| Autor: |
Srinivasulu Ale, Qiong Su, Jasdeep Singh, Sushil Himanshu, Yubing Fan, Blake Stoker, Eric Gonzalez, Bala Ram Sapkota, Curtis Adams, Keith Biggers, Emi Kimura, James Wall |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Smart Agricultural Technology, Vol 5, Iss , Pp 100270- (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2772-3755 |
| DOI: |
10.1016/j.atech.2023.100270 |
| Popis: |
Irrigation decision support tools are a means by which crop producers can improve the efficiency of water they apply. However, the available tools generally have poor adoption rates due to high costs, requirement for technical background and 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 aide cotton producers in the Texas Rolling Plains and High Plains regions to increase water-use efficiency while maintaining higher crop yields. 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 enables output 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 improved 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 Texas Rolling Plains 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 apply limited irrigation water more efficiently. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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