| Literature DB >> 30149522 |
Jianhua Wang1,2, Yuanyuan Deng3, Hanyu Diao4.
Abstract
The main objective of this study is to examine how market returns and external pressure influence farmers' standardized pesticide application and to investigate the moderating role of information acquisition. Data were collected from 986 farmers following a multi-stage sampling method from five major agricultural provinces in China. A hierarchical regression analysis was performed to test the hypothesis. The results show that market returns and information acquisition of pesticide application had a significant and positive influence on standardized pesticide application. Also, interaction effects were found between acquisition of policy information and market returns, and also between acquisition of pesticide application information and external pressure. The policy implication is that the improvement of market returns of safe agricultural products is a potential way to improve farmers' pesticide usage behaviors. Policy information and pesticide application information should be widely provided to farmers in order to facilitate the transition to standardized pesticide application.Entities:
Keywords: farmers’ behavior; hierarchical regression analysis; information; pesticide use
Mesh:
Substances:
Year: 2018 PMID: 30149522 PMCID: PMC6165372 DOI: 10.3390/ijerph15091829
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 3.390
Figure 1Conceptual Framework.
Characteristics of the sample farmers.
| Characteristics | Categories | Frequency | % | Characteristics | Categories | Frequency | % |
|---|---|---|---|---|---|---|---|
| Age (years) | <18 | 9 | 0.91 | Education level | Primary school or Below | 295 | 29.92 |
| 18–25 | 75 | 7.61 | Junior middle school | 483 | 48.99 | ||
| 26–45 | 351 | 35.60 | senior high school | 153 | 15.52 | ||
| 45–60 | 409 | 41.58 | Junior college | 26 | 2.64 | ||
| >60 | 142 | 14.30 | Bachelor or above | 29 | 2.93 | ||
| Family size (persons) | 1–2 | 68 | 6.89 | Annual household income | 10000–20000 Yuan | 117 | 11.87 |
| 3 | 244 | 24.75 | 20000–30000 Yuan | 333 | 33.77 | ||
| 4 | 339 | 34.38 | 30000–50000 Yuan | 338 | 34.28 | ||
| ≥5 | 335 | 33.98 | 50000–100000 Yuan | 196 | 19.88 | ||
| Gender | Male | 590 | 59.84 | >100000 Yuan | 1 | 0.10 | |
| Female | 396 | 40.16 |
Notes: 6.25 Yuan ≈ $1 (2013 data).
Measurement of the variables.
| Variables | Indicators | Factor Loadings | Cronbach’ α |
|---|---|---|---|
| Standardized Pesticide Application | Apply the pesticide according to the pesticide instructions or under the guidance of professional technicians | 0.774 | 0.670 |
| Comply with the pesticide safety interval | 0.661 | ||
| Actively learn the technology and knowledge of the pesticide and its applications | 0.622 | ||
| Market Returns | Products can get more market returns with standardized pesticide application | 0.633 | 0.756 |
| Products can get a higher price with standardized pesticide application | 0.716 | ||
| Standardize the production process control of high-quality agricultural products | 0.693 | ||
| Products can meet buyers’ requirements with standardized pesticide application | 0.713 | ||
| Products can be recognized by customers with standardized pesticide application | 0.568 | ||
| External Pressure | Influences of family attitude to my safe production | 0.656 | 0.653 |
| Influences of my friends’ attitude to my safe production | 0.751 | ||
| Influences of other farmers’ attitude to my safe production | 0.711 | ||
| Pesticides Application Information | Check the extent of pesticide residues | 0.573 | 0.691 |
| Large amounts of pesticides are used to cause pesticide residues | 0.576 | ||
| Know the safe intervals between pesticides | 0.564 | ||
| Higher pesticide residues can affect the edibility of agricultural products | 0.526 | ||
| Market Information | Get information on agricultural markets | 0.794 | 0.745 |
| Is it difficult to sell products at a good price? | 0.766 | ||
| Policy Information | The local government conducts technical training on pesticides application | 0.763 | 0.763 |
| The local government is promoting the safe production of agricultural products | 0.648 | ||
| The local government imposes penalties for violation of the safe production of agricultural products | 0.645 | ||
| Local agricultural products are tested when they are purchased | 0.736 | ||
| The government sets production and planting standards for safe agricultural products | 0.729 | ||
| Age | Under 18 years old = 1; 18–25 years old = 2; 26–45 years old = 3; 46–60 years old = 4; 60+ years old = 5 | ||
| Education level | Primary School or Below = 1; junior middle school = 2; senior high school = 3; junior college = 4; Bachelor or above = 5 | ||
| Farm Size | Scored 1 if 1–2 mu; 2 if 2–3 mu; 3 if 3–6 mu; 4 if more than 6 mu | ||
Notes: 1 mu ≈ 6.1 acres.
Basic statistics and Pearson’s correlation coefficients (N = 986).
| Variables | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
|---|---|---|---|---|---|---|---|---|---|---|
| 1. Gender | ||||||||||
| 2. Age | −0.074 * | |||||||||
| 3. Education level | 0.026 | −0.476 ** | ||||||||
| 4. Farm Size | −0.166 ** | −0.034 | −0.118 ** | |||||||
| 5. Market Returns | −0.012 | −0.010 | 0.127 ** | −0.012 | ||||||
| 6. External Pressure | −0.049 | −0.124 ** | 0.127 ** | 0.074 * | 0.216 ** | |||||
| 7. Pesticides Information | −0.039 | 0.069 * | −0.010 | −0.009 | 0.431 ** | 0.069 * | ||||
| 8. Policy Information | 0.071 * | 0.001 | 0.064 * | 0.065 * | 0.169 ** | 0.201 ** | 0.185 ** | |||
| 9. Market Information | 0.026 | 0.094 ** | 0.021 | −0.104 ** | 0.078 * | −0.106 ** | 0.241 ** | 0.188 ** | ||
| 10. Pesticide Behaviors | −0.062 | 0.049 | −0.026 | 0.106 ** | 0.434 ** | 0.068 * | 0.588 ** | 0.135 ** | 0.169 ** | |
| Mean Value | 0.40 | 3.61 | 2.00 | 2.59 | 3.748 | 3.147 | 3.435 | 0.390 | 2.534 | 3.784 |
| S.D. | 0.490 | 0.855 | 0.908 | 1.072 | 0.489 | 0.872 | 0.704 | 0.349 | 0.757 | 0.585 |
Notes: ** p < 0.01, * p < 0.05.
Results of the hierarchical regression analysis.
| Independent Variables | Model 1 | Model 2 | Model 3 | Model 4 |
|---|---|---|---|---|
|
| ||||
| Gender (0 = Male, 1 = Female) | −0.049 | −0.046 | −0.028 | −0.032 |
| Age | 0.033 | 0.014 | −0.003 | −0.006 |
| Education Level | 0.008 | −0.032 | −0.022 | −0.017 |
| Farm Size | 0.060 ** | 0.059 *** | 0.064 *** | 0.064 *** |
|
| ||||
| Market Returns | 0.262 *** | 0.137 *** | 0.148 *** | |
| External Pressure | −0.016 | −0.009 | −0.005 | |
| Pesticides Application Information | 0.279 *** | 0.272 *** | ||
| Policy Information | −0.001 | −0.011 | ||
| Market information | 0.028 | 0.025 | ||
|
| ||||
| Market Returns × Pesticides Application Information | −0.040 ** | |||
| Market Returns × Policy Information | 0.054 *** | |||
| Market Returns × Market Information | 0.038 * | |||
| External Pressure × Policy Information | 0.026 * | |||
| External Pressure × Pesticides Application Information | 0.063 *** | |||
| External Pressure × Market Information | −0.036 * | |||
|
| 0.016 | 0.208 | 0.403 | 0.432 |
| Adjust | 0.012 | 0.204 | 0.398 | 0.424 |
| 3.925 ** | 42.967 *** | 73.243 *** | 49.282 *** |
Notes: Correlation variables have been centralized and reported as non-standardized coefficients. *p < 0.05, ** p < 0.01, *** p < 0.001.
Simple effect and simple result analysis.
| Dependent Variable | Independent Variable | Regulated Variable | Standard | Coefficients |
|
|---|---|---|---|---|---|
| Standardized Pesticide Application | Market Returns | Pesticide Application information | High | 0.12 *** | 0.000 |
| Low | 0.137 *** | 0.000 | |||
| Policy Information | High | 0.349 *** | 0.000 | ||
| Low | 0.74 *** | 0.000 | |||
| Market Information | High | 0.291 *** | 0.000 | ||
| Low | 0.192 *** | 0.000 | |||
| External Pressures | Pesticide Application Information | High | 0.077 *** | 0.003 | |
| Low | −0.015 | 0.417 | |||
| Policy Information | High | 0.054 ** | 0.020 | ||
| Low | −0.014 | 0.595 | |||
| Market Information | High | 0.081 *** | 0.001 | ||
| Low | 0.019 | 0.467 |
Notes: *p < 0.05, ** p < 0.01, *** p < 0.001.
Figure 2Plots of the interactions between pesticide application information and market benefits in predicting standardized pesticide application.
Figure 3Plots of the interactions between policy information and market benefits in predicting standardized pesticide application.
Figure 4Plots of the interactions between market information and market benefits in predicting standardized pesticide application.
Figure 5Plots of the interactions between pesticide application information and external pressure in predicting standardized pesticide application.
Figure 6Plots of the interactions between policy information and external pressure in predicting standardized pesticide application.
Figure 7Plots of the interactions between market information and external pressure in predicting standardized pesticide application.