Multi-agent agro-economic simulation of irrigation water demand with climate services for climate change adaptation
AbstractFarmers’ irrigation practices play a crucial role in the sustainability of crop production and water consumption, and in the way they deal with the current and future effects of climate change. In this study, a system dynamic multi-agent model adopting the soil water balance provided by the Food and Agriculture Organization (FAO) Irrigation and Drainage Paper 56 was developed to explore how farmers’ decision making may affect future water needs and use with a focus on the role of climate services, i.e. forecasts and insurance. A climatic projection record representing the down-scaled A1B market scenario (a balance across all sources) of the assessment report of the Intergovernmental Panel on Climate Change (IPCC) is used to produce future daily data about relative humidity, precipitation, temperature and wind speed. Two types of meteorological services are made available: i) a bi-weekly bulletin; and ii) seasonal forecasts. The precision of these services was altered to represent different conditions, from perfect knowledge to poor forecasts. Using the available forecasts, farming agents take adaptation decisions concerning crop allocation and irrigation management on the basis of their own risk attitudes. Farmers’ attitudes are characterized by fuzzy classifications depending on age, relative income and crop profitability. Farming agents’ adaptation decisions directly affect the crop and irrigation parameters, which in turn affect future water needs on a territorial level. By incorporating available and future meteorological services, the model allows the farmer’s decision making-process to be explored together with the consequent future irrigation water demand for the period 2015 to 2030. The model prototype is applied to a data set of the Venice Lagoon Watershed, an area of 2038 km2 in north-east Italy, for a preliminary test of its performance and to design future development objectives.
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Copyright (c) 2013 Stefano Balbi, Sabindra Bhandari, Animesh K. Gain, Carlo Giupponi
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