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Abstract. Although crop water requirement thought to be solely a function of evapotranspiration (ET), it may depend on many other parameters as well. Especially plant intake needs to be considered in the crop water requirement quantification process, since a plant takes water from its root zone where localized soil condition can play a significant role. Additionally, the traditional ET based irrigation technique may cause over or under irrigation due to a significant amount of uncertainty associated with ET and crop coefficient. Although the use of new technology for irrigation is gaining more attention, the correct method to determine the irrigation amount with high accuracy is still a high priority especially for the state like California, where a 10 percent water savings in Central Valley can save about 2.1 million acre-ft of water annually (DeOreo et al. 2011). A comprehensive analysis is thus required to understand the correlations between crop water requirement and the associated field and weather data. This analysis would identify the important parameters required to determine the irrigation amount with higher accuracy. Several numerical techniques including single and multi linear regression, principal component analysis (PCA), and artificial neural network (ANN) modeling approaches have been used in this study to observe and identify the effects of different important parameters for quantifying crop water requirement for an olive field at California State University Fresno. The ET, SR, AT, ST, RH, CET, and CPSM are found to be the more important parameters. Results also show that correlation coefficient increases with a higher number of independent variables in a combination. At a lower lag time (2 days) a plant responses faster in summer causing higher correlation coefficient. Three (3) or four (4) principal components are identified to be sufficient to capture almost 85% of the system variability. The ANN model can successfully capture the general pattern and dynamics of the crop water requirement.