Improving Irrigation Management of Cotton with Small Unmanned Aerial Vehicle (UAV) in Texas High Plains

Author:

Risal Avay1ORCID,Niu Haoyu2,Landivar-Scott Jose Luis3ORCID,Maeda Murilo M.1,Bednarz Craig W.4,Landivar-Bowles Juan3,Duffield Nick2ORCID,Payton Paxton5,Pal Pankaj3,Lascano Robert J.6,Goebel Timothy6,Bhandari Mahendra3

Affiliation:

1. Texas A&M, AgriLife Research and Extension Center, Lubbock, TX 79403, USA

2. Texas A&M Institute of Data Science, Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA

3. Texas A&M, AgriLife Research and Extension Center, Corpus Christi, TX 78406, USA

4. Semi-Arid Agricultural Systems Institute, West Texas A&M University, Canyon, TX 79016, USA

5. Goanna Ag, Goondiwindi, QLD 4390, Australia

6. Cropping System Research Laboratory, United States Department of Agriculture, Agriculture Research Service, Lubbock, TX 79403, USA

Abstract

The rapid decline in water availability for irrigation on the Texas High Plains (THP) is a significant problem affecting crop production and the viability of a large regional economy worth approximately USD 7 billion annually. This region is the largest continuous cotton-producing area in the United States, and the timely delivery and efficient use of irrigation water are critical to the sustainability and profitability of cotton production in this region. Current irrigation scheduling must be improved to reduce water consumption without compromising crop production. Presently, irrigation scheduling based on reference evapotranspiration (ETo) is limited due to the lack of reliable and readily available in-field weather data and updated crop coefficients. Additionally, in-field variability in crop water demand is often overlooked, leading to lower irrigation efficiency. To address these challenges, we explored the potential use of an unmanned aerial vehicle (UAV)-based crop monitoring system to support irrigation management decisions. This study was conducted in Lubbock, Texas, in 2022, where high temporal and spatial resolution images were acquired using a UAV from a cotton field experiment with four irrigation levels. Soil moisture and canopy temperature sensors were deployed to monitor crop response to irrigation and rainfall. The results indicated a significant effect of water stress on crop growth (revealed by UAV-based canopy cover (CC) measurements), yield, and fiber quality. Strong correlations between multi-temporal CC and lint yield (R2 = 0.68 to 0.88) emphasized a clear trend: rainfed treatments with lower yields exhibited reduced CC, while irrigated plots with higher CC displayed increased yields. Furthermore, irrigated plots produced more mature and uniform fibers. This study also explored various evapotranspiration calculation approaches indicating that site-specific CC measurements obtained from a UAV could significantly reduce irrigation application. A regression model linking evapotranspiration to canopy cover demonstrated promising potential for estimating water demand in crops with an R2 as high as 0.68. The findings highlight the efficacy of UAV-based canopy features in assessing drought effects and managing irrigation water in water-limited production regions like the THP.

Funder

Texas Water Research Institute

Publisher

MDPI AG

Reference32 articles.

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