Evaluating the efficiency of solar radiation estimation models in simulating the yield and water requirement of wheat

Document Type : Research Paper

Authors

1 Department of Horticulture Science and Engineering, High Educational Complex of Torbat-e Jam, khorasan Razavi, Iran

2 Department of Water Science and Engineering, University of Torbat-e Jam, Khorasan Razavi, Iran

3 Disasters and Climate Change Research Group- CRI (RIMAS), Mashhad, Iran.

Abstract

Solar radiation is one of the main inputs to crop growth models to estimate growth and yield. This study was conducted with the aim of evaluating the impact of radiation estimation accuracy by different models on the accuracy of simulating wheat evapotranspiration and yield by CSM-CERES-Wheat model in Razavi Khorasan province. The efficiency of simple models including Angstrom-Prescott, Angstrom-Prescott suggested by FAO, Hargreaves-Samani, Hargreaves-Samani suggested by FAO, Kermani, Hunt and Power have been evaluated in this study. The results of the radiation estimation for different models in the calibration stage showed that Kermani, Angstrom-Prescott calibrated, Angstrom-Prescott models with FAO recommended coefficients, Power, Hunt and Hargreaves-Samani gave the closest estimates compared to the measured radiation in Mashhad, respectively. In the validation stage of the calibrated models, it is found that the Power, Kirmani, Angstrom-Prescott models had the most accurate estimation of daily radiation compared to the measured radiation in the study areas, respectively. Furthermore, the lowest difference between simulated yield and evapotranspiration of wheat using the observed radiation data and the estimated radiation data is respectively obtained from the models of Power, calibrated Angstrom-Prescott, Angstrom-Prescott with FAO suggested coefficients and finally the Kermani. Considering the accuracy and extensive spatial coverage of the radiation data in the Power model, it is recommended to use it as the best radiation estimation method for use in the crop models.

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  • Receive Date: 14 October 2023
  • Revise Date: 28 December 2023
  • Accept Date: 25 January 2024
  • Publish Date: 21 June 2024