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Abstract
The study first identified fully efficient farmers and then estimated technical efficiency of inefficient farmers, identifying their determinants by applying a Zero Inefficiency Stochastic Frontier Model (ZISFM) on a sample of 300 rice farmers from central-northern Thailand. Next, the study developed scenarios of potential production increase and resource conservation if technical inefficiency was eliminated. Results revealed that 13% of the sampled farmers were fully efficient, thereby justifying the use of our approach. The estimated mean technical efficiency was 91%, implying that rice production can be increased by 9%, by reallocating resources. Land and labor were the major productivity drivers. Education significantly improved technical efficiency. Farmers who transplanted seedlings were relatively technically efficient as compared to those who practised manual and/or mechanical direct seeding methods. Elimination of technical inefficiency could increase output by 8.64% per ha, or generate 5.7–6.4 million tons of additional rice output for Thailand each year. Similarly, elimination of technical inefficiency would potentially conserve 19.44% person-days of labor, 11.95% land area, 11.46% material inputs and 8.67% mechanical power services for every ton of rice produced. This translates into conservation of 2.9–3.0 million person-days of labor, 3.7–4.5 thousand km2 of land, 10.0–14.5 billion baht of material input and 7.6–12.8 billion baht of mechanical power costs to produce current level of rice output in Thailand each year. Policy implications include investment into educating farmers, and improving technical knowledge of seeding technology, to boost rice production and conserve scarce resources in Thailand.
Highlights
The measurement of productive efficiency of a farm relative to other farms, or the “best practice”for an industry, has long been of interest to agricultural economists
Yi = xi0 β + vi − ui with probability 1 − p where the error term in the Stochastic frontier model (SFM) is defined as ε i = vi − ui, yi, which represents the output of firm i, xi denotes a K × 1 vector whose values are functions of inputs and other explanatory variables, β is the vector of parameters corresponding to explanatory variables, vi is assumed to be i.i.d random errors and normal distribution with mean 0 and unknown variance σv2, ui are non-negative unobservable random variables following a half normal distribution with mean 0 and unknown variance σu2, p is a function that represents the proportion of firms that are fully efficient, and p is specified as p = exp(γ)/[1 + exp(γ)]
It is clear that the pseudo-likelihood ratio (PLR)
Summary
The measurement of productive efficiency of a farm relative to other farms, or the “best practice”for an industry, has long been of interest to agricultural economists. The measurement of productive efficiency of a farm relative to other farms, or the “best practice”. From an applied perspective, measuring inefficiency or efficiency is important because this is the first step in a process that can lead to substantial resource savings [1]. These resource savings have important implications for both policy formulation and farm management [2]. For individual farms, gain in efficiency is important in periods of financial stress. Efficient farms are likely to generate higher incomes, and stand a better chance of surviving and prospering.
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