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Technical Efficiency of Khao Dawk Mali 105 Rice Variety in Chiang Mai Province

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Technical Efficiency of Khao Dawk Mali 105 Rice Variety in Chiang Mai Province International Journal of Advanced Science and Technology Vol. 29, No.9s, (2020), pp.4498-4507 Technical Efficiency of Khao Dawk Mali 105 Rice Variety in Chiang Mai Province Wongchai Anupong Department of Agricultural Economy and Development, Faculty of Agriculture, Chiang Mai University, Thailand Abstract This research aimed to measure technical efficiency and to analyze factors affecting the technical inefficiency of rice production from Khao Dawk Mali 105 variety in Chiang Mai Province .An in-depth interview was conducted in this study using quota sampling, a total of 11 Districts in Chiang Mai Province .A Stochastic Frontier Analysis was theoretically applied to measure the technical efficiency’s score with the Maximum Likelihood Estimation( MLE )method .Finally, an Ordinary Least Square (OLS )was theoretically employed to analyze factors affecting the technical inefficiency of rice production in Chiang Mai Province. The technical efficiency’s score of Khao Dawk Mali 105 rice variety was a low level at an average level of 0.664 (66.40 )which can more increase by 33.60 .%The highest technical efficiency score was about 0.978( 97.80)%, while the lowest technical efficiency score was about 0.022( 2.20 .)%The factors that showed a different significant level towards technical inefficiency are cultivation technologies, drought conditions, and government policy .These three variables had a positive relationship which contrasts with the basic assumptions . Furthermore, the other three factors, including farmers ’training, farming experience, as well as research and development had an insignificant level, implying these three insignificant factors were unable to describe the technical inefficiency of rice production in Chiang Mai Province .Therefore, the technical efficiency’s score of rice production from Khao Dawk Mali 105 rice variety can be more increased by improving the cultivation technologies, drought conditions, and government policy. Keywords :Chiang Mai Province, Khao Dawk Mali 105 Variety, Rice Production, Technical Efficiency, Stochastic Frontier Analysis INTRODUCTION Rice is one of the most important economic crops in the world, especially in Asia. Due to the environment that is suitable for production and population, rice consumption is ubiquitous .Most rice production and consumption are located in Asia, with Thailand being the world’s major rice producer and exporter .Due to the rapid population growth in Asia, Thailand has supported rice production to meet domestic demand and still expansively exports rice to overseas .As a result, the world rice trade increases from 36.48 million tons in the crop year 2010/11 to 46 million tons in the crop year 2014/15, accounting for three percent per year( .Office of Agricultural Economics, 2015) The northern region is an important area for rice cultivation because most farmers have cultivated rice for household consumption and also produce rice products cultivated in the country .Therefore, rice cultivation is the main occupation that generates farmer’s incomes living in the northern region which has 17 major rice cultivated areas, namely Chiang Rai, Phayao, Lamphun, Lampang, Chiang Mai, Mae Hong Son, Kamphaeng Phet, Sukhothai, Nan, Uttaradit, Phitsanulok, Phichit, Nakhon Sawan, Uthai Thani, and Phetchabun, accounting for two percent of the country’s total rice cultivation area, followed by the Northeastern region which makes up 75percent based on statistical data from Office of Agricultural Economics in the year 2015 .The harvesting farmland and major rice production in the northern region have continuously expanded from 2002until 2002 as shown in Table 0. The major popular rice variety in the northern region is Khao Dawk Mali 105 rice variety, the special characteristic of which provides fragrant and soft rice when cooked .The Khao Dawk Mali 007 rice variety is developed from the native rice varieties, derived from farmers in Bang Khla District, Chachoengsao Province through the pure breeding process .The Khao Dawk Mali 105 rice variety which has special characteristics meets the needs of the market .Therefore, the Thai government has highly supported and promoted the propagation of Khao Dawk Mali 007 rice variety, starting from 25th May, 1959 onwards (Wisitchawong, 2003 .)Given the agricultural area of the northern region in which has an irrigation system to handle a large amount of rainwater ISSN: 2005-4238 IJAST 4498 Copyright ⓒ 2020 SERSC International Journal of Advanced Science and Technology Vol. 29, No.9s, (2020), pp.4498-4507 and most farmers prefer to grow rice in large plots, cultivation of Khao Dawk Mali 007 rice variety is most suitable .Also, there are other areas in Thailand where are appropriately irrigated, including the central provinces; Nakhonsawan Province, Ayutthaya Province, Phichit Province, as well as the northern provinces; Ubonratchathani Province, Udonthani Province, Nong Bua Lam Phu Province, and Loei Province. Table 1 :The statistics of rice production in the northern region, including planted area, harvested area, and productivity from 2009 to 2014 Year Planted Area (ha) Harvested Area (ha) Productivity (tons) 2009 2,038,452 1,909,120 6,556,307 2010 2,293,119 2,144,614 7,315,131 2011 2,424,693 1,952,214 7,121,027 2012 2,388,413 2,347,751 8,744,836 2013 2,347,792 2,296,456 8,637,165 2014 2,328,821 2,285,184 8,624,408 Source :Office of Agricultural Economics (2016) Most of the farmers in Chiang Mai Province preferred to grow Khao Dawk Mali 105 rice variety to meet the demand of the local market .In addition to household consumption, most local people in the northern region prefer to eat sticky rice .However, there are some local people who prefer to cultivate Khao Dawk Mali 105 rice variety for trading in both the domestic and international markets. The cultivation is in the main District of Chiang Mai Province that has a large rice field’s appropriate irrigation system, namely Mae Rim District, Mae Taeng District, Chiang Dao District, Fang District, Mae Ai District, San Sai District, Phrao District, Doi Saket District San Kamphaeng District, Hang Dong District, and Mae Chaem District .The most cultivated area of Khao Dawk Mali 105 rice variety is in Phrao District where the research of the Phrao model’s project of the Faculty of Agriculture, Chiang Mai University, was successfully carried out. Therefore, this research aims (1 )to study the basic information of the rice farmers who grow Khao Dawk Mali 105 rice variety as the main crop in Chiang Mai Province; (2 )to measure the technical efficiency of rice production from Khao Dawk Mali 105 rice variety employing a Stochastic Frontier Analysis with Maximum Likelihood Estimation; ( 3 )to analyze the factors affecting the technical inefficiency of rice production from Khao Dawk Mali 105 rice variety in Chiang Mai Province using an Ordinary Least Square (OLS.) LITERATURE REVIEW Relevant researches have mainly emphasized two aspects, focusing on a technical efficiency measurement that applies Stochastic Frontier Analysis (SFA )in diverse fields, together with a study on rice production efficiency in many areas .In terms of the technical efficiency measurement using SFA’s method, Coelli et al( .1999) studied the influence of environmental variables based on SFA of 32 national airlines in the Asia-Oceania, Europe, and North America regions .The study found that environmental variables were important in estimating and unable to ignore by adding environmental variables in both production equations and performance equations .Coelli et al . (2005) applied the SFA’s method to assess environmental efficiency .They stated that productive factors and the production caused by the environment can be considered as variables in the equation .The environmental variables can be used in both production equations and performance equations which are important in the decision-making process. Likewise, Ueasin et al( .2007) studied the technical efficiency of rice husk which is used to produce electricity in Thailand .Their research’s objective was to measure the technical efficiency of power production from rice husks .The secondary data used in the study was collected from The Energy Policy Office in Thailand in 2002, a total of 57 biomass power plants .The empirical results showed that the SFA model has a higher technical efficiency score of 0.755, followed by CRS-DEA (0.720) and the VRS-DEA (0.522), respectively .Ghosh and Kathuris (2016) investigated the impact of institutional quality on the performance of thermal power plants in India. This research applied a translog stochastic frontier model to estimate the technical efficiency of 77 coal- based thermal power plants. The findings revealed that the average technical efficiency’s score was 76.7%, pointing out that there is a broad scope for efficiency improvements in each sector. Moreover, the results had a ISSN: 2005-4238 IJAST 4499 Copyright ⓒ 2020 SERSC International Journal of Advanced Science and Technology Vol. 29, No.9s, (2020), pp.4498-4507 powerful towards a diverse specified model and represented that state-level regulators provide a positive impact on power sector performance. Besides, Alshammari et al( .2019 )studied the impact of oil prices and the financial market on cost efficiency in the insurance and Takaful sectors in the Gulf Cooperation Council’s (GCC) insurance industry. This research mainly applied a stochastic frontier cost function with data from 2009–2016. Their results revealed that the relationship between technical efficiency and oil prices goes from positive to negative when the oil prices are increased. However, the relationship between technical efficiency and the financial market showed negatively. Therefore, no clear evidence of the oil price’s impacts was empirically found between conventional insurance and Takaful sectors. Finally, Boyd and Lee (2019) analyzed the electric and fuel energy efficiency for five different metal-based durable manufacturing industries in the United States. This research collected the secondary data from 1987- 2012 from U.S. Economic Census at the three digits North American Industry Classification System (NAICS) level.
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