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Farmer typology and implications for policy design – An unsupervised machine learning approach

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  • Graskemper, Viktoria
  • Yu, Xiaohua
  • Feil, Jan-Henning

Abstract

Within the European Union, there is currently a vivid debate about the European Green Deal with its Farm to Fork Strategy and the related future design of the Common Agricultural Policy post 2020. This paper contributes to this debate by providing a clustering of German farmers analysing objective data (N = 812) using Partitioning Around Medoids (PAM) as a crucial pre-requisite for an effective design and communication of future agricultural policies. Accordingly, German farmers can be clustered into three different groups. The conventional growers are the oldest group of farmers, showing the highest land growth rate, and are characterized by a focus on traditional and politically subsidised activities. The versatile youngsters are rather young in age and the majority of them have completed some form of higher education. Their business profile is diverse. The third group of family-based farmers has the highest shares of family support within their farming business and consists mostly of dairy farmers. Policy and communication design needs to consider all these different profiles. Especially new and innovative programs could be developed and tested together with the versatile youngsters. Furthermore, aspects ensuring an effective and economically rewarding production of agricultural goods should be taken into account to offer a perspective for the conventional growers and for food security. Moreover, the family-based farmers constitute a promising target group for rural development programs.

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  • Graskemper, Viktoria & Yu, Xiaohua & Feil, Jan-Henning, 2021. "Farmer typology and implications for policy design – An unsupervised machine learning approach," Land Use Policy, Elsevier, vol. 103(C).
    • Handle: RePEc:eee:lauspo:v:103:y:2021:i:c:s026483772100051x
    DOI: 10.1016/j.landusepol.2021.105328
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    Cited by:

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    3. Ölkers, Tim & Liu, Shuang & Mußhoff, Oliver, 2023. "A typology of Malian farmers and their credit repayment performance - An unsupervised machine learning approach," 97th Annual Conference, March 27-29, 2023, Warwick University, Coventry, UK 334547, Agricultural Economics Society - AES.
    4. María de Lourdes Maldonado-Méndez & José Luis Romo-Lozano & Julio Baca del Moral & Alejandro Ismael Monterroso-Rivas, 2022. "Multidimensional Typology of Mexican Farmers in the Context of Climate Change," Agriculture, MDPI, vol. 12(8), pages 1-18, July.
    5. Wang, Hanjie & Feil, Jan-Henning & Yu, Xiaohua, 2023. "Let the data speak about the cut-off values for multidimensional index: Classification of human development index with machine learning," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    6. Huber, Robert & Bartkowski, Bartosz & Brown, Calum & El Benni, Nadja & Feil, Jan-Henning & Grohmann, Pascal & Joormann, Ineke & Leonhardt, Heidi & Mitter, Hermine & Müller, Birgit, 2024. "Farm typologies for understanding farm systems and improving agricultural policy," Agricultural Systems, Elsevier, vol. 213(C).
    7. Anne Gobin & Ann Van Herzele, 2023. "A Data-Driven Farm Typology as a Basis for Agricultural Land Use Decisions," Land, MDPI, vol. 12(11), pages 1-18, November.
    8. Wang, Hanjie & Yu, Xiaohua, 2023. "Carbon dioxide emission typology and policy implications: Evidence from machine learning," China Economic Review, Elsevier, vol. 78(C).
    9. Netshipale, A.J. & Raidimi, E.N. & Mashiloane, M.L. & de Boer, I.J.M. & Oosting, S.J., 2022. "Farming system diversity and its drivers in land reform farms of the Waterberg District, South Africa," Land Use Policy, Elsevier, vol. 117(C).
    10. Matthew C. LaFevor, 2022. "Characterizing Agricultural Diversity with Policy-Relevant Farm Typologies in Mexico," Agriculture, MDPI, vol. 12(9), pages 1-19, August.

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