Applying Data Envelopment Analysis (DEA) to Optimize the Fertilizer and Pesticides Consumption in Wetland Rice Cultivation in Malaysia

Résumé

Abstract Fertilizers and pesticides are the most effective and costly inputs in rice production in wetland rice cultivation in Malaysia. Farmers need to know the optimum amount of these inputs to produce the same rice yield. In this study, Data Envelopment Analysis (DEA) was used to estimate the farm input efficiencies of rice production based on seven inputs including N, P, K, and chemicals pesticides liquid insecticides, solid insecticides, fungicides, and herbicides, and one output of rice grain yield. The study aimed to benchmark the inefficient farms to the efficient farms to optimize the fertilizer and pesticide inputs, and also maximize the rice grain yield output. We used three main models Charnes–Cooper–Rhodes (CCR), Banker–Charnes–Cooper (BCC), and Slacks-Based Measure (SBM) models in both direction input and output-oriented to evaluate the performance of every model and determine the effective one. The technical, pure technical, scale and cross efficiencies were calculated for rice production through the three models. The results revealed the average technical were (0.89 and 0.95) for CCR, (0.76 and 0.87) for SBM-I-C, (0.89 and 0.95) for SBM-O-C, and pure technical efficiencies were (0.95 and 0.97) for BCC-I, (0.97 and 0.99) for BCC-O, (0.79 and 0.9) for SBM-I-V, and (0.96 and 0.99) for SBN-O-V for first and second season respectively. The results showed that out of the total number of farms the efficient farms were 16.7% and 43.3% for CCR models, and 26.7% and 56.7% for BCC models for the first and second season respectively.

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