Document Type : Original Article
Authors
1 Assistant Professor, Department of Agricultural Economics, University of Sistan and Baluchestan
2 Ph.D. Graduate in Agricultural Economics, University of Sistan and Baluchestan
3 Assistant Professor, Faculty of Agriculture, University of Birjand, Birjand, Iran
4 Assistant Professor, Faculty of Agriculture, University of Zabol
Abstract
This research investigates the direct and indirect effects of spatial spillovers from temperature, relative humidity, and precipitation on saffron yield in South Khorasan counties from 2011 to 2023. Iran is the largest producer of saffron in the world, and South Khorasan Province is the second largest producer of this product in the country after Khorasan Razavi Province, which shows the importance of examining the effects of climate change on its yield. In this study, data related to saffron yield were obtained from the Agricultural Jihad website and climate data from the meteorological website of South Khorasan Province. Data estimation and analysis were performed using the spatial panel model and Stata17 software. The results are based on the SAC model, which has been selected as the optimal model in spatial econometric models. It was shown that in general, precipitation, relative humidity, and maximum temperature index do not have a significant effect on saffron yield, but the temperature index has a negative and significant effect on saffron yield, and the minimum temperature index had a positive and significant effect. Spatial parameters also showed positive and significant spatial correlation, indicating the importance of spatial effects in data analysis. Based on the results of this research, by improving irrigation methods and using optimal agricultural techniques, monitoring and predicting climate and temperature, and conducting further research into the effects of climate change and spatial spillovers on saffron, farmers and policymakers can be helped to improve the productivity and performance of this crop in the coming years by better predicting and managing climate conditions.
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