Risk Cognition, Social Learning, and Farmers’ Adoption of Conservation Agriculture Technology
Abstract
:1. Introduction
- When examining the relationship between risk cognition and the adoption of CA, multiple CA techniques were discussed in this study that could break through the problem of a single CA technique in previous studies. Also, in this study, the critical role of social learning, including offline practical learning through “learning by doing” and online learning with ICT, was considered, and a relatively complete family CA adoption behavioral response mechanism was constructed from the three aspects of cognition, learning, and action.
- Using questionnaire surveys to quantify risk cognition, online and offline social learning, and CA adoption is more objective.
- Additionally, the CA indicators are divided into three alternative measurement indicators of tillage, biology, and engineering, to test the model’s robustness and enhance the research results’ reliability.
- This study simultaneously measured economic, ecological, and social benefit cognition when measuring risk cognition and considered both online and offline learning in processing social learning variables.
2. Literature Review and Theoretical Framework
2.1. The Impact of Risk Cognition on the Adoption of CA
2.2. The Role of Social Learning in the Impact of Risk Cognition on the Adoption of CA
3. Data and Methods
3.1. Data Sources
3.2. Indicator Selection
3.3. Variable Measurement
3.4. Model Selection
4. Results
4.1. Reliability and Validity Analysis
4.2. Descriptive Statistics and Correlation Matrix Analysis of the Variables
4.3. Empirical Results
4.4. Robustness Test
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Variables | Measurements | Cronbach’s Alpha | CR | AVE | SFL | Cumulative Explained Variance | KMO |
---|---|---|---|---|---|---|---|
Farmers’ risk cognition | Cognition of economic benefits: Do you believe that green production and planting can reduce agricultural production costs and improve the quality and efficiency of agricultural products? (1 = yes) | 0.906 | 0.941 | 0.842 | 0.927 | 0.841 | 0.792 |
Cognition of ecological benefits: Do you believe green production and planting can improve cultivated land quality and the ecological environment? (1 = yes) | 0.898 | ||||||
Cognition of social benefits: Do you believe green production and planting are practical and beneficial to human health? (1 = yes) | 0.927 | ||||||
Offline practical learning through “learning by doing” | Learning from government institution (1 = yes) | 0.823 | 0.893 | 0.739 | 0.937 | 0.734 | 0.818 |
Learning from non-governmental institution (1 = yes) | 0.915 | ||||||
Learning from others (1 = yes) | 0.709 | ||||||
Online learning with ICT | Mobile phone: Do you use a mobile phone to access agricultural technology information? (1 = yes) | 0.831 | 0.898 | 0.748 | 0.747 | 0.747 | 0.805 |
Tablet: Do you use a tablet to access agricultural technology information? (1 = yes) | 0.917 | ||||||
Computer: Do you use a computer to access agricultural technology information? (1 = yes) | 0.919 |
Variables | Mean | Standard Deviation | VIF | Tolerance | CA Adoption | Risk Cognition | Offline Learning | Online Learning |
---|---|---|---|---|---|---|---|---|
CA adoption | 0.51 | 0.500 | 1 | |||||
Risk cognition | 0.31 | 0.170 | 0.884 | 1.027 | 0.107 ** | 1 | ||
Offline learning | 0.10 | 0.300 | 0.895 | 1.117 | 0.123 *** | 0.106 * | 1 | |
Online learning | 0.184 | 0.387 | 0.893 | 1.119 | 0.130 *** | 0.073 *** | 0.154 ** | 1 |
Gender | 0.85 | 0.430 | 0.957 | 1.043 | 0.051 ** | |||
Age | 50.83 | 9.006 | 0.996 | 1.059 | −0.080 ** | |||
Education | 7.93 | 3.599 | 0.945 | 1.05 | 0.072 * | |||
Land holding size | 9.32 | 10.410 | 0.970 | 1.031 | 0.060 | |||
Have investment or not | 0.15 | 0.358 | 0.959 | 1.004 | 0.118 *** | |||
Has credit or not | 0.39 | 0.492 | 0.952 | 1.045 | 0.151 ** |
Variables | Adoption of CA | |||||
---|---|---|---|---|---|---|
Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | |
Risk cognition | 0.137 *** (0.046) | 0.144 *** (0.046) | 0.187 *** (0.060) | 0.127 *** (0.046) | 0.087 * (0.048) | |
Offline practical learning through “learning by doing” | 0.483 *** (0.146) | 0.445 *** (0.113) | ||||
Risk cognition * Offline learning | 0.495 *** (0.184) | |||||
Online learning with ICT | 0.367 *** (0.106) | 0.124 (0.135) | ||||
Risk cognition * Online learning | 0.396 *** (0.135) | |||||
Gender | 0.297 *** (0.099) | 0.397 *** (0.105) | 0.399 *** (0.105) | −0.171 (0.118) | 0.371 *** (0.105) | 0.361 *** (0.105) |
Age | −0.012 *** (0.004) | −0.012 *** (0.004) | −0.011 *** (0.004) | 0.000 (0.004) | −0.011 *** (0.004) | −0.011 *** (0.004) |
Education level | 0.153 *** (0.042) | 0.147 *** (0.042) | 0.146 *** (0.042) | 0.088 * (0.046) | 0.139 *** (0.042) | 0.137 *** (0.042) |
Land holding size | 0.004 * (0.002) | 0.003 (0.002) | 0.003 (0.002) | 0.001 (0.001) | 0.003 (0.002) | 0.001 (0.002) |
Has investment or not | −0.241 (0.083) | −0.211 (0.083) | −0.188 (0.084) | −1.136 (0.088) | −0.231 (0.084) | −0.243 (0.084) |
Has credit or not | 0.378 *** (0.085) | 0.320 *** (0.088) | 0.266 *** (0.089) | 1.063 *** (0.092) | 0.281 *** (0.088) | 0.292 *** (0.089) |
LR Chi2 | 69.64 | 78.92 | 90.16 | 533.94 | 91.27 | 99.80 |
Pseudo-R2 | 0.043 | 0.049 | 0.056 | 0.312 | 0.057 | 0.062 |
Prob > chi2 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
Log likelihood | −769.717 | −765.075 | −759.227 | −587.530 | −758.900 | −754.634 |
Variables | Coefficient | Standard Error | Confidence Interval | |
---|---|---|---|---|
UCL | LCL | |||
Low level of offline practical learning through “learning by doing” | 0.055 | 0.106 | 0.213 | −0.019 |
High level of offline practical learning through “learning by doing” | 0.044 ** | 0.018 | 0.124 | 0.265 |
Low level of online learning with ICT | 0.034 | 0.033 | 0.310 | −0.032 |
High level of online learning with ICT | 0.022 ** | 0.170 | 0.124 | 0.265 |
Variables | BM | TM | EM | BM | TM | EM | BM | TM | EM | BM | TM | EM | BM | TM | EM |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Risk cognition | 0.026 *** (0.011) | 0.049 *** (0.010) | 0.017 *** (0.006) | 0.024 ** (0.011) | 0.047 *** (0.010) | 0.016 *** (0.006) | 0.024 *** (0.009) | 0.021 *** (0.010) | 0.018 *** (0.006) | 0.025 ** (0.011) | 0.048 *** (0.010) | 0.017 ** (0.006) | 0.024 ** (0.011) | 0.047 *** (0.010) | 0.020 *** (0.006) |
Offline learning | 0.096 (0.041) | 0.145 (0.037) | 0.115 * (0.023) | 0.104 * (0.044) | 0.096 (0.045) | 0.089 (0.046) | |||||||||
Risk cognition * Offline learning | 0.185 ** (0.093) | 0.108 * (0.1463) | 0.022 *** (0.0786) | ||||||||||||
Online learning | 0.016 (0.031) | 0.062 * (0.028) | 0.022 *** (0.017) | 0.005 (0.045) | 0.044 (0.041) | 0.015 (0.025) | |||||||||
Risk cognition * Online learning | 0.029 ** (0.046) | 0.026 *** (0.042) | 0.052 ** (0.025) | ||||||||||||
Control variables | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y | Y |
R-sq | 0.051 | 0.202 | 0.722 | 0.055 | 0.212 | 0.728 | 0.086 | 0.028 | 0.071 | 0.051 | 0.205 | 0.723 | 0.051 | 0.205 | 0.724 |
Adj R-sq | 0.046 | 0.198 | 0.721 | 0.049 | 0.207 | 0.726 | 0.030 | 0.175 | 0.263 | 0.045 | 0.200 | 0.720 | 0.045 | 0.200 | 0.721 |
Prob > F | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
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Ren, Y.; Feng, H.; Gao, T. Risk Cognition, Social Learning, and Farmers’ Adoption of Conservation Agriculture Technology. Agriculture 2023, 13, 1644. https://doi.org/10.3390/agriculture13081644
Ren Y, Feng H, Gao T. Risk Cognition, Social Learning, and Farmers’ Adoption of Conservation Agriculture Technology. Agriculture. 2023; 13(8):1644. https://doi.org/10.3390/agriculture13081644
Chicago/Turabian StyleRen, Yaqin, Hui Feng, and Tianzhi Gao. 2023. "Risk Cognition, Social Learning, and Farmers’ Adoption of Conservation Agriculture Technology" Agriculture 13, no. 8: 1644. https://doi.org/10.3390/agriculture13081644