Abstract

Canal operators need information to manage water deliveries to irrigators, especially in the case of on-demand irrigation supply systems. Short-term irrigation demand forecasts can provide potentially valuable information for a canal operator who must manage such a system, especially if these forecasts could be generated by using readily available information about bio-physical conditions of the irrigated area and the decision-making processes of irrigators. Additionally, Bayesian models of irrigation behavior can provide insight into the likely criteria which farmers use to make irrigation decisions. This paper develops a Bayesian belief network (BBN) to infer irrigation decision-making behavior of farmers based on factor interaction and posterior probabilities. The model discussed here was built from a combination of data about biotic, climatic, and edaphic conditions under which observed irrigation decisions were made. From all the possible initial trials, the model which was built from data comprising of conditions on days the irrigation decision was made, and a day before it, was found to be the best and is presented and discussed here. The paper includes a case study using data collected from the Canal B region of the Sevier River, near Delta, Utah. Alfalfa, barley and corn are the main crops in the Canal B area. The model has been tested with a portion of the data to affirm the model predictive capabilities. It was found that most of the farmers used consistent rules throughout all years and across different types of crops. Soil moisture stress, was found to be the most likely, significant predictive variable of the irrigation decision. Irrigation decisions appeared to be triggered by a farmer’s perception of soil stress (or a surrogate thereof), or by a perception of combined factors such as information about a neighbor irrigating or an apparent preference to irrigate on a weekend. Soil stress resulted in irrigation probabilities (chance that the farmer will irrigate) of 94.4% for alfalfa. Prediction accuracy of the timing for irrigations of alfalfa was observed to be 81.0%, and 61.0% for barley and corn. The study shows that BBNs can be a prospective tool to analyse likely decisions about irrigation in an on-demand system with good accuracy.

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