A stochastic analysis of cropland utilization and resource allocation under climate change

Chih-Chun Kung, Xiaoyong Cao, Yongrok Choi and Shan-Shan Kung

Technological Forecasting and Social Change, 2019, vol. 148, issue C

Abstract: Agriculture is considered to be highly vulnerable to global climate change which has significant influences on regional temperature and precipitation, thereby altering crop yields, resource allocation, land-use patterns, distribution of cultivars, farmers' behavior, and social welfare. To understand how agricultural activities respond to climate change, therefore, it is necessary to investigate farmers' behavior in the face of climate-induced crop yield changes. We extend the deterministic agricultural sector model to a two-stage stochastic programming with recourse (SPR) model to evaluate the potential changes in cropland utilization and agro-economic measures under climate impacts in Taiwan. We show that when farmers are uncertain the risk, land use could alter considerably. In such cases, approximately 55% and 26% of corn land and peanut land, respectively, will be used to plant other crops. When changes in crop yield are certain, farmers can maintain their income at a cost of higher government expenditures on aside-land subsidy and rice repurchase program. Because yields of many crops present a positive response to climate change, in some cases the net social welfare might increase by NT$800 million dollars (after subtracting NT$5.2 billion dollars of government expenditure) or 0.2% of total agricultural production, and most of the benefits are captured by farmers. These issues, as well as policy implications such as wealth redistribution and resource allocation are discussed in detail.

Keywords: Climate impacts; Mathematical programming; Policy analysis; Yield response (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:148:y:2019:i:c:s0040162517318383

DOI: 10.1016/j.techfore.2019.119711

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