Calculation of regional agricultural production efficiency and empirical analysis of its influencing factors-based on DEA-CCR model and Tobit model

2021 ◽  
pp. 1-14
Author(s):  
Pengfei Zhou ◽  
Siyuan Yang ◽  
Xiaohang Wu ◽  
Yang Shen
Keyword(s):  
Empirical Analysis ◽  
Influencing Factors ◽  
Agricultural Production ◽  
Production Efficiency ◽  
Malmquist Index ◽  
Tobit Model ◽  
Annual Growth Rate ◽  
Productivity Level ◽  
Agricultural Modernization ◽  
Ccr Model

The improvement of agricultural production efficiency and the transformation of production mode are the core of promoting agricultural modernization. Taking Chongqing as a sample case, this paper uses DEA-CCR Model, Malmquist Index and Tobit Model to calculate and analyze its agricultural production efficiency and its influencing factors, and accurately identifies the problems existing in its agricultural transformation and development. It has an important policy reference value for improving agricultural innovation and competitiveness, promoting the steady development of rural revitalization, and realizing agricultural modernization, which also provides some reference and enlightenment for countries or regions with similar characteristics of mountain agriculture development in the world to enhance regional agricultural production efficiency. Through empirical analysis and investigation, it is found that the overall agricultural production efficiency of Chongqing remains at the productivity level of 0.8 from 2009 to 2018, with an average annual growth rate of 12.8%, but there is a large gap in the level of regional development. Through Malmquist Index decomposition, it is found that agricultural technology progress has the greatest contribution to the improvement of production efficiency. Financial support for agriculture, urbanization level, regional economic development level and highway mileage have a significant positive impact on production efficiency, while the level of farmers’ disposable income has a negative impact on the increase of production efficiency, and the income gap between urban and rural residents fails to pass the significance test.

scholarly journals The Measurement and Influencing Factors of Total Factor Productivity in the Chinese Rural Distribution Industry

2021 ◽  
Vol 13 (15) ◽  
pp. 8581
Author(s):  
Chaoxun Ding ◽  
Ruidan Zhang
Keyword(s):  
Total Factor Productivity ◽  
Influencing Factors ◽  
Data Model ◽  
Industrial Structure ◽  
Positive Impact ◽  
Malmquist Index ◽  
Annual Growth Rate ◽  
Factor Productivity ◽  
The Sustainable Development ◽  
Urbanization Rate

Total factor productivity (TFP) is critical to the sustainable development of the rural distribution industry. Improvements in productivity of the rural distribution industry can promote the high-quality development of the Chinese distribution industry. Studying the characteristics and influencing factors of total factor productivity in regard to the rural distribution industry in China is significant for promoting the transformation and development of the rural distribution industry. This paper uses the DEA–Malmquist Index to measure the total factor productivity (TFP) of the Chinese rural distribution industry and its decomposition index, and uses a panel data model to empirically study its influencing factors. The results show that, from 2008 to 2018, the TFP of the Chinese rural distribution industry showed a trend of rising first and then fluctuating and declining, with an average annual growth rate of 2.93%; the fluctuation direction of the TFP of the rural distribution industry in the eastern and western regions of China is basically the same, which has had a reverse change relationship with the central and northeast regions for many years. The industrial structure, urbanization rate, rural informatization rate, and conditions of the transportation facilities have significant impacts on the TFP of the rural distribution industry, among which the informatization rate has the greatest positive impact.

scholarly journals Increasing Remuneration for Agricultural Workers in the Irkutsk Region as a Result of Increasing of Agricultural Production Efficiency

2021 ◽  
Vol 12 (3) ◽  
Author(s):  
Inna Savchenko ◽  
Nikolay Anikienko ◽  
Sergey Savchenko
Keyword(s):  
Labor Productivity ◽  
Agricultural Production ◽  
Financial Incentives ◽  
High Performance ◽  
Production Efficiency ◽  
Agricultural Workers ◽  
Selling Price ◽  
Productivity Level ◽  
Wage Share ◽  
Irkutsk Region

The provision of regions with local products will be achieved on the basis of increasing labor productivity in agriculture. Financial incentives of workers are of great importance. The article substantiates the need to increase labor productivity in agricultural production. The achieved productivity level of agricultural crops and livestock, as well as labor intensity of production according to product types are considered by the example of agricultural organizations of the Irkutsk region. The level of workers’ wages in agricultural production in the region within 2015–2019 is analyzed. The data on the wage share in the production cost and in the selling price are given. The ways of agricultural production development, such as increasing soil fertility, improving growing crops technology, providing high-performance equipment, improving labor management and remuneration, are considered. On the basis of the proposed measures, it is possible to increase the wage level of agricultural workers.

scholarly journals Evaluation and Influencing Factors of Agricultural Green Efficiency in Jianghuai Ecological Economic Zone

2021 ◽  
Vol 14 (1) ◽  
pp. 30
Author(s):  
Genzhong Li ◽  
Decai Tang ◽  
Valentina Boamah ◽  
Zhiwei Pan
Keyword(s):  
Influencing Factors ◽  
Agricultural Production ◽  
Technological Progress ◽  
Production Efficiency ◽  
Environmental Efficiency ◽  
Policy Implications ◽  
Environmental Damage ◽  
Desirable Output ◽  
Full Analysis ◽  
Positive Effect

Traditionally, in the pursuit of economic development, ecological health was sacrificed, but this is no longer tenable considering the vast environmental damage that is difficult to control and expensive to repair. This is especially true for China’s Jianghuai River Basin (JRB). As a result, this paper uses the slacks-based measure (SBM) directional distance function and the tobit model on panel data from various cities and counties in the Jiangsu section of the HRB to empirically analyze regional green agricultural production efficiency and influencing factors from 2005 to 2019. The results illustrate that agricultural and environmental efficiency fluctuate upward in provincial areas. Still, a downward trend is observed in both redundancy and insufficiency rates of undesirable and desirable output. While this indicates improving regional agricultural and environmental efficiency, there is no readily detectable positive effect of technological progress and technical efficiency on green agricultural production efficiency improvements. Following a full analysis, policy implications are presented and discussed.

scholarly journals An Analysis of Agricultural Production Efficiency of Yangtze River Economic Belt Based on a Three-Stage DEA Malmquist Model

2022 ◽  
Vol 19 (2) ◽  
pp. 958
Author(s):  
Zhiwei Pan ◽  
Decai Tang ◽  
Haojia Kong ◽  
Junxia He
Keyword(s):  
Agricultural Production ◽  
Yangtze River ◽  
Technological Progress ◽  
Production Efficiency ◽  
Malmquist Index ◽  
Data Envelopment ◽  
Factor Productivity ◽  
Strategic Development ◽  
Technological Efficiency ◽  
Development Area

The Yangtze River Economic Belt (YREB) is a major national strategic development area in China, and the development of the YREB will greatly promote the development of the entirety China, so research on its agricultural production efficiency is also of great significance. This paper is committed to studying the agricultural production efficiency of 11 provinces in the YREB and adopts a combination of the Data Envelopment Analysis (DEA) model and the Malmquist index to make a dynamic and static analysis on the YREB’s agricultural production efficiency from 2010 to 2019. Then, a three-stage DEA Malmquist model that eliminates the factors of random interference and management inefficiency is compared to a model without elimination. The results show that the adjusted technological efficiency changes, technological progress, and total factor productivity increased by −0.1%, 0.24%, and 0.22%, respectively. When comparing these values to the pre-adjustment values, the results indicate that the effect of environmental variables cannot be ignored when studying the agricultural production efficiency of the YREB. At the same time, the differences in the agricultural production efficiency in the YREB are reasonably explained, and feasible suggestions are put forward.

OPTIMAL ANALYSIS OF FARM AGRICULTURAL MACHINERY EQUIPMENT BASED ON MATHEMATICAL MODELLING

2020 ◽  
pp. 333-340
Keyword(s):  
Mathematical Modelling ◽  
Production Efficiency ◽  
Programming Model ◽  
Constraint Equation ◽  
Economic Benefits ◽  
Agricultural Machinery ◽  
Optimization Analysis ◽  
Agricultural Modernization ◽  
Actual Equipment ◽  
Optimal Allocations

With the development of science and technology, the degree of agricultural mechanization is getting higher and higher. Agricultural machinery is an important support for the development of agricultural modernization. Optimizing the allocation of agricultural machinery is conducive to improving agricultural production efficiency and economic benefits. In this paper, mathematical modelling method is mainly used in the analysis and optimization of agricultural machinery configuration. By determining the objective function and constraint equation, combined with the actual situation of Xinjiang Production and Construction Corps, the linear programming model and workload model of agricultural machinery and equipment optimization are established. Finally, the actual number of agricultural machinery and equipment and the number of optimal allocations of Xinjiang Production and Construction Corps farm were compared. The effectiveness of the optimization model is verified by comparing the optimized agricultural machinery equipment with the actual equipment. The results show that the optimized equipment model has good optimization effect. On the basis of reducing the number of agricultural machinery and equipment, the matching rate of agricultural machinery is improved, and the operation cost of agricultural machinery is effectively reduced. It is hoped that this study can provide certain reference and reference for the optimization analysis of agricultural machinery and equipment based on mathematical modelling.

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