New methods to adjust the design and operation of center pivot and micro-irrigation systems for improved uniformity

In recent years, two effective and efficient types of pressurized irrigation methods have emerged as the leading technologies --- micro-irrigation and center pivots. Due to the limitations imposed on the agricultural water, it is very important to properly evaluate the factors that affect the water application uniformity of these systems. In micro-irrigation, the uniformity is mostly controlled by the hydraulic characteristics of the system while in center pivots, wind drift and evaporation losses (WDELs) play the major role. For micro-irrigation laterals, a new and accurate energy grade line (EGL) that could account for the Darcy-Weisbach friction factor variation, the velocity head change as well as the friction and local losses was developed. Using this EGL, analytical methods were proposed to determine the uniformity index of micro-irrigation laterals and also to optimize their length for a desired level of uniformity. As for center pivots, a new, cost effective and easy-to implement methodology was developed and validated against the widely-accepted catch can test. The advantage of the proposed method is that it permits to monitor the dynamic variation in WDELs under different weather conditions and for relatively long time periods. We then applied the methodology to continuously monitor the WAE under a stationary linear move irrigation system during the 2014 growing season. It was found that the WAE is very dynamic and can experience abrupt changes up to 16.5% even during very short timing intervals (5-min) as a result of sudden changes in wind speed and direction as well as the solar radiation. The WAE was maximum on a calm, cold and humid day and was minimum in the afternoon of a very windy day (WS ≈ 6 m/s). The maximum and minimum measured WAE during our experiments were determined as 97.5% and 73.6%. Wind speed and temperature followed by vapor pressure deficit were found to be the strongest drivers of the WAE. Predictive equations were also developed to estimate the hourly WAE as a function of weather parameters. The empirical model has the capability to calculate the WAE with a maximum absolute error of 4% and with high level of confidence.