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Utah State University (2012)

Analysis of Irrigation Decision Behavior and Forecasting Future Irrigation Decisions

Sanyogita Andriyas

Titre : Analysis of Irrigation Decision Behavior and Forecasting Future Irrigation Decisions

Auteur : Sanyogita Andriyas

Université : Utah State University

Grade : Doctor of Philosophy (PhD) 2012

Résumé
Farmers play a pivotal role in food production. To be economically successful, farmers must make many decisions during the course of a growing season about the allocation of inputs to production. For farmers in arid regions, one of these decisions is whether to irrigate. This research is the first of its kind to investigate the reasons that drive a farmer to make irrigation decisions and use those reasons/factors to forecast future irrigation decisions. This study can help water managers and canal operators to estimate short-term irrigation demands, thereby gaining information that might be useful in management of irrigation supply systems. This work presents three approaches to study farmer irrigation behavior : Bayesian belief networks (BBNs), decision trees, and hidden Markov models (HMMs). All three models are in the class of evolutionary algorithms, which are often used to analyze problems in dynamic and uncertain environments. These algorithms learn the connections between observed input and output data and can make predictions about future events. The models were used to study behavior of farmers in the Canal B command area, located in the Lower Sevier River Basin, Delta, Utah. Alfalfa, barley, and corn are the major cropsFarmers play a pivotal role in food production. To be economically successful, farmers must make many decisions during the course of a growing season about the allocation of inputs to production. For farmers in arid regions, one of these decisions is whether to irrigate. This research is the first of its kind to investigate the reasons that drive a farmer to make irrigation decisions and use those reasons/factors to forecast future irrigation decisions. This study can help water managers and canal operators to estimate short-term irrigation demands, thereby gaining information that might be useful in management of irrigation supply systems. This work presents three approaches to study farmer irrigation behavior : Bayesian belief networks (BBNs), decision trees, and hidden Markov models (HMMs). All three models are in the class of evolutionary algorithms, which are often used to analyze problems in dynamic and uncertain environments. These algorithms learn the connections between observed input and output data and can make predictions about future events. The models were used to study behavior of farmers in the Canal B command area, located in the Lower Sevier River Basin, Delta, Utah. Alfalfa, barley, and corn are the major crops

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Page publiée le 22 mai 2013, mise à jour le 16 décembre 2019