Abstract

Small farmers are the main organizational form of agricultural production and management in China, and it is very important to study their organic fertilizer application behavior to promote the development of green agriculture in China. Based on the survey data of 334 small farmers in Sichuan Province, this paper uses bivariate Probit model to analyze the influence of social network and environmental cognition on organic fertilizer application behavior. The research shows that:(1) The application of commercial organic fertilizer and farmyard manure by small farmers has a significant complementary effect.(2) Social network has a significant positive impact on organic fertilizer application behavior, and there are significant differences between kinship social network and friendship social network on organic fertilizer application behavior.(3) The level of environmental cognition has a significant positive effect on the application behavior of organic fertilizer. Therefore, it is necessary not only to make full use of and expand the social network of small farmers, but also to pay attention to improving their environmental awareness and promoting organic fertilizer application behavior.

Highlights

  • As one of the important agricultural production factors, chemical fertilizer has played a positive role in improving grain yield and ensuring national food security

  • Based on the survey data of 334 small farmers in Sichuan Province, this paper uses bivariate Probit model to analyze whether there is complementary or substitution effect between different technical choices when farmers are faced with commercial organic fertilizer and farmyard manure application choices.Is there any difference in the influence of social network and environmental cognition on different technology choices? So as to provide reference for the development of green agriculture in China

  • The results show that kinship social network has a positive effect on the application of commercial organic fertilizer and farmyard manure by small farmers, which is significant at the level of 10% and 5%, respectively, while friendship social network has no significant effect on the application of commercial organic fertilizer by small farmers, but has a positive effect on the application of farmyard manure, which is significant at the level of 10%

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Summary

Introduction

As one of the important agricultural production factors, chemical fertilizer has played a positive role in improving grain yield and ensuring national food security. Jiang Weijun et al [15] pointed out that social networks further affect the willingness to adopt technology through trust and learning mechanisms.Under the condition of mastering the relevant information of organic fertilizer technology,farmers' own cognition is very important. From the research perspective,the existing literature pays attention to the influence of social network or farmers' cognition on organic fertilizer application behavior, but few literatures bring social network and environmental cognition into the same analysis framework to analyze their influence on technology adoption. Based on the survey data of 334 small farmers in Sichuan Province, this paper uses bivariate Probit model to analyze whether there is complementary or substitution effect between different technical choices when farmers are faced with commercial organic fertilizer and farmyard manure application choices.Is there any difference in the influence of social network and environmental cognition on different technology choices? Based on the survey data of 334 small farmers in Sichuan Province, this paper uses bivariate Probit model to analyze whether there is complementary or substitution effect between different technical choices when farmers are faced with commercial organic fertilizer and farmyard manure application choices.Is there any difference in the influence of social network and environmental cognition on different technology choices? So as to provide reference for the development of green agriculture in China

Theoretical analysis and research hypothesis
Model building
Findings
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