The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Supporting transition toward conservation agriculture: a framework to analyze the learning processes of farmers
Open accessAbstract
Conservation agriculture (CA) is based on 3 principles, namely reduced soil disturbance, permanent soil cover, and more complex and legume-rich rotations; and multiple studies have shown its positive impacts. Because CA relies on a variety of ecological processes, it is more deeply rooted in a specific ecological context than conventional agriculture. The complexity of these processes makes it difficult to elaborate general recipes to be applied by farmers, who therefore need to learn to make their own choices adapted to their own agroecosystem. Consequently, helping farmers to move toward CA requires supporting them in learning to develop their own practices. Farmers’ learning remains poorly investigated at the individual level, with in particular very little work focusing on learning in CA. We hypothesize that the processes involved in learning to practice CA may differ from those involved in conventional agriculture: for instance, the current lack of detailed reference documents may induce farmers to experiment more. Against this background, we here aimed at describing how farmers experienced in CA learn, by qualifying their learning mechanisms and processes. To do so, we conducted five comprehensive interviews with farmers experienced in CA, and then inductively analyzed the data to explore the diversity of learning mechanisms involved, i.e. the elementary actions or cognitive activities which, organized together, constitute a learning process. We, thus, propose a descriptive framework of non-ordered and non-obligatory learning mechanisms that appear to be mobilized by farmersexperienced in conservation agriculture, as a first step toward a deeper analysis of their learning processes. We further emphasize the often unintentional aspect of learning, as well as the importance, for farmers who wish to implement CA practices, of developing new standards of comparison. A better understanding of these learning processes would help improving extension services and training for farmers.
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Such an advisory approach strengthens horizontal knowledge structures and enhances the development of farmer skills to solve complex problems arising in their specific context, which makes it more effective.
“Such an advisory approach strengthens horizontal knowledge structures (Anderson et al. 2019a, Bourne et al. 2021) and enhances the development of farmer skills to solve complex problems arising in their specific context (Cristofari et al. 2017, Charatsari et al. 2019, Bourne et al. 2021, Krafft et al. 2022), which makes it more effective (Ataei et al. 2019, Anderson et al. 2019a).”
From A heuristic framework to portray agroecological transition initiatives in reflexive arrangements, illustrated with a conservation agriculture network in Denmark (2025) · cites this paper as Cristofari et al. 2017
In “The different learning sources” (page unavailable), the paper says farmers use peers’ input for specific local problems and scientific input to adapt practices to their own systems. “Practical implications” proposes using these insights to improve extension and training, but does not show that an advisory approach enhances farmers’ problem-solving skills. (LLM, conf=0.82)
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Cites
The Discovery of Grounded Theory; Strategies for Qualitative Research
Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968) · Nursing Research · doi:10.1097/00006199-196807000-00014 · Cited by 39,449
Productivity limits and potentials of the principles of conservation agriculture
Pittelkow, C. M., Liang, X., Linquist, B. A., van Groenigen, K. J., Lee, J., Lundy, M. E., van Gestel, N., Six, J., Venterea, R. T., & van Kessel, C. (2015) · Nature · doi:10.1038/nature13809 · Cited by 1,688
Chantre, E., & Cardona, A. (2014) · Agroecology and Sustainable Food Systems · doi:10.1080/21683565.2013.876483 · Cited by 137
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- Basch, G., Friedrich, T., Kassam, A. and Gonzalez- Sanchez, E. 2015. Conservation Agriculture in Europe. In Conservation Agriculture. Eds.: Farooq, M. and Siddique, K.H.M. Springer International Publishing, 357-389.
- Darnhofer, I., Bellon, S., Dedieu, B. and Milestad, R. 2010. Adaptiveness to enhance the sustainability of farming systems. A review. Agronomy for Sustainable Development 30. (3): 545-555.
- Dordas, C. 2015. Nutrient management perspecti- ves in conservation agriculture. In Conservation Agriculture. Eds.: Farooq, M. and Siddique, K.H.M., Springer International Publishing, 79-107.
- Eisenhardt, K.M. 1989. Building theories from case study research. Academy of Management Review 14. (4): 532-550. FAO 2012. Conservation agriculture. http://www.fao. org/ag/ca/fr/.
- Farooq, M. and Siddique, K.H.M. 2015. Conservation Agriculture. Springer International Publishing.
- Hobbs, P.R., Sayre, K. and Gupta, R. 2008. The role of conservation agriculture in sustainable agriculture. Philosophical Transactions of the Royal Society B. Biological Sciences 363. (1491): 543-555.
- Ingram, J. 2010. Technical and Social Dimensions of Farmer Learning: An Analysis of the Emergence of Reduced Tillage Systems in England. Journal of Sustainable Agriculture 34. (2): 183-201.
- Kilpatrick, S. and Johns, S. 2003. How farmers learn: different approaches to change. The Journal of Agricultural Education and Extension 9. (4): 151-164.
- Millar, J. and Curtis, A. 1997. Moving Farmer Knowledge beyond the Farm Gate: An Australian Study of Farmer Knowledge in Group Learning. European Journal of Agricultural Education and Extension 4. (2): 133-142.
- Toffolini, Q. 2016. Produire des connaissances action- nables pour la re-conception pas-à-pas de systèmes de culture vers l'agroécologie. PhD Thesis, Paris, AgroParisTech.
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Cassart, P., Frei, A., Hauggaard-Nielsen, H., Kruse Rasmussen, H., Swartebroeckx, A., Froidmont, E., Stilmant, D., & Rossing, W. (2025) · Ecology and Society · doi:10.5751/es-15770-300210 · Cited by 4
Metadata
- Authors
- Cristofari, H., Girard, N., & Magda, D.
- Affiliations
- Institut National Polytechnique de Toulouse (FR)
- ORCID
- Girard, Nathalie
- Venue
- Hungarian Geographical Bulletin
- Published
- 2017-03-31
- Volume
- 66
- Issue
- 1
- Pages
- 65-76
- ISSN
- 2064-5031 (ISSN-L)
- 2064-5147
- OpenAlex
- W2604143868
- Cited by
- 9
- References
- 3
- Citations by year
- 2026: 1 · 2024: 1 · 2021: 1 · 2020: 2 · 2019: 1 · 2018: 3
- FWCI
- 4.32
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Hungarian Geographical Bulletin (publishedVersion, cc-by-nc-nd)
- HAL (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Agence Nationale de la Recherche (ANR-15-CE21-0006)
- Topics
- Life Sciences › Agricultural and Biological Sciences › General Agricultural and Biological Sciences › Agricultural Innovations and Practices
- Social Sciences › Development › Agriculture, Land Use, Rural Development
- Social Sciences › Anthropology › Indigenous Knowledge Systems and Agriculture
- Keywords
- conservation agriculture, farmer learning, learning mechanisms, permanent soil cover, agricultural extension, farmer training, comparison standards
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