Multi-path Driven by Farmers' Adoption Behavior of Dry Farming Green Agricultural Technology in Loess Plateau from the Perspective of Trust Theory — A Study Based on fsQCA

doi:10.21203/rs.3.rs-4867260/v1

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Multi-path Driven by Farmers' Adoption Behavior of Dry Farming Green Agricultural Technology in Loess Plateau from the Perspective of Trust Theory — A Study Based on fsQCA

https://doi.org/10.21203/rs.3.rs-4867260/v1

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Enhancing farmers' performance in embracing novel green agricultural technologies in the arid Loess Plateau regions holds significant potential for boosting agricultural productivity, augmenting farmers' income, and fostering environmental and ecological benefits in local agricultural production. Drawing upon interviews with 488 farmers, this study utilizes the fuzzy set qualitative comparative analysis to establish an integrative model encompassing willingness, value trust, external technology institutional trust, and adoption performance enhancement. The model unravels the conditional configurations and diverse pathways wherein value trust and institutional trust factors influence farmers' adoption of new green agricultural technology. The findings indicate that a singular behavioral decision-making factor is not a prerequisite for farmers to effectively adopt new green agricultural technologies. Instead, the interplay of multiple decision-making factors culminates in three distinct types of farmer technology adoption performance. These conditional configurations are classified as endowment will-dominance mode, internal and external joint constraint mode, and external context-dominant mode. This research not only offers a fresh analytical perspective for studying the enhancement of farmers' adoption behavior of green agricultural technology on the Loess Plateau, but also serves as a crucial theoretical and practical reference for fostering high-quality agricultural development in the region.

Earth and environmental sciences/Environmental sciences

Earth and environmental sciences/Environmental social sciences

Green agricultural technology

Farmer adoption

Qualitative comparative analysis method

Performance improvement

Configuration pathway

The adoption of green agricultural technology in the arid areas of the Loess Plateau has effectively improved the local ecological landscape, significantly improved the agricultural production capacity of the Loess Plateau and diversified the industrial model. However, due to the lack of rationality of the overall vegetation restoration allocation, the ecological environment of the Loess Plateau degraded and deteriorated in the later period. The fragile background of the loess ecological environment has not been fundamentally changed, and the risk of extreme natural disasters has always been high, which in turn leads to low land use efficiency and brings the problem of livelihood fragility of farmers. The application of new agricultural technologies can optimize the input structure of agricultural production factors, improve crop varieties to improve crop environmental adaptability, and the joint adoption of multiple new agricultural technologies can make up for the shortcomings and shortcomings of a single agricultural technology. The adoption of new green agricultural technologies can effectively enhance farmers' ability to control the growth environment and growth cycle of crops and master the efficient and high-quality production methods and management measures of agricultural products while protecting the agricultural production environment(Ren et al., 2022). Promoting farmers to actively adopt new green agricultural technologies with the trinity of "increasing yield, increasing income, and improving ecological and environmental benefits", such as water-saving irrigation, soil testing and formula fertilization, and water adaptation and soil improvement, is a solid foundation for high-quality agricultural development༈Niu et al., 2022༉. Therefore, it is of great theoretical significance to explore the path and positive factors of the performance improvement of new green agricultural technology adoption by dryland farmers on the Loess Plateau༈Elahi et al., 2022༉, and to improve the sinking diffusion degree of new green agricultural technology, which is a practical problem that needs to be solved urgently.

Farmers are the implementers and operators of green agricultural technology, and their willingness to adopt them is directly related to the scope and extent of the "implementation" of the technology(Guo et al., 2022). Green agricultural technology has the characteristics of high investment, long return period, and positive externalities, and farmers' adoption behavior is affected by multiple factors༈Zheng et al., 2022༉. In this paper, we found that the characteristics of farmers' own endowments, such as age, gender, and education level, were the primary factors influencing farmers' adoption behavior, and secondly, the characteristics of rural households, such as the number of working-age laborers, land management area, the proportion of households with agricultural income༈Chen et al., 2022༉, and the degree of part-time agricultural employment, constituted the structural factors of farmers' adoption behavior. The effective promotion of agricultural technology needs to uphold the working idea of "farmer-oriented, agricultural production for use"༈Luo et al., 2022༉. At the level of technology promotion, factors such as the frequency of training meetings, the ability of agricultural technology extension personnel to communicate and teach, the accuracy and intensity of technology extension, and the degree of Internet access of farmers affect the willingness of farmers to release their endowments༈Yang et al., 2022༉, technology adopts incentive and subsidy measures, introduces the participation of financial institutions, and effectively integrates various resources to maximize the role of the bottom line and reduce the adoption of risk risks༈Jiang et al., 2022༉. At the level of market demand, the demand for "green, organic and pollution-free" food and the health awareness of residents' food have a positive guiding effect on the adoption behavior of farmers༈Li et al., 2022༉. In addition, some scholars have demonstrated the diversity and differences in the starting point of farmers' willingness to adopt green agricultural technology from the perspectives of "bounded rationality" and "external environment and institutional factors". Some scholars have also studied the technology adoption behavior of farmers around the type of farmer operation༈Passarelli et al., 2022). Li Xian bao believes that due to the different production and operation objectives of various agricultural business entities, there are obvious differences in the allocation of production factors such as labor, land, and capital and the adoption of technology. Kong Xianzhi believes that new agricultural business entities, such as large planters, family farms, professional farmer cooperatives, and agricultural enterprises, will adopt advanced agricultural production technologies to reduce the growing production costs of labor and land. When Gou Lufeng et al. studied the influencing factors of farmers' technology adoption from the perspective of farmer differentiation, they found that cooperatives can provide sufficient technology supply for farmers༈Foguesatto et al., 2022༉, agricultural enterprises can use their own favorable thermal conditions to guide technical support, and farmers who operate family farms often have a high demand for new technologies༈Rizzo et al., 2024༉. The existing literature provides an important theoretical basis for this paper and has important reference value for this paper, but there is still room for further improvement: first, in terms of research methods, most of the existing studies focus on the macro, qualitative and theoretical analysis of the adoption behavior of green agricultural technology farmers or the traditional regression analysis model can be applied, and the variables are difficult to meet the preconditions of mutual independence, linear correlation, and causal symmetry༈Campos, 2022༉, and it is difficult to explain the interaction effects of more than three interactive variables. The micro empirical research on the combination of deep-seated factors influencing farmers' adoption behavior needs to be strengthened. Second, in terms of research content, the adoption of new green farming technologies by farmers may be a decision-making process in which multiple independent variables (more than 3) depend on each other, and it is still unknown whether different factors are combined with each other to form different configurations༈Sarkar, 2022༉, and how these configurations systematically affect the effectiveness of farmers' green technology adoption behaviors. The process of configuration theorization expands and enriches the research on the correlation between individual factors and farmers' behavior and its moderating effect. Previous research results often present several independent factors and corresponding countermeasures for the factors, and the synergistic impact of multi-factor interaction and combination on the improvement of farmers' technology adoption performance should be considered.

Based on this, it is assumed that the improvement of farmers' performance in adopting new green agricultural technologies is not only restricted by their own endowment conditions, subjective cognition, release willingness and other value trust, but also by the technical information matching platform. This study is based on the construction of a willingness(Mgomezulu, 2023), value trust, external technology system trust, and adoption performance improvement model, combined with the arid region of the Loess Plateau in Shaanxi Province5 Based on the county micro survey data, the qualitative comparative analysis method was used to explore the synergy of multiple factors "technology transformation༈Giua et al., 2022༉, endowment willingness, organizational model, government measures, and market vitality" on farmers' technology adoption performance impact, identifying the configuration pathways (i.e., the combination of different factors/criteria that produce a certain outcome) to improve the farmer's adoption performance. This study is helpful to deepen the rational understanding of the configuration path of improving the performance of dryland farming farmers on the Loess Plateau, and also provides a new analytical approach for the synergistic influence of multiple factors on the adoption of new green farming technologies by dryland farmers on the Loess Plateau.

This paper constructs an integration model of willingness, value trust, external technology system trust, and adoption performance improvement. It is believed that the adoption of new green agricultural technology by farmers is the result of the combined effect of endogenous factors such as individual participation cognition and endowment release willingness and externalities such as the construction of green agricultural technology information matching platform, policy guarantee, market, and ecological risk(Ochieng et al., 2022), and explains the influence effect of a single factor and the multiple concurrent causal relationship of each factor༈Oyetunde-Usman, 2022༉. Performance improvement refers to the adoption of green agricultural technologies by farmers, and which factors and combinations can be positive factors that can enhance farmers' willingness to adopt and promote farmers' adoption behavior༈Andati et al., 2022༉. There is a high degree of consistency between performance improvement and adoption behavior, and the enthusiasm and initiative of farmers to adopt new green agricultural technologies directly affect the degree of performance improvement༈Chi, 2022༉.

Willingness value trust refers to the composition of the internal driving factors of farmers' adoption performance, which is the degree of farmers' willingness to release their own endowments to accept new green agricultural technologies(Laksono et al., 2022), including their own endowments, land attachment, environmental protection concepts, and large household drives༈Bagheri et al., 2022༉. The stronger the willingness to Xi technology, the more it can stimulate the subjective initiative of farmers to learn new technologies, and the higher the probability of adopting new technologies in agricultural production practice.

Institutional trust in external technology refers to the external factors of farmers' adoption performance(de Lauwere, 2022), which is the reality and institutional environment that farmers need to face and consider in adopting green agricultural technology, including policies, subsidies, insurance, markets, risks, etc༈Ren, 2023༉. Technology transfer and government measures are important prerequisites for farmers' willingness and behavior changes, and they are also important dominating forces for farmers' adoption. The government's regulatory measures such as agricultural product quality supervision and administrative penalties can regulate and restrain farmers' production behaviors༈Zheng, 2022༉, and various government-led training and promotion projects on reducing and increasing efficiency can stimulate farmers' enthusiasm for adopting green agricultural technologies༈Mgendi, 2022༉. The supporting use of various policies can better play the role of the government in driving farmers' technology to adopt green production.

2.1 Research Area

The Loess Plateau, located in the north-central part of China, in the middle reaches of the Yellow River, is a world-famous large-area loess covered plateau, with an area of 635,000 km2, and is one of the four major plateaus in China. The Loess Plateau is an important energy and chemical base in China. The loess particles are fine, the soil is soft, rich in mineral nutrients, except for a few stony mountains, the thickness of the loess is between 50 ~ 80m, and the thickest is up to 150 ~ 180m. The long history of agricultural reclamation in the basin and river valley makes the ecological environment of the Loess Plateau more fragile and more responsive to climate change. For a long time, the development of the Loess Plateau region has faced serious problems such as serious soil erosion, sparse vegetation, high population pressure and low productivity, and is one of the areas with the most concentrated contradictions in Chinese population, resources and environment.

The popularization and adoption of green agricultural technology on the Loess Plateau have effectively improved the local ecological landscape, significantly improved the agricultural production capacity of the Loess Plateau, and the industrial model has become increasingly diversified. However, due to the lack of rationality of the overall vegetation restoration allocation, the ecological environment of the Loess Plateau degraded and deteriorated in the later period. The fragile background of the loess ecological environment has not been fundamentally changed, and the risk of natural disasters has always been high, which leads to low land use efficiency and brings the problem of livelihood fragility of farmers. The key to the high-quality development of the ecological environment and social economy of the Loess Plateau still needs to rely on science and technology, and it is urgent to give full play to the role of green agricultural technology in resource allocation and scientific decision-making, so as to consolidate the effectiveness of comprehensive ecological management of the ecological environment of the Loess Plateau and help the ecological protection and high-quality social and economic development of the Loess Plateau(Figure 1).

2.2 Research Methods

Qualitative Comparative Analysis (QCA) is based on the ideas of Boolean algebra and set theory proposed by Charles Raging in his book "Comparative Methods: Beyond Qualitative and Quantitative Measurement8 in 1987Based on the case analysis, this paper seeks the research methods of multiple concurrent causal relationships and multiple configurations between the explanatory variables and the explanatory variables(Ragin C et al., 2006). QCA treats multiple causes in each case as conditional variable configuration, which is suitable for case studies with small and medium-sized sample sizes and complex causal relationships, which solves the problems of poor external deduction and lack of depth of traditional quantitative analysis methods in traditional qualitative analysis methods, and is committed to analyzing and explaining the comprehensive effects of various antecedent conditions, which is a kind of research that goes beyond qualitative and quantitative method. The method can be divided into several analysis techniques, such as clear set analysis (cs QCA), multi-value set analysis (mvQCA), and fuzzy set analysis (fsQCA) ༈Fiss P C., 2007༉. In this paper, the fuzzy set analysis method will be used. The analysis steps are as follows: first, the conditions and result variables are calibrated and converted into sets. Secondly, a necessity analysis of a single condition (including its non-set) is carried out to analyze whether a single conditional variable constitutes a necessary condition for the result Finally, the original consistency threshold, PRI threshold and case frequency threshold are set, and the configuration path of the result is obtained by combining counterfactual thinking and Boolean minimization operation. Consistency and coverage are two measures of individual conditional necessity and configuration paths. Consistency (Eq. (1)) refers to the extent to which conditions and their combinations can derive results. Coverage (Eq. (2)) refers to the degree to which the conditions and their combinations interpret the results. Since there is more than one condition or combination that produces the same result, there will be overlap between the conditional configurations to interpret the results. Therefore, the coverage is further divided into the original coverage, the unique coverage, and the coverage of the overall solution. Raw coverage refers to the degree to which results are interpreted with overlap between configurations; Unique coverage refers to the degree to which a single configuration interprets the results after removing the overlaps༈Schneider C et al., 2012༉. The coverage of the overall solution is a type of total coverage, which refers to the degree to which the results are interpreted by all configurations.

$$\:Consistency({X}_{i}\le\:{Y}_{i})=\sum\:_{i=1}^{n}\left[\text{min}\left({X}_{i},{Y}_{i}\right)\right]/\sum\:_{i=1}^{n}\left({X}_{i}\right)$$

$$\:Coverage\left({X}_{i}\le\:{Y}_{i}\right)=\sum\:_{i=1}^{n}[min({X}_{i},{Y}_{i})]/\sum\:_{i=1}^{n}\left({Y}_{i}\right)$$

where Consistency denotes the consistency of the overall solution; Coverage represents the coverage of the overall solution. There are $\:{\:X\:}_{i}$six conditional variables: technology transformation, willingness endowment, organizational model, market vitality, regulatory government measures, and encouraging government measures;$\:{Y}_{i}$It is the first farmer to increase the performance of adopting new green agricultural technologies(Fiss P.C., 2022).

2.3 Selection of study samples

According to the research method adopted in this paper, the following four basic criteria are adopted for the selection of cases in this paper: (1) Number of samples: The number of cases required by the clear set qualitative comparative analysis method is between 10 ~ 60, and 5 cases are selected according to the characteristics of "farmers' technology adoption performance" of the research object (2) Credibility: The selected cases are all from first-hand field interviews, official websites such as local government affairs platforms and the Department of Agriculture and Rural Affairs, which are authoritative to a certain extent(3) Coverage: The selected cases cover all types of local farmers with green agricultural techniques;(4) Differences: On the basis of coverage, special attention is paid to the selection of different factor combination models of different types of farmers, so as to conduct a comparative analysis of multiple causes and effects based on the same results.

From July to August 2023, the research team will go deep into the demonstration counties of the project of "Water Adaptation and Soil Improvement and Collaborative Improvement of Production Capacity in Dryland Agriculture on the Loess Plateau" - W County of YA City and D County of YL City, J County, M County, and S County, and carried out field research on the performance improvement of the adoption of new green agricultural technologies and the configuration path of dryland farmers. The key to the high-quality development of the ecological environment and social economy of the Loess Plateau still needs to rely on science and technology, and it is urgent to give full play to the role of green agricultural technology in resource allocation and scientific decision-making, so as to consolidate the effectiveness of comprehensive management of the ecological environment of the Loess Plateau and help the ecological protection and high-quality social and economic development of the Loess Plateau. Therefore, in response to the urgent practical needs, this paper will take the rural households in the five counties as the research object, and adopt the method of stratified random sampling, with 3 townships randomly selected in each county, and 3—4 administrative villages, 10 questionnaires were randomly distributed in each village, and a total of 525 questionnaires were collected, of which 37 questionnaires did not meet the screening conditions, and 488 valid questionnaires were collected, with an effective rate of 92.95%. Using a combination of semi-structured interviews and questionnaires, one-to-one or two-to-one open-ended interviews were conducted around the influencing factors of green agricultural technology adoption, and the interview time was 20 ~ 30 minutes per person Secondly, a structured questionnaire was issued, which was completed by the interviewed farmers and assisted by the enumerator, and detailed in-depth interview data and first-hand data were obtained, and 488 research cases in this paper were collated.

According to the above-mentioned intention value trust-external technology system trust-performance improvement integrated analysis framework, performance improvement is taken as the outcome variable, technology transformation, endowment willingness, organizational model, government measures, and market vitality5 variables were used as antecedents. Among them, endowment willingness is the internal driving agent variable of psychological perception, and technology transformation, organizational model, government measures and market vitality are Extrinsic proxy variables for regulatory contexts. All items in the questionnaire are in the form of a five-level Likert scale, and the answer method is individual subjective assignment, ranging from "no effect" to "very effective", "insufficient mastery" to "fully mastered". The value of each variable is the sum of the items of the relevant dimension, and the higher the value, the higher the degree of the corresponding variable.

3.1 Technology transformation

Technology transformation and the construction of a green agricultural technology promotion information matching platform are of fundamental significance for farmers' adoption. At present, the scope of China's agricultural production has expanded somewhat compared with the past, and the production links are not limited to the traditional ones but have touched on the subsequent links such as the processing and sales of agricultural products. Therefore, the new green agricultural technology can not only be applied to the production of agricultural products but must also have a certain applicability and pertinence to the follow-up links. Only by applying the right agricultural technology and agricultural machinery in the right place can we effectively improve the level of agricultural development in our country. As far as China's current situation is concerned, although the country has paid great attention to agricultural development, China's agricultural development level is still low, and the application rate of new green agricultural technology and agricultural machinery is not satisfactory. The successful construction and operation of the information matching platform requires the complementarity and coordination of scientific research institutes, universities, agricultural enterprises, and grassroots extension workers, which is related to the opportunities for farmers to get in touch with green agricultural technology and promotes the exchange and sharing of technological achievements and effective transformation. On the one hand, there is farmers' perception of the technology itself, such as the degree of understanding of new green agricultural technologies ^[12]; On the other hand, it is necessary to consider farmers' perception of the function or effect of technology, including the importance of improving agricultural production skills and management level.

3.2 Endowment intention

Based on previous research, this paper identifies the age, gender, education level of farmers, and their family characteristics, such as the number of laborers of appropriate age, land management area, proportion of households with agricultural income, degree of part-time agricultural business, and the nature of differentiated agricultural management entities Farmers' environmental awareness and land attachment complex are also important factors to consider in terms of farmers' willingness to release their endowments.

3.3 Endowment will

In this paper, agricultural cooperatives are identified as cooperative organizations in a broad sense, including agricultural production cooperatives, large planters, family farms, and "soil experts". It can be defined as the establishment of a model interaction mechanism between agricultural cooperative organizations and farmers, and a series of formal or informal institutional arrangements such as unified production management, technology publicity and promotion, and product sales and services are made to members in the form of contracts, so as to change farmers' awareness of green agricultural technologies and form internal or external incentives and constraints for technology adoption behaviors. The topics set in this article are "The requirements of cooperatives (agricultural enterprises) for the quality and quality of agricultural products purchased or helped to sell" and "The needs of cooperatives (agricultural enterprises) for farmers in the production process. “Meet the "training efforts of cooperatives (agricultural enterprises) to farmers on new green agricultural technologies".

3.4 Government measures

In the process of promoting green agricultural technologies, as: popularize. One of the main subjects of the government. Promote adoption by farmers R&D and implementation of policies dynamics. It can be divided into two types: regulatory measures and incentive measures. Visit Farmers “Law Enforcement Brigade regulatory enforcement in the agricultural production process" and "due to the quality and safety of agricultural products detect problem and the possibility of being penalized ".Regulatory measures. Assign a value;Ask the farmer "Government subsidy." Distribution to adopt green agriculture. The impact of technology on government organizations. Green agriculture. Satisfaction with technical training" "Government departments right green agriculture technology advocacy and "agricultural technology extension stations (agricultural technicians) to adopt Green agriculture whether technology is helpful" to government regulation behavior. Assign a value.

3.5 Market vitality

Market vitality refers to the guiding role of the market, which can be divided into positive orientation and negative orientation. Positive orientation refers to the high demand and stable profits of green agricultural products have an important role in countering and guiding the adoption of new technologies by farmers, while negative orientation refers to the inherent lag and blindness of the market and the fierce competition brought about by high homogeneity and low integration of agricultural products in the county.

3.6 Performance improvement

Performance improvement will be evaluated from three dimensions: economic effect (operationalized into production increase effect, income increase effect), technical effect, and ecological benefit. Performance improvement includes three dimensions: economic effect, technical effect, and management effect of adopting green agricultural technology. and the benefit of increasing income is achieved through "reducing labor expenditure." "Improving the profit per mu" and "reducing the use of water, electricity and other resources" are measured, and the productivity benefit is measured by "green agricultural technology operability", "guidance effect of technical extension personnel" and "green agricultural technology production conversion rate". The ecological benefits are measured by "the degree of improvement of the ecological environment after the use of the technology" and "the evaluation of the alleviation of soil compaction after the use of the technology".

Table 1

Variable determination
Type	Variable	Variable measurement content	Variable assignment
Conditional variables	Technology Transfer	How do you rate your mastery of the technology?	Very Ignorant = 1, Not Very Aware = 2, Fair = 3, Somewhat Aware = 4, Very Aware = 5
		Do you think green agriculture technologies are important for increasing yields?	Very unimportant = 1, not too important = 2, fair = 3, somewhat important = 4, very important = 5
		Do you think green agricultural technology is important for ecological and environmental protection?	Very unimportant = 1, not too important = 2, fair = 3, somewhat important = 4, very important = 5
	Endowment willingness	Are you willing to adopt green agriculture technologies?	Very reluctant = 1, not very willing = 2, fair = 3, somewhat willing = 4, very willing = 5
		How do you recognize your ability to adopt technology?	Very Low = 1, Relatively Low = 2, Fair = 3, Relatively High = 4, Very High = 5
		Without external involvement, would you take the initiative to adopt green agricultural technologies?	Definitely not = 1, not very likely = 2, generally = 3, probably = 4, definitely = 5
	Organizational model	What is the degree of guidance given by cooperatives to green agricultural techniques?	Very Low = 1, Relatively Low = 2, Fair = 3, Relatively High = 4, Very High = 5
		What is the evaluation of the work attitude of the technical promoter?	Very bad = 1, relatively bad = 2, fair = 3, relatively good = 4, very good = 5
		Evaluation of the promotion form of the technical promoter?	Very unsuitable = 1, relatively unsuitable = 2, fair = 3, relatively suitable = 4, very suitable = 5
	Government measures	Do you have any assessment of the government subsidy guarantee?	Very out of place = 1, Relatively out of place = 2, Fair = 3, Relatively in place = 4, Very in place = 5
		Are you satisfied with the green agricultural technology training organized by the government?	Very Dissatisfied = 1, Somewhat Dissatisfied = 2, Fair = 3, Somewhat Satisfied = 4, Very Satisfied = 5
		Is the government promoting green agricultural technology strongly?	Very small = 1, relatively small = 2, fair = 3, relatively large = 4, very large = 5
		How strong is the supervision and enforcement of the law enforcement brigade in the agricultural production process?	Very small = 1, relatively small = 2, fair = 3, relatively large = 4, very large = 5
		The possibility of being penalized for problems arising from the quality inspection of agricultural products?	Very small = 1, relatively small = 2, fair = 3, relatively large = 4, very large = 5
	Market dynamism	How much does good sales of green produce affect your technology adoption?	Very small = 1, relatively small = 2, fair = 3, relatively large = 4, very large = 5
	Market dynamism	To what extent has sluggish sales of green produce affected your technology adoption?	Very small = 1, relatively small = 2, fair = 3, relatively large = 4, very large = 5
Outcome variables	Performance improvement	How much has the quality of agricultural products improved significantly?	Completely invalid = 1, Relatively invalid = 2, Fair = 3, Somewhat effective = 4, Very effective = 5
		How well do green agricultural technologies match local agricultural production?	Very Low = 1, Relatively Low = 2, Fair = 3, Relatively High = 4, Very High = 5
		How much has the output of agricultural products increased significantly?	Completely invalid = 1, Relatively invalid = 2, Fair = 3, Somewhat effective = 4, Very effective = 5
		What is the operability and transformation ability of green agricultural technology?	Very Low = 1, Relatively Low = 2, Fair = 3, Relatively High = 4, Very High = 5
		How obvious is the implementation of green agricultural technology on the protection of the local ecological environment?	Completely invalid = 1, Relatively invalid = 2, Fair = 3, Somewhat effective = 4, Very effective = 5
		How much does the economic return of farmers increase?	Very Low = 1, Relatively Low = 2, Fair = 3, Relatively High = 4, Very High = 5

SPSS24.0 was used to test the reliability and validity of 5 antecedent variables and 1 outcome variable, and the results are shown in Table 2. Except for the slightly lower Cronbach's α coefficient of endowment willingness and government measures, the Cronbach's α coefficient of the other variables was greater than the recommended value of 0.7, which passed the reliability test, and the questionnaire results met the test requirements of stability and consistency. The overall KMO value of the questionnaire was 0.775, indicating that the measurement index could effectively reflect the underlying traits of the common factors, and the chi-square approximation of Bartlett's spherical test was 706.413, with a significance level of 0.000, indicating that the scale is suitable for factor analysis, see Table 1 for data sources.

Table 2

Reliability test
Variable name	Number of items	Cronbach's α coefficient
Technology Transfer	3	0.751
Endowment willingness	3	0.668
Organizational model	3	0.769
Government measures	5	0.642
Market dynamism	2	0.715
Performance improvement	6	0.869

The Fuzzy Set Qualitative Comparative Analysis (fsQCA) method analyzes the set relationship rather than the variable and needs to calibrate the original variable dimension into the set membership, that is, whether the research case belongs to a certain set. Converting variables to set membership using fuzzy sets generally requires three thresholds: full membership, intersection points, and no membership at all. To avoid subjectivity, the values of each variable at 75%, 50% and 25% are used as the calibration parameters, and the specific calibration anchor points are shown in Table 3.

Table 3

Fuzzy calibration of the collection
Gather	Calibrate the anchor point
Gather	Not affiliated at all (25%)	Intersection (50%)	Full subordination (75%)
Technology Transfer	12	13	14
Endowment willingness	8	9	10
Organizational model	7	8	11
Government measures	6	7	8
Market dynamism	16	17	18
Performance improvement	32	33	34
Note: The six variables of technology transformation, endowment willingness, organizational model, regulatory government measures, encouraging government measures, and performance increase are fully subordinated. The intersection points and the three calibration points that were not affiliated at all were set as the upper quartile(75%), median (50%), and lower quartile (25%) of the descriptive statistics of the case sample, respectively༈Figure 2).

4.1 Necessity analysis of individual conditions

fsQCA based on set theory uses consistency indicators to identify sufficient or necessary subsets between different conditional variables, the configuration formed by the conditional variables and the outcome variables, the necessary conditions refer to the conditions that always exist when the results occur, and the results always occur when sufficient conditions appear, QCA mainly analyzes the necessity of a single condition and the adequacy of the conditional configuration. The coverage index reflects the explanatory power of the condition in the process of the outcome, and the higher its value, the stronger the explanatory power. The necessity test is an important step in QCA, and if the consensus value of a single antecedent condition is greater than 0.9, it means that the condition is a necessary condition for the outcome variable. Table 4 shows the test results of whether each antecedent condition constitutes a necessary condition to improve the effect of farmers' behavior. It can be seen that the consistency of the five conditional variables selected in this paper is less than 0.9, indicating that technology transformation, endowment willingness, organizational model, regulatory government measures and None of the encouraging government measures are necessary for farmers to effectively adopt the technology of reducing chemical fertilizer application and increasing efficiency, that is, the improvement of performance is not caused by a certain antecedent condition, and the influence of each conditional variable on the behavior effect is interdependent, and there may be "multiple concurrent causation", which needs to be further investigated the effect of its configuration path.

Table 4

Necessity test of individual conditions
Conditional variables	Consistency	Coverage
Technology Transfer	0.648	0.600
~Technology Transfer	0.439	0.580
Endowment willingness	0.681	0.625
~Endowed willingness	0.439	0.587
Organizational model	0.547	0.557
~Organizational model	0.564	0.555
Regulatory government measures	0.409	0.610
~Regulatory government measures	0.670	0.555
Incentivized government measures	0.516	0.584
~Incentive-based government measures	0.530	0.577
Note: "~" means "not" in fuzzy set operations.

4.2 Adequacy analysis of conditional configurations

Adequacy analysis uses a truth table algorithm to identify whether a configuration is a sufficient configuration of a result, and when the agreement between a particular configuration and the result is greater than or equal to 0.75, the configuration can be considered as the adequacy configuration of the result ^[12]. Referring to the widely accepted practice in previous studies ^[15], the original consistency threshold is set at 0.8 in this paper. In addition to the consistency threshold, the number of cases covered by a particular configuration is also a screening criterion for a particular configuration to enter the Boolean minimization process. The frequency threshold of small and medium-sized samples (10ཞ100 cases) should not be less than 1, and the frequency threshold of large samples can be appropriately increased. In this paper, the case frequency threshold is set to 2, and the complex, concise and intermediate solutions of the model are calculated by fsQCA 3.0 software, and Table 5 shows the conditional configuration analysis results for farmers to effectively adopt the technology of reducing chemical fertilizer application and increasing efficiency. According to the theory of Richwood and Larkin(2017), this paper mainly refers to the intermediate solution to explain the processing results of QCA and combines the degenerate solution and the intermediate solution to identify the core conditions and edge conditions of the configuration. If an antecedent condition occurs in both the concise solution and the intermediate solution, it is the core condition; If the antecedent condition occurs only in the intermediate solution, it is an edge condition. A solid circle (●) indicates that a condition exists, a crossed circle (⊗) indicates a condition is missing, a large circle indicates a core condition, and a small circle indicates an edge condition. There were three antecedent configurations with explanatory power to improve the behavioral effect of farmers, and their overall consistency was 0.868, which was higher than the theoretical threshold of 0.8, and the consistency of single antecedent configuration was 0.916, 0.888, and 0.811, respectively, which were also higher than 0.8, indicating that these three antecedent configurations were sufficient conditions for improving the behavioral effect. The overall coverage rate was 0.537, indicating that these three configurations could explain the real-world cases to a certain extent. In general, these three different configurations can better improve the behavior effect of farmers' adoption of technology, and they are equivalent, that is, "different paths to the same end".

Configuration 1 is summarized as the endowment willing-dominated model, which indicates that even if the farmers' psychological cognitive level is low, and the constraints of the external context such as regulatory and encouraging government measures and organizational models are weak, the willingness to actively participate can improve the adoption of farmers. Improved performance of green agricultural technologies. Among them, technology transformation, endowment willingness, and encouraging government measures are the core conditions, and the organizational model and regulation are the core conditions type of government measures are marginal conditions. This configuration can be expressed as: when the effect of technology sinking application transformation has not appeared, and a series of encouragement and regulatory measures such as government supervision cannot play an effective role, the local area. When agricultural cooperatives and leading enterprises fail to provide substantive technical training services, and farmers do not have a general understanding of new green agricultural technologies, if farmers. The willingness to adopt endowment plays a leading role, and the willingness and behavior can also maintain a high level of consistency. Through in-depth interviews, it is learned that the farmers who meet this configuration have obvious common characteristics: they have actively participated in the new green agricultural technology training organization meeting for many times and adopted green agriculture. The quality of the agricultural products produced has also been significantly improved, and above-average profits can be realized, which has become a strong driving force for the transformation of farmers' endowment intentions into sustainable behaviors and has produced good performance improvement after the adoption of technology. Furthermore, it is found that different agricultural business entities are based on examining the cost of technology adoption, the profit margin of technology adoption, the risk of technology adoption, and the ability to apply technology, the business endowment of the subject is judged. Among them, the demand for new green agricultural technology for small-scale subsistence farmers and small-scale part-time farmers is mainly reflected in the substitution of labor factors through technology, reducing the labor burden, reducing the negative impact of the transfer of high-quality labor to non-agricultural fields, and stabilizing the output level of production factors such as labor and land. Its business scale is generally small, so the growth of its technology update income is very limited, and it is unwilling to bear the high cost of technology adoption, showing the characteristics of strong technology adoption risk aversion; family farms or large planters are guided by market demand and pursue the goal of high-yield, high-quality, and high-efficiency agricultural production The expected benefits brought by new green agricultural technologies are more attractive to them, and the ability of this type of farmers to bear the adoption risk and cost is higher than that of small-scale farmers.

Configuration 2 is summarized as an internal and external joint constraint model, which indicates that the improvement of farmers' performance requires external situational conditions such as regulatory government measures and market vitality, and at the same time, farmers' subjective cognition is internal. The foundation, endowment willingness, organizational model and encouragement government measures play a supporting role, and various internal and external factors interact with each other as antecedent conditions. Among them, the organizational model, regulatory government measures, and encouraging government measures are the core conditions, endowment willingness, and market vitality is an edge condition. This configuration shows that the establishment of individual behavior of farmers in the middle stage of technical training depends on the cognition of the technical information environment, and the initial decision of farmers to adopt new green agricultural technologies requires regulatory government measures Under the guidance of external compulsory system changes, the cognition formed is often more concrete, clear and stable, which is more conducive to the transformation of farmers' adoption behavior from passive acceptance behavior to active sustainable behavior. In the field visit and research, it was found that most farmers were aware of environmental problems such as the deterioration of the agricultural production environment and agricultural non-point source pollution, and generally had the awareness of green agricultural production and clean and circular production, but the degree of implementation of the behavior was still there. It is relatively low, showing the contradictory phenomenon of "high cognition and low behavior". If the government imposes mandatory regulatory measures to strengthen the supervision and punishment of violations of the treaty on the quality and safety of green agricultural products, farmers, as rational smallholders, will first consider the additional costs incurred, and when the marginal benefits of individuals are less than the marginal costs, farmers will take the initiative to adopt and Xi learn green agriculture driven by loss aversion Technology. Regulatory government measures can guide and regulate farmers' production behaviors, and promote the transformation of cognition into endogenous power, that is, the combined pressure of subjective psychological factors and external situations can guide farmers to effectively adopt green agricultural technologies.

Configuration 3 is summarized as an external context-led model, in which the effective adoption of green agricultural technologies by farmers mainly depends on the role of external contexts such as organizational models and regulatory government measures. The complementary role of endowment willingness and encouraging government measures. Among them, endowment willingness, organizational model and encouraging government measures are the core conditions, and technology transformation and regulation government measures are marginal conditions. This configuration reflects that the farmers who participated in the technical training at the beginning are not willing to increase their awareness of green agricultural technologies and their endowments. The supervision of environmental pollution and the quality and safety of agricultural products can enhance the restraining effect on farmers' non-standard behaviors, and agricultural cooperative organizations can effectively play an incentive and guiding role to make up for the shortcomings of farmers' scattered production behaviors. On the one hand, the traditional production mode of high input and high output makes farmers have a strong willingness to change, and in the early stage of farmers' transformation of fertilization behavior, the government clearly puts forward requirements and restrictions on the production process of agricultural products, which reduces the possibility of farmers' speculative behavior to a certain extent, and farmers have to do so under high-intensity supervision Change the traditional production mode and adopt clean and circular green agricultural production technology. On the other hand, the organizational model is to induce institutional changes, and the cooperative organization provides technical training services for members, unifies the production factors such as materials required for technology, and implements standardized management of the production process, unified purchase and sales of agricultural products and other socialized services, which can be realized by farmers under large-scale production "High quality, good price", long-term and stable economic income stimulates farmers to form strong behavioral motivation, and further improves the performance of farmers in adopting green agricultural technologies(Figure 3).

Comparing configuration 2 and configuration 3, it is found that there is a substitution relationship between technology transformation and organizational mode when regulatory government measures occur. Only one condition exists in the technology transformation and organization model to improve the behavior effect of farmers' adoption of technology. Summarizing the above three configurations, the effectiveness of psychological perception and external situation factors is different in the farmers at different stages of technical training, and the farmers in the early stage need more joint pressure from the external environment such as regulatory government measures and organizational models, and the farmers in the middle stage gradually form pairs. However, the guiding and normative role of regulatory government measures is still indispensable, and the willingness of farmers' endowment occupies a dominant position in the later stage, which can directly promote the effective adoption of farmers Green agricultural technology.

Table 5

Conditional configuration for effective technology adoption by farmers in fsQCA
Antecedent conditions	Configuration one	Configuration two	Configuration three
Technology Transfer	⊗	●	⊗
Endowment willingness	●	⊗	⊗
Organizational model	⊗	⊗	●
1	⊗	●	●
Encouraging government measures	⊗	⊗	⊗
Market dynamism	⊗	⊗	⊗
consistency	0.917	0.888	0.812
Raw coverage	0.270	0.165	0.176
Unique coverage	0.230	0.129	0.130
Overall consistency		0.867
Overall coverage		0.537
Note: ●=Core conditions exist; ⊗ = core condition is missing; ●=Edge condition presents; ⊗ = Edge condition is missing.

4.3 Robustness test

After the consistency level was increased from 0.8 to 0.85, the test results screened out the original configuration 3, and the other configurations did not change, and the overall consistency became 0.908, and the overall coverage rate became 0.508, there was no significant change, indicating that the results of the study were generally reliable, as shown in Table 6.

Table 6

Robustness test to increase the consistency threshold
Antecedent conditions	Configuration one	Configuration two
Technology Transfer	⊗	●
Endowment willingness	●	⊗
Organizational model	⊗	⊗
Regulatory government measures	⊗	●
Encouraging government measures	⊗	⊗
Market dynamism	⊗	⊗
consistency	0.917	0.888
Raw coverage	0.270	0.165
Unique coverage	0.243	0.137
Overall consistency	0.908
Overall coverage	0.508
Note: ●=Core conditions exist; ⊗ = core condition is missing; ●=Edge conditionpresents; ⊗ = Edge condition is missing.

Based on the in-depth interview data of 488 smallholder farmers in the demonstration counties of the project, this paper constructs a theoretical model of willingness, value trust, external technology trust, institutional trust, and performance enhancement, and uses the fuzzy set qualitative comparative analysis method(fsQCA) to explain the multiple concurrent causal mechanisms of various behavioral decision-making factors affecting the behavior effect of farmers' adoption of green agricultural technologies. The results show that a single antecedent condition is not a necessary condition for farmers to effectively adopt technology, and the joint action of multiple decision-making factors forms three conditional configurations to promote the improvement of farmers' adoption performance, which are summarized as the dominant model of endowment willingness, Internal and external joint constraint model, and external situational dominant model. First, when the external situational conditions such as farmers' psychological cognition, organizational connection, and government regulation play a limited role, endowment willingness is the key to improve the effect of farmers' adoption of technology. Second, the joint constraints of farmers' subjective psychological cognition and external regulatory government measures are the main ways to realize farmers' effective adoption of fertilizer reduction and efficiency improvement technologies. Third, when the internal conditions such as technology transformation and endowment willingness are not available, external situational factors such as regulatory government measures and organizational models play a role in improving rural households Adopt important ways to improve performance. In addition, when the compulsory government measures occur, there is a substitution relationship between endowment intention and organizational model, and there is a substitution relationship between farmers' cognition and organizational model. The existence of only one condition can improve the behavioral effect of farmers' adoption of technology.

Based on the three conditional configurations to improve the performance of farmers' green agriculture technology, the following enlightenment is obtained in this paper:

Pay attention to cultivating and enhancing the willingness of rural households to participate in green production and use new green agricultural technologies. Improve the degree of organization of rural households, provide new green agricultural technologies, services, and information for rural households through professional cooperatives and leading enterprises, actively guide rural households to carry out green production, and promote the improvement of the quality of agricultural products. Actively establish technology-based village entry projects, with agricultural experts, technical lecturers, technology demonstration families, and ordinary rural households. The four-in-one agricultural technology is used as a promotion model to enter the village, shorten the distance between agricultural technology and farmers, and promote the transformation of agricultural scientific and technological achievements. Through the brand effect of green agricultural products, product premiums are formed, farmers' agricultural income is improved, and farmers' willingness to green production is ultimately promoted. Under the circumstance that it is difficult for some farmers to change their objective resource endowment conditions quickly, some subjective and controllable conditions can be changed in a targeted manner, and the substitution role of institutional rules and social culture on resource endowment can be brought into play to improve farmers' new green agricultural technology Performance.

Strengthen government supervision, give full play to the active role of the government, and improve the level of farmers' active awareness of green agricultural production. Publicize the significance of developing green agriculture through mobile phones, WeChat, radio, the Internet, and other means, promote and demonstrate new green agricultural technology models, and guide farmers to form green production awareness. Through a series of regulatory measures such as quality inspection of agricultural products and supervision of the production process, the government has appropriately increased the punishment of opportunistic behaviors such as excessive application of chemical fertilizers and substandard quality of agricultural products and standardized and guided farmers to switch to green production behaviors.

It is necessary to promote the active integration of farmers into the green development of agriculture, agricultural cooperative organizations should provide technical, information, service and financial support for small farmers who lack production factors, and government departments should implement differentiation and differentiation according to the heterogeneity of the endowment characteristics of agricultural technology audiences. Precise policy guidance actively guides and regulates farmers' technology adoption behaviors through incentive and restraint mechanisms, and effectively promotes the performance improvement of farmers' adoption of green agricultural technologies.

Strengthen the construction of market order for green agricultural products, build an embedded global perspective on the development of green agriculture, improve the database of green agricultural information resources, and grasp the demand for various green agricultural products, price and other information monitoring and early warning release; Improve the quality inspection of green agricultural product sales, and comply with residents' demand for ecological, organic and healthy consumption of agricultural products Increasing subsidies for the use of new green agricultural technologies to improve the quality of green agricultural products and increasing farmers' incomes, and with the support of local policies, promoting the construction of core areas for the production of green agricultural products It is necessary to speed up the building of regional brands of high-quality agricultural products and give full play to the leading role of local leading enterprises; extend the industrial chain of agricultural products, deeply integrate and develop with secondary and tertiary industries, and process agricultural and sideline products; Develop a collectivized management model of green agriculture, attract the participation of rural households, and encourage large households to help small households, small households operate in groups, and promote a good cycle of green agricultural technology promotion.

Through measures such as cultivating farmers' sense of belonging to their hometown and strengthening farmers' awareness of the importance of green agricultural technology, form a loess culture with strong emotional identity and high-value cognition, guide everyone to develop green agriculture, and participate in training activities on the adoption of green agricultural technology to improve the performance of farmers' technology adoption. Therefore, the performance improvement strategy of new green agricultural technologies is not one-size-fits-all and must be based on local conditions and adapted to farmers' conditions. From the perspective of global integration, it is necessary to focus on the configuration path of multiple factors, and through the permutation and combination of various factors, it is feasible to break the objective constraints such as its own conditions and external environment and form a characteristic rural household technology adoption High-performance path.

Declaration of interests

☐ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

☒ The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

The financial support was provided by Major projects of the National Social Science Fund.

Ethics statement

The study was approved by the Institutional Ethics Committee of Northwest Agriculture and Forestry University, China (Permission No.20210011) and was conducted in accordance with the People's Republic of China Academic Ethical Procedures in Guidelines. No other specific permissions were required for these activities. All parents, guardians or next of kin provided written informed consent for the minors to participate in this study. All adult participants provided written informed consent to participate in this study.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

Data will be made available on request.

Acknowledgement

This study is supported by Major Project of National Social Science Foundation of China "Research on Building a Social Governance System of Co-construction, Co-governance and Sharing" (22ZDA101)

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Multi-path Driven by Farmers' Adoption Behavior of Dry Farming Green Agricultural Technology in Loess Plateau from the Perspective of Trust Theory — A Study Based on fsQCA

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Abstract

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1. Introduction

2. Materials and Methods

2.1 Research Area

2.2 Research Methods

2.3 Selection of study samples

3. Data Analysis

3.1 Technology transformation

3.2 Endowment intention

3.3 Endowment will

3.4 Government measures

3.5 Market vitality

3.6 Performance improvement

4. Results

4.1 Necessity analysis of individual conditions

4.2 Adequacy analysis of conditional configurations

4.3 Robustness test

5. Conclusions

Declarations

References

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