期刊
JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING
卷 146, 期 6, 页码 -出版社
ASCE-AMER SOC CIVIL ENGINEERS
DOI: 10.1061/(ASCE)IR.1943-4774.0001464
关键词
Evapotranspiration (ET) and water balance; Soil water status; Plant-based measurements; Model based
资金
- National Key Research and Development Program of China [2017YFC0403303]
- Natural Science and Engineering Research Council of Canada-Discovery program [RGPIN 04784-14]
- Fundamental Research Funds for the Central Universities [2019B18214]
- National Natural Science Foundation of China [51909064]
- China Postdoctoral Science Foundation [2019M651684]
- Belt and Road Special Foundation of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering [2019490411]
- China Scholarship Council (CSC) program
In an effort to improve plant growth and to achieve high yield and/or quality, irrigation scheduling (IS) seeks to provide plants with appropriate quantities of water at appropriate times. To better understand irrigation scheduling's main processes and principles, its four most common methods of operation-(1) evapotranspiration and water balance (ET-WB), (2) soil moisture (Theta) status, (3) plant water status, and (4) models-along with their pros and cons are introduced and compared. Irrigation applications, including software, programs, and associated controllers are introduced. Given that some of these methods focus on Theta or plant responses to soil moisture, the determination of target soil moisture levels, along with estimates (either calculated or measured) of current soil moisture status are key to both scheduling irrigations, and the precise replenishment of soil moisture to target levels. Accordingly, factors in the soil-crop-atmosphere system affecting soil moisture must be considered in the scheduling process. As all four types of IS methods focus on soil water content, which serves as a bridge between irrigation management and crop water requirements for growth, future scheduling methods should focus on the management of soil moisture based on an advanced understanding of its effects on crop growth either by the integration of existing IS methods or the development of new models, using intelligent algorithms. Using these approaches, more practical, accurate, and easily adaptable IS applications should be developed for real-time farming operations. Weather station networks and online data access should be enhanced to better serve these IS applications.
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