Development and evaluation of a decision support mobile application for cotton irrigation management

Authors: ALE, SRINIVASULU - Texas A&M Agrilife; SU, QIONG - Clemson University; SINGH, JASDEEP - University Of Illinois; HIMANSHU, SUSHIL - Asian Institute Of Technology; FAN, YUBING - Texas A&M Agrilife; STOKER, BLAKE - Texas A&M University; GONZALES, ERIC - Texas A&M University; SAPKOTA, BALA - Texas A&M University; Adams, Curtis; BIGGERS, KIETH - Texas A&M University

Submitted to: Smart Agricultural Technology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/10/2023
Publication Date: 6/11/2023
Citation: Ale, S., Su, Q., Singh, J., Himanshu, S., Fan, Y., Stoker, B., Gonzales, E., Sapkota, B., Adams, C.B., Biggers, K. 2023. Development and evaluation of a decision support mobile application for cotton irrigation management. Smart Agricultural Technology. 5:Article 100270. https://doi.org/10.1016/j.atech.2023.100270.
DOI: https://doi.org/10.1016/j.atech.2023.100270

Interpretive Summary: Irrigation decision support tools are a means by which crop producers can improve the efficiency of water they applyimportant to apply deficit irrigation water and optimize crop yield. However, the available tools have generally had poor adoption ratesremained less adopted by producers due to the high costs of hardware and software, requiring high requirement for technical background to use the toolsand skills, need for extensive input data, lack of economic analysis support, and/or low accuracy. Here, we aimed to develop an inexpensive and easy-to-use decision support mobile app called Irrigation Decision-support for Conserving Resources and Optimizing Production (idCROP), to aidehelp cotton producers in the Texas Rolling Plains (TRP) and High Plains (THP) regions to increase water-use efficiency and optimize crop yield. The app was built based on the crop simulation model, Decision Support System for Agrotechnology Transfer (DSSAT), and a newly developed economic model to provide real-time irrigation schedules and projected economic outcomes based on water use and production goals. The app integrates real-time management information from users with historic, real-time, and forecasted short-term and seasonal weather data., This integration enablesallowing output of a full-season forecast of efficient and situationally relevant irrigation schedules and forecasts of associated cotton yield and economic returns. This enables users to choose an irrigation strategy that best suits their irrigation capacities and expected returns. The irrigation schedules can be improvedoptimized by optional remote detection of plant water stress using a sensor platform mounted on a pivot irrigation system, but the app can be used without the sensors in conjunction with any type of irrigation system. The crop model-based irrigation scheduling in idCROP was validated at the Texas A&M AgriLife Chillicothe Research Station in the TRP region, and acceptable results were obtained. The idCROP app provides producers with a simple but powerful tool that takes the guess work out of irrigation management, helping them secure financial incentives with limited irrigation, indicating great individual, regional, and national economic significance.

Technical Abstract: Irrigation decision support tools are a means by which crop producers can improve the efficiency of water they applyimportant to apply deficit irrigation water and optimize crop yield. However, the available tools have generally had poor adoption ratesremained less adopted by producers due to the high costs of hardware and software, requiring high requirement for technical background to use the toolsand skills, need for extensive input data, lack of economic analysis support, and/or low accuracy. Here, we aimed to develop an inexpensive and easy-to-use decision support mobile app called Irrigation Decision-support for Conserving Resources and Optimizing Production (idCROP), to aidehelp cotton producers in the Texas Rolling Plains (TRP) and High Plains (THP) regions to increase water-use efficiency and optimize crop yield. The app was built based on the crop simulation model, Decision Support System for Agrotechnology Transfer (DSSAT), and a newly developed economic model to provide real-time irrigation schedules and projected economic outcomes based on water use and production goals. The app integrates real-time management information from users with historic, real-time, and forecasted short-term and seasonal weather data., This integration enablesallowing output of a full-season forecast of efficient and situationally relevant irrigation schedules and forecasts of associated cotton yield and economic returns. This enables users to choose an irrigation strategy that best suits their irrigation capacities and expected returns. The irrigation schedules can be improvedoptimized by optional remote detection of plant water stress using a sensor platform mounted on a pivot irrigation system, but the app can be used without the sensors in conjunction with any type of irrigation system. The crop model-based irrigation scheduling in idCROP was validated at the Texas A&M AgriLife Chillicothe Research Station in the TRP region, and acceptable results were obtained. The idCROP app provides producers with a simple but powerful tool that takes the guess work out of irrigation management, helping them secure financial incentives with limited irrigation, indicating great individual, regional, and national economic significance.