Application of the CERES-Rice Model for Rice Yield Gap Analysis

Bhanusree, D.; Srinivasachary, D.; Nirmala, B.; Supriya, K.; Naik, B. Balaji; Rathod, Santosha; Brajendra,; Reddy, Bojjareddy Nanda Kumar; Bellamkonda, Jyostna

doi:10.9734/ijecc/2022/v12i111396

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…The model yielded an average district yield of 5350 kg/ha, comparable to the official statistics of 5014 kg/ha [53]. Based on the observations, it can be asserted that the model exhibits a high degree of reliability in accurately projecting crop yields over a range of different management approaches.…”

Section: Discussionsupporting

confidence: 51%

Crop Yield Assessment Using Field-Based Data and Crop Models at the Village Level: A Case Study on a Homogeneous Rice Area in Telangana, India

Mandapati,

Gumma,

Metuku

et al. 2023

AgriEngineering

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Crop yield estimation has gained importance due to its vital significance for policymakers and decision-makers in enacting schemes, ensuring food security, and assessing crop insurance losses due to biotic and abiotic stress. This research focused on rice yield estimation at the field level in the Karimnagar district of Telangana during 2021 and 2022 by employing the leaf area index (LAI) as the primary criterion for integrating remote sensing technology and crop simulation models. Using Sentinel-2 satellite data, the rice crop was mapped with the help of ground data and machine learning algorithms, attaining an accuracy of 93.04%. Crop management data for the DSSAT tool were collected during the field visits; the model results revealed a 0.80 correlation between observed and predicted yields. Due to its strong correlation with LAI (0.82), the normalized difference vegetation index (NDVI) was selected as the critical element for integration with the model. A spatial LAI map was generated using the linear equation developed between the NDVI and LAI. The relationship between LAI and yield was used to create a spatial yield map. The study’s findings show that assimilating remote sensing data with crop models enhances the precision of rice yield prediction for insurance companies and policy- and decision-makers.

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Section: Discussionsupporting

confidence: 51%

Crop Yield Assessment Using Field-Based Data and Crop Models at the Village Level: A Case Study on a Homogeneous Rice Area in Telangana, India

Mandapati,

Gumma,

Metuku

et al. 2023

AgriEngineering

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show abstract

“…From the figure it was clear that observed data points closely align with the simulated data, exhibiting a tightly clustered distribution along the line of best fit. This indicates a high degree of agreement between the observed and simulated datasets [20][21][22][23].…”

Section: Model Performance Evaluation During Validationmentioning

confidence: 53%

Evaluation of CERES –Rice Model for Simulating Rice Yield and Phenophases

V.,

Das,

Chandniha

et al. 2024

Int. J. Plant Soil Sci.

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In this study, the performance of the CERES-Rice model in simulating the growth and yield of the Rajeshwari variety in the Raipur district of Chhattisgarh, India, was evaluated. Utilizing observed data from 2021 and 2022, the model was calibrated and validated using key parameters such as days to anthesis, physiological maturity, and yield. Calibration involved adjusting genetic coefficients to improve simulation accuracy, with validation ensuring the model's reliability beyond the calibration period. The comparison between observed and simulated data for crop performance parameters showed that the model performed reasonably well. For days to Anthesis, the RMSE was 4.32 with a d-stat of 0.59, and an error of 5.4%. For Days to Panicle initiation, the RMSE was 1.83, the d-stat was 0.82, and the error was -4.7%. For days to PM, the RMSE was 6.7, the d-stat was 0.65, and the error was 3.0%. Yield showed an RMSE of 472.4, a d-stat of 0.81, and an error of 7.7%. F The mean simulated values closely matched the observed means, indicating overall good model accuracy. In this study, fine tuning the genetic coefficients of CERES rice model for the variety Rjeshwari was done and can be used for further studies and applications.

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Application of the CERES-Rice Model for Rice Yield Gap Analysis

Cited by 2 publications

References 7 publications

Crop Yield Assessment Using Field-Based Data and Crop Models at the Village Level: A Case Study on a Homogeneous Rice Area in Telangana, India

Crop Yield Assessment Using Field-Based Data and Crop Models at the Village Level: A Case Study on a Homogeneous Rice Area in Telangana, India

Evaluation of CERES –Rice Model for Simulating Rice Yield and Phenophases

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