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. 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.
    2. 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).
    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. 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).
    5. 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.
    6. 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.
    7. 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.
    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. Izolda Pristojkovic Suko & Magdalena Holter & Erwin Stolz & Elfriede Renate Greimel & Wolfgang Freidl, 2022. "Acculturation, Adaptation, and Health among Croatian Migrants in Austria and Ireland: A Cross-Sectional Study," IJERPH, MDPI, vol. 19(24), pages 1-15, December.
    5. Manda, Julius & Feleke, Shiferaw & Mutungi, Christopher & Tufa, Adane H. & Mateete, Bekunda & Abdoulaye, Tahirou & Alene, Arega D., 2024. "Assessing the speed of improved postharvest technology adoption in Tanzania: The role of social learning and agricultural extension services," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    6. Nana Kim & Daniel M. Bolt & James Wollack, 2022. "Noncompensatory MIRT For Passage-Based Tests," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 992-1009, September.
    7. B Kelsey Jack, "undated". "Market Inefficiencies and the Adoption of Agricultural Technologies in Developing Countries," CID Working Papers 50, Center for International Development at Harvard University.
    8. Ghadir Asadi & Mohammad H. Mostafavi-Dehzooei, 2022. "The Role of Learning in Adaptation to Technology: The Case of Groundwater Extraction," Sustainability, MDPI, vol. 14(12), pages 1-37, June.
    9. Huffman, Wallace E., 2001. "Human capital: Education and agriculture," Handbook of Agricultural Economics, in: B. L. Gardner & G. C. Rausser (ed.), Handbook of Agricultural Economics, edition 1, volume 1, chapter 7, pages 333-381, Elsevier.
    10. Mi Jung Lee & Daejin Kim & Sergio Romero & Ickpyo Hong & Nikolay Bliznyuk & Craig Velozo, 2022. "Examining Older Adults’ Home Functioning Using the American Housing Survey," IJERPH, MDPI, vol. 19(8), pages 1-13, April.
    11. Li Yu & Peter F. Orazem, 2014. "O-Ring production on U.S. hog farms: joint choices of farm size, technology, and compensation," Agricultural Economics, International Association of Agricultural Economists, vol. 45(4), pages 431-442, July.
    12. Peng Wang & Yuanxin Zheng & Mingzhu Zhang & Kexin Yin & Fei Geng & Fangxiao Zheng & Junchi Ma & Xiaojie Wu, 2024. "Methods for measuring career readiness of high school students: based on multidimensional item response theory and text mining," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-15, December.
    13. 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.
    14. Qian Wu & Monique Vanerum & Anouk Agten & Andrés Christiansen & Frank Vandenabeele & Jean-Michel Rigo & Rianne Janssen, 2021. "Certainty-Based Marking on Multiple-Choice Items: Psychometrics Meets Decision Theory," Psychometrika, Springer;The Psychometric Society, vol. 86(2), pages 518-543, June.
    15. Renan P. Monteiro & Gabriel Lins de Holanda Coelho & Paul H. P. Hanel & Emerson Diógenes Medeiros & Phillip Dyamond Gomes Silva, 2022. "The Efficient Assessment of Self-Esteem: Proposing the Brief Rosenberg Self-Esteem Scale," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 17(2), pages 931-947, April.
    16. 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.
    17. T. W. G. Meer & E. Ouattara, 2019. "Putting ‘political’ back in political trust: an IRT test of the unidimensionality and cross-national equivalence of political trust measures," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(6), pages 2983-3002, November.
    18. Zhang, Congying & Xiang, Jingru & Chang, Qian, 2023. "Does Informatization Cause the Relative Substitution Bias of Agricultural Machinery Inputs for Labor Inputs? Evidence from Apple Farmers in China," Research on World Agricultural Economy, Nan Yang Academy of Sciences Pte Ltd (NASS), vol. 4(3), September.
    19. Chun Wang, 2015. "On Latent Trait Estimation in Multidimensional Compensatory Item Response Models," Psychometrika, Springer;The Psychometric Society, vol. 80(2), pages 428-449, June.
    20. 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.

    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.