Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/95925
Title: Evaluation of the factors explaining the use of agricultural land: A machine learning and model-agnostic approach
Authors: Viana, Cláudia M.
Santos, Maurício
Freire, Dulce 
Abrantes, Patrícia
Rocha, Jorge
Keywords: Artificial intelligence; Cropland; Interpretability; LIME; xAI
Issue Date: 2021
Publisher: Elsevier
Project: SFRH/BD/115497/2016 
UIDB/00295/2020 
UIDP/00295/2020 
Serial title, monograph or event: Ecological Indicators
Volume: 131
Abstract: o effectively plan and manage the use of agricultural land, it is crucial to identify and evaluate the multiple human and environmental factors that influence it. In this study, we propose a model framework to identify the factors potentially explaining the use of agricultural land for wheat, maize, and olive grove plantations at the regional level. By developing a machine-learning model coupled with a model-agnostic approach, we provide global and local interpretations of the most influential factors. We collected nearly 140 variables related to biophysical, bioclimatic, and agricultural socioeconomic conditions. Overall, the results indicated that biophysical and bioclimatic conditions were more influential than socioeconomic conditions. At the global interpretation level, the proposed model identified a strong contribution of conditions related to drainage density, slope, and soil type. In contrast, the local interpretation level indicated that socioeconomic conditions such as the degree of mechanisation could be influential in specific parcels of wheat. As demonstrated, the proposed analytical approach has the potential to serve as a decision-making tool instrument to better plan and control the use of agricultural land. © 2021 The Author(s)
URI: http://hdl.handle.net/10316/95925
ISSN: 1470160X
DOI: 10.1016/j.ecolind.2021.108200
Rights: openAccess
Appears in Collections:FEUC- Artigos em Revistas Internacionais

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