The Relationship Between Amounts of Barley Yield and Basic Production Components in Wadi Irawan Agricultural Project
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Inputs, Outputs, Barley Production Process, MLR, Modeling, Irawan Project
Abstract
The study aimed to estimate the best regressive relation for the effect of basic production components on the amounts of barley yield in the Irawan project. Five components have been determined: Area, Water, Basic Fertilizer (BF), Additional Fertilizer (AF), and Liquid Fertilizer (LF) as inputs, beside the Production (criterion) as an output. Observed data collected along twenty-two successive agricultural seasons. Using multiple linear regression (MLR) tools, three models were estimated. First: A five-predictor (MLR) model. The model's diagnosis showed that it was suffering from some regression problems. Model's assessment: Overall it was significant, where 72% of the variability in production could be accounted for by variability in the considered predictors, but not all the regression coefficients were significant. Moreover, two predictors (BF and LF) have negative effects, so both should be dropped. Area and water were highly correlated; water should be dropped. Second: A two-predictor (refined) MLR model. Diagnosis showed that it was free of regression problems. Assessment: Overall the model was significant, where 70% of the variability in production could be accounted for by variability in the area and AF. The regression coefficient of area was significant. Third: simple LR model. Diagnosis showed that it was free of regression problems. Assessment: Overall the model was significant, with 65% of the variability in production being accounted for by variability in the area; its regression coefficient was significant. The second model seems to be the best fit for describing the barley production process in the Wadi Irawan agricultural project and could be used for both estimation and prediction.
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