Response of Landscape Groundcovers to Deficit Irrigation and Site-Specific Management of Alfalfa in Southern California

Southern California is home to different landscape groundcovers and a major alfalfa producer (Medicago sativa L.), which consume significant irrigation water. Therefore, developing water conservation strategies for landscape groundcovers and site-specific management zones for alfalfa production is necessary to increase resource use efficiency. In this dissertation, an effort has been made to optimize landscape groundcovers' irrigation water use and delineate alfalfa management zones for site-specific applications of agricultural inputs.A two-year (2020-2021) study was conducted in Riverside, CA, to evaluate the effect of irrigation rates on the growth, physiological performance, and canopy temperature dynamics of ten landscape groundcovers. Four reference evapotranspiration (ETo) based irrigation treatments (ranging from 24- to 99-% ETo) and ten groundcovers were laid in a randomized complete block design and replicated three times. Groundcovers, including Rhadogia spinescens, Baccharis x 'Starn' Thompson, and Eriogonum fasciculatum 'Warriner Lytle' were found to maintain their growth and quality at 24% ETo deficit irrigation. In addition, the groundcover water response function was developed for the first time to help estimate the effect of irrigation rates on the quality of different landscape groundcovers. Irrigation-included cooling was evident in most of the groundcovers irrigated at higher rates; however, Rhagodia spinescens and Baccharis x Starn Thompson were found to perform well in terms of quality, cooling ability and overall growth development. Addressing variability in the field is helpful for site-specific alfalfa management. A study on four alfalfa fields in southern California was conducted to evaluate the potential of multispectral and thermal images acquired using unmanned aerial vehicles to estimate within- and between-field alfalfa production. A multiple linear regression model showed the highest accuracy with R2 = 0.83, RMSE = 142.99 kg ha-1, and MAE = 109.30 kg ha-1. The models accurately ...