The Role of Contract Farming in Green Smart Agricultural Technology

Abstract

Promoting green agricultural production is becoming increasingly important in order to address resource and environmental issues and meet consumers’ demand for safe agricultural products. Green agriculture mainly refers to the adoption of green, smart agriculture technologies in agricultural production. Farmers are the main body of agricultural production, so guiding farmers to adopt green, smart agriculture technologies is of great significance for safeguarding the ecological environment. However, due to the combined influence of internal and external factors, the current level of adoption of green, smart agriculture technologies by farmers is not high. Contract farming can have an impact on farmers’ production behavior through various channels, such as guiding and standardizing production, and may become an internal driving force for improving the adoption of green, smart agriculture technologies. How do farmers make contract farming selection decisions? How does the choice of contract farming affect farmers’ adoption of green, smart agriculture technologies? Further research on the above issues can provide useful references for promoting the development of green agriculture and ensuring the quality of agricultural products in China. Against this backdrop, this paper, using research data about rice farmers in the Jiangsu Province of China, adopted a gradual regression method for checking the mediating and moderating effects to discover the mechanism of how contract farming influences rice farmers’ choice of green, smart agriculture technologies. The research results showed that: (1) contract farming has an evidently positive influence on farmers’ choice of green, smart agriculture technologies; (2) the high ecological value standard plays a complete mediating role in the process of contract farming influencing farmers’ choice of green, smart agriculture technologies; and (3) the moderating effect of planting rice income exists and is significant. When the income from cultivation is high, the positive relationship between the high ecological value standard and farmers’ choice of green, smart agriculture technologies is strengthened. Income from rice cultivation does have a moderating effect. Therefore, the government can actively guide farmers to participate in contract farming by increasing its publicity and support of contract farming. The implementation of the high ecological value standard in contract farming should be refined. The government should complete the mechanism for selling quality agricultural products at good prices. Contract farming can make farmers and corporations involved have deep cooperation, increase the non-agricultural income of farmers, and further enhance the overall income of their households. Through these measures, the development of green agriculture can be achieved.

1. Introduction

At present, China is undergoing a transition from traditional agriculture to modern agriculture, and the negative externalities of the extensive production methods in traditional agriculture are becoming increasingly evident. The announcement about issuing the 2021–2025 Plan for National Agricultural Product Quality and Safety Improvement by the Ministry of Agriculture and Rural Affairs (MARA) specifies that the quality and safety of agricultural products should be highly valued, and flourishing agriculture through green agriculture should be promoted and implemented by reducing the use and increasing the effect of fertilizers and pesticides [1].

In 2020, the output of agricultural fertilizers in China reached 54.96 million tons (net), the use of agricultural fertilizers hit 52.507 million tons, the output of chemical pesticides was 2.148 million tons (100% of effective components), the use of pesticides amounted to 1.313 million tons, and the use of agricultural membrane registered 2.389 million tons [1]. These values decreased slightly after MARA issued the announcement of the Action Plan for Zero Increase in Fertilizer Usage After 2020 and the Action Plan for Zero Increase in Pesticide Usage After 2020. However, the decline was minor, and some even bounced back in individual years. Overall, the use of pesticides and fertilizers per Mu (Chinese unit of area, equivalent to 1/15 or about 0.066 hectares) in China is rather high. The effect of policies on tackling agricultural pollution is limited.

Contract farming refers to a form of agricultural operation in which farmers sign legally effective production and sales contracts with enterprises or intermediary organizations before agricultural production, thereby determining the rights and obligations of both parties. Farmers organize production according to the contract, and enterprises or intermediary organizations purchase products produced by farmers according to the contract. Contract farming, as an important form in the industrial chain development of agriculture, can transfer the production and operation risks of the operating entity of rice cultivation and effectively reduce the income fluctuation of farming households, which will improve their incomes [2]. Moreover, contract farming provides a standardized production process and advanced production technologies, which can optimize the links in the workflow of the operating entities and promote the use of green, smart agriculture technologies [3,4]. Apart from these, contract farming, as an important model innovation, enhances farming households ability to choose and effectively use modern production technologies to improve production [5].

Apart from the positive impact of contract farming on proliferating farming technologies, the forms of contracts also have different influences on proliferation [6,7]. Contract farming with corporations produces better results than that with third parties such as cooperatives [8]. Among all farming technologies, those designed for safe production are promoted the most evidently by contract farming. In terms of the content of contract farming, quality testing, quality encouragement, and technical guidance in contract farming can effectively improve farmers’ green production [9,10].

More and more scholars pay attention to the impact of contract farming on farmers’ green, smart technology adoption behavior. Most scholars believe that contract farming is conducive to promoting the use of green, smart technology by farmers [11,12,13,14]. At present, the research on contract farming and agricultural green technology adoption mainly focuses on the following two aspects: First, contract farming affects the adoption of agricultural green technology. Miyata et al. [11] pointed out that farmers in developing countries can use advanced equipment and technology for production and gain economies of scale after signing contracts with large companies. Zhang Kun [12] found that establishing a close contractual relationship between enterprises and farmers does not only directly improve the income level of farmers but also provides high-quality production factors and advanced production technology to farmers through contractual production. Based on the survey of 754 pig farmers, Pan Dan [13] analyzed the influencing factors of contract farming, such as pig farmers’ green technology adoption behavior, and found that industrial organization is an important factor affecting green technology adoption. Tan Yongfeng [14], using surveyed data from 468 pig farmers, found that contract farming participation can promote the green production transformation for farmers.

Second, contract farming improves farmers’ safe production behavior. Based on the data collected from 410 farmers, Lin Li [15] used the propensity score matching model to evaluate the impact of contractual relationships on the safe production behavior of farmers in China. The results found that contractual agriculture had a positive impact on the safety of farmers production [15]. Based on field experiment data on broiler farmers in China, Mao Hui [16] found that pollution from livestock and poultry is the main source of rural pollution, which directly affects the rural ecological environment as well as the quality and safety of agricultural products. This paper analyzes farmers’ cleaner production behavior from the perspective of incomplete contracts and social trust and also finds that contract farming can promote farmers’ cleaner production behavior [16]. Vilber [17] found that contract farming, a common strategy among multi-national companies in the global food sector, can help achieve safe agricultural production behavior as part of the world’s sustainable development goals (SDGs). However, it largely depends on whether or not small farmers can participate in contract farming programs, which has been a major issue globally. Our main objective in this systematic literature review is to identify the factors that drive small farmer participation in contract farming.

This paper has the following two contributions and new ideas: First, the research on the adoption mechanisms of green and smart agricultural technology has been expanded. Most of the existing studies focus on the impact of contract farming on agricultural green technology adoption but fail to reveal the mechanism and implementation path of contract farming. This paper illustrates the mechanism of contract farming and the adoption of agricultural green, smart technology through the study of mediating effect and moderating effect, which is a good supplement to the existing research. Second, this paper extends the research objective to food crops. There are few discussions on contract farming of food crops [15,16]. In 2022, the proportion of grain crops in Jiangsu’s total cultivated area reached 77.64% [1], and the cultivated area is huge. Therefore, taking rice as the research object has certain theoretical and practical value.

China is a major producer of rice in the world, and rice is the staple crop in China. According to the Structural Adjustment Plan for the National Cultivation Industry (2016–2020), the area of rice crops reached thirty million hectares [1]. Therefore, rice cultivation has the capability to promote green, smart agriculture technologies. Jiangsu Province boasts a large area of rice crops, but due to the sufficient water and warm weather in the plant environment, rice, compared to other crops, is subject to more plant diseases, pests, and weeds. As a result, more pesticides are used. The area where rice is produced is in a river network, and flood irrigation is usually used, which means a large number of pesticides infiltrate into the soils and drain into rivers, lakes, and seas. As such, the source of pollution caused by rice pesticides cannot be underestimated. Meanwhile, the per capita GDP of Jiangsu Province hit 21,200 US dollars in 2021, exceeding the threshold of income of developed countries, which had reached the economic level of developed countries [1]. Therefore, it has the economic capability to promote green, smart agriculture technologies. Promoting the green development of rice production and building a “great green barn” in a high-level manner is an important measure to maintain the harmony between humans and nature, accelerate the creation of ecological civilization, and ensure food safety in China. The measure can also lay a solid foundation for the well-being of the present and future generations of the Chinese nation; thus, it embodies both practical and historical significance [18,19].

Therefore, does contract farming facilitate the rice farmers’ transition to green, smart agriculture? What factors influence their behaviors in such a transition, and how do they work? Against the backdrop of strong advocation for green agricultural development in China, the topic of how to help farmers lower carbon emissions in rice cultivation and achieve green, smart cultivation is worth studying.

2. Theoretical Analysis and Research Hypothesis

2.1. Influence of Contract Farming on Farmers’ Choice of Green, Smart Agricultural Technologies

With the aid of the industrial chain organizational model, contract farming provides advanced production technologies and real-time agricultural consultation capable of expanding the applications of new technologies, products, or varieties. Therefore, enterprises involved in the contracts can provide farmers with production technology services related to new varieties of rice crops, cultivation technologies, and technologies for disease and pest elimination through speeches, demonstrations, and guidance [20,21]. As a result, contract farming can promote the development or advancement of green and low-carbon agriculture. Based on this, Hypothesis 1 (H1) is proposed, which is that contract farming has a positive influence on farmers’ choice of green, smart agriculture technologies.

2.2. High Ecological Value Standard Serves as a Medium between Contract Farming and Farmers’ Choice of Green, Smart Agricultural Technologies

Many farmers are running medium-scale rice cultivation paddies. Their cultivation behaviors are more standardized and modern, but they still face problems in production practices such as scattered paddies, lack of cultivation standards, and a low management level. It is hard to align the cultivation standards since the standards are conveyed from the town level-one cultivation conservation stations and agricultural technology stations to the farming entities at certain scales [22]. Conversely, contract farming normally has certain cultivation standard systems and other advantages, such as converged production and normalized management. Contract farming can regulate and optimize the productive behavior of rice planting, which is conducive to the adoption of green and smart agricultural technology by farmers [23]. Based on this, Hypothesis 2 is proposed, which is that contract farming indirectly influences farmers’ choice of green, smart agriculture technologies through the medium of high ecological value standard.

2.3. Moderating Effect of Crop Cultivation Income on the Process of High Ecological Value Standard

Farmers whose income from crop cultivation is high usually have higher overall incomes and accumulate enough family assets. They are less constrained by economic factors and are able to bear operational and technological risks to a certain extent [24]. The economic accumulation by farmers with high incomes can provide certain material guarantees for farmers to choose green, smart agriculture technologies.

The purpose of farmers using green, smart agriculture technologies is to enjoy the benefits of premium green, organic, and quality agricultural products and increase their overall income from crop cultivation [25,26,27]. Based on the above analysis, this paper proposes Hypothesis 3, which is that crop cultivation income can moderate the influence of high ecological value standards on farmers’ choice of green, smart agriculture technologies. The logical framework of this paper is shown in Figure 1.

3. Data Source, Model Building, and Variable Setting

3.1. Date Source

In this paper, the key data used in the study was collected by sorting statistical materials based on field research in rice production areas [in the Jiangsu Province]. The Jiangsu Province was chosen as the research area for the following reasons: For one thing, Jiangsu Province is one of the four major rice cultivation areas in China and a key working area to facilitate the modern development of the rice industry and improve people’s well-being. For another, the province has a time-honored history of rice cultivation, a prosperous economy, the advantages of the new organization model and adoption of green, smart agriculture technologies, and a variety of rice contract farming models, making it suitable for conducting this study.

The data used in this paper was collected from household surveys in rural areas of the Jiangsu Province from January 2020 to December 2021. The survey personnel conducted face-to-face interviews with farmers based on questionnaires, with a focus on farmers’ choices of using green, smart agriculture technologies. The survey adopts a multi-stage random sampling survey method to select sample counties and farmers, and a total of 816 samples are recovered. Samples with missing and abnormal data are removed, and a final total of 782 valid questionnaires is obtained. The effective rate of the questionnaire was calculated at 95.83%.

3.2. Characteristics of the Respondents

The sample features are listed in

Table 1. Basic characteristics of farmers.

Variable	N	Mean	Sd	Min	Max
Gender (Male = 1; female = 0)	782	0.9309	0.2537	0	1
Age (year)	782	52.9962	8.6871	20	82
Educational level (year)	782	9.016	3.0121	0	18

. In terms of gender, 93% of the sample is male because the survey mostly interviewed household heads. The average age of farmers is 53 years because a major part of the labor force in agricultural production consists of the elderly, and the proportion of young people is small. The average length of education the interviewees received was approximately 9 years, which is in line with the fact that most of them had finished junior high school education. The most educated farmer has a master’s degree or 18 years of education, which means some well-educated people are also willing to participate in agricultural production. The distribution of sample features is reasonable, so the sample can be representative of farmers in the area.

3.3. Variable Definitions and Descriptive Statistical Analysis

3.3.1. Independent Variable

The independent variable in this paper is the green, smart agriculture technologies, which, as defined by this paper, include seven indicators: (1) whether to use physical prevention and control technologies (such as colored panel traps and insect light traps) (no = 0, yes = 1); (2) whether to use biological prevention and control technologies (using insects or fungus to kill pests). (no = 0, yes = 1); (3) whether to use unmanned devices (including drones and unmanned operation machines). (no = 0, yes = 1); (4) whether to adopt the straw returning technology (no = 0, yes = 1); (5) whether to use the water-fertilizer combination technology (no = 0, yes = 1); (6) whether to use formula fertilization by soil testing (no = 0, yes = 1); and (7) whether to utilize organic fertilizers (no = 0, yes = 1). The number of farmers who have not used any green, smart farming technology is the smallest, and the number of farmers using six technologies is the largest. On average, each farmer uses at least one form of technology. Generally speaking, farmers in the area currently do not use many green, intelligent farming technologies.

3.3.2. Key Dependent Variables

The key dependent variable in this paper is contract farming. As defined by this paper, contract farming refers to farmers signing rice cultivation contracts with corporations. Production materials that farmers need, including seeds, pesticides, fertilizers, and other cultivation technology services for the production, are provided by the corporations, who then buy the rice produced by farmers at the price they agree to and write in the contract [28,29,30]. Rice farmers are responsible for providing the land for cultivation and the whole process of cultivating rice crops; 1 denotes participating in contract farming, and 0 denotes non-participation.

3.3.3. Mediating and Moderating Variables

The mediating variable defined in this paper is the high ecological value standard. Agriculture with high ecological value has gradually become the trend in modern agriculture. The high ecological value standard has standardized features of a low-carbon circular economy, involving advanced technologies and high added values. The scores in standardization were used as a proxy variable for the high ecological value standard [31].

More specifically, there are five scoring items, including whether there are specifications for the crop variety, the type of pesticides and fertilizers, the ratio of pesticides to fertilizers, the type of machine for applying fertilizers and pesticides, and circular cultivation [32,33,34]. If there is a specification for a certain item, then the farmer scores 1; if there are none, then the farmer scores 0 for that item. The scores accumulated measured the standardization of rice production.

3.3.4. Controlled Variables

Risk preferences were measured by the Holt-Laury experiment [35]. Among all theories and methods in experimental economics used for measuring farmers’ attitudes towards risks, academia uses the lottery method invented by Holt and Laury [35], in which the mechanism is to use the subject’s preferences in choosing game options to judge their risk attitudes. This paper adopted the Holt-Laury experiment to suit the knowledge of rice farmers. The motivation and urgency of farmers to seize development opportunities were measured with a focus on farmers’ attitudes towards risks. As shown in

Table 2. Measurement of Farmers’ Risk Preference.

Risk Preference	Option A		Option B
	Probability (%)	Amount of Money (USD)	Probability (%)	Amount of Money (USD)	Probability (%)	Amount of Money (USD)
1	100%	250	50%	200	50%	300
2	100%	250	50%	150	50%	350
3	100%	250	50%	100	50%	400
4	100%	250	50%	50	50%	450
5	100%	250	50%	0	50%	500

, Option A has 100% possibility, and Option B has 50% possibility. If a farmer chooses A, then the risk-bearing level is 0. Options B correspond to levels of 1 to 5, meaning farmers are more risk-seeking. It can be seen from

Table 3. Basic statistics of variables.

Var Name	Obs	Mean	SD	Min	Max
green, smart agriculture technologies (Number of uses of technology)	782	1.247	1.116	0	6
contract farming (Yes = 1; no = 0)	782	0.139	0.347	0	1
high ecological value standard (Number of standards)	782	0.208	0.682	0	5
income from rice cultivation (thousand)	782	23.298	31.948	0	270
Risk Preference (The value ranges from 0 to 1. A larger value indicates a higher risk)	782	1.115	1.815	0	5
average number of laborers (The number of agricultural labor)	782	2.177	0.858	0	7
agriculture technology training (times)	782	2.870	2.040	0	30
agricultural investment in rural infrastructure (Million)	782	15.000	46.465	0	300
government subsidy (USD/hectare)	782	125.522	69.154	0	1165

that in the farmer risk test, the minimum value is 0, the maximum value is 5, and the average value is 1.115, indicating that the vast majority of farmers are unwilling to bear risks and are risk averse.

As shown in Table 3, the average number of laborers in a farming household is 2.18, indicating that rice crops are cultivated by the couples in most households. In terms of agriculture technology training, the highest number of training sessions in a year that a household receives is 30, and the lowest is 0, and there is a huge difference in technology training for different operation modes. More specifically, the number of training sessions that farmers engaged in contract farming attended is clearly higher than that of those farmers not engaged in contract farming. With respect to agricultural investment in rural infrastructure (such as building paddy ridges and repairing water channels), the lowest figure is 0, and the highest is $300 million [Table 3]. The amount of investment varies evidently among different areas and different production and operation modes of rice cultivation. The government subsidy for each hectare of rice paddy can reach as high as $1165, including subsidies for cultivation and agricultural machinery.

As shown in Table 3, farmers in the sample have the mediating variable scored at only 0.208, indicating the lack of specifications for the high ecological value standard in contract farming. The moderating variable is the income from rice cultivation. Farmers in the sample have an average income from rice production of USD 23.30, mainly because some farmers have large areas of crop paddies, and their incomes improve the average figure.

3.4. Model Setting

Mediation with regulation means that the mediation process is affected by the regulation variable; that is, the size of the mediation effect will change with the different values of the regulation variable. Wen Zhonglin [36] discussed how to use the sequential test method to test the mediation model, in which the latter half of the mediation process is regulated. Wen Zhonglin systematically summarized six regulated intermediary models (three models that only regulate intermediary effects and three models that simultaneously regulate intermediary effects and direct effects), reviewed the analysis methods of three regulated intermediary models: sequential test, interval test of coefficient product, and difference test of intermediary effects, and gave a test flow. Guo Yu’s research [37] confirmed that organizations have a significant positive impact on green agricultural technology adoption, the level of professionalism plays a positive intermediary role, and income plays a positive regulatory role. The Moderated mediator addresses both mechanism and boundary issues. Through the modified mediator, the mechanism by which contract farming influenced the use of green, smart agricultural technology was answered (mechanism question). At the same time, it also answered whether contract farming promotes the use of green, smart agricultural technology in all cases and whether this relationship is likely to change in certain situations (boundary issues)? Therefore, this method is suitable for the research in this article.

Based on the above analysis, the high ecological value standard influences the conduction mechanism of contract farming and then further impacts farmers’ choices of green, smart technologies. By referencing Baron’s gradual regression method [37] for checking the mediating effect and the steps in the mediating and moderating model suggested by Wen Zhonglin [37], this paper establishes a model as shown in Equations (1)–(4).

First of all, Y is regressed to X alone. There is no intermediary variable, but other control variables can be added. The second regression equation is to regress m to X. Finally, the third regression is to regress y to m and X at the same time. The following conditions are required for the establishment of a mediation effect: first, the ${\mathsf{\alpha }}_1$ of Equation (1) is significant; Second, ${\mathsf{\beta }}_1$ in Equation (2) is significant; Thirdly, ${\mathsf{\gamma }}_2$ of Equation (3) is significant; Fourth, if ${\mathsf{\delta }}_2$ = 0, it means that the direct effect is 0, and it is completely affected by the indirect effect, which is called complete mediation. If ${\mathsf{\delta }}_2$ ≠ 0, it means that the direct effect is not 0, and it means that X has both direct and indirect effects on y, which is called partial mediation.

The detailed form of the model is shown below:

$${\mathrm{Y}}_{\mathrm{i}}={\mathsf{\alpha }}_0+{\mathsf{\alpha }}_1{\mathrm{D}}_{\mathrm{i}}+{\mathsf{\alpha }}_2{\mathrm{C}}_1+{\mathsf{\mu }}_1$$

(1)

$${\mathrm{m}}_{\mathrm{i}}={ \mathsf{\beta }}_0+{\mathsf{\beta }}_1{\mathrm{D}}_{\mathrm{i}}+{\mathsf{\beta }}_2{\mathrm{C}}_2+{\mathsf{\mu }}_2$$

(2)

$${\mathrm{Y}}_{\mathrm{i}}={\mathsf{\gamma }}_0+{\mathsf{\gamma }}_1{\mathrm{m}}_{\mathrm{i}}+{\mathsf{\gamma }}_2{\mathrm{D}}_{\mathrm{i}}+{\mathsf{\gamma }}_3{\mathrm{C}}_3+{\mathsf{\mu }}_3$$

(3)

$${\mathrm{Y}}_{\mathrm{i}}={\mathsf{\delta }}_0+{\mathsf{\delta }}_1{\mathrm{m}}_{\mathrm{i}}+{\mathsf{\delta }}_2{\mathrm{D}}_{\mathrm{i}}+{\mathsf{\delta }}_3\mathrm{Z}+{\mathsf{\delta }}_4{\mathrm{m}}_{\mathrm{i}}\mathrm{Z}+{\mathsf{\delta }}_5{\mathrm{C}}_4+{\mathsf{\mu }}_4$$

(4)

where, ${\mathrm{Y}}_{\mathrm{i}}$ denotes the measurement indicator for green, smart agriculture technologies, ${\mathrm{D}}_{\mathrm{i}}$ is the virtual variable in contract farming, and ${\mathrm{m}}_{\mathrm{i}}$ stands for the mediating variable of the high ecological value standard. ${\mathrm{C}}_1$, ${\mathrm{C}}_2$, ${\mathrm{C}}_3$ and ${\mathrm{C}}_4$ denote the controlled variables, and ${\mathsf{\mu }}_1$, ${\mathsf{\mu }}_2$, ${\mathsf{\mu }}_3$ and ${\mathsf{\mu }}_4$ stand for the random perturbation variables in their respective regression functions. In Equation (1), the coefficient ${\mathsf{\alpha }}_1$ can be viewed as the overall effect of contract farming on farmers’ choice of green, smart agriculture technologies, while ${\mathsf{\beta }}_1$ in Equation (2) stands for direct effects. By substituting Equation (2) into Equation (3), the coefficients ${\mathsf{\gamma }}_1$ and ${\mathsf{\gamma }}_2$ stand for the mediating effect of each mediating variable. After computing the relevant coefficients and the evident level, the checking method related to mediating effects can be used to determine whether there is a mediating effect. Equation (4) denotes that contract farming indirectly influences farmers’ choice of green, smart agriculture through the high ecological value standard moderated by rice cultivation income [38,39,40,41,42].

4. Results and Discussion

After referencing the above-mentioned checking steps for a moderated mediating effect model, this paper used the Stata 13.1 software to detect the mediating effect of the high ecological value standard and the moderating effect of rice cultivation income. Model 1 is a regression model of the relationship between contract farming and farmers’ choices of green, smart agriculture technologies. Model 2 is the regression measuring the relationship between contract farming and the high ecological value standard. Model 3 measures the effect of contract farming and the high ecological value standard on farmers’ choices of green, smart agriculture technologies through regression. The last model, Model 4, adds a moderating variable, income from rice cultivation, and the interaction between the moderating variable and the mediating variable, namely the income from cultivation and the high ecological value standard based on Model 3. The estimated results from all models are shown in

Table 4. Estimated results of the influence of contract farming and high ecological value standardon green, smart agriculture technologies.

	(1)	(2)	(3)	(4)
	Green, Smart Agriculture Technologies	High Ecological Value Standard	Green, Smart Agriculture Technologies	Regulating Effect
contract farming (number of uses of technology)	0.5887 ***	1.4599 ***	0.0697	0.0104
	(4.0231)	(13.1028)	(0.3226)	(0.0721)
Gender (Male = 1; female = 0)	−0.0147	−0.0125	−0.0103	−0.0315
	(−0.1120)	(−0.2532)	(−0.0811)	(−0.2574)
Age (year)	−0.0021	−0.0002	−0.0020	−0.0012
	(−0.5267)	(−0.1098)	(−0.5037)	(−0.3004)
Educational level (year)	0.0082	0.0076	0.0055	0.0036
	(0.6566)	(1.2528)	(0.4452)	(0.3254)
Risk Preference (value ranges from 0 to 1. A larger value indicates a higher risk)	−0.0232	−0.0131	−0.0186	−0.0197
	(−1.2751)	(−1.3900)	(−1.0456)	(−1.1480)
average number of laborers (number of agricultural labor)	0.0470	−0.0226	0.0551	0.0434
	(1.1753)	(−1.0874)	(1.3837)	(1.1852)
agriculture technology training (times)	0.0170	−0.0048	0.0187	0.0170
	(1.3294)	(−0.6470)	(1.4757)	(1.0988)
agricultural investment in rural infrastructure (million)	−0.0003	−0.0005 *	−0.0001	0.0000
	(−0.4281)	(−1.6616)	(−0.1815)	(0.0043)
government subsidy (USD/hectare)	0.0006	0.0001	0.0006	0.0005
	(1.6426)	(0.3824)	(1.5706)	(1.1141)
high ecological value standard (number of standards)			0.3555 ***	0.2592 ***
			(2.8796)	(3.0040)
income from rice cultivation (thousand)				0.0033 ***
				(2.7000)
c.high ecological value standard#c.income from rice cultivation				0.0014 *
				(1.7988)
Urban effect	control	control	control	control
_cons	0.5701 *	−0.0308	0.5811 *	0.5071 *
	(1.8585)	(−0.2222)	(1.8968)	(1.6692)
N	782	782	782	782
R-Square	0.4023	0.5916	0.4216	0.4369
Adj.R-Square	0.3898	0.5831	0.4087	0.4228

Notes: * p < 0.1, and *** p < 0.01.

.

The regression results in Table 4 demonstrate that contract farming has an evident positive influence on farmers’ choice of green, smart agriculture technologies (coefficient = 0.5887 and p < 0.01), so Hypothesis 1 is proved correct. Farmers engaged in contract farming receive information about technologies, technological guidance, technical training, and market trends, so their knowledge about green, smart agriculture technologies is effectively improved. They also have higher recognition of technologies, improving their income and optimizing the environment. Meanwhile, massive technical guidance and training can make it easier for farmers to master green, smart agriculture technologies and result in higher acceptance of the technologies. Contract farming can transfer the risks to farmers and stabilize their income [11]. After farmers get stable incomes, they are more capable of bearing the risks of losses in adopting the agricultural technologies, which in turn can further increase their acceptance of the technologies.

Model 2 in Table 4 shows that contract farming has a significant positive effect on high ecological value standards (coefficient = 1.4599 and p < 0.01). From the results of Model 3 in Table 4, it can be found that contract farming has no significant effect on farmers’ choice of green, smart agriculture technologies, and the high ecological value standard has a significant positive effect on farmers’ choice of green, smart agriculture technologies (coefficient = 0.3555 and p < 0.01), indicating that the high ecological value standard plays a completely mediating role in the choice of green, smart agriculture technologies by farmers. In contract farming, contract enterprises and rice farmers agree on a series of high ecological value standard-related clauses, such as rights, obligations, ways of fulfilling contracts, and breach clauses, which can restrict the production behaviors of rice farmers.

In Model 4 in Table 4, it can be found that the high ecological value standard, income from rice cultivation, and the interaction between the two variables have a clear positive influence on farmers’ choice of green, smart agriculture technologies, with coefficients of 0.2592, 0.0033, and 0.0014, respectively. The effects decline sharply after levels of 0.1%, 1%, and 10%. After adding the variable of income from rice production and the interaction item between income and the high ecological value standard, the R-Square model improves significantly, indicating the notable moderating effect of the income variable. When the income from cultivation is high, the positive relationship between the high ecological value standard and farmers’ choice of green, smart agriculture technologies is strengthened. Therefore, Hypothesis 3 is proved correct, meaning income from rice cultivation does have a moderating effect. By providing more complete conditions for production and using all materials needed for modern production in an intensive and highly efficient manner on the basis of good infrastructure and modern material equipment, contract farming improves agriculture production efficiency, increases product prices, and increases the overall incomes of farmers. It also transfers the risks of rice cultivation, which encourages farmers to be more willing to choose green, smart agriculture technologies. Using such technologies in contract farming can achieve the sustainable utilization of agricultural resources such as water and land and create a positive ecological circulation. Rice cultivation per se will also become a circular ecological system.

5. Robustness Check

To check the robustness of the estimation results from the models, the sample from more developed areas, namely the southern part of Jiangsu, was eliminated. Samples from the middle and northern parts of Jiangsu were used for re-estimation in Models 1 to 4. The results gained are listed in

Table 5. Estimated results of the influence of contract farming and high ecological value standardon green, smart agriculture technologies of different area.

	(1)	(2)	(3)	(4)
	Green, Smart Agriculture Technologies	High Ecological Value Standard	Green, Smart Agriculture Technologies	Regulating Effect
contract farming (number of uses of technology)	0.7257 ***	1.4565 ***	0.2154	0.1769
	(4.7370)	(12.5072)	(0.9458)	(1.1599)
Gender (Male = 1; female = 0)	−0.0158	−0.0102	−0.0122	−0.0315
	(−0.1213)	(−0.2053)	(−0.0974)	(−0.2580)
Age (year)	0.0009	−0.0011	0.0013	0.0020
	(0.2248)	(−0.5364)	(0.3130)	(0.5080)
Educational level (year)	0.0105	0.0098	0.0070	0.0054
	(0.7926)	(1.4702)	(0.5369)	(0.4645)
Risk Preference (value ranges from 0 to 1. A larger value indicates a higher risk)	−0.0215	−0.0141	−0.0166	−0.0176
	(−1.1158)	(−1.3967)	(−0.8822)	(−0.9918)
average number of laborers (number of agricultural labor)	0.0751 *	−0.0183	0.0815 **	0.0716 *
	(1.8397)	(−0.8170)	(2.0001)	(1.8668)
agriculture technology training (times)	0.0126	−0.0063	0.0148	0.0134
	(0.9656)	(−0.8046)	(1.1482)	(0.8410)
agricultural investment in rural infrastructure (million)	−0.0005	−0.0005	−0.0003	−0.0002
	(−0.6182)	(−1.5650)	(−0.3837)	(−0.2610)
government subsidy (USD/hectare)	0.0001	0.0001	0.0000	−0.0000
	(0.1845)	(0.5729)	(0.0841)	(−0.0298)
high ecological value standard (number of standards)			0.3503 ***	0.2413 ***
			(2.6335)	(2.6059)
income from rice cultivation (thousand)				0.0027 **
				(2.1189)
c.high ecological value standard#c.income from rice cultivation				0.0015 *
				(1.8202)
Urban effect	control	control	control	control
_cons	0.3964	−0.0173	0.4025	0.3423
	(1.2539)	(−0.1173)	(1.2663)	(1.0879)
N	696	696	696	696
R-Square	0.3807	0.5930	0.4006	0.4145
Adj.R-Square	0.3680	0.5847	0.3874	0.3998

Notes: * p < 0.1, ** p < 0.05, and *** p < 0.01.

. Each estimation result for different variables in the table is close to that in Table 4 in terms of the direction of the influence and the level of significance, which demonstrates that the estimation results in this paper are robust.

6. Conclusions and Insights for Policies

The following are the major conclusions drawn from the study. First, as a new mode of agriculture production and operation, contract farming has an evident positive influence on farmers’ choice of green, smart agriculture technologies. Second, contract farming has not only a clear positive effect on farmers’ choice of green, smart agriculture technologies but also a positive influence on the high ecological value standard, which in turn makes more farmers choose the technologies. This indicates that the high ecological value standard plays a mediating role in the process of contract farming by influencing farmers’ choices of technologies, so it can serve as the mediator path for such influence. Finally, income from rice cultivation has an evident positive moderating effect in the process of the high ecological value standard influencing farmers’ choice of green, smart agriculture technologies. The research results show that with high income from rice cultivation, the positive effect of the standard on farmers’ choices of technologies is magnified.

Based on the above research conclusions, this paper puts forward the following suggestions to encourage farmers to protect cultivated land: First, farmers need to be actively guided to engage in contract farming. Farmers joining contract farming play an important role in promoting more farmers’ choice of green, smart agriculture technologies. For one thing, the government should increase the publicity of contract farming by holding a series of activities such as contract farming signing ceremonies, speeches about green, smart agriculture technologies given by professionals, field research on contract farming bases, awarding ceremonies for exemplars of contract farming, and dividend meetings for contract farming. By doing so, the government can improve the farmers’ recognition of contract farming and green, smart agriculture technologies, making them realize that contract farming is a new operation and production mode. By doing so, the government can facilitate long-term cooperation between enterprises and farmers and make contract farming a sustainable mode of modern agricultural production and operation. The Jiangsu Provincial Government needs to allocate special funds to develop high-quality farmer cultivation projects to support rice processing enterprises in developing contract farming production. Governments at all levels in Jiangsu Province have formulated policies to support rice processing enterprises in organizing contract farming training for farmers. The training plan and target audience were proposed by leading agricultural enterprises engaged in contract farming production and then reviewed and organized by a high-quality farmer cultivation project team to implement and bear reasonable costs, thereby improving the accuracy of training the targeted audience and the applicability of the training content.

Second, the implementation of the high ecological value standard in contract farming should be refined. When providing support and subsidies to farmers and enterprises involved in contract farming, the government should also introduce corresponding requirements to regulate some compulsory items on the contracts and every aspect of farming, including the crop variety, the type of pesticides and fertilizers, the frequency of using pesticides and fertilizers, the machine used, circular agriculture, etc. The government should also encourage contract farming enterprises to set up green, smart agriculture production funds, implement measures such as purchasing green organic products at protective prices and premium subsidies, make enterprises and farmers involved in contract farming have a shared goal and supervise each other, and gradually refine the supply chain division between the enterprises and farmers. Jiangsu Province has utilized its economic advantages to introduce special policies to support high-quality rice contract farming production. The Jiangsu Provincial Government provides subsidies to contract farming for planting rice on more than 50 hectares, with a price increase in more than 5% compared to the national minimum purchase price. At the same time, the Jiangsu Provincial Government relies on universities, research institutes, agricultural technology promotion institutions, and others to develop Green, smart agriculture technologies, supervise contract farmers to use Green, smart agriculture technologies, establish production accounts, and achieve traceability of the production process.

Finally, the farmers’ incomes should be steadily increased. For one thing, the government should complete the mechanism for selling quality agricultural products at good prices. The investment required in contract farming is higher relative to normal production, so governmental departments should coordinate the interest relations between corporations, cooperatives, farmers, and other entities. They should amplify farmers’ voices in contract farming, supervise the contract-fulfilling behaviors of corporations and farmers, increase punishment for violations, and encourage corporations and cooperatives to buy contract products at a premium, which can give farmers motivation to join contract farming. For another, due to its feature of whole industrial chain production, contract farming can make farmers and corporations involved have deep cooperation, and the relatively remaining labor (spare labor or labor in off-seasons) can transfer to the second and third industries, participate in seasonal work or services for contract farming corporations, increase the non-agricultural income of farmers, and further enhance the overall income of their households.

Due to limitations of time and ability, this study failed to obtain a large range of samples. When analyzing the adoption of green and smart agricultural planting technology by farmers, only Jiangsu Province was selected as the sample area for investigation. Although a questionnaire survey was conducted in strict accordance with the research content and model requirements, the survey and analysis results of Jiangsu Province alone may not fully reflect the overall situation of the adoption of green and smart agricultural planting technology. Future research will further expand the survey sample and explore whether other crop samples can reach the same conclusion.