Sensor feedback system enables automated deficit irrigation scheduling for cotton

Authors: O`Shaughnessy, Susan; Colaizzi, Paul; BEDNARZ, CRAIG - West Texas A & M University

Submitted to: Frontiers in Plant Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/14/2023
Publication Date: 3/9/2023
Citation: O'Shaughnessy, S.A., Colaizzi, P.D., Bednarz, C.W. 2023. Sensor feedback system enables automated deficit irrigation scheduling for cotton. Frontiers in Plant Science. 14:1-14. https://doi.org/10.3389/fpls.2023.1149424.
DOI: https://doi.org/10.3389/fpls.2023.1149424

Interpretive Summary: Irrigated crops produce two to three times the yields of dryland crops and reduce the risk of crop failure during drought conditions. In the Texas High Plains region where water levels in the non-replenishing Ogallala Aquifer are in decline and drought conditions prevail, deficit irrigation management could help sustain irrigated crop production and stabilize yields. However, deficit irrigation strategies require continuous monitoring of the crop to ensure that the deficit levels do not significantly reduce yields or yield quality. Scientists from ARS (Bushland) and West Texas A&M University managed cotton under deficit irrigation levels using a variable rate irrigation center pivot system outfitted with canopy temperature sensors on the pivot lateral and soil water sensors strategically located in various locations in the cropped field to continuously monitor plant and soil water status. Deficit irrigation scheduling methods were able to maintain seed cotton yields as compared to fully irrigated cotton, with a minimum 20% water savings. The tested system requires much less labor and expense than the use of other methods and could be used by producers to save time in implementing deficit irrigation strategies.

Technical Abstract: When water resources for agriculture are limited, deficit irrigation (DI) management is one method of sustaining irrigated cropland. Precision irrigation technologies using sensor feedback can provide dynamic decision support for irrigation scheduling. Such technologies could be used to help farmers implement DI strategies. However, few studies have reported on the use of these systems for DI management. This two-year study was conducted in Bushland, Texas to investigate the performance of the GIS-based irrigation scheduling supervisory control and data acquisition (ISSCADA) system as a tool to manage deficit irrigation scheduling for cotton (Gossypim hirsutum L). Two different irrigation scheduling methods automated by the ISSCADA system - (1) a plant feedback (designated C) - based on integrated crop water stress index iCWSI) thresholds, and (2) a hybrid (designated H) method, created to combine soil water depletion and the iCWSI thresholds, were compared with a benchmark manual irrigation scheduling (M) that used weekly neutron probe readings. Each method applied irrigation at levels designed to be equivalent to 25 percent, 50 percent, and 75 percent replenishment of soil water depletion to near field capacity (designated I25, I50 and I75) using the pre-established thresholds stored in the ISSCADA system or the designated percent replenishment of soil water depletion to field capacity in the M method. Fully irrigated and extremely deficit irrigated plots were also established. Relative to the fully irrigated plots, deficit irrigated plots at the I75 level for all irrigation scheduling methods maintained seed cotton yield, while saving water. In 2021, the irrigation savings was a minimum of 20 percent, while in 2022, the minimum savings was 16 percent. Comparing the performance of deficit irrigation scheduling between the ISSCADA system and the manual method showed that crop response for all three methods were statistically similar at each irrigation level. Because the M method requires labor intensive and expensive use of the highly regulated neutron probe, the automated decision support provided by the ISSCADA system could simplify deficit irrigation management of cotton in a semi-arid region.