Intraseasonal Management Strategies for Deficit Irrigation

Authors

Isaac I. N. Mortensen, University of Nebraska-LincolnFollow

First Advisor

Raymond J. Supalla

Second Advisor

Derrel L. Martin

Date of this Version

4-2011

Document Type

Thesis

Citation

A thesis presented 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: Agricultural Economics

Under the supervision of Professors Raymond J. Supalla and Derrel L. Martin

Lincoln, Nebraska, April 2011

Comments

Copyright 2011, Isaac Mortensen

Abstract

Reduced availability of irrigation water to producers has led to the need for intraseasonal management strategies that efficiently use the limited supply of irrigation water. Historical weather data was used to develop a range of conditions experienced at the location. Sound weather data improves the dependability of management strategies. Data from weather stations on the Automated Weather Data Network and the Colorado AgMet network were evaluated based upon net radiation and dew point temperature observations expected in an irrigated agricultural setting. This weather data was used to create a relationship between the Penman-Montieth evapotranspiration (ET) and Hargreaves ET and the geographical location of the weather stations. The AquaCrop model was calibrated to data from the Carbon Sequestration Project at Mead, Nebraska. The model was able to accurately predict canopy cover (R² = 0.96), biomass production (R² = 0.98), and yields (R² = 0.84, RMSE = 0.72 Mg ha^-1). The model was also able to track ET throughout the growing season. The weather data and calibrated model were used to simulate the impact of irrigation timing throughout the growing season and to determine the timing of irrigation events that produced the highest marginal yields for different system capacities and initial soil water contents. Using the optimized irrigation distribution, a management strategy was developed to deficit irrigate corn based upon days after planting, initial soil water content, well-waterd ET, and a yield goal. The model predicted yields within 10% of the yield goal for the majority of simulations. It translated geographically and expressed the ability to account for differences in system capacity.

Advisers: Raymond J. Supalla and Derrel L. Martin