Optimum sowing dates for soybean in central India using CROPGRO and ClimProb symbiosis

Kumar, Rajesh; Singh, K. K.; Gupta, B. B. P.; Baxla, A. K.; Rathore, L. S.; Attri, S. D.

doi:10.1017/s1350482702002104

Cited by 12 publications

(9 citation statements)

References 7 publications

(9 reference statements)

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“…Changing the value of vernalization sensitivity resulted in a lengthy or reduced vegetative growth period, thereby affecting the biomass accumulation in APSIM wheat. Our findings regarding the total dry matter in CERES-Wheat were in accordance to some other studies [50,56] under semi-arid environment while a sub-tropical study was supported by Andarzian et al [57]. In the ricewheat cropping system, rice straw acts as surface mulch which alters soil temperature in the seed zone that ultimately affects the rate of germination, development, and growth [58].…”

Section: Leaf Area Index (Lai) and Total Dry Mattersupporting

confidence: 91%

Crop Models: Important Tools in Decision Support System to Manage Wheat Production under Vulnerable Environments

et al. 2021

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Decision support systems are key for yield improvement in modern agriculture. Crop models are decision support tools for crop management to increase crop yield and reduce production risks. Decision Support System for Agrotechnology Transfer (DSSAT) and an Agricultural System simulator (APSIM), intercomparisons were done to evaluate their performance for wheat simulation. Two-year field experimental data were used for model parameterization. The first year was used for calibration and the second-year data were used for model evaluation and intercomparison. Calibrated models were then evaluated with 155 farmers’ fields surveyed for data in rice-wheat cropping systems. Both models simulated crop phenology, leaf area index (LAI), total dry matter and yield with high goodness of fit to the measured data during both years of evaluation. DSSAT better predicted yield compared to APSIM with a goodness of fit of 64% and 37% during evaluation of 155 farmers’ data. Comparison of individual farmer’s yields showed that the model simulated wheat yield with percent differences (PDs) of −25% to 17% and −26% to 40%, Root Mean Square Errors (RMSEs) of 436 and 592 kg ha−1 with reasonable d-statistics of 0.87 and 0.72 for DSSAT and APSIM, respectively. Both models were used successfully as decision support system tools for crop improvement under vulnerable environments.

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Section: Leaf Area Index (Lai) and Total Dry Mattersupporting

confidence: 91%

Crop Models: Important Tools in Decision Support System to Manage Wheat Production under Vulnerable Environments

et al. 2021

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“…DSSAT shell allows the users to organize and manipulate crop, soil and weather data, to run crop models in various ways, and to analyze their outputs 43 , 44 . CROPGRO-Soybean which is one of the models running under DSSAT, is a generic physiological process-oriented legume crop model 45 . The CSM-CROPGRO-Soybean model within the DSSAT suite simulates plant growth and development from sowing to maturity using a daily time step, and ultimately predicts yield 24 , 26 , 46 .…”

Section: Methodsmentioning

confidence: 99%

Evaluation and application of the CROPGRO-soybean model for determining optimum sowing windows of soybean in the Nigeria savannas

Bebeley

Kamara

Jibrin

et al. 2022

Sci Rep

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Soybean production is limited by poor soil fertility and unstable rainfall due to climate variability in the Nigeria savannas. There is a decline in the amount and duration of rainfall as one moves from the south to north of the savanna zones. The use of adapted soybean varieties and optimum sowing windows are avenues to increase productivity in the face of climate variability. Crop simulation models can be used as tools for the evaluation of alternative management options for a particular location, including fertilizer application rates, plant density, sowing dates and land use. In this study, we evaluated the performance of the Cropping System Model (CSM)-CROPGRO-Soybean to determine optimum sowing windows for three contrasting soybean varieties (TGX1835-10E, TGX1904-6F and TGX1951-3F) cultivated in the Nigeria savannas. The model was calibrated using data from ten field experiments conducted under optimal conditions at two sites (BUK and Dambatta) in Kano in the Sudan savanna (SS) agro-ecology over four growing seasons. Data for model evaluation were obtained from independent experiment for phosphorus (P) response trials conducted under rainfed conditions in two locations (Zaria and Doguwa) in the northern Guinea savanna (NGS) zone. The model calibration and evaluation results indicated good agreement between the simulated and observed values for the measured parameters. This suggests that the CROPGRO-Soybean model was able to accurately predict the performance of soybean in the Nigeria savannas. Results from long-term seasonal analysis showed significant differences among the agro-ecologies, sowing windows and the soybean varieties for grain yield. Higher yields are simulated among the soybean varieties in Zaria in the NGS than in Kano the SS and Jagiri in the southern Guinea savanna (SGS) agro-ecological zones. Sowing from June 1 to July 5 produced optimal yield of TGX1951-3F and TGX1835-10E beyond which yield declined in Kano. In Zaria and Jagiri the simulated results show that, sowing from June 1 to July 12 are appropriate for all the varieties. The variety TGX1951-3F performed better than TGX1904-6F and TGX1835-10E in all the agro-ecologies. The TGX1951-3F is, therefore, recommended for optimum grain yield in the savannas of northern Nigeria. However, the late maturing variety TGX1904-6F is not recommended for the SS due to the short growing season in this zone.

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“…Tovihoudji [14] and several other researchers notified that the DSSAT CERES-Maize module implemented in DSSAT [15] for simulating maize growth and yield is suitable for a broad range of soil, management, and climatic conditions in comparison to APSIM [16][17][18][19]. Many researchers have already used the DSSAT model to get good crop management practices to minimize production risks [20][21][22][23][24][25]. Soler et al [26] used this model to forecast maize yield.…”

Section: Introductionmentioning

confidence: 99%

Optimum Sowing Window and Yield Forecasting for Maize in Northern and Western Bangladesh Using CERES Maize Model

Choudhury

Molla

Zahan³

et al. 2021

Agronomy

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Determination of the optimum sowing window not only can improve maize yield significantly but also can fit maize in the existing cropping pattern. To get the advantages of sowing maize at the optimum time, a study was designed and carried out at the research field of Bangladesh Agricultural Research Institute, Rangpur, Bangladesh during 2015–2017. Another aim of the study was to forecast the yield of maize for the northern and western regions of Bangladesh using the CERES-Maize model. The study considered 5 November, 20 November, 5 December, 20 December, and 5 January as sowing dates for maize to identify the optimum sowing window. Three hybrid maize varieties, viz., BARI Hybrid Maize-9 (BHM-9), NK-40, and Pioneer30V92 were used. The study was laid out in a split-plot design, assigning the sowing dates in the main plot and the varieties in the sub-plot. To forecast the yield, the daily weather data of 2017 were subjected to run the model along with thirty years (1986–2015) of weather data. The genetic coefficients of the tested maize varieties were obtained through calibration of the model by using the observed field data of 2015–2016 and through validation by using the data of 2016–2017. The seasonal analysis was done using the DSSAT CERES-Maize model to confirm the experimental findings for optimizing the sowing window for maize at the northern region (Rangpur) of the country and subsequently adjusted the model for the western region (Jashore). The model performances were satisfactory for crop phenology, biomass, and grain yield. The NRMSE for anthesis was 0.66% to 1.39%, 0.67% to 0.89% for maturity date, 1.78% to 3.89% for grain yield, and 1.73% to 3.17% for biomass yield. The optimum sowing window for maize at the Rangpur region was 5 November to 5 December and 5 to 20 November for the Jashore region. The CERES-Maize model was promising for yield forecasting of the tested maize varieties. It gave a realistic yield forecast at approximately 45 days prior to the harvest of all the tested varieties. The study results are expected to be useful for both the farmers and the policy planners to meet up the future maize demands.

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Optimum sowing dates for soybean in central India using CROPGRO and ClimProb symbiosis

Abstract: The optimum sowing dates for soybean cv. Gaurav

Cited by 12 publications

References 7 publications

Crop Models: Important Tools in Decision Support System to Manage Wheat Production under Vulnerable Environments

Crop Models: Important Tools in Decision Support System to Manage Wheat Production under Vulnerable Environments

Evaluation and application of the CROPGRO-soybean model for determining optimum sowing windows of soybean in the Nigeria savannas

Optimum Sowing Window and Yield Forecasting for Maize in Northern and Western Bangladesh Using CERES Maize Model

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