Huber R., Bartkowski B., Brown C., El Benni N., Feil J.-H., Grohmann P., Joormann I., Leonhardt H., Mitter H., Muller B. (2024). Farm typologies for understanding farm systems and improving agricultural policy. Agricultural systems, 01/01/2024, vol. 213, p. 103800.
|Farm typologies for understanding farm systems and improving agricultural policy (2024)
|R. Huber ; B. Bartkowski ; C. Brown ; N. El Benni ; J.-H. Feil ; P. Grohmann ; I. Joormann ; H. Leonhardt ; H. Mitter ; B. Muller
|Type de document :
|Agricultural systems (vol. 213, January 2024)
|Article en page(s) :
|Langues du résumé :
Catégories principales06 - AGRICULTURE. FORÊTS. PÊCHES ; 6.5 - Gestion des Exploitations
Thésaurus IAMMEXPLOITATION AGRICOLE ; CLASSIFICATION ; SYSTEME DE PRODUCTION ; POLITIQUE AGRICOLE
Farm typologies help to identify patterns across a wide range of farm systems and describe heterogeneity in agriculture concisely. They can also support the design of agricultural policies by providing information and knowledge about policy target groups. For example, voluntary agri-environmental schemes could be tailored to specific agricultural landscapes and farm types. Farm typologies, however, are often developed from scratch, with limited connection to previous studies and policy making.
The objective of this study is to clarify the purposes of farm typologies in research and agricultural policy making and to develop a framework that allows to increase the usefulness and usability of farm typologies for agricultural policy making.
Based on a review of 13 systematically identified overview studies on farm typologies, we develop a framework that establishes connections between the purposes of farm typologies along the different stages of the policy process.
RESULTS AND CONCLUSIONS
We find multiple purposes for farm typologies, the two most common of which are for understanding the characteristics, heterogeneity, and development of farm systems and for policy making. The newly developed framework suggests that connecting knowledge across these purposes could improve the validity, transferability, and relevance of farm typologies for agricultural policy making. Our framework also provides an entry point for encouraging cooperation between developers and users of typologies, and for the improvement of typologies through new data (including behavioural data) and methods such as machine learning. We conclude that future research can build on the existing work on farm typologies but must be aware of the specific challenges that are associated with the use of farm typologies in the policy process.
Knowledge of the prospects and challenges of using farm typologies allows to increase the usefulness and usabilityof these typologies and can contribute to the design of targeted and tailored agricultural policy instruments. By increasing the acceptance, perceived fairness, and legitimacy, this can improve their effectiveness and efficiency, which is urgently needed for a successful transformation to a more sustainable agricultural sector.
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