2013•He'nan nongye daxue xuebaoRequires access

Analysis of food production technical efficiency and total factor productivity of Henan province

Qi Liu

Open publisher page 2 citations

Abstract

The non-parametric DEA method was used to analyze the composition of the average food production technical efficiency,pure technical efficiency,scale efficiency and total factor productivity and change of Henan Province from 2000 to 2011.The results show that Henan Province grain production technical efficiency growth is mainly related to the improvement of the pure technical efficiency and the low scale efficiency restricting the increase.The total factor productivity growth was attributed to the technical efficiency and technical progress,but the total factor productivity growth was very slow,some years appear even reverse phenomenon.The food total factor productivity change of each city is different.The construction of agricultural infrastructure in the grain production in Henan Province should be strengthened,and investment in science and technology should be increased,and the promotion of agricultural science and technology should also be strengthened.

About this research paper

What this paper is about

The non-parametric DEA method was used to analyze the composition of the average food production technical efficiency,pure technical efficiency,scale efficiency and total factor productivity and change of Henan Province from 2000 to 2011.The results show that Henan Province grain production technical efficiency growth is mainly related to the improvement of the pure technical efficiency and the low scale efficiency restricting the increase.The total factor productivity growth was attributed to the technical efficiency and technical progress,but the total factor productivity growth was very slow,some years appear even reverse phenomenon.The food total factor productivity change of each city is different.The construction of agricultural infrastructure in the grain production in Henan Province should be strengthened,and investment in science and technology should be increased,and the promotion of agricultural science and technology should also be strengthened.

Why it matters

OpenAlex reports 2 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

The non-parametric DEA method was used to analyze the composition of the average food production technical efficiency,pure technical efficiency,scale efficiency and total factor productivity and change of Henan Province from 2000 to 2011.The results show that Henan Province grain production technical efficiency growth is mainly related to the improvement of the pure technical efficiency and the low scale efficiency restricting the increase.The total factor productivity growth was attributed to the technical efficiency and technical progress,but the total factor productivity growth was very slow,some years appear even reverse phenomenon.The food total factor productivity change of each city is different.The construction of agricultural infrastructure in the grain production in Henan Province should be strengthened,and investment in science and technology should be increased,and the promotion of agricultural science and technology should also be strengthened.

Key concepts: Productivity, Total factor productivity, Production (economics), Technical change, Agricultural productivity, Agricultural economics, Agriculture, Technical progress

Related papers

Back to paper searchBrowse research topicsOriginal source
Analysis of food production technical efficiency and total factor productivity of Henan province — Research Paper | ScholarLens