The Impact of Agricultural Credit on Planting Structure: An Empirical Test of Factor Allocation

Abstract

Rural finance provides financial support for agricultural production. Agricultural credit, as the most important rural financial resource, is designed to regulate rural economic activity and guide the rational adjustment of the rural economy and industrial structure. However, the relationship between the availability of credit to farmers and their choice of cropping behavior in the agricultural production process remains unexplored in depth. To fill this gap, this study constructs an analytical framework for ‘Agricultural credit-production factor allocation-planting structure decision-making behaviour’. Using data from a large-scale rural survey in China, this paper empirically examines the impact of agricultural credit on the specialization and ‘grain-oriented’ of farm households’ planting structure using the OLS model, the mediated effects model, and the 2SLS model. In addition, this study explores the mechanism of the allocation of agricultural production factors in this process. This has enriched the theoretical and practical research on rural finance for agricultural development. Studies have shown that agricultural credit contributes significantly to the specialization and ‘grain-oriented’ of the planting structure. The findings of the study also show that agricultural credit promotes cropping restructuring among farmers through large-scale operations, technological advancement, and green production. In addition, there are differences in the impact of agricultural credit on the planting structure depending on the type of food-producing area, the scale of operation, the development of digital infrastructure, the education of the head, and the source of credit. These findings suggest that increasing rural financial support and promoting the restructuring of land improvement, agricultural machinery, and green production factors may be an effective path to optimizing the cropping structure and improving the efficiency of production factor utilization.

1. Introduction

As a nation’s most fundamental concern, food security plays a pivotal role. In 2024, China’s grain production had another bumper harvest, maintaining a steady level of over 650 million tons for 10 years. This builds a solid foundation for advancing agricultural modernization. However, the ‘non-grainization’ in cultivated land use remains a prominent issue, driven by factors such as rural labor migration, epidemics, and agricultural policies [1,2,3,4]. Combined with the multiple unfavorable factors of international geopolitical conflicts, trade protectionism, and extreme global climate shocks, uncertainty in the food supply chain has increased. The global fight against hunger has regressed rather than advanced, and the number of people facing hunger has continued to grow for many years. Holding on to the Chinese rice bowl seems particularly daunting. The share of sown area dedicated to food grains in relation to the total sown area of China has declined from 71.42% in 2016 to 69.61% in 2022. China’s food production and demand remain in a tight balance, which is unlikely to change in the long term.

Meanwhile, the planting structure of food crops is undergoing significant shifts, highlighted by the fact that the district of maize planted and sown changes from year to year. In the agricultural context of a ‘small-scale peasant economy of the large country’, planting structure adjustment is essentially a strategic effort to maximize benefits or minimize risks. One pivotal production mode is ‘small and complete’. Although this approach is more advantageous than specialization in reducing business risks [5], it also entails certain challenges. Subject to factors such as information search and negotiation, diversified farmers need to purchase a large variety of means of production with low demand for each, thus increasing transaction costs [6]. Consequently, this contributes to increased uncertainty in food security. Therefore, encouraging the adjustment of planting structure toward specialization is of paramount importance, aligning with the broader goals of promoting Chinese-style modernization [7].

The focus on agricultural policies has garnered considerable attention within the academic community. The existing literature analyzed planting structure from multiple perspectives, such as resource endowment [8,9], technological advancement [10,11], and national policies [12,13]. First, human capital is pivotal in the transformation of conventional agriculture. The shift toward cash crops in the planting structure is contingent upon an elevated standard of human capital and enhanced adaptability to technological advancements [14]. Non-farm employment plays a key role in strengthening farmers’ food security [15] and facilitating agricultural specialization [16]. The aging agricultural population has promoted food crop cultivation [17], and the return of migrant workers has made a profound contribution to the advancement of farmer specialization. Additionally, natural resources form the foundation of agricultural production. Water endowment directly impacts planting structure adjustment [17]. Extreme climate events encourage farmers to cultivate resilient vegetable crops that are less susceptible to adverse weather conditions. Moreover, improving rural road accessibility boosts agricultural specialization [18]. Second, mechanization is a cornerstone of agricultural modernization. The incorporation of mechanical factors can reduce manual labor inputs [19], thereby enhancing production efficiency [20]. Ultimately, policies play a crucial role in shaping planting structure. The increasing cost of water, for example, may discourage farmers from cultivating high-water-consumption crops [21]. Policies such as burn bans may undermine food security [22]. Although extensive research has been conducted, the role of rural finance, particularly in the credit for agricultural activities related to planting structure, remains underexplored.

In recent years, the rapid development of the financial sector has significantly influenced rural areas and the livelihoods of farmers. The government’s financial support for the ‘three rural areas’ has also been increasing. China’s National Economic and Social Development Statistics Bulletin shows that RMB loan balances of rural financial institutions, including rural credit unions, rural co-operative banks, and rural commercial banks, have shown rapid growth over the period 2010–2023. It grew from RMB 5.7 trillion in 2010 to RMB 29.4 trillion in 2023. According to the 2017 Chinese Family Database (CFD) survey data, the share of rural households that incurred borrowing or lending in agricultural production and operation was about 17%, and the share of farm households that needed funds for agricultural production and operation was about 16%. Farmers still have financial gaps in their production and business activities. China’s Ministry of Agriculture and Rural Development (MARD) has identified financial services as a powerful initiative for impelling the production of food and crucial agricultural products. This role has been validated in the literature. For instance, Hu et al. emphasized the importance of finance in supporting quality agriculture, particularly in major grain-producing regions [23]. Ren et al. highlighted the crucial role of green finance in propelling agricultural transformation [24]. Additionally, the financial capital, such as household income, available to rural households profoundly impacts the diversification of cereal crop structures [25]. Credit constraints limit farmers’ choice of food crop varieties to grow [26]. Due to inadequate credit allocation stemming from the asymmetry in the credit market, small-scale farmers in rural China are impeded from enhancing agricultural welfare [27].

Overall, various aspects of planting structure have been extensively explored. However, there is still room for further development and refinement, including (1) that existing research on planting structure largely focuses on ‘grain-oriented’ and ‘non-grainization’ types, with insufficient attention paid to restructuring within food or cash crops (for example, reducing maize cultivation and increasing rice cultivation). Meanwhile, the literature on agricultural specialization remains limited. (2) Few studies have analyzed the impact of agricultural credit on planting structure, especially from the perspective of farmers. (3) The mechanisms through which agricultural credit affects planting structure have yet to be fully explored.

With the objective of addressing these gaps, this paper makes the following contributions:

First, from the perspective of the agricultural division, an index is constructed to assess the specialization degree of the planting structure. It is supplemented by the share of area sown to food crops. This expands the research scope.

Second, from the perspective of farmers’ credit, this paper constructs an analytical framework of “credit-factor allocation-planting decision-making behaviour”, exploring the role of financial services in agricultural factor allocation. This enriches the theoretical body of knowledge in agricultural production.

Third, utilizing representative data with a Chinese context, this paper explores whether agricultural credit has a heterogeneous effect on planting structure. This informs sustainable agricultural development in similar settings.

The remainder of this paper is organized as follows. Section 2 examines the impact of agricultural credit on planting structure and presents four hypotheses. Section 3 outlines the data and methods. Section 4 discusses the evaluation results. Section 5 concludes with key findings and policy implications.

2. Theory and Hypothesis

Whether a farmer’s cropping structure shifts towards specialization is a rational decision to maximize profits. It is also constrained by the stock of resources and the structure of their allocation, in particular by the abundance of financial resources for agricultural production. As the most important source channel of agricultural production funds, the key to analyzing the impact of agricultural credit on the degree of specialization lies in examining whether abundant agricultural production funds have optimized the allocation of agricultural production factors. Therefore, on the basis of summarizing existing studies, this paper constructs a theoretical analytical framework for the impact of agricultural credit on farmers’ planting structure. It mainly covers the direct impact of agricultural credit on the degree of specialization in the planting structure of farm households, as well as the transmission mechanism of the allocation of factors of production. Among them, the allocation of agricultural production factors in this paper mainly includes three aspects: land scale management, agricultural technology progress, and green production.

2.1. Effect of Agricultural Credit on Planting Structure

Food security has always been a top priority for the Chinese government. As the most basic and direct decision-making unit for food production, farmers’ production behavior is critical to guarantee food security. In practice, farmers’ cropping decisions are closely linked to their capital stock and the costs of agricultural production. Increased levels of rural finance can contribute to high-quality agricultural development [28]. Farmers’ demand for agricultural finance plays a crucial role in driving structural upgrades within agriculture. Stimulation from the demand side is the key to resolving the financial constraints faced by farmers and promoting agricultural industrialization [29]. Traditional smallholders are generally conservative farmers with low financing needs. They prefer small-scale, decentralized operations and a diversified planting structure [30]. In contrast, farmers with agricultural credit tend to be more risk-averse. They have a higher demand for farm income and profits through specialized production. Consequently, their willingness to scale up, industrialize, and intensify production is stronger. Meanwhile, the specialization of planting structure impacts the inputs of agricultural production. Large-scale cultivation of a single crop over time not only reduces the cost of information searching but also saves on learning and the time and transport costs of switching workplaces between plots [5]. In summary, agricultural credit can transform a planting structure from a diversified model with high transaction costs to a specialized model with reduced input costs, thereby promoting agricultural production. Therefore, this research proposes:

Hypothesis 1 (H1): 

Agriculture credit facilitates planting structure adjustment toward specialization.

2.2. Mechanisms for the Allocation of Factors of Production in Agriculture

Agricultural production factor allocation refers to agricultural production decision-making, production resources through administrative or market means, and the combination or distribution among business entities. Among other things, agricultural production resources are a collection of elements that can be utilized in agricultural production, which are the basis of agricultural production and are fundamental to the formation of agricultural products. It is difficult for a single agricultural resource to meet the needs of agricultural production development, and farmers must consider a variety of factors when making production decisions and make a rational allocation of them. In this paper, the allocation of agricultural production factors is examined in terms of land transfer, agricultural machinery, and organic fertilizer.

2.2.1. The Effects of Agricultural Credit on Planting Structure from the Land Transfer Perspective

Upgrading the agricultural industrial structure is a crucial prerequisite for achieving the high-quality development of Chinese agriculture. Land-scale operations are a key pathway for promoting changes in the agricultural structure. However, inadequate rural credit products and financial service models impede the development of land transfer markets, thereby inhibiting farmland expansion. This challenge becomes more pronounced as the scale of land management grows [31]. Agricultural credit availability to farmers is a driver of agricultural transformation and upgrading. As agricultural production funds increase, farmers’ willingness to invest in agriculture gradually shifts from small-scale purchases of agricultural materials to larger-scale investments in land expansion, such as land transfer and farmland infrastructure construction [32]. This shift further implies and suggests that, in order to enhance farm infrastructure efficiency, farmland transfer typically involves the centralized consolidation of small-scale, decentralized lands into larger, contiguous plots. This creates conditions for specialization in planting structure. By sharing various costs, including the indivisibility of production facilities, the transport costs of the means of production, and the movement costs of mechanical operations, contiguous cultivation promotes economies of scale [33]. Consequently, this process fosters industrial specialization and scaling of agricultural production. Thus, this research puts forth the following hypothesis:

Hypothesis 2 (H2): 

Agricultural credit promotes specialization in the planting structure by facilitating the transfer of land.

2.2.2. The Impact of Agricultural Credit on Planting Structure from the Perspective of Agricultural Machinery

One of the central challenges in China’s agricultural development is the low production efficiency caused by inadequate labor division. The ‘capitalization’ of traditional agriculture [34] and the substitution of labor with agricultural technology represent critical pathways for improving smallholder productivity [35]. Households with high agricultural credit may have various motivations for ‘capitalization’, and investment in agricultural machinery may be a significant avenue for this transition [36,37]. These households either adopt machinery services or purchase farm machinery for self-service to enhance productivity. Due to the ‘lock-in effect’, these households are apt to specialize in their planting structure, thus elevating the likelihood of transitioning to specialized farming operators. Furthermore, the greater the agricultural credit, the more likely it is to stimulate incentives for technology adoption, which in turn, generates market demand for related services [7]. This dynamic encourages the supply side to expand investments in agricultural machinery, driven by its profit-seeking nature. As a result, a broader range of farmers begins cultivating crops that align with the operational goals of agricultural service providers, which ultimately fosters increased specialization in regional and continuous cultivation. Consequently, this research puts forth the following notion:

Hypothesis 3 (H3): 

Agricultural credit promotes specialization in the planting structure by facilitating machinery.

2.2.3. The Impact of Agricultural Credit on Planting Structure from the Perspective of Organic Fertilizer Application

Green production in agriculture can profoundly enhance the efficiency of resource utilization and increase the yield and market price of agricultural products. According to the 2023 China Consumer Trend Report, over 70% of consumers prefer green brands, with the ‘post-90s’ generation more willing to accept premium prices for green products. By the end of 2022, the total volume of green food and organic agricultural products in China had reached 60,254. Despite the accelerated commercialization of green agricultural products in China, the development of green agriculture encounters significant challenges due to the substantial financial investment required for green transformation [38,39]. From the perspective of farmers’ decision-making, the contradiction between diversified financing demand and limited financial resources creates traditional agriculture transformation constraints. Agricultural green investment behavior is the result of farmers’ decision-making with the goal of maximizing benefits, taking into account factors such as production technology, product yields, and markets. Consequently, farmers may weigh their investments in green agricultural products across consumption expenditure categories according to their production preferences. Regarding future agriculture and green finance development, farmers with agriculture credit tend to seek to meet the green development trend, thus increasing the procurement of green agricultural materials [40]. They are more inclined to adopt specialized planting structures to achieve standardized and industrialized agricultural production and earn product premiums. The application of organic fertilizers is pivotal in ensuring product safety at the source and impelling sustainable agricultural development. Farmers with financing needs prefer to apply organic fertilizers to improve product quality, thus securing product premiums. Further, specialization has become the favored choice to reduce the transportation and storage costs of organic fertilizers. In summary, this research proposes the following hypothesis:

Hypothesis 4 (H4): 

Agricultural credit promotes specialization in the planting structure by encouraging the application of organic fertilizer.

The analysis framework is depicted in Figure 1.

3. Materials and Methods

3.1. Data Sources

The data were sourced from the 2017 dataset of the Chinese Family Database (CFD) (https://ssec.zju.edu.cn/2020/0424/c86173a3031114/page.htm (accessed on 10 January 2025)). The database is a collaborative effort between Zhejiang University and its partner institutions, encompassing data from 608 rural communities across 29 provinces in China. Stratified, three-stage random sampling was used to ensure a representative and diverse sample. The research included various aspects, such as basic information about the village, community economy, agricultural production and operation of households, land use, and agricultural/industrial and commercial credit. Since the data provided in this study are exclusively for online use, the analysis was conducted solely on the CFD platform of Zhejiang University. First, we kept the sample for rural areas only. Second, data on households, villages, and individual farmers were matched to exclude samples that did not engage in agricultural production behavior. Finally, samples with missing values and outliers for key variables were processed. After processing, this study obtained 6864 valid samples. It is estimated that about 27%, 44%, and 29% of the samples were from provinces in the eastern, central, and western regions, respectively. These data provide a better picture of China’s agricultural operations.

3.2. Measurement of Crucial Variables

In this study, planting structure specialization (PSP) was treated as the dependent variable. Specialized management practices in agricultural production aim to reduce crop variety to concentrate resources and production factors on superior agricultural products. This study followed Bradshaw’s insights. Using the Herfindhal–Hirschman Index (HHI), Bradshaw assessed crop diversity in Saskatchewan, Canada, from 1994 to 2000 [41]. In subsequent studies, Chinese scholars used this method to evaluate the degree of specialization of Chinese farmers in farming [33,42]. The HHI ranges from 0 to 1, with higher values indicating greater levels of specialization in crop varieties. An HHI of 1 represents complete specialization, where farmers focus exclusively on a specific product. Conversely, an HHI of 0 indicates full diversification. Given the characteristics of the research data, this study mainly focused on the specialization for ten major crops: rice, wheat, corn, potato, sweet potato, beans, peanuts, rapeseed, cotton, and tobacco. These 10 crops do not cover all crop varieties and will result in an overestimation of the HHI index. However, these 10 major crops are grown on a large scale, cover a wide area of arable land, and have a higher contribution to regional agricultural output. The accuracy of their data acquisition is significantly better than that of the secondary crops grown on a decentralized basis, making the results of the calculations more operational. At the same time, priority is given to measuring crops that are widely distributed and account for a high proportion of the sown area, and secondary crops are eliminated. This facilitates the focusing of policy interventions and guides the prioritization of funding, technology, and human resources to support major regions and major crops, thereby reducing the complexity of policy implementation. The calculated results are as follows:

$$HHI=\sum _{\mathrm{j}=1}^{\mathrm{n}}{S_{ij}}^2=\sum _{\mathrm{j}=1}^{\mathrm{n}}{\left({\displaystyle \frac{H_{ij}}{H_i}}\right)}^2$$

Here, S_ij represents the ratio of the sown area H_ij for crop j in the farmer i’s total sown area H_i.

Furthermore, the nature of the planting structure (‘grain-oriented’ or ‘non-grainization’) was investigated, using the ratio of the cultivated area devoted to three major grain crops (rice, wheat, and corn) to the total cultivated area as an indicator [21,43]. An increase in this value signifies a ‘grain-orientated’ planting structure (GOPS), while a decrease indicates a ‘non-grainization’ planting structure.

The explanatory variable in this paper is agricultural credit. The transformation of traditional agriculture to modern agriculture is an inevitable trend in China’s agricultural development, but the transformation and upgrading of industry require agricultural capital investment. Credit financing, bond financing, and lease financing are important sources of agricultural financing. In particular, credit, as an intrinsic driver of sustainable agricultural operations, is a major source of funding for the transformation of the agricultural industry and the adoption of new technologies [44]. There are two forms of agricultural credit: one is loans from financial institutions such as banks and credit unions, and the other is private borrowing from friends and relatives. In conjunction with the survey questions in the CFD database, this paper defines a farm household that borrows or takes out a loan as a result of its agricultural operations as an agricultural credit-available household, and vice versa, as a non-agricultural credit-available household. Thus, it reflects the agricultural credit situation of farm households.

With regard to the mechanism variables, drawing from the existing literature [8,31], the first mechanism variable is land transfer. A farmer is defined as a farmer with land transfers if he or she has transferred land from other farmers in agricultural production and used it to expand his or her operation. If the sample farmers did not transfer land, they were defined as farmers without land transfers. The second one is agricultural machinery adoption, which was evaluated based on the use of machinery in six stages of household agricultural production: land preparation, fertilization, sowing, harvesting, transportation, and pesticide application. If no machinery was utilized in any of these stages, the agricultural machinery adoption variable was assigned a value of 0. The third mechanism variable is organic fertilizer application. If the sample farmer has applied organic fertilizer in the agricultural production chain, it is marked as 1, and vice versa as 0.

The control variables were categorized into three main categories: household head, family, and village endowment characteristics [17,32]. Household head characteristics include gender, education, and political affiliation. Political affiliation refers to membership in the Communist Party of China (CPC). Family characteristics consist of four indicators: average health, the share of the agricultural labor force, and social capital. Social capital denotes the involvement of family members in village leadership roles. Village endowment characteristics are composed of four indicators: traffic condition, public information service platform, location, and the economic level of the village. In addition, regional variables were controlled for, taking into account regional differences, including the eastern, central, and western provinces. A brief description of these variables is provided in

Table 1. Descriptive statistics.

Variable	Description	Mean	SD
PSP	Based on HHI	0.6757	0.2650
GOPS	The cultivated area dedicated to three major grain crops/total cultivated area	0.8101	0.2745
Agricultural credit	Do farmers have access to agricultural credit funds (1 = yes; 0 = no)	0.1761	0.3810
Gender	1 = male; 0 = Female	0.9168	0.2762
Education	Level [1,9]	2.4844	0.9192
Political identity	Whether the head of the household is a member of the Communist Party of China (1 = yes; 0 = no)	0.4017	0.4903
Average health	The average health level of family members	3.2594	0.8325
Agricultural labor scale	Share of agricultural labor force	0.6068	0.2741
Social capital	Whether the family members of farmers have been village cadres (1 = yes; 0 = no)	0.0683	0.2523
Traffic condition	Hardening of roads leading to the center of the county	2.4599	0.5246
Platform	Whether the rural area has a public information service platform (1 = yes; 0 = no)	0.3435	0.4749
Location	The distance between the village committee and the township government (km)	7.1598	6.6688
Poor village	Whether the village is poor (1 = yes; 0 = no)	0.3164	0.4651
Land transfer	1 = yes; 0 = no	0.1578	0.3646
Machinery adoption	1 = yes; 0 = no	0.7592	0.4276
Organic fertilizer application	1 = yes; 0 = no	0.5211	0.4996

.

3.3. Methods

In quantitatively evaluating the effect of agricultural credit on planting structure, the dependent variable is continuous, so we need to establish an OLS model.

$$Y_i={\alpha }_0+{\alpha }_1{X}_i+{\alpha }_2{Z}_i+{\epsilon }_i$$

(1)

where the dependent variable Y_i, respectively, denotes PSP and GOPS. The core independent variable X_i is the characteristics of the farmer’s agricultural credit. Z_i represents the control variables. ${\alpha }_0$ is a constant term. ${\epsilon }_i$ is the error term, while ${\alpha }_1$ and ${\alpha }_2$ are the parameters to be estimated.

Since the availability of agricultural credit to farmers affects production and business decisions, however, cropping structure is closely related to farm business returns and repayment capacity, which in turn, may affect the availability of agricultural credit to farmers. In addition, although the model controls for a range of control variables, there may still be factors that are difficult to quantify directly, such as agricultural policy and level of economic development, that bias the results of the benchmark regression. To solve the endogeneity of the model, such as omitted variables and reverse causation, this paper selects instrumental variables related to agricultural credit for regression analysis, and the 2SLS model is set as follows:

$$\stackrel{\wedge }{X_i}={\rho }_0+{\rho }_{1}I{V}_i+{\rho }_2{Z}_i+{\epsilon }_i$$

(2)

$$Y_i={\sigma }_0+{\sigma }_1\stackrel{\wedge }{X_i}+{\sigma }_3{Z}_i+{\epsilon }_i$$

(3)

where IV_i stands for an instrumental variable. Regarding the selection of instrumental variables for agricultural credit, there are three main types of existing studies. One is the associated subject counterpart variables, for example, selecting variables such as the number of financial items used by the family members of agricultural households as instrumental variables. The second is to take the lagged period of endogenous explanatory variables as instrumental variables. The third is to select the mean under the same regional dimension as the instrumental variables. Due to the lack of variables in the data of this paper, such as the number of farm household financial programs used, the availability of credit, and the one-period lag of credit. Therefore, based on the peer effects theory, the village-level adoption rate of agricultural credit availability is introduced as an instrumental variable (IV) to examine its potential impact on farmers’ adoption decisions. Peer effect theory suggests that an individual’s behavior is influenced by the behaviors of others. The village-level agricultural credit availability ratio is strongly correlated with the agricultural credit traits of farm households, but this ratio is less likely to affect individual business decisions, making it a desirable instrumental variable (IV₁). In addition, to further minimize the interference of current farmers in the village-level rate, we also use as an instrumental variable the agricultural credit availability rate of the remaining farmers in the same village, excluding current farmers (IV₂).

4. Results and Discussion

The presence of multicollinearity, as a whole, cannot be called a major problem, but if the model has severe multicollinearity, it may lead to inaccurate estimation results. To address possible multicollinearity, the variance inflation factor (VIF) was used to test for multicollinearity. The larger the VIF value, the more serious the multicollinearity problem. In general, the VIF value is taken as 10 as the judgment threshold. When VIF < 10, there is no serious multicollinearity. When VIF ≥ 10, there is strong multicollinearity. Based on the test results in

Table 2. Variance inflation factors for each variable.

Variables	VIF	1/VIF
Agricultural credit	1.02	0.9809
Gender	1.04	0.9589
Education	1.10	0.9104
Political identity	1.01	0.9912
Average health	1.06	0.9444
Agricultural labor scale	1.04	0.9640
Social capital	1.03	0.9664
Traffic condition	1.01	0.9925
Platform	1.01	0.9891
Location	1.02	0.9845
Poor village	1.02	0.9810
Mean VIF	1.03	——

, it can be seen that the maximum VIF value of the explanatory variables of the model is 1.10, the minimum VIF value is 1.01, and the mean value is 1.03, the results of which are much smaller than the critical value of 10. This indicates that there is no serious multicollinearity among the variables in the model, which improves the credibility of the estimation results.

4.1. Benchmark Regression

In order to minimize the effect of factors such as heteroskedasticity, the model is estimated robustly.

Table 3. Benchmark regression results.

Variables	Model (1) PSP	Model (2) GOPS	Model (3) PSP	Model (4) GOPS
Agricultural credit	0.0562 ***	0.0170 **	0.0598 ***	0.0187 **
	(0.0079)	(0.0085)	(0.0080)	(0.0089)
Gender			−0.0191	0.0098
			(0.0116)	(0.0127)
Education			−0.0002	0.0005
			(0.0036)	(0.0039)
Political identity			−0.0142 **	0.0051
			(0.0063)	(0.0067)
Average health			−0.0037	−0.0014
			(0.0038)	(0.0042)
Agricultural labor scale			−0.0253 **	−0.0338 ***
			(0.0115)	(0.0124)
Social capital			−0.0222 *	−0.0167
			(0.0123)	(0.0130)
Traffic condition			−0.0193 ***	0.0019
			(0.0059)	(0.0065)
Platform			0.0612 ***	0.0309 ***
			(0.0065)	(0.0068)
Location			−0.0005	−0.0013 ***
			(0.0005)	(0.0005)
Poor village			0.0215 ***	0.0158 **
			(0.0066)	(0.0070)
Cons	0.7009 ***	0.8376 ***	0.7758 ***	0.8389 ***
	(0.0061)	(0.0066)	(0.0255)	(0.0283)
Regional	YES	YES	YES	YES
Observations	6864	6864	6864	6864
R-squared	0.0676	0.0161	0.0843	0.0221

Note: Robust standard errors are in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.

presents the regression results of agricultural credit on specialization and grain orientation, suggesting a positive effect of agricultural credit on both specialization and grain orientation. Diversification inevitably leads to diseconomies of scale due to the finite nature of resources. Meanwhile, output diversification exposes marketing to the same dilemma. These circumstances make diversified agricultural production activities have very high transaction costs. In order to maximize profits, farmers tend to expand the area dedicated to a particular crop to achieve economies of scale. Furthermore, under the constraints of off-farm labor transfer and rural population aging, the adoption of agricultural socialization services has become increasingly necessary, and food crops with a higher level of mechanization have become the preferred choice. Consequently, the planting structure exhibits a ‘grain-oriented’ structure. In terms of sample characteristics, farmers with agricultural credit are more likely to scale up their operations through land transfer. Their average sowing area is about 1.65 hectares, and approximately 80% of them use agricultural machinery during production, much higher than those without agricultural credit (the average sowing area of the sample was about 0.821 hectare, and about 76% of the farmers use agricultural machinery. The average sowing area of the sample farmers without agricultural credit was about 0.644 hectare, and about 75% of the farmers used agricultural machinery).

Regarding control variables, at the household head level, being a CPC party member has a significant negative effect on specialization. At the family level, farmers with a higher share of agricultural labor and those with village cadres are more inclined to diversify their cultivation. At the village level, transport conditions have a significant negative impact on specialization. The low economic level of the village and having a public information service platform are significantly and positively related to specialization. The percentage of the labor force in agriculture and village location conditions significantly and negatively affect the degree of grain orientation. The low economic level of the village and having a public information service platform have a significant positive effect on grain orientation.

4.2. Robustness and Endogeneity Discussion

4.2.1. Substitution of the Dependent Variable

The species concentrations (1/number of crop species grown) were used as substitutes for PSP, and the logarithm of the sown area of three major grain crops was used as a substitute for GOPS, respectively. The enhancement of specialization usually means that individuals engage in fewer types of activities within a given time frame. Agricultural specialization means reducing the number of types of crops grown and concentrating on the production of superior agricultural products. Therefore, the concentration in the number of types can, to some extent, reflect the degree of specialization in the planting structure of farmers. As shown in columns (1) and (2) of

Table 4. Robustness test.

Variables	OLS		Tobit Model		OLogit Model
	(1)	(2)	(3)	(4)	(5)	(6)
Agricultural credit	0.0553 *** (0.0092)	0.4483 *** (0.0326)	0.0852 *** (0.0122)	0.0594 *** (0.0193)	0.4630 *** (0.0603)	0.1498 ** (0.0669)
Cons	0.7219 *** (0.0289)	0.3019 *** (0.0917)	0.9025 *** (0.0381)	1.1172 *** (0.0621)
Control variables	YES	YES	YES	YES	YES	YES
Regional	YES	YES	YES	YES	YES	YES
N	6864	6864	6864	6864	6864	6864
F/Wald chi2	43.68	64.28	47.10	18.31	522.69	217.16
R2/Pseudo R2	0.0752	0.1237	0.0669	0.0201	0.0342	0.0146
Log pseudolikelihood					−8295.984	−7258.0984

Note: Robust standard errors are in parentheses; *** p < 0.01, ** p < 0.05.

, the effect of agricultural credit on PSP and GOPS remains significantly positive, indicating that the baseline results are robust.

4.2.2. Replacement of Measurement Models

Considering that both PSP and GOPS are continuous variables with values between zero and one, the results were re-estimated using both the Tobit and OLogit models based on the OLS estimation method. From columns (3) to (6) of Table 4, the effect of agricultural credit on PSP and GOPS remains significantly positive, confirming that the benchmark regression results are robust.

4.2.3. Regression of Instrumental Variables

There may be omitted variables in the effect of agricultural credit on planting structure, potentially leading to biased estimates in the benchmark regression. In order to address this issue, the 2SLS model was employed for re-estimation. According to columns (1) and (4) of

Table 5. Endogeneity test.

Variables	(1)	(2)	(3)	(4)	(5)	(6)
	First Stage	Second Stage	Second Stage	First Stage	Second Stage	Second Stage
IV1	0.6417 *** (0.0370)
IV2				0.5927 *** (0.0344)
Agricultural credit		0.5654 *** (0.0440)	0.2152 *** (0.0391)		0.5700 *** (0.0446)	0.2149 *** (0.0392)
Cons	0.1606 *** (0.0368)	0.6695 *** (0.0332)	0.7977 *** (0.0309)	0.1585 *** (0.0368)	0.6686 *** (0.0334)	0.7977 *** (0.0309)
Control variables	YES	YES	YES	YES	YES	YES
Regional	YES	YES	YES	YES	YES	YES
N	6864			6864

Note: Robust standard errors are in parentheses; *** p < 0.01.

, both instrumental variables IV₁ and IV₂ in the first-stage regression results are correlated with agricultural credit and are significant at the 1% statistical level. The first-stage F-statistic values all exceed 10. None of the models suffer from weak instrumental variable problems. The results of the second-stage regression demonstrate that agricultural credit significantly promotes planting structure specialization and grain orientation, and the direction of the coefficients of the core independent variable remains consistent and significant with the benchmark regression results, verifying that the instrumental variables are valid and that the benchmark regression results are robust.

4.3. Mechanism Analysis

This section investigates the mechanisms through which agricultural credit influences PSP, specifically, the changes in production factors, including land transfer, agricultural machinery adoption, and organic fertilizer application.

Column (1) of

Table 6. Mechanism analysis.

Variables	(1) Land Transfer	(2) Machinery	(3) Organic Fertilizer
Agricultural credit	0.0862 *** (0.0131)	0.0648 *** (0.0126)	0.0396 *** (0.0154)
Cons	0.0605 * (0.0351)	0.3519 *** (0.0431)	0.2853 *** (0.0492)
Control variables	YES	YES	YES
Regional	YES	YES	YES
N	6864	6864	6864
R2	0.0187	0.0654	0.0514

Note: Robust standard errors are in parentheses; *** p < 0.01, * p < 0.1.

exhibits the impact of agricultural credit on land transfer. The results indicate a positive relationship at the 1% significance level, suggesting that agricultural credit contributes to an increased degree of PSP through land transfer. Agricultural credit reflects farmers’ production willingness, and those with credit are inclined to leverage land as a transferable asset, thus adjusting their planting structure. In other words, farmers with agricultural credit are more dependent on farmland and have strong incentives to expand the size of their land by transferring into it, thus promoting specialization. Column (2) shows that the effect of agricultural credit availability on the adoption of agricultural mechanization is significantly positive at the 1% statistical level, demonstrating that agricultural credit substantially enhances specialization by promoting agricultural machinery adoption. Mechanization is a critical component of agricultural development, and farmers with sufficient capital for production tend to improve productivity through enhanced machinery adoption.

Column (3) shows that agricultural credit positively influences the application of organic fertilizers at the level of 1%, implying that agricultural credit remarkably promotes the specialization of planting structures by encouraging the use of organic fertilizers. Organic fertilizer application is vital for enhancing soil fertility and the quality of agricultural products. The transition from chemical fertilizers to organic fertilizers requires additional time and capital investment. Moreover, organic fertilizers are susceptible to external environmental factors during storage and transportation. Crops have varying micronutrient requirements, necessitating the use of distinct types of organic fertilizers. The diversification of agricultural production increases the costs associated with purchasing, storing, and transporting agricultural materials, whereas specialized production facilitates improvements in land quality and labor productivity.

4.4. Heterogeneity Analysis

This section explores the heterogeneity based on the functionality of China’s grain production areas, the scale of operations, and the digital infrastructure. The results are reported in

Table 7. Heterogeneity analysis of regions and scale.

Variables	Major Grain-Producing Regions		Scale
	(1) Yes	(2) No	(3) <2 Hectares	(4) ≥2 Hectares
Agricultural credit	0.0848 *** (0.0107)	0.0293 ** (0.0117)	0.0507 *** (0.0087)	0.0518 ** (0.0222)
Cons	0.6378 *** (0.0347)	0.8810 *** (0.0367)	0.7976 *** (0.0265)	0.4948 *** (0.1053)
Control variables	YES	YES	YES	YES
Regional	YES	YES	YES	YES
N	3881	2983	6346	518
R2	0.0988	0.1557	0.0836	0.1047

Note: Robust standard errors are in parentheses; *** p < 0.01, ** p < 0.05.

and

Table 8. Heterogeneity analysis of digital, education, and source.

Variables	Digital		Head of Household Education			Source
	(1) Yes	(2) No	(3) Low	(4) Medium	(5) High	(6) Formal
Agricultural credit	0.0607 *** (0.0087)	0.0449 ** (0.0204)	0.0542 *** (0.0109)	0.0665 *** (0.0129)	0.0600 ** (0.0289)	0.0412 *** (0.0095)
Agricultural credit × Formal						0.0578 *** (0.0149)
Cons	0.7640 *** (0.0277)	0.8428 *** (0.0696)	0.7915 *** (0.0327)	0.7479 *** (0.0457)	0.6932 *** (0.0858)	0.7829 *** (0.0256)
Control variables	YES	YES	YES	YES	YES	YES
Regional	YES	YES	YES	YES	YES	YES
N	5958	906	3640	2490	734	6864
R2	0.0840	0.1220	0.0965	0.0825	0.0541	0.0861

Note: Robust standard errors are in parentheses; *** p < 0.01, ** p < 0.05.

.

4.4.1. Analysis of Heterogeneity in Major Grain-Producing Regions

Cultivation decisions on agricultural land cannot be separated from specific spatial and temporal conditions and national policy support. In China, the role of each province in guaranteeing national food security may influence the planting structure. In this study, the full sample was divided into major grain-producing regions and non-major grain-producing regions. Columns (1) and (2) of Table 7 demonstrate that the impact of agricultural credit on PSP is significant at the 1% and 5% statistical levels for farmers in main and non-main production areas, respectively. Moreover, the coefficients for the main grain-producing areas are larger than those for the non-main grain-producing areas. This disparity likely stems from the concentrated availability of credit resources and production inputs in major grain-producing regions, which promote specialization in the planting structure by channeling resources toward dominant agricultural products. In recent years, China has attached great importance to the issue of national food security and has continued to increase its support for large grain-producing counties, tilting the allocation of credit resources in favor of large grain-producing counties in the main grain-producing areas. At the same time, the gross agricultural output value of the provinces in the main grain-producing areas accounts for a larger share of the regional GDP, and there is more room for adjusting the structure of agricultural production. They are more easily contiguous.

4.4.2. Analysis of Heterogeneity in the Scale of Operations

Farmers in China with less than 2 hectares of land are at the bottom of specialization. The small-scale, decentralized production model will continue in the long term. A threshold of 2 hectares of sown area is used to distinguish between small-scale and large-scale farmers. The results in columns (3) and (4) of Table 7 indicate a positive effect of agricultural credit on PSP for both small-scale and large-scale farmers, with statistical significance at the 1% and 5% levels, respectively. This suggests that, when farmland is operated on a small scale, and side employment is the primary livelihood strategy for farm households and is dominated by non-farm employment, agricultural specialization can reduce the cost of farm purchases and allow farmers to allocate more time to non-farm work. For large-scale farmers, the increase in labor costs prompts them to weigh the production costs of various crops and expand the share of high-yield crops. Since high-yield crops usually exhibit lower levels of mechanization, they pose greater challenges to agricultural technologies. Specialization is conducive to saving labor costs, advancing agricultural technologies, and improving business returns.

4.4.3. Analysis of Heterogeneity in Digital Infrastructure

Broadband, as one of the most accessible and efficient digital technologies, lays a critical foundation for agricultural production and marketing. In this study, the entire sample was divided into regions with well-developed and underdeveloped digital infrastructure according to the existence of broadband installation in farm households. The results in columns (1) and (2) of Table 8 show that the effect of agricultural credit availability on specialization in cropping structure is significant at the 1% and 5% levels for farmers with perfect and imperfect digital infrastructure, respectively. This may be attributed to the fact that the development of digital infrastructure encourages farmers to acquire information about production and marketing. Through broadband, farmers can swiftly grasp the market situations of agricultural products, identify high prices for agricultural products, and adjust their planting structure in a timely manner, thus enhancing efficiency. At the same time, through broadband, farmers can more easily improve their production techniques and search for lower-cost agricultural materials, thus saving costs.

4.4.4. Analysis of Heterogeneity in Education

The low level of human capital in rural areas and the difficulty of coordinating it with technological progress are important factors affecting the structural reform of the agricultural supply side. The study categorized the sample into the three levels of low, medium, and high education according to the educational qualifications of the head of the household and excluded the control variable of the educational qualifications of the head of the household from the model. The results in columns (3) and (4) of Table 8 show that the effect of agricultural credit on specialization is significant at the 1% level for farmers in the low and medium education groups and at the 5% level for those with high education. The possible reason for this is that farmers are rational economic agents whose level of education influences the adoption of agricultural technology, the ability to access market information, the opportunity cost of labor, and thus, decision-making toward planting behavior. Farmers in the low and medium education groups have a weaker ability to understand and apply market signals, agricultural policies, and new agricultural technologies, as well as a lack of off-farm employment skills and a high level of dependence on agriculture, which may be based on the inertia of growing one or more crops to reduce risk. Highly educated farmers have a higher level of human capital, implying a stronger profit motive for agriculture, and they are more capable of absorbing and applying new knowledge and technology. To reduce the cost of information collection and agricultural procurement, it cooperates and communicates more frequently with agricultural service organizations. At the same time, as highly educated farmers tend to actively learn to imitate professional organizations and to improve agricultural returns, they are more likely to obtain financial support through non-credit channels of financing (such as intra-cooperative fund transfers, contract farming), reducing their dependence on agricultural credit financing.

4.4.5. Analysis of Heterogeneity in Credit Source

Constrained financing of agricultural production is a shortcoming of China’s rural finance, and financial support from formal institutions, such as banks or credit unions, is an important means to promote high-quality development in the plantation industry and rural revitalization. To examine the impact of formal credit financing channels on planting structure, this paper introduces formal variables based on the source of funds. That is, the access of farmers to financial support through banks or credit unions is assigned a value of one and zero otherwise. Then, the interaction term between agricultural credit and formal sources is constructed for estimation. The results in column (6) of Table 8 show that the interaction term between agricultural credit and formal sources has a positive effect on PSP and is significant at the 1% level. This suggests that the level of PSP is significantly higher for households benefiting from formal credit sources than for those not benefiting when farmers are engaged in agricultural production operations through loans. The likely reason for this is that financial institutions are profit-seeking, and banks tend to choose farmers with low transaction costs and stable returns for lending to maximize their interests. The decentralized operating pattern raises the cost of obtaining information for banks. Meanwhile, China’s policy on lending focuses on supporting the financial supply of important agricultural products and expanding financial service scenarios for the whole industrial chain. This has also effectively promoted the adjustment of agricultural production to specialization, scale, and industrialization.

4.5. Discussion

4.5.1. Key Findings

We briefly discuss the main findings of the empirical results. It is worth noting that agricultural credit contributes to both specialization and “grain-orientation”. However, there is no necessary causal relationship between the two, and they may show synergy or vice versa. Specialized planting is often based on resource endowment through the optimal allocation of production factors to achieve large-scale, centralized planting of advantageous crops. Its core lies in improving production efficiency and reducing production costs through specialization. Grain orientation reflects the upward trend in the share of sown area, inputs, and outputs of food crops in the agricultural production system in the process of structural adjustment of agricultural cultivation. While agricultural credit facilities provide new opportunities for agricultural production, the long cycle of cash crop production and the high demand for labor will further exacerbate the financial constraints faced by farmers once they expand their production. At the same time, since the price of organic fertilizer is much higher than chemical fertilizer, and the amount of fertilizer needed for cash crops such as fruits and vegetables is greater than that for food crops, it is difficult for farmers growing cash crops to have sufficient funds to purchase organic fertilizer. In contrast, growing cash crops, although more costly, is far more profitable than growing food. Thus, in the context of specialized production by farmers through credit funds, “grain-orientation” or “non-grainization” is a comprehensive consideration of the repayment risk, expected returns, and resource endowment of farmers. For example, as an important grain-producing area in China, the black-soil region of the northeast has a vast expanse of land that is suitable for large-scale mechanization. Coupled with policy support, such as arable land use control and grain subsidies, specialized planting is likely to promote the development of the planting structure in the direction of food crops, i.e., to show a “grain-orientation”.

4.5.2. Extensibility Analysis

Currently, the aging of China’s rural population and the defarming of the young and strong labor force are prominent [45]. It has been noted that the rising opportunity cost of labor due to agricultural labor migration motivates farmers to plant more mechanized crops and thus increase productivity [46]. Households with off-farm employment are likely to expand their cereal area to cope with liabilities and income instability arising from migration [32]. That is, non-farm employment drives farmers to expand food production and increase production efficiency [5]. The aging of the rural population impairs agricultural production and leads to the abandonment of farmland, and the more severe the aging, the greater the likelihood of abandonment [47]. China’s labor migration and population aging have become irreversible trends. The impact of agricultural credit, as a more operational financial capital, on agricultural production is more worthy of in-depth exploration than factors that are difficult to reverse, such as nonfarm employment and aging. This study makes several innovative contributions to analyzing how agricultural credit affects cropping structure. In particular, this is through the intermediation of three key production factor allocations: land transfer, agricultural machinery, and organic fertilizer application. These aspects will be examined in depth below to explore the mechanisms of their impact on cropping structure under the influence of agricultural credit.

First is the indirect impact of land transfer. This study finds that land transfer plays an important mediating role between agricultural credit and cropping structure. The development of rural finance has met the demand for loans for farmers’ production and operation, broadened the channels of agricultural financing, and promoted the growth of farmers’ agricultural income. Farmers’ access to agricultural credit funds solves the problem of a shortage of funds for agricultural production, thus encouraging farmers to transfer to land, develop moderate-scale operations [48], and improve agricultural returns. Compared with traditional smallholder farmers, scale creates conditions for the application of modern agricultural technology, increases labor productivity [31], and promotes the transformation of planting structure. And with the continuous improvement of China’s land system reform, the land transfer market is gradually maturing, making large-scale, continuous production possible. However, there is a lack of items used as collateral security for agricultural operations. At the same time, small farmers, family farms, and other agricultural business entities have unsound financial systems, low product standardization, and weak risk-resistance, making it difficult for business entities to obtain agricultural credit funds, thus exacerbating the irrational use of agricultural land. Thus, farmers’ access to agricultural credit funds can indirectly contribute to the continuous optimization of the cropping structure by stimulating farmers to transfer land and expand the scale of agricultural production. This finding is consistent with the existing literature. The optimal reorganization of land resources can accelerate agricultural transformation, especially for farmers with access to agricultural credit facilities.

Second is the indirect impact of agricultural machinery. While there is a significant positive impact of credit finance on cropping structure, it should also be noted that the estimated coefficient for mechanization is not high. In reality, the use of credit funds as the main method of financing has created conditions for increasing the level of agricultural mechanization. However, the current specialization of smallholder farmers in China is far from reaching the level corresponding to the “technical efficiency inflection point” [49]. The reason for this, first, is that a large amount of China’s rural light and strong labor force has been transferred. It is difficult for the existing agricultural population to absorb new technologies and equipment. This is coupled with the dilemma of an aging workforce, low levels of education, a shortage of effective labor, and financing constraints. Farmers either urgently pursue income from agricultural operations and turn to cash crops with high yields and low mechanization, or they pursue non-farm income and plant field crops with a relatively high level of mechanization and less time spent in agriculture. Second, the use of agricultural machinery needs to be premised on topography and landscape, and most large machines are more suitable for farming in plains. In recent years, agricultural services, mainly contract farming and production trusteeship, have significantly increased the mechanization rate and improved overall production capacity [31,50]. Guiding contiguous planting through specialized agricultural services is an important path that responds to the transformation and development of China’s agricultural business model. Agricultural credit funds also provide important support for the purchase of agricultural services by farmers.

Finally, there are the indirect effects of organic fertilizer application. This study found that organic fertilizer application plays an important mediating role between agricultural credit and cropping structure. Organic fertilizer substitution technology is a key means to improve soil quality and the quality of agricultural products and plays a crucial role in improving the rural ecological environment and promoting sustainable agricultural development. Since the price of organic fertilizers is much higher than that of chemical fertilizers, and different crops require different varieties of organic fertilizers, this not only increases the cost of storing and transporting organic fertilizers for farmers but also exacerbates technical barriers to agriculture. Economic capital improves the ability to adopt new technologies, resilience to technological risks, and profoundly affects production behavior. The stronger the capital, the more farmers tend to adopt alternative technologies to organic fertilizers [51]. Compared to farmers who do not adopt organic fertilizers, those who apply organic fertilizers will spontaneously reduce crop varieties and expand the scale of cultivation of the same crop to reduce the cost and technical constraints of converting sites for production materials. Facilitate the capture of agricultural premiums by farmers through technical support and large-scale operations to increase comparative returns to agriculture. As a result, farmers’ access to abundant capital through agricultural credit can promote organic fertilizer application and indirectly promote cropping structure adjustment. This finding is consistent with the existing literature that economic capital endowment directly affects the intensity of farmers’ investment in organic fertilizer [52], which in turn, affects farmers’ production behavior.

4.6. Research Limitations and Perspectives

It is worth noting that our study has some limitations, and future ones can further explore in many ways. First, this study uses cross-sectional data, limiting data generalizability to the time dimension in the future. Second, while this paper analyzes the impact of agricultural credit on cropping structure, it does not specifically measure and infer the extent of its impact. On this basis, in the future, indicators such as climate change, credit limits, interest rates, types of guarantees, and farmers’ perception of credit can be identified for more precise quantitative analysis. In addition, land transfer, agricultural machinery, and organic fertilizer application were selected as mediating variables in this paper, but the strength of the effect was not discussed in detail. In the future, we can determine more precise effect values through refined variables such as the area of land transferred in and out, the cost of agricultural mechanization, and the cost of organic fertilizer application, to further clarify the causal relationship. Finally, there is further incorporation of digital technologies in the context of artificial intelligence and the big data boom. We aim to conduct field research in a follow-up study to explore factors such as climate change risk, digital technology enablement, and policy interventions, which will further enhance the usefulness of the study, and to expand the study to the time dimension, using panel data to examine the long-term dynamic effects.

5. Conclusions and Policy Implications

5.1. Conclusions

Based on the significance of China’s food security and the rapid expansion of rural finance, this paper empirically examines the impact of agricultural credit on planting structure using data from the CFD. The findings uncover the following key insights:

(1)

Agricultural credit has a positive effect on specialization and grain orientation;

(2)

Agricultural credit further influences planting structure by shaping the drivers behind changes in production factors. Specifically, the transfer of agricultural land, the adoption of machinery, and the application of organic fertilizers play a mediating role in this process;

(3)

Heterogeneity analysis identifies significant differences in the impact of agricultural credit on planting structure between the functionality of food production areas, scale of operations, digital infrastructure development, education of heads, and the source of credit. The agricultural credit contributes more significantly to specialization in food-producing regions, while contributing relatively less to non-food-producing regions. Agricultural credit contributes significantly to the specialization of both smallholders and large-scale households, and more so for large-scale households. Agricultural credit has a significant effect on specialization for both perfect and imperfect digital infrastructure farmers, but the coefficient is larger for perfect digital infrastructure farmers. Agricultural credit contributes significantly to the specialization of the cropping structure of farmers in low, medium, and high education groups, but with different levels of significance. Households benefiting from formal sources of credit have a higher level of specialization when farmers have agricultural credit funds.

5.2. Policy Implications

First, agricultural information should be disseminated through multiple channels, such as television, radio, newspapers, and magazines, guiding farmers toward cultivating specialized agricultural products. The government can propel the adjustment of agricultural production toward industrialization, branding, and specialization by creating characteristic and advantageous agricultural industrial belts.

Second, joint efforts should be made to resolve credit constraints from both the supply and demand sides. On the supply side, the government should promote structural reforms in rural finance, encourage diverse entities to participate in rural financial services, and foster financial product innovation. The focus is on increasing financial support for agricultural service organizations and encouraging co-operatives, agribusinesses, and other organizations to provide interest-free and low-interest financial loan services to alleviate the financial constraints of farmers in agricultural production. At the same time, it is also necessary to strengthen agricultural technology training and insurance services and to improve the agricultural production skills of farmers, especially large-scale households. To increase the innovation and transformation of agricultural scientific and technological achievements, utilizing agricultural insurance, resources are actively tilted toward large-scale households. On the demand side, a focus should be placed on cultivating a group of rural financial leaders. Through the combination of active publicity and demonstration, farmers’ demand for credit should be directed toward large-scale and specialized cultivation areas, thereby accelerating the process of Chinese-style agricultural modernization.

Third, comprehensive land improvement initiatives should be implemented across regions. Farmers should be encouraged to advance the centralized improvement of arable land, creating conditions for agricultural mechanization. It should also actively research and develop new types of agricultural machinery, increase the adaptability of agricultural machinery to terrain and crop varieties, and improve the efficiency of resource utilization. Additionally, attention should be paid to the innovative allocation of resources, adapting to local conditions, coordinating the promotion of arable land upgrading and renovation, and training professional farmers. These actions will help accelerate agricultural productivity development and guarantee national food security.