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Technical Efficiency Analysis of Organic Rice Production in Leyte, Philippines: A Stochastic Frontier Approach

Authors: Allen Glennie P. Lambert;

Technical Efficiency Analysis of Organic Rice Production in Leyte, Philippines: A Stochastic Frontier Approach

Abstract

This study investigates the technical efficiency of organic (uncertified) lowland irrigated rice farming in selected areas of Leyte Province. Technical efficiency indices were derived from a stochastic frontier production function analysis employing the maximum likelihood estimation. Organic (uncertified) rice farmers observed mean technical efficiency was high, with 92% for the dry season and 90% for the wet season, which is comparatively higher than the conventional practices findings available in the literature. Results showed that technical efficiency was significantly affected by farming experience, education, trainings attended, income from other sources, affiliation with organizations, technical services from the Department of Agriculture, and homemade sources of inputs. Results of the study suggest that farmers should apply optimal levels of organic inputs and continue water management to control weeds. For the government and non-government agencies to further promote the use of organic rice farming systems and intensive extension services is necessary. In addition, it is recommended that the government should create a policy and assistantship for organic farmers, especially small-scale farmers, in having their products certified at a lesser price or better a cost-free program. This will help farmers have a justified price for their products and will also further motivate other farmers to convert to organic farming.

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Keywords

HB1-3840, stochastic frontier production function, Commercial geography. Economic geography, Economic theory. Demography, HF1021-1027, organic rice production, technical efficiency

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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