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The Economic Impacts Of Water Scarcity In Agriculture:the Chilean Case

Congress: 2015
Author(s): Roberto Ponce (Concepcion)


Keyword(s): Sub-theme 17: Climate change, impacts and adaptation,
AbstractThe agricultural sector could be one of the most vulnerable economic sectors to the impacts of climate change in the coming decades. Climate change impacts are related to changes in the growth period, extreme weather events, and changes in temperature and precipitation patterns, among others. All of these impacts may have significant consequences on agricultural production. In order to address the challenges imposed by climate change from an economic perspective, an approach that provides a detailed picture of the agricultural sector and the relationships within it, is essential. In this regard, bottom-up approaches (i.e. in particular models applied at local level, but driven by global scenario driving forces) could be an effective tool to evaluate the economic impacts of climate change on the agricultural sector. Bottom-up approaches, such as bio-economic agricultural models, simulate the agents' (e.g. farmers') behavior, allowing for an ex-ante evaluation of policy interventions. Agricultural supply models represent the agricultural sector through a series of behavioral equations, which are solved in order to maximize the farm income or the regional income, subject to technological, environmental, and institutional constraints. (Howitt 2005). This paper presents a non-linear agricultural supply model for the analysis of the economic impacts of changes in water availability due to climate change. The agricultural model is designed specifically for the analysis of the Chilean agricultural sector. The Agricultural Supply model (ASM) is a mathematical programming model designed to analyze the agricultural sector with high geographical disaggregation. It includes the major agricultural activities within the area, and differentiates between water provision systems (rainfed and irrigated), among other features. The core of the ASM includes the behavior of the agricultural producers, which is characterized by detailed information at the producer level in order to represent a system of outputs supply and inputs demand, which is the result of the assumed profit maximization behavior. The information is differentiated by activity and geographical area, including: area planted, yield, variable costs, and labor demand, which is used to compute total costs, gross margin, and net revenues. The information presented above is complemented with supply elasticities for each activity. The core model is optimized considering a series of endowment restrictions, such as: total land, irrigated land, and water availability. Using Positive Mathematical Programming (PMP), the model is calibrated to the reference period. Using this method it is possible to achieve a perfect calibration for area planted avoiding the dependency between parameters and constraints (Howitt 1995). At the national level, the expected changes in water availability have a minor impact on the total land allocation. However, as expected the estimated impacts across regions is uneven, with the largest impacts in the northern region. Results by zone and activity show that there is not a direct relationship between the expected change in water availability and the final change in land allocation. This apparent contradiction is because the final land allocated to each activity is function of its relative profit respect to the other activities. In this regard, water availability is one component of the profit level, along with prices and costs. Climate change will have vast and diverse impacts on the agricultural sector across the world, with developing countries presenting the most vulnerable regions. Considering the high level of policy intervention that the agricultural sector already has, a modeling approach that considers all the connections within it is essential; and the model presented in this study fulfills this requirement. In addition, the model presents a very detailed picture of the agricultural sector, with a high level of geographical detail aiming to identify local conditions that could influence the final economic consequences of climate change. The model depicted here is a tool flexible enough to be applied in diverse situations in which information access is a constraint. As shown above, the main source of information here was the Agricultural Census, complemented with secondary data that should be easy to collect if the objective is to use this model in other countries. Climate change impacts on the Chilean Agricultural sector are vast, with considerable economic consequences across regions. At the regional level, our model shows substantial re-allocations of land, with the northern zone showing larger changes. However, this land reallocation does not seriously impact the total agricultural production at the national level. Therefore, according to the results, even if climate change may not have large absolute consequences, it may produce large distributional consequences, with fruits producers being worst-off than crops producers. In this regard, climate change could threaten a key economic sector, since fruits account for 31% of the total food export. This redistribution of rents could worsen the inequity that already exists in Chile, presenting additional challenge for coping with climate change. Howitt, R. “Agricultural and Environmental Policy Models: Calibration, Estimation and Optimization.” University of Carlifornia. UCDavis, 2005. Howitt, R. “Positive Mathematical Programming.” American Journal of Agricultural Economics, 1995: 329-342.
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