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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #421027
Research Project: Improving Irrigated Crop Management System for Humid and Sub-humid Climates

Location: Cropping Systems and Water Quality Research

Title: Combining mobile and penetrating ECa sensors to understand variability in irrigated cotton production

Author
item Sudduth, Kenneth
item TIAN, FENGKAI - University Of Missouri
item WILSON, BRADLEY - University Of Missouri
item ZHOU, JIANFENG - University Of Missouri

Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 11/12/2024
Publication Date: 11/12/2024
Citation: Sudduth, K.A., Tian, F., Wilson, B.R., Zhou, J. 2024. Combining mobile and penetrating ECa sensors to understand variability in irrigated cotton production [abstract]. 2024 ASA-CSSA-SSSA International Annual Meeting, November 10-13, 2024, San Antonio, Texas. Paper No. 158190. Available: https://scisoc.confex.com/scisoc/2024am/meetingapp.cgi/Paper/158190

Interpretive Summary:

Technical Abstract: Crop growth and yield in irrigated cotton production can vary within fields due to soil texture variations that impact plant water availability. We present two case studies illustrating how apparent soil electrical conductivity (ECa) sensors can provide dense spatiotemporal data quantifying soil texture and soil water content, variables that can then be used for irrigation decision support. In the first study, data from mobile coulter-based ECa sensors were processed with commercial inversion software to create layer maps of conductivity to a 1-m depth. These layer maps were validated with data from an ECa-sensing penetrometer (R2 = 0.81). Conductivity layer maps were then transformed to texture maps through calibration to soil core texture data, with good results (R2 = 0.81) across six Missouri fields. These three-dimensional representations of soil texture could then be used as input data for spatially explicit crop and environmental modeling. The goal of the second study was to determine if soil water content (WC) could be estimated directly from multitemporal ECa surveys obtained within the growing season in a cotton field with highly variable soil texture. Soil ECa data were collected prior to planting and three times during the growing season across a 5-ha irrigated cotton field using a commercial electromagnetic induction instrument. Concurrently, WC was measured at four depths for five within-field locations. Using these data, regressions estimating WC from ECa were developed (R2 = 0.79) and used to map spatially variable WC. Changes in ECa-estimated WC between measurement dates corresponded reasonably well with a mapped water balance. The ECa-based WC maps developed in this research provide much higher spatial resolution for soil-based irrigation scheduling than would be possible with point WC measurements alone.