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Assessment of the Impacts of Climate Change on Saffron Yield in Hamadan

Samavaki, Amir Abbas | 2025

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 58207 (09)
  4. University: Sharif University of Technology
  5. Department: Civil Engineering
  6. Advisor(s): Moghim, Sanaz
  7. Abstract:
  8. The effect of climate change on agricultural productivity and efficiency is a major concern and challenge for the agricultural industry. Different hydrometeorological variables, such as extreme temperature, precipitation, and their variations affect the growth and yield of the agricultural products. Saffron is one of the most important agricultural products in Iran. Iran produces the largest amount of Saffron globally and Hamadan Province is one of the major saffron-producing regions in Iran. This study uses different Artificial Intelligence methods not only for clustering and sensitivity analysis of the hydroclimatological variables but also for evaluating the impacts of climate change on Saffron yield in Hamadan Province. We developed extreme climate indices (related to temperature and precipitation as two main indicators of climate change) and combined them with indices obtained from ETCCDI (34 indices). These climate indices are used as inputs in different artificial intelligence methods, including Random Forest, XGBoost, AdaBoost, Support Vector Machine (SVM), and Linear Regression, to assess the impact of extreme climate change on saffron yield. Additionally, this study used Perturbation method for sensitivity analysis to explore the relationships between extreme climate change indices and saffron yield. Results indicates that the Random Forest algorithm performs the best for sensitivity analysis among all algorithms. Extreme climate change indices, particularly those related to the monthly maximum and minimum temperatures, have the highest negative impact on saffron yield compared to other hydroclimatological indices. Furthermore, the minimum temperature has a more significant negative impact on saffron yield compared to the maximum temperature. Additionally, the counties of Malayer, Nahavand, and Asadabad, located in the south and west of Hamadan Province, exhibited the highest accuracy in sensitivity analysis. The results indicate that a 20% change in the maximum monthly temperature exerts an average impact of ±1.5% on saffron yield. In comparison, variations in the minimum monthly temperature demonstrate a diminished effect, contributing to less than a 1% change in production. The findings suggest that monthly extreme temperatures can be used to assess the risk of saffron production, increase agricultural productivity, and improve decision-making for the cultivation of this product
  9. Keywords:
  10. Climate Change ; Artificial Intelligence ; Agriculture ; Saffron ; Hamadan Province

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