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Journal of Resources and Ecology >

2025 , Vol. 16 >Issue 5: 1499 - 1514

DOI: https://doi.org/10.5814/j.issn.1674-764x.2025.05.021

Agro-ecosystem and Rural Revitalization

Regional Differences, Dynamic Evolution, and Obstacle Factors in the Development of Agricultural New Quality Productive Forces in China

  • QIN Lingui ,
  • LIU Songqi ,
  • WANG Wanzhi ,
  • MIAO Fengsheng ,
  • XIE Fengjie , *
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  • The College of Economics and Management, Shenyang Agricultural University, Shenyang 110866, China
* XIE Fengjie, E-mail:

QIN Lingui, E-mail:

Received date: 2025-03-05

  Accepted date: 2025-07-15

  Online published: 2025-10-14

Supported by

The Major Program of National Social Science Foundation of China(23&ZD108)

The General Program of National Social Science Founda tion of China(23BJY171)

The China Postdoctoral Special Funding Project(2024T170590)

The Liaoning Province “Xingliao Talent Program” Project(XLYC2410051)

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Abstract

Agricultural new quality productive forces are the key foundation for realizing high-quality agricultural development. This study constructs the evaluation indicator system of agricultural new quality productive forces (ANQPFs) from three dimensions: agricultural laborers, agricultural labor objects, and agricultural labor resources. The equal weight method, entropy method, and CRITIC method are comprehensively applied to measure ANQPFs in China from 2011 to 2021. The Dagum's Gini coefficient, variance decomposition, kernel density estimation, Markov chain, and obstacle degree model are used to analyze regional differences, structural differences, dynamic evolution, and obstacle factors of ANQPFs. The findings show that: (1) There is an upward trend in ANQPFs in the national and the three major regions during the study period, while there are significant differences in ANQPFs by regions, which are characterized by a decreasing distribution from the east to the central, and then to the west. (2) The overall differences in ANQPFs have tended to widen, with inter-regional differences being the main source. (3) Agricultural labor object differences and agricultural labor resource differences are the main structural sources of ANQPFs development differences in China, with agricultural labor resource differences replacing agricultural labor object differences as the top source of ANQPFs differences after 2016. (4) The ANQPFs of the national and three regions show the distribution dynamics of “overall increase, absolute differences widen”, and there is the phenomenon of “club convergence” in ANQPFs. (5) The number of Taobao villages, rural entrepreneurial activities, the number of agricultural science and technology patents per capita, and expenditure on agricultural science and technology activities are the main factors obstructing the development of ANQPFs.

Key words： agricultural new quality productive forces; regional differences; structural differences; dynamic evolution; obstacle factors

Cite this article

QIN Lingui , LIU Songqi , WANG Wanzhi , MIAO Fengsheng , XIE Fengjie . Regional Differences, Dynamic Evolution, and Obstacle Factors in the Development of Agricultural New Quality Productive Forces in China[J]. Journal of Resources and Ecology, 2025 , 16(5) : 1499 -1514 . DOI: 10.5814/j.issn.1674-764x.2025.05.021

1 Introduction

Around the world, achieving “zero hunger” (United Nations Sustainable Development Goal SDG 2) faces unprecedented challenges. The frequent occurrence of extreme weather events caused by climate change, supply chain disruptions due to geopolitical conflicts, and uneven economic recovery following the pandemic continue to threaten global food security. According to data from the 2024 State of Food Security and Nutrition in the World report, global hunger levels have remained high for the third consecutive year. In 2023, the number of hungry people was between 713 million and 757 million, an increase of approximately 152 million people compared to 2019. In this context, improving agricultural productivity has become the core path and consensus among countries to solve the global food security dilemma.
In September 2023, Chinese leaders proposed the concept of “new quality productive forces (NQPFs)” for the first time. In 2025, Chinese policymakers first proposed the concept of “agricultural new quality productive forces (ANQPFs)”, emphasizing “leading the aggregation of advanced production factors with scientific and technological innovation, and developing ANQPFs following local conditions”. ANQPFs gather elements of agricultural science and technology innovation and promote the deep transformation of agricultural production methods. It can not only promote effective qualitative improvement and reasonable quantitative growth in agricultural development, but also take high-quality agricultural development to construct a strong agricultural country in order to realize the Chinese path to modernization. Therefore, it is important to clarify the theoretical connotation of ANQPFs, construct a scientific and reasonable ANQPFs evaluation indicator system, measure the development level of ANQPFs, clarify the regional and structural differences of ANQPFs, analyze the dynamic evolution characteristics of ANQPFs, and diagnose the obstacle factors of ANQPFs. This has significant theoretical and practical implications for the promotion of the ANQPFs’ development and agricultural high-quality development.
Within this context, NQPFs have jumped to become a hot issue in scholarly research, with existing studies predominantly concentrating on the following three domains: The first is the theoretical connotation and the logic behind the formation of NQPFs. Zhou and Xu (2023) argue that NQPFs are different from traditional productivity, which is led by science and technology innovation. It is a novel form of productivity that diverges from the conventional growth trajectory, aligning with the criteria for high-quality development. Pu and Xiang (2024) define NQPFs as a new type of productivity consisting of “high-quality” laborers, “new medium” labor resources, and “new material” labor objects. Regarding the formation logic of NQPFs, some scholars, based on the perspective of Marxist productivity theory, have argued that NQPFs represent the leap and qualitative change of productivity, which is the inheritance and development of Marxist productivity theory (Deng et al., 2024; Mi, 2024; Xie et al., 2024). From the economics perspective, some other scholars have demonstrated the basis for the formation of NQPFs from the view of science and technology innovation (Zhang, 2024a; Zheng and Xu, 2024), capital market (Lin and Wen, 2024), and digital economy (Yao and Wang, 2024; Zhu and Chen, 2024), respectively. The second is the organic link between NQPFs and high-quality agricultural development. Wang and Yang (2023) discuss the realization path of digital NQPFs to promote high-quality development of agriculture by focusing on the relationship among digital technology, digital NQPFs, and high-quality development of agriculture. Hou et al. (2024) provide an in-depth analysis of the theoretical logic, key issues, and practical paths for NQPFs to empower high-quality agricultural development in Northeast China. In addition, other scholars have explored the implementation paths of NQPFs- enabled agricultural high-quality development from the perspective of digital village construction (Zhang, 2024b). The third is the measurement of NQPFs. Through combing the literature, it is found that the construction of the evaluation indicator system of NQPFs can be divided into two types. One type is based on the perspective of labor factors. Such literature mainly focuses on the three dimensions including laborers, labor resources, and labor objects, which construct and measure NQPFs evaluation indicator systems at different spatial scales (Han et al., 2024; Huang et al., 2024; Lin et al., 2024; Liu and He, 2024; Wang and Wang, 2024; Liu et al., 2025). The other type is based on the multidimensional feature perspective of NQPFs. Such literature integrates several aspects, including innovation, industry, digital, and green to measure NQPFs (Li et al., 2024; Lu et al., 2024; Sun and Guo, 2024; Dai and Zheng, 2025). In addition, some scholars have quantitatively measured NQPFs at the firm level based on the dual perspectives of labor and production tools (Song et al., 2024a; Tan et al., 2024; Zhao and Li, 2024). In terms of measuring ANQPFs, Zhu and Ye (2024) examined the spatio-temporal characteristics and dynamic evolution of ANQPFs in 31 provinces in China from 2012 to 2021. Song et al. (2024b) similarly measured the dynamic level of ANQPFs in Chinese provinces from 2013 to 2022.
In summary, although the above studies provide rich experience for this paper, there are still some deficiencies. With regard to research perspectives, the extant literature mainly concentrates on the theoretical connotation and level measurement of NQPFs, while there are fewer interpretations of the connotation and construction of the indicator system of ANQPFs, and differences in the existing evaluation indicator systems are obvious, which have not yet reached a consensus. Regarding the content of the study, the current literature predominantly focuses on the spatial- temporal characteristics and regional differences of ANQPFs, which lacks the analysis of the structural differences, dynamic evolution, and obstacle factors of ANQPFs. In view of this, this study first constructs an evaluation index system for ANQPFs and uses the equal weight method, entropy method, and CRITIC method to measure the development level of ANQPFs. Second, this study applies the Dagum's Gini coefficient and variance decomposition to identify regional and structural differences and sources of ANQPFs’ development in China. Third, we utilizes kernel density estimation and Markov chain analysis to comprehensively grasp the dynamic evolution trend of ANQPFs’ development in China. Finally, it further diagnoses the obstacle factors hindering the development of ANQPFs in China through the obstacle degree model, and clarifies the key points for promoting ANQPFs’ development in the next stage.

2 Theoretical connotation and indicator system construction of ANQPFs

2.1 Theoretical connotation of ANQPFs

Compared with the three elements of traditional productivity, namely, laborers, labor objects, and labor resources, NQPFs is a new material productive force generated under the conditions of digitization, informatization, and intelligence, with science and technology innovation as its leading factor, by realizing key disruptive technological breakthroughs. NQPFs pay more attention to the combination of “new”, “quality” and “productivity” (Wang et al., 2024), and its essence is based on the scientific and technological innovation and quality improvement of laborers, labor objects, and labor resources (Wang, 2024). To realize the transformation of productivity from spiritual to material, science and technology need to be combined with the three elements of productivity (Zhou and Xu, 2023). This shows that NQPFs not only focus on technological innovation and quality improvement but also pay more attention to the optimal combination of the three elements within productivity.
ANQPFs are the specific embodiment of NQPFs in agriculture, which is a multidimensional and multilevel concept that distinguishes between traditional agricultural productivity and agricultural high-quality development. The efficient, coordinated, and sustainable development of agricultural productivity is a prerequisite for promoting the construction of modern agriculture (Li et al., 2016), while high-quality agricultural development places greater emphasis on high quality, high returns, and high efficiency (Wang, 2021). While ANQPFs can be regarded as the center of agricultural high-quality development (Luo and Geng, 2024), the state of agricultural productivity through scientific and technological innovation has been upgraded to a certain stage. Its content should include the three dimensions of “agricultural laborers”, “agricultural labor objects”, and “agricultural labor resources”. So this study summarizes the theoretical connotation of ANQPFs as follows: based on traditional productivity, focusing on the agricultural high-quality development, and forming the organic combination and leap of agricultural laborers, agricultural labor objects, and agricultural labor resources through deepening scientific & technological innovation and upgrading the quality. The theoretical connotation is deeply consistent with a series of ANQPFs-related policies introduced by the Chinese government (Table 1).
Table 1 Policies related to ANQPFs from 2024 to 2025
Time Policy document Main content
2024.10 Guiding Opinions on Vigorously Developing Smart Agriculture Propose to comprehensively enhance the application level of smart agriculture, including promoting seven key tasks: precision farming of major crops, digitization of facility farming, smart livestock farming, intelligent fishery production, intelligent seed breeding, and production, digitization of the entire agricultural industry chain, and digitization of rural management and services
2024.12 Central Rural Work Conference Propose promoting collaborative research by agricultural science and technology forces, accelerating the large-scale promotion and application of scientific and technological achievements, and developing ANQPFs following local conditions
2025.02 China outlines key tasks to deepen rural reforms, advance rural revitalization in 2025 Propose that scientific & technological innovation should lead to the accumulation of advanced production factors, and develop ANQPFs following local conditions
2025.04 Plan for Accelerating the Construction of China into an Agricultural Powerhouse (2024-2035) Propose to accelerate agricultural scientific and technological innovation with a focus on the seed industry, guided by key core agricultural technologies and the urgent needs of the industry, promote major agricultural scientific and technological breakthroughs, and advance the construction of an agricultural powerhouse by developing ANQPFs
Firstly, agricultural laborers are the core strength of ANQPFs, which is mainly reflected in two aspects: the agricultural labor potential and the agricultural labor productivity. To match ANQPFs, agricultural new quality laborers must first have high development potential with richer knowledge and skill reserves, more advanced cognitive and practical abilities, and higher innovation literacy (Zhu and Ye, 2024). Thus, agricultural quality laborers tend to have a higher level of education and a sense of innovation and entrepreneurship. Meanwhile, the mastery of new scientific instruments and labor techniques can make the production process more intelligent, informative, and efficient (Lu et al., 2024), which in turn improves labor productivity. Hence, higher agricultural labor productivity is a concrete expression of agricultural new quality laborers.
Secondly, the agricultural labor objects are the processing objects of ANQPFs, which are mainly reflected in the two aspects, the agricultural industry development level and the agricultural green development level. On the one hand, the agricultural industry development level should be suitable for the development of ANQPFs, which is based on traditional industries and committed to upgrading the traditional agricultural industry as well. Through deep cultivation of traditional agriculture, scientific and technological innovations will be transformed into industrial development, which will lead to the growth of the emerging agricultural industry (Mao and Zhang, 2024). At the same time, it adheres to the development path of high-end, industrialization, and intelligence, thus promoting the construction of future agriculture. Therefore, the agricultural new quality labor objects are embodied in the agricultural industry development level, specifically in the following three components: traditional agricultural upgrading, emerging agricultural growing, and future agricultural constructing. On the other hand, based on the concept of “ecology is a resource, ecology is productivity”, it emphasizes that the development of productivity is guided by green development concepts such as ecological development and environmental protection. This is different from the traditional productivity development that relies on resource consumption, and it is a leap towards “ecologization” (Pu and Huang, 2023), which promotes the green development of agriculture. Therefore, upgrading the agricultural green development level is the materialization of the agricultural new quality labor objects.
Thirdly, agricultural labor resources are an important foundation of ANQPFs, both in terms of agricultural material labor resources and agricultural intangible labor resources. Agricultural material labor resources mainly include agricultural digital infrastructure formed in the context of digital economic development, while agricultural intangible labor resources are embodied in agricultural scientific & technological innovation and agricultural digital level (Wang and Wang, 2024). The core of the agricultural new quality labor resources is the digital intelligence of labor tools, and intelligent agricultural equipment provides strong support for agricultural production not in quality but in efficiency, thus promoting the sustainable development of agriculture. Hence, the great abundance of agricultural material resources and the continuous upgrading of agricultural intangible resources are the two aspects that make up the agricultural new quality labor resources.

2.2 Indicator system construction of ANQP

Based on the interpretation of the theoretical connotation of ANQPFs in section 2.1, the following principles of scientificity, comprehensiveness, comparability and data availability, and the ideas of existing literature, this study constructs the evaluation indicator system of ANQPFs in China. It has three sub-dimensions: agricultural laborers, agricultural labor objects, and agricultural labor resources, which include 17 specific indicators selected as shown in Table 2.
Table 2 Evaluation indicator system of ANQPFs
Primary indicator Secondary indicator Tertiary indicators Explanation Attribute
Agricultural
laborers		 Agricultural labor
potential		 Rural human capital Average years of education among rural residents (X1) +
Rural entrepreneurship awareness Rural entrepreneurship activity level (X2) +
Agricultural labor
productivity		 Agricultural economic output Agricultural production efficiency (X3) +
Agricultural economic income Per capita disposable income of rural residents (X4) +
Agricultural
labor objects		 Agricultural industry
development level		 Traditional agriculture upgrading Proportion of agricultural product processing industry (X5) +
Emerging agriculture growing Number of national leading enterprises in agricultural industrialization (X6) +
Future agriculture construction Number of digital industrialized agricultural enterprises (X7) +
Agricultural green
development level		 Agricultural ecology
development		 Forest coverage rate (X8) +
Number of green agricultural enterprises (X9) +
Agricultural environmental
protection		 Pesticide application intensity (X10) −
Fertilizer application intensity (X11) −
Agricultural
labor resources		 Agricultural material
labor resource		 Agricultural digital infrastructure Rural cell phone penetration rate (X12) +
Rural Internet penetration rate (X13) +
Agricultural intangible
labor resource		 Agricultural science &
technology innovation		 Number of agricultural science and technology patents per capita (X14) +
Expenditures on agricultural science and technology activities (X15) +
Agricultural digital level Number of Taobao villages (X16) +
Active participation of enterprises in e-commerce (X17) +
The first is that agricultural laborers, as the core force of ANQPFs, should possess richer knowledge reserves, practical abilities, and entrepreneurial qualities. Therefore, the average years of education and entrepreneurial activity among rural residents were used to reflect the agricultural labor potential. Agricultural production efficiency and per capita disposable income of rural residents were selected to represent agricultural labor productivity. The second is that agricultural labor objects are the processing objects of ANQPFs, which embody profound ideas of green agricultural development and agricultural industrial transformation. As a result, the proportion of the agricultural processing industry, the number of national leading enterprises in agricultural industrialization, and the number of digitalized agricultural enterprises are used to indicate the agricultural industry development level. Forest coverage rate, the number of green agricultural enterprises, pesticide application intensity, and fertilizer application intensity are employed to measure the agricultural green development level. The third is that agricultural labor resources are an important foundation for ANQPFs, including both material labor resources and intangible production resources. So, rural cell phone penetration rate and rural internet penetration rate were used to show agricultural material labor resources. The number of agricultural science and technology patents per capita, expenditures on agricultural science and technology activities, the number of Taobao villages, and active participation of enterprises in e-commerce were used to show agricultural intangible labor resources.
Compared with existing research, the ANQPFs evaluation index system constructed in this study is significantly innovative in terms of its systematicity and the novelty of its indicators. On the one hand, the system framework overcomes the limitations of a single or partial dimension and systematically integrates the core elements of ANQPFs based on the three elements of the productivity framework. On the other hand, the indicators selected are closely linked to the characteristics of the times and reflect innovation. Through the introduction of characteristic indicators such as “rural entrepreneurial activity”, “number of digital industrialized agricultural enterprises”, “number of green agricultural enterprises”, “number of agricultural science and technology patents per capita”, and “number of Taobao villages”, it accurately measures and responds to the core characteristics of ANQPFs, such as new industries, new models, and new momentum. The inclusion of these distinctive indicators and their integration with a systematic framework is what distinguishes this study from other indicator systems.

3 Methods and data

3.1 Methods

3.1.1 Measurement of ANQPFs

To avoid the shortcomings brought about by a single assignment method, this study employs the combined assignment method, which combines the subjective and objective assignment methods to measure the ANQPFs of the provinces in China. Among them, subjective weights are determined by the equal weight method, the entropy method, and CRITIC method are applied to determine the objective weights, and then the arithmetic average of the weights obtained by the three methods is used as the final weights of each indicator. The detailed calculation steps are as follows:
First, standardize the evaluation indicators:
Positive indicator: $ X_{i j}^{\prime}=\frac{X_{i j}-\min \left(X_{i j}\right)}{\max \left(X_{i j}\right)-\min \left(X_{i j}\right)}$
Negative indicator: $X_{i j}^{\prime}=\frac{\max \left(X_{i j}\right)-X_{i j}}{\max \left(X_{i j}\right)-\min \left(X_{i j}\right)}$
where $i$ is the provincial subscript, $j$ is the evaluation indicator subscript; ${{X}_{ij}}$ and $X_{i j}^{\prime}$ are the original data and standardized data of the j-th indicator in the i-th province, respectively.
Second, the standardized indicator data is weighted to calculate the scores for each indicator:
$D_{i j}=W_{j} \times X_{i j}^{\prime}$
where ${{D}_{ij}}$ is the score of the j-th indicator in the i-th province; ${{W}_{j}}$ is the weight of the j-th indicator.
Third, the scores of the indicators are synthesized to measure ANQPFs:
${{U}_{i}}=\underset{j=1}{\overset{n}{\mathop \sum }}\,{{D}_{ij}}$
where ${{U}_{i}}$ is the ANQPFs development level of the i-th province, with the larger value indicating a higher ANQPFs development level and vice versa.

3.1.2 Dagum's Gini coefficient decomposition

Dagum's Gini coefficient is more capable of identifying regional differences and their sources (Dagum, 1997). Thus, in this study, Dagum's Gini coefficient is used to measure and decompose the regional differences of ANQPFs in China. The specific formula is as follows:
$G=\underset{j=1}{\overset{k}{\mathop \sum }}\,\underset{h=1}{\overset{k}{\mathop \sum }}\,\underset{i=1}{\overset{{{n}_{j}}}{\mathop \sum }}\,\underset{r=1}{\overset{{{n}_{h}}}{\mathop \sum }}\,\left| {{y}_{ji}}-{{y}_{hr}} \right|/2{{n}^{2}}\bar{y}$
Where $G$ represents the overall Gini coefficient, and its value indicates the greater the overall difference. $k$ is the number of regional divisions, $n$ is the number of provinces, j and h are regional subscripts, with i and r being provincial subscripts; ${{n}_{j}}$(${{n}_{h}}$) as the number of provinces in the j (h) region, and ${{y}_{ji}}$(${{y}_{hr}}$) is ANQPFs of the province $i$($r$) in the $j$($h$) region; $\bar{y}$ is the arithmetic mean of the ANQPFs.
Furthermore, the regions can be ranked according to the mean value of the ANQPFs, and Dagum's Gini coefficient is decomposed into three parts: intra-regional differences (${{G}_{w}}$), inter-regional differences (${{G}_{nb}}$), and intensity of transvariation (${{G}_{t}}$), $G={{G}_{w}}+{{G}_{nb}}+{{G}_{t}}$. The specific formulas are as follows:
${{G}_{jj}}=\frac{\frac{1}{2{{{\bar{y}}}_{j}}}\underset{i=1}{\overset{{{n}_{j}}}{\mathop \sum }}\,\underset{r=1}{\overset{{{n}_{j}}}{\mathop \sum }}\,\left| {{y}_{ji}}-{{y}_{jr}} \right|}{n_{j}^{2}}$
${{G}_{w}}=\underset{j=1}{\overset{k}{\mathop \sum }}\,{{G}_{jj}}{{p}_{j}}{{s}_{j}}$
${{G}_{jh}}=\frac{\underset{i=1}{\overset{{{n}_{j}}}{\mathop \sum }}\,\underset{r=1}{\overset{{{n}_{h}}}{\mathop \sum }}\,\left| {{y}_{ji}}-{{y}_{hr}} \right|}{{{n}_{j}}{{n}_{h}}\left( {{{\bar{y}}}_{j}}+{{{\bar{y}}}_{h}} \right)}$
${{G}_{nb}}=\underset{j=2}{\overset{k}{\mathop \sum }}\,\underset{h=1}{\overset{j-1}{\mathop \sum }}\,{{G}_{jh}}\left( {{p}_{j}}{{s}_{h}}+{{p}_{h}}{{s}_{j}} \right){{D}_{jh}}$
${{G}_{t}}=\underset{j=2}{\overset{k}{\mathop \sum }}\,\underset{h=1}{\overset{j-1}{\mathop \sum }}\,{{G}_{jh}}\left( {{p}_{j}}{{s}_{h}}+{{p}_{h}}{{s}_{j}} \right)\left( 1-{{D}_{jh}} \right)$
${{D}_{jh}}=\frac{{{d}_{jh}}-{{p}_{jh}}}{{{d}_{jh}}+{{p}_{jh}}}$
${{d}_{jh}}=\mathop{\int }_{0}^{\infty }\text{d}{{F}_{j}}\left( y \right)\mathop{\int }_{0}^{y}\left( y-x \right)\text{d}{{F}_{h}}\left( x \right)$
${{p}_{jh}}=\mathop{\int }_{0}^{\infty }\text{d}{{F}_{h}}\left( y \right)\mathop{\int }_{0}^{y}\left( y-x \right)\text{d}{{F}_{j}}\left( x \right)$
where ${{p}_{j}}={{n}_{j}}/n$, ${{s}_{j}}={{n}_{j}}{{\bar{y}}_{j}}/n\bar{y}$; Djh represents the relative impact of the ANQPFs between regions j and h; djh is the difference of ANQPFs between regions, which represents the mathematical expectation of the sum of all yji–yhr>0 sample values in regions j and h; pjh is the hyper-variable first-order moment, which represents the mathematical expectation of the sum of all yhr–yji>0 sample values in regions j and h; Fj(Fh) expresses the cumulative distribution function of ANQPFs in the region j(h).

3.1.3 Variance decomposition

Variance decomposition can explore the extent to which different dimensions contribute to the variance of a target (Li et al., 2023). In this research, the ANQPFs development level ($U$) is measured by the three dimensions of agricultural laborers (${{U}_{1}}$), agricultural labor objects (${{U}_{2}}$), and agricultural labor resources (${{U}_{3}}$), that is, $U={{U}_{1}}+{{U}_{2}}+{{U}_{3}}$. Therefore, based on the structural decomposition perspective, the variance decomposition method is used to reveal the contribution degree of the differences in the different dimensions of ANQPFs. The specific formula is as follows:
$\begin{align} & var\left( U \right)=cov\left( U\text{,}\ {{U}_{1}}+{{U}_{2}}+{{U}_{3}} \right) \\ & \ \ \ \ \ \ \ \ \ \ =cov\left( U\text{,}\ {{U}_{1}} \right)+cov\left( U\text{,}\ {{U}_{2}} \right)+cov\left( U\text{,}\ {{U}_{3}} \right) \\ \end{align}$
Both sides are obtained by dividing $var\left( U \right)$ simultaneously:
$1=\frac{cov\left( U\text{,}\ {{U}_{1}} \right)}{var\left( U \right)}+\frac{cov\left( U\text{,}\ {{U}_{2}} \right)}{var\left( U \right)}+\frac{cov\left( U\text{,}\ {{U}_{3}} \right)}{var\left( U \right)}$
where $var$ is the variance and $cov$ is the covariance.

3.1.4 Kernel density estimation

Kernel density estimation can use continuous density function curves to describe the dynamic distribution characteristics of random variables (Rosenblatt, 1956). Assuming that $f\left( x \right)$ is the density function of the random variable $x$, which is evaluated by the formula:
$f\left( x \right)=\frac{1}{Nh}\underset{i=1}{\overset{N}{\mathop \sum }}\,K\left( \frac{{{X}_{i}}-x}{h} \right)$
where $N$ represents the number of observations; ${{X}_{i}}$ is the number of independently and identically distributed observations; $x$ is the mean of the observations; $K\left( \cdot \right)$ is the kernel density function; and h denotes the bandwidth.

3.1.5 Markov chain

Markov chain is a method used to study random transition problems in discrete time and states without prior conditions. For this study, the probability distribution of ANQPFs rank in the year $t$ is represented by a $1\times k$ vector of state probabilities $P=\left[ {{P}_{1\text{,}t}}\text{,}\ {{P}_{2\text{,}t}}\text{,}\ \cdots \text{,}\ {{P}_{k\text{,}t}} \right]$, while the transfer process among ANQPFs ranks in different years can be represented by a $k\times k$ Markov transfer probability matrix ${{M}_{ij}}.$ ${{M}_{ij}}$ represents the probability value that a province belonging to level $i$ in year $t$ transforms to level $j$ in year $t+1$, which is calculated as follows:
${{M}_{ij}}=\frac{{{n}_{ij}}}{{{n}_{i}}}$
where ${{n}_{ij}}$ is the sum of the number of provinces that transferred from level $i$ in year $t$ to level $j$ in year $t+1$ in terms of ANQPFs’ development level during the study period. ${{n}_{i}}$ is the sum of the number of provinces whose ANQPFs is at level $i$ in all the years of the study period.

3.1.6 Obstacle degree model

In evaluating ANQPFs in China, it is important not only to measure the level of development of regional ANQPFs, but more importantly, to identify the key factors hindering the development of ANQPFs to provide a scientific basis for formulating ANQPFs’ development policies. Therefore, the study adopts the obstacle degree model into the analytical framework of ANQPFs to pathologically diagnose the underlying indicators of ANQPFs. The specific formula is as follows:
${{O}_{i}}=\frac{{{I}_{i}}{{F}_{i}}}{\sum\limits_{i=1}^{m}{{{I}_{i}}{{F}_{i}}}}\times 100 \%$
where ${{O}_{i}}$ is the degree of obstacle of the i-th indicator to ANQPFs, the larger its value, the greater the degree of influence of the indicator on ANQPFs. ${{F}_{i}}$ is the degree of contribution of the i-th indicator to ANQPFs; ${{I}_{i}}$ is the gap between the actual value of the i-th indicator and the optimal target value, expressed as the differences between 1 and the standardized value r for each indicator, $I=1-r$.

3.2 Data

This study selects 30 provinces in China during 2011-2021 as the research sample (due to severe data deficiencies in Tibet and Hong Kong, Macao, and Taiwan, these regions are not included in the research sample). The data mainly come from the China Statistical Yearbook, China Rural Statistical Yearbook, China Population and Employment Statistical Yearbook, China Environmental Statistical Yearbook, China Science and Technology Statistical Yearbook, China Agricultural Products Processing Industry Yearbook, China Trade and Foreign Economy Statistical Yearbook, statistical yearbooks of each province, China Academy for Rural Development-Qiyan China Agri-research Database (CCAD), China Knowledge Network Patent Database and Ali Research Institute Report. It should be noted that individual missing data are imputed using linear interpolation, and price-related data are deflated using 2011 as the reference period.

4 Results and discussion

4.1 Results of ANQPFs

4.1.1 National and regional levels

Figure 1 reports the measurement results of ANQPFs in the national and three major regions. From the national level, ANQPFs are generally on the rise from 0.2002 in 2011 to 0.3893 in 2021, with an average annual growth rate of 6.88%, which indicates that the development of ANQPFs in China has been effective, but the general situation is still at a comparatively low level (mean value of 0.2809), and there is a large room for improvement. Further, a comparison of the mean and median of the national ANQPFs from 2011- 2021 shows that the mean is consistently higher than the median, with the gap being particularly pronounced after 2018. This indicates that the Chinese provinces’ ANQPFs are characterized by a clear right skew; in other words, most of the provinces have low ANQPFs, while there are also a few provinces with exceptionally high ANQPFs. At the regional level, all three major regional ANQPFs show a steady upward trend. Specifically, the eastern region rise from 0.2431 in 2011 to 0.4745 in 2021, with an average annual growth rate of 6.92%, which is significantly higher than the national mean. The central and western regions increase from 0.1782 and 0.1732 in 2011 to 0.3586 and 0.3265 in 2021, with average annual growth rates of 7.25% and 6.54%, respectively, which is slightly lower than the national mean level. In summary, ANQPFs are highest in the eastern region, followed by the central region, while the western region has the lowest level of development. However, the growth rate of ANQPFs in western region is the highest, indicating that despite the relatively low level of development in western region, there is a clear catch-up effect and good momentum for development.
Figure 1 Measurement results of ANQPFs at the national and regional levels

Note: The eastern region includes Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong and Hainan; the central region includes Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, and Hunan; the western region includes Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang.

4.1.2 Provincial level

Table 3 presents the measurement results of ANQPFs for each province in China. In terms of the ranking of the development level of ANQPFs in various provinces, Beijing, Zhejiang, Fujian, Guangdong, and Shanghai are the top five provinces in the country, forming the first echelon with eastern provinces such as Jiangsu, Shandong, Tianjin, and Hainan. Jiangxi, Sichuan, Shaanxi, Chongqing, Hunan, Hubei, and Liaoning follow closely behind to form the second tier, while Guangxi, Anhui, Guizhou, Jilin, Heilongjiang, and other central and western provinces lag in terms of development. This further reflects the fact that ANQPFs have become a lead in the eastern provinces, significantly higher than in the central and western regions, corroborating the results of the previous district-level measurements. Therefore, promoting the development of ANQPFs in the central and western regions is the key to enhancing the national ANQPFs. From the viewpoint of the ANQPFs growth rate of each province, the average annual growth rate of 30 provinces in China is positive. The top 5 provinces in terms of growth rate are Anhui, Shandong, Henan, Guizhou, and Hubei, and the bottom 5 provinces are Beijing, Tianjin, Liaoning, Inner Mongolia, and Jilin. The possible reason for this is that central and western provinces such as Anhui, Henan, and Guizhou have a low initial level of ANQPFs and a huge potential for development space. At the same time, they can benefit from specialized national policy support, which enables their ANQPFs to develop rapidly. Eastern provinces such as Beijing and Tianjin, where the ANQPFs development level is already much higher than that of other provinces, are at the bottleneck of ANQPFs development, and thus, the development process is slow.
Table 3 Measurement results of ANQPFs at the provincial level
Province 2011 2013 2015 2017 2019 2021 Mean GR (%) Ranking
Beijing 0.3472 0.3674 0.4187 0.4527 0.4866 0.5691 0.4394 5.07 1
Tianjin 0.2289 0.2682 0.3079 0.3128 0.3150 0.3608 0.2987 4.66 8
Hebei 0.1689 0.1877 0.2300 0.2639 0.3166 0.3834 0.2558 8.54 20
Shanxi 0.1630 0.1815 0.2036 0.2213 0.2395 0.2752 0.2131 5.38 27
Inner Mongolia 0.1888 0.2029 0.2175 0.2242 0.2422 0.2751 0.2243 3.84 25
Liaoning 0.2251 0.2525 0.2610 0.2668 0.2934 0.3401 0.2714 4.21 16
Jilin 0.2177 0.2370 0.2542 0.2519 0.2634 0.2955 0.2527 3.10 21
Heilongjiang 0.1960 0.2200 0.2326 0.2524 0.2863 0.3269 0.2499 5.25 22
Shanghai 0.2723 0.2867 0.3260 0.3677 0.4032 0.4544 0.3519 5.25 5
Jiangsu 0.2115 0.2660 0.3177 0.3558 0.4142 0.4925 0.3409 8.82 6
Zhejiang 0.2874 0.3211 0.3633 0.4258 0.5233 0.6085 0.4180 7.79 2
Anhui 0.1528 0.1787 0.2325 0.2701 0.3287 0.4032 0.2585 10.19 18
Fujian 0.2701 0.2963 0.3439 0.3906 0.4703 0.5366 0.3816 7.11 3
Jiangxi 0.2034 0.2292 0.2698 0.2874 0.3394 0.3962 0.2856 6.89 10
Shandong 0.2012 0.2422 0.2875 0.3420 0.3867 0.5210 0.3256 9.98 7
Henan 0.1378 0.1565 0.1888 0.2278 0.2739 0.3551 0.2195 9.93 26
Hubei 0.1712 0.2147 0.2533 0.2811 0.3397 0.4033 0.2760 8.95 15
Hunan 0.1833 0.2091 0.2463 0.2869 0.3466 0.4134 0.2790 8.47 14
Guangdong 0.2491 0.2744 0.3307 0.3787 0.4593 0.5705 0.3708 8.64 4
Guangxi 0.1967 0.2196 0.2598 0.2916 0.3165 0.3529 0.2707 6.02 17
Hainan 0.2127 0.2349 0.2710 0.2995 0.3316 0.3831 0.2885 6.06 9
Chongqing 0.1905 0.2169 0.2645 0.3014 0.3394 0.3813 0.2807 7.19 13
Sichuan 0.1970 0.2141 0.2554 0.2920 0.3400 0.4150 0.2839 7.74 11
Guizhou 0.1559 0.1898 0.2283 0.2907 0.3172 0.3680 0.2571 8.97 19
Yunnan 0.1713 0.1899 0.2177 0.2452 0.2914 0.3405 0.2405 7.11 23
Shaanxi 0.2160 0.2390 0.2655 0.2939 0.3240 0.3631 0.2828 5.33 12
Gansu 0.1257 0.1427 0.1712 0.2041 0.2413 0.2776 0.1937 8.25 29
Qinghai 0.1452 0.1583 0.1810 0.2059 0.2280 0.2581 0.1958 5.92 28
Ningxia 0.1727 0.1921 0.2186 0.2313 0.2571 0.2901 0.2270 5.32 24
Xinjiang 0.1457 0.1623 0.1783 0.1908 0.2266 0.2697 0.1931 6.35 30

Note: Due to space limitations, this research only shows ANQPFs in odd years for each province; the means in the table are averages during 2011-2021, the growth rates (GR) are average annual growth rates during 2011-2021, and rankings of provinces are given based on their means.

4.2 Regional differences in ANQPFs

4.2.1 Overall and intra-regional differences

Figure 2 displays the evolutionary tendency of the overall differences and the intra-regional differences of ANQPFs. Regarding the overall disparities, China's ANQPFs exhibit an oscillating upward trajectory during the examination period, with a mean Gini coefficient of 0.126. The coefficient climbed from 0.129 in 2011 to 0.135 in 2021, representing a 4.65% increase and an average annual rise of 0.46%. Notably, a decline occurred from 0.132 to 0.117 between 2012 and 2016, followed by a rapid ascent, forming a V-shaped pattern. One potential explanation for this shift in focus is the 18th CPC National Congress, at which point the CPC Central Committee began to assign significant importance to the “Three Rural Issues”. In response, a series of policies were introduced to strengthen and benefit agriculture. This has accelerated the development of modern agricultural industrial systems, production systems, and business systems across regions, which contributed to a temporary narrowing of the overall disparities in ANQPFs development. After the 19th CPC National Congress, agriculture entered a critical period of reform on the supply side, and the varying efficiency in transitioning from old to new growth drivers across regions led to a resurgence in the overall differences of ANQPFs.
Figure 2 Trends in overall and intra-regional Gini coefficient of ANQPFs
With regard to intra-regional differences, the internal disparities in both the eastern and western regions show an oscillating upward trend, with mean Gini indices of 0.098 and 0.085, respectively. These figures climbed from 0.106 and 0.086 in 2011 to 0.108 and 0.088 in 2021, representing increases of 1.89% and 2.33%, and an average annual rise of 0.19% and 0.23%. Conversely, the central region's intra-regional differences exhibit a fluctuating downward trajectory, with its Gini coefficient averaging 0.068. It declined from 0.081 in 2011 to 0.077 in 2021, a drop of 4.94%, and an average annual decrease of −0.51%. In general, while the central region shows narrowing differences, the intra-regional differences in ANQPFs are widening nationally and within the other two regions. The level of within-region inequality for ANQPFs across the three regions ranks as follows: eastern>western>central, indicating that the imbalance in ANQPFs is more pronounced in the eastern and western regions relative to the central region.

4.2.2 Inter-regional differences

Figure 3 illustrates the evolutionary tendency of inter-regional differences in ANQPFs. As a whole, the differences between the eastern-western and central-western regions exhibit an undulating upward movement, while the eastern- central disparity displays a fluctuating downward trajectory. Specifically, the mean Gini coefficients for the eastern- western and central-western pairings are 0.177 and 0.082, respectively. These values climbed from 0.175 and 0.085 in 2011 to 0.192 and 0.092 in 2021, representing increases of 9.71% and 11.76%, with average annual rises of 0.93% and 1.12%. Conversely, the mean Gini coefficient for the eastern- central region is 0.155, declining from 0.163 in 2011 to 0.156 in 2021, a reduction of 4.29% and an average annual decrease of -0.44%. The possible reasons for this are that, firstly, compared with the western regions, the eastern and central regions possess relatively greater economies and a superior foundation of agricultural industrial upgrading, resource integration, and the practical application of agricultural science and technology innovations. This has spurred the rapid development of regional ANQPFs, thereby widening the gap between the eastern-central regions collectively and the western regions. Secondly, the eastern and central regions, leveraging their distinct resource endowments, industrial foundations, and location advantages, have prioritized coordinated planning for inter-regional modern agricultural spatial layouts. Additionally, through strengthening the integration of the agricultural industry chain, resource sharing, and technical exchanges across regions, the development disparity in ANQPFs between the eastern and central regions has been effectively reduced.
Figure 3 Trends in inter-regional Gini coefficient of ANQPFs

4.2.3 Sources of regional differences and their contributions

Table 4 lists the sources of regional differences and their contributions to ANQPFs. Regarding the magnitude of the contribution rates, inter-regional differences of ANQPFs contribute substantially more than both intra-regional differences and intensity of trans-variation, with a mean rate of 65.28%, spanning from 62.68% to 67.47%. Conversely, the contributions of inter-regional differences and intensity of trans-variation are comparatively lower, averaging 24.01% and 10.71%, and ranging from 23.07% to 25.03% and 9.35% to 12.29%, respectively. This demonstrates that intra-regional differences are the main contributing factor to overall differences in ANQPFs. The above results are similar to the findings of Wang and Wang (2024), who found that inter-regional differences are also the main reason for regional differences in NQPFs development. Examining the temporal evolution of contribution rates, the share attributable to intra-regional differences exhibits a fluctuating downward movement, declining from 25.03% in 2011 to 24.34% in 2021. This represents a 2.76% decline and an average annual reduction of -0.28%. In contrast, the contribution of inter-regional differences displays an undulating upward trajectory, rising from 62.68% in 2011 to 65.57% in 2021, a 4.61% increase and an average annual rise of 0.45%. Intensity of trans-variation, which captures the contribution of cross-regional overlap to the aggregate gap, declined from 12.29% in 2011 to 10.09% in 2021, a drop of 17.90% and an average annual decrease of -1.95%, indicating a diminishing effect of regional overlap. Consequently, reducing inter-regional differences in ANQPFs emerges as the foremost priority for fostering coordinated regional ANQPFs development and advancing the Chinese agricultural high-quality development.
Table 4 Sources of differences and contribution rates
Year Intra-regional Inter-regional Intensity of trans-variation
Source Contribution (%) Source Contribution (%) Source Contribution (%)
2011 0.032 25.03 0.081 62.68 0.016 12.29
2012 0.030 23.07 0.088 66.91 0.013 10.02
2013 0.029 23.25 0.082 66.16 0.013 10.59
2014 0.028 23.13 0.081 67.40 0.011 9.48
2015 0.028 23.18 0.081 67.47 0.011 9.35
2016 0.028 23.60 0.076 64.85 0.013 11.55
2017 0.029 23.87 0.079 64.63 0.014 11.51
2018 0.031 24.78 0.080 64.18 0.014 11.04
2019 0.032 25.00 0.081 64.02 0.014 10.97
2020 0.033 24.91 0.084 64.16 0.014 10.92
2021 0.033 24.34 0.088 65.57 0.014 10.09

4.3 Structural differences in ANQPFs

Figure 4a shows the results of the structural decomposition of the national ANQPFs development gap. Judging from the size of the contribution rate, the differences in agricultural labor objects and agricultural labor resources are the main structural sources of developmental differences in ANQPFs, with a mean contribution rate of 37.30% and 36.37%, respectively. The contribution of differences in agricultural laborers is also large, with a mean contribution of 26.32%. Therefore, reducing the agricultural labor object gap and the agricultural labor resource gap is the key to narrowing the development gap of China's ANQPFs. Concerning the evolution of the contribution rate, the contribution rates of agricultural laborers differences and agricultural labor objects differences show a clear downward trend, from 27.38% and 43.59% in 2011 to 20.17% and 33.62% in 2021, respectively, with a decrease of 26.33% and 22.87%, and an average annual growth rate of −3.01% and −2.56% respectively. The contribution rate of agricultural labor resource differences shows a significant upward trend, from 29.03% in 2011 to 46.21% in 2021, with an increase of 59.18%, and an average annual growth rate of 4.76%, replacing the agricultural labor object differences after 2016 as the main source of the development differences in China's ANQPFs. It shows that the development of ANQPFs in the new period depends more on agricultural digital infrastructure, agricultural science and technology innovation, and agricultural digital level, so regions should shift their development focus to agricultural labor resources.
Figure 4 Structural differences of ANQPFs
Figure 4b to 4d depict the structural decomposition results of ANQPFs developmental disparities in the eastern, central, and western regions in that order. Overall, there is significant regional heterogeneity in the structural sources of ANQPFs development differences across the three regions. Among them, the ANQPFs development differences between the eastern and central regions mainly originated from the agricultural labor object differences until 2018, and its average contribution rates were 39.53% and 67.02%, respectively, but the contribution rate of the agricultural labor object differences shows a decreasing trend, with an average annual growth rates of −3.81% and −8.60%, respectively; the contribution of agricultural labor resource differences shows a significant upward trend, with average annual growth rates of 7.84% and 21.45%, respectively, and rapidly overtook agricultural labor object differences as the main source of ANQPFs development differences after 2017; the contribution of agricultural labor differences is relatively small and decreasing, with mean values of 24.28% and 7.62%, respectively, and average annual growth rates of −6.58% and −9.55%. The main structural source of ANQPFs development disparity in the western region has always been the agricultural labor object disparity, with a mean contribution of 64.52% and an average annual growth rate of 1.92%, with no shift in the source of disparity during the period under examination; the contribution of agricultural labor resource differences does not vary much, with a mean value of 29.12% and an average annual growth rate of 1.04%; the contribution of agricultural labor differences is the smallest and on a downward trend, with a mean value of only 6.36% and an average annual growth rate of −19.92%.
In summary, the combined contribution of agricultural labor object differences and agricultural labor resource differences accounted for more than 70% of the structural sources of ANQPFs development differences in the national and the three major regions. And except for the western region, the main structural source of ANQPFs development disparity has gradually shifted from agricultural labor object disparity to agricultural labor resource disparity over time.

4.4 Dynamic evolution of ANQPFs

4.4.1 Dynamic distribution characteristics

Figure 5a illustrates the distributional dynamics that characterize ANQPFs’ development at the national level. From the distribution location, the kernel density curve's primary peak is constantly shifted to the right, and the rate of movement is increasing year by year, indicating that the national ANQPFs are rising, and the overall development is rapid. This coincides with the results of the previous time-series evolution characteristics. From the distribution patterns, it becomes evident that the height of the main peak in the curve decreases gradually, while the width of the main peak expands smoothly. This indicates that the absolute inter-provincial differences in ANQPFs across the country are widening. In terms of distribution extensibility, there is a clear right-trailing phenomenon and broadening in the curve, indicating that there are some provinces with significantly higher ANQPFs than others, such as Beijing, Shanghai, Zhejiang, Fujian, and Guangdong. In terms of polarization, the curves show a slight biphasic pattern during the research period, suggesting that polarization phenomena exist in the development of ANQPFs.
Figure 5 Dynamic distribution characteristics of ANQPFs
Figure 5b to 5d depict the distributional dynamics of ANQPFs development in the eastern, central, and western regions, respectively. Judging from the distribution location, all three regions align with national trends, exhibiting consistent rightward displacement of their primary density peaks. This signifies rising ANQPFs levels across all regions. Regarding distribution pattern, the main peak height in eastern and central regions follows an inverted V-shaped trajectory (initial increase followed by decline), while peak width initially narrows before expanding. This suggests fluctuating and increasing dispersion of intra-regional ANQPFs. Conversely, the western region shows progressive reduction in main peak height alongside oscillatory peak broadening, indicating widening internal disparities in ANQPFs. In distribution extensibility, late-stage curves reveal left-skewed tails in eastern and central regions, highlighting persistent lower-development provinces (such as Hebei, Liaoning in the east; Shanxi, Henan in the central). Western regions display pronounced right-skewed tails, reflecting generally lower ANQPFs despite having higher-performing provinces like Chongqing, Sichuan, and Shaanxi. Concerning polarization, all regional curves evolve from unimodal to multimodal distributions over time. Crucially, subsidiary peaks in eastern/central regions remain substantially lower and flatter than dominant peaks, whereas western regions exhibit elevated subsidiary peaks. This demonstrates more acute polarization and greater intra-regional disparity in western region compared to eastern and central regions.
In general, the ANQPFs in both the national and the three major regions show the distributional dynamics of “overall increase, absolute differences widen”. As a matter of fact, with the deepening and clarification of the national “three rural” policy as well as the establishment of agricultural high-tech industrial demonstration zones, modern agricultural industry science and technology innovation centers, and agricultural science and technology parks, the development of ANQPFs will be good in the long run. However, due to insufficient endowments of resources and concomitant underdeveloped economic infrastructure in specific regions, there is inadequate support for the ANQPFs, consequently resulting in comparatively lower ANQPFs in these areas, thus engendering a substantial disparity with other regions.

4.4.2 Transition probability analysis

To investigate the internal mobility patterns of ANQPFs development and transition probabilities, this research employs a Markov transfer probability matrix. For comparison, the ANQPFs in all provinces during the research period are divided into four tiers utilising the quartile method: Low (I), medium-low (II), medium-high (III), and high (IV). The transfer probability matrix of ANQPFs at 1-year intervals is calculated, and the results are shown in Table 5.
Table 5 Markov transition probability matrix of ANQPFs
t/(t+1) I II III IV N
I 0.7262 0.2738 0.0000 0.0000 84
II 0.0000 0.6875 0.3125 0.0000 80
III 0.0000 0.0000 0.7237 0.2763 76
IV 0.0000 0.0000 0.0000 1.0000 60
First, diagonal elements consistently exhibit higher transition probabilities than off-diagonal counterparts, indicating ANQPFs’ stationarity and confirming club convergence. Specifically, retention probabilities for provinces in tiers I, II, III, and IV are 72.62%, 68.75%, 72.37%, and 100.00% respectively. Second, terminal diagonal values exceed central diagonal values, revealing stronger rank persistence at both extremes (tier I and IV). This demonstrates more pronounced club convergence at the lowest and highest development levels. Third, probabilities to the right of the diagonal dominate those to the left, signifying that upward mobility exceeds downward shifts. This reflects ANQPFs’ predominant upward trajectory. Finally, rank transfer occurs only between neighboring types, and the probability of upward transfer varies across ranks. Among them, the probabilities of upward transfer by one level for tiers I, II, and III are 27.38%, 31.25%, and 27.63%, respectively. This indicates that the progression of ANQPFs is a gradual and protracted process, making it difficult to achieve leapfrog transfers. Therefore, the development obstacles faced by different tiers vary significantly. The reason for this is that the underlying conditions vary from one level to another, and thus, the selected paths and difficulty degrees of ANQPFs development also vary considerably. In addition, the probabilities on the left side of the main diagonal are all zero, showing that the ANQPFs in the provinces are better developed and have not yet been at risk of recession.

4.5 Obstacle factors of ANQPFs

This study utilises the Obstacle Degree model to identify the obstacle factors affecting the development of ANQPFs. Considering the large number of indicators, this paper only reports the top 5 obstacle factors and their obstacle degrees during 2011-2021, and the results are shown in Table 6.
Table 6 Major obstacle factors of ANQPFs
Year Ranking of major obstacle factors
1st obstacle factor 2nd obstacle factor 3rd obstacle factor 4th obstacle factor 5th obstacle factor
2011 X16 (13.69) X2 (8.98) X15 (7.53) X14 (7.49) X9 (7.00)
2012 X16 (13.92) X2 (9.08) X14 (7.50) X15 (7.44) X9 (7.03)
2013 X16 (14.13) X2 (9.15) X14 (7.50) X15 (7.36) X9 (7.02)
2014 X16 (14.36) X2 (9.24) X14 (7.57) X15 (7.39) X7 (6.97)
2015 X16 (14.64) X2 (9.41) X14 (7.56) X15 (7.51) X7 (7.04)
2016 X16 (14.88) X2 (9.51) X14 (7.64) X15 (7.56) X7 (7.05)
2017 X16 (14.94) X2 (9.53) X14 (7.62) X15 (7.59) X7 (7.00)
2018 X16 (15.04) X2 (9.64) X15 (7.70) X5 (7.65) X14 (7.49)
2019 X16 (15.25) X2 (9.85) X5 (7.99) X14 (7.95) X15 (7.62)
2020 X16 (15.51) X2 (10.22) X5 (8.34) X14 (7.86) X15 (7.29)
2021 X16 (15.84) X2 (10.83) X5 (8.66) X14 (7.85) X15 (7.35)

Note: Parentheses represent the degree of obstruction (%) for each obstructive factor in different years.

In terms of obstacle factors frequency, the number of Taobao villages, rural entrepreneurship activities, the number of agricultural science and technology patents per capita, and the expenditure on agricultural science and technology activities ranked first, with a frequency of 11 times. Subsequently, the number of agricultural products processing industries and the digitally industrialized agricultural enterprises appear 4 times, while the number of green agricultural enterprises appears only 3 times. It can be seen that the number of Taobao villages, rural entrepreneurial activities, the number of agricultural science and technology patents per capita, and the expenditure on agricultural science and technology activities are the main factors hindering the development of ANQPFs. It is recommended that in the future, provinces place greater emphasis on the enhancement of digital capabilities in the agricultural domain, particularly on the establishment of Taobao villages, the reinforcement of agricultural scientific and technological innovation, and the promotion of rural entrepreneurship.
From the rank of obstacle factors, the number of Taobao villages and rural entrepreneurship activities always ranked the first and the second in the examination period, with the mean values of obstacle degree of 14.75% and 9.59%, respectively. This indicates that the number of Taobao villages and rural entrepreneurship activities are key factors in the development process of ANQPFs, which is an important point of exertion to improve ANQPFs. At the same time, the number of agricultural science and technology patents per capita and the expenditure on agricultural science and technology activities have always been alternately ranked as the third to the fifth, with the mean values of obstacle degree of 7.64% and 7.48%, respectively. This suggests that special attention needs to be paid to agricultural science and technology innovation in the process of ANQPFs development, which is in line with the viewpoint of “science and technology innovation is a key element in the development of NQPFs” in the NQPFs theory. It is notable that the share of the agro-processing industry increased from the fourth obstacle factor in 2018 to the third obstacle factor in 2019, and has remained unchanged since then. This suggests that traditional agriculture remains the foundation of ANQPFs’ development, and the development of ANQPFs can be supported by the resources of traditional agriculture. In addition, the number of digital industrialized agricultural enterprises and green agricultural enterprises ranked as the fifth barrier factor during 2014-2017 and 2011-2013, respectively, while exiting the top five obstacle factors after 2018 and 2014, respectively. It shows that China has achieved excellent results in future agriculture construction and green agriculture development.

5 Conclusions and policy implications

5.1 Conclusions

Based on the basic connotation of ANQPFs, this research constructs the ANQPFs evaluation indicator system. The equal weight method, entropy method, and CRITIC method are comprehensively measuring China's ANQPFs from 2011 to 2021. Dagum Gini coefficient, variance decomposition, kernel density estimation, Markov chain, and obstacle degree model are utilized to analyze and test the regional differences, structural differences, dynamic evolution, and obstacle factors of ANQPFs across the national and three major regions. The major research conclusions are as follows:
From the development level trend, the ANQPFs of the national and three major regions show an upward trend during the research period, with an average annual growth rate of 6.88%, 6.92%, 7.25%, and 6.54%, respectively. However, there are notable differences in ANQPFs of each region, which are characterized by the distribution of the eastern, central, and western regions in decreasing order.
In terms of regional differences, the overall differences in ANQPFs show a widening trend, with inter-regional differences being its main source, to which the eastern-western region contributes the most. Meanwhile, the intra-regional differences among the three major regions show different evolutionary trends, with a widening trend in the eastern and western regions and a narrowing trend in the central region.
Judging from the structural differences, agricultural labor object differences and agricultural labor resource differences are the main structural sources of ANQPFs development differences in China, with agricultural labor resources replacing agricultural labor object differences as the first major source of ANQPFs differences after 2016. In addition, there is obvious regional heterogeneity in the structural sources of ANQPFs development differences in the three major regions. Among them, the main structural source of ANQPFs development differences in the eastern and central regions shifted from differences in agricultural labor objects to differences in agricultural labor resources, and in the western region, there are always differences in agricultural labor objects.
Regarding the dynamic evolution, the ANQPFs in the national and three regions show the distributional dynamics of “overall increase, absolute differences widen”, but the evolution process is different. In addition, there is a phenomenon of “club convergence” in ANQPFs, whereby grade shifts occur only between neighboring types, and the probability of upward shifts varies from grade to grade. ANQPFs have not yet experienced the risk of grade decline and have demonstrated a stable and positive development trend.
According to the obstacle factors, the number of Taobao villages, rural entrepreneurial activities, the number of agricultural science and technology patents per capita, and the expenditure on agricultural science and technology activities are the main factors obstructing the development of ANQPFs. Among them, the number of Taobao villages and the degree of rural entrepreneurial activities consistently ranked as the first and the second obstacle factors during the examination period, with the mean values of their obstacle degrees of 14.75% and 9.59%, respectively, which further reflects that the number of Taobao villages and the degree of rural entrepreneurial activities are the key factors to promote the development of ANQPFs.

5.2 Policy implications

First, building an upward trend in ANQPFs and focusing on the sustainable development of ANQPFs. For the central and western regions, where the level of ANQPFs is relatively low, the core driving force for transformation and upgrading should be consolidated by increasing human capital and technological investment. At the same time, the efficiency of agricultural production and the added value of agricultural products should be improved, while the skills training and comprehensive quality of agricultural laborers should be strengthened to build a high-quality team of new professional farmers. Proactively positioning ourselves in future agricultural industries such as biological breeding, intelligent equipment, and deep processing, as well as deeply integrating and promoting ecological and circular agriculture models to achieve synergistic benefits in both economic and ecological terms. Accelerating the optimization and upgrading of digital infrastructure, deepening the application and integration of digital intelligence technologies such as big data, the Internet of Things, and artificial intelligence across all agricultural scenarios, and driving the transformation toward intelligent production management and precise services.
Second, paying attention to the increasing regional differences in ANQPFs and building a mechanism for the regional synergistic development of ANQPFs. On the one hand, it focuses on enhancing investment in agricultural science and technology innovation and quality level improvement in the central and western regions, strengthening exchanges and cooperation with the eastern regions, and narrowing the gap with ANQPFs in the eastern regions. On the other hand, the accuracy and efficiency of resource allocation should be strengthened to fully leverage the eastern region's technological leadership, capital intensity, and market first-mover advantage. Through diversified approaches such as industrial collaboration, technology transfer, and talent exchange, the eastern region can effectively radiate and drive the coordinated advancement of the central, western, and northeastern regions, thereby preventing the further widening of regional disparities in the course of dynamic development.
Third, comprehensively grasping the structural characteristics of ANQPFs development, and driving the leap in ANQPFs according to the differences in the contribution of different productivity factors to ANQPFs. From the perspective of national development differences, agricultural labor resource disparities became the most prominent issue after 2016 and had the greatest impact on ANQPFs. It should be committed to addressing the developmental imbalances within the agricultural labor resources to drive down the ANQPFs disparities. From the perspective of regional development differences, the eastern regions should promote the rational use transformation and upgrading of agricultural labor resources, thereby reducing intra-regional differences; the key to reducing ANQPFs differences in the central regions is to optimize the means of production, change material resources, and material conditions, and reduce differences in agricultural labor resources; the focus of ANQPFs development in the western regions is to balance ANQPFs development utilizing synergistic development of production forces and internal optimization.
Fourth, focusing on the volatility of ANQPFs development, implementing a dynamic monitoring mechanism, and strengthening risk assessment, as well as early warning. On the one hand, by the dynamic evolution of ANQPFs, which is characterized by a “general increase, absolute differences widen”, a dynamic monitoring mechanism has been set up to regularly assess the development of ANQPFs in each region and provide a scientific basis for policy adjustments. On the other hand, for various potential obstacle factors, it is necessary to conduct risk assessment and early warning in advance, establish a comprehensive risk governance framework of “identification-early warning-disposal” formulate detailed graded intervention plans, transform fluctuation risks into opportunities for structural optimization, and ensure the stable development of ANQPFs through dynamic management.

5.3 Limitations and future research

This study also has the following limitations and improvements: first, due to the availability of indicator data, this study can only measure the level of ANQPFs in China at the provincial level. Future research can be expanded to the municipal or county level to more accurately characterize the development characteristics of ANQPFs and provide richer empirical evidence for the formulation of differentiated policies for different regions. Second, with the rapid improvement in ANQPFs, their characteristics may change over time. Future research may expand the ANQPFs evaluation index system to include more factors, such as artificial intelligence and precision agriculture.
Third, this study only discusses regional differences, dynamic evolution, and obstacle factors in the development of ANQPFs, without analyzing the driving factors behind their development. Future research can use methods such as geographic detectors and geographically weighted regression to reveal the driving mechanisms behind the development of ANQPFs following local conditions.

References

Publishing order | Descend order by publishing year | Descend order by cited within
[1]
Dagum C. 1997. A new approach to the decomposition of the Gini income inequality ratio. Empirical Economics, 22(4): 515-531.

[2]
Dai D, Zheng Y. 2025. The new quality productive force, science and technology innovation, and optimization of industrial structure. Sustainability, 17(10): 4439. DOI: 10.3390/su17104439.

[3]
Deng Z, Wu H J, Yang D Y. 2024. Accelerating the development of new quality productive forces in the industrial field: History, characteristics, and pathways. Journal of Beijing University of Technology (Social Sciences Edition), 24(4): 107-117. (in Chinese)

[4]
Han W L, Zhang R S, Zhao F. 2024. The measurement of new quality productivity and new driving force of the Chinese economy. Journal of Quantitative & Technological Economics, 41(6): 5-25. (in Chinese)

[5]
Hou G Y, Zhang Z Y, Dong S W. 2024. New quality productive forces empowering high-quality agricultural development in Northeast China: Theoretical logic, key issue and practical paths. Social Sciences in Hunan, (1): 69-76. (in Chinese)

[6]
Huang Q, Guo W, Wang Y. 2024. A study of the impact of new quality productive forces on agricultural modernization: Empirical evidence from China. Agriculture, 14(11): 1935. DOI: 10.3390/agriculture14111935.

[7]
Li J, Zhang Z Y, Mao X F, et al. 2016. Evaluation and suggestions about China agricultural layout based on resources and ecology. Issues in Agricultural Economy, 37(3): 26-33. (in Chinese)

[8]
Li R, Huang X L, Liu Y B. 2023. Spatio-temporal differentiation and influencing factors of China's urbanization from 2010 to 2020. Acta Geographica Sinica, 78(4): 777-791. (in Chinese)

DOI

[9]
Li Y, Chen H L, Tian M Z. 2024. Statistical measurement and spatiotemporal evolution characteristics of new quality productive force level. Statistics & Decision, 40(9): 11-17. (in Chinese)

[10]
Lin C, Wen X O. 2024. Capital markets empowering the formation of new quality productive forces: Theoretical logic, practical problems, and upgrading paths. Journal of Shenzhen University (Humanities & Social Sciences), 41(2): 66-75. (in Chinese)

[11]
Lin L, Gu T Y, Shi Y. 2024. The influence of new quality productive forces on high-quality agricultural development in China: Mechanisms and empirical testing. Agriculture, 14(7): 1022. DOI: 10.3390/agriculture14071022.

[12]
Liu Y, Duan D, Feng Z. 2025. The impact of new quality productive forces on the high-quality development of China's foreign trade. Systems-Basel, 13(5): 367. DOI: 10.3390/systems13050367.

[13]
Liu Y, He Z C. 2024. Synergistic industrial agglomeration, new quality productive forces and high-quality development of the manufacturing industry. International Review of Economics & Finance, 103373. DOI: 10.1016/j.iref.2024.103373.

[14]
Lu J, Guo Z A, Wang Y P. 2024. Levels of development of new quality productivity, regional differences and paths to enhancement. Journal of Chongqing University (Social Science Edition), 30(3): 1-17. (in Chinese)

[15]
Luo B L, Geng P P. 2024. New quality agricultural productivity: Theoretical framework, core concepts, and enhancement pathways. Issues in Agricultural Economy, (4): 13-26. (in Chinese)

[16]
Mao S P, Zhang C. 2024. Promote the construction of an agricultural power through the development of new quality agricultural productivity. Issues in Agricultural Economy, (4): 36-46. (in Chinese)

[17]
Mi J. 2024. The theoretical development, qualitative composition and practical value of new quality productive forces. Journal of Jishou University (Social Sciences), 45(3): 147-160. (in Chinese)

[18]
Pu Q P, Huang Y Y. 2023. Generation logic, theoretical innovation and time value of general secretary Xi Jinping's important exposition on new quality productivity. Journal of Southwest University (Social Sciences Edition), 49(6): 1-11. (in Chinese)

[19]
Pu Q P, Xiang W. 2024. New quality productivity and its utilizations—New driving force for Chinese modernization. Journal of Xinjiang Normal University (Edition of Philosophy and Social Sciences), 45(1): 77-85. (in Chinese)

[20]
Rosenblatt M. 1956. Remarks on some nonparametric estimates of a density function. The Annals of Mathematical Statistics, 27: 832-837.

[21]
Song J, Zhang J C, Pan Y. 2024a. Research on the impact of ESG development on new quality productive forces of enterprises—Empirical evidence from Chinese A-share listed companies. Contemporary Economic Management, 46(6): 1-11. (in Chinese)

[22]
Song Z J, Leng M N, Zhou B, et al. 2024b. New quality agricultural productive forces in China: Evaluation system construction, dynamic evolution and policy implications. Journal of Agro-Forestry Economics and Management, 23(4): 425-434. (in Chinese)

[23]
Sun L W, Guo J H. 2024. Construction of new quality productive force evaluation index system and empirical measurement. Statistics & Decision, 40(9): 5-11. (in Chinese)

[24]
Tan H Y, Liu J L, Li Z J, et al. 2024. The impact of institutional investor shareholding on new quality productive forces of enterprises. Journal of Yunnan University of Finance and Economics, 40(8): 56-71. (in Chinese)

[25]
Wang F, Han X Y, Chen R H. 2024. Empowering modern industrial system with new quality productive forces: Intrinsic logic and implementation path. Contemporary Economic Management, 46(6): 12-19. (in Chinese)

[26]
Wang J. 2021. Measurement and evaluation of China's agricultural high quality development. Journal of Jiangxi University of Finance and Economics, (2): 93-106. (in Chinese)

[27]
Wang J. 2024. New productive forces: A theoretical frame and index system. Journal of Northwest University (Philosophy and Social Sciences Edition), 54(1): 35-44. (in Chinese)

[28]
Wang J, Wang R J. 2024. New quality productivity: Index construction and spatiotemporal evolution. Journal of Xi’an University of Finance and Economics, 37(1): 31-47. (in Chinese)

[29]
Wang Q M, Yang J G. 2023. Research on digital new quality productivity and high-quality development of Chinese agriculture. Journal of Shaanxi Normal University (Philosophy and Social Sciences Edition), 52(6): 61-72. (in Chinese)

[30]
Xie F S, Jiang N, Kuang X L. 2024. Towards an accurate understanding of ‘new quality productive forces’. Economic and Political Studies, 13(1): 1-15.

[31]
Yao S J, Wang J F. 2024. The theoretical logic and implementation path of digital economy promoting the development of new quality productive forces. Journal of Yantai University (Philosophy and Social Science Edition), 37(2): 1-12. (in Chinese)

[32]
Zhang X N. 2024a. Analysis on the core element of developing new quality productive forces through scientific and technological innovation. Ideological & Theoretical Education, (4): 20-26. (in Chinese)

[33]
Zhang Z Y. 2024b. New quality productive forces empowering digital village construction: Transformation logic and implementation paths. Academic Exchange, (1): 93-107. (in Chinese)

[34]
Zhao G Q, Li J T. 2024. Does enterprise digital transformation enable new quality productivity development of enterprises? Based on micro evidence from Chinese listed firms. Review of Industrial Economics, (4): 23-34. (in Chinese)

[35]
Zheng Y N, Xu L M. 2024. Innovation and new quality productivity. Bulletin of Chinese Academy of Sciences, 39(7): 1131-1140. (in Chinese)

[36]
Zhou W, Xu L Y. 2023. On new quality productivity: Connotative characteristics and important focus. Reform, (10): 1-13. (in Chinese)

[37]
Zhu D, Ye L X. 2024. Agricultural new quality productive force in China: Level measurement and dynamic evolution. Statistics & Decision, 40(9): 24-30. (in Chinese)

[38]
Zhu H H, Chen L Q. 2024. Theoretical logic and path choice of digital economy enabling new quality productive forces. Social Sciences in Xinjiang, (4): 27-37. (in Chinese)

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