IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v14y2024i5p680-d1383930.html
   My bibliography  Save this article

How Does Information Acquisition Ability Affect Farmers’ Green Production Behaviors: Evidence from Chinese Apple Growers

Author

Listed:
  • Zheng Li

    (College of Economics and Management, Northwest A&F University, Yangling 712100, China
    These authors contributed equally to this work.)

  • Disheng Zhang

    (College of Economics and Management, Northwest A&F University, Yangling 712100, China
    These authors contributed equally to this work.)

  • Xiaohuan Yan

    (College of Economics and Management, Northwest A&F University, Yangling 712100, China
    Western Rural Development Research Center, Northwest A&F University, Yangling 712100, China)

Abstract

Green production is crucial in promoting sustainable agricultural practices, ensuring food safety, and protecting the rural ecological environment. Farmers, as the main decision makers of agricultural production, and their green production behaviors (GPBs), directly determine the process of agricultural green development. Based on the survey data of 656 apple growers in Shaanxi and Gansu provinces in 2022, this paper uses a graded response model to measure the information acquisition ability (IAA) of farmers and constructs an ordered Logit model to empirically explore the influence mechanisms of IAA, green benefit cognition (GBC), and new technology learning attitude (NTLA) on farmers’ GPBs. The results show the following: (1) IAA has a significantly positive impact on the adoption of GPBs by farmers, and farmers with a high IAA are more conscious to adopt green production technologies; (2) in the process of IAA affecting farmers’ adoption of GPBs, GBC plays a positive mediating role; (3) NTLAs have a positive moderating effect on the process of GBC affecting farmers’ GPB adoption; (4) there are generational, educational and regional differences in the impact of IAA on farmers’ GPBs. Policy makers should improve rural information facilities, strengthen agricultural technology promotion and training, improve farmers’ IAA and benefit awareness level, and formulate relevant policies to mobilize farmers’ enthusiasm for learning new technologies.

Suggested Citation

  • Zheng Li & Disheng Zhang & Xiaohuan Yan, 2024. "How Does Information Acquisition Ability Affect Farmers’ Green Production Behaviors: Evidence from Chinese Apple Growers," Agriculture, MDPI, vol. 14(5), pages 1-17, April.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:5:p:680-:d:1383930
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/14/5/680/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/14/5/680/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yakubu Abdul-Salam & Euan Phimister, 2017. "Efficiency Effects of Access to Information on Small-scale Agriculture: Empirical Evidence from Uganda using Stochastic Frontier and IRT Models," Journal of Agricultural Economics, Wiley Blackwell, vol. 68(2), pages 494-517, June.
    2. Shoumin Yue & Ying Xue & Jie Lyu & Kangkang Wang, 2023. "The Effect of Information Acquisition Ability on Farmers’ Agricultural Productive Service Behavior: An Empirical Analysis of Corn Farmers in Northeast China," Agriculture, MDPI, vol. 13(3), pages 1-26, February.
    3. Zhou, Xinyao & Zhang, Yongqiang & Sheng, Zhuping & Manevski, Kiril & Andersen, Mathias N. & Han, Shumin & Li, Huilong & Yang, Yonghui, 2021. "Did water-saving irrigation protect water resources over the past 40 years? A global analysis based on water accounting framework," Agricultural Water Management, Elsevier, vol. 249(C).
    4. Khataza, Robertson R.B. & Doole, Graeme J. & Kragt, Marit E. & Hailu, Atakelty, 2018. "Information acquisition, learning and the adoption of conservation agriculture in Malawi: A discrete-time duration analysis," Technological Forecasting and Social Change, Elsevier, vol. 132(C), pages 299-307.
    5. Chalmers, R. Philip, 2012. "mirt: A Multidimensional Item Response Theory Package for the R Environment," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i06).
    6. Ma, Jun & Gao, Huixian & Cheng, Changgao & Fang, Zhou & Zhou, Qin & Zhou, Haiwei, 2023. "What influences the behavior of farmers' participation in agricultural nonpoint source pollution control?—Evidence from a farmer survey in Huai'an, China," Agricultural Water Management, Elsevier, vol. 281(C).
    7. Zhiyun Zhou & Haoling Liao & Hua Li, 2023. "The Symbiotic Mechanism of the Influence of Productive and Transactional Agricultural Social Services on the Use of Soil Testing and Formula Fertilization Technology by Tea Farmers," Agriculture, MDPI, vol. 13(9), pages 1-26, August.
    8. Wozniak, Gregory D, 1993. "Joint Information Acquisition and New Technology Adoption: Late versus Early Adoption," The Review of Economics and Statistics, MIT Press, vol. 75(3), pages 438-445, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Caiyan Yang & Weihong Huang & Yu Xiao & Zhenhong Qi & Yan Li & Kun Zhang, 2024. "Adoption of Fertilizer-Reduction and Efficiency-Increasing Technologies in China: The Role of Information Acquisition Ability," Agriculture, MDPI, vol. 14(8), pages 1-18, August.
    2. Chengze Li & Dianwei Zhang & Qian Lu & Jiajing Wei & Qingsong Zhang, 2024. "Production Process Outsourcing, Farmers’ Operation Capability, and Income-Enhancing Effects," Agriculture, MDPI, vol. 14(9), pages 1-20, August.
    3. Yihan Chen & Wen Xiang & Minjuan Zhao, 2024. "Impacts of Capital Endowment on Farmers’ Choices in Fertilizer-Reduction and Efficiency-Increasing Technologies (Preferences, Influences, and Mechanisms): A Case Study of Apple Farmers in the Province," Agriculture, MDPI, vol. 14(1), pages 1-25, January.
    4. Weihong Huang & Caiyan Yang & Ke Liu & Rui Min, 2023. "Information Acquisition Ability and Farmers’ Herd Behavior in Rice–Crayfish Coculture System Adoption," Agriculture, MDPI, vol. 13(10), pages 1-15, September.
    5. Yuying Yang & Yubin Wang, 2024. "The Impact of Government Subsidies and Quality Certification on Farmers’ Adoption of Green Pest Control Technologies," Agriculture, MDPI, vol. 15(1), pages 1-15, December.
    6. Luo, Nanyu & Ji, Feng & Han, Yuting & He, Jinbo & Zhang, Xiaoya, 2024. "Fitting item response theory models using deep learning computational frameworks," OSF Preprints tjxab, Center for Open Science.
    7. Melissa Gladstone & Gillian Lancaster & Gareth McCray & Vanessa Cavallera & Claudia R. L. Alves & Limbika Maliwichi & Muneera A. Rasheed & Tarun Dua & Magdalena Janus & Patricia Kariger, 2021. "Validation of the Infant and Young Child Development (IYCD) Indicators in Three Countries: Brazil, Malawi and Pakistan," IJERPH, MDPI, vol. 18(11), pages 1-19, June.
    8. Björn Andersson & Tao Xin, 2021. "Estimation of Latent Regression Item Response Theory Models Using a Second-Order Laplace Approximation," Journal of Educational and Behavioral Statistics, , vol. 46(2), pages 244-265, April.
    9. Victoria T. Tanaka & George Engelhard & Matthew P. Rabbitt, 2020. "Using a Bifactor Model to Measure Food Insecurity in Households with Children," Journal of Family and Economic Issues, Springer, vol. 41(3), pages 492-504, September.
    10. Klaas Sijtsma & Jules L. Ellis & Denny Borsboom, 2024. "Recognize the Value of the Sum Score, Psychometrics’ Greatest Accomplishment," Psychometrika, Springer;The Psychometric Society, vol. 89(1), pages 84-117, March.
    11. Çetin Toraman & Güneş Korkmaz, 2023. "What is the “Meaning of School†to High School Students? A Scale Development and Implementation Study Based on IRT and CTT," SAGE Open, , vol. 13(3), pages 21582440231, September.
    12. Ruifeng Liu & Jian Wang & Mengling Tian & Yefan Nian & Wei Ren & Hengyun Ma & Fei Liang, 2025. "Farmers’ adoption of green prevention and control technology in China: does information awareness matter?," Palgrave Communications, Palgrave Macmillan, vol. 12(1), pages 1-16, December.
    13. West, Steele, 2021. "The Estimation of Farm Business Inefficiency in the Presence of Debt Repayment," 2021 Conference, August 17-31, 2021, Virtual 315048, International Association of Agricultural Economists.
    14. Yikun Luo & Qipeng Chen & Jianyong Chen & Peida Zhan, 2024. "Development and validation of two shortened anxiety sensitive index-3 scales based on item response theory," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-7, December.
    15. Yinrong Chen & Yanqing Qin & Qingying Zhu, 2023. "Study on the Impact of Social Capital on Agricultural Land Transfer Decision: Based on 1017 Questionnaires in Hubei Province," Land, MDPI, vol. 12(4), pages 1-22, April.
    16. Cervantes, Víctor H., 2017. "DFIT: An R Package for Raju's Differential Functioning of Items and Tests Framework," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i05).
    17. Elina Tsigeman & Sebastian Silas & Klaus Frieler & Maxim Likhanov & Rebecca Gelding & Yulia Kovas & Daniel Müllensiefen, 2022. "The Jack and Jill Adaptive Working Memory Task: Construction, Calibration and Validation," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-29, January.
    18. Zarezadeh, Mahboubeh & Delavar, Majid & Morid, Saeed & Abbasi, Hamid, 2023. "Evaluating the effectiveness of macro-level water-saving policies based on water footprint sustainability indicators," Agricultural Water Management, Elsevier, vol. 282(C).
    19. Joshua B. Gilbert & James S. Kim & Luke W. Miratrix, 2023. "Modeling Item-Level Heterogeneous Treatment Effects With the Explanatory Item Response Model: Leveraging Large-Scale Online Assessments to Pinpoint the Impact of Educational Interventions," Journal of Educational and Behavioral Statistics, , vol. 48(6), pages 889-913, December.
    20. Bing Li & Cody Ding & Huiying Shi & Fenghui Fan & Liya Guo, 2023. "Assessment of Psychological Zone of Optimal Performance among Professional Athletes: EGA and Item Response Theory Analysis," Sustainability, MDPI, vol. 15(10), pages 1-15, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:14:y:2024:i:5:p:680-:d:1383930. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.