Scientific and Technological Innovation Effects on High-Quality Agricultural Development: Spatial Boundaries and Mechanisms

Abstract

This study investigates the spatial boundaries and mechanisms of the effect of scientific and technological innovation (STI) on high-quality agricultural development (HQA) to enhance agricultural practices. By employing a double-fixed spatial Durbin model and analyzing panel data from 167 prefectural-level cities in major grain-producing regions spanning from 2004 to 2021, we revealed significant spatiotemporal variations in the impact of STI on HQA in both local and adjacent cities. Our findings remained robust after rigorous testing. The study identified the spillover range of STI to be 420 km, displaying a distinctive inverted U-shaped trend around 170 km. Mechanism analysis indicates that both agricultural industry upgrades and human capital levels within 420 km amplify the influence of STI on local HQA, with only the latter demonstrating spillover effects. Within 170 km, both factors effectively regulate HQA in adjacent cities, while beyond this distance, only human capital regulatory impact continues to exhibit spillover effects. These insights offer theoretical guidance for designing effective agricultural scientific and technology promotion policies aimed at elevating the quality of HQA.

1. Introduction

Although the concept of high-quality agricultural development (HQA) originated in China, it aligns closely with the global agricultural development model, which emphasizes that agricultural development should focus not only on speed and scale, but also quality, efficiency, and ecological protection. As the global population is projected to reach 9.7 billion by 2050, HQA is essential to ensure food security, sustainability, and environmental stewardship [1,2]. As a crucial driver of HQA, scientific and technological innovation (STI) plays a significant role in enhancing agricultural productivity, production efficiency, and resilience [3,4,5]. Although the coordinated development level of STI and HQA has been significantly improved [6], the adoption and impact of STI vary across different spatial boundaries [7,8]. Understanding the spatial mechanisms of STI can help policymakers and stakeholders identify areas of high potential for HQA and target interventions effectively. This study aimed to explore the spatial boundaries and mechanisms of the effect of STI on HQA, shedding light on how to promote sustainable agricultural development.

Scholars have extensively researched four aspects of HQA: connotation definition, measurement and evaluation, driving factors, and realization paths. There is a general consensus on the importance of promoting HQA, although there is debate on whether to use a comprehensive evaluation method or a single-indicator method. Some scholars argue that the multidimensional nature of HQA supports the use of a comprehensive evaluation method [9,10]. Others believe that measuring HQA with a single indicator, such as green total factor productivity, which encompasses technological progress, efficiency improvement, and environmental factors, is more appropriate [11,12]. A comparison of the two methods shows that the comprehensive evaluation method may result in weaker horizontal comparability due to subjectivity and variability in indicator selection, while the single-indicator method allows for easier comparison between results. For example, Wang and Xie (2019) found that technological progress pushed the level of HQA up by 3.1% per year using the SBM-ML index [13]; Gong et al. (2020) used the same data and methods to reach a similar conclusion [14]. Given that this study focuses on exploring the spillover boundaries and mechanisms of STI with respect to HQA, it therefore opts for a single-indicator method using agricultural green total factor productivity as a proxy for HQA.

As the cornerstone of Chinese agriculture, STI plays a crucial role in achieving HQA [15,16]. Previous studies have primarily examined the impact of STI on various aspects, such as the agricultural economy [17], peasant household income [18], and rural development [19]. Some scholars also pay attention to the impact of STI on agricultural quality and efficiency improvement based on the perspective of total factor productivity [20]. With China transitioning toward high-quality development, research on STI and HQA has yielded valuable insights. While theoretical research generally agrees that STI is essential for promoting HQA and driving the modernization of agriculture [3,21], empirical studies present varying perspectives. Some researchers argue that STI positively influences HQA and leads to significant spillover effects [22,23], while others suggest a negative impact on neighboring regions [24]. Additionally, scholars have highlighted that the spatial spillover direction of STI is not uniform, as it is influenced by factors such as geographical distance and intellectual property rights [7]. Despite the focus on spillover effects in empirical studies, the concept of spillover boundaries has been overlooked. According to the innovation diffusion theory, the diffusion of STI is influenced by multiple factors, including geographical location and absorptive capacity, resulting in regional disparities in benefiting from spillovers [25]. Discrepancies in agricultural resources, economic development levels, and regional fragmentation create boundaries for scientific and technology spillovers.

The main purpose of the study was to reveal the spatial boundaries and mechanisms of STI on HQA, and provide a theoretical reference for rationally formulating agricultural scientific and technology promotion policies and helping improve the HQA. Previous research has laid a solid theoretical foundation for this study, but there is still room for further improvement. Compared with previous research, the main marginal contributions were reflected in the following three aspects. Firstly, we investigated the spillover boundaries of STI on HQA. We not only confirmed the existence of the spillover effect of STI, but also delineated its spatial boundaries. By establishing these boundaries, we can effectively grasp the scope of STI, offering valuable insights for the formulation of appropriate agricultural scientific and technological extension policies. Secondly, we revealed the mechanisms through which STI drives HQA. By elucidating the logical connections between these two elements, we can clearly delineate the specific pathways through which STI impacts HQA, thereby providing guidance for the development of innovation policies aimed at enhancing agricultural performance. Finally, we focused on research at the prefectural level. Compared with the provincial level, the spatial scope of prefecture-level cities is relatively small, which can help formulate agricultural development policies based on local conditions and improve the precision of policy interventions.

This article is structured as follows: Section 2 delves into the theoretical relationship between STI and HQA, presenting research hypotheses. Section 3 outlines the selection of variables and the construction of an econometric model. The empirical analysis is discussed in Section 4, the discussion of the article is placed in Section 5, while Section 6 presents the conclusions and specific recommendations.

2. Theoretical Analysis

2.1. Scientific and Technological Innovation and High-Quality Agricultural Development

According to the endogenous growth theory, STI plays a crucial role in advancing HQA. STI acts as a primary driver of HQA by introducing new production technologies, management concepts, and methods, thereby enhancing agricultural production efficiency and product quality, and reducing pollution levels. Disparities in agricultural conditions and STI adoption among municipalities exist, but as a knowledge-based product, STI can spread between regions through technology exchange, information networks, and technician mobility, impacting neighboring regions’ HQA. However, the diffusion of STI is influenced by various factors, such as socioeconomic development, agricultural resources, geography, and absorptive capacity, leading to a spatial boundary in its dissemination. Research indicates that the spillover effect of STI diminishes beyond a specific distance [8]. Building on this analysis, Hypothesis 1 is presented.

Hypothesis 1: 

The promotion effect of STI on the improvement of HQA is heterogeneous, and its spillover effect has spatial boundaries.

2.2. Scientific and Technological Innovation, Agricultural Industry Upgrading, and High-Quality Agricultural Development

The demand-driven effect triggered by industrial upgrading is a key factor in promoting STI [26]. The efficiency improvements sought through the upgrading of the agricultural industry heavily rely on technological advancements, such as the advancement of precision agriculture, which, in turn, drives the demand for intelligent and information agricultural technology. This, in effect, fosters innovation in intelligent agricultural machinery and equipment, as well as agricultural Internet of Things technology. Additionally, as resource and environmental constraints become more stringent, despite steady growth in grain output in primary production areas, issues such as severe land degradation, increasing production costs, and declining biodiversity necessitate a pressing shift in agricultural production methods [27]. Achieving efficient and environmentally sustainable agricultural practices requires technological innovations in eco-agriculture and recycling agriculture, thereby driving research and development in areas such as soil testing, precision fertilization, pest management, and water-saving irrigation. Building on this analysis, Hypothesis 2 is proposed for this study.

Hypothesis 2: 

Upgrading the agricultural industry can moderate the impact of STI on HQA.

2.3. Scientific and Technological Innovation, Human Capital Enhancement, and High-Quality Agricultural Development

The endogenous growth theory highlights the pivotal role of human capital in driving economic development. By fostering STI, the enhancement of human capital can effectively enhance the overall production efficiency [28]. An escalation in the human capital level signifies an increase in the knowledge and skills of agricultural producers. This not only enables them to better assimilate and implement STI advancements, but also empowers them to recognize the deficiencies in existing technologies and production methods. By amalgamating their own knowledge and experience in practical production, they can stimulate a sense of improvement or innovation. Human capital serves as both the core element of STI and a significant catalyst in facilitating the conversion and dissemination of STI outcomes. Proficient agricultural producers who adopt novel technologies and methods can achieve higher agricultural yields by enhancing production techniques and efficiency. By leveraging the ‘demonstration effect’, they can boost the acceptance and adoption of new technologies among neighboring farmers, thereby escalating the demand for STI outcomes and propelling the advancement of STI endeavors. Building upon this analysis, this study posits Hypothesis 3.

Hypothesis 3: 

Human capital enhancement can moderate the impact of STI on HQA.

3. Materials and Methods

3.1. Variable Selection and Explanation

3.1.1. Explained Variables

High-quality agricultural development (HQA) is often assessed using agricultural green total factor productivity, which encompasses environmental factors, technological progress, and efficiency improvements in the agricultural production process. This metric serves as a proxy variable for HQA and allows for cross-sectional comparisons in research. In this study, the super-efficient Epsilon-Based Measure and global Malmquist–Luenberger (EBM-GML) index method was employed to measure HQA in the main grain-producing areas, converting it into a fixed-base index with 2004 as the base period during the regression process. Referring to previous research [29,30], the input variables in the calculation process included labor (employees in the primary industry), land (sown crop area), machinery (total power of agricultural machinery), chemical fertilizer (fertilizer discount, scalar quantity), and irrigation (effective irrigation area). The expected output was the total output value of agriculture, forestry, animal husbandry, and fishery (constant prices in 2004), while the undesired output was agricultural non-point-source pollution. The correlation coefficients required for accounting for agricultural non-point-source pollution were primarily derived from the research results of Tao et al. (2021) [31]. The pollution sources mainly included chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) produced by chemical fertilizers (nitrogen fertilizers, phosphate fertilizers, and compound fertilizers), farmland solid waste (rice, wheat, corn, soybeans, potatoes, oilseeds, and vegetables), and animal husbandry (cattle, sheep, and pigs). Among them, the calculation method for fertilizer pollution yield is determined by multiplying the fertilizer conversion rate, the fertilizer pollution yield coefficient, and the factor (1—fertilizer utilization rate). The yield of solid waste pollution from farmland is calculated by multiplying the crop yield by the proportion of straw in the crop yield, the straw pollution coefficient, and the conversion coefficient (1—comprehensive utilization rate of straw). Similarly, the pollution output from animal husbandry is derived by multiplying the amount of animal husbandry by the pollution production coefficient of animal husbandry and the factor (1—comprehensive utilization rate of animal manure). Due to a serious lack of data on pesticides, agricultural film, and poultry breeding in prefecture-level cities, they were not measured here.

3.1.2. Explanatory Variables

Scientific and technological innovation (STI) has been a focal point in research. Previous studies often relied on input-based indicators such as R&D expenditures and the number of R&D personnel, yet faced challenges with double counting. Some researchers turned to the number of patents as an indicator, but this failed to capture their true socioeconomic value, resulting in limited comparability across industries. In a departure from prior approaches, this study leveraged the findings of Kou and Liu (2017) [32] by focusing on innovation output. Specifically, it utilized the innovation index at the prefecture level and city level as a proxy for STI, thus adjusting for the patent value and enhancing the measurement accuracy. This methodology, which has been endorsed by numerous scholars, aims to address the limitations of previous studies [33]. The regression analysis in this study involved the use of logarithms to further explore the relationship.

3.1.3. Control Variables

HQA is influenced by various factors, and to mitigate endogeneity bias resulting from omitted variables, this study incorporated additional variables into the econometric model.

Financial support for agriculture: An increase in the financial support for agriculture will help raise agricultural productivity and increase agricultural output, laying a solid foundation for HQA. However, it may also exacerbate chemical factor inputs and adversely affect the agri-ecological environment. This study utilized prefecture-level city expenditures on agriculture, forestry, and water affairs as its proxy indicator (unit: billion CNY).

Urbanization: The increase in the urbanization level is an important variable affecting HQA by absorbing surplus agricultural labor and providing conditions for the large-scale operation of agriculture, as well as providing market, technical, and financial support for the development of agriculture. In this study, the proportion of the urban population in the total population was used to measure the urbanization level, and non-agricultural population data were used as a substitute for cities with a missing urban population (unit: percentage).

Industrialization: The increase in industrialization promotes the rapid development of petroleum agriculture, which improves the efficiency of agricultural production while reducing the efficiency of agricultural ecology; at the same time, the rapid development of industrialization increases the demand for primary agricultural products, which, in turn, has an impact on HQA. The proportion of the value added of the secondary industry in the regional GDP of prefecture-level cities was used as a proxy indicator in this study (unit: percentage).

Economic development: The improvement of the regional economic development level can provide sufficient factor support for agricultural development through the polarization effect, and can promote the improvement of HQA. In this study, the per capita GDP of prefecture-level cities was used as a proxy indicator for the level of economic development (unit: billion CNY).

Environmental regulation: Environmental regulation refers to the sum of various policies and measures formulated and implemented for the purpose of environmental protection. The design of effective environmental governance policies is an important topic in the achievement of HQA. As a potential external constraint of agricultural development, environmental regulation directly affects the expenditure and cost of agricultural production entities and is an important external force affecting HQA [1]. The increase in environmental regulation can increase the production cost and reduce the profit of agricultural production through the “compliance cost effect”. On the other hand, through the “innovation compensation effect”, it can also push farmers to improve their production methods, improve the quality of their agricultural products, and increase their agricultural production efficiency [34]. Regarding the selection of variables for agricultural environmental regulation, a relatively unified standard has not yet been formed. Some scholars have suggested that the intensity of environmental protection policies is proportional to the strictness of regional environmental protection, and this practice has been widely recognized in the field of environmental economics [35]. Therefore, learning from the calculation methods of Chen and Chen (2018) [36] for environmental regulation, this study selected words and phrases related to environmental protection from the work reports of prefecture-level municipal governments from 2004 to 2021 for quantitative statistics, and took them as proxy variables for environmental regulation (the unit is represented by “units”). In the actual regression, in order to avoid the influence of zero values, they were processed by adding 1 to take the logarithm.

Agricultural planting structure: Since 2004, China’s grain production has increased year after year, which has laid a solid foundation for promoting HQA, but, at the same time, there is a critical situation of a simultaneous increase in output, inventory, and imports. On the other hand, the supply of water and soil resources is becoming increasingly tight, and the consumption structure of residents is constantly upgrading. Adjusting the agricultural planting structure has become a realistic choice to promote HQA. In this study, the proportion of the sown area of grain crops to the sown area of crops in prefecture-level cities was used as a proxy indicator (unit: percentage).

3.1.4. Moderating Variables

Agricultural industry upgrading: The optimization and upgrading of China’s agricultural industrial structure have resulted in a consistent enhancement of agricultural green total factor productivity, significantly improving the overall quality of agricultural development. This study applied the Cotyledon–Clark theorem to assess the ratio of the total output value of agriculture, forestry, animal husbandry, and fishery services to the total output value of agriculture, forestry, animal husbandry, and fishery in prefecture-level cities (unit: percentage).

Human capital: Human capital plays a crucial role in modern agricultural development and is essential for ensuring national food security. Enhancing human capital facilitates the adoption of advanced production and management methods, providing valuable external support for promoting HQA. The level of human capital is assessed by the proportion of students enrolled in general undergraduate programs in prefecture-level cities to the total population at the end of the year (unit: percentage).

3.2. Model Construction

To reveal the mechanism and spatial scope of STI with respect to HQA, this study constructed the following spatial econometric model [37]:

$$y_{it}=\rho \sum _{j=1}^n{W}_{ij}{y}_{jt}+\beta X_{it}+\phi \sum _{j=1}^n{W}_{ij}{x}_{it}+{\mu }_i+{\nu }_t+{\epsilon }_{it}$$

(1)

In Equation (1), subscript i denotes the prefecture-level city, and t is the year. y_it denotes the level of HQA in the prefecture-level city. ρ is the spatial lag coefficient of HQA. X_it is the set of explanatory variables. φ is the spatial regression coefficient of the explanatory variables. μ_i, v_t, and ε_it represent the spatial effect, the time effect, and the random error term, respectively. W_ij is the spatial weight, and the inverse of the Euclidean distance of prefecture-level cities (W₁) is used as the spatial weight in the section on the benchmark regression. The economic behavior of prefecture-level cities is considered in the section on the robustness test, and a comprehensive weight matrix (W₂) containing the geographic distance and economic behavior is constructed based on the gravity model.

3.3. Data Sources and Descriptive Statistics

This study focused on 167 prefectural-level cities in China’s 13 main grain-producing regions from 2004 to 2021. The choice of starting in 2004 is significant for two reasons. Firstly, the Chinese government defined the main grain-producing areas geographically in December 2003, and from 2004 onward, it began to provide increased policy support for agricultural development in these areas. Secondly, the issuance of Central Document No. 1 in 2004 marked a turning point in boosting the agricultural output in the main grain-producing regions, setting the stage for subsequent years of growth and laying a strong foundation for HQA. Data for this study were primarily sourced from the EPSDATA database, with missing values being filled in by referencing statistical yearbooks of the municipalities or using the mean value method. The descriptive statistics are shown in

Table 1. Descriptive statistics.

Variable Type	Variable Name	Symbol	Obs	Mean	S.D.	Min.	Max.
Dependent variable	High-quality agricultural development	HQA	3006	1.93	0.99	0.20	8.50
Independent variable	Scientific and technological innovation	STI	3006	11.10	37.11	0.02	568.20
Control variables	Financial support for agriculture	Fsup	3006	30.18	26.87	0.17	141.90
	Urbanization	Urban	3006	0.50	0.16	0.10	0.96
	Industrialization	Industry	3006	0.47	0.11	0.11	0.86
	Economic development	Pgdp	3006	41,568.20	31,326.91	3089.00	218,118.00
	Environmental regulation	Environment	3006	34.04	20.56	0	136.00
	Agricultural planting structure	Structure	3006	0.72	0.13	0.28	0.99
Moderating variables	Agricultural industry upgrading	Upgrade	3006	0.04	0.03	0.01	0.97
	Human capital	Hcapital	3006	1.91	2.42	0.02	18.68

.

4. Results

4.1. Spatial Autocorrelation Test of Variables

The global Moran index of HQA and STI for prefecture-level cities was calculated using the Stata16.0 software, and the results are presented in

Table 2. Global Moran index of HQA and STI with different spatial weights.

Year	W1				W2
	HQA		STI		HQA		STI
	I	P	I	P	I	P	I	P
2005	0.032	0.000	0.041	0.000	0.083	0.001	0.123	0.000
2006	0.080	0.000	0.043	0.000	0.191	0.000	0.123	0.000
2007	0.042	0.000	0.045	0.000	0.116	0.000	0.130	0.000
2008	0.046	0.000	0.049	0.000	0.128	0.000	0.140	0.000
2009	0.073	0.000	0.055	0.000	0.193	0.000	0.153	0.000
2010	0.090	0.000	0.061	0.000	0.232	0.000	0.169	0.000
2011	0.101	0.000	0.068	0.000	0.280	0.000	0.185	0.000
2012	0.109	0.000	0.073	0.000	0.288	0.000	0.199	0.000
2013	0.099	0.000	0.081	0.000	0.255	0.000	0.213	0.000
2014	0.088	0.000	0.087	0.000	0.235	0.000	0.225	0.000
2015	0.079	0.000	0.095	0.000	0.218	0.000	0.243	0.000
2016	0.062	0.000	0.104	0.000	0.183	0.000	0.260	0.000
2017	0.089	0.000	0.107	0.000	0.289	0.000	0.265	0.000
2018	0.095	0.000	0.112	0.000	0.288	0.000	0.272	0.000
2019	0.068	0.000	0.114	0.000	0.212	0.000	0.274	0.000
2020	0.087	0.000	0.118	0.000	0.221	0.000	0.278	0.000
2021	0.060	0.000	0.122	0.000	0.181	0.000	0.283	0.000

. Our analysis revealed that regardless of the spatial weights used, both variables exhibited significant correlation at the 1% level, suggesting the presence of spatial dependence. This underscores the importance of incorporating spatial econometric modeling into the examination of the relationship between STI and HQA. Therefore, researchers should be mindful of spatial correlations to mitigate potential biases in regression outcomes.

4.2. Model Testing and Selection

After confirming the presence of spatial autocorrelation between STI and HQA, a spatial econometric model was selected and tested.

Table 3. Results of the spatial measurement model tests.

Testing Indicator	Statistical Value	p-Value
LM_spatial_lag	473.459	≤0.0001
LM_spatial_error	2769.205	≤0.0001
Robust LM_spatial_lag	108.030	≤0.0001
Robust LM_spatial_error	2404.205	≤0.0001
Wald_spatial_lag	31.140	≤0.0001
Wald_spatial_error	30.060	≤0.0001
LR_spatial_lag	30.910	≤0.0001
LR_spatial_error	29.850	≤0.0001
Hausman test	147.310	≤0.0001
LR test (time fixed)	2073.270	≤0.0001
LR test (individual fixed)	119.750	≤0.0001

shows that both the LM test and the robust LM test were statistically significant at the 1% level, indicating the coexistence of spatial lag models and spatial error models. The Wald test and LR test ruled out any degradation in the spatial Durbin model. These findings suggested that choosing the spatial Durbin model was appropriate [37]. The Hausman test suggested that the spatial Durbin model with fixed effects was the preferred option. Since the fixed effects of the spatial Durbin model included various types, the LR test was then used to assess the combined significance of temporal and spatial fixed effects, revealing that the spatiotemporal fixed-effect model outperformed the temporal and spatial fixed-effect model. Consequently, we finally adopted the spatial Durbin model with nested spatiotemporal double-fixed effects as the baseline model for analysis.

4.3. Benchmark Regression

This study utilized the spatial Durbin model with double-spatiotemporal-fixed effects to analyze the influence of STI in prefecture-level cities on HQA. To address heteroskedasticity, logarithms of absolute-value variables were used in the regression. The results in

Table 4. Full sample estimation and decomposition of spatial effects.

Variable	STI	Fsup	Urban	Industry	Pgdp	Environment	Structure
Main	0.129 ***	−0.204 ***	−0.675 ***	−0.751 ***	0.192 ***	−0.022 *	0.514 ***
	(0.0242)	(0.0355)	(0.1618)	(0.1922)	(0.0560)	(0.0114)	(0.1938)
Wx	0.857 **	−0.305	−6.854 ***	−0.745	0.180	−0.109	−8.966 ***
	(0.3719)	(0.4774)	(2.0885)	(2.8042)	(0.6809)	(0.1650)	(2.1936)
Direct effect	0.123 ***	−0.207 ***	−0.614 ***	−0.738 ***	0.182 ***	−0.0202 *	0.587 ***
	(0.0238)	(0.0302)	(0.1688)	(0.2072)	(0.0665)	(0.0109)	(0.1979)
Indirect effect	0.354 **	−0.070	−3.242 ***	0.070	0.019	−0.052	−4.833 ***
	(0.1736)	(0.2444)	(1.2300)	(1.6761)	(0.3979)	(0.0793)	(1.1398)
Total effect	0.477 ***	−0.277	−3.856 ***	−0.669	0.201	−0.0721	−4.247 ***
	(0.1780)	(0.2429)	(1.2218)	(1.6896)	(0.3894)	(0.0811)	(1.1147)
ρ	−0.945 *** (0.1706)
σ2	0.212 *** (0.0055)
R2	0.234
N	3006

Note: * p < 0.1, ** p < 0.05, *** p < 0.01.

reveal a significant spatial autocorrelation coefficient of −0.945 at the 1% level, indicating a negative impact of local HQA on neighboring prefectures and suggesting a “siphoning effect” of HQA. Due to bias in the regression coefficients of the spatial Durbin model, this section further dissects the spatial effects of each factor using the partial differential decomposition method, with detailed results presented in Table 4.

Both the direct and indirect coefficients of STI were significantly positive at the 1% level, suggesting that enhancing STI can effectively enhance HQA in both local and neighboring cities. This finding aligns with the results of Li et al. (2021) [22]. The adoption of new knowledge, technologies, and methods can boost local HQA by transforming traditional agricultural practices, enhancing production efficiency, and reducing pollution levels. Additionally, neighboring cities can indirectly enhance their HQA by leveraging technological innovations through knowledge diffusion, training, and the exchange of scientific personnel. Therefore, Hypothesis 1 is partially supported.

The direct effect of financial support for agriculture was significantly negative at the 1% level, while the indirect effect was negative but not statistically significant. This suggested that improvements in financial support suppressed the local HQA but not significantly for neighboring areas. The reason is that the focus on quantity rather than quality of agricultural output in China’s main grain-producing regions, due to the importance of food security, has influenced the distribution of financial support [38]. Additionally, local governments prioritize local agricultural development over that of neighboring cities due to the zero-sum-game nature of political promotion and performance comparisons [39].

Both the direct and indirect effect coefficients of urbanization were significantly negative at the 1% level, suggesting that urbanization improvements hindered the increase in HQA in local and neighboring cities. This was due to urbanization providing market, financial, and technological support for agricultural development, while also consuming valuable arable land and attracting population migration to cities. As a result, the optimization of agricultural production resources became challenging, leading to a lack of positive momentum for HQA in these areas [24].

The direct effect coefficient of industrialization was significantly negative at the 1% level, while the indirect effect coefficient was not significant. This suggested that the increase in industrialization hindered local HQA, but its impact on neighboring prefecture-level cities was not emphasized. This was mainly due to the rapid development of petroleum agriculture and the promotion of agricultural production efficiency resulting from the increase in industrialization. This led to the cumulative effect of “reverse ecologization” in agricultural production becoming more prominent [40], ultimately negatively affecting HQA.

The direct effect coefficient of economic development was significantly positive at the 1% level, while the indirect effect coefficient was not significant. This suggested that the growth in economic strength positively impacted the local HQA but did not have a significant effect on adjacent HQA. This could be attributed to the fact that the enhancement of regional economic strength led to the absorption of agricultural resources and elements from surrounding prefectures through the polarization effect, subsequently fostering local agricultural development through agglomeration and scale effects.

The direct impact of environmental regulations on local HQA was significantly negative, while the indirect impact was not significant. This suggested that increased environmental regulations impeded the development of HQA, as the attention given by local governments to environmental issues indirectly raised the prices of agricultural production inputs. This, in turn, crowded out productive investment, increased agricultural production costs, weakened marketplace competitiveness, and, ultimately, hindered the improvement of HQA. Moreover, competitive dynamics between local governments make it challenging for environmental regulations at the local level to influence agricultural development in neighboring regions.

The adjustment of the agricultural planting structure not only enhanced the local areas’ agricultural quality, but also impacted neighboring cities. Optimizing the allocation of production factors allows for crop production advantages to be leveraged, agricultural productivity to be improved, and, ultimately, agricultural quality to be enhanced. However, this restructuring may concentrate on specific production factors, hindering neighboring regions’ access to resources and impeding their agricultural quality improvement.

4.4. Robust and Endogenous Processing

In order to further validate the baseline regression results, robustness tests and endogeneity adjustments were conducted across seven different components, with detailed results presented in

Table 5. Results of the robustness and endogeneity treatment.

Variable	Replacement Weight	Tail Reduction	Replace Dependent Variable	Replace Independent Variable	Adjustment Sample	Add Omitted Variable	Dynamic SDM
W × HQA−1							−2.073 ***
							(0.2869)
STI	0.125 ***	0.122 ***	0.114 ***	8.012 ***	0.084 ***	0.239 ***	0.119 ***
	(0.0238)	(0.0221)	(0.0127)	(1.0572)	(0.0208)	(0.0245)	(0.0261)
Agglomeration						0.906 ***
						(0.0847)
Agglomeration2						−0.061 ***
						(0.0114)
W × STI	0.192 *	0.975 ***	0.744 ***	37.410 **	0.797 **	1.074 ***	1.003 **
	(0.0991)	(0.3478)	(0.1959)	(16.1788)	(0.3349)	(0.3826)	(0.4033)
W × Agglomeration						−3.268 ***
						(1.1702)
W × Agglomeration2						0.467 ***
						(0.1637)
ρ	−0.135 ***	−0.907 ***	−0.886 ***	−0.959 ***	−1.008 ***	−0.872 ***	0.383 **
	(0.0515)	(0.1697)	(0.1596)	(0.1713)	(0.2043)	(0.1681)	(0.1751)
σ2	0.216 ***	0.174 ***	0.058 ***	0.210 ***	0.088 ***	0.194 ***	0.220 ***
	(0.0056)	(0.0045)	(0.0015)	(0.0054)	(0.0027)	(0.0050)	(0.0055)
R2	0.621	0.672	0.84	0.547	0.45	0.324	0.554
N	3006	3006	3006	3006	2171	3006	2839

Note: * p < 0.1, ** p < 0.05, *** p < 0.01.

.

Replacement weights: The spatial weights constructed in the previous analysis primarily focused on the spatial geographic distance, overlooking the spatial correlation of economic activities. To address this, this study incorporated both spatial correlation in the economy and distance among prefecture-level cities by constructing an economic–geographical weight matrix based on the gravity model. The analysis indicated that altering the weights did not alter the direction or significance of the estimated coefficients for the main explanatory variables. The only difference lay in the magnitude of the coefficients, suggesting that the baseline regression findings remained reliable.

Tail reduction treatment: Excluding extreme values of variables can provide a more accurate representation of the relationship between STI and HQA. In this study, a bilateral shrinkage of the 1% quantile was conducted, followed by re-regression. The results revealed that the regression coefficient for STI remained significantly positive, suggesting that the influence of STI on HQA was robust.

Replacement of the dependent variable: The results of HQA calculated based on the EBM-GML measure were further decomposed into green technological progress and green technological efficiency, where green technological efficiency reflected changes in actual production relative to the production frontier due to factors such as institutional and policy changes affecting factor allocation efficiency at the current technological level. By replacing the dependent variable with green technology efficiency, the study continued to examine the influence of STI on HQA. The findings reaffirmed a positive relationship between STI and HQA, underscoring the robustness of the empirical results presented here.

Replacement of the independent variable: In the benchmark regression, a comprehensive index was utilized to measure the STI in prefecture-level cities. However, for this analysis, a different approach was taken, whereby a single indicator, specifically, the number of invention patents granted, was used as a proxy indicator for STI. The re-regression results indicated that the positive impact of STI on HQA remained significantly positive.

Adjustment of samples: Since the report of the 19th Party Congress in 2017 suggested that China has transitioned to a phase of high-quality development, the significance of high-quality development has been gaining more attention from various sectors. This study specifically selected samples from before 2017 to re-evaluate, aiming to minimize the influence of policy adjustments. The findings indicated that the regression coefficient of STI remained positive.

Addition of omitted variables: This study addressed potential omitted variables by incorporating agricultural industrial agglomeration and its quadratic term to mitigate endogeneity concerns. Industrial agglomeration plays a crucial role in reducing agricultural production costs, enhancing resource utilization efficiency, and elevating the overall HQA. This research employed location entropy as a metric to quantify agricultural industry agglomeration using the formula Agglomeration = (Y_ia/Y_i)/(Y_a/Y), where Agglomeration represents the location entropy; Y_ia denotes the total output value of agriculture, forestry, animal husbandry, and fishery in city i; Y_i signifies the total output value of all industries in city i; Y_a indicates the national output value of agricultural, forestry, animal husbandry, and fisheries; and Y represents the national total output value of all industries. Regression analysis revealed an inverted U-shaped correlation between agricultural industrial agglomeration and local HQA, as well as a U-shaped connection with neighboring HQA. Notably, even after controlling for additional variables, the positive impact of STI on HQA remained statistically significant, reaffirming the reliability of the benchmark regression results.

Dynamic SDM: To address endogeneity concerns related to mutual causality, this study incorporated the spatial lag of the dependent variable into the baseline model and re-estimated it using the dynamic spatial Durbin model. The findings revealed a negative spatial lag term for HQA, suggesting that local HQA in the previous year significantly affected the neighboring HQA in the current year. Accounting for endogeneity, the direction of STI remained consistent with the benchmark regression results.

Through multiple tests, it was found that the direction and significance of STI with respect to HQA did not fundamentally change; only the coefficient size of the difference changed, which fully explained the robustness and reliability of this study’s findings.

4.5. Spatial Overflow Boundary Exploration

The first law of geography posits that the correlations between phenomena are strongly influenced by their geographical proximity. After confirming the spatial spillover effect of STI on HQA, this study further investigated the spatial attenuation boundary of STI with respect to HQA using the double-fixed SDM. Following a methodology outlined in a previous publication, a distance threshold was established where if city i was beyond the threshold from city j, a weight of 1/d_ij was assigned; otherwise, a weight of 0 was assigned. Given that the minimum Euclidean distance between major grain-producing regions was 17.64 km, a conservative initial distance threshold of 20 km was set for computational ease. The regression analysis was then conducted in 50 km intervals until the results reached statistical insignificance. The detailed findings are presented in

Table 6. Model estimation results at different spatial distances.

Spatial Distance (km)	Variable
	Spillover Effect	Control Variable	ρ	σ2	R2	N
20–70	0.155 ***	Control	−0.045 **	0.213 ***	0.000	3006
	(0.0300)		(0.0227)	(0.0055)
70–120	0.127 ***		−0.055 *	0.216 ***	0.042	3006
	(0.0360)		(0.0299)	(0.0056)
120–170	0.053 **		−0.086 ***	0.216 ***	0.091	3006
	(0.0285)		(0.024)	(0.0056)
170–220	−0.160 ***		0.017	0.215 ***	0.003	3006
	(0.0396)		(0.0284)	(0.0055)
220–270	−0.128 ***		0.031	0.214 ***	0.350	3006
	(0.0396)		(0.0294)	(0.0055)
270–320	−0.110 ***		0.055 **	0.216 ***	0.360	3006
	(0.0361)		(0.0270)	(0.0056)
320–370	−0.079 *		0.151 ***	0.213 ***	0.164	3006
	(0.0412)		(0.0305)	(0.0055)
370–420	−0.072 **		−0.047	0.214 ***	0.072	3006
	(0.0362)		(0.0288)	(0.0055)
420–470	−0.001		−0.114	0.215 ***	0.111	3006
	(0.0039)		(0.2043)	(0.0056)
470–520	−0.016		0.013	0.217 ***	0.155	3006
	(0.0258)		(0.0200)	(0.0056)

Note: * p < 0.1, ** p < 0.05, *** p < 0.01.

.

The study findings revealed that the spillover effect of STI on HQA was statistically significant at a minimum level of 10% within a distance range of 420 km. Beyond this distance, the spillover effect lost significance, suggesting that the impact of STI on HQA followed a pattern of diminishing influence with increasing geographical distance.

Specifically, the change in the spatial spillover of STI could be categorized into three intervals. The first interval spanned up to 170 km, covering approximately two prefecture-level cities. Within this range, local STI played a significant role in enhancing HQA in neighboring cities, with a negative correlation between size and distance. This indicated that the positive spillover impact of STI on HQA was primarily observed in nearby cities. This phenomenon can be attributed to the similarities in agricultural production factors, natural climate conditions, and socioeconomic development among adjacent cities. By effectively disseminating and diffusing local STI achievements, they could be integrated with the agricultural production conditions of neighboring cities to facilitate the enhancement of HQA in those regions.

Between 170 and 420 km, there was a spatial adjacency relationship that extended beyond the typical range of neighboring cities. Within this distance range, the spillover effect of STI on HQA outcomes was notably negative. As the distance increased, the magnitude of this negative effect decreased. This was due to the higher costs associated with disseminating STI achievements over greater distances, as well as variations in agricultural resources among distant cities. Simply replicating STI achievements without considering local agricultural conditions could result in a mismatch between STI and HQA, leading to adverse spillover effects on HQA.

The third interval was more than 420 km. The decrease in spatial units in the spatial weight matrix led to a random fluctuation in the spatial spillover coefficient. Within this distance, the spillover effect of STI on HQA was no longer statistically significant. Therefore, Hypothesis 1 was further supported.

The spillover range of STI on HQA was found to be effective up to 420 km, with a boundary at 170 km. The spillover effect of STI showed an initial promotion followed by inhibition with respect to HQA, demonstrating a notable inverted U pattern.

4.6. Heterogeneity of the Effective Range

The effective spatial boundary of STI with respect to HQA was determined to be 420 km. To account for potential spatiotemporal heterogeneity, the spatial weight matrix was reconstructed with this boundary, and the double-fixed SDM was used to analyze the spillover effect. The findings are presented in

Table 7. Results of the heterogeneity test.

Variable	Geographical Heterogeneity		Development Level Heterogeneity		Environmental Shock Heterogeneity
	North	South	High-Level	Low-Level	2004–2020	2020–2021
Direct effect	−0.044	0.379 ***	0.104 ***	0.123 ***	0.095 ***	0.157
	(0.0405)	(0.0285)	(0.0324)	(0.0315)	(0.0232)	(0.4234)
Indirect effect	0.302 ***	−0.120	0.408 ***	−0.195 *	0.139 *	4.085 ***
	(0.1110)	(0.0796)	(0.1001)	(0.1007)	(0.0769)	(1.4548)
Total effect	0.258 **	0.259 ***	0.512 ***	−0.072	0.234 ***	4.243 ***
	(0.1128)	(0.0804)	(0.1094)	(0.1029)	(0.0801)	(1.5440)
Control variable	Control
R2	0.494	0.325	0.625	0.105	0.182	0.0139
N	1530	1476	1494	1512	2672	334

Note: * p < 0.1, ** p < 0.05, *** p < 0.01.

.

Geographical heterogeneity: Previous studies have recognized variations in HQA across different regions [41]. To investigate whether the impact of STI on HQA varied regionally, the major grain-producing regions were split into northern and southern sections at the Qinling–Huaihe River. Separate analyses were conducted for each section. The results of the regressions showed a positive spillover effect of STI in the northern regions, while this effect was not statistically significant in the southern regions. This difference may be attributed to the significant outflow of scientific and technological talent from the northern regions, leading to a shortage of talent needed for HQA, whereas neighboring regions benefited from the influx of these professionals, improving their own HQA. On the other hand, the southern regions, characterized by a rugged terrain, complex geography, and diverse languages, faced greater challenges in promoting and disseminating STI. The geographical and cultural diversity in the south increased the associated costs, resulting in STI primarily enhancing HQA within the region, with a less pronounced spillover effect.

Heterogeneity at the development level: The level of agricultural development also requires different levels of STI. To examine whether the impact of STI varied with the level of HQA, the yearly averages of HQA were divided into cold spots (low-level) and hot spots (high-level) using the natural discontinuity method, and new regressions were conducted. The findings revealed that in regions with high-level HQA, STI had a significant positive role in promoting HQA in local and neighboring cities. On the other hand, in regions with less advanced HQA, while the direct effects of STI remained positive, the indirect effects were negative. This discrepancy was attributed to the weaker agricultural resource endowments and socioeconomic conditions in regions with less advanced HQA. The uncritical adoption and implementation of local STI practices by neighboring areas resulted in a mismatch between STI and HQA, consequently hindering the enhancement of HQA.

Heterogeneity in environmental shock: The 2020 global public health security incidents had a significant impact on agricultural production [42]. To investigate whether the effect of STI on HQA varied with sudden environmental changes, the sample was divided based on the year 2020. The findings revealed that prior to 2020, STI positively influenced HQA in both local and neighboring cities. However, post-2020, STI only had indirect spillover effects, with no direct impacts. This was attributed to emergency measures such as traffic control and restrictions on personnel movement that were implemented to contain the epidemic, which hindered STI’s role in local agricultural development. Nonetheless, the use of online communication platforms facilitated the spread of STI to neighboring areas, thereby enhancing HQA in those regions.

In summary, the impact of STI on HQA varied significantly due to changes in geographical location, the level of HQA, and the external environment, resulting in spatiotemporal heterogeneity. Consequently, Hypothesis 1 was fully validated.

4.7. Adjustment Effect of the Effective Range

The results of the benchmark regression demonstrated that STI can effectively enhance HQA in both local and neighboring cities. Building on this finding, this section delves deeper into the impact of STI on HQA. Initially, a spatial weight matrix was reconstructed using a 420 km boundary, and interaction terms related to the upgrading of the agricultural industry and human capital were incorporated into the double-fixed SDM. The estimated coefficients of these interaction terms were then analyzed to assess their moderating effects. Subsequently, considering the varying directions of STI’s spillover effects before and after 170 km, the moderating roles of agricultural industry upgrading and human capital were further investigated using 170 km as a dividing line. The detailed results are presented in

Table 8. Results of the moderating effect.

Variable	(1)	(2)	(3)	(4)	(5)	(6)
STI	0.069 *** (0.0249)	0.052 ** (0.0249)	0.072 *** (0.0246)	0.092 *** (0.0245)	0.074 *** (0.0238)	0.115 *** (0.0240)
Upgrade	1.620 *** (0.3916)	1.735 *** (0.3967)	1.598 *** (0.3933)
STI × Upgrade	1.199 *** (0.1652)	1.221 *** (0.1668)	1.167 *** (0.1664)
Hcapital				−0.005 (0.0101)	−0.0062 (0.0100)	−0.004 (0.0100)
ST I × Hcapital				0.017 *** (0.0025)	0.018 *** (0.0025)	0.017 *** (0.0025)
W × STI	0.159 (0.1159)	−0.040 (0.0455)	0.349 *** (0.1177)	0.309 *** (0.1111)	0.079 * (0.0433)	0.486 *** (0.1059)
W × Upgrade	1.428 (1.3967)	0.803 (0.7976)	−0.003 (1.5136)
W × STI × Upgrade	0.709 (0.7356)	0.807 ** (0.3683)	−0.148 (0.7857)
W × Hcapital				0.020 (0.0476)	0.082 *** (0.0209)	−0.086 * (0.0511)
W × STI × Hcapital				−0.023 ** (0.0097)	−0.0268 *** (0.0047)	0.031 *** (0.0111)
Interactive item	STI × Upgrade			STI × Hcapital
Direct effect	1.205 *** (0.1594)	1.229 *** (0.1614)	1.185 *** (0.1598)	0.018 *** (0.0024)	0.018 *** (0.0024)	0.017 *** (0.0024)
Indirect effect	0.284 (0.5840)	0.726 ** (0.3517)	−0.318 (0.6629)	−0.022 *** (0.0078)	−0.025 *** (0.0046)	0.023 ** (0.0097)
Total effect	1.490 ** (0.6042)	1.955 *** (0.3901)	0.867 (0.6947)	−0.004 (0.0081)	−0.007 (0.0053)	0.040 *** (0.0100)
Control variable	Control
ρ	−0.303 *** (0.0519)	−0.031 (0.0280)	−0.217 *** (0.0511)	−0.286 *** (0.0520)	0.008 (0.0280)	−0.221 *** (0.0510)
σ2	0.208 *** (0.0054)	0.212 *** (0.0055)	0.209 *** (0.0054)	0.209 *** (0.0054)	0.209 *** (0.0054)	0.209 *** (0.0054)
N	3006	3006	3006	3006	3006	3006

Note: * p < 0.1, ** p < 0.05, *** p < 0.01.

.

As indicated in Table 8, the direct regression coefficient of the interaction term between agricultural industry upgrading and STI was 1.199, showing a significant positive relationship at the 1% level within a 420 km range. On the other hand, the spatially lagged term of 0.709 did not pass the test, suggesting that agricultural industry upgrading moderated the influence of STI on HQA. Further analysis using partial differential decomposition revealed that the direct effect of the interaction term was 1.205, which was significantly positive at the 1% level, while the spillover effect, although positive, was not statistically significant. This indicated that the optimization and upgrading of the agricultural industry strengthened the role of STI in enhancing local HQA, but the spillover effect on neighboring areas was not yet prominent. Disaggregation within 170 km (as shown in column (2) of Table 8) showed that agricultural industry upgrading reinforced the impact of STI on HQA in both local and neighboring municipalities. However, beyond 170 km (column (3) of Table 8), the moderating effect lacked significant spillover effects. This was attributed to the market demand created by industrial transformation and upgrading for agricultural STI, leading to enhanced innovation across the industry chain. The integration of the industry and innovation chains thereby boosted the capacity of agricultural STI [43]. The advancement of knowledge, methods, and technologies in agricultural development enhanced the quality assurance of local agriculture. However, the current agricultural industry’s limited scale made it challenging to extend the impact of these advancements beyond certain geographical boundaries, limiting the demand for technology and innovation in distant cities. As a result, the regulatory effect remained confined to a relatively small area and did not extend to distant cities.

According to column (4) of Table 8, the direct regression coefficient of the interaction term between human capital level and technological innovation within a 420 km range showed a significant positive effect at the 1% level, while the spatial lag coefficient was significantly negative at the 5% level. This suggested that the human capital level could effectively moderate the impact of STI on HQA. Further analysis of the interaction term decomposition revealed that the direct effect coefficient and the indirect effect coefficient were significantly positive and negative, respectively, at the 1% level. This indicated that the human capital level enhanced the impact of STI on local HQA but suppressed its impact on neighboring HQA. Upon closer examination, within 170 km, human capital’s moderating effect remained consistent, but beyond 170 km, the enhancement of human capital strengthened the impact of STI on HQA in both local and neighboring municipalities. This was attributed to the improvement in human capital facilitating the application and transformation of agricultural STI achievements, thereby boosting the quality and efficiency of agricultural development. Additionally, the local growth of human capital may attract high-quality agricultural laborers from neighboring cities, leading to a shortage of necessary human capital in those areas and hindering their HQA enhancement. However, with the increase in geographic distance, the cost of human capital flow between cities also increased. This led to a decrease in the level of human capital flow and agglomeration, resulting in a weakening siphon effect on talent as the distance increased. Consequently, the siphon effect failed to impact human capital in more distant cities, allowing their human capital to be utilized for local agricultural production and the improvement of their HQA.

In summary, this study demonstrated that within a 420 km range, the upgrading of the agricultural industry and levels of human capital played a crucial role in moderating the impact of STI on HQA. Specifically, the impact of agricultural industry upgrading was significant within a radius of 170 km, while human capital levels exhibited a weakening effect within this radius and a strengthening effect beyond it. These findings validate Hypotheses 2 and 3.

5. Discussion

The purpose of this study was to reveal the spatial boundaries and mechanisms of the effect of STI on HQA, and provide a reference for the formulation of STI policies to promote HQA. This mainly included three aspects: the first was the analysis of the spatial spillover effect of STI on HQA, the second was the exploration of the spatial spillover boundary of STI with respect to HQA, and the third was revealing the mechanism of STI affecting HQA. We first used the super-efficient EBM-GML index to measure HQA and then studied the above problems based on the spatial Dobbin model. We found that the impact of STI on HQA had a spatial boundary, and the upgrading of the agricultural industry and the level of human capital had a significant regulatory effect. The conclusion of this study was consistent with the results of previous research on the impact of STI on agricultural development. For example, Li et al. (2022) found that STI can effectively promote the improvement of HQA in local and adjacent areas, and the spillover effect is significantly greater than the local effect [22]; Chen et al. (2022) also confirmed that there is a peer effect on the impact of STI on HQA [23]. As expected, we found that STI can promote the HQA of local areas by changing the traditional mode of agricultural production and improving the efficiency of agricultural production; with the spread of STI, this also helps improve the HQA in adjacent areas.

It is worth noting that we further explored the spillover boundaries of STI and found that the intensive area of its spillover effect is within 420 km. Unlike in previous studies, Zhang and Song (2022) suggested that within 500 km, the positive role of STI in promoting agricultural development was relatively strong, and beyond this distance, it continued to weaken until it was no longer significant [8]. However, we found that within 420 km, the impact direction of STI on HQA had an inverted U-shaped feature of first promoting and then inhibiting. The main reason was that the agricultural resource endowments of adjacent cities were relatively similar. Through the exchange and learning of STI achievements, the level of regional agricultural development can be effectively promoted. With the increase in distance, the simple imitation and replication of agricultural STI achievements may lead to a mismatch between STI achievements and agricultural development needs, causing the spillover effect of STI on HQA to go from positive to negative [24].

The main contribution of this study is that it reveals the spatial spillover boundaries and mechanisms of the impact of STI on the HQA at the prefecture level and provides a theoretical reference for local governments to formulate agricultural scientific and technological extension policies to promote HQA. The disadvantage is that, due to the lack of macro-level data, this study only reveals the average effect of STI on HQA, and fails to further explore the heterogeneity between the two systems from within prefecture-level cities, but this does not affect the reliability of the conclusions of this article. In future research, we will further collect micro-level data to explore the impact of STI on HQA from the perspective of farmers and provide a theoretical reference for policymakers.

6. Conclusions and Recommendations

6.1. Research Conclusions

This study employed the dual fixed-effect spatial Durbin model and balanced panel data from 167 prefecture-level cities in China’s major grain-producing areas from 2004 to 2021 to reveal the spatial boundaries and mechanisms of the effect of STI on HQA. The specific findings are as follows:

(1)

STI contributed to enhancing HQA in both local and neighboring cities. The conclusion remains strong even after rigorous testing for robustness, including altering weights, addressing outliers, substituting variables, adjusting samples, and addressing endogeneity issues. Furthermore, the impact of STI on HQA displays notable spatiotemporal heterogeneity due to variations in geographical location, agricultural progress, and external factors.

(2)

The spatial boundaries of the spillover effect of STI on HQA are clearly evident. It is statistically significant within a range of 420 km but loses significance beyond this distance. At approximately 170 km, the spillover effect of STI on HQA exhibits a distinct inverted U-shaped pattern.

(3)

The optimization and upgrading of the agricultural industry and human capital both play significant moderating roles in enhancing the positive impact of STI on local HQA within a 420 km radius. Within a 170 km radius, these factors effectively moderate the influence of STI on HQA in neighboring prefecture-level cities. However, beyond this distance, only the moderating effect of human capital continues to exert its influence.

6.2. Recommendations

(1)

Agricultural scientific and technological promotion policies should be formulated according to local conditions. This study found that the impact of STI on HQA has significant spatiotemporal heterogeneity. Therefore, we should formulate agricultural scientific and technological promotion policies according to local conditions to help realize HQA. On the one hand, in the major grain-producing areas in the north, the government should formulate reasonable policies to attract and retain talent so as to avoid the excessive loss of talent, which makes it difficult to apply STI achievements. In the main grain-producing areas in the south, we should establish a sharing platform for scientific and technological achievements, reduce the cost of its dissemination and diffusion, and strengthen the exchange and cooperation among cities. On the other hand, in cities with relatively high levels of agricultural development, financial and human support for STI research and promotion should be further increased to promote its positive role. In cities with a relatively low level of agricultural development, agricultural scientific and technological innovations should be developed and introduced in combination with the actual situation of local agricultural production to avoid the mismatch between the introduced scientific and technological achievements and agricultural development.

(2)

The optimization and upgrading of the agricultural industry should be promoted. This research shows that although the upgrading of the agricultural industry strengthens the positive impact of STI on the local HQA, the positive spillover effect of its regulatory effect is limited to a small range, so we should further promote the optimization and upgrading of the agricultural industry and promote the realization of its spillover effect. On the one hand, we should strengthen the construction of agricultural infrastructure, increase the density of rural public roads, and focus on their construction quality. At the same time, a co-construction and sharing platform should be built for urban and rural logistics facilities to provide material support for the upgrading of the agricultural industry. On the other hand, we should extend the agricultural industry chain and promote the deep integration of rural primary, secondary, and tertiary industries on the premise of ensuring national food security and an effective supply of major agricultural products. The government should guide leading enterprises to establish agricultural product processing plants in rural areas, save the transportation costs of raw materials, promote the circulation and operation of agricultural products in different regions with the help of public transport facilities and logistics platforms, and improve the competitiveness of agricultural products and agricultural production efficiency.

(3)

Human resources should be reasonably allocated. This study found that the improvement of the human capital level can strengthen the positive impact of STI on local HQA, but its regulatory effect has a negative spillover effect in a certain range, so we should reasonably allocate human resources and weaken its negative spillover effect. On the one hand, human resource needs should be clarified. All regions should combine with the actual situation of agricultural production, give full play to the internal advantages of the rural scientific and technological commissioner system, attract the required agricultural talent to serve in the countryside through reasonable welfare policies, and find talent according to the demand to avoid the talent highland problem caused by the excessive concentration of human resources, weaken its negative spillover effect, and help the realization of HQA. On the other hand, the allocation mechanism of human resources should be improved in different regions. Prefecture-level cities within the main grain-producing areas can be guided by the government to establish an agricultural talent information database, which can be updated and improved regularly to comprehensively grasp the main information of agricultural talents within the region. Sharing information with surrounding cities can promote the cross-regional flow of agricultural talents and avoid the excessive concentration of human resources in a certain region.