The study aims to investigate Thai rice production efficiency with the copula-based stochastic frontier model to draft general statistical data for Thai agriculture development in the future. The empirical results show that land and fertilizer have an indisputably positive and significant effect on rice production. The SFM with Gaussian copula has the lowest value regarding to AIC. The best average TE score belongs to the northern region followed by the central region, the north-eastern region, and the southern region with their average scores of 0.86, 0.819, 0.76 and 0.74, respectively. The interesting point is that, even with highest efficiency score, there is the largest gap between the highest and the lowest scores within central region provinces, followed by north-eastern, central and northern regions with their values of 0.2, 0.18, 0.15 and 0.08, respectively.

This study purposes to estimate climate change effect on agriculture sector in ASEAN by using the copula-based stochastic frontier approach to evaluate the technical efficiency and factors that affect agriculture production. Panel data of land, labour, fertilizer, and temperature in seven countries in ASEAN including Thailand, Vietnam, Myanmar, Philippines, Indonesia, Cambodia, and Malaysia collected from 2002 - 2016 were used for estimating the model. The results presented that the land, labour, and fertilizer consumption according to the agriculture have positive and significant effects on agricultural production. The most interesting point from this study, found that there is a negative effect on agriculture production related by the climate change. Additionally, this study provides the most appropriate tools to analyse climate change impacts on ASEAN agriculture and the potential options for adaptation in the agriculture sector.

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