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中文题名:

 平台化农户绿色生产意愿与行为偏差的影响因素研究    

姓名:

 杨超群    

学号:

 2019810089    

保密级别:

 公开    

论文语种:

 chi    

学科代码:

 095    

学科名称:

 农学    

学生类型:

 硕士    

学位:

 农业硕士    

学校:

 南京农业大学    

院系:

 人文与社会发展学院    

专业:

 农村发展(专业学位)    

研究方向:

 农业农村发展    

第一导师姓名:

 余德贵    

第一导师单位:

 南京农业大学    

第二导师姓名:

 储呈平    

完成日期:

 2021-05-20    

答辩日期:

 2021-05-31    

外文题名:

 Research on the Influencing Factors of Platform Farmers’ Green Production Willingness and Behavior Deviation    

中文关键词:

 农户 ; 绿色生产 ; 计划行为理论 ; 结构方程模型    

外文关键词:

 Farmers ; Green production ; Planning behavior theory ; Structural equation model    

中文摘要:

当前,我国正处于农业绿色发展的关键时期,农业资源约束趋紧、环境污染和生态破坏呈现日益严重的趋势,严重制约着我国绿色农业的发展。为改善目前严峻的农业生态环境,我国政府高度重视,发布了一系列相关政策法规。在这样的大背景下,推进农业绿色发展对我国农业可持续发展和乡村振兴战略实施具有重要意义。

   随着农村地区互联网的普及,“互联网+农业”逐渐成为农业发展新的趋势。农户作为农业生产的直接参与者,其绿色生产意愿与行为存在偏差,关系着农业绿色发展的进程。本文在计划行为理论的基础上,参考其他专家学者的研究思路,建立了农户绿色生产意愿和行为偏差的影响路径,并根据理论模型做出相关假设。随后基于江苏省新沂市327个云农场平台使用农户的调研数据,利用结构方程模型和分层回归分析进行实证分析。最终研究结如下:

   在个体特征方面:农户绿色生产的意愿和行为在性别上没有差异性,会随着年龄的增大呈下降趋势,而不同文化程度的农户在绿色生产的意愿和行为方面也有显著的差别,同时耕地面积的大小也会影响农户的意愿和实际行为,另外村干部身份的农户绿色生产的意愿和行为比普通农户要高。在云农场平台使用方面:使用云农场平台熟练的农户,往往具有较高的绿色生产意愿和较多的实际行为;同时经常从云农场平台上获取生产信息的农户也与获取信息较少的农户具有显著的差异性。在影响路径方面:农户的行为态度、主观规范、感知行为控制都直接正向影响农户绿色生产的意愿,并通过农户的意愿间接影响农户的行为,而且感知行为控制也直接影响农户的行为。此外,在农户的意愿转化的行为的过程中受到风险感知和组织化因素的调节作用:产量波动负向调节农户意愿到行为的转化,预期收益、合作社因素、云农场平台因素则是正向调节。

在文章的最后给出了政策建议:①在政府层面,要完善绿色农业发展的法律法规, 落实对绿色生产以及农产品的补贴政策,鼓励农户进行农业绿色生产;②在组织层面, 云农场平台要继续完善平台架构,方便于服务使用农户,同时要积极吸收农户进入经 营主体,鼓励和引导农户进行农业绿色生产,完善利益分配机制;③在个体层面,农 户要提升自身环境意识和绿色意识,参加相关培训,学习和掌握绿色生产技术,积极参加农业绿色生产。

外文摘要:

At present, my country is in a critical period of agricultural green development. The tightening of agricultural resource constraints, environmental pollution and ecological damage are showing increasingly serious trends, which seriously restrict the development of green agriculture in China. In order to improve the current severe agricultural ecological environment, the Chinese government attaches great importance to it and has issued a series of relevant policies and regulations. In this context, the promotion of green agricultural development is of great significance to the implementation of my country's sustainable agricultural development and rural revitalization strategy.

With the popularization of the Internet in rural areas, "Internet + agriculture" has gradually become a new trend in agricultural development. As farmers are direct participants in agricultural production, whether they conduct green production or not is related to the process of agricultural green development. Based on the theory of planned behavior, this paper refers to the research ideas of other experts and scholars, establishes the influence path of farmers' green production willingness and behavior, and makes relevant assumptions based on the theoretical model. Subsequently, based on the survey data of farmers using 327 cloud farm platforms in XinYi City, Jiangsu Province, the structural equation model was used for empirical analysis. The final research results are as follows:

In terms of individual characteristics: there is no gender difference in the willingness and behavior of farmers' green production, and will decline with age, while farmers with different education levels also have significant differences in their willingness and behavior in green production. The size of the arable land will also affect the willingness and actual behavior of farmers. In addition, farmers with the status of village cadres have higher willingness and behaviors for green production than ordinary farmers. In terms of the use of the cloud farm platform: farmers who are skilled in using the cloud farm platform tend to have a higher willingness to green production and more practical behaviors; at the same time, farmers who often obtain production information from the cloud farm platform are also related to those who have less information. There are significant differences. In terms of the path of influence: farmers’ behavior attitude, subjective norms, and perceived behavior control all directly and positively affect farmers’ willingness to green production, and indirectly affect farmers’ behavior through their wishes, and perceived behavior control also directly affects farmers’ behavior. In addition, the behavior of farmers’ willingness transformation is regulated by risk perception and organizational factors: output fluctuations negatively regulate the transformation of farmers’ willingness to behavior, while expected returns, cooperative factors, and cloud farm platform factors are positively regulated.

Policy recommendations are given at the end of the article: ①At the government level, we must improve laws and regulations on green agricultural development, implement subsidies for green production and agricultural products, and encourage farmers to carry out green agricultural production; ②At the organizational level, the cloud farm platform must continue to improve the platform structure to facilitate the service of farmers, and at the same time, it must actively attract farmers to enter the main body of business, encourage and guide farmers to green agriculture Production, improve the benefit distribution mechanism; ③At the individual level, farmers should enhance their environmental awareness and green awareness, participate in relevant training, learn and master green production techniques, and actively participate in agricultural green production.

参考文献:

1.报刊类

[1] Abebaw D , Haile M G. The impact of cooperatives on agricultural technology adoption:Emprical evidence from Ethiopia [J].Food Policy, 2013,48(2):82-91.

[2] Adisa R S , Balogun K S . Impact of improved technologies on small-scale soybean production: Empirical evidence from Benue state, Nigeria[J]. Pakistan Journal of Agricultural Sciences, 2013, 50(2):305-310.

[3] Asfaw A, Admassie A. The role of education on the adoption of chemical fertiliser under different socioeconomic environments in Ethiopia [J]. Agricultural economics, 2004, 30(3): 215-228.

[4] Bjornlund H, Nicol L, Klein K K. The adoption of improved irrigation technology and management practices —A study of two irrigation districts in Alberta, Canada[J]. Agricultural Water Management, 2009, 96(1): 121-131.

[5] Bowman M S, Zilberman D. Economic factors affecting diversified farming systems [J]. Ecology and society, 2013, 18(1):126-153.

[6] Cai X, Rosegrant M W.Irrigation technology choices under hydrologic uncertainty: A casestudy from Maipo River Basin, Chile[J]. Water Resources Research, 2004, 40(4):1-10.

[7] Carter M R , Laajaj R , Yang D . Subsidies and the Persistence of Technology Adoption:Field Experimental Evidence from Mozambique[J]. nber working papers, 2014.

[8] Doris Lapple. Adoption and Abandonment of Organic Farming: An Empirical Investigation of the Irish Drystock Sector[J]. Journal of Agricultural Economics, 2010, 61(3):697-714.

[9] Espinosa-Goded M,Jesús Barreiro-Hurlé, Ruto E. What Do Farmers Want From Agri-Environmental Scheme Design? A Choice Experiment Approach[J].Journal of Agricultural Economics, 2010,61(2):259-273.

[10] Francis H. D'Emden, Rick S. Llewellyn, Michael P. Burton. Adoption of conservation till agein Australian cropping regions: An application of duration analysis[J]. technological forecasting & social change, 2006, 73(6):630-647.

[11] Herath C S . Does intention lead to behaviour? A case study of the Czech Republic farmers[J].Agricultural Economics (AGRICECON), 2013, 59(3):143-148.

[12] Leggesse Dadi, Michael Burton, Adam Ozanne.Duration Analysis of Technological Adoption in Ethiopian Agriculture[J].Journal of Agricultural Economics, 2004, 55.

[13] Maertens A , Barrett C B . Measuring Social Networks’ Effects on Agricultural TechnologyAdoption[J]. American Journal of Agricultural Economics, 2013, 95(2):353-359.

[14] Mohapatra R . Farmers' Education and Profit Efficiency in Sugarcane Production: A Stochastic Frontier Profit Function Approach[J]. Iup journal of agricultural economics,2011.

[15] Nkamleu G B, Adesina A A. Determinants of chemical input use in peri-urban lowland systems: bivariate probit analysis in Cameroon [J]. Agricultural systems, 2000, 63(2): 111-121.

[16] Olawuyi S O , Mu shunje A . Social Capital and Adoption of Alternative Conservation Agricultural Practices in South-Western Nigeria[J]. Sustainability, 2019, 11.90 Toxicology, 2011, 86(3):p.307-313.

[17] Prive J C, LEeviston Z.Predicting pro-environmental agricultural practices:The social,psychological and contextual influences on land management[J].Journal of Rural Studies,2014,34(34):879-903.

[18] Sheikh A D , Rehman T , Yates C M , et al. Logit models for identifying the factors thatin fluence the uptake of new ‘no-tillage’ technologies by farmers in the rice-wheat and thecotton-wheat farming systems of Pakistan's Punjab[J]. Agricultural Systems, 2003,75(1):79-95.

[19] Toma L, Mathijs E .Environmental risk perception, environmental concern and propensity to participate in organic farming programmes [J].Journal of Environmental Management,2007,83(2):145-157.

[20] 白亚娟.我国农业科技推广体系研究[D].西北农林科技大学,2014.

[21] 陈楠.乡村振兴视角下农户绿色生产行为的影响因素研究[J].农业与技术,2021,41(04):144-149.

[22] 程鹏飞,于志伟,李婕,程广华.农户认知、外部环境与绿色生产行为研究——基于新疆的调查数据[J].干旱区资源与环境,2021,35(01):29-35.

[23] 邓生菊,陈炜.乡村振兴与甘肃美丽乡村建设[J].开发研究,2018(05):98-103.

[24] 段文婷,江光荣.计划行为理论述评[J].心理科学进展,2008(02):315-320.

[25] 杜运伟,景杰.乡村振兴战略下农户绿色生产态度与行为研究[J].云南民族大学学报(哲学社会科学版),2019,36(01):95-103.

[26] 国亮,侯军岐.影响农户采纳节水灌溉技术行为的实证研究[J].开发研究,2012(03):104-107.

[27] 黄杰. 合作社对稻农绿色农业生产技术采用行为的影响研究[D].安徽财经大学,2020.

[28] 黄炎忠,罗小锋,李容容,张俊飚.农户认知、外部环境与绿色农业生产意愿——基于湖北省632个农户调研数据[J].长江流域资源与环境,2018,27(03):680-687.

[29] 康晓梅.何处是“田园净土”?农业污染已超工业[J].生态经济,2015,31(06):6-9.

[30] 罗颖,郑逸芳,许佳贤.农户参与土地信托流转意愿与行为选择偏差研究——基于福建省沙县农户的调查数据[J].中共福建省委党校学报,2019(05):115-123.

[31] 马文奇,马林,张建杰,张福锁.农业绿色发展理论框架和实现路径的思考[J].中国生态农业学报(中英文),2020,28(08):1103-1112.

[32] 倪书阳.“互联网+”背景下发展绿色农业的思考及建议[J].中国农业信息,2016(20):12-13+22.

[33] 齐萌萌.农户清洁生产意愿、行为选择及其偏差的实证研究[D].山东农业大学,2018.

[34] 孙小燕,刘雍.土地托管能否带动农户绿色生产?[J].中国农村经济,2019(10):60-80.

[35] 檀勤良,邓艳明,张兴平,张充,杨海平.农业秸秆综合利用中农户意愿和行为选择研究[J].兰大学学报(社会科学版),2014,42(05):105-111.

[36] 魏昊,夏英,李芸.信贷需求抑制视角下农户环境友好型农业技术采纳行为分析[J].华中农业大学学报(社会科学版),2020(01):56-66+164.

[37] 汪名富.农业生态环境污染现状及治理对策[J].现代农业科技,2019(08):186+188.

[38] 王思博,李冬冬.耕地调整方式、耕地流转与农户绿色生产行为选择——以广昌县白莲绿色生产为例[J].福建农林大学学报(哲学社会科学版),2020,23(03):1-11.

[39] 温忠麟.张雷,侯杰泰,刘红云.中介效应检验程序及其应用[J].心理学报,2004(05):614-620.

[40] 温忠麟,侯杰泰,张雷.调节效应与中介效应的比较和应用[J].心理学报,2005(02):268-274.

[41] 徐胜,齐振宏,黄炜虹,杨彩艳,刘可.公共农技推广对农户施药行为的影响——基于PSM模型的实证研究[J].江苏农业科学,2021,49(02):229-236.

[42] 许增巍,姚顺波,苗珊珊.意愿与行为的悖离:农村生活垃圾集中处理农户支付意愿与支付行为影响因素研究[J].干旱区资源与环境,2016,30(02):1-6

[43] 杨彩艳,齐振宏,黄炜虹,陈雪婷.效益认知对农户绿色生产技术采纳行为的影响——基于不同生产环节的异质性分析[J].长江流域资源与环境,2021,30(02):448-458.

[44] 于法稳.新时代农业绿色发展动因、核心及对策研究[J].中国农村经济,2018(05):19-34.

[45] 于法稳.发展生态农业,助力农业供给侧结构性改革[J].农村工作通讯,2016(17):60.

[46] 严功岸. 农户绿色生产行为与认证意愿研究[D].河南农业大学,2018.

[47] 尤小文. 农户:一个概念的探讨[J]. 中国农村观察, 1999(05):19-21.

[48] 于艳丽,李桦.社区监督、风险认知与农户绿色生产行为——来自茶农施药环节的实证分析[J].农业技术经济,2020(12):109-121.

[49] 张慧仪.政府介入、市场激励对农户采纳绿色防控技术行为的影响分析[J].福建茶叶,2020,42(03):55-56.

[50] 张康洁,吴国胜,尹昌斌,钱小平.绿色生产行为对稻农产业组织模式选择的影响——兼论收入效应[J].中国农业大学学报,2021,26(04):225-239.

[51] 庄丽娟,张杰,齐文娥.广东农户技术选择行为及影响因素的实证分析——以广东省 445户荔枝种植户的调查为例[J].科技管理研究,2010,30(08):90-92.

[52] 张燕媛,张忠军.农户生产环节外包需求意愿与选择行为的偏差分析——基于江苏、江西两省水稻生产数据的实证[J].华中农业大学学报(社会科学版),2016(02):9-14+134.

2.学位论文类

[1]陈瑶.农户标准化生产遵从行为与意愿悖离及影响因素研究[D].西北农林科技大学,2020.

[2]高萌. “双重网络”视角下农户绿色生产技术采纳行为研究[D].西北农林科技大学,2020.

[3]何悦.农户绿色生产行为形成机理与实践路径研究[D].四川农业大学,2019.

[4]李晓蕾.基于农户视角的河北省甜瓜绿色生产意愿与行为研究[D].河北农业大学,2019.

[5]慕宏杰.社会规范视角下农户参与农业绿色生产行为研究[D].兰州财经大学,2019.

[6]钱龙.非农就业、土地流转与农户农业生产变化[D].浙江大学,2017.

[7]徐冬梅.农户转出林地产权的行为研究[D].石河子大学,2018.

中图分类号:

 F32    

开放日期:

 2021-06-20    

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