Document Type : Research Article
Authors
1
Department of Irrigation and Reclamation Engineering, Faculty of Agriculture, University of Tehran
2
Ferdowsi University of Mashhad
10.22067/jcesc.2024.89213.1344
Abstract
Introduction
Ensuring food security has significant importance to countries with arid and semiarid climates and inadequate water for irrigation, like Iran which is quite vulnerable to climate change consequences. Strategic decision-making is crucial for effective production of agricultural crops especially cereals. Wheat is a strategic crop for achieving food security in Iran which is cultivated both rain-fed and irrigated.Crop models are mathematical expressions of the plant growth and development under different environmental and management conditions. These models’ performance and accuracy rely on high-quality and long-term input data especially observed or generated climatic databases. The aim of this study is to simulate the yield of irrigated wheat in seven cities of Razavi Khorasan province using DSSAT crop model and AgMERRA reanalysis data.
Materials and Methods
In this study, required daily weather data of seven stations located across Khrosan Razavi province namely Mashhad, Neishabour, Gonabad, Torbet-Haidaryeh, Torbet-Jam, Sabzevar, and Kashmer for the period of 1980-2010 were collected and used. The observed daily data included rainfall, maximum and minimum temperatures, wind speed, and sunshine hours. Corresponding period AgMERRA reanalysis data were also retrieved from the database to be used as an alternative input. AgMERRA is a global gridded daily weather dataset that was originally generated using NASA’s MERRA model (the National Aeronautics and Space Administration, Modern-Era Retrospective Analysis for Research and Applications). The AgMERRA global gridded climate dataset (0.25×0.25) has a horizontal resolution of approximately 25 km. It provides daily, high-resolution, and continuous meteorological datasets for the period 1980-2010. It is proven to be useful for agricultural and meteorological studies. Annual irrigated wheat yield data, soil information (including texture, depth, nitrogen content, and moisture), and management data i.e. variety, planting date, planting depth, and row spacing were obtained from agricultural stations across the province. The Crop Simulation Model (CERES-wheat module) of the DSSAT version 4.6 was used to simulate irrigated wheat yield. DSSAT (Decision Support System for Agrotechnology Transfer) is a package of several dynamic simulation models for over 42 crops that has been tested and applied for more than 30 years in more than174 countries with acceptable results. The reported genetic coefficients for selected wheat variety from previous studies in the region were used. The statistical indices, including the coefficient of determination (R2), root mean square error (RMSE) and normalized root mean square error (NRMSE) were used for comparisons and evaluation of the model performance for both runs using observed and reanalysis weather data.
Results and Discussion
The comparison between observed and reanalysis AgMERRA climate data in all seven study stations revealed a good agreement with correlation coefficient ranging from 0.67 to 0.92 and highest correlation was observed in air temperature time series.Besides, the error indies range for AgMERRA dataset determined as MAE from 3.87 to 4.11 and RMSE from 4.93 to 7.76. The model was run for simulation of Pishtaz variety yield, which has already been calibrated and evaluated in Khorasan Razavi province, using observed and AgMERRA climatic data. According to NRMSE, RMSE, and R2 statistical indices application of observed climatic data for simulation of the selected wheat yield is more accurate with R2 between 0.63-0.72 in study stations compared to AgMERRA data application with R2 ranging from 0.50 to 0.67
Conclusion
According to statistical metrics, the use of observed data comparing to AgMERRA reanalysis provided better estimations of irrigated wheat in all study stations. Although the AgMERRA may also be used as a suitable alternate data with acceptable accuracy. Therefore, the climate datasets can be recommended as an input of crop models in regions with limited or non-reliable climate data. Further studies using another climate datasets and other crops is required for more scrutiny.
Acknowledgment
Authors would like to acknowledge the Seed and Plant Improvement Institute, Iran Ministry of Agriculture, and also Iran Meteorological Organization for their assistance and providing required data
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