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Modelling farm behaviour on soil fertility with the policy variable: a case from Africa


This paper describes a new approach to make predictions on the behaviour of farms in Africa related to soil fertility management. Not only sectorial factors, but also the larger socio-economic context including policies influence the behaviour of small-scale farms. Science does not yet understand this context due to its vast heterogeneity, contingencies and complexity. We collected and processed qualitative data and sociological parameters to transform them into numbers. The model we constructed is based on probabilistic estimates of behaviour of farm classes under two scenarios in Mali (Mafèya) and Zambia (Chipata). We propose seven distinct farm classes to simulate the likeliness for a change from one class to another under defined policy regimes and other social conditions. In real life, network of actors, institutions and other social formations couple and decouple farmer’s identities and farms in highly dynamic social and ecological processes. We constructed a simplified model based on selected social theories, interpretative sociological inquiry and Markov chains in order to allow simulations under the two policy scenarios “as-is” and “to-be”. Our simulations result in significantly different outcomes per locality and scenario. This approach allows practical simulations of farm, food and agriculture systems and comparative research. We expect a better understanding of the dynamics of farms, faster adoption of innovations and a better base for a research-led dialogue of practitioners and policy makers. The paper demonstrates the primordial role of policies, influencing directly farmers’ behaviour, rural and labour markets as well as food systems and rural development.

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The detailed data can be obtained from the authors.


  1. 1.

    Unfortunately often divided instead of unified.

  2. 2.

    This term would need more clarification. Based on White (2008; p.8) we see “disciplines” as self-constituting conveners of social action, built around commitment of identities and centered on processes rather than structure. Examples of disciplines are farmer cooperatives (interface), a national government (council) or a research department (arena).

  3. 3.

    Alternative concept than behaviour: farm trajectories (Falconnier et al. 2015)

  4. 4.

    A function of social constellations like rights, connectivity to networks and resources provided by disciplines. We will not enter here in more details. The debate started with the origins of the new discipline “Sociology of Agriculture (Newby 1983)

  5. 5.

    To note that income has not only an economic, but as well a social dimension. We look here on the latter.

  6. 6.

    We could, with White (2008) define organic agriculture as a style, or a particular social formation, in which the social is strongly intertwined with the cultural and economic.

  7. 7.

    As a farm decides from year to year based on the context rather than in a fixed historic or traditional pattern.

  8. 8.

    See here:

  9. 9.

    Defined as a rule telling the decision makers what to do

  10. 10.

    Including here interface, arenas and councils (White 2008)

  11. 11.

    This corresponds to the calculations of Falconnier et al. (2015), who analyzed the trajectories of farms around Koutiala (Mali) between 1994 and 010: SQ (“hanging”) =70%, A (“stepping-up”) =17% and R (“falling-down”) =13%

  12. 12.

    We do not use here the term “society”, as this is rather a mirage, or at least a shortcut term and not fruitful for a scientific contextualization covering processes from individual/person to global level.


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Without the commitment of the many enumerators collecting the impressive data sets and the patient and grateful farmers from Mali and Zambia in 2016, the survey as base of this paper would not have served its purpose.


This research paper became possible because of the ORM4Soil project funded by the Swiss National Science Foundation and Swiss Agency for Development and Cooperation (SDC) under the Swiss Program for Research on Global Issues for Development.

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Correspondence to Gian L. Nicolay.

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Nicolay, G.L., Chikopela, L., Diarra, B. et al. Modelling farm behaviour on soil fertility with the policy variable: a case from Africa. Org. Agr. 10, 43–56 (2020).

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  • Soil fertility management
  • Smallholder farm behaviour
  • Social networks
  • Policies
  • Africa
  • Research technology
  • Modelling
  • Predictions