Integrating data and knowledge to support the selection of service plant species in agroecology - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Journal Articles Computers and Electronics in Agriculture Year : 2024

Integrating data and knowledge to support the selection of service plant species in agroecology

Abstract

Highlights: • Plant functional traits can be linked to ecosystem services (ES) using a logical rule-based language. • An ontology-based data access approach enables to identify the most relevant species for a desired ES. • The management of missing and redundant trait values in databases is key to the reliability of species selection. • The proposed tool ranks service plant species similarly to published and expert knowledge. Abstract: There is a crucial need for tools to help researchers, technicians and farmers designing sustainable agroecosystems based on agroecology Indeed, such agroecosystems are inherently complex and their design requires to integrate various data and unstabilised scientific knowledge. In this paper, we consider the issue of selecting service plant species according to their potential to provide ecosystem services. To tackle that issue, we adopt an approach based both on a formalized representation of domain knowledge, which enables reasoning, and on the exploitation of available data, collected independently of the targeted application. More specifically, we rely on the one hand on recent scientific results in agronomy linking functional traits (i.e., measurable characteristics of plant species) to ecosystem services, and on the other hand on data about functional traits collected by the research community in ecology. The architecture of our system is inspired by the ontologybased data access paradigm, which allows to combine data and knowledge in a principled way. We provide a methodology to acquire scientific knowledge in the form of diagrams linked to data sources, as well as a formalization in a logical rule-based language. Importantly, our rules are independent from specific diagrams and data, to ensure genericity and facilitate the evolution of the system. We detail the construction of a knowledge base devoted to vine grassing, i.e., installing herbaceous service plants in vineyards, and present an evaluation of the system's results on this use case. We finally discuss the lessons learned and further challenges to be met.
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Avant la publication
Saturday, July 13, 2024
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Saturday, July 13, 2024
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Dates and versions

lirmm-04393663 , version 1 (14-01-2024)

Licence

Attribution - NonCommercial - NoDerivatives

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Elie Najm, Marie-Laure Mugnier, Christian Gary, Jean-François Baget, Raphael Métral, et al.. Integrating data and knowledge to support the selection of service plant species in agroecology. Computers and Electronics in Agriculture, 2024, 217, pp.108594. ⟨10.1016/j.compag.2023.108594⟩. ⟨lirmm-04393663⟩
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