Cropping System Model Research Articles

Model‐based multi‐genotype characterization of drought stress Target Population of Environments for the common bean in East Africa

AbstractEfforts toward the development of common bean varieties that can perform better under the drought conditions in Eastern Africa are constrained by significant genotype–environment–management (G × E × M) interactions. To address this, an attempt was made previously to assess drought stress for Eastern Africa using the Target Population of Environments (TPE) approach, albeit by using a single cultivar. Here, we extend earlier TPE work with a larger sample of representative genotypes from East Africa including commercial and breeding lines. By using a larger genotype pool, we are able to (1) assess the extent to which TPEs identified vary across genotypes (i.e., generality of the TPEs) and (2) identify best‐performing genotypes for the various environments identified. To perform the analyses, we used the CROPGRO‐DRYBEAN cropping system model available in the Decision Support System for Agrotechnology Transfer (DSSAT) v4.7 calibrated and evaluated from field trials in Africa and Colombia with 10 genotypes. Our findings indicate that despite some level of genotype‐related variation, the dominant drought stress patterns, and their priorities—drought stress‐free conditions remain the dominant TPE, followed by terminal drought stress—remain unaltered. Moreover, we find that it is possible to reduce the extent and intensity of drought stress in various TPEs by adequately targeting genotypes from the pool considered herein. Importantly, the genotype characteristics for stress‐free, extreme terminal drought and moderate terminal drought stress are significantly different, confirming a differentiated breeding strategy is required between the TPEs. The findings from this study are useful for the modernization of the bean breeding programs in Africa.

Beyond assimilation of leaf area index: Leveraging additional spectral information using machine learning for site-specific soybean yield prediction

Assimilating external observations of crop state in cropping system models is essential for making spatially explicit predictions of crop variables relevant in precision agriculture. Satellite-based leaf area index (LAI) estimates have been the most frequent variable used as a proxy of actual crop growth. However, additional information beyond LAI, like canopy N content, water content, and structure, can be retrieved from satellite observations. Including such variables by data assimilation directly is difficult because many crop models do not have corresponding state variables or the relationship between the observations and the process that regulates crop growth is unclear. Therefore, other approaches are required to include such information. In this study, we investigate the improvement in the predicted yield and feature impact on model outputs by using a hybrid approach that combines observations from Sentinel-1 and 2 time-series with the outputs from a process-based model embedded in a data assimilation framework and uses the Gradient-boosted trees regressor (GBTR) as predictive model. We used two regions with soybean fields: the US (13 K points) and Uruguay (400 K points). We found an advantage when using the GBTR as the predictive model (reduced RRMSE by ∼16%) compared to data assimilation. Adding the vegetation indices had a marginal improvement (reduced RRMSE by ∼1%), while the impact of adding reflectance and backscatter values was negative. The satellite-based features had a very small importance score, while features' impact on prediction was predominantly unclear, explaining the marginal predictive power added by satellite-based features. We found that features from the reproductive stages had the highest importance, while the importance of an index related to drought stress (NMDI) across the growing season provided insights for further improvement of data assimilation methods. However, more studies are required to better disentangle pathways towards further improvement in constraining crop models by ingesting satellite observations.

A sound understanding of a cropping system model with the global sensitivity analysis

The capability of cropping system models of depicting the crop and soil-related processes implies a high number of parameters. The aim of this work was to detect the key parameters, and the associated processes, of the ARMOSA cropping system model, considering two target outputs, crop yield and nitrogen leaching. A global sensitivity analysis (SA) was carried out in two steps: (1) the Morris method considering the whole set of parameters; (2) Sobol analysis was applied to the Morris outcome. The simulation was run on winter wheat in four soil types in Marchfeld (Austria, 2010–2018). Parameters affecting crop yield was the critical nitrogen concentration, the potential CO2 assimilation rate, and the drought sensitivity parameter. Nitrogen leaching was mainly affected by the decomposition of litter and the early aboveground biomass growth. The parameters ranking did not appreciably change across soil types. This study offers a quick and replicable methodology for model calibration.

Evaluation of a regional crop model implementation for sub-national yield assessments in Kenya

CONTEXTCropping system models can be used to both assess regional food security and to monitor and predict agricultural drought. Agriculture in Kenya is extremely important to both the economy and food security of the country. OBJECTIVEThis study evaluated a regional implementation of a widely used crop model, the Decision Support System for Agrotechnology Transfer (DSSAT), within a coupled modeling framework, the Regional Hydrologic Extremes Assessment System (RHEAS), over Kenya. The goal of this study was to assess the ability of RHEAS to simulate the annual variability of maize yields at the county level and evaluate the uncertainty inherent in the model and inputs. METHODSThe RHEAS system implements a stochastic ensemble approach to account for field scale variabilities in crop management practices and underlying soil and weather conditions. Satellite-derived datasets were used to evaluate the land surface component of the system and seasonally disaggregated yield for 5 years was used to assess the performance of the cropping system model. RESULTS AND CONCLUSIONSThe median correlation between RHEAS and satellite-derived soil moisture and evapotranspiration estimates were 0.78, and 0.51, respectively, indicating that the model is able to capture the key drivers of the hydrological budget. Overall, RHEAS simulated yearly yield variations with a median correlation of 0.7 with reported yields, with the best performance in the short rains season. However, across both seasons, the RHEAS model was positively biased on the order of ∼1.6 MT/ha. The overall median unbiased RMSE was 0.66 MT/ha. The RHEAS system shows skill at simulating extreme departures in anomalies, and a majority of the time (62.5%) the reported yields fall within the interquartile range of the simulations. SIGNIFICANCEOne of the most important areas of improvement for the next generation of agricultural data and models is to better understand and communicate the inherent uncertainties. This is especially critical in data-limited regions. Here we present a modeling system and its implementation that begins to address these concerns. We demonstrate the ability to simulate broad trends in yields at the county level for sub-annual yields with skills that commensurate previous national/annual level studies.

High spatial resolution seasonal crop yield forecasting for heterogeneous maize environments in Oromia, Ethiopia

Seasonal climate variability determines crop productivity in Ethiopia, where rainfed smallholder farming systems dominate in the agriculture production. Under such conditions, a functional and granular spatial yield forecasting system could provide risk management options for farmers and agricultural and policy experts, leading to greater economic and social benefits under highly variable environmental conditions. Yet, there are currently only a few forecasting systems to support early decision making for smallholder agriculture in developing countries such as Ethiopia. To address this challenge, a study was conducted to evaluate a seasonal crop yield forecast methodology implemented in the CCAFS Regional Agricultural Forecasting Toolbox (CRAFT). CRAFT is a software platform that can run pre-installed crop models and use the Climate Predictability Tool (CPT) to produce probabilistic crop yield forecasts with various lead times. Here we present data inputs, model calibration, evaluation, and yield forecast results, as well as limitations and assumptions made during forecasting maize yield. Simulations were conducted on a 0.083° or ∼ 10 km resolution grid using spatially variable soil, weather, maize hybrids, and crop management data as inputs for the Cropping System Model (CSM) of the Decision Support System for Agrotechnology Transfer (DSSAT). CRAFT combines gridded crop simulations and a multivariate statistical model to integrate the seasonal climate forecast for the crop yield forecasting. A statistical model was trained using 29 years (1991–2019) data on the Nino-3.4 Sea surface temperature anomalies (SSTA) as gridded predictors field and simulated maize yields as the predictand. After model calibration the regional aggregated hindcast simulation from 2015 to 2019 performed well (RMSE = 164 kg/ha). The yield forecasts in both the absolute and relative to the normal yield values were conducted for the 2020 season using different predictor fields and lead times from a grid cell to the national level. Yield forecast uncertainties were presented in terms of cumulative probability distributions. With reliable data and rigorous calibration, the study successfully demonstrated CRAFT’s ability and applicability in forecasting maize yield for smallholder farming systems. Future studies should re-evaluate and address the importance of the size of agricultural areas while comparing aggregated simulated yields with yield data collected from a fraction of the target area.

Global sensitivity analysis of APSIM-wheat yield predictions to model parameters and inputs

The performance of cropping system models is often limited by uncertainties in model parameters and inputs. This study aims to explore how model yield prediction responds to these uncertainties under various environmental and management conditions. The Sobol’ method was used to investigate the sensitivity of the Agricultural Production Systems SIMulator (APSIM)-Wheat yield prediction to the following factors: air temperature (maximum and minimum), precipitation, initial soil nitrogen content, nitrogen fertilization amount, and soil hydraulic parameters. Eighteen scenarios were defined to consider a combination of three weather conditions (wet, normal, and dry growing season, based on the historical climatology of the Wimmera district of Victoria, Australia), three soil types (sandy, loamy, and clayey soils), and two nitrogen fertilization applications (50 and 100 kg N/ha). Eight thousand APSIM simulations were used to calculate the first order and total sensitivity indices for each scenario. The effects of weather, soil water characteristics and nitrogen availability were estimated by measuring their impacts on sensitivity indices. Our results show that yield was more sensitive either to variables that control water availability (precipitation and soil type) or variables that control nitrogen availability, depending on which was more limiting to wheat growth under the scenario. For example, in case of low nitrogen fertilization scenario, the initial nitrogen content sensitivity ranked first. Variation of this factor contributed 64% to 79% to the variance in simulated yield for clayey soils, nearly four times higher than the second-ranked factor (nitrogen fertilization amount). Soil hydraulic parameters and precipitation were most important when the crop growth was more constrained by water availability than by nitrogen availability. In the case of sandy soils in dry years with high nitrogen fertilization level, soil parameters and precipitation accounted for 83% of the yield variability. Minimum temperature consistently ranked as the least important factor under all scenarios. This work will help researchers better understand the model inputs’ impacts on the simulated yield variability under various soil, management, and weather conditions. The results also support agronomists and practitioners in the optimal use of the APSIM-Wheat model as a management tool.

Coupling the CSM-CROPGRO-Soybean crop model with the ECOSMOS Ecosystem Model – An evaluation with data from an AmeriFlux site

Process-based simulation models, such as land surface (LSM) and crop models, are useful tools for studying the impacts of the environment, management and genotype on agricultural production. LSM are capable of simulating crop development and growth, but not in as much detail as the processes embedded in crop models. Crop models, on the other hand, do not usually have the ability to solve the surface water, energy and carbon balances, which can help to assess the feedbacks between climate and agricultural systems. The goals of this study were first to implement the well-known Cropping System Model (CSM)-CROPGRO-Soybean model of the Decision Support System for Agrotechnology Transfer (DSSAT) into the Ecosystem Model Simulator (ECOSMOS) LSM and then to evaluate this coupling to simulate surface fluxes and crop performance of irrigated and rainfed soybean agroecosystems with comprehensive data from an AmeriFlux site. After model coupling, simulations were benchmarked against multiple flux (carbon dioxide, energy, and water), phenology, growth, and yield observations obtained from field scale experiments across 14 seasons from irrigated and rainfed fields near Mead, Nebraska. Calibration of the physiological parameters of ECOSMOS and of the CROPGRO-Soybean genetic coefficients was conducted. Common metrics were employed to assess model performance. Overall, the coupled model reproduced both the magnitude and seasonal patterns of the energy balance, carbon dioxide exchange, evapotranspiration and soil water balance. The crop model coupling into ECOSMOS preserves the known high skill of the CROPGRO-Soybean model in simulating soybean phenology and growth dynamics. The simulated yields were consistent with the observations at field level. Although the ECOSMOS-CROPGRO-Soybean model is sufficiently robust to simulate surface fluxes and crop performance of soybean agroecosystems at field scale for the environments of eastern Nebraska, further evaluation for a wide range of climatic and soil conditions and management practices is warranted.

Scientific agenda for climate risk and impact assessment of West African cropping systems

Rainfed agriculture is at the centre of many West African economies and a key livelihood strategy in the region. Highly variable rainfall patterns lead to a situation in which farmers’ investments to increase productivity are very risky and will become more risky with climate change. Process-based cropping system models are a key tool to assess the impact of weather variability and climate change, as well as the effect of crop management options on crop yields, soil fertility and farming system resilience and widely used by the West African scientific community. Challenges to use are related to their consideration of the prevailing systems and conditions of West African farms, as well as limited data availability for calibration. We outline here a number of factors need to be considered if they are to contribute to the scientific basis underlying transformation of farming systems towards sustainability. These include: capacity building, improved models, FAIR data, research partnerships and using models in co-development settings.

APSIM next generation mungbean model: A tool for advancing mungbean production

Mungbean, a grain legume with high nutritional value, is grown widely throughout Asia and increasingly in Australia. Despite growing interest amongst farmers, mungbean remains an inconsistent and thus risky crop to plant in Australia. Cropping system models like the Agricultural Production Systems sIMulator (APSIM) are valuable tools for helping farmers to examine options for improving crop management and assess production risks across potential growing regions for mungbean. This paper outlines the simulation capacity of a new mungbean crop model parameterized using the Plant Modelling Framework in APSIM Next Generation, the newest version of the APSIM framework. The aim of the paper is to document the parameterization and validation processes of the model. The new mungbean model was built using data from 28 field experiments to simulate measured phenology, canopy development, biomass accumulation/partitioning, stress responses, N fixation, root growth, and yield across a wide range of environments. The root mean squared error (RMSE) in predictions for grain weight and aboveground weight were 25.4 g m−2 and 91.4 g m−2, respectively. The model successfully captured the dynamics of crop response to sowing dates, water/irrigation regimes, and climate. The new mungbean model is a robust and accurate tool for use in Australia and tropical/sub-tropical Asia. Researchers can use the new mungbean model to determine best management practices such as the optimal time to sow mungbeans in different environments. The output from model simulations can help farmers assess risks associated with sowing at different times and soil water conditions specific to their region. Such risk analysis can improve farmer decision-making confidence in mungbean, increasing its potential production for Australia. Overall, the new APSIM mungbean model can be used effectively to identify and close the mungbean yield gap, mitigate risk of crop failure, and increase profits for mungbean farmers in Australia and tropical/sub-tropical Asia; it has the capacity to assist with increasing mungbean production globally under changing climate conditions.

Assessment of Future Climate and Kharif Paddy Yields using Ceres-Rice in the State of Andhra Pradesh

Climate change considerably impacts water needs for agricultural production, particularly in paddy crops (Oryza Sativa). The paddy crop response is uncertain and also heterogeneous due to climate change. Climate variables rainfall and temperature directly impact crop productivity. A precise understanding of crop yields is required for agricultural production management to plan sustainable food demand in the future at the state level. The climate change scenario's impact on rice yield at 0.25° × 0.25° spatial resolution was assessed in Andhra Pradesh and presented in this paper. The use of representative concentration Pathway 8.5 scenario in projections made by the Global Climate Model (GCM) were downscaled for mid-century (2048-2078) using statistical tools. In this study, the Cropping System Model (CSM) and Crop Estimation through Resource and Environment Synthesis (CERES) modules for rice, as part of the Decision Support System for Agro-technology Transfer (DSSAT) package, were utilized. The use of the Regional Crop Yield Estimation System (RCYES) for the Cropping System Model (CSM) within the Decision Support System for Agro-technology Transfer (DSSAT) was facilitated through Python in this study. It is observed that rainfall will decrease during winter and pre-monsoon seasons related to the baseline period (1988–2018) for RCP 8.5. From July to October, there was a significant increase in rainfall. The most considerable change in the rain was 50.7 mm in September. A notable variation between the maximum and minimum temperatures of 2.3 and 2.5 degrees Celsius in June and April respectively. Rainfall is expected to increase in Anantapur, Kurnool and Nellore districts during the mid-century 2040's. The correlation between the baseline and DES mean yield was 0.87, with a maximum yield of 0.86 and a minimum yield of 0.82. Decrease paddy yields by up to 10.7% in West Godavari, Krishna, Guntur, Nellore and Prakasam districts. At the same time, an increase in paddy yields up to 9.8% is anticipated in Srikakulam, Visakhapatnam, Vizianagaram, East Godavari, Anantapur, Chittoor, Kadapa, and Kurnool. In contrast, a maximum decrease of 189.9 mm of rainfall is expected in the Vizianagaram district. These results could assist in devising adaptation measures to reduce the negative effect of climate change on rice crops in Andhra Pradesh.

Modelling maize yield impacts of improved water and fertilizer management in southern Africa using cropping system model coupled to an agro-hydrological model at field and catchment scale

AbstractThis study quantifies the effect of fertilizer and irrigation management on water use efficiency (WUE), crop growth and crop yield in sub-humid to semi-arid conditions of Limpopo Province, South Africa. An approach of coupling a cropping system model (DSSAT) with an agro-hydrological model (SWAT) was developed and applied to simulate crop yield at the field and catchment scale. Simulation results indicated that the application of fertilizer has a greater positive effect on maize yield than irrigation. WUE ranged from 0.10–0.57 kg/m3 (rainfed) to 0.84–1.39 kg/m3 (irrigated) and was positively correlated with fertilizer application rate. The combined application of the variants with deficit irrigation and fertilizer rate (120:60 kg N:P/ha) for maize turned out to be the best option, giving the highest WUE and increasing average yield by up to 5.7 t/ha compared to no fertilization and rainfed cultivation (1.3 t/ha). The simulated results at the catchment scale showed the considerable spatial variability of maize yield across agricultural fields with different soils, slopes and climate conditions. The average annual simulated maize yield across the catchment corresponding to the highest WUE ranged from 4.0 to 7.0 t/ha. The yield gaps ranged from 3.0 to 6.0 t/ha under deficit irrigation combined with 120N:60P kg/ha and ranged from 0.2 to 1.5 t/ha when only applying deficit irrigation but no fertilizer. This information can support regional decision makers to find appropriate interventions that aim at improving crop yield and WUE for catchments/regions.

Adapting the CROPGRO model to simulate growth and yield of guar, Cyamopsis tetragonoloba L, an industrial legume crop

Guar (Cyamopsis tetragonoloba L.) is a relatively unexplored seed legume crop with a high interest for production of galactomannan (guar gum), which is highly valued for industrial purposes. Until now no crop simulation models exist for guar. This paper describes the adaptation of the CROPGRO module of the Cropping System Model (CSM) to simulate guar growth and yield based on literature knowledge as well as optimization against growth analysis and yield data collected in multiple field experiments conducted in Texas and New Mexico. CSM-CROPGRO is a mechanistic model that simulates daily crop growth and development dependent on daily weather, soil properties, crop management, and genetic parameters. Model adaptation started with CROPGRO-Soybean as the template and involved setting tissue compositions, and especially the cardinal temperature dependencies of processes including the rate of leaf appearance, rate of progress to reproductive events, photosynthesis, pod addition, and seed growth rate, all parameterized in the species file. Adapted cultivar parameters include life cycle phase durations, duration to end of leaf area expansion, pod adding duration, leaf photosynthesis, seed size, seed protein, seed lipid, and individual seed filling duration. Compared to soybean, guar appears more sensitive to cold temperature because the cardinal base temperatures for photosynthesis, rate of leaf appearance, leaf area expansion, and seed growth had to be increased by 2–4 ˚C. The model adaptations highlight the drought tolerance of guar, with slower leaf and root senescence and greater tolerance of N-fixation under drought. The model simulated well the time-series growth of two cultivars under irrigated and rainfed conditions at Chillicothe, TX and gave acceptable simulations of end-of-season biomass, pod, and seed yield of guar in response to different weather, irrigation, soils, and seasons across three sites. This is the first creation of a crop model for guar and will be released in DSSAT.

Impact of Spatial Soil Variability on Rainfed Maize Yield in Kansas under a Changing Climate

As the climate changes, a growing demand exists to identify and manage spatial variation in crop yield to ensure global food security. This study assesses spatial soil variability and its impact on maize yield under a future climate in eastern Kansas’ top ten maize-producing counties. A cropping system model, CERES-Maize of Decision Support System for Agrotechnology Transfer (DSSAT) was calibrated using observed maize yield. To account for the spatial variability of soils, the gSSURGO soil database was used. The model was run for a baseline and future climate change scenarios under two Representative Concentration Pathways (RCP4.5 and RCP8.5) to assess the impact of future climate change on rainfed maize yield. The simulation results showed that maize yield was impacted by spatial soil variability, and that using spatially distributed soils produces a better simulation of yield as compared to using the most dominant soil in a county. The projected increased temperature and lower precipitation patterns during the maize growing season resulted in a higher yield loss. Climate change scenarios projected 28% and 45% higher yield loss under RCP4.5 and RCP8.5 at the end of the century, respectively. The results indicate the uncertainties of growing maize in our study region under the changing climate, emphasizing the need for developing strategies to sustain maize production in the region.

A new module to simulate surface crop residue decomposition: Description and sensitivity analysis

In the agroecosystem, surface crop residues are widely recognized as affecting many processes such as soil water dynamics, crop growth, nitrogen and carbon cycling. For this reason, developing models that simulate the effect of surface residues and their decomposition is crucial, especially while modeling conservation agriculture. To date, even though many cropping systems and C-oriented models differently simulate the evolution of surface residue biomass, a comprehensive approach is still missing. In this study, we developed a new simulation module that explicitly simulates the decomposition of surface residues, by including all the variables and processes that are relevant for agroecosystem's simulation. This module has been later integrated into the ARMOSA cropping system model. To quantify the contribution of each parameter to the simulated outputs (i.e., decomposed biomass), a sensitivity analysis (SA) was conducted, comparing the result with the APSIM model used as a benchmark. The SA was conducted on four different crop residues (maize, rye, soybean and wheat) over three different years. In addition, for each crop residue, we verified whether parameters changed their relevance depending on the considered time period. The most critical parameters of the new module reflected the importance of air temperature, soil water content and residue biomass in the decomposition process. The potential decomposition rate had minor importance, highlighting that, when setting crop-specific values, other environment-related parameters are more relevant for the actual decomposition rate. In the case of APSIM model, the potential decomposition rate and the optimum temperature for this process resulted in the first two ranks. Finally, concordance coefficients were used to compare SA outputs: compared to APSIM, the new model showed higher concordance passing from one crop residue to another, even when comparing the different simulation periods within the same crop. In summary, this work presented a novelty in surface crop residue representation and provided a deep survey of the module behavior and characteristics.

Application of Parameter Optimization Methods Based on Kalman Formula to the Soil—Crop System Model

Soil-crop system models are effective tools for optimizing water and nitrogen application schemes, saving resources and protecting the environment. To guarantee model prediction accuracy, we must apply parameter optimization methods for model calibration. The performance of two different parameter optimization methods based on the Kalman formula are evaluated for a parameter identification of the soil Water Heat Carbon Nitrogen Simulator (WHCNS) model using mean bias error (ME), root-mean-square error (RMSE) and an index of agreement (IA). One is the iterative local updating ensemble smoother (ILUES), and the other is the DiffeRential Evolution Adaptive Metropolis with Kalman-inspired proposal distribution (DREAMkzs). Our main results are as follows: (1) Both ILUES and DREAMkzs algorithms performed well in model parameter calibration with the RMSE_Maximum a posteriori (RMSE_MAP) values were 0.0255 and 0.0253, respectively; (2) ILUES significantly accelerated the process to the reference values in the artificial case, while outperforming in the calibration of multimodal parameter distribution in the practical case; and (3) the DREAMkzs algorithm considerably accelerated the burn-in process compared with the original algorithm without Kalman-formula-based sampling for parameter optimization of the WHCNS model. In conclusion, ILUES and DREAMkzs can be applied to a parameter identification of the WHCNS model for more accurate prediction results and faster simulation efficiency, contributing to the popularization of the model.

Crop model determined mega-environments for cassava yield trials on paddy fields following rice

The Cropping System Model (CSM)-MANIHOT-Cassava provides the opportunity to determine target environments for cassava (Manihot esculenta Crantz) yield trials by simulating growth and yield data for various environments. The aim of this research was to investigate whether cassava production on paddy fields in Northeast, Thailand could be grouped into mega-environments using the model. Simulations for four different cassava genotypes grown on paddy field following rice harvest was conducted for various soil types and the weather data from 1988 to 2017. The genotype main effect plus genotype by environment interaction (GGE biplot) technique was used to group the mega-environments. The analyses of yearly data showed inconsistent results across years for environment grouping and for the winning genotypes of the individual environment group. An analysis using GGE biplot with the average value of the simulated storage root dry weight (SDW) for 30 years indicated that all 41 environments were grouped into two different mega-environments. This study demonstrated the ability of the CSM-MANIHOT-Cassava to help identify the mega-environments for cassava yield trials on paddy field during off-season of rice that could help reduce both time and resources.

Evaluation of MSWX gridded data for modeling of wheat performance across Iran

Cropping system models (CSMs) are useful tools for developing and optimizing crop management strategies under specific soil and climate conditions. This requires good quality weather data, which is often lacking in many agricultural areas of the world. Studies exposed the capability of gridded weather data to be a proper source in situations where measured weather data is not available. This study assessed the accuracy of a recently published gridded weather dataset called MSWX for modeling of wheat performance in Iran. MSWX has a 0.1°× 0.1° spatial and 3–hourly temporal resolution and covers 1979 to present, and it was initially generated using European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 gridded data. For this study, the CSM-NWheat model within Decision Support System for Agrotechnology Transfer (DSSAT) was applied for modeling of wheat production of three irrigated and three rainfed cultivars at six sites in Iran during 2000–2010 with observed daily weather data. Results showed that MSWX maximum and minimum temperatures (Tmax and Tmin) had higher accuracy than AgERA5, while MSWX solar radiation had weaker performance than AgERA5. The bias of MSWX solar radiation was significantly greater than AgERA5 in some of sites. Comparing the NWheat model outputs based on measured and MSWX temperature (solar radiation) data, grain yield was estimated using MSWX with a MAE of 172 (133) kg/ha and a NRMSE of 11.4% (7.6%). The MSWX temperature (solar radiation) data had high accuracy for simulation of flowering and maturity dates with MAE of 3 and 5 (1 and 3) days and NRMSE of 5.4% and 7% (2.1% and 4.5%). The MSWX temperature data was found as the best replacement for measured temperature data in the study area. Also, it seems that MSWX solar radiation has the potential to be an adequate substitution for measured solar radiation data after further bias correction.

Combining Soil Water Content Data with Computer Simulation Models for Improved Irrigation Scheduling

Highlights Stand-alone irrigation scheduling models were compared to models combined with soil water content data. Use of soil water content data reduced irrigation recommendations compared to stand-alone models. Cotton fiber yield and water productivity were often not significantly impacted by the reduced irrigation amounts. Weather station aridity and other measurement uncertainties must be addressed to improve the methodology. Abstract. Irrigation scheduling models can be used to guide irrigation management decisions, but their simulations often deviate from reality. Combining in-season field data with models may improve the simulations, leading to better irrigation decisions and improved agronomic outcomes. The objective of this study was to evaluate cotton fiber yield and water productivity outcomes from a field trial that compared three computer simulation models for irrigation scheduling: (1) AquaCrop-OSPy (AQC), (2) the CROPGRO-Cotton module within the DSSAT Cropping System Model (CSM), and (3) the pyfao56 evapotranspiration-based, soil water balance model (FAO). Six irrigation scheduling treatments were established, including the three models used as stand-alone scheduling tools (AQC, CSM, and FAO) and the use of the three models in combination with weekly soil water content (SWC) data from neutron moisture meters (AQCSWC, CSMSWC, and FAOSWC). Two cotton varieties were also evaluated (NexGen 3195 and NexGen 4936). The field trial was conducted during the 2021 and 2022 cotton growing seasons at Maricopa, Arizona. Seasonal irrigation amounts were different among irrigation scheduling treatments (p < 0.05), with 9–21% less water recommended for the AQCSWC, CSMSWC, and FAOSWC treatments as compared to AQC, CSM, and FAO. In 2021, the differences in irrigation amount did not lead to any statistical differences in fiber yield among irrigation treatments, but water productivity for the stand-alone CSM model was significantly reduced compared to the other five irrigation treatments (p < 0.05). In 2022, treatments based on soil water content data reduced yield by 15% as compared to stand-alone model treatments, but the reduction was significant only for FAOSWC. Water productivity differences in 2022 were due to the choice of model rather than the inclusion of soil water content data. In both years, the shorter-season cotton variety (NexGen 3195) yielded greater than the longer-season variety (NexGen 4926), and the former achieved greater water productivity than the latter through the yield improvements (p < 0.05). Taken together, the results suggest that combining soil water content data with irrigation scheduling models was useful for reducing irrigation amounts while often maintaining cotton fiber yield and water productivity; however, issues with weather station aridity and other measurement uncertainties must be addressed to improve the methodology. Keywords: Cotton, Evapotranspiration, Irrigation, Management, Sensor, Simulation, Water.

Comparing CSM-CROPGRO and APSIM-OzCot Simulations for Cotton Production and Eddy Covariance-Based Evapotranspiration in Mississippi

Optimizing irrigation water use efficiency (WUE) is critical to reduce the dependency of irrigated cotton (Gossypium spp.) production on depleting aquifers. Cropping system models can integrate and synthesize data collected through experiments in the past and simulate management changes for enhancing WUE in agriculture. This study evaluated the simulation of cotton growth and evapotranspiration (ET) in a grower’s field using the CSM-CROPGRO-cotton module within the Decision Support System for Agrotechnology Transfer (DSSAT) and APSIM (Agricultural Production Systems simulator)-OzCot during 2017–2018 growing seasons. Crop ET was quantified using the eddy covariance (EC) method. Data collected in 2017 was used in calibrating the models and in 2018 validating. Over two cropping seasons, the simulated seedling emergence, flowering, and maturity dates were varied less than a week from measured for both models. Simulated leaf area index (LAI) varied from measured with the relative root mean squared errors (RRMSE) ranging between 20.6% to 38.7%. Daily ET deviated from EC estimates with root mean square errors (RMSEs) of 1.90 mm and 2.03 mm (RRMSEs of 63.1% and 54.8%) for the DSSAT and 1.95 mm and 2.17 mm (RRMSEs of 64.7% and 58.8%) for APSIM, during 2017 and 2018, respectively. Model performance varied with growing seasons, indicating improving ET simulation processes and long-term calibrations and validations are necessary for adapting the models for decision support in optimizing WUE in cotton cropping systems.

Effects of Conventional Tillage and No-Tillage Systems on Maize (Zea mays L.) Growth and Yield, Soil Structure, and Water in Loess Plateau of China: Field Experiment and Modeling Studies

Cropping system models can be useful tools for assessing tillage systems, which are both economically and environmentally viable. The objectives of this study were to evaluate the decision support system for agrotechnology transfer (DSSAT) CERES-Maize model’s ability to predict maize growth and yield, as well as soil water dynamics, and to apply the evaluated model to predict evapotranspiration processes under conventional tillage (CT) and no-tillage (NT) systems in a semi-arid loess plateau area of China from 2014 to 2016. The field experiment results showed that NT increased the surface soil bulk density and water-holding capacity but decreased the total porosity for the surface soil and the maize grain yield. Model calibration for maize cultivar was achieved using grain yield measurements from 2014 to 2016 for CT, and model evaluation was achieved using soil and crop measurements from both CT and NT for the same 3 yr period. Good agreement was reached for CT grain yields for model calibration (nRMSE = 4.02%; d = 0.87), indicating that the model was successfully calibrated. Overall, the results of model evaluation were acceptable, with good agreement for NT grain yields (nRMSE = 4.26%; d = 0.86); the agreement for LAI ranged from good to moderate (RMSE = 0.30‒0.31; d = 0.84‒0.85); the agreement for soil water content was good for NT (RMSE = 0.03‒0.08; d = 0.81‒0.95), but ranged from good to poor for CT (RMSE = 0.06‒0.09; d = 0.42‒0.88); the overall agreement between measured and simulated soil water varied from poor to good depending on soil depth and tillage. It was concluded that the DSSAT CERES-Maize model provided generally good-to-moderate simulations of continuous maize production (yield and LAI) for a short-term tillage experiment in the loess plateau, China, but generally good-to-poor simulations of soil water content.