Impact of Mobile Internet Application on Farmers’ Adoption and Development of Green Technology

Abstract

Mobile Internet refers to a new mode of information interaction through mobile terminals and the use of wireless communication to access information services. The mobile Internet has provided a new model for the traditional information circulation mode, rural economic structure, and social transformation. With an association between the mobile Internet and farmers’ adoption behavior of new green technology, this paper investigated the impact of mobile Internet application on farmers’ adoption of green technology through a mediating effect model. From the perspective of new agricultural technology diffusion and based on the perspective of factor substitution, we decompose the mechanism that mobile Internet applications affect the adoption of new wheat varieties. Along the path of information transmission, evaluation, and adoption of new wheat varieties, we deeply explored the impact of the mobile Internet on the adoption of new wheat varieties by different types of wheat growers. The results show that there is a significant mediating effect of information acquisition ability, risk attitude, and expected return on the influence of the mobile Internet on green technology adoption. For the adoption mechanism of new green varieties, there was a distinct difference among various farmers.

1. Introduction

With the rapid growth of agriculture, non-point source pollution has become increasingly severe. A large number of chemical fertilizers, pesticides, and other agrochemicals are used to promote agricultural production, which is a major reason for non-point source pollution. Data from the “China Geochemical Survey Report (2016)” released by the China Geological Survey show that 84.8 million hm² (1.272 billion mu) of arable land in China is free of heavy metal pollution, accounting for 91.8% of the surveyed arable land. Excessive use of chemicals not only endangers ecological safety and agricultural safety, affects the sustainable development of agriculture, but it also affects the improvement of the international competitiveness of agricultural products in China [1,2]. Therefore, the widespread use of new green technology and varieties is an important path for sustainable development in the future. The promotion of new agricultural green technologies and new varieties is essentially a process of technology diffusion, which is a technical exchange activity requiring the demonstration and propaganda from the agricultural technology department to guide farmers to adopt the technology. As a vital role in agricultural development, technology is also important in the sustainable development of modern agriculture [3]. The main function of promotion is to establish an information link between new technology and farmers so that farmers can master it to achieve reduced inputs or increased incomes. Most domestic farmers usually prefer to adopt techniques that can be mastered through simple imitation and are more secure for personal experience in agricultural production, due to their limited knowledge of new varieties and weak anti-risk ability. So, the promotion of agricultural technology is an important part of the sustainable development of agriculture, and it is “The Last Kilometer” of modern agricultural green technology from the laboratory to the field [4,5].

From a global perspective, agricultural technology support has become an important means of sustainable agricultural development in major agricultural-producing countries. After decades of agricultural technology promotion, China has formed a relatively complete promotion system, which has played a vital role in the progress of agricultural technology in the relatively backward period. However, the traditional approach is hard to adapt the diffusion of green technology in the new age [6,7,8]. The emergence of the mobile Internet has realized the efficient transmission of technical information among farmers, narrowing the distance between agricultural scientists and farmers and changing the way farmers make production decisions. Mobile Internet refers to a new mode of information interaction through mobile terminals and the use of wireless communication to access information services, which usually includes three parts: mobile terminals, application software, and system software. Mobile terminals generally refer to hardware devices used to connect to the Internet, such as smartphones, tablets. Compared to the traditional Internet, users can access the Internet network to obtain information anytime and anywhere. The penetration rate of the mobile Internet in rural China is more than 60%, which is far more than landline Internet. Therefore, the mobile Internet is the main channel for farmers to obtain network information. Based on this situation, this paper chooses mobile Internet as the analysis object. In economics, farmers are often considered to be the “rational economic man”, and subjectively, the decisive factor in whether they are willing to use new technologies is whether farmers engaged in agricultural production decisions believe that new technologies can bring higher returns [9]. Usually, if farmers are familiar with the technology they use, they can better play the advantages of it and thus increase agricultural productivity, but this effect will gradually weaken over time until the new technology variety completely replaces the old technology [10]. This can be an overly ideal assumption when making adoption decisions, which is reflected in the significant elimination rates of many new varieties [11]. A typical model hypothesis is that farmers produce and learn by observing experiments by others, which increases the uncertainty of information quality compared to “Learn by doing”, partly due to the inaccuracy of farmers’ knowledge (e.g., planting techniques, soil quality). Thus, farmers participate in “incomplete learning” and measure each piece of information proportionally according to its value [12].

Many factors affect farmers’ choice of new technology [13], including regional cultural differences, individual characteristics of farmers, cost benefits, information channels, etc. In the market economy environment, farmers will consider economic benefits more as the core element of individual production decision-making. Yu [14] selected the main factors affecting agricultural economic growth, such as labor force, capital input, cultivated land area, fertilizer input, and information input. The amount of information input was measured by the number of websites, the number of books, magazines, and newspapers published, the number of home computers per 100 households, the number of agricultural science and technology service personnel, the number of entertainment and service expenses, the number of mobile phones per 100 households, and the communication expenses of farmers. Gao [15] constructed the informatization evaluation index system of Henan Province based on the principle of index representativeness. Agricultural informatization input quantity, agricultural informatization facility quantity, agricultural informatization efficiency, and the number of agrarian informatization talents are taken into consideration.

This study seeks to clarify the mechanism and influence of mobile Internet application on farmers’ choice of new green technologies, so as to provide a reliable basis for the implementation of agricultural technology promotion through the mobile Internet. This study will use data related to rural mobile Internet penetration and adoption of new green technologies by farm households as a case study to explore the mechanism of the influence of mobile Internet use on the adoption of new technologies by farm households, which will be useful for a comprehensive understanding of the effectiveness achieved and the dilemmas in the development of the implementation of information into villages and households, and provide some reference for the formulation of policies and strategies for agricultural and rural informatization development.

To analyze the impact of mobile Internet application on farmers’ adoption of green technology, wheat was selected as the research object in this study. Wheat is one of the three major food crops in China, and its planting area is second only to rice. Compared to the traditional wheat planting mode, the popularization and application of green wheat technology can effectively reduce the occurrence and prevalence of pests and diseases, avoid the adverse effects of various natural disasters on wheat growth, and the yield per unit area of wheat is higher, and the yield increase effect is significant. Moreover, the application of green planting technology in the whole process from wheat soaping to harvest can effectively reduce the use of fertilizers and pesticides, reduce harm to the farmland’s ecological environment, and ensure food safety. Focusing on the promotion of green wheat varieties in recent years, based on understanding mobile Internet application and farmers’ behavior, the mechanism of farmers’ adoption of new technologies in recent years is analyzed. This study will combine information economics, search theory, and farmer behavior theory, and use the field micro-survey data of wheat farmers in Henan Province to conduct a targeted quantitative analysis.

2. Model and Data

In general, it is believed that the development of information technology in rural China, especially mobile Internet technology, can promote the dissemination of new technologies among farmers. Firstly, the application of mobile Internet has expanded the network of agricultural technology resources, making it more convenient for farmers to access professional information on new technologies and guidance on planting techniques. The richer and more comprehensive the amount of data on new technologies that farmers can access through the mobile Internet, the stronger the farmers’ perception of the high productivity, stability, and practicality of new technologies, and the more likely they are to plant new varieties. Secondly, the application of the mobile Internet enables farmers to overcome the initial implementation barriers of new technologies and increases farmers’ confidence in planting, so more information on new technologies that farmers can access through the mobile Internet has a positive impact on their adoption. Once again, the financial cost to farmers of accessing information about new technologies from the mobile Internet is low, and information can be efficiently disseminated from the source to the farmer’s end.

The ease with which farmers can access information on new technologies from the mobile Internet can, to some extent, increase the speed with which farmers can master new technologies and reduce the time cost of learning new technologies. These reasons will encourage farmers to be more willing to adopt new technologies. The framework of influence pathways of mobile Internet applications on the adoption of new green technologies is shown in Figure 1.

The analytical model in this paper assumes that for all farmers, new technologies are ‘better’ than old technologies in terms of greater production benefits. At the same time, information accessibility has a significant positive effect on the adoption of green technologies by farmers, and by improving this, farmers’ aversion to green technologies is significantly reduced, thus promoting their adoption.

Mobile Internet is believed to affect farmers’ adoption of new technology by changing their ability to obtain information on new green varieties, risk attitudes, and expected return. The influence path relationship is shown in Figure 2. The study will examine the influence path of new green varieties adopted by farmers using the mobile Internet through the mediating effect model, which uses the evaluation value of farmers’ new green varieties information access ability, risk aversion degree, and expected return as intermediary variables to reflect the influence mechanism of the mobile Internet on the adoption of new varieties by farmers. Mediating effect analysis is a research method that analyses the path of target development by analyzing the process and mechanism of influence of the explanatory variables on the explanatory variables. Compared to the common method which only analyzes the influence of explanatory variables on the explained variables, the mediating effect analysis method is not only a methodological advancement, but also allows for more valuable findings. The impact of mobile Internet applications on green technology adoption studied in this paper can establish a mediating effects model for econometric analysis, which can analyze and identify the mediating effects of mobile internet applications influencing the process of green technology adoption on farmers’ ability to obtain information about new varieties, risk attitudes, and expected returns [16].

In the effect model,

$$Y_i={\delta }_1+{\alpha }_1{X}_i+{\beta }_1{D}_i+e_1$$

(1)

$$M_i={\delta }_2+{\alpha }_2{X}_i+{\beta }_2{D}_i+e_2$$

(2)

$$Y_i={\delta }_3+{\alpha }_3{X}_i+{\beta }_3{D}_i+{\gamma }_3{M}_i+e_3$$

(3)

In the model, $Y_i$ is the adoption degree of the $i^{th}$ farmer’s new wheat green variety, $X_i$ is the related factors affecting the adoption degree of the $i^{th}$ farmer’s new wheat green variety. $D_i$ is the treatment variable, that is, whether the $i^{th}$ farmer uses the mobile Internet. If $D_i$ = 1, it means using the mobile Internet; if $D_i$ = 0, it means not using the mobile Internet. $M_i$ is the mediator variable, ${\alpha }_j$, ${\beta }_j$, ${\gamma }_3$, ${\delta }_j$ (j = 1,2,3) is the parameter to be estimated. Where: ${\alpha }_1$ is the total effect on the adoption degree of new green wheat varieties by the $i^{th}$ farmer except the mobile Internet; ${\alpha }_2$ is its influence on the mediating variable; ${\alpha }_3$ is its Average Direct Effect (ADE, Average Direct Effect) on the adoption degree of the new green wheat variety by the $i^{th}$ farmer; ${\beta }_1$ is the total effect of mobile Internet application on the adoption of new green wheat varieties; ${\beta }_2$ is the influence of mobile Internet application on mediating variables; ${\beta }_3$ is the ADE of mobile Internet application on the adoption degree of new green wheat varieties by the $i^{th}$ farmer; ${\delta }_j$ is a constant term; ${\gamma }_3$ is Average Causal Mediating Effect (ACME). When the $D_i$ = 1, it can only be observed after wheat growers to use the mobile Internet, it cannot be observed without wheat farmers after the use of the mobile Internet, when $D_i$ = 0, it can only be observed after wheat farmers did not use the mobile Internet, it cannot be observed after wheat growers to use the mobile Internet. $e_1$, $e_2$, and $e_3$ are residual terms. The mediating effect model used in this paper adopts the coefficient product test method proposed by Sobel (1982) [12]. The null hypothesis is given in this paper $H_0:{\beta }_2\cdot {\gamma }_3=0$, When the null hypothesis is rejected, ${\beta }_2\cdot {\gamma }_3\ne 0$, it can be proved that ${\beta }_2$ and ${\gamma }_3$ are both significant, and the mediating effect exists.

The empirical data came from the field survey of wheat farmers in Henan Province. The questionnaire included farmer questionnaires and rural questionnaires in the main wheat growing areas of Henan Province [17,18]. Through sorting and screening, 572 questionnaires were finally confirmed as the empirical analysis samples of this study.

3. Variable Definition and Descriptive Analysis

The analysis variables of the influence of mobile Internet application on the adoption mechanism of new green wheat varieties by farmers include explained variables, core explanatory variables, mediating variables, and control variables [19]. This study focused on the core explanatory variable as the application of the mobile Internet on farmers, and the intermediary variable focused on three indicators of information acquisition ability, risk attitude, and expected return of wheat farmers. Model reference has been studied, defined, and described the above variables, as shown in

Table 1. Variable definition and description.

The Variable Name	Variable Definitions
Explained variables:
Adoption rate of new green wheat varieties	Planting proportion of green pollution-free wheat varieties.
Explanatory variables:
Mobile Internet applications	It is determined by the comprehensive judgment of mobile terminal and mobile software application, and the use the value is 1. If not used, the value is 0.
Mediating variables:
Information acquisition ability	Through the comprehensive measurement of eight indicators, for details. The value ranges from 0 to 4.
New variety Name Information	Names of new green varieties obtained by farmers, ranging from 0 to 4.
New variety certification information	Approval status of new green varieties obtained by farmers, value range 0–4.
New variety characteristic information	Cultivation characteristics of new green varieties obtained by farmers, value range 0–4.
Sowing technology information	Information on planting technology of new green varieties obtained by farmers, value range 0–4.
Technical information on field management	Field management technology of new green varieties obtained by farmers, value range 0–4.
Wheat market price information	Farmers obtain the market price of wheat, which ranges from 0 to 4.
Seed price information	Farmers get wheat seed prices, value range 0–4.
Price information of pesticides and fertilizers	The price of wheat pesticides and fertilizers obtained by farmers ranges from 0 to 4.
Risk attitude	To measure the risk aversion of farmers by experimental methods, see 6.3.2 for details. The value ranges from 0 to 1.6.
Prospective earnings	Comprehensive measurement is carried out through five indicators. For details, see 6.3.3. The value ranges from 0 to 4.
Production per unit area expected	The new variety can improve the expected degree of wheat yield per unit area. The value ranges from 0 to 4.
Expectations of stress resistance	The new variety can improve the expected degree of wheat stress resistance. The value ranges from 0 to 4.
Quality expectations	The new variety can improve the expected degree of wheat quality. The value ranges from 0 to 4.
Expectation of growth period	The new variety has the expected degree of suitable growth period, the value range is 0–4.
Input cost expectation	New variety can reduce the expected degree of input cost, value range is 0–4.
Control variables:
Age	Actual age (One full year of life).
Gender	1 = male; 0 = female.
Years of education	Years of education for decision-makers in Agricultural Production and Operation (years).
Wheat planting experience	Engaged in wheat planting time, according to the actual value (years).
Whether to return to farming personnel	Return to the town full-time engaged in agricultural production and management activities? 1 is true; 0 is false.
Family	In terms of actual numbers (person).
Number of family farm workers	Number of family members engaged in agricultural production (persons).
The number of times the family attended agricultural technology training	According to the actual value (times).
Annual household income	Total household income, the natural logarithm of the actual value.
Wheat revenue share	Wheat production income to total household income ratio.
Wheat planting scale	The natural logarithm of wheat planting scale is taken according to the actual mu.
Whether family members serve as village officials	Are the family members village cadres or township cadres or above? 1 is true; 0 is false.
Participation in Cooperatives	Do you participate in agricultural cooperatives? 1 is true; 0 is false.
Information acquisition channel	Access to agricultural information channel number.

below [20,21].

3.1. Information Acquisition Ability

In order to describe the ability of farmers to learn information about new technologies, it is difficult to comprehensively reflect the degree of farmers’ understanding of information about new varieties through a single index [22]. In this study, the index system is set up based on the ontology information, planting information, and market information of new wheat varieties. The ontology information includes the variety name, approval date, variety characteristics, and other information that can be obtained by farmers. Planting information includes planting technology information of new varieties and information of field management technology. The market information includes the information on market price of kinds of wheat, the information on seed price, and the information on the price of pesticides and fertilizers needed in the planting process.

The data used in this paper come from the farmers in Henan Province, and the researchers are graduate students from the China University of Mining and Technology and the information staff of Henan Yinong Information Society. In the process of the questionnaire collection, the survey data will be cleaned and verified. The questionnaires with poor information quality will be eliminated to ensure data quality. About 10 households in each village are randomly selected as samples, and finally, 637 farmer questionnaires from 10 administrative counties are obtained. Through sorting and screening, 572 questionnaires are finally confirmed as empirical analysis samples of this study. The map of the investigation area is shown in Figure 3.

The information acquisition ability of 572 farmers is counted according to the frequency of the scoring interval. As shown in Figure 4. The abscissa is the score of the information acquisition ability, and the ordinate is the number of farmers per 0.1 scoring interval. It can be seen that the ability to obtain information on farmers is characterized by a “normal distribution” of high in the middle and low on both sides. Recipients of information often switch off after a bit in the so-called cognitive continuum when the recipient thinks they have read enough to accept, reject, or modify the information being received.

3.2. Risk Attitude

Based on prospect theory and utility theory, this study measures the attitude toward the risk of farmers in the decision-making of new varieties by means of experimental economics [12]. The utility function model of farmers is as follows:

$$U\left(x,p:y,q\right)=\{\begin{array}{c}g\left(p\right)·f\left(x\right)+g\left(q\right)·f\left(y\right) if x<0<y\\ f\left(y\right)+g\left(p\right)·\left[f\left(x\right)-f\left(y\right)\right] else\end{array}$$

(4)

where:

$$g\left(p\right)=e^{-{\left(-lnp\right)}^{\beta }}$$

$$f\left(x\right)=\{\begin{array}{c}{x}^{1-\alpha } if x>0\\ -\gamma {\left(-x\right)}^{1-\alpha } else\end{array}$$

$\mathrm{where} U\left(x,p:y,q\right)$represents the utility function of farmers and $f\left(x\right)$ is a real-valued function, which refers to the utility that a certain amount of bonus can bring in the experiment. $g\left(p\right)$ represents the weight of probability$p$in the utility function, and $g\left(q\right)$ represents the weight of probability$\mathrm{q}$in the utility function.$x, y$ represent two different bonus amounts in the same option in the experiment, respectively. $\alpha$ indicates the degree of risk aversion of farmers. The smaller the value, the more radical the attitude toward risk of farmers toward new varieties, and vice versa; $\beta$ indicates the degree to which farmers attach importance to small probability risk events. The smaller the value, the less farmers attach importance to small probability events, the more aggressive their behavior is, and the more conservative they are. $\gamma$ indicates the attitude toward loss of farmers. The smaller the value, the less the loss caused by the reduction of the award amount is than the gain caused by the increase of the award amount. The more willing the farmers are to take risks, and the more conservative the behavior is. By referring to [16], we can get the estimated value of$\alpha$ by measuring the median value of inequality interval. The distribution of measurement results about the attitude toward risk of 572 farmers is shown in Figure 5 below.

3.3. Expected Return

The expected return of farmers before planting new green varieties is the intermediary variable that this study focuses on. In order to measure the expected return of farmers, this study will conduct a comprehensive evaluation from five aspects: the expectation of farmers for the improvement of yield per unit area of new varieties, the expectation of stress resistance (disease resistance, lodging resistance, drought resistance, etc.), the expectation of quality improvement, the expectation of suitable growth period, and the expectation of cost saving. The interval distribution of the comprehensive score of farmers’ expected return is shown in Figure 6. From the overall point of view, the average value of farmers’ expected return is 1.907 and the standard deviation is 0.336, which indicates that the expected return of farmers for new green varieties is neutral. On the other hand, it can be seen that the promotion of current agricultural technology is insufficient, which failed to make farmers fully understand the benefits of planting new green varieties of wheat.

Descriptive statistics of core variables are shown in

Table 2. Descriptive statistics of core variable grouping.

Variable Name	Total Sample		With Mobile Internet		Without Mobile Internet
	Mean Value	Standard Deviation	Mean Value	Standard Deviation	Mean Value	Standard Deviation
Explained variable:
Adoption degree of New Wheat Varieties	0.455	0.195	0.582	0.111	0.257	0.118
Mediating variable:
Information acquisition ability	2.261	0.511	2.637	0.205	1.680	0.205
Aversion toward risk	0.811	0.301	0.671	0.241	1.027	0.249
Expected return	1.907	0.366	2.152	0.196	1.529	0.215

. According to the path of influencing the adoption behavior of new green wheat varieties, the farmers who use the mobile Internet have higher access to new variety information and expected return than those who do not use the mobile Internet, and their aversion toward risks of new varieties is less than those who do not use the mobile Internet. Intuitively, the mobile Internet application has a positive correlation with the ability of farmers to obtain information on new green varieties and the expected return, and a negative correlation with the aversion toward risk of farmers. The positive sign of the model results in the table indicates the positive correlation between the variables, and the negative sign indicates the negative correlation between the variables. The number of * is used to represent the significance of the model result, the more *, the more reliable the conclusion. The standard error of partial sample results is in () in the table. The smaller the value is, the smaller the error of the result is.

4. Results and Discussion

4.1. Mediating Effect of Information Acquisition Ability

In

Table 3. Analysis of the influence mechanism of mobile Internet applications on new wheat varieties’ adoption.

Variable Name	Model 1	Model 2		Model 3		Model 4
	Adoption Levels of New Wheat Varieties	Information Acquisition Ability		Attitude Toward Risk		Expected Return
		Values of Information Acquisition Ability	Adoption Levels of New Wheat Varieties	Risk Aversion	Adoption Levels of New Wheat Varieties	Expected Return	Adoption Levels of New Wheat Varieties
Whether to use the mobile Internet	0.254 ***	0.918 ***	0.228 ***	−0.196 ***	0.248 ***	0.619 ***	0.191 ***
	(0.012)	(0.027)	(0.021)	(0.026)	(0.012)	(0.018)	(0.021)
Values of information-obtaining capacity			0.028 *
			(0.015)
Risk aversion					−0.031 *
					(0.015)
Expected return index							0.102 ***
							(0.027)
Controlled variable	Under control	Under control	Under control	Under control	Under control	Under control	Under control
F value	141.654 ***	203.458 ***	133.251 ***	51.665 ***	133.342 ***	229.598 ***	136.754 ***
ACEM		0.026		0.006		0.063
Proportion of mediating effect in total effect		10.24%		2.36%		24.80%
Sobel test		0.029 **		0.009 *		0.065 ***
		(0.011)		(0.005)		(0.014)
Sample size	572

Table Captions: ***, **, * represent significant at 1%, 5%, and 10% levels, respectively. The data in () is the standard error.

, the regression results of “Values of information acquisition ability” in Model 2 indicate that mobile Internet applications significantly contribute to the increase in the value of information acquisition ability after controlling other factors such as farmers’ individuals and families. In other words, mobile Internet applications can improve farmers’ ability to obtain information about new varieties. The results of “Adoption levels of new wheat varieties” in Model 2 indicate that both mobile Internet applications and the improvement of information acquisition ability have a significant positive effect on the adoption of new varieties. However, compared with Model 1, the influence coefficient of mobile Internet applications has changed. After controlling the mobile Internet application variables, the increase in farmers’ information acquisition ability can further improve the adoption levels of new wheat varieties. In general, the ability to obtain new varieties of information is one of the main ways to influence the explanatory variables in this study. Looking further, the total effect is 0.254, and the average mediating causal effect (ACEM) is 0.026, accounting for 10.24% of the total effect. It is indicated that mobile Internet applications can promote the adoption of new varieties by improving the information acquisition ability of farmers.

4.2. Mediating Effect of Risk Attitude

In Table 3, the regression results of “Attitude Toward Risk” in Model 3 indicate that mobile Internet applications have a significant negative correlation with farmers’ Attitude Toward Risk at 1% level. The results of “Adoption levels of new wheat varieties” indicate that mobile Internet applications have a significant positive affect on the adoption of new wheat varieties at the 5% level, and the influence coefficient is 0.248. While Attitude Toward Risk has a significant negative effect on the adoption levels of new wheat varieties at 1% level, the influence coefficient is -0.031. For farmers who are more risk averse, they will try to avoid the risk. The most direct approach is to choose the most familiar old varieties. Compared with Model 1, the influence coefficient of mobile Internet applications has also changed significantly. This indicates that risk aversion is one of the main ways to affect farmers’ adoption behavior of new varieties. Further analysis shows that the total effect is 0.254, and the mediating causal effect is 0.006, accounting for 2.36% of the total effect.

4.3. Mediating Effect of Expected Return

The analysis shows that the total effect is 0.254, and the average mediating causal effect is 0.063, accounting for 24.80% of the total effect. It means that the effect of mobile Internet applications on the adoption of new wheat varieties, by the effect of expected return, is approximately 24.80%. It suggests that the mobile Internet can promote the adoption of new varieties by enhancing farmers’ expected return.

So, this study examines the mediating effect of farmers’ information acquisition ability, risk attitude, and expected return on new technology adoption behavior. The results of different influence mechanism formulas show that information acquisition ability, risk aversion, and expected return have significant positive effects in the process of new technology adoption, and all of them pass the Sobel test. Among the three mediating variables, the effect of expected return is the largest and risk aversion is the smallest. The results are shown in Table 3, and Model 1 is the direct effect mechanism of mobile Internet applications on the adoption of new varieties by farmers.

4.4. Discussion

The improvement of new technology information acquisition ability is an important way to promote the diffusion of new agricultural technologies. Generally, different information access ways have different effects on the diffusion of new wheat technologies. The information access ways that significantly improve the new technology information acquisition ability of wheat farmers have a greater impact on the new technology adoption behavior of farmers. The application of the mobile Internet can make farmers perceive more comprehensive risk information, which will help farmers to reevaluate planting behavior, reduce farmers’ concerns about the uncertainty of new technology adoption, and then change farmers’ risk attitude towards new technology. Farmers are more inclined to choose wheat varieties with higher future income expectations when making production decisions. Through the mobile Internet, farmers can master more information about agricultural products’ planting and market and can better understand the price of wheat input and output, the output under the conditions of input use, the past profitability, or the best combination of input when planting with new technology.

5. Conclusions

The iteration of new crop varieties is an important way to improve agricultural productivity. If the adoption and implementation process of new varieties can be accelerated, it can effectively promote the technological progress of the agricultural industry, improve the benefits of large-scale planting of varieties, and solve the problems such as scattered regional variety layouts. Based on the micro-survey data of farmers and using the method of mediating effect analysis, this paper proves that the application of the mobile Internet can promote the adoption of new green technologies by improving farmers’ ability to obtain information, changing farmers’ risk attitudes towards new green technologies, and improving the path of expected return.

Firstly, this paper quantitatively measures the ability of farmers to obtain information on new varieties of wheat from the ontology information, planting information, and market information of new green varieties of wheat. By using the method of experimental economics, the risk aversion degree of wheat farmers in Henan Province is measured through three groups of experiments, and the result shows that the average risk aversion degree of farmers is 0.811, and most of them belong to the risk aversion type. However, among the risk-averse growers, the distribution of growers with lower, medium, and higher risk aversion is relatively uniform; through the comprehensive scoring method, we measure the expected return of wheat growers and obtain the comprehensive score of expected return. Secondly, we prove the information acquisition ability, risk attitude, and expected return by using the mediating effect model, which has significant mediating effects on the impact of mobile Internet applications on new technology adoption. Among them, the mediating effect of expected return is the highest at 24.80%, the mediating effect of risk attitude is the lowest at 2.36%, and the mediating effect of information acquisition ability is 10.24%. In the process of information dissemination, mobile Internet applications promote the adoption of new green varieties by improving farmers’ information acquisition ability. In the face of uncertain risks, mobile Internet applications promote the adoption of new green varieties by reducing farmers’ risk aversion. In the evaluation and decision-making process, mobile Internet applications promote the adoption of new green varieties by increasing farmers’ expected return.

These research conclusions have important practical significance for in-depth research to solve the problems of rural information gap and sustainable development of new green technologies. On the path of green technology diffusion, the mobile Internet potentially affects farmers to adopt behavioral factors from the aspects of cognition, decision-making, and evaluation, which has a positive impact on farmers’ adoption of new green technologies. From the perspective of long-term development, the mobile Internet, as a new information dissemination method, will help promote the sustainable development of green agriculture and play an important catalytic role in the process of agricultural modernization. In the process of promoting new green technologies, the application of the mobile Internet can change the inertial thinking formed in the traditional planting process of farmers to a certain extent. It brings inspiration to the current green agricultural development work or policy guidance and provides an effective way to solve problems such as rural green technology promotion.