In-field Optimization of Soil Water Parameters for Irrigation Scheduling

Authors

Ishani Lal, University of Nebraska-LincolnFollow

First Advisor

Haishun Yang

Second Advisor

Derek M. Heeren

Date of this Version

5-2024

Document Type

Thesis

Citation

A thesis submitted to the faculty of the Graduate College at the University of Nebraska in partial fulfillment of requirements for the degree of Master of Science

Major: Agronomy

Under the supervision of Professors Haishun Yang and Derek M. Heeren

Lincoln, Nebraska, May 2024

Comments

Copyright 2024, Ishani Lal. Used by permission

Abstract

Several precision technologies are being developed to assist farmers in informed irrigation decision-making. These technologies aim to enhance irrigation water efficiency, check overirrigation, boost crop water productivity, and promote the sustainable utilization of water resources. We hypothesize that soil moisture parameters used in irrigation decision tools need to be validated to best represent the correct state of soil moisture in the field. This is an important factor that affects the accuracy of the irrigation recommendations forecasted by the decision support tools. In this research, we evaluated different methods of estimating field capacity (FC) and wilting point (WP) to optimize soil moisture parameters for irrigation scheduling. Several studies have emphasized the importance of observational FC but none have been able to quantify and compare various methods relative to observational FC (FC_obs). To test our hypothesis, we used one irrigation decision support system (IDSS) and compared it to Web Soil Survey (WSS) and Pedo Transfer Functions (PTF) for FC. For WP, we compared the IDSS, WSS and PTF methods to laboratory method (LAB). IDSS was categorized into two different types of methods- Single Data Point Optimization (SDPO) and Time Series Optimization (TSO), based on how they estimate FC and WP. Furthermore, we quantified the uncertainty of predicting the soil moisture parameters by those methods using RMSPE (Root Mean Square Prediction Error). The study area covered eight locations in three states across the Great Plains of North America including North and South Dakota, Eastern and Western Nebraska, and Kansas for the 2023 growing season. The results showed that WSS had the highest RMSPE_WSS of 18.6% with a potential delay in irrigation of 15 days and 6 fewer irrigation events followed by PTF with RMSPE_PTF of 13.6% with a potential delay in irrigation of 11 days and 5 fewer irrigation events. The SDPO had the lowest RMSPE_SDPO of 0.7% with a potential delay in irrigation of -1 day and no missed irrigation event. TSO had an RMSPE_TSO which reflects the temporal variability in the estimation of FC, and it was around 0.8%. Soil Water Depletion (SWD) was plotted for each of the four methods to compare them with the baseline (FC_obs and WP_LAB). This study will benefit irrigation scheduling practices by providing a better understanding of the insights available from field and lab data and how to best manage the data from the soil moisture probes.