Thenail C., Moonen A.C., Lardon S., Marraccini E., Rizzo Davide. (2022). Landscape agronomy : lessons learned and challenges ahead, from a European perspective. In :
Rizzo Davide (ed.), Marraccini E. (ed.), Lardon S. (ed.). Landscape agronomy : advances and challenges of a territorial approach to agricultural issues. Cham : Springer, 271-294. ISBN 978-3-031-05261-3.
Titre du document
Landscape agronomy : lessons learned and challenges ahead, from a European perspective
Année de publication
2022
Type de document
Partie d'ouvrage
Auteurs
Thenail C., Moonen A.C., Lardon S., Marraccini E., Rizzo Davide
In
Rizzo Davide (ed.), Marraccini E. (ed.), Lardon S. (ed.), Landscape agronomy : advances and challenges of a territorial approach to agricultural issues
Source
Cham : Springer, 2022,
271-294 ISBN 978-3-031-05261-3
Landscape Agronomy is expected to contribute to the observation, understanding and support of agriculture at the landscape level. The conceptual model of Landscape Agronomy was developed to integrate assessment and monitoring beyond the farm level and across multiple temporal and spatial scales. In this chapter, we evaluate the relevance of the Landscape Agronomy conceptual model by analysing three issues of integrated governance and public action. The three examples highlight the three parts of the conceptual model: (a) core agricultural landscape dynamics, illustrated by farmers'collective projects of agroecological transition; (b) the three poles of the conceptual model-practices, resources and patterns-illustrated by Integrated Pest Management as an example of requirements for sustainable agroecosystem management; and (c) temporal and spatial interactions, illustrated by the concept of territorial circularity as a way to integrate landscape-based management into global issues related to nutrient cycles. Although it captures the key issues addressed by such perspectives, the Landscape Agronomy conceptual model still faces some challenges, such as explicit consideration of temporal dimensions and explicit inclusion of new (invisible) patterns emerging from newly available data and digitalisation.
