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ARS Home » Research » Publications at this Location » Publication #113684
Title: PRECISION IRRIGATION MANAGEMENT USING MODELING AND REMOTE SENSING APPROACHES

Author
item Barnes, Edward
item Pinter Jr, Paul
item Kimball, Bruce
item Hunsaker, Douglas - Doug
item Wall, Gerard - Gary
item La Morte, Robert

Submitted to: Decennial National Irrigation Symposium
Publication Type: Proceedings
Publication Acceptance Date: 11/14/2000
Publication Date: 12/15/2000
Citation: Barnes, E.M., Pinter Jr, P.J., Kimball, B.A., Hunsaker, D.J., Wall, G.W., La Morte, R.L. 2000. Precision irrigation management using modeling and remote sensing approaches. Dicennial National Irrigation Symposium. pp. 332-337

Interpretive Summary: Many agricultural producers are adopting a new management practice called precision farming, whereby crop needs are determined as finely as every few feet. The amount of water available to plants from the soil is one of the factors that can vary this finely and is difficult to manage because it is not practical to measure the amount of water in the soil that extensively. Methods combining soil maps with models also are impractical to determine soil water content because of the difficulty in quantifying all of the possible variables. Likewise, methods using images from an aircraft or satellite to detect crop water status are impractical because of the frequency with which images are needed. In this study, it was determined that estimates of the extent to which a plant needs water (plant water stress) obtained with infrared thermometers, combined with predictions of water stress from a crop simulation model, can improve the model's prediction of the amount of water available to the plant. Ultimately, this approach could provide agricultural producers and consultant a new tool to apply irrigation water in the varying amounts required over a field.

Technical Abstract: A synergy between remote sensing and crop simulation models is proposed as a new method for managing irrigations in precision agriculture. The remote sensing component provides the ability to assess plant water status at high spatial resolution and the crop model provides data at high temporal frequency. The objective of this study was to integrate the crop water stress index (CWSI) and the simulation model CERES-Wheat to provide data on within-field variability in plant water requirements and yield response. The accuracy of the procedure was evaluated using a data set collected during the Free Air Carbon Dioxide Enhancement (FACE) wheat experiments conducted at the Maricopa Agricultural Center in Arizona. The method was very sensitive to overestimation of the CWSI under dry conditions with a potential for inaccurately predicted soil water contents. However, the combined approach allowed the model to provide reasonable yield prediction of water stressed plots using only CWSI measurements during the season to indicate inadequate plant available water. These initial results are encouraging; however, additional analysis of the data on a plot-by-plot basis is necessary before specific conclusions can be made about the suitability of this method for precision farming applications.