Crop Simulation Models Research Articles

Climate mitigation sustains agricultural research and development expenditure returns for maize yield improvement in developing countries

Governmental expenditure on agricultural research and development (R&D) has played a substantial role in increasing crop yields in recent decades. However, studies suggest that annual yield growth rates would decline in a warming climate compared to that in a non-warming climate. Here, we present how projected climate could alter maize yield gain owing to a US$ 1 billion increase in agricultural R&D expenditure (referred to as yield response) for 71 maize-producing countries using global gridded crop model simulations with socioeconomic and climate scenarios as inputs. For the middle of this century (2041–2060) under the low warming scenario (shared socioeconomic pathways: SSP126), the median yield response between countries is estimated to be the highest at 27.2% in the low-income group, followed by 6.6% in the lower-middle-income group, 1.0% in the high-income group, and 0.1% in upper-middle-income group. The projected median yield response for lower (the low- and lower-middle)-income groups under the high warming scenario (SSP585) was approximately half than that under the low warming scenario: 27.2% → 15.6% for the low-income, 6.6% → 1.7% for the lower-middle-income, and 1.0% → 0.6% for the high-income groups. For the upper-middle-income group, where there is limited room for adopting high-yielding technology and management already being used in higher (the high- and higher-middle)-income groups, the negative impacts of climate change cannot be offset and yields are projected to decline, even with continued R&D investments (0.1% → –0.2%). Even if the R&D expenditures increase at the same value, expected yield gains will depend on future warming levels. This finding suggests that climate mitigation is a prerequisite for maintaining the yield returns from agricultural R&D investments in developing countries.

An Improved Approach of Winter Wheat Yield Estimation by Jointly Assimilating Remotely Sensed Leaf Area Index and Soil Moisture into the WOFOST Model

The crop model data assimilation approach has been acknowledged as an effective tool for monitoring crop growth and estimating yield. However, the choice of assimilated variables and the mismatch in scale between remotely sensed observations and crop model-simulated state variables have various effects on the performance of yield estimation. This study aims to examine the accuracy of crop yield estimation through the joint assimilation of leaf area index (LAI) and soil moisture (SM) and to examine the scale effect between remotely sensed data and crop model simulations. To address these issues, we proposed an improved crop data-model assimilation (CDMA) framework, which integrates LAI and SM, as retrieved from remotely sensed data, into the World Food Studies (WOFOST) model using the ensemble Kalman filter (EnKF) approach for winter wheat yield estimation. The results showed that the yield estimation at a 10 m grid size outperformed that at a 500 m grid size, using the same assimilation strategy. Additionally, the winter wheat yield estimation accuracy was higher when using the bivariate data assimilation method (R2 = 0.46, RMSE = 756 kg/ha) compared to the univariate method. In conclusion, our study highlights the advantages of joint assimilating LAI and SM for crop yield estimation and emphasizes the importance of finer spatial resolution in remotely sensed observations for crop yield estimation using the CDMA framework. The proposed approach would help to develop a high-accuracy crop yield monitoring system using optical and SAR retrieved parameters.

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.

Projection of Yield of Rice Crop over Prayagraj with Future Climatic Scenario

The purpose of this study was to evaluate the effects of climate change on productivity of rice cultivars Swarna sub-1, Sarjoo-52, Pant Dhan 4, and NDR-359 for the years 2030, 2050, 2070, and 2090 for the Prayagraj district of Uttar Pradesh using the Marksim and DSSAT crop simulation model under four different climate change scenarios, namely RCPs 2.6, 4.5, 6.0, and 8.5. The model evaluation indicated good performance with both calibration (PE=2.43,6.26,4.31,3.79, RMSE= 132.79, 345.13, 237.44, 237.85, nRMSE = 2.53, 7.31, 4.46, 3.97) and validation (PE =3.58, 10.03, 4.28, 6.09, RMSE = 197.88, 456.35, 238.37, 366.52, nRMSE = 3.95, 10.55, 4.67, 6.39) for Swarna sub-1, Sarjoo-52, Pant Dhan 4 and NDR-359 cultivars respectively, which showed good agreement between anticipated and observed values. For the Prayagraj region, NDR-359 yields the most among the four varieties, followed by Pant Dhan 4, Swarna Sub-1, and Sarjoo-52. By examining the future climate data that MarkSim's weather generator downloaded, it was noticed that all other weather variables, such as solar radiation, average maximum and minimum temperature increases while rainfall decreases. The findings of the study reveal that, among all projection scenarios, the grain yield is greater for RCP 2.6 and lowest for RCP 8.5. Analysis of the expected climate scenario data with yield revealed that the yield was higher in 2030 and lower in 2050. Additionally, it was observed that again in 2090 the yield gets increased for RCP 2.6 & decreased for RCP 4.5,6.0 & 8.5. Among all the four cultivars the yield of Sarjoo-52 will be reduced for RCP 4.5,6.0 & 8.5 for the year of 2050,2070 & 2090.These findings may offer insightful information about possible climate change effects on rice yield and suitable adaptive strategies to minimize the negative effects of future climate change.

Narrowing maize yield gaps across smallholder farming systems in Zambia: what interventions, where, and for whom?

Maize production in Zambia must increase with a view towards improved food security and reduced food imports whilst avoiding cropland expansion. To achieve this, it is important to understand the causes behind the large maize yield gaps observed in smallholder farming systems across the country. This is the first study providing a yield gap decomposition for maize in Zambia, and combining it with farm typology delineation, to identify the key limiting factors to maize yield gaps across the diversity of farms in the country. The analysis builds upon a nationally representative household survey covering three growing seasons and crop model simulations to benchmark on-farm maize yields and N application rates. Three farm types were delineated, including households for which maize is a marginal crop, households who are net buyers of maize, and households who are market-oriented maize producers. Yield gap closure was about 20% of the water-limited yield, corresponding to an actual yield of 2.4 t ha− 1. Market-oriented maize farms yielded slightly more than the other farm types, yet the drivers of yield variability were largely consistent across farm types. The large yield gap was mostly attributed to the technology yield gap indicating that more efficient production methods are needed to raise maize yields beyond the levels observed in highest yielding fields. Yet, narrowing efficiency and resource yield gaps through improved crop management (i.e., sowing time, plant population, fertilizer inputs, and weed control) could more than double current yields. Creating a conducive environment to increase maize production should focus on the dissemination of technologies that conserve soil moisture in semi-arid areas and improve soil health in humid areas. Recommendations of sustainable intensification practices need to consider profitability, risk, and other non-information constraints to improved crop management and must be geographically targeted to the diversity of farming systems across the country.

Crop models for assessing impact and adaptation options under climate change

Increased amount of green house gases (GHG) in the atmosphere will cause climate change that will adversely impact crop production especially in the arid and semi-arid regions of the developing countries. Development and implementation of field level adaptations measures to cope up with climate change are necessary to the farmers whose livelihood depends on crop-based income. Crop simulation models that incorporate soil-crop-climate processes of plant growth and that are sensitive to climate change factors can be used to quantify impact of climate change on crop production and evaluating and prioritizing adaptation measures at farm level. This paper analyses the impacts of climate change and plausible agronomic, land and water management and genetic adaptations options for the major crops of the semi-arid tropical region with examples from selected sites in India and other developing countries. The crop models need to be linked to the improved pest, disease and weed models to analyze and predict yield losses, especially those due to climate change. The simulation models also need to incorporate the impact of extreme weather events on crop production that is projected to increase with climate change.

Revisiting statistical spectral-agrometeorological wheat yield models for Punjab using MODIS EVI and NCMRWF re-analysis temperature data

Use of space-based spectral information with weather inputs for wheat yield modeling by empirical and crop simulation models is reviewed and extended with enhanced spectral modeling approach for districts in central Punjab. The study uses multi-date and multi-year MODIS data at 250m resolution to both identify wheat crop and develop temporal spectral Enhanced Vegetation Index (EVI) profile for 2001-2019 period. Recently developed high resolution (12km) gridded temperature data from NCMRWF, namely India Monsoon Data Assimilation and Analysis (IMDAA) has been used for computing district-level average of daily (AV) and night-time (NT) temperatures. Multiple linear regression analysis with statistical tests on significance of coefficients for various inputs is used to investigate significance of various input parameters in yield models on multi-district data set. Results identify both area under spectral profile (AS-EVI) and mean peak value (PK-EVI) have significant control on yield. Individual district-level trend based yield (YT) is a significant coefficient in the multi-district models. Model performance is significantly improved by the inclusion of phase-specific temperatures and at specific post-flowering phases the night temperature figured in best models. Significance of the results in development of spatially resolved yields for applications like yield forecast, crop insurance and climate change studies is discussed.

Modelling nitrogen management in hybrid rice for coastal ecosystem of West Bengal, India.

Hybrid rice requires adequate nitrogen (N) management in order to achieve good yields from its vegetative and reproductive development. With this backdrop, a field experiment was conducted at Regional Research Station (Coastal Saline Zone), Bidhan Chandra Krishi Viswavidyalaya, Kakdwip, West Bengal (India) to record growth and yield performance of hybrid rice (cv. PAN 2423) under varied N-fertilizer doses. A modelling approach was adopted for the first time in hybrid rice production system under coastal ecosystem of West Bengal (India). In the present study, the Agricultural Production Systems Simulator (APSIM) model was calibrated and validated for simulating a hybrid rice production system with different N rates. The APSIM based crop simulation model was found to capture the physiological changes of hybrid rice under varied N rates effectively. While studying the relationship between simulated and observed yield data, we observed that the equations developed by APSIM were significant with higher R2 values (≥0.812). However, APSIM caused an over-estimation for calibrate data but it was rectified for validated data. The RMSE of models for all the cases was less than respective SD values and the normalized RMSE values were ≤20%. Hence, it was proved to be a good rationalized modelling and the performance of APSIM was robust. On the contrary, APSIM underestimated the calibrated amount of N (kg ha-1) in storage organ of hybrid rice, which was later rectified in case of validated data. A strong correlation existed between the observed and APSIM-simulated amounts of N in storage organ of hybrid rice (R2 = 0.94** and 0.96** for the calibration and validation data sets, respectively), which indicates the robustness of the APSIM simulation study. Scenario analysis also suggests that the optimal N rate will increase from 160 to 200 kg N ha-1 for the greatest hybrid rice production in coming years under elevated CO2 levels in the atmosphere. The APSIM-Oryza crop model had successfully predicted the variation in aboveground biomass and grain yield of hybrid rice under different climatic conditions.

Modeling adaptation of sorghum in Ethiopia with APSIM—opportunities with G×E×M

Sorghum is an important food and feed crop in the dry lowland areas of Ethiopia. Farmers grow both early-sown long-duration landraces and late-sown short-duration improved varieties. Because timing and intensity of drought stress can vary in space and time, an understanding of major traits (G), environments (E), management (M), and their interactions (G×E×M) is needed to optimize grain and forage yield given the limited available resources. Crop simulation modeling can provide insights into these complex G×E×M interactions and be used to identify possible avenues for adaptation to prevalent drought patterns in Ethiopia. In a previous study predictive phenology models were developed for a range of Ethiopian germplasm. In this study, the aims were to (1) further parameterize and validate the APSIM-sorghum model for crop growth and yield of Ethiopian germplasm, and (2) quantify by simulation the productivity-risk trade-offs associated with early vs late sowing strategies in the dry lowlands of Ethiopia. Field experiments involving Ethiopian germplasm with contrasting phenology and height were conducted under well-watered (Melkassa) and water-limited (Miesso) conditions and crop development, growth and yield measured. Soil characterization and weather records at the experimental sites, combined with model parameterization, enabled testing of the APSIM-sorghum model, which showed good correspondence between simulated and observed data. The simulated productivity for the Ethiopian dry lowlands environments showed trade-offs between biomass and grain yield for early and late sowing strategies. The late sowing strategy tended to produce less biomass except in poor seasons, whereas it tended to produce greater grain yield except in very good seasons. This study exemplified the systems approach to identifying traits and management options needed to quantify the production-risk trade-offs associated with crop adaptation in the Ethiopian dry lowlands and further exemplifies the general robustness of the sorghum model in APSIM for this task.

Remote sensing crop group-specific indicators to support regional yield forecasting in Europe

Operational crop yield forecasting services typically provides crop yield forecasts based on regression models between official yields and agro-environmental variables, among which meteorological data, crop simulation model or satellite-derived indicators. The reliability with which these variables infer on yields depends, among many other factors, also on their aggregation in the space domain, for example on the type of the crop masks utilized in the aggregation process from point to regional scale. This work investigates how the yield explanatory power of satellite-derived indicators is changing moving from time-stable arable land masks to annual crop group-specific masks. We compare in particular the linkage between time series of regional crop yield in Europe and the Normalized Difference Vegetation Index (NDVI) from Moderate Resolution Imaging Spectroradiometer (MODIS), when generic arable land masks or crop group-specific and year-specific information are applied to aggregate pixel values at regional level. Regional (Eurostat level NUTS-2) crop yield statistics were collected from official databases for the period 2003–2019, while NDVI data were derived from MODIS daily products at 250 m spatial resolution for the same reference period. Regional NDVI profiles were retrieved by averaging single pixels time series according to the information of five crop masks, including generic arable land masks, crop group-specific static masks (separating winter and spring crops from summer crops), and annual crop group-specific masks (distinguishing between the two crop groups and varying in time). A compared correlation analysis between yield data and regional temporal NDVI profiles was performed assuming a linear regression model. Coefficient of determination R2 and Root Mean Squared Error (RMSE) were computed to assess the models’ errors and to analyze the effect of the aggregation on the different crop masks. Results indicated an improvement in yield estimation when using annual crop group-specific indicators with respect to generic and static products. Although not homogeneously distributed throughout Europe, advantages have been highlighted both in terms of accuracy and timeliness of the prediction. In most regions, the introduction of annual masks allowed to reduce RMSE values by 0.3 t/ha and advanced forecast times by up to 30 days. The added value in the use of annual crop group-specific masks concerned the two European most cultivated crops, namely soft wheat and grain maize.

Developing a tactical nitrogen fertilizer management strategy for sustainable wheat production

Syncronizing plant available water with soil nitrogen (N) remains a critical aspect of agronomic management to enhance crop yield, grain quality, farmers’ profit, and environmental sustainability. Their interaction is essential expecially in landscapes characterized by a highly spatial and temporal range of pedoclimatic conditions. To support farmers in making more informed decisions, validate dynamic process based crop simulation models have been successfully used to optimize N fertlization. In this study, we aimed to develop a tactical N fertilizer management strategy to increase profitability, improve grain quality and reduce N losses. The SALUS model was tested against measured durum wheat grain yield and grain quality data collected across independent farmers’ fields in Tuscany (Italy). The model was then used to optimize N fertilization under different potential plant extractable soil water (PESW) conditions at the second topdressing N fertilization timing. The model was tested against measurements of grain yield and protein concentration at harvest, as well as phenological stage, biomass, and plant N content during the growing season. Simulations were carried out for 30 years of available weather, using different N rates. The simulations allowed the identification of optimal N rates for each PESW condition and soil type concerning economic and environmental sustainability. Results showed higher yield and higher leaching for the silty clay soil (Quercia; QUE) than for the loamy soil (Arbia; ARB). No major differences were predicted for protein content across soils. Profitability and emissions increased as N rate increased. The N fertilization strategy locally adopted by farmers was also analyzed across different PESW conditions at 2nd topdressing fertilization in comparison to other adopted N management strategies (timing of application and fertilization rates). The model showed that the conventional fertilization strategy does not maximize socio-ecological benefits, but was also the lowest among all the strategies tested. The maximum economic benefit for farmers was reached by applying 90 kg N at 1st and 60 kg N at 2nd topdressing fertilization, both in dry (due to sufficient soil water storage in the soil) and wet years. These results provide valuable insights for developing strategies that balance sustainability, resilience, quality, and profitability in wheat crop production in central Italy.

Calibration and Validation of CROPGRO-cowpea Model of DSSAT for Four Cowpea Varieties under Drought Stress

The Decision Support System for Agrotechnology Transfert (DSSAT) is a software comprising crop simulation models. The aim of this study was to: Calibrate the CROPGRO-cowpea model of DSSAT for four cowpea varieties, and Validate the model using a data set collected under drought stress For this purpose, experiments were carried out in 2020 and 2021 dry seasons in Burkina Faso at Kamboinsin in the Centre of Environmental and Agricultural Research and Training (CREAF). The model calibration was done using data including days to 50% flowering, days to maturity, above-ground biomass, and grain yield per hectare collected from an experiment without water and nutrient stress, and the GenCalc software was used for estimating the genetic coefficients of the varieties. Data from two drought stress treatments, such as D1: drought at seedling stage, and D2: drought at flowering stage, were used for validating the model. The results of the calibration showed that the model excellently simulated the days to flowering with a normalized root mean square error (nRMSE) of less than 10% and a high degree of agreement for all the varieties. The simulation of the days to physiological maturity was excellent for 50% of the varieties (nRMSE<10%) and good for the others (10 < nRMSE < 20%). The simulation of the grain yield per hectare ranged from excellent to good. Poor prediction of the above-ground biomass was attained for 75% of the varieties (nRMSE ≥ 40%), while the fair simulation was recorded for 25% (nRMSE=27%) during the model calibration. The statistics of the validation process showed an excellent simulation of the days to flowering (nRMSE= 1.85%; R2=0.98; d-index=0.99) and a good prediction of the days to maturity (nRMSE=13.82; R2=0.87; d-index=0.53) for all the varieties. Simulated above-ground biomass was in poor agreement with the observed values (nRMSE=106.62%; R2=0.92; d-index=0.36). Fair prediction of the grain yield by the model was achieved during the validation (nRMSE = 27%). From these results, it can be concluded that the DSSAT model can be considered an efficient tool for predicting cowpea phenology, growth, and yield in optimum conditions of development. However, in drought stress conditions, the sensitivity or tolerance status to the drought of the variety can reduce the accuracy of the grain yield prediction by the model.

On the sustainability of paddy rice cultivation in the Paraíba do Sul river basin (Brazil) under a changing climate

Located in Southeast Brazil, the Paraíba do Sul River Basin is considered one of the most strategic in the country since it is the main source of fresh water for more than 14.3 million people. The population growth in recent decades, in combination with increased demand for water for industrial and agricultural activities, has generated concerns about how the combined effect of land uses and global climate change might affect water availability in the future. Once irrigation is one of the main water consumers, especially for paddy rice cultivation, in this study we used a crop simulation model to determine the water requirements of paddy rice under current and future climate conditions based on three different regionalized climate scenarios and for different land use and land cover change projections. In addition, we estimated water footprints for different irrigation and soil fertility strategies. Finally, we explored adaptation actions that can lead to more sustainable production. Projections indicate an increase in the productivity and efficiency of irrigated rice. Intermittent flooding proved to be an efficient alternative, rather than the traditional practice of continuous ponding. Most scenarios projected a gradual decrease in water footprint for rice cultivated using irrigation and soil fertility management. On the other hand, the water footprint under rainfed and non-optimum soil fertility resulted in higher water footprints. Due to changes in land use and land cover, mainly urban sprawl, the rice areas are expected to decrease in the future reducing the production in the region, which is expected to decrease by almost 10% in the period 2016–2035 and up to 38% in the period 2036–2055. This trend can be compensated by adopting sustainable practices in rice cultivation, such as rational soil fertility and water management, which can boost the current level of productivity by 50% and reduce water use by up to 15%.

ELEVATION OF WHEAT CULTIVARS AT VARIOUS LEVELS OF NITROGEN THROUGH FIELD EXPERIMENTATION AND MODELING APPROACH IN PUNJAB, PAKISTAN


 
 
 The present study was conducted to evaluate the growth of wheat cultivars at different nitrogen (N) levels through experimental and crop simulation model in Sialkot District of Punjab, Pakistan. The data was collected from the fields as well as the weather and soil data obtained from the relevant departments. The experiment was conducted for two wheat cultivars with four treatments of N i.e. 0, 55, 110, 220 kg N per hector. Overall, the results indicated that the field level observed data showed significantly positive correlation with the model simulated growth and yield variables. The findings of this study showed that out of the two wheat cultivars i.e. Faisalabad-2008 showed earlier anthesis and maturity as compared to the Sehar-2006, however, the wheat yield, leaf area index, and harvest index was higher for Sehar-2006. From these results, it can be concluded that Sehar-2006 has more productivity and must be encouraged as compared to Faisalabad-2008.
 
 

Evaluating Agronomic Onset Definitions in Senegal through Crop Simulation Modeling

Rainfed agriculture in Senegal is heavily affected by weather-related risks, particularly timing of start/end of the rainy season. For climate services in agriculture, the National Meteorological Agency (ANACIM) of Senegal has defined an onset of rainy season based on the rainfall. In the field, however, farmers do not necessarily follow the ANACIM’s onset definition. To close the gap between the parallel efforts by a climate information producer (i.e., ANACIM) and its actual users in agriculture (e.g., farmers), it is desirable to understand how the currently available onset definitions are linked to the yield of specific crops. In this study, we evaluated multiple onset definitions, including rainfall-based and soil-moisture-based ones, in terms of their utility in sorghum production using the DSSAT–Sorghum model. The results show that rainfall-based definitions are highly variable year to year, and their delayed onset estimation could cause missed opportunities for higher yields with earlier planting. Overall, soil-moisture-based onset dates determined by a crop simulation model produced yield distributions closer to the ones by semi-optimal planting dates than the other definitions, except in a relatively wet southern location. The simulated yields, particularly based on the ANACIM’s onset definition, showed statistically significant differences from the semi-optimal yields for a range of percentiles (25th, 50th, 75th, and 90th) and the means of the yield distributions in three locations. The results emphasize that having a good definition and skillful forecasts of onset is critical to improving the management of risks of crop production in Senegal.

Effect of variation in the observations on the prediction uncertainty in crop model simulation: Use ORYZA (v3) as a case study

The reliability of crop models for predicting ecophysiological processes determines applicability of models in modern agricultural research and production. However, many uncertainties in the modeling processes are strangling the extension of model application. Model parameterization is one of the major sources of prediction uncertainties, which depends on the Observations used for model Calibration (OC). In this study, we conducted a two-year field experiment to evaluate the effect of variation in OC on model prediction error and uncertainty. Model performance was analyzed after the calibration of each dataset. We found that model bias was the dominant factor for the overall prediction error with the low variation of OC. Model uncertainty would contribute more to overall prediction error when OC increased to a threshold of circa 14.5%. The low variation of OC resulted in a lower variation of prediction error (0.04%–26.8%) than that of the high variation of OC (0.4%–71.8%). The variation of prediction error was different across environments that model simulated. Besides, the prediction error and uncertainty of in-season variables, such as biomass and leaf area index, etc., were also more significant than end-season variables, such as final grain yield. Our findings highlighted that the variability in observations was a major contributor to model prediction uncertainty during the calibration process. We recommend that efforts in uncertainty reduction should include quantitatively assessing in the suitability of datasets for the purpose of calibration, together with making a specific calibration strategy based on the simulation targets.

Modeling management of continuous dryland cotton with an intervening winter wheat cover crop in a semiarid climate

Although winter cover crop residue can mitigate the stresses of dryland production in semi-arid regions, cover crops can also reduce soil moisture and cash crop yields. In some field studies of dryland cotton grown after terminated winter wheat in the U.S. Southern High Plains (SHP) and Texas Rolling Plains cotton yields were increased relative to continuous cotton, while others had no significant yield or soil water effect. These uncertain outcomes may be due to the trial's limited sampling of seasonal rainfall conditions. To estimate the probabilities of cover crop effects under more representative SHP climate conditions, 294 station-years of crop model simulations of terminated winter wheat followed by dryland cotton were conducted. These simulations were driven by weather data from 21 SHP weather stations during 2005–2019. Each station-year's simulations were repeated under 54 combinations of wheat planting, termination, and cotton planting dates, 2 soil series with different water capacities, and 10 initial soil moisture conditions. When simulations begin with fall soil moisture at field capacity optimal management options for both soils plant wheat early and cotton late, but have different wheat termination dates. Before cotton planting winter cover crop effects are dominated by reduced surface evaporation and increased transpiration, with greater transpiration effects producing decreased column soil moisture (CSM) at wheat termination. Some soil moisture recharge occurs between termination and cotton planting, but cover crops reduce CSM at cotton planting in both soils in ~75% of simulations. Reduced soil evaporation and soil moisture recovery continues after cotton planting, resulting in positive effects on seed cotton yield in 50% of the silty clay loam simulations and in 67% of the fine sandy loam simulations. Gradually reducing initial fall soil moisture in the silty clay loam reduces wheat biomass but increases the incidence of positive effects on seed cotton yields and CSM at cotton planting and harvest. By contrast, drier initial soil moisture in the fine sandy loam had relatively minor yield and CSM effects. In both soils terminated wheat residue led to increased CSM at cotton harvest in at least 70% of the simulations regardless of soil moisture at wheat planting.

Climate adaptive rice planting strategies diverge across environmental gradients in the Indo-Gangetic Plains

Abstract The timing of rice planting has a profound influence on the productivity of the rice-wheat cropping pattern in the Indo-Gangetic Plains (IGP), a system that provides the foundation for food security in South Asia. Nevertheless, strategies for adaptive rice planting in a rapidly changing climate are not well established. In this ex-ante analysis, regional gridded crop model simulations are deployed to investigate the impact of different rice planting strategies on system level productivity, resilience, and environmental benefits. Our results suggest that synchronizing rice planting dates with the monsoon onset substantially outperforms farmer practice (+41%) and static state recommendations in the Eastern IGP. However, planting long-duration rice with the monsoon onset is ineffective in the Northwestern IGP since the later arrival of the monsoon increases the probability of cold damage to rice and terminal heat stress in wheat. Here, fixed planting dates (+12.5%) or planting medium duration varieties at monsoon onset (+18%) performed best. We conclude that resilient and productive rice planting strategies must account for interannual weather variability and divergent climate conditions across sub-regions in the IGP.

Combining remote sensing-derived management zones and an auto-calibrated crop simulation model to determine optimal nitrogen fertilizer rates

CONTEXTCotton is an economically important crop in Australia that requires high resource application, particularly that of nitrogen (N) fertilizers. Determining optimal N fertilizer rates that reach both economic and environmental objectives is a key challenge in cotton systems because of the inherent within-field variability and relatively low N fertilizer use efficiency (NFUE). OBJECTIVEThis study aimed to model optimal N fertilizer rates by accounting for within-field variability through management zone (MZ) delineation across a cotton field in Queensland, Australia. METHODSMZs were delineated using satellite-derived normalized difference vegetation index (NDVI) and the crop simulation model, Decision Support System for Agrotechnology Transfer (DSSAT), was automatically calibrated with a grid-search optimization algorithm and validated across two cotton seasons. A total of 336 different N fertilizer scenarios were subsequently evaluated at pre-planting and top-dressing to observe the effect on profit margin and NFUE. RESULTS AND CONCLUSIONSThe MZ delineation analysis determined that within-field variability could be best represented by two MZs, one of which displaying subsoil constraints due to high carbonate concentrations. The use of the auto-calibration algorithm led to a successful validation of the model with a Wilmott d-index of agreement ranging between 0.75 (soil nitrate) and 0.96 (aboveground biomass and plant N), respectively. The subsequent N scenario simulations indicated that by reducing N fertilizer rates by 80 and 30 kg N ha−1 across the two MZs, respectively, compared to the current industry average, profits could be maximized through maintaining yields while reducing N inputs. SIGNIFICANCEOverall, these results demonstrate the potential of combining remote sensing-derived MZs and crop model auto-calibration techniques to support the cotton industry in achieving improved resource efficiency and profit margins.

Climate Change Impacts Assessment Using Crop Simulation Model Intercomparison Approach in Northern Indo-Gangetic Basin of Bangladesh

The climate change impacts of South Asia (SA) are inextricably linked with increased monsoon variability and a clearly deteriorating trend with more frequent deficit monsoons. One of the most climate-vulnerable nations in the eastern and central Indo-Gangetic Basin is Bangladesh. There have been numerous studies on the effects of climate change in Bangladesh; however, most of them tended to just look at a small fraction of the impact elements or were climatic projections without accounting for the effects on agriculture. Additionally, simulation studies using the CERES-Rice and CERES-Wheat models were conducted for rice and wheat to evaluate the effects of climate change on Bangladeshi agriculture. However, up to now, Bangladesh has not implemented farming system ideas by integrating cropping systems with other income-generating activities. This study was conducted as part of the Indo-Gangetic Basin (IGB) regional evaluations using the protocols and integrated assessment processes of the Agricultural Model Intercomparison and Improvement Project (AgMIP). It was also done to calibrate crop models (APSIM and DSSAT) using rice and wheat. To assist policymakers in creating national and regional plans for anticipated future agricultural systems, our work on the integrated evaluation of climate change impacts on agricultural systems produced realistic predictions. The outcome of this research prescribes a holistic assessment of climate change on future production systems by including all the relevant enterprises in the agriculture sector. The findings of the study suggested two major strategies to minimize the yield and increase the profitability in a rice-wheat cropping system. Using a short-term HYV (High Yielding Variety) of rice can shift the sowing time of wheat by 7 days in advance compared to the traditional sowing days of mid-November. In addition, increasing the irrigation amount by 50 mm for wheat showed a better yield by 1.5-32.2% in different scenarios. These climate change adaptation measures could increase the per capita income by as high as 3.6% on the farm level.