Universidad del Desarrollo1, Universidad de Concepción2
The agricultural sector could be one of the most vulnerable economic sectors to the impacts of climate change in the coming decades. Regarding crop production, the impacts will be a function of the geographical location with an increase in yields in high-latitude areas with rising temperatures, and a decrease in yields in low-latitude areas. Simulation results show that the positive impacts of climate change outweigh the negative ones (Parry, et al. 2007). Changes in water availability will have a direct impact on the agricultural sector. Simulation results show an increase in the irrigation demand at the global level throughout the 21st century, in order to cope with both climate change and population growth (Doll 2002, Fisher, et al. 2006, Alcamo, et al. 2003, Arnell, et al. 2011). The magnitude of climate change impacts will demand an urgent policy response in order to cope with the consequences. Considering the high level of policy intervention that the agricultural sector already experiences (quotas, taxes, band prices), the required climate change adaptation policies could lead to undesirable outcomes if all the potential linkages within the agricultural sector are not considered as part of a single system. The welfare consequences of poor policies could be large, especially for developing countries where the agricultural sector not only has economic relevance, but is also a keystone for food security (FAO 2010). The effectiveness of public policies will depend on local characteristics, such as: climate and socioeconomic conditions. 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 could be an effective tool to evaluate the economic impacts of climate change on the agricultural sector. Agricultural models simulate the agents' optimal behaviour, allowing for an ex-ante evaluation of policy intervention. Agricultural models range from studies at farm level, to studies including the whole agricultural sector. The main difference is related to price assumptions. Agricultural supply models represent the agricultural sector through a series of behavioural equations, which are optimized in order to maximize the farm income or the regional income, subject to technological, environmental, and institutional constraints. The core equations of an agricultural supply model include prices, supply, costs, and stock variables (Howitt 2005). One of the most relevant stock variables is the water available for agricultural production. By linking both the agricultural supply and the water availability is possible to have a good picture about the agricultural sector by developing an hydro economic model. Hydro economic models have been widely used to analyze water related problems in several countries (Brower and Hofkes 2008, McKinney, et al. 1999, Lee and Howitt 1996, Pulido-Velazquez, et al. 2008, Heinz, et al. 2007), but with few applications in developing countries (Cai et al. 2003; Maneta et al. 2009). In this paper we developed a hydro economic model at river basin scale aimed to account for the economic impacts of changes in water availability, as consequence of climate change, at river basin scale. The model has two components: supply and demand. The water supply is modeled using the SWAT hydrologic model, while the water demand is represented by the agricultural sector. In economics, the water demand of the agricultural sector is considered a derived demand because the optimum amount of water demanded by the farmers is function of the amount of final products that are produced for those farmers along with the demand of other inputs. In this sense, the derived water demand is a sub-product within the procedure of computing the optimum land allocation among crops. The derived water demand could be computed either using econometrics or mathematical programming. In the first case, it is necessary knowing both the water prices and the water quantities demanded at each price, among others variables. On the other hand, using the mathematical programming method implies to know the technology as well as the input/output prices. Considering that in most of the cases, the water used by the agricultural sector is free of charge (or it has a non-competitive price), the use of econometric techniques is not suitable. Our approach has three steps. In the first one, the water supply is modeled using the SWAT model, which is calibrated and validate for the study area. On the other hand, climate change impacts are modeled as changes in both temperatures and precipitations, using a downscaled climate model. The interaction between the climate and hydrologic model will deliver the expected changes in both precipitations and river flow changes, that will shock the water supply used by the agricultural model. In the second step the agricultural water demand is modeled using a non-linear agricultural supply model that optimizes the farmer net income subject to land, water, and institutional constraints. Finally, the third step implies the integration of the demand and supply side. This is done following an optimization approach in which the agricultural demand is integrated into a single framework by linking it with the water supply. All the components of the integrated framework are spatially differentiated. The objective of this optimization module is to maximize the farmers net subject to water, land and institutional constraints. The model is applied to the Vergara River Basin in Chile. It has an extension of 4.260,5 km2, with 150.000 inhabitants. The main economic activities include: agriculture, forestry, and energy. According to the preliminary results, the expected changes in both temperature and precipitation will drive a 13% decrease in the water availability within the basin. This change in water availability will drive a complete land reallocation. Under this new land allocation, farmers' income will decrease 5% in average. With the pears producers in Ercilla municipality the most affected by the change in water availability. Maneta, M, y otros. Â«A Spatially Distributed Hydroeconomic Model to Assess the Effects of Drought on Land Use, Farm Profits, and Agricultural Employment .Â» Water Resources Research, 2009. Brouwer, R, and M Hofkes. 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Â“Integrated Hydro-Economic Modelling: Approaches, Key Issues, and Future Research Directions.Â” Ecological Economics 66 (2008): 16-22.