Project Summaries

12-346  Project Manager: E. M. Barnes

DEVELOPING AN EFFECTIVE SENSOR-BASED IRRIGATION SCHEDULING TECHNIQUE FOR COTTON PRODUCTION IN COASTAL PLAIN SOILS

Ahmad Khalilian, Clemson University

Fields in the southeastern Coastal Plain region have a high degree of variability in soil type and topography, with some areas of the field wetter or dryer than other areas. Efficient irrigation in these fields is best achieved using a sensors-based variable rate irrigation (VRI) system which takes all of these variations into consideration. Although variable-rate water application technology is commercially available and over 100 units have already been installed on growers' pivots in SC and GA, many cotton growers either don't have access to this technology or don't have the time or comfort level with higher-tech gadgets. Therefore, until VRI systems become simpler for grower use, an efficient and affordable sensor-based irrigation scheduling technique for cotton needs to be developed to account for field variability in the southeastern Coastal Plain soils. Increasing crop water use efficiency (WUE) and use of more drought tolerance cotton varieties also help resource conservation.

The overall objective of this study was to determine the most accurate and affordable sensor technology, the optimum sensor location in a production field, and the number of moisture sensors required for an effective sensor-based irrigation scheduling for cotton production in coastal plain soils.

Replicated tests were conducted during 2012 growing season at the Edisto Research and Educational Center to develop an effective sensor-based irrigation scheduling technique for cotton production in coastal plain soils. The test field was divided into management zones based on soil EC data and the following irrigation treatments were replicated four times in each zone using a randomized complete block experimental design: Treatments 1 to 4: irrigation rates calculated based on sensor readings from Zones 1 to 4. Treatment 1 in each zone was irrigated based on sensor readings from Zone 1, while Treatment 2 in each zone was irrigated based on sensor readings from Zone 2. The same procedure was repeated for Treatments 3 and 4. Treatment 5 was ET-based irrigation scheduling. Three different soil moisture sensors (AquaSpy, Watermark, and Decagon EC-5) were installed in plots of each treatment for soil moisture monitoring. In addition, neutron probe access tubes were installed in the same plots next to the other moisture sensors.

The VMC measurements using Decagon sensor were strongly correlated (R2=0.81) with the neutron probe's calculated VMC. The Watermark and AquaSpy sensors readings were poorly correlated with those of neutron probe (R2<0.5). Except in Zone 2, ET-based irrigation scheduling yielded significantly lower than the rest of the irrigation treatments. The results suggested that, in a production field with soil variability, it would be beneficial to install moisture sensors in management zones with higher EC readings to obtain maximum yield. However, this may not be true during a dry year. Water use efficiency was affected by soil texture (EC). There were no significant differences in WUE among the five irrigation treatments in higher EC zones. However, irrigating based on sensor readings from Zone 2 and ET-based irrigation scheduling, significantly increased WUE in lower EC zones.

 

Project Year: 2012
 

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