Template-Type: ReDIF-Article 1.0
Author-Name: Foad Eshghi
Author-Name-First: Foad
Author-Name-Last: Eshghi
Author-Email: fesh.foad@gmail.com
Author-Workplace-Name: Department of Agricultural Economics, Faculty of Agricultural Engineering, Sari University of Agricultural Sciences and Natural Resources, Sari, Iran.
Author-Name: Tahereh Ranjbar
Author-Name-First: Tahereh
Author-Name-Last: Ranjbar
Author-Email: taherehranjbar.m@gmail.com
Author-Workplace-Name: Department of Agricultural Economics, Sari Agricultural Sciences and Natural Resources University, Sari, Iran.
Author-Name: Nemat Allah Nemati
Author-Name-First: Nemat Allah
Author-Name-Last: Nemati
Author-Email: nemat.zeus@gmail.com
Author-Workplace-Name: Department of Agricultural Economics, Faculty of Agricultural Engineering, Sari Agricultural Sciences and Natural Resources University, Sari, Iran.
Author-Name: Hafezeh Hoseini
Author-Name-First: Hafezeh
Author-Name-Last: Hoseini
Author-Email: fargol74h@gmail.com
Author-Workplace-Name: Department of Agricultural Economics, Sari Agricultural Sciences and Natural Resources University, Sari, Iran.
Author-Name: Masoud Sharifi Khatir
Author-Name-First: Masoud
Author-Name-Last: Sharifi Khatir
Author-Email: masoudkhatir123@gmail.com
Author-Workplace-Name: Master of Agricultural Economics, Tarbiat Modares University, Tehran, Iran.
Title: Impacts of Drought on Food Security of Strategic Crops in Iran: A Machine Learning–Based Scenario Analysis
Abstract: Drought-driven hydroclimatic stress is increasingly disrupting agricultural supply in arid and semi-arid environments. This study quantifies future supply vulnerability for three major cereal crops in Iran, namely wheat, rice, and barley, using a multivariate Long Short-Term Memory (LSTM) time-series framework. Annual data for 1961–2023 were compiled from international databases and include crop production, imports, harvested area, and precipitation as a climate proxy. The modelling workflow proceeds in two steps: (i) domestic production is projected for 2024–2028 using crop-specific multivariate LSTM models; and (ii) the forecasts are translated into implied import requirements. In this study, implied import requirement is a gap-based accounting indicator, calculated as the non-negative difference between the calibrated baseline domestic requirement and forecasted production, rather than a direct machine-learning forecast of import volumes. To stress-test vulnerability, three drought shock scenarios, including 10%, 30%, and 50% production reductions, are applied to the baseline production pathway, and scenario-specific import gaps are recomputed. The results show that drought shocks substantially increase external supply exposure across all crops. Under the severe 50% shock scenario, average implied import requirements during 2024–2028 increase by 172.6% for wheat, 137.1% for rice, and 65.4% for barley compared with the baseline pathway. In aggregate, the three-crop import gap rises by 135.7%, indicating a sharp increase in food-security vulnerability under severe drought stress. Methodologically, the study demonstrates how LSTM-based production forecasts can be converted into policy-relevant import-gap scenarios. From a policy perspective, the findings support integrated drought-risk management through water productivity improvement, strategic grain reserves, and forward import planning.
Classification-JEL: JEL
Keywords: Long short-term memory networks (LSTM); Cereal Supply Risk; Hydroclimatic Stress; Scenario Analysis
Journal: International Journal of Resistive Economics
Pages: 64-84
Volume: 14
Issue: 3
Year: 2026
Month: July
DOI: 10.22034/oajre.2026.576145.1193
File-URL: https://www.oajre.ir/article_245600_ad027c454a6ff9ba5726a4f7afd35a2e.pdf
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File-URL: https://www.oajre.ir/article_245600.html
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Handle: RePEc:trd:journl:245600