Studying Crop Yield Response to Supplemental Irrigation and the Spatial Heterogeneity of Soil Physical Attributes in a Humid Region

Abstract

West Tennessee’s supplemental irrigation management at a field level is profoundly affected by the spatial heterogeneity of soil moisture and the temporal variability of weather. The introduction of precision farming techniques has enabled farmers to collect site-specific data that provide valuable quantitative information for effective irrigation management. Consequently, a two-year on-farm irrigation experiment in a 73 ha cotton field in west Tennessee was conducted and a variety of farming data were collected to understand the relationship between crop yields, the spatial heterogeneity of soil water content, and supplemental irrigation management. The soil water content showed higher correlations with soil textural information including sand (r = −0.9), silt (r = 0.85), and clay (r = 0.83) than with soil bulk density (r = −0.27). Spatial statistical analysis of the collected soil samples (i.e., 400 samples: 100 locations at four depths from 0–1 m) showed that soil texture and soil water content had clustered patterns within different depths, but BD mostly had random patterns. ECa maps tended to follow the same general spatial patterns as those for soil texture and water content. Overall, supplemental irrigation improved the cotton lint yield in comparison to rainfed throughout the two-year irrigation study, while the yield response to supplemental irrigation differed across the soil types. The yield increase due to irrigation was more pronounced for coarse-textured soils, while a yield reduction was observed when higher irrigation water was applied to fine-textured soils. In addition, in-season rainfall patterns had a profound impact on yield and crop response to supplemental irrigation regimes. The spatial analysis of the multiyear yield data revealed a substantial similarity between yield and plant-available water patterns. Consequently, variable rate irrigation guided with farming data seems to be the ideal management strategy to address field level spatial variability in plant-available water, as well as temporal variability in in-season rainfall patterns.