Simian LI

Economics and Management School, Yangtze University, Jingzhou 434023, China

Abstract [Objectives] Hubei Province has a superior geographical location, and is located in the middle and lower reaches of the Yangtze River, with pleasant climate and abundant natural resources. It is an important province of population, agriculture and resources in China. [Methods] Based on the data of Statistical Yearbook of Hubei 2018, the agricultural economic indicators of the cities and prefectures in Hubei Province were analyzed with principal component analysis method by using SPSS19.0. [Results] The comprehensive scores and rankings of the agricultural economic development level of the 17 cities and prefectures in Hubei Province were obtained. They were divided into four agricultural development levels. [Conclusions] According to the analysis results, corresponding policy recommendations were put forward to promote the development of agricultural economy in Hubei Province.

Key words Agricultural economic development level, Principal component analysis, Comprehensive evaluation

1 Introduction

China has always attached great importance to the development of agriculture. With the development of urbanization and industrialization in China, the development of agriculture is increasingly restricted by resources and the environment. Hubei Province is one of the important provinces in the central region. Due to its geographical location and differences in various congenital factors such as natural resources, the agricultural development of various cities and prefectures in Hubei Province is uneven. Therefore, the comprehensive evaluation of the agricultural development level of each city and the regional division according to the corresponding standards has clear guidance for the proposal of corresponding policies, and will also promote the implementation of policies.

At present, there has been rich research on agricultural economic development in China. Li Fangli conducted research from the level of influencing factors, considering that factor input is still the main driving force for the development of China’s agricultural economy, and conducted in-depth analysis from the four aspects of material, capital, human capital and technology[1]. Starting from the current situation of agricultural economic development, Liu Fei pointed out the problems in the development of China’s agricultural economy, such as low level of agricultural production technology, sharp decline in labor force, lack of effective constraints and support from the national government, and lack of sufficient foreign investment[2]. Liu Yonghe pointed out the restrictive factors such as the irrational industrial structure of the agricultural economy, the imperfect agricultural economic foundation, and the slow development of new agricultural technologies in the development of agricultural economy[3]. Regarding the comprehensive evaluation of the agricultural economic development level, Wu Jiao[4]conducted a comprehensive evaluation of the agricultural economic development level of Sichuan Province by constructing an index system and using the principal component analysis method. In summary, the research on the level of agricultural economic development in China has a certain foundation. However, most of the regions studied in the literature are in the economically developed areas such as the eastern coast and the western and other economically backward areas, and there is less research on the relevant provinces in the central region. In this context, studying the current development level of agricultural economy in Hubei Province, one of the important provinces in the central region and one of China’s main grain producing areas, is very necessary.

2 Empirical analysis of the level of agricultural economic development in the cities and prefectures of Hubei Province

2.1 Introduction to the modelPrincipal component analysis studies how to transform multiple variables into several uncorrelated comprehensive variables with certain representativeness and explanatory power. Through the idea of dimensionality reduction, it simplifies complex problems, reduces dimensions, and visualizes abstract problems. The calculation steps of principal component analysis are summarized as follows: (i) calculating the correlation coefficient matrix according to the data table; (ii) calculating the eigenvalue and eigenvector; (iii) calculating the principal component contribution rate and cumulative contribution rate and extracting the principal components corresponding to eigenvalues with a cumulative contribution rate of 85%-95%; (iv) calculating the load and score of each principal component.

2.2 Selection of indicatorsAccording to the principles of comparability, operability and comprehensiveness, and with reference to relevant literature, 8 explanatory variables (X1, total power of agricultural machinery;X2, fertilization rate;X3, rural electricity consumption;X4, cultivated land area;X5, effective irrigation area;X6, total grain output;X7, total output value of agriculture, forestry, animal husbandry and fishery;X8, rural employees) were selected in this study, and they can accurately make a comprehensive evaluation of the level of agricultural economic development of each city and prefecture. The original data used in the model came from theStatisticalYearbookofHubeiProvince2018.

2.3 Correlation analysis of original dataBecause the dimensions of each indicator are different, before the principal component analysis, the data needs to be standardized first. Then, the correlation coefficient matrix between the 8 indicators was calculated by using SPSS19.0 software (Table 1). The correlation coefficients or the absolute values of the correlation coefficients indicate that the greater the value, the stronger the correlation between the indicators, and the greater the representativeness of the principal component to the variable. If the correlation coefficient value is small, it is not suitable for principal component analysis. As shown in Table 1, there was a certain degree of overlap in agricultural economic information among these eight indicators. For example, the total grain output had a very significant correlation with the total power of agricultural machinery and the area of cultivated land and had a relatively significant correlation with variables such as effective irrigation area and fertilization rate.

Table 1 Matrix of correlation coefficients

2.4 Principal component analysis of dataThe variance contribution rate and cumulative variance contribution rate obtained by running the SPSS19.0 software are shown in Table 2. The value of the first component accounted for 83.041% of the total variance, and the cumulative value of the first two components accounted for 92.345% of the total variance, showing that the value of the first 2 components can explain 92.345% of all indicators. Since the total explanation of the principal components should reach 85% or more, and therefore, the extraction of the first two principal components can meet the requirements. Thus, the original 8 indicators could be transformed into 2 indicators by dimensionality reduction.

According to the principal component score coefficient matrix of Table 3, the linear expressions of the two principal components were obtained:Y1=0.141X1+0.135X2+0.117X3+0.147X4+0.139X5+0.142X6+0.140X7+0.133X8;Y2=-0.063X1+0.054X2+0.774X3-0.258X4-0.089X5-0.101X6-0.268X7-0.442X8.

Table 3 Component score coefficient matrix

Table 2 Eigenvalue and variance contribution rate of each component

Then, the variance contribution rate of the two principal components was divided by the sum of the variance contribution rates of the two principal components, respectively to obtain their weights. The comprehensive evaluation function of the agricultural economic development level of each city and prefecture in Hubei Province was as follows:F=0.899Y1+0.101Y2.

2.5 Comprehensive score of each city and prefectureAccording to the comprehensive evaluation function, the comprehensive scores and rankings of the cities and prefectures in Hubei Province were calculated by using Excel. The collated results are shown in Table 4.

Table 4 Comprehensive scores of the agricultural economic development level of each city and prefecture in Hubei Province

3 Analysis of empirical results and policy recommendations

3.1 Analysis of empirical resultsThe comprehensive agricultural economic strength of various regions in Hubei Province is uneven, with large regional differences. With the province’s average comprehensive score (0) as the baseline, the 17 cities and prefectures were divided into 4 levels, namely high level, medium-to-high level, low-to-medium level and low level (Table 4). Among them, Jingzhou City, Xiangyang City and Huanggang City had comprehensive scores of more than 1 point, belonging to high-level regions; the comprehensive scores of Jingmen City, Xiaogan City, Yichang City, Wuhan City and Enshi Prefecture were all above the average, but less than 1, belonging to middle-to-high-level regions: the comprehensive scores of Suizhou City, Xianning City and Shiyan City were all lower than the average level, but greater than -0.5, belonging to low-to-medium-level regions; and Huangshi City, Tianmen City, Xiantao City, Qianjiang City, Ezhou City and Shennongjia had comprehensive scores of less than -0.5, and were low-level regions.

3.2 Policy recommendationsIn order to further improve the unbalanced agricultural economic development of the cities and prefectures in Hubei Province, the following policy recommendations are proposed. (i) Increasing investment in infrastructure. The government should improve the infrastructure construction in different regions and focus. To a certain extent, it should focus on policy funding support for the development of backward areas. (ii) Increasing the introduction of agricultural professionals. There are few agricultural colleges in Hubei Province. Relatively speaking, there are fewer agricultural professionals. Therefore, the government should issue an effective talent introduction policy to attract more agricultural talents, providing support for the development of agricultural science and technology in Hubei Province. (iii) Vigorously developing characteristic agriculture. Each region should identify its advantages and utilize its own advantages to create a distinctive agricultural industry. At the same time, the local government should develop and build an agricultural industry that better caters to market needs combined with the consumption power and consumption tendency of the region. (iv) Strengthening agricultural cooperation between regions. Regions with better agricultural economic development in the province can carry out agricultural cooperation and assistance to regions with relatively backward agricultural economy, and backward regions can find some better reference points suitable for the development of their respective regions, thereby promoting the balanced development of the agricultural economy in the province.