Development of machinery operations and quality field practices assessment for wetland rice cultivation area in Malaysia

Rice is one of the most essential staple foods for a large part of the world’s population including Malaysia, and there is a need to increase production for filling the gap between production and consumption. Maximum paddy productivity could be achieved through the implementation of the best and most effective agricultural practices and the maximum quality of operations output. The Rice Check system is a holistic integrated crop management component that provides objective recommendations and a method of use to improve the yields, grain quality, and profitability of rice growing. In this study, in situ comprehensive evaluation of farm operations and practices conducted in 30 farms for two seasons at Sungai Burong, North-West Integrated Agricultural Development Authority Selangor Malaysia was conducted. The study aims to evaluate the quality of agricultural practices and machinery operations based on the Rice Check and determine the most effective key parameter for improving the quantity production, then optimize the effective input by using an optimization model. To evaluate practices and operations quality, six benchmarking parameters were selected, namely (1) seeding, (2) tillage, (3) water management, (4) fertilization, (5) chemical spraying, and (6) harvesting. A comprehensive evaluation of the quality and effectiveness of all agricultural operations and practices was performed to assess the degree of perfection and quality index of these farming practices, agricultural operations, and the effect of that on the crop in general and the yield as final output was performed. The evaluation was done based on four rights, right source, the right amount, right timing, and right placing and quality of these operations. Multiple linear regression was used to rank the parameters of the cultural practices based on the most effective yielding and the key parameter subjected to the DEA optimization model for high-yielding rice. The result showed that for land preparation just 22.6% of farmers follow exactly the requirement of the Rice Check, for tillage practices 61.9% of farmers follow, for planting 26.2% of farmers follow, for pesticides 44.4% of farmers follow, for fertilizer, 41.1% of farmers follow, and for harvesting, 37.5% of farmers follow the Rice check recommendations. Farmers do not follow the standard in terms of amount, source, and timing of operations in total. In terms of operation quality, tillage is ranked as first achieving 100% of the quality index, and pesticide operation the lowest-achieving 56.4%. Fertilizer has 78.7% Q.I, (quality index), planting achieved 86.7%, harvesting 78.7%, and water irrigation 62.4%. For ranking the parameters, multiple linear regression showed that fertilizer is the most effective parameter on rice yield/production with R2 = 0.85, then planting with R2 = 0.80 followed by pesticides R2 = 0.52. Fertilizer and pesticide inputs were subjected to data envelopment analysis optimization models by using the GAMS optimization package. Running the models showed there were just 5 (16.7%) efficient plots for CCR-I and CCR-O, just 7 (23.3%) efficient plots for BCC-I, and 8 (26.7%) efficient plots for the BCC-O model. Results showed that there was excess use of input materials more than the required optimum ranges from 13.3% (18.1 kg/ha) for Nitrogen, 12% (10.9 kg/ha) for Phosphorus 17.1% (11.4 kg/ha) for Potassium, Fungicides 32.6%, Liquid Insecticides 34.2%. Powder Insecticide 39.0%, and Herbicides 28.9%. It could be concluded that the quality indices for agricultural practices and operations were (61.9 and 100%) for tillage, (26.2 and 86.73%), for planting, (37.7 and 56.4%) for pesticides, (41.4 and 78.71%) for fertilizer, and (37.5 and 78.7%) for harvesting respectively, for water management the quality index was 67.1%. The most effective parameter on yield of rice was the fertilizer, 2nd was the planting, 3rd was the pesticides, 4th was the harvesting, and 5th was the soil factor.

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