A model-based evaluation of farmers' income variability under climate change (case study: autumn crops in Iran)

Abstract

Abstract The study strives to analyze the potential variations of farmers' income under climate change by using Ricardian approach. The case study was Mazandaran province of Iran and three autumn crops, i.e. wheat, barley and canola were considered as the investigated crops. The Long Ashton Research Station Weather Generator (LARS-WG) model was selected to downscale the climate data. Three climate variables were downscaled for the years 2020-2080 under three climate scenarios: optimistic (RCP2.6), medium (RCP4.5), and pessimistic (RCP8.5). The Ricardian approach was also employed to predict the economics of climate change. Accordingly, the mean monthly temperature of the province is projected to have an upward trend under all climate scenarios, however, the rainfall pattern would be varied. The results of economic impacts of climate change also approved that the net income of investigated crops would be different trends under climate change scenarios. Accordingly, the variations of air temperature and rainfall would lead that the net income increases for wheat and barley, while it decreases for canola.