Title : Developing site-specific water response functions using historical soil moisture data
Abstract:
Variable-rate irrigation (VRI) is an important technology in precision agriculture due to its ability to tailor water application to distinct areas of the agricultural field according to the heterogenous within-field conditions. To obtain the optimal results from variable-rate irrigation, a crucial step is the development of accurate site-specific crop water response functions, which form the basis for determining optimal irrigation levels across different field segments. However, comprehensive development of these functions is still lacking in existing literature. There are two principal challenges in developing site-specific water response functions. The first challenge is data collection. The second challenge involves the dynamic nature of water in production field. Many crop water response studies that have used irrigated water as the independent variable have resulted in inconclusive findings. Crop science research often uses plant water evapotranspiration, however, evapotranspiration itself is an outcome of crop growth, rather than a manageable input which could lead to endogeneity problems. Hence, the main objective of this research is to estimate the crop water production function using soil moisture levels as the explanatory variable. In this study, we propose using soil moisture data as the independent variable to estimate site-specific crop water response functions, considering that soil moisture level can better represent the water accessible to crops and is a manageable input factor. Data was gathered from an 18-hectare production field in Brooksville, Mississippi. Soil moisture sensors were installed at 44 grid points in the field, recording hourly measurements at various depths from 2018 to 2020. These recorded soil water tension (SWT) as a proxy for soil moisture level. The study employs both global ordinary least squares regression and geographically weighted regression (GWR) to estimate the relationship between soil water tension and crop yield. The geographically weighted regression allows coefficients to vary across locations, capturing spatial heterogeneity in yield response. The results from the global regression provided a baseline although the model suggested a weak and mostly insignificant relationship between crop yields and soil water tension (SWT). Unlike the global OLS regression which smoothed out everything across the field, the GWR model allowed the relationships to vary from one sensor location to another, and this revealed localized patterns across the field between crop yields and SWT that was entirely masked in the global OLS regression. The GWR result maps revealed consistent variability patterns in the Yield-SWT relationship, especially during different plant growth stages and soil depths. Overall, soybean’s vegetive stage is the most sensitive period to soil moisture stress. A farmer with this information will be able to tailor their irrigation application, concentrating more on areas of the field that are most sensitive to water stress, and exactly what period of the growth stage of the crop. Furthermore, the estimated economic value of site-specific VRI application revealed that using GWR-based variable rate irrigation led to gains of $1.07 per acre in 2018 for soybeans, $25.28 per acre in 2019 for corn, and $18.54 per acre in 2020 for soybeans, compared to the traditional OLS-based uniform irrigation approach.
