Micro-Irrigation System Design and Management: Monitoring, Modeling, and Optimization for Best Management Practices

Increase in human population, and thus increase in demand for food, limitation in expansion of agricultural land and their limited potential in increasing the crop production all indicate the need for increase in efficiency of irrigation and fertigation practices and as a result crop production. Toward addressing these concerns, conventional surface irrigation systems were switched to micro irrigation systems, which have been proven to be effective in boosting productivity and improving water/nutrient use efficiency. However, optimum design of micro irrigation systems and irrigation/fertigation management are the key elements in achieving desired water/nutrient use efficiency.;Almond's high water/nitrogen demand and acreage across the Central Valley in the California make it vulnerable to be recognized as one of the sources of nitrate contamination of groundwater. Alfalfa consumes about 20% of California's developed water, indicating the importance of optimizing water use efficiency in alfalfa. Therefore, a field monitoring study was conducted to evaluate water use efficiency and its uniformity across an almond orchard. We also conducted a numerical modeling study where HYDRUS-2D model was coupled with a multi-objective optimization tool, AMALGAM, looking for optimum subsurface drip irrigation system design/management for alfalfa.;In almond orchard, monitoring of leaching was introduced as an index for evaluation of irrigation management practices. Water balance approach and Darcy's law were used to estimate the leaching of water below the root zone of almond in both tree and field scales under drip and micro-jet irrigation systems. It was shown that the uncertainty introduced by spatial variation in actual evapotranspiration and applied water dominated the temporal changes in average leaching at field scale suggesting that the field-scale water balance is not the appropriate method for leaching assessment.;We used Tempe Cell, Neural Network approach and inverse modeling to estimate the soil hydraulic conductivity for Darcy law. Whereas the tree scale actual ET is known, it was shown that the estimated soil hydraulic properties by inverse modeling are greatly influenced by the ET values used in inverse modeling. It was concluded that the combination of spatial variation in soil matric potential, h, heterogeneity in soil properties, and approach used for estimation of soil hydraulic properties are the main sources of uncertainty in leaching rate estimated using Darcy law approach. Therefore, the soil hydraulic conductivity is the main limiting factor in using DL approach. Monitoring of soil matric head, h, was also found to be a good index of occurrence of leaching since the total head gradient in deep soil profile is expected to be close to unity and the magnitude of soil hydraulic conductivity and thus leaching rate is a direct function of soil matric head, h..;In the numerical study for alfalfa we used the optimization framework to optimize drip-line installation depth and distance, irrigation duration, and irrigation frequency, while maximizing root water uptake and minimizing irrigation water losses by leaching in alfalfa field. These optimal parameters are determined for different root distribution (uniform and linear), and soil types (sandy loam, loam, and clay loam). It was shown that root distribution and soil properties have a large effect on optimal applied irrigation water, irrigation water scheduling, and deep percolation losses.