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Mario Andres Fernandez

FernandezEach year, the Universities Council on Water Resources organizes a competition for Best Dissertation in water resources.  For 2014, Dr. Mario Fernandez dissertation on “Decadal Climate Variability: Economic Implications for Agriculture and Water in the Missouri River Basin” under the direction of his chair, Dr. Bruce McCarl  has won in the Water Policy and SocioEconomics category!

The award carries an award of $750 plus reimbursement of up to $1000 in travel expenses to the annual conference in June 2014. See Dr. Ferandez’s abstract below.

Learn more at www.ucowr.org .

Abstract:

Economic research on climate and productivity effects of ocean phenomena has mostly focused on interannual cases such as the El Niño Southern Oscillation. Here Decadal climate variability (DCV) refers to ocean related climate influences of duration from seven to twenty years. The specific phenomena analyzed here are the Pacific Decadal Oscillation, the Tropical Atlantic Gradient and the West Pacific Warm Pool. Their positive and negative phases, occurring individually or in combination, are associated with variations in crop and water yields.

This dissertation examines the value of DCV information to agriculture and water users in the Missouri river basin using a price endogenous agricultural and non-agricultural model that depicts cropping and water use. The model is used to evaluate the welfare gains and adaptations given various levels of DCV information.

The analysis shows the value for a perfect forecast is about 5.2 billion dollars, though 86% of this value, 4.55 billion dollars, can be obtained by a less perfect forecast based on already available data in the form of the prediction of DCV phase under transition probabilities. The results indicate that appreciating DCV state is important, stimulating adaptive responses in the acreage of major crops plus water use adjustments by residential, agricultural and industrial users, though for the most part, we find little evidence that further improvements in predictive capabilities are of great value.

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