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ARS Home » Plains Area » Bushland, Texas » Conservation and Production Research Laboratory » Soil and Water Management Research » Research » Publications at this Location » Publication #381752

Research Project: Precipitation and Irrigation Management to Optimize Profits from Crop Production

Location: Soil and Water Management Research

Title: Optimal irrigation scheduling under limited water supply

item ANDRADE, MANUEL - University Of Nevada
item CHOLULA, URIEL - University Of Nevada
item O`Shaughnessy, Susan
item Evett, Steven - Steve

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 3/10/2021
Publication Date: 7/12/2021
Citation: Andrade, M.A., Cholula, U., O'Shaughnessy, S.A., Evett, S.R. 2021. Optimal irrigation scheduling under limited water supply [abstract]. ASABE 2021 Annual International Meeting, Virtual and On Demand, July 11-14, 2021, in Reno, Nevada. Paper No. 2100945.

Interpretive Summary:

Technical Abstract: Increasing water demands and diminishing water resources require a more efficient use of water applied to crops. Declining aquifers, periods of drought, and changing snowmelt patterns are forcing many farmers in the Western U.S. to grow crops under limited seasonal irrigation budgets that are insufficient to meet consumptive demands. This study introduces an irrigation scheduling method that aims to maximize yield subject to a given seasonal irrigation budget. The method is meant to be executed every day preceding a potential irrigation event in order to determine if an irrigation is required the following day and, if so, how much water should be applied in order to attain the maximum yield that can be achieved without surpassing a seasonal budget. The method integrates a crop model to estimate the potential impact on yield of many different irrigation scheduling scenarios and a heuristic optimization method to identify the best scenario. The crop model is supported by weather, soil water, and plant sensing systems to ensure that the effect of previous irrigation and rainfall events is accounted for by the method as it proceeds to identify the optimal irrigation amount to be applied during the following irrigation event. In order to account for the effect of uncertain future weather conditions during the rest of the irrigation season, the method integrates a stochastic weather data generator that feeds the crop model with estimates of weather conditions derived from historic measurements. Our hypothesis is that the integration of a crop model supported by the daily assimilation of sensing data will allow the heuristic optimization method to identify growth stages when crops are more tolerant to water stress. The proposed irrigation scheduling method can then act as an expert system capable of reducing irrigation amounts during such stages in order to minimize the economic impact of a limited water supply.