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Research Project: Sustainable Climate-Resilient Peanut Cropping Systems

Location: National Peanut Research Laboratory

Title: Incorporating volumetric water content (capacitance) sensors as an automated data entry solution for Irrigator Pro

Author
item GALLIOS, IOANNIS - University Of Georgia
item BUTTS, CHRIS - Retired ARS Employee
item Sorensen, Ronald
item PORTER, WESLEY - University Of Georgia
item VELLIDIS, GEORGE - University Of Georgia

Submitted to: Journal of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/24/2024
Publication Date: 12/24/2024
Citation: Gallios, I., Butts, C., Sorensen, R.B., Porter, W., Vellidis, G. 2024. Incorporating volumetric water content (capacitance) sensors as an automated data entry solution for Irrigator Pro. Journal of the ASABE. 67(6):1561-1574. https://doi.org/10.13031/ja.16052.
DOI: https://doi.org/10.13031/ja.16052

Interpretive Summary: Irrigator Pro is a web-based decision support tool developed by engineers at the USDA, ARS National Peanut Research Laboratory to guide growers in scheduling irrigation for their peanut crops. The current version renders advice based on past rainfall/irrigation amounts and minimum/maximum soil temperature measured under the peanut canopy or on data from sensors that measure soil water potential in the field. This collaborative research between the USDA, ARS and the University of Georgia was undertaken to develop the rationale for using data from sensors that measure volumetric soil water content in addition to the existing data sources for irrigation decisions. A 3-year study (2021-2023) was conducted at two sites in south Georgia that compared Irrigator Pro irrigation decisions using minimum/maximum soil temperature, soil water potential, and volumetric water content. The peanut yields and water use in the fields irrigated guided by the volumetric water content data were as good as or better than the yields and water use in peanut fields irrigated using the minimum/maximum soil temperature or the soil water potential. As a result of this study, Irrigator Pro has been updated to include logic for irrigation scheduling using data from volumetric water content in addition to the other two data sources and is available for the 2024 peanut production season.

Technical Abstract: Irrigator Pro (IP) is a public domain irrigation scheduling decision support tool (DST) developed by the United States Department of Agriculture’s National Peanut Research Laboratory (NPRL) which is widely used by peanut farmers and consultants in the southeastern U.S. The original version of the DST utilizes min/max soil temperature and precipitation to provide yes/no irrigation decisions for peanuts. Recently scientists at NPRL developed a sensor-based soil water balance model version of Irrigator Pro that automates the data collection process. This version allows either manual data entry or automated entry of soil matric potential data from sensors located at 0.1, 0.2, and 0.4 m. To make Irrigator Pro more accessible to growers and consultants, the work described here focused on incorporating volumetric water content (VWC) also known as capacitance sensors as an automated data entry solution for Irrigator Pro. The deliverable of this project was to release a new version of Irrigator Pro that uses VWC data directly from capacitance probes. During the 2021 growing season, data were collected from two field sites in southwestern Georgia, USA, using replicated plot studies to develop the VWC version of Irrigator Pro. The VWC version was evaluated during the 2022 and 2023 growing seasons by comparing its performance to that of the other two versions of the Irrigator Pro and to other advanced irrigation scheduling tools. The results indicated that the VWC version performed at least as well as the temperature and matric potential versions of Irrigator Pro in terms of irrigation water use and peanut yields. The VWC version of Irrigator Pro was then integrated into the Irrigator Pro platform and made available for public use. Ongoing evaluation of the model will continue in subsequent growing seasons with on-farm trials.