected the rate of crop development compared with prior default parameters; thus, we found it necessary to recalibrate genetic coefficients that affect life cycle phases and growth simulated by the model. The model was recalibrated and evaluated with 10 growth analyses data sets collected in field experiments conducted at three locations in Florida (Bradenton, Quincy, and Gainesville) from 1991 to 2007. Use of modified parameters sufficiently improved model performance to provide accurate prediction of crop and fruit DM accumulation throughout the season. Overall, the average root mean square error (RMSE) over all experiments was reduced 44% for leaf area index, 71% for fruit number, and 36% for both aboveground biomass and fruit dry weight simulations with the modified parameters compared with the default. The Willmott d index was higher and was always above 0.92. The CROPGRO-Tomato model with these modified cardinal temperature parameters will predict more accurately tomato growth and yield response to temperature and thus be useful in model applications.

Anticipated climate change will alter the temperature and rainfall characteristics of crop growing seasons. This will require genetic improvement of crops for adapting to future climates for higher yields. The CROPGRO model for groundnut was used to evaluate genetic traits of Virginia and Spanish types of groundnut for various climate scenarios of India. The analysis revealed that productivity of groundnut can be increased in current and future climates by adjusting the duration of various life-cycle phases, especially the seed-filling to physiological maturity (SD-PM). Increased maximum leaf photosynthesis rate (AMAX), increased partitioning to reproductive organs (XFRT) and increased individual seed-fill duration (SFDUR) all contributed to the increase in pod yield in all climates. More determinate pod set (shorter PODUR) was beneficial only in the water deficit environments. The positive effect of increasing specific leaf area (SLA) and leaf size (SIZLF) on pod yield was greater in environments more favourable for plant growth. Increasing reproductive tolerance to high temperature by 2 °C increased pod yield of groundnut in warmer environments, especially where the crop often suffers from drought. Increased adaptive partitioning to roots (ATOP) increased drought resistance of groundnut on high water-holding capacity soils. Combination of traits had additive effects and pod yield increased substantially. These results indicate that the CROPGRO model can be used to assess the potential of individual or combination of plant traits for guiding breeding of improved groundnut varieties for current and future climates.