Multilevel Correlation Analysis of Influencing Factors on Grain Total Factor Productivity in Main Grain Producing Provinces of China

Abstract

In order to analyze the influencing factors of TFP (the abbreviation of Total Factor Productivity) deeply, this paper calculates and decomposes the TFP of the main grain producing provinces in China from 2006 to 2015 by the DEA-Malmquist index model. On the basis of this, the grey correlation analysis model based on super-efficiency DEA is used to quantitatively analyze the influencing factors of total factor productivity, technological progress and technical efficiency. The results show that the proportion of grain sowing, the level of food economic development, the level of grain mechanization, the average scale of operations and the level of fertilizer using have the most influence on grain total factor productivity. Finally, according to the results of the analysis, some suggestions are put forward to improve the TFP of different provinces in the main grain producing areas.

Spatiotemporal Heterogeneity of Total Factor Productivity of Grain in the Yangtze River Delta, China

“…The impact coefficient of the proportion of the graingrowing population and the impact coefficient of grain output per unit area show the spatial heterogeneity characteristics of low in the southeast and high in the northwest. This indicates that the promoting effect of both on the TFPG is the increasing spatial distribution from southeast to northwest, as shown in Figure 6 [8,48]. The spatial heterogeneity of the impact coefficient of per capita GDP in the Yangtze River Delta is not obvious; the spatial distribution of the impact coefficient of the grain economic development level shows the characteristics of being high in the east and low in the west, and the distribution of the low and high values of the impact coefficient is relatively concentrated.…”

“…The impact coefficient of the proportion of the grain-growing population and the impact coefficient of grain output per unit area show the spatial heterogeneity characteristics of low in the southeast and high in the northwest. This indicates that the promoting effect of both on the TFPG is the increasing spatial distribution from southeast to northwest, as shown in Figure 6 [8,48].…”

“…Based on the concept of TFP, the TFPG refers to the development and utilization efficiency of various factors, such as labor, capital, and technology, in the process of grain production, which can comprehensively reflect the development level of labor conditions, resource allocation, technological progress, and other factors [7]. And increasing the TFPG is a critical way to ensure grain security and ecological security and to achieve rural revitalization [8]. There are a lot of studies on the TFPG, including its decomposition, measurement, and critical role to GDP.…”

The role of agricultural machinery in improving green grain productivity in China: Towards trans-regional operation and low-carbon practices

“…In addition, the severe labor shortage in the major grain-marketing areas has forced technological reforms in agricultural machinery. With the advantage of developed economic conditions, new machinery and technology were easily popularized, thus generating a technological spillover effect [ 82 ]. The lack of significant spatial spillover effects in producing-marketing balance areas can be attributed to the absence of locational and technological advantages ( Fig.…”

Evaluation of the Response of Grain Productivity to Different Arable Land Allocation Intensities in the Land Use Planning System of China

“…"Keeping the bowl firmly in our own hands" and "maintain a high grain self-sufficiency" were considered to be the basic national policies of China. Since the 1970s, China has assigned 13 provinces as "China's major grain producing areas" (abbreviated as CMGPA hereafter)-including Heilongjiang, Jilin, Liaoning, Inner Mongolia, Hebei, Henan, Shandong, Jiangsu, Jiangxi, Hubei, Hunan, Anhui and Sichuan-in order to guarantee the long-term national food supply [3].…”