Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/58563
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dc.contributor.authorJi Maen_US
dc.contributor.authorJianxu Liuen_US
dc.contributor.authorSongsak Sriboonchittaen_US
dc.date.accessioned2018-09-05T04:26:19Z-
dc.date.available2018-09-05T04:26:19Z-
dc.date.issued2018-01-01en_US
dc.identifier.issn1860949Xen_US
dc.identifier.other2-s2.0-85037852103en_US
dc.identifier.other10.1007/978-3-319-70942-0_33en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85037852103&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/58563-
dc.description.abstract© Springer International Publishing AG 2018. This paper imposed the translog stochastic frontier production model to analyze the China’s province-level agriculture productivity by using panel data during 2002–2012 on 31 provinces in China. The results show that China’s province-level agriculture productivity has been improved for over 11 years. Hunan, Bejing and Shanghai approached the agriculture technical efficiency frontier. The agriculture technical efficiencies in underdeveloped area such like Guizhou, Yunnan and Anhui increased sharply and approached to the national province-level mean, 60%, in terms of the technical efficiencies over 11 years which, however, still have 40% space to be improved. We recommend that the provinces with lower technical efficiency, such as Anhui, Yunnan and Guizhou, should learn experiences from those provinces that have high technical efficiency so that improving the agricultural productivities of themselves.en_US
dc.subjectComputer Scienceen_US
dc.titleTechnical efficiency analysis of China’s agricultural industry: a stochastic frontier model with panel dataen_US
dc.typeBook Seriesen_US
article.title.sourcetitleStudies in Computational Intelligenceen_US
article.volume753en_US
article.stream.affiliationsChiang Mai Universityen_US
article.stream.affiliationsYunnan Academy of Social Sciencesen_US
Appears in Collections:CMUL: Journal Articles

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