Impacts of the Use of Short-Term Weather Forecast Data in Irrigation Scheduling on Nutrient and Water use Efficiency in Humid Climates: Experimental Results in Corn and Cotton
Abstract
Agriculture accounts for 62% of the total water consumption, the highest consumption among all water use sectors in 2015 in the United States (US). With the increasing climate variability during the growing season, irrigation adoption is increasing for providing supplement water to crops, promoting crop yield and quality, and enhancing profits. Enormous benefits of irrigation have been observed when used to offset short-term in-season droughts, especially at the peak of the growing season resulting from improved nutrient use efficiency (NUE). However, improper irrigation scheduling, especially in humid regions, results in waterlogging and surface runoff, leading to nutrient leaching and runoff, ultimately resulting in lower NUE, lower Water Use Efficiency (WUE), poor yields, and water quality impacts. This study was conducted at Tidewater research station in Suffolk to test the effects of a rule-based irrigation scheduling methodology on Yield, NUE, and WUE in corn and cotton under three irrigation treatments (rainfed, traditional, and forecast-informed irrigation) and four nitrogen application treatments (increments of 80lb for corn and 40lb for cotton). The methodology incorporated real-time soil water availability, crop physiological status, water needs, and short-term weather forecast information from National Weather Service. The results showed an improved yield, NUE, and WUE using forecast-informed irrigation compared to traditional corn irrigation scheduling. It was observed that early irrigation might impact the NUE when followed by irrigation in the later months, especially for cotton. The field study also highlights some of the practical challenges associated with implementing precision irrigation in practice, including labor and equipment issues and uncertainty in forecast accuracy.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFM.H25O1287S