IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v14y2024i12p2148-d1529971.html
   My bibliography  Save this article

Explicating the Role of Agricultural Socialized Services on Chemical Fertilizer Use Reduction: Evidence from China Using a Double Machine Learning Model

Author

Listed:
  • Lulu Wang

    (College of Economics and Management, Shenyang Agricultural University, Shenyang 110866, China)

  • Jie Lyu

    (College of Economics and Management, Shenyang Agricultural University, Shenyang 110866, China)

  • Junyan Zhang

    (College of Economics and Management, Shenyang Agricultural University, Shenyang 110866, China)

Abstract

Reducing chemical usage, particularly chemical fertilizers, is a crucial measure for advancing sustainable agricultural development. This study utilized field survey data from 894 maize farmers across three northeastern provinces of China. A double machine learning modeling framework was established to empirically examine the impact and mechanism of agricultural socialized services on chemical fertilizer use of farm households. The model addresses numerous stringent constraints of conventional causal inference models and effectively mitigates the “curse of dimensionality” issue. Current research indicates that agricultural socialized services can substantially decrease chemical fertilizer use among farmers. Further investigation reveals that these services facilitate this reduction by enhancing the mechanization level, promoting the use of organic fertilizers, and providing a labor substitution effect. The region heterogeneity test indicates that the impact of agricultural socialized services is more pronounced in Liaoning and Heilongjiang provinces geographically. Regarding the heterogeneity analysis of food crop income levels, agricultural socialized services can decrease chemical fertilizer use among farmers more effectively with elevated food crop income levels. Consequently, the findings imply that the socialization of agricultural services has substantial potential to be an effective chemical fertilizer reduction strategy to support the agricultural green transition, which can be enhanced through promoting the degree of mechanization, organic fertilizer application, and labor division and specialization.

Suggested Citation

  • Lulu Wang & Jie Lyu & Junyan Zhang, 2024. "Explicating the Role of Agricultural Socialized Services on Chemical Fertilizer Use Reduction: Evidence from China Using a Double Machine Learning Model," Agriculture, MDPI, vol. 14(12), pages 1-16, November.
  • Handle: RePEc:gam:jagris:v:14:y:2024:i:12:p:2148-:d:1529971
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/14/12/2148/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/14/12/2148/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hu, Yumeng & Liu, Yu, 2024. "Impact of fertilizer and pesticide reductions on land use in China based on crop-land integrated model," Land Use Policy, Elsevier, vol. 141(C).
    2. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    3. Helmut Farbmacher & Martin Huber & Lukáš Lafférs & Henrika Langen & Martin Spindler, 2022. "Causal mediation analysis with double machine learning [Mediation analysis via potential outcomes models]," The Econometrics Journal, Royal Economic Society, vol. 25(2), pages 277-300.
    4. HU, Ruifa & YANG, Zhijian & KELLY, Peter & HUANG, Jikun, 2009. "Agricultural extension system reform and agent time allocation in China," China Economic Review, Elsevier, vol. 20(2), pages 303-315, June.
    5. Yiriyibin Bambio & Salima Bouayad Agha, 2018. "Land tenure security and investment: Does strength of land right really matter in rural Burkina Faso?," Post-Print hal-04328928, HAL.
    6. Anna Baiardi & Andrea A Naghi, 2024. "The value added of machine learning to causal inference: evidence from revisited studies," The Econometrics Journal, Royal Economic Society, vol. 27(2), pages 213-234.
    7. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    8. Xi Yu & Xiyang Yin & Yuying Liu & Dongmei Li, 2021. "Do Agricultural Machinery Services Facilitate Land Transfer? Evidence from Rice Farmers in Sichuan Province, China," Land, MDPI, vol. 10(5), pages 1-14, April.
    9. Tao Chen & Muhammad Rizwan & Azhar Abbas, 2022. "Exploring the Role of Agricultural Services in Production Efficiency in Chinese Agriculture: A Case of the Socialized Agricultural Service System," Land, MDPI, vol. 11(3), pages 1-18, February.
    10. Philipp Bach & Victor Chernozhukov & Malte S. Kurz & Martin Spindler & Sven Klaassen, 2021. "DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R," Papers 2103.09603, arXiv.org, revised Jun 2024.
    11. Xiaoxuan Chen & Tongshan Liu, 2023. "Can Agricultural Socialized Services Promote the Reduction in Chemical Fertilizer? Analysis Based on the Moderating Effect of Farm Size," IJERPH, MDPI, vol. 20(3), pages 1-16, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yan Xu & Jie Lyu & Dandan Yuan & Guanqiu Yin & Junyan Zhang, 2025. "The Impact of Agricultural Machinery Services on Food Loss at the Producer Level: Evidence from China," Agriculture, MDPI, vol. 15(3), pages 1-20, January.

    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. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
    2. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP72/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2022. "Urban economics in a historical perspective: Recovering data with machine learning," Regional Science and Urban Economics, Elsevier, vol. 94(C).
    4. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP54/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Daniel Goller, 2023. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Annals of Operations Research, Springer, vol. 325(1), pages 649-679, June.
    6. Yiyi Huo & Yingying Fan & Fang Han, 2023. "On the adaptation of causal forests to manifold data," Papers 2311.16486, arXiv.org, revised Dec 2023.
    7. Zhang, Han, 2021. "How Using Machine Learning Classification as a Variable in Regression Leads to Attenuation Bias and What to Do About It," SocArXiv 453jk, Center for Open Science.
    8. Julius Schaper, 2025. "Residualised Treatment Intensity and the Estimation of Average Partial Effects," Papers 2502.10301, arXiv.org.
    9. Giacomo De Giorgi & Costanza Naguib, 2022. "Life after Default: Credit Hardship and its Effects," Diskussionsschriften dp2206, Universitaet Bern, Departement Volkswirtschaft.
    10. Mark Kattenberg & Bas Scheer & Jurre Thiel, 2023. "Causal forests with fixed effects for treatment effect heterogeneity in difference-in-differences," CPB Discussion Paper 452, CPB Netherlands Bureau for Economic Policy Analysis.
    11. Michael C Knaus, 2022. "Double machine learning-based programme evaluation under unconfoundedness [Econometric methods for program evaluation]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 602-627.
    12. Ruiyu Hu & Zemenghong Bao & Zhisen Lin & Kun Lv, 2024. "The Innovative Construction of Provinces, Regional Artificial Intelligence Development, and the Resilience of Regional Innovation Ecosystems: Quasi-Natural Experiments Based on Spatial Difference-in-D," Sustainability, MDPI, vol. 16(18), pages 1-37, September.
    13. Jiquan Peng & Zihao Zhao & Lili Chen, 2022. "The Impact of High-Standard Farmland Construction Policy on Rural Poverty in China," Land, MDPI, vol. 11(9), pages 1-20, September.
    14. Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Sep 2023.
    15. Aysegül Kayaoglu & Ghassan Baliki & Tilman Brück & Melodie Al Daccache & Dorothee Weiffen, 2023. "How to conduct impact evaluations in humanitarian and conflict settings," HiCN Working Papers 387, Households in Conflict Network.
    16. Bas Bosma & Arjen Witteloostuijn, 2024. "Machine learning in international business," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 55(6), pages 676-702, August.
    17. Huber, Martin & Meier, Jonas & Wallimann, Hannes, 2022. "Business analytics meets artificial intelligence: Assessing the demand effects of discounts on Swiss train tickets," Transportation Research Part B: Methodological, Elsevier, vol. 163(C), pages 22-39.
    18. Rahul Singh & Liyuan Xu & Arthur Gretton, 2020. "Kernel Methods for Causal Functions: Dose, Heterogeneous, and Incremental Response Curves," Papers 2010.04855, arXiv.org, revised Oct 2022.
    19. Yuchen Lu & Jiakun Zhuang & Jun Chen & Chenlu Yang & Mei Kong, 2025. "The Impact of Farmland Transfer on Urban–Rural Integration: Causal Inference Based on Double Machine Learning," Land, MDPI, vol. 14(1), pages 1-30, January.
    20. Kangqi Jiang & Xiaofeng Chen & Jiayun Li & Mengling Zhou, 2025. "Technology adoption and extreme stock risk: Evidence from digital tax reform in China," Palgrave Communications, Palgrave Macmillan, vol. 12(1), pages 1-20, December.

    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:12:p:2148-:d:1529971. 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.