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The paper jointly evaluates the determinants of switching to Jasmine rice and its productivity while allowing for production inefficiency at the level of individual producers. Model diagnostics reveal that serious selection bias exists, justifying use of a sample selection framework in stochastic frontier models. Results from the probit variety selection equation reveal that gross return (mainly powered by significantly higher Jasmine rice price), access to irrigation and education are the important determinants of choosing Jasmine rice. Results from the stochastic production frontier reveal that land, irrigation and fertilisers are the significant determinants of Jasmine rice productivity. Significantly lower productivity in Phitsanulok and Tung Gula Rong Hai provinces demonstrate the influence of biophysical and environmental factors on productivity performance. The mean level of technical efficiency is estimated at 0.63 suggesting that 59% [(100  -  63)/63] of the productivity is lost due to technical inefficiency. Policy implications include measures to keep Jasmine rice price high, increase access to irrigation and fertiliser availability, as well as investment in education targeted to farm households which will synergistically increase adoption of Jasmine rice as well as farm productivity. Copyright (c) 2009 The Authors. Journal compilation (c) 2009 The Agricultural Economics Society.

In the standard stochastic frontier model, the two-sided error term V and the one-sided technical inefficiency error term W are assumed to be independent. In this paper, we relax this assumption by modeling the dependence between V and W using copulas. Nine copula families are considered and their parameters are estimated using maximum simulated likelihood. The best model is then selected using the AIC or BIC criteria. This methodology was applied to coffee production data from Northern Thailand. For these data, the best model was the one based on the Clayton copula. The main finding of this study is that the dependence between V and W is significant and cannot be ignored. In particular, the standard stochastic frontier model with independence assumption grossly overestimated the technical efficiency of coffee production. These results call for a reappraisal of previous production efficiency studies using the SFM with independence assumption, which may occasionally lead to overoptimistic conclusions.

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This study examines the dependence structure of extreme realization of growth rate between palm

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 km 2 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.