# Spatiotemporal Coupling and Driving Factors of Farmland Transfer and Labor Transfer Based on Big Data: The Case of Xinjiang, China

Authors: Yijie Wang; Guoyong Liu; Xiaohu Liu
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

Agriculture big data can use the advantages of information technology to provide data support and technical support for improving the level of farmland transfer and labor transfer. And the coupling and coordinated development of the two is an essential way to ensure the effective promotion of the rural revitalization strategy and complete the building of a moderately prosperous society in an all-round path. First, aided by the coupling coordination model and exploratory spatial data analysis, a detailed analysis of the spatiotemporal patterns and dynamics of farmland transfer and labor transfer was conducted. The driving factors were quantitatively examined using the gray correlation model. The results indicate that the degree of coupling and coordination between Xinjiang’s farmland transfer and labor transfer ranges from 0.50 to 0.70, basically on the between of slight and primary coordination, and the degree of coordination among the regions varies greatly. The labor resource endowment, agricultural equipment level, farmland ownership confirmation, nonagricultural industry development level, and urbanization level are significantly impacting the degree of coupling and coordination between Xinjiang’s farmland transfer and labor transfer.


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## Body

## 1. Introduction

As the key factor of production in rural economic and social development, the effective allocation of land resources and labor has a profound impact on economic development in China. With the acceleration of urbanization, the improvement of the agricultural mechanization level, and the nonagricultural industrial progress, the phenomenon of farmland transfer and labor transfer has become very common in vast rural areas. The farmland transfer has become the optimal path to achieve large-scale agricultural management, which plays a crucial role in promoting agrarian modernization and enabling the effective allocation of farmland resources [1]. The gradual transfer of rural labor force to urban and nonagricultural industries is conducive to ease employment pressure of the rural labor force, promote the development of agricultural economy, and promote the integrated development of urban and rural areas. As a major agricultural province in western China, Xinjiang enriches in farmland resources and labor resources. Affect by the continuous promotion of rural land system reform and the rapid development of new urbanization, nonagricultural industry, and agricultural and rural modernization, the levels of farmland transfer and labor transfer in Xinjiang have improved steadily but it also exposed the problem of disconnection between the two in the process of development. In 2018, the proportion of transferred land in the area of household-contracted land in Xinjiang was 24.33% and the proportion of nonagricultural employment rural labor force in the total rural labor force was 44.62%. The high level of labor transfer was not accompanied by a high level of farmland transfer. This disjointed phenomenon makes the farmland transfer and labor transfer face the risk of a vicious circle of mutual restriction, which directly impedes the implementation of the rural revitalization strategy and the synchronous development of the four modernizations in Xinjiang. Therefore, in the context of China’s emphasis on the rural revitalization strategy, it is urgent to explore the coordinated development of farmland transfer and labor transfer in relation to the rural economic development and the practical requirements of the agricultural and rural modernization.Existing research stresses that the farmland transfer and labor transfer have an inverted “U-type” relationship [2]. Instability of contracted land management rights and the fine fragmentation of land will hinder the transfer of rural labor forces [3, 4]. The establishment of a land market can promote land transactions, can have a direct effect on the distribution of family labor force and gender division of labor, and is conducive to improving the level of labor force transfer [5, 6]. Meanwhile, labor transfer can realize land redistribution and reuse and strengthen the development of farmland transactions [7]. The coordinated development is affected by various factors. Previous research paid more attention to relevant factors such as the relative abundance of peasant factors, traditional concepts, systems, information, and the degree of national empowerment [8, 9]. The coupling and coordination levels between farmland transfer and labor transfer have been less discussed by the quantitative analysis method in the existing literature. As the development levels of farmland transfer and labor transfer continue to increase in China, the degree of coordination between the two shifted from mild dysregulation to forced overcoordination and the coupling coordination levels between the middle and east and west are the moderate coupling of the farmland transfer lag type. The coupling and coordination of various regions have not yet formed a benign resonance in time and space [10, 11]. However, most of the existing research lacks research on the build evaluation index system of farmland transfer and labor transfer coupling coordination is less, using separate indicators for simple measurement analysis, and research utilized administrative division, such as the province, as the evaluation unit for the coupling and coordination level between farmland transfer and labor transfer. Therefore, this topic has not been adequately addressed in previous studies. To fill this gap, by referring to other research results, this study constructed the coordinated evaluation index system of farmland transfer and labor transfer coupling from three aspects of basic conditions, current situation, and external conditions and detailed an empirical investigation into the spatiotemporal differentiation of and influencing factors on the coupling and coordinated development of farmland transfer and labor transfer in Xinjiang.The paper is structured as follows. Section2 introduces the data source and methodology. Section 3 describes the spatiotemporal differentiation between farmland transfer and labor transfer; it analyzes the driving factors for the coordinated development of the two. We outline the conclusions and state the scope for future work in Section 4.


## 2. Methodology

### 2.1. Data Source

The research data mainly includes farmland transfer data, labor transfer data, and the social-economic development data. The farmland transfer data and labor transfer data come from the Statistical Report of Rural Operation and Management of Xinjiang (2009–2018). Social-economic development data comes from the Statistical Yearbook of Xinjiang (2010–2019), in which the relative indicators are calculated according to the corresponding original data. Missing data or abnormal values were supplemented and corrected by means of the mean replacement method according to the data of known years. In addition, selected evaluation index data do not include county-level cities directly under the central government of the autonomous region (Bingtuan city) and 14 prefectures in Xinjiang are finally determined as the research units.


### 2.2. Research Methods

#### 2.2.1. The Construction of an Index System and Identification of Weights

The coordinated evaluation index system of coupling of farmland transfer and labor transfer in this study is constructed by the aspects of the farmland transfer level and labor transfer level. The farmland transfer level is constructed with the farmland and operating status, farmland transfer strength, and the external conditions of farmland transfer as the first-grade indexes. The labor transfer level is constructed with the aspects of the rural labor status, labor transfer intensity, and external conditions of labor transfer as the first-grade indexes. The research constructs the evaluation index system, which is divided into 2 dimensions, 6 first-grade indexes, and 30 second-grade indexes (Table1). The weights of the indexes at each level are calculated by the entropy evaluation method.Table 1
Evaluation index system of coupling and coordination degree of farmland transfer and labor transfer and weight identification.
DimensionFirst-grade indexSecond-grade indexIndex weightFarmland transferFarmland status and management statusAnnual growth rate of the number of farmers engaged in agricultural operation0.0308Planting proportion of grain crops0.1041Average farmland area per household0.1033Proportion of large-scale operation household in the total number of households0.0734Proportion of land management rights issued0.0649Farmland transfer intensityFarmland transfer rate0.0706Year-on-year growth rate of farmland transfer area0.0322Proportion of transfer in area of new business entities in total transfer area0.0802Proportion of transfer out households in total households0.0687External conditions of farmland transferYear-on-year growth rate of the number of new agricultural business entities0.0635Agricultural equipment level0.0375Service level of agricultural socialization0.0632Agricultural labor productivity0.0379Annual growth rate of farmers’ disposable income0.0616Proportion of output value of planting industry in output value of agriculture, forestry, animal husbandry, and fishery0.1081Labor transferRural labor force transfer situationProportion of agricultural labor force in rural labor force0.0647Number of labor force per household0.0716Proportion of labor force with high school education or above in rural labor force0.0848The intensity of labor transferLabor transfer rate0.0685Annual growth rate of labor transfer0.0293Proportion of seasonal transfer in rural labor force0.0615Proportion of perennial transfer in rural labor force0.0605Proportion of part-time households in total rural households0.0766Proportion of people transferred outside the county in rural labor force0.0717The external conditions of labor transferIncome ratio of urban and rural residents0.0580Family burden ratio0.0757Annual growth rate of wage income0.0481Proportion of nonagricultural added value in regional GDP0.0813Urbanization rate0.0930Social security expenditure ratio of urban and rural residents0.0549


#### 2.2.2. Comprehensive Evaluation Model

(1)
Standardization processing of the evaluation indexTo compare the evaluation indicators of different units and degree, it is important to make standardization processing for each evaluation index. The research used the extreme value method to standardize the raw data of the evaluation indicators. The computational formula is indicated as follows:(1)Xij′=Xij−XminXmax−Xmin.In the formula,Xij′ refers to the standardized value of the evaluation index, Xmin indicates the minimum value of the evaluation index, and Xmax represents the maximum value of the evaluation index
(2)
Computation of the farmland transfer index and labor transfer indexThe farmland transfer index and labor transfer index are identified by the weighted sum of the affiliated and standardized-grade evaluation index. The computational formula is indicated as follows:(2)fx=∑i=1mwixij,(3)gx=∑j=1nwjxij.In the formula,fx,gx represent the evaluation index of farmland transfer and labor transfer, wi and wj, respectively, represent the index weight of farmland transfer and labor transfer, and xij represents the index value after standardization.


#### 2.2.3. Coupling Coordination Model

To accurately reflect the degree of coupling and coordination between farmland transfer and labor transfer, drawing on the coupling theory in physics, referring to the relevant research results of Zhizhang et al. [12], the coupling coordination model was constructed. The computation formula is indicated as follows:
(4)C=fx×gxfx+gx/221/2,D=C×T,T=αfx+βgx.In the formula,C is the coupling degree, which represents the interaction strength and overall coupling synergy between farmland transfer and labor transfer; D is the coupling coordination degree, which indicates the coordination level of farmland transfer and labor transfer and the degree of coordination between subsystems; T is the comprehensive evaluation index, reflecting the comprehensive development level of farmland transfer and labor transfer. α, β are the undetermined coefficients of farmland transfer and labor transfer. The research assumes that farmland transfer and labor transfer are in the same position in the coupling and coordination research, so the undetermined coefficient is determined as the same: α=β=0.5. Based on Jing et al. and Chengyu and Shiqiang’s research [13, 14] on the classification of the coupling coordination degree, the coupling coordination degree is divided into 10 levels with 0.1 as the interval length. The classification standards for coupling coordination levels are shown in Table 2.Table 2
Classification standards for coupling coordination levels.
LevelsCoupling coordination degreeExplanationLevelsCoupling coordination degreeExplanation10.00–0.09Extreme coupling imbalance60.50–0.59Slightly coupled coordination20.10–0.19Severe coupling imbalance70.60–0.69Primary coupling coordination30.20–0.29Moderate coupling imbalance80.70–0.79Intermediate-coupling coordination40.30–0.39Mild coupling imbalance90.80–0.89Good coupling and coordination50.40–0.49Near coupling imbalance100.90–1.00High-quality coupling coordination


#### 2.2.4. Exploratory Spatial Data Analysis Method

Exploratory spatial data analysis methods are usually divided into global autocorrelation and local autocorrelation [15]. The global spatial autocorrelation is calculated by global Moran’s I method to illustrate the distribution of the coupling coordination level of farmland transfer and labor transfer in the overall space. The calculation formula is as follows:
(5)I=MS0×∑i=1M∑j=1MwijXi−X¯Xj−X¯∑j=1MXi−X¯2.In the formula,i≠j, M is the number of study areas and Xi is the observed value and X¯ is the average value and wijis the spatial weight matrix between the study area i and the study area j. The spatial adjacency is 1, and the spatial non adjacency is 0. Moran’s I ranges from [−1, 1]. When Moran’s I is greater than 0, it is a positive correlation, and when less than 0, it is a negative correlation.The local spatial autocorrelation test is calculated by localMoran’s I to test the spatial correlation and difference of the coupling coordination level of farmland transfer and labor transfer in adjacent prefectures. The calculation formula is as follows:
(6)Ii=Zi∑i=1MWijZj.Among them,ZiandZj are the standardized values of the observed values in the study area and Wij is the spatial weight.


#### 2.2.5. Grey Correlation Model

The grey correlation model can reflect the main factors driving the coupling and coordinated development of farmland transfer and labor transfer more objectively and truly [16]. The specific calculation steps are as follows:First, determine the feature sequence and factor sequence. This study selects the coupling coordination degree of farmland transfer and labor transfer as the characteristic sequence, which is recorded asY0m,t, and the sequence of driving factors is recorded as Xim,t.Second, calculate the grey correlation coefficient. As the core index value of the grey correlation degree model, the grey correlation coefficient determines the relationship between the characteristic sequence and the factor sequence. The calculation formula is(7)rim,t=mini,m,tY0′m,t−Xi′m,t+ρ×maxi,m,tY0′m,t−Xi′m,tY0′m,t−Xi′m,t+ρ×maxi,m,tY0′m,t−Xi′m,t.In the formula,Y0′m,t and Xi′m,t represent the characteristic sequence and factor sequence after standardization, respectively, ρ is the resolution coefficient, and the value is set as 0.5: ρ=0.5.Third, calculate the grey correlation degree. The calculation formula is as follows:(8)ri=1M×T∑m=1M∑t=1Nrim,t.According to the value ofri, we can analyze the correlation between the driving factors and the coupling coordination of farmland transfer and labor transfer. The greater the value of ri, the stronger the correlation is and on the contrary, the weaker the correlation.


## 2.1. Data Source

The research data mainly includes farmland transfer data, labor transfer data, and the social-economic development data. The farmland transfer data and labor transfer data come from the Statistical Report of Rural Operation and Management of Xinjiang (2009–2018). Social-economic development data comes from the Statistical Yearbook of Xinjiang (2010–2019), in which the relative indicators are calculated according to the corresponding original data. Missing data or abnormal values were supplemented and corrected by means of the mean replacement method according to the data of known years. In addition, selected evaluation index data do not include county-level cities directly under the central government of the autonomous region (Bingtuan city) and 14 prefectures in Xinjiang are finally determined as the research units.


## 2.2. Research Methods

### 2.2.1. The Construction of an Index System and Identification of Weights

The coordinated evaluation index system of coupling of farmland transfer and labor transfer in this study is constructed by the aspects of the farmland transfer level and labor transfer level. The farmland transfer level is constructed with the farmland and operating status, farmland transfer strength, and the external conditions of farmland transfer as the first-grade indexes. The labor transfer level is constructed with the aspects of the rural labor status, labor transfer intensity, and external conditions of labor transfer as the first-grade indexes. The research constructs the evaluation index system, which is divided into 2 dimensions, 6 first-grade indexes, and 30 second-grade indexes (Table1). The weights of the indexes at each level are calculated by the entropy evaluation method.Table 1
Evaluation index system of coupling and coordination degree of farmland transfer and labor transfer and weight identification.
DimensionFirst-grade indexSecond-grade indexIndex weightFarmland transferFarmland status and management statusAnnual growth rate of the number of farmers engaged in agricultural operation0.0308Planting proportion of grain crops0.1041Average farmland area per household0.1033Proportion of large-scale operation household in the total number of households0.0734Proportion of land management rights issued0.0649Farmland transfer intensityFarmland transfer rate0.0706Year-on-year growth rate of farmland transfer area0.0322Proportion of transfer in area of new business entities in total transfer area0.0802Proportion of transfer out households in total households0.0687External conditions of farmland transferYear-on-year growth rate of the number of new agricultural business entities0.0635Agricultural equipment level0.0375Service level of agricultural socialization0.0632Agricultural labor productivity0.0379Annual growth rate of farmers’ disposable income0.0616Proportion of output value of planting industry in output value of agriculture, forestry, animal husbandry, and fishery0.1081Labor transferRural labor force transfer situationProportion of agricultural labor force in rural labor force0.0647Number of labor force per household0.0716Proportion of labor force with high school education or above in rural labor force0.0848The intensity of labor transferLabor transfer rate0.0685Annual growth rate of labor transfer0.0293Proportion of seasonal transfer in rural labor force0.0615Proportion of perennial transfer in rural labor force0.0605Proportion of part-time households in total rural households0.0766Proportion of people transferred outside the county in rural labor force0.0717The external conditions of labor transferIncome ratio of urban and rural residents0.0580Family burden ratio0.0757Annual growth rate of wage income0.0481Proportion of nonagricultural added value in regional GDP0.0813Urbanization rate0.0930Social security expenditure ratio of urban and rural residents0.0549


### 2.2.2. Comprehensive Evaluation Model

(1)
Standardization processing of the evaluation indexTo compare the evaluation indicators of different units and degree, it is important to make standardization processing for each evaluation index. The research used the extreme value method to standardize the raw data of the evaluation indicators. The computational formula is indicated as follows:(1)Xij′=Xij−XminXmax−Xmin.In the formula,Xij′ refers to the standardized value of the evaluation index, Xmin indicates the minimum value of the evaluation index, and Xmax represents the maximum value of the evaluation index
(2)
Computation of the farmland transfer index and labor transfer indexThe farmland transfer index and labor transfer index are identified by the weighted sum of the affiliated and standardized-grade evaluation index. The computational formula is indicated as follows:(2)fx=∑i=1mwixij,(3)gx=∑j=1nwjxij.In the formula,fx,gx represent the evaluation index of farmland transfer and labor transfer, wi and wj, respectively, represent the index weight of farmland transfer and labor transfer, and xij represents the index value after standardization.


### 2.2.3. Coupling Coordination Model

To accurately reflect the degree of coupling and coordination between farmland transfer and labor transfer, drawing on the coupling theory in physics, referring to the relevant research results of Zhizhang et al. [12], the coupling coordination model was constructed. The computation formula is indicated as follows:
(4)C=fx×gxfx+gx/221/2,D=C×T,T=αfx+βgx.In the formula,C is the coupling degree, which represents the interaction strength and overall coupling synergy between farmland transfer and labor transfer; D is the coupling coordination degree, which indicates the coordination level of farmland transfer and labor transfer and the degree of coordination between subsystems; T is the comprehensive evaluation index, reflecting the comprehensive development level of farmland transfer and labor transfer. α, β are the undetermined coefficients of farmland transfer and labor transfer. The research assumes that farmland transfer and labor transfer are in the same position in the coupling and coordination research, so the undetermined coefficient is determined as the same: α=β=0.5. Based on Jing et al. and Chengyu and Shiqiang’s research [13, 14] on the classification of the coupling coordination degree, the coupling coordination degree is divided into 10 levels with 0.1 as the interval length. The classification standards for coupling coordination levels are shown in Table 2.Table 2
Classification standards for coupling coordination levels.
LevelsCoupling coordination degreeExplanationLevelsCoupling coordination degreeExplanation10.00–0.09Extreme coupling imbalance60.50–0.59Slightly coupled coordination20.10–0.19Severe coupling imbalance70.60–0.69Primary coupling coordination30.20–0.29Moderate coupling imbalance80.70–0.79Intermediate-coupling coordination40.30–0.39Mild coupling imbalance90.80–0.89Good coupling and coordination50.40–0.49Near coupling imbalance100.90–1.00High-quality coupling coordination


### 2.2.4. Exploratory Spatial Data Analysis Method

Exploratory spatial data analysis methods are usually divided into global autocorrelation and local autocorrelation [15]. The global spatial autocorrelation is calculated by global Moran’s I method to illustrate the distribution of the coupling coordination level of farmland transfer and labor transfer in the overall space. The calculation formula is as follows:
(5)I=MS0×∑i=1M∑j=1MwijXi−X¯Xj−X¯∑j=1MXi−X¯2.In the formula,i≠j, M is the number of study areas and Xi is the observed value and X¯ is the average value and wijis the spatial weight matrix between the study area i and the study area j. The spatial adjacency is 1, and the spatial non adjacency is 0. Moran’s I ranges from [−1, 1]. When Moran’s I is greater than 0, it is a positive correlation, and when less than 0, it is a negative correlation.The local spatial autocorrelation test is calculated by localMoran’s I to test the spatial correlation and difference of the coupling coordination level of farmland transfer and labor transfer in adjacent prefectures. The calculation formula is as follows:
(6)Ii=Zi∑i=1MWijZj.Among them,ZiandZj are the standardized values of the observed values in the study area and Wij is the spatial weight.


### 2.2.5. Grey Correlation Model

The grey correlation model can reflect the main factors driving the coupling and coordinated development of farmland transfer and labor transfer more objectively and truly [16]. The specific calculation steps are as follows:First, determine the feature sequence and factor sequence. This study selects the coupling coordination degree of farmland transfer and labor transfer as the characteristic sequence, which is recorded asY0m,t, and the sequence of driving factors is recorded as Xim,t.Second, calculate the grey correlation coefficient. As the core index value of the grey correlation degree model, the grey correlation coefficient determines the relationship between the characteristic sequence and the factor sequence. The calculation formula is(7)rim,t=mini,m,tY0′m,t−Xi′m,t+ρ×maxi,m,tY0′m,t−Xi′m,tY0′m,t−Xi′m,t+ρ×maxi,m,tY0′m,t−Xi′m,t.In the formula,Y0′m,t and Xi′m,t represent the characteristic sequence and factor sequence after standardization, respectively, ρ is the resolution coefficient, and the value is set as 0.5: ρ=0.5.Third, calculate the grey correlation degree. The calculation formula is as follows:(8)ri=1M×T∑m=1M∑t=1Nrim,t.According to the value ofri, we can analyze the correlation between the driving factors and the coupling coordination of farmland transfer and labor transfer. The greater the value of ri, the stronger the correlation is and on the contrary, the weaker the correlation.


## 2.2.1. The Construction of an Index System and Identification of Weights

The coordinated evaluation index system of coupling of farmland transfer and labor transfer in this study is constructed by the aspects of the farmland transfer level and labor transfer level. The farmland transfer level is constructed with the farmland and operating status, farmland transfer strength, and the external conditions of farmland transfer as the first-grade indexes. The labor transfer level is constructed with the aspects of the rural labor status, labor transfer intensity, and external conditions of labor transfer as the first-grade indexes. The research constructs the evaluation index system, which is divided into 2 dimensions, 6 first-grade indexes, and 30 second-grade indexes (Table1). The weights of the indexes at each level are calculated by the entropy evaluation method.Table 1
Evaluation index system of coupling and coordination degree of farmland transfer and labor transfer and weight identification.
DimensionFirst-grade indexSecond-grade indexIndex weightFarmland transferFarmland status and management statusAnnual growth rate of the number of farmers engaged in agricultural operation0.0308Planting proportion of grain crops0.1041Average farmland area per household0.1033Proportion of large-scale operation household in the total number of households0.0734Proportion of land management rights issued0.0649Farmland transfer intensityFarmland transfer rate0.0706Year-on-year growth rate of farmland transfer area0.0322Proportion of transfer in area of new business entities in total transfer area0.0802Proportion of transfer out households in total households0.0687External conditions of farmland transferYear-on-year growth rate of the number of new agricultural business entities0.0635Agricultural equipment level0.0375Service level of agricultural socialization0.0632Agricultural labor productivity0.0379Annual growth rate of farmers’ disposable income0.0616Proportion of output value of planting industry in output value of agriculture, forestry, animal husbandry, and fishery0.1081Labor transferRural labor force transfer situationProportion of agricultural labor force in rural labor force0.0647Number of labor force per household0.0716Proportion of labor force with high school education or above in rural labor force0.0848The intensity of labor transferLabor transfer rate0.0685Annual growth rate of labor transfer0.0293Proportion of seasonal transfer in rural labor force0.0615Proportion of perennial transfer in rural labor force0.0605Proportion of part-time households in total rural households0.0766Proportion of people transferred outside the county in rural labor force0.0717The external conditions of labor transferIncome ratio of urban and rural residents0.0580Family burden ratio0.0757Annual growth rate of wage income0.0481Proportion of nonagricultural added value in regional GDP0.0813Urbanization rate0.0930Social security expenditure ratio of urban and rural residents0.0549


## 2.2.2. Comprehensive Evaluation Model

(1)
Standardization processing of the evaluation indexTo compare the evaluation indicators of different units and degree, it is important to make standardization processing for each evaluation index. The research used the extreme value method to standardize the raw data of the evaluation indicators. The computational formula is indicated as follows:(1)Xij′=Xij−XminXmax−Xmin.In the formula,Xij′ refers to the standardized value of the evaluation index, Xmin indicates the minimum value of the evaluation index, and Xmax represents the maximum value of the evaluation index
(2)
Computation of the farmland transfer index and labor transfer indexThe farmland transfer index and labor transfer index are identified by the weighted sum of the affiliated and standardized-grade evaluation index. The computational formula is indicated as follows:(2)fx=∑i=1mwixij,(3)gx=∑j=1nwjxij.In the formula,fx,gx represent the evaluation index of farmland transfer and labor transfer, wi and wj, respectively, represent the index weight of farmland transfer and labor transfer, and xij represents the index value after standardization.


## 2.2.3. Coupling Coordination Model

To accurately reflect the degree of coupling and coordination between farmland transfer and labor transfer, drawing on the coupling theory in physics, referring to the relevant research results of Zhizhang et al. [12], the coupling coordination model was constructed. The computation formula is indicated as follows:
(4)C=fx×gxfx+gx/221/2,D=C×T,T=αfx+βgx.In the formula,C is the coupling degree, which represents the interaction strength and overall coupling synergy between farmland transfer and labor transfer; D is the coupling coordination degree, which indicates the coordination level of farmland transfer and labor transfer and the degree of coordination between subsystems; T is the comprehensive evaluation index, reflecting the comprehensive development level of farmland transfer and labor transfer. α, β are the undetermined coefficients of farmland transfer and labor transfer. The research assumes that farmland transfer and labor transfer are in the same position in the coupling and coordination research, so the undetermined coefficient is determined as the same: α=β=0.5. Based on Jing et al. and Chengyu and Shiqiang’s research [13, 14] on the classification of the coupling coordination degree, the coupling coordination degree is divided into 10 levels with 0.1 as the interval length. The classification standards for coupling coordination levels are shown in Table 2.Table 2
Classification standards for coupling coordination levels.
LevelsCoupling coordination degreeExplanationLevelsCoupling coordination degreeExplanation10.00–0.09Extreme coupling imbalance60.50–0.59Slightly coupled coordination20.10–0.19Severe coupling imbalance70.60–0.69Primary coupling coordination30.20–0.29Moderate coupling imbalance80.70–0.79Intermediate-coupling coordination40.30–0.39Mild coupling imbalance90.80–0.89Good coupling and coordination50.40–0.49Near coupling imbalance100.90–1.00High-quality coupling coordination


## 2.2.4. Exploratory Spatial Data Analysis Method

Exploratory spatial data analysis methods are usually divided into global autocorrelation and local autocorrelation [15]. The global spatial autocorrelation is calculated by global Moran’s I method to illustrate the distribution of the coupling coordination level of farmland transfer and labor transfer in the overall space. The calculation formula is as follows:
(5)I=MS0×∑i=1M∑j=1MwijXi−X¯Xj−X¯∑j=1MXi−X¯2.In the formula,i≠j, M is the number of study areas and Xi is the observed value and X¯ is the average value and wijis the spatial weight matrix between the study area i and the study area j. The spatial adjacency is 1, and the spatial non adjacency is 0. Moran’s I ranges from [−1, 1]. When Moran’s I is greater than 0, it is a positive correlation, and when less than 0, it is a negative correlation.The local spatial autocorrelation test is calculated by localMoran’s I to test the spatial correlation and difference of the coupling coordination level of farmland transfer and labor transfer in adjacent prefectures. The calculation formula is as follows:
(6)Ii=Zi∑i=1MWijZj.Among them,ZiandZj are the standardized values of the observed values in the study area and Wij is the spatial weight.


## 2.2.5. Grey Correlation Model

The grey correlation model can reflect the main factors driving the coupling and coordinated development of farmland transfer and labor transfer more objectively and truly [16]. The specific calculation steps are as follows:First, determine the feature sequence and factor sequence. This study selects the coupling coordination degree of farmland transfer and labor transfer as the characteristic sequence, which is recorded asY0m,t, and the sequence of driving factors is recorded as Xim,t.Second, calculate the grey correlation coefficient. As the core index value of the grey correlation degree model, the grey correlation coefficient determines the relationship between the characteristic sequence and the factor sequence. The calculation formula is(7)rim,t=mini,m,tY0′m,t−Xi′m,t+ρ×maxi,m,tY0′m,t−Xi′m,tY0′m,t−Xi′m,t+ρ×maxi,m,tY0′m,t−Xi′m,t.In the formula,Y0′m,t and Xi′m,t represent the characteristic sequence and factor sequence after standardization, respectively, ρ is the resolution coefficient, and the value is set as 0.5: ρ=0.5.Third, calculate the grey correlation degree. The calculation formula is as follows:(8)ri=1M×T∑m=1M∑t=1Nrim,t.According to the value ofri, we can analyze the correlation between the driving factors and the coupling coordination of farmland transfer and labor transfer. The greater the value of ri, the stronger the correlation is and on the contrary, the weaker the correlation.


## 3. Results and Discussion

### 3.1. Time Series Evolution Analysis of the Comprehensive Level of Farmland Transfer and Labor Transfer

It can be observed in Figure1 that the level of labor transfer evaluation index in Xinjiang is relatively high, with an obvious upward trend of fluctuation, from 0.4414 in 2009 to 0.5576 in 2018, with an average annual growth rate of 2.63%. Except for a small decline in 2015 and 2017, it shows a good upward trend in other years. The evaluation index of farmland transfer fluctuated from 0.3132 in 2009 to 0.3084 in 2018, showing a downward trend as a whole, but fluctuated significantly. The growth rate in 2013 was 4.46%, and the decline rate in the next year was 4.46%. The development level of farmland transfer is unstable. With the upward development of the evaluation indexes of the two subsystems of farmland transfer and labor transfer, the comprehensive evaluation index of farmland transfer and labor transfer in Xinjiang also shows an obvious upward trend. The comprehensive evaluation index during the study period has increased from 0.3773 in 2009 to 0.4330 in 2018, with an average annual growth rate of 1.63%.Figure 1
Xinjiang farmland transfer and labor transfer evaluation index and comprehensive evaluation index (2009–2018).It can be seen in Table3 that the levels of farmland transfer, labor transfer, and comprehensive evaluation index in all prefectures of Xinjiang are not high. From 2009 to 2018, the average values of the evaluation index of farmland transfer and labor transfer in Xinjiang were only 0.3012 and 0.5403, respectively, while the average value of comprehensive evaluation index was only 0.4162. Prefectures with a relatively high-evaluation index of farmland transfer are mainly distributed in northern Xinjiang, such as Tacheng administrative offices, Bortala Mongol autonomous prefecture, and Changji Hui autonomous prefecture. Prefectures with a low evaluation index of farmland transfer is also concentrated in northern Xinjiang, such as Urumqi city and Turpan administrative offices. The high and low evaluation indexes of farmland transfer are concentrated in northern Xinjiang, which are mainly affected by land resource endowment conditions, economic and social development levels, and crop planting structure. Prefectures with a relatively high-labor transfer evaluation index are also distributed in northern Xinjiang, such as Urumqi city, Ili Kazak autonomous prefecture, and Hami administrative offices. Prefectures with relatively low evaluation index are concentrated in southern Xinjiang, such as Aksu Administrative Offices and Kashgar Administrative Offices. The prefectures with a relatively high comprehensive evaluation index of farmland transfer and labor transfer are concentrated in northern Xinjiang, such as Bortala Mongol Autonomous Prefecture, Hami Administrative Offices, and Ili Kazak Autonomous Prefecture, while the prefectures with relatively low comprehensive evaluation index are concentrated in southern Xinjiang, such as Aksu Administrative Offices and Kashgar Administrative Offices.Table 3
The average value of the evaluation index and comprehensive evaluation index of farmland transfer and labor transfer in various prefectures in Xinjiang (2009–2018).
PrefectureFarmland transfer indexLabor force transfer indexComprehensive evaluation indexPrefectureFarmland transfer indexLabor force transfer indexComprehensive evaluation indexNorthern Xinjiang0.33510.58710.4611Southern Xinjiang0.29290.43060.3618Urumqi City0.25510.58490.4200Bayangol Mongol Autonomous Prefecture0.33230.41770.3750Karamay city0.33000.47460.4023Aksu Administrative Offices0.30920.34390.3265Turpan Administrative Offices0.27400.46760.3708Kizilsu Kirgiz Autonomous Prefecture0.32070.44560.3832Hami Administrative Offices0.30180.55260.4272Kashgar Administrative Offices0.29170.38670.3392Changji Hui Autonomous Prefecture0.34850.47840.4135Hotan Administrative Offices0.29580.42810.3619Ili Kazak Autonomous Prefecture0.29700.56080.4289Xinjiang0.30120.54030.4207Tacheng Administrative Offices0.40670.42830.4175Altay Administrative Offices0.30360.47190.3877Bortala Mongolian Autonomous Prefecture0.39000.47760.4338


### 3.2. Analysis on Spatiotemporal Evolution of Coupling Coordination Degree between Farmland Transfer and Labor Transfer

From the perspective of time evolution, the coupling and coordinated development level of farmland transfer and labor transfer in Xinjiang shows a fluctuating upward trend, rising from 0.6053 in 2009 to 0.6302 in 2018, reaching the primary coupling and coordination level as a whole, but the growth rate is relatively slow, with an average annual growth rate of only 0.41% (Table4). As far as northern and southern Xinjiang is concerned, the coupling and coordination level of farmland transfer and labor transfer shows an upward trend. Among them, the coupling coordination degree of northern Xinjiang was 0.6281 in 2009, the maximum value was 0.6718 in 2013, and the coupling coordination degree was 0.6553 in 2018, reaching the medium-term stage of primary coupling coordination, with an average annual growth rate of 0.41%. In 2009, it was 0.5700 in southern Xinjiang, which was forced coupling. After reaching the primary coupling coordination from 2012 to 2013, it returned to the middle and late stages of forced coupling coordination again, which was 0.5912 in 2018, with an average annual growth rate of 0.37%. It can be seen that the coupling and coordination level of farmland transfer and labor transfer in Xinjiang from 2009 to 2018 was “high in the north and low in the south.”Table 4
Coordination degree of the coupling degree of farmland transfer and labor transfer in Xinjiang (2009–2018).
YearsXinjiang provinceGrowth rateNorthern XinjiangGrowth rateSouthern XinjiangGrowth rate20090.6053/0.6281/0.5700/20100.61210.01120.63840.01640.57880.015420110.61630.00690.66180.03670.58210.005720120.62150.00840.6607−0.00170.61180.051020130.63340.01910.67540.02220.61410.003820140.6247−0.01370.6461−0.04340.5985−0.025420150.62470.00000.66040.02210.5900−0.014220160.6221−0.00420.6572−0.00480.5784−0.019720170.62270.00100.6477−0.01450.58310.008120180.63020.01200.65360.00910.59120.0139It can be seen from Table5 that the coupling degree of farmland transfer and labor transfer in all prefectures of Xinjiang is consistent with the overall coupling degree of Xinjiang, reaching a high level of coupling. Except Urumqi city, the coupling degree of farmland transfer and labor transfer in the other 13 prefectures is more than 0.900. The coupling coordination level of farmland transfer and labor transfer in various prefectures of Xinjiang is not high, the average value of the whole Xinjiang is 0.6213, and the coupling coordination degree of most prefectures is between 0.60 and 0.66, which is the primary coupling coordination level. Specifically, for the coupling and coordination of farmland transfer and labor transfer, the prefectures higher than the average value of Xinjiang are mainly concentrated in northern Xinjiang such as Bortala Mongolian Autonomous Prefecture, Tacheng Administrative Offices, and Changji Hui Autonomous Prefecture, while the prefectures lower than the average value are mainly concentrated in southern Xinjiang such as Aksu Administrative Offices, Kashgar Administrative Offices, and Hotan Administrative Offices. The land resource endowment in northern Xinjiang has comparative advantages, the levels of agricultural equipment and agricultural labor production efficiency are high, the farmland transfer market is well developed, and the level of farmland transfer is higher than that in southern Xinjiang. At the same time, the level of economic and social development, the level of new urbanization, and the development level of nonagricultural industry are high, which can provide a large number of employment opportunities for rural labor forces, fully meet the requirements of part-time or nonagricultural employment of rural labor force, and make it have certain advantages in the coupling and coordination of farmland transfer and labor transfer. However, due to the influence of less cultivated land per capita, poor agricultural cultivation conditions, large rural labor force population, and poor comprehensive quality, the coupling and coordination level of farmland transfer and labor transfer in southern Xinjiang is relatively low.Table 5
The average value of the coupling degree and coupling coordination degree of farmland transfer and labor transfer in various prefectures in Xinjiang (2009–2018).
PrefectureCoupling degreeCoupled coordination degreePrefectureCoupling degreeCoupled coordination degreeNorthern Xinjiang0.92500.6529Southern Xinjiang0.96220.5898Urumqi City0.84550.5957Bayangol Mongol Autonomous Prefecture0.98550.6078Karamay city0.96340.6222Aksu Administrative Offices0.99530.5697Turpan Administrative Offices0.92890.5864Kizilsu Kirgiz Autonomous Prefecture0.96920.6093Hami Administrative Offices0.91090.6237Kashgar Administrative Offices0.97870.5760Changji Hui Autonomous Prefecture0.97420.6346Hotan Administrative Offices0.96440.5904Ili Kazak Autonomous Prefecture0.90450.6228Xinjiang0.91840.6213Tacheng Administrative Offices0.99770.6453Altay Administrative Offices0.95200.6074Bortala Mongolian Autonomous Prefecture0.98730.6542


### 3.3. Spatial Autocorrelation Analysis of Coupling Coordination between Farmland Transfer and Labor Transfer in Xinjiang

#### 3.3.1. Global Spatial Autocorrelation Analysis

The estimated values of globalMoran’s I index of the coupled coordination of farmland transfer and labor transfer from 2009 to 2018 are positive, showing a spatial positive correlation as a whole. Except that Moran’s I from 2010 to 2013 failed the significance test (z>1.96, P<0.05), the absolute value of Z in other years was greater than 1.96, indicating that Moran’s I indexes in 2009 and 2014–2018 had strong significance. Global Moran’s I index in some years is low, indicating that the spatial autocorrelation characteristics are not obvious and the degree of agglomeration is low. Among them, it showed a continuous downward trend from 2009 to 2013, from 0.3623 to 0.2032, rebounded after falling to the lowest point, reached the maximum value of 0.6484 in 2015, and then began to develop in a downward upward cycle. The coupling coordination degree of farmland transfer and labor transfer showed a development trend of agglomeration dispersion over time (Figure 2).Figure 2
GlobalMoran’s I index of the coupling coordination degree of farmland transfer and labor transfer in Xinjiang.


#### 3.3.2. Local Spatial Autocorrelation Analysis

The proportion of the H-H agglomeration area in the two stages increased from 28.57% to 50.00%, becoming the type area with the highest proportion, indicating that the development level of farmland transfer and labor transfer in Xinjiang has entered a high level and the benign coupling and coordination situation of mutual promotion between the two has basically formed. The spatial agglomeration distribution of this type of area is further strengthened, and the distribution scope tends to the agglomeration of northern Xinjiang. From the perspective of the L-L agglomeration area, many prefectures in southern Xinjiang are located in this type of area. The number of inland states has not changed in the two stages, accounting for 35.71%. The spatial agglomeration trend of this type of area has been strengthened and concentrated to southern Xinjiang. From the perspective of the L-H agglomeration area, the proportion of this type of area decreased from 21.43% to 14.29%, indicating that low-value areas are greatly affected by high-value areas and gradually transition to high-value areas. In terms of spatial pattern, it basically shows an aggregation trend in northern Xinjiang. The spatial distribution in the two stages is concentrated in northern Xinjiang and tends to gather in the northeast of northern Xinjiang. From the perspective of the H-L agglomeration area, the number of prefectures in this type of area fluctuated greatly and the proportion decreased from 14.29% to 0.00%. In the first stage, the spatial distribution of this type of area was decentralized. Affected by the coupling and coordination levels of surrounding prefectures, it was transferred to the H-H agglomeration area and L-L agglomeration area in the second stage. Overall, the coupling coordination degree of farmland transfer and labor transfer in Xinjiang shows significant agglomeration characteristics in space and tends to be stable. The strong-strong agglomeration type is mainly distributed in northern Xinjiang, and the weak-weak agglomeration type is mainly distributed in southern Xinjiang (Table6).Table 6
Changes in the spatial correlation of the coupling and coordination of farmland transfer and labor transfer in various states in Xinjiang (2009–2018).
YearsH-HL-HL-LH-LPrefecture%Prefecture%Prefecture%Prefecture%2009–2013Hami Administrative Offices, Ili Kazak Autonomous Prefecture, Tacheng Administrative Offices and Bortala Mongolian Autonomous Prefecture (4)28.57Turpan Administrative Offices, Altay Administrative Offices and Karamay City (3)21.43Urumqi City, Bortala Mongolian Autonomous Prefecture, Aksu Administrative Offices, Kashgar Administrative Offices and Hotan Administrative Offices (5)35.71Changji Hui Autonomous Prefecture and Kizilsu Kirgiz Autonomous Prefecture (2)14.292014–2018Karamay City, Hami Administrative Offices, Changji Hui Autonomous Prefecture, Ili Kazak Autonomous Prefecture, Tacheng Administrative Offices, Altay Administrative Offices and Bortala Mongolian Autonomous Prefecture (7)50.00Urumqi City and Turpan Administrative Offices (2)14.29Bortala Mongolian Autonomous Prefecture, Aksu Administrative Offices, Kizilsu Kirgiz Autonomous Prefecture, Kashgar Administrative Offices and Hotan Administrative Offices (5)35.71


### 3.4. Analysis on Driving Factors of Coupling Coordination of Farmland Transfer and Labor Transfer in Xinjiang

Comprehensively considering the coupling and coordination levels of farmland transfer and labor transfer in 14 prefectures of Xinjiang, consulting the opinions and suggestions of relevant experts in this field, the following factors are finally determined as the main driving factors: labor resource endowment (X1), farmland ownership (X2), agricultural equipment level (X3), urbanization rate (X4), and nonagricultural industry development level (X5). According to the selected index data, the grey correlation degree model is used to obtain the correlation degree between the coupling coordination degree in 14 prefectures of Xinjiang and the indexes of 5 driving factors.It can be seen that the average correlation degree between each driving factor and the coupling coordination levels of farmland transfer and labor transfer are higher than 0.60, indicating that each driving factor index is closely related to the coupling and coordinated development of the two. According to the degree of influence, the driving factors are labor resource endowment > agricultural equipment level > farmland ownership confirmation > nonagricultural industry development level > urbanization level (Table7).
(1)
The average correlation between labor resource endowment and coupling coordination level is the highest, with an average of 0.6544, indicating that the number of family labor force has the greatest impact on coupling coordination development. This is because the endowment of family labor resources has a significant impact on farmers’ family decision-making behavior. If the family labor resources are rich, in order to improve the family economic income level, farmers will make decisions to transfer part of the labor force to employment or transfer to land to expand the scale of production and operation. These two decisions are conducive to the development of farmland transfer and labor transfer. The spatial correlation between the two shows the distribution pattern of southern Xinjiang > northern Xinjiang, indicating that the labor resource endowment in southern Xinjiang has a greater impact on the coupling and coordination level than that in northern Xinjiang. This is because the average household labor force in southern Xinjiang is higher than that in northern Xinjiang, which has a more prominent advantage in surplus labor resources. Therefore, the influence on the coupling coordination degree of the two is higher than that in northern Xinjiang(2)
The average correlation degree between the agricultural equipment level and the coupling coordination level is 0.6525, which has the second impact on the coupling and coordinated development of the two. The level of agricultural equipment is directly proportional to agricultural production efficiency. The improvement of agricultural production efficiency can not only promote the development of farmland transfer but also liberate more agricultural surplus labor force and promote the coupling and coordinated development of farmland transfer and labor transfer. The spatial correlation between the two shows the distribution pattern of northern Xinjiang > southern Xinjiang, indicating that the impact of agricultural equipment level on the coupling coordination degree of northern Xinjiang is higher than that of southern Xinjiang. The main reason is that the per capita income level, land management scale, and capital investment of rural residents in northern Xinjiang are better than those in southern Xinjiang economic and social development has incomparable inherent advantages in southern Xinjiang(3)
The average correlation degree between farmland ownership confirmation and coupling coordination level is 0.6456, indicating that farmland ownership confirmation can promote the coupling and coordinated development of the two. The confirmation of farmland right can stabilize the land contract management right, solve the problem of fuzzy farmland property right, enable farmers to actively participate in the farmland transfer, improve the probability and level of farmland transfer, reduce the binding between fragmented farmland management and low-efficiency agricultural production labor force, promote the transfer of rural labor force, and improve the long-term and stability of transfer employment. The correlation degree of the two shows the distribution pattern of northern Xinjiang > southern Xinjiang in space. This is because the land resource endowment conditions and agricultural planting level of northern Xinjiang are higher than those of southern Xinjiang and clear property rights are more conducive to the development of farmland transfer and labor transfer. Therefore, the impact on the coupling and coordinated development level of northern Xinjiang is significantly higher than that of southern Xinjiang(4)
The average correlation degree between the proportion of nonagricultural added value in regional GDP and the coupling coordination level is 0.6255. The development level of nonagricultural industry can promote regional economic development, provide more jobs to absorb rural surplus labor force, drive the development of farmland transfer and labor transfer, and improve the coupling and coordination level of farmland transfer and labor transfer. The spatial correlation between the two shows the distribution pattern of southern Xinjiang > northern Xinjiang. This is because the development level of nonagricultural industry in southern Xinjiang is low and its development changes are more sensitive to the coupling and coordination level of farmland transfer and labor transfer and the degree of influence is also higher than that in northern Xinjiang(5)
The average correlation degree between the urbanization rate and the coupling coordination level is 0.6225. Urbanization is an important pulling force of labor transfer. The level of urbanization directly affects the quantity and quality of jobs provided for the transferred labor force and can promote the improvement of the coupling and coordination level of farmland transfer and labor transfer to a certain extent. The spatial correlation between the two shows the distribution pattern of southern Xinjiang > northern Xinjiang. The possible reason is that the urbanization level of southern Xinjiang is lower than that of northern Xinjiang. The change of the urbanization level has a more effective impact on the coupling and coordinated development level, and the degree of impact is also higher than that of northern XinjiangTable 7
Correlation degree of coupling and coordination driving factors of farmland transfer and labor transfer in various states in Xinjiang.
PrefectureX1X2X3X4X5Northern XinjiangUrumqi City0.66410.61540.65400.63950.5818Karamay city0.53450.58190.69480.57190.7210Turpan Administrative Offices0.65090.67120.68030.58600.5782Hami Administrative Offices0.66320.68110.59150.60120.5068Changji Hui Autonomous Prefecture0.55640.68680.64740.64520.5358Ili Kazak Autonomous Prefecture0.58800.73300.58480.71040.6238Tacheng Administrative Offices0.64640.46510.70010.52380.7558Altay Administrative Offices0.65560.75540.81830.54880.6983Bortala Mongolian Autonomous Prefecture0.75760.62620.71050.63830.6418Southern XinjiangBayangol Mongol Autonomous Prefecture0.75500.53450.65960.62110.6770Aksu Administrative Offices0.71590.70160.54480.69220.6517Kizilsu Kirgiz Autonomous Prefecture0.63290.59880.60970.65590.5759Kashgar Administrative Offices0.62130.77620.54980.73170.5464Hotan Administrative Offices0.71910.61130.68970.54870.6629Average0.65440.64560.65250.62250.6255


## 3.1. Time Series Evolution Analysis of the Comprehensive Level of Farmland Transfer and Labor Transfer

It can be observed in Figure1 that the level of labor transfer evaluation index in Xinjiang is relatively high, with an obvious upward trend of fluctuation, from 0.4414 in 2009 to 0.5576 in 2018, with an average annual growth rate of 2.63%. Except for a small decline in 2015 and 2017, it shows a good upward trend in other years. The evaluation index of farmland transfer fluctuated from 0.3132 in 2009 to 0.3084 in 2018, showing a downward trend as a whole, but fluctuated significantly. The growth rate in 2013 was 4.46%, and the decline rate in the next year was 4.46%. The development level of farmland transfer is unstable. With the upward development of the evaluation indexes of the two subsystems of farmland transfer and labor transfer, the comprehensive evaluation index of farmland transfer and labor transfer in Xinjiang also shows an obvious upward trend. The comprehensive evaluation index during the study period has increased from 0.3773 in 2009 to 0.4330 in 2018, with an average annual growth rate of 1.63%.Figure 1
Xinjiang farmland transfer and labor transfer evaluation index and comprehensive evaluation index (2009–2018).It can be seen in Table3 that the levels of farmland transfer, labor transfer, and comprehensive evaluation index in all prefectures of Xinjiang are not high. From 2009 to 2018, the average values of the evaluation index of farmland transfer and labor transfer in Xinjiang were only 0.3012 and 0.5403, respectively, while the average value of comprehensive evaluation index was only 0.4162. Prefectures with a relatively high-evaluation index of farmland transfer are mainly distributed in northern Xinjiang, such as Tacheng administrative offices, Bortala Mongol autonomous prefecture, and Changji Hui autonomous prefecture. Prefectures with a low evaluation index of farmland transfer is also concentrated in northern Xinjiang, such as Urumqi city and Turpan administrative offices. The high and low evaluation indexes of farmland transfer are concentrated in northern Xinjiang, which are mainly affected by land resource endowment conditions, economic and social development levels, and crop planting structure. Prefectures with a relatively high-labor transfer evaluation index are also distributed in northern Xinjiang, such as Urumqi city, Ili Kazak autonomous prefecture, and Hami administrative offices. Prefectures with relatively low evaluation index are concentrated in southern Xinjiang, such as Aksu Administrative Offices and Kashgar Administrative Offices. The prefectures with a relatively high comprehensive evaluation index of farmland transfer and labor transfer are concentrated in northern Xinjiang, such as Bortala Mongol Autonomous Prefecture, Hami Administrative Offices, and Ili Kazak Autonomous Prefecture, while the prefectures with relatively low comprehensive evaluation index are concentrated in southern Xinjiang, such as Aksu Administrative Offices and Kashgar Administrative Offices.Table 3
The average value of the evaluation index and comprehensive evaluation index of farmland transfer and labor transfer in various prefectures in Xinjiang (2009–2018).
PrefectureFarmland transfer indexLabor force transfer indexComprehensive evaluation indexPrefectureFarmland transfer indexLabor force transfer indexComprehensive evaluation indexNorthern Xinjiang0.33510.58710.4611Southern Xinjiang0.29290.43060.3618Urumqi City0.25510.58490.4200Bayangol Mongol Autonomous Prefecture0.33230.41770.3750Karamay city0.33000.47460.4023Aksu Administrative Offices0.30920.34390.3265Turpan Administrative Offices0.27400.46760.3708Kizilsu Kirgiz Autonomous Prefecture0.32070.44560.3832Hami Administrative Offices0.30180.55260.4272Kashgar Administrative Offices0.29170.38670.3392Changji Hui Autonomous Prefecture0.34850.47840.4135Hotan Administrative Offices0.29580.42810.3619Ili Kazak Autonomous Prefecture0.29700.56080.4289Xinjiang0.30120.54030.4207Tacheng Administrative Offices0.40670.42830.4175Altay Administrative Offices0.30360.47190.3877Bortala Mongolian Autonomous Prefecture0.39000.47760.4338


## 3.2. Analysis on Spatiotemporal Evolution of Coupling Coordination Degree between Farmland Transfer and Labor Transfer

From the perspective of time evolution, the coupling and coordinated development level of farmland transfer and labor transfer in Xinjiang shows a fluctuating upward trend, rising from 0.6053 in 2009 to 0.6302 in 2018, reaching the primary coupling and coordination level as a whole, but the growth rate is relatively slow, with an average annual growth rate of only 0.41% (Table4). As far as northern and southern Xinjiang is concerned, the coupling and coordination level of farmland transfer and labor transfer shows an upward trend. Among them, the coupling coordination degree of northern Xinjiang was 0.6281 in 2009, the maximum value was 0.6718 in 2013, and the coupling coordination degree was 0.6553 in 2018, reaching the medium-term stage of primary coupling coordination, with an average annual growth rate of 0.41%. In 2009, it was 0.5700 in southern Xinjiang, which was forced coupling. After reaching the primary coupling coordination from 2012 to 2013, it returned to the middle and late stages of forced coupling coordination again, which was 0.5912 in 2018, with an average annual growth rate of 0.37%. It can be seen that the coupling and coordination level of farmland transfer and labor transfer in Xinjiang from 2009 to 2018 was “high in the north and low in the south.”Table 4
Coordination degree of the coupling degree of farmland transfer and labor transfer in Xinjiang (2009–2018).
YearsXinjiang provinceGrowth rateNorthern XinjiangGrowth rateSouthern XinjiangGrowth rate20090.6053/0.6281/0.5700/20100.61210.01120.63840.01640.57880.015420110.61630.00690.66180.03670.58210.005720120.62150.00840.6607−0.00170.61180.051020130.63340.01910.67540.02220.61410.003820140.6247−0.01370.6461−0.04340.5985−0.025420150.62470.00000.66040.02210.5900−0.014220160.6221−0.00420.6572−0.00480.5784−0.019720170.62270.00100.6477−0.01450.58310.008120180.63020.01200.65360.00910.59120.0139It can be seen from Table5 that the coupling degree of farmland transfer and labor transfer in all prefectures of Xinjiang is consistent with the overall coupling degree of Xinjiang, reaching a high level of coupling. Except Urumqi city, the coupling degree of farmland transfer and labor transfer in the other 13 prefectures is more than 0.900. The coupling coordination level of farmland transfer and labor transfer in various prefectures of Xinjiang is not high, the average value of the whole Xinjiang is 0.6213, and the coupling coordination degree of most prefectures is between 0.60 and 0.66, which is the primary coupling coordination level. Specifically, for the coupling and coordination of farmland transfer and labor transfer, the prefectures higher than the average value of Xinjiang are mainly concentrated in northern Xinjiang such as Bortala Mongolian Autonomous Prefecture, Tacheng Administrative Offices, and Changji Hui Autonomous Prefecture, while the prefectures lower than the average value are mainly concentrated in southern Xinjiang such as Aksu Administrative Offices, Kashgar Administrative Offices, and Hotan Administrative Offices. The land resource endowment in northern Xinjiang has comparative advantages, the levels of agricultural equipment and agricultural labor production efficiency are high, the farmland transfer market is well developed, and the level of farmland transfer is higher than that in southern Xinjiang. At the same time, the level of economic and social development, the level of new urbanization, and the development level of nonagricultural industry are high, which can provide a large number of employment opportunities for rural labor forces, fully meet the requirements of part-time or nonagricultural employment of rural labor force, and make it have certain advantages in the coupling and coordination of farmland transfer and labor transfer. However, due to the influence of less cultivated land per capita, poor agricultural cultivation conditions, large rural labor force population, and poor comprehensive quality, the coupling and coordination level of farmland transfer and labor transfer in southern Xinjiang is relatively low.Table 5
The average value of the coupling degree and coupling coordination degree of farmland transfer and labor transfer in various prefectures in Xinjiang (2009–2018).
PrefectureCoupling degreeCoupled coordination degreePrefectureCoupling degreeCoupled coordination degreeNorthern Xinjiang0.92500.6529Southern Xinjiang0.96220.5898Urumqi City0.84550.5957Bayangol Mongol Autonomous Prefecture0.98550.6078Karamay city0.96340.6222Aksu Administrative Offices0.99530.5697Turpan Administrative Offices0.92890.5864Kizilsu Kirgiz Autonomous Prefecture0.96920.6093Hami Administrative Offices0.91090.6237Kashgar Administrative Offices0.97870.5760Changji Hui Autonomous Prefecture0.97420.6346Hotan Administrative Offices0.96440.5904Ili Kazak Autonomous Prefecture0.90450.6228Xinjiang0.91840.6213Tacheng Administrative Offices0.99770.6453Altay Administrative Offices0.95200.6074Bortala Mongolian Autonomous Prefecture0.98730.6542


## 3.3. Spatial Autocorrelation Analysis of Coupling Coordination between Farmland Transfer and Labor Transfer in Xinjiang

### 3.3.1. Global Spatial Autocorrelation Analysis

The estimated values of globalMoran’s I index of the coupled coordination of farmland transfer and labor transfer from 2009 to 2018 are positive, showing a spatial positive correlation as a whole. Except that Moran’s I from 2010 to 2013 failed the significance test (z>1.96, P<0.05), the absolute value of Z in other years was greater than 1.96, indicating that Moran’s I indexes in 2009 and 2014–2018 had strong significance. Global Moran’s I index in some years is low, indicating that the spatial autocorrelation characteristics are not obvious and the degree of agglomeration is low. Among them, it showed a continuous downward trend from 2009 to 2013, from 0.3623 to 0.2032, rebounded after falling to the lowest point, reached the maximum value of 0.6484 in 2015, and then began to develop in a downward upward cycle. The coupling coordination degree of farmland transfer and labor transfer showed a development trend of agglomeration dispersion over time (Figure 2).Figure 2
GlobalMoran’s I index of the coupling coordination degree of farmland transfer and labor transfer in Xinjiang.


### 3.3.2. Local Spatial Autocorrelation Analysis

The proportion of the H-H agglomeration area in the two stages increased from 28.57% to 50.00%, becoming the type area with the highest proportion, indicating that the development level of farmland transfer and labor transfer in Xinjiang has entered a high level and the benign coupling and coordination situation of mutual promotion between the two has basically formed. The spatial agglomeration distribution of this type of area is further strengthened, and the distribution scope tends to the agglomeration of northern Xinjiang. From the perspective of the L-L agglomeration area, many prefectures in southern Xinjiang are located in this type of area. The number of inland states has not changed in the two stages, accounting for 35.71%. The spatial agglomeration trend of this type of area has been strengthened and concentrated to southern Xinjiang. From the perspective of the L-H agglomeration area, the proportion of this type of area decreased from 21.43% to 14.29%, indicating that low-value areas are greatly affected by high-value areas and gradually transition to high-value areas. In terms of spatial pattern, it basically shows an aggregation trend in northern Xinjiang. The spatial distribution in the two stages is concentrated in northern Xinjiang and tends to gather in the northeast of northern Xinjiang. From the perspective of the H-L agglomeration area, the number of prefectures in this type of area fluctuated greatly and the proportion decreased from 14.29% to 0.00%. In the first stage, the spatial distribution of this type of area was decentralized. Affected by the coupling and coordination levels of surrounding prefectures, it was transferred to the H-H agglomeration area and L-L agglomeration area in the second stage. Overall, the coupling coordination degree of farmland transfer and labor transfer in Xinjiang shows significant agglomeration characteristics in space and tends to be stable. The strong-strong agglomeration type is mainly distributed in northern Xinjiang, and the weak-weak agglomeration type is mainly distributed in southern Xinjiang (Table6).Table 6
Changes in the spatial correlation of the coupling and coordination of farmland transfer and labor transfer in various states in Xinjiang (2009–2018).
YearsH-HL-HL-LH-LPrefecture%Prefecture%Prefecture%Prefecture%2009–2013Hami Administrative Offices, Ili Kazak Autonomous Prefecture, Tacheng Administrative Offices and Bortala Mongolian Autonomous Prefecture (4)28.57Turpan Administrative Offices, Altay Administrative Offices and Karamay City (3)21.43Urumqi City, Bortala Mongolian Autonomous Prefecture, Aksu Administrative Offices, Kashgar Administrative Offices and Hotan Administrative Offices (5)35.71Changji Hui Autonomous Prefecture and Kizilsu Kirgiz Autonomous Prefecture (2)14.292014–2018Karamay City, Hami Administrative Offices, Changji Hui Autonomous Prefecture, Ili Kazak Autonomous Prefecture, Tacheng Administrative Offices, Altay Administrative Offices and Bortala Mongolian Autonomous Prefecture (7)50.00Urumqi City and Turpan Administrative Offices (2)14.29Bortala Mongolian Autonomous Prefecture, Aksu Administrative Offices, Kizilsu Kirgiz Autonomous Prefecture, Kashgar Administrative Offices and Hotan Administrative Offices (5)35.71


## 3.3.1. Global Spatial Autocorrelation Analysis

The estimated values of globalMoran’s I index of the coupled coordination of farmland transfer and labor transfer from 2009 to 2018 are positive, showing a spatial positive correlation as a whole. Except that Moran’s I from 2010 to 2013 failed the significance test (z>1.96, P<0.05), the absolute value of Z in other years was greater than 1.96, indicating that Moran’s I indexes in 2009 and 2014–2018 had strong significance. Global Moran’s I index in some years is low, indicating that the spatial autocorrelation characteristics are not obvious and the degree of agglomeration is low. Among them, it showed a continuous downward trend from 2009 to 2013, from 0.3623 to 0.2032, rebounded after falling to the lowest point, reached the maximum value of 0.6484 in 2015, and then began to develop in a downward upward cycle. The coupling coordination degree of farmland transfer and labor transfer showed a development trend of agglomeration dispersion over time (Figure 2).Figure 2
GlobalMoran’s I index of the coupling coordination degree of farmland transfer and labor transfer in Xinjiang.


## 3.3.2. Local Spatial Autocorrelation Analysis

The proportion of the H-H agglomeration area in the two stages increased from 28.57% to 50.00%, becoming the type area with the highest proportion, indicating that the development level of farmland transfer and labor transfer in Xinjiang has entered a high level and the benign coupling and coordination situation of mutual promotion between the two has basically formed. The spatial agglomeration distribution of this type of area is further strengthened, and the distribution scope tends to the agglomeration of northern Xinjiang. From the perspective of the L-L agglomeration area, many prefectures in southern Xinjiang are located in this type of area. The number of inland states has not changed in the two stages, accounting for 35.71%. The spatial agglomeration trend of this type of area has been strengthened and concentrated to southern Xinjiang. From the perspective of the L-H agglomeration area, the proportion of this type of area decreased from 21.43% to 14.29%, indicating that low-value areas are greatly affected by high-value areas and gradually transition to high-value areas. In terms of spatial pattern, it basically shows an aggregation trend in northern Xinjiang. The spatial distribution in the two stages is concentrated in northern Xinjiang and tends to gather in the northeast of northern Xinjiang. From the perspective of the H-L agglomeration area, the number of prefectures in this type of area fluctuated greatly and the proportion decreased from 14.29% to 0.00%. In the first stage, the spatial distribution of this type of area was decentralized. Affected by the coupling and coordination levels of surrounding prefectures, it was transferred to the H-H agglomeration area and L-L agglomeration area in the second stage. Overall, the coupling coordination degree of farmland transfer and labor transfer in Xinjiang shows significant agglomeration characteristics in space and tends to be stable. The strong-strong agglomeration type is mainly distributed in northern Xinjiang, and the weak-weak agglomeration type is mainly distributed in southern Xinjiang (Table6).Table 6
Changes in the spatial correlation of the coupling and coordination of farmland transfer and labor transfer in various states in Xinjiang (2009–2018).
YearsH-HL-HL-LH-LPrefecture%Prefecture%Prefecture%Prefecture%2009–2013Hami Administrative Offices, Ili Kazak Autonomous Prefecture, Tacheng Administrative Offices and Bortala Mongolian Autonomous Prefecture (4)28.57Turpan Administrative Offices, Altay Administrative Offices and Karamay City (3)21.43Urumqi City, Bortala Mongolian Autonomous Prefecture, Aksu Administrative Offices, Kashgar Administrative Offices and Hotan Administrative Offices (5)35.71Changji Hui Autonomous Prefecture and Kizilsu Kirgiz Autonomous Prefecture (2)14.292014–2018Karamay City, Hami Administrative Offices, Changji Hui Autonomous Prefecture, Ili Kazak Autonomous Prefecture, Tacheng Administrative Offices, Altay Administrative Offices and Bortala Mongolian Autonomous Prefecture (7)50.00Urumqi City and Turpan Administrative Offices (2)14.29Bortala Mongolian Autonomous Prefecture, Aksu Administrative Offices, Kizilsu Kirgiz Autonomous Prefecture, Kashgar Administrative Offices and Hotan Administrative Offices (5)35.71


## 3.4. Analysis on Driving Factors of Coupling Coordination of Farmland Transfer and Labor Transfer in Xinjiang

Comprehensively considering the coupling and coordination levels of farmland transfer and labor transfer in 14 prefectures of Xinjiang, consulting the opinions and suggestions of relevant experts in this field, the following factors are finally determined as the main driving factors: labor resource endowment (X1), farmland ownership (X2), agricultural equipment level (X3), urbanization rate (X4), and nonagricultural industry development level (X5). According to the selected index data, the grey correlation degree model is used to obtain the correlation degree between the coupling coordination degree in 14 prefectures of Xinjiang and the indexes of 5 driving factors.It can be seen that the average correlation degree between each driving factor and the coupling coordination levels of farmland transfer and labor transfer are higher than 0.60, indicating that each driving factor index is closely related to the coupling and coordinated development of the two. According to the degree of influence, the driving factors are labor resource endowment > agricultural equipment level > farmland ownership confirmation > nonagricultural industry development level > urbanization level (Table7).
(1)
The average correlation between labor resource endowment and coupling coordination level is the highest, with an average of 0.6544, indicating that the number of family labor force has the greatest impact on coupling coordination development. This is because the endowment of family labor resources has a significant impact on farmers’ family decision-making behavior. If the family labor resources are rich, in order to improve the family economic income level, farmers will make decisions to transfer part of the labor force to employment or transfer to land to expand the scale of production and operation. These two decisions are conducive to the development of farmland transfer and labor transfer. The spatial correlation between the two shows the distribution pattern of southern Xinjiang > northern Xinjiang, indicating that the labor resource endowment in southern Xinjiang has a greater impact on the coupling and coordination level than that in northern Xinjiang. This is because the average household labor force in southern Xinjiang is higher than that in northern Xinjiang, which has a more prominent advantage in surplus labor resources. Therefore, the influence on the coupling coordination degree of the two is higher than that in northern Xinjiang(2)
The average correlation degree between the agricultural equipment level and the coupling coordination level is 0.6525, which has the second impact on the coupling and coordinated development of the two. The level of agricultural equipment is directly proportional to agricultural production efficiency. The improvement of agricultural production efficiency can not only promote the development of farmland transfer but also liberate more agricultural surplus labor force and promote the coupling and coordinated development of farmland transfer and labor transfer. The spatial correlation between the two shows the distribution pattern of northern Xinjiang > southern Xinjiang, indicating that the impact of agricultural equipment level on the coupling coordination degree of northern Xinjiang is higher than that of southern Xinjiang. The main reason is that the per capita income level, land management scale, and capital investment of rural residents in northern Xinjiang are better than those in southern Xinjiang economic and social development has incomparable inherent advantages in southern Xinjiang(3)
The average correlation degree between farmland ownership confirmation and coupling coordination level is 0.6456, indicating that farmland ownership confirmation can promote the coupling and coordinated development of the two. The confirmation of farmland right can stabilize the land contract management right, solve the problem of fuzzy farmland property right, enable farmers to actively participate in the farmland transfer, improve the probability and level of farmland transfer, reduce the binding between fragmented farmland management and low-efficiency agricultural production labor force, promote the transfer of rural labor force, and improve the long-term and stability of transfer employment. The correlation degree of the two shows the distribution pattern of northern Xinjiang > southern Xinjiang in space. This is because the land resource endowment conditions and agricultural planting level of northern Xinjiang are higher than those of southern Xinjiang and clear property rights are more conducive to the development of farmland transfer and labor transfer. Therefore, the impact on the coupling and coordinated development level of northern Xinjiang is significantly higher than that of southern Xinjiang(4)
The average correlation degree between the proportion of nonagricultural added value in regional GDP and the coupling coordination level is 0.6255. The development level of nonagricultural industry can promote regional economic development, provide more jobs to absorb rural surplus labor force, drive the development of farmland transfer and labor transfer, and improve the coupling and coordination level of farmland transfer and labor transfer. The spatial correlation between the two shows the distribution pattern of southern Xinjiang > northern Xinjiang. This is because the development level of nonagricultural industry in southern Xinjiang is low and its development changes are more sensitive to the coupling and coordination level of farmland transfer and labor transfer and the degree of influence is also higher than that in northern Xinjiang(5)
The average correlation degree between the urbanization rate and the coupling coordination level is 0.6225. Urbanization is an important pulling force of labor transfer. The level of urbanization directly affects the quantity and quality of jobs provided for the transferred labor force and can promote the improvement of the coupling and coordination level of farmland transfer and labor transfer to a certain extent. The spatial correlation between the two shows the distribution pattern of southern Xinjiang > northern Xinjiang. The possible reason is that the urbanization level of southern Xinjiang is lower than that of northern Xinjiang. The change of the urbanization level has a more effective impact on the coupling and coordinated development level, and the degree of impact is also higher than that of northern XinjiangTable 7
Correlation degree of coupling and coordination driving factors of farmland transfer and labor transfer in various states in Xinjiang.
PrefectureX1X2X3X4X5Northern XinjiangUrumqi City0.66410.61540.65400.63950.5818Karamay city0.53450.58190.69480.57190.7210Turpan Administrative Offices0.65090.67120.68030.58600.5782Hami Administrative Offices0.66320.68110.59150.60120.5068Changji Hui Autonomous Prefecture0.55640.68680.64740.64520.5358Ili Kazak Autonomous Prefecture0.58800.73300.58480.71040.6238Tacheng Administrative Offices0.64640.46510.70010.52380.7558Altay Administrative Offices0.65560.75540.81830.54880.6983Bortala Mongolian Autonomous Prefecture0.75760.62620.71050.63830.6418Southern XinjiangBayangol Mongol Autonomous Prefecture0.75500.53450.65960.62110.6770Aksu Administrative Offices0.71590.70160.54480.69220.6517Kizilsu Kirgiz Autonomous Prefecture0.63290.59880.60970.65590.5759Kashgar Administrative Offices0.62130.77620.54980.73170.5464Hotan Administrative Offices0.71910.61130.68970.54870.6629Average0.65440.64560.65250.62250.6255


## 4. Conclusions

This paper focuses on the major strategic background of rural revitalization strategy and agriculture big data. It analyzes the spatiotemporal differentiation and influencing factors of the coupling and coordinated development of farmland transfer and labor transfer in Xinjiang. The main conclusions are as follows:(1)
From 2009 to 2018, the level of farmland transfer in Xinjiang showed a slight downward trend and the fluctuation and upward trend of labor transfer level was obvious. The comprehensive evaluation levels of the two continued to rise, and the difference between prefectures and states gradually narrowed, forming a development pattern of northern Xinjiang > southern Xinjiang(2)
The measurement results of the coupling coordination show that the coordination degree of Xinjiang’s farmland transfer and labor transfer is in a fluctuated growing, primary state of equilibrium. Among them, northern Xinjiang has always been in the primary coupling and coordination stage for ten years, while southern Xinjiang has changed alternately between reluctantly coupling and coordination and primary coupling and coordination, forming a distribution pattern of “high in the north and low in the south”(3)
The coupling coordination degree of farmland transfer and labor transfer in Xinjiang has a significant global spatial positive correlation, showing a trend of agglomeration dispersion over time, with obvious fluctuation. There is a certain regularity in local space, and the spatial agglomeration state tends to be stable, forming a distribution pattern in which strong agglomeration types are concentrated in northern Xinjiang and weak agglomeration types are concentrated in southern Xinjiang(4)
The driving factors of the coupling coordination of farmland transfer and labor transfer are labor resource endowment, agricultural equipment level, farmland ownership, nonagricultural industry development level, and urbanization level. The driving factors of the coupling coordination of farmland transfer and labor transfer in southern and northern Xinjiang are differentThe research initially attempts to construct a complete evaluation index system of the coupling coordination degree of farmland transfer and labor transfer, but there are also some cases of missing individual indicators. In the future, the subsequent relevant research can consider further the index selection and expanding the coverage of the index selection, so as to establish a more multisource and comprehensive evaluation index system. In addition, how to use farmers survey data to better analyze the coupling and coordination of farmland transfer and labor transfer is a new research direction for our study in the future..



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*Source: 7604448-2022-04-15.xml*