Assessment of Mycological Possibility Using Machine Learning Models for Effective Inclusion in Sustainable Forest Management

Raquel Martínez-Rodrigo, Beatriz Águeda (), Teresa Ágreda, José Miguel Altelarrea, Luz Marina Fernández-Toirán and Francisco Rodríguez-Puerta
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Raquel Martínez-Rodrigo: Fundación Cesefor, Calle C, E-42005 Soria, Spain
Beatriz Águeda: iuFOR-EiFAB, Campus de Soria, Universidad de Valladolid, E-42004 Soria, Spain
Teresa Ágreda: iuFOR-EiFAB, Campus de Soria, Universidad de Valladolid, E-42004 Soria, Spain
José Miguel Altelarrea: Fundación Cesefor, Calle C, E-42005 Soria, Spain
Luz Marina Fernández-Toirán: iuFOR-EiFAB, Campus de Soria, Universidad de Valladolid, E-42004 Soria, Spain
Francisco Rodríguez-Puerta: iuFOR-EiFAB, Campus de Soria, Universidad de Valladolid, E-42004 Soria, Spain

Sustainability, 2024, vol. 16, issue 13, 1-15

Abstract: The integral role of wild fungi in ecosystems, including provisioning, regulating, cultural, and supporting services, is well recognized. However, quantifying and predicting wild mushroom yields is challenging due to spatial and temporal variability. In Mediterranean forests, climate-change-induced droughts further impact mushroom production. Fungal fruiting is influenced by factors such as climate, soil, topography, and forest structure. This study aims to quantify and predict the mycological potential of Lactarius deliciosus in sustainably managed Mediterranean pine forests using machine learning models. We utilize a long-term dataset of Lactarius deliciosus yields from 17 Pinus pinaster plots in Soria, Spain, integrating forest-derived structural data, NASA Landsat mission vegetation indices, and climatic data. The resulting multisource database facilitates the creation of a two-stage ‘mycological exploitability’ index, crucial for incorporating anticipated mycological production into sustainable forest management, in line with what is usually done for other uses such as timber or game. Various Machine Learning (ML) techniques, such as classification trees, random forest, linear and radial support vector machine, and neural networks, were employed to construct models for classification and prediction. The sample was always divided into training and validation sets (70-30%), while the differences were found in terms of Overall Accuracy (OA). Neural networks, incorporating critical variables like climatic data (precipitation in January and humidity in November), remote sensing indices (Enhanced Vegetation Index, Green Normalization Difference Vegetation Index), and structural forest variables (mean height, site index and basal area), produced the most accurate and unbiased models (OA training = 0.8398; OA validation = 0.7190). This research emphasizes the importance of considering a diverse array of ecosystem variables for quantifying wild mushroom yields and underscores the pivotal role of Artificial Intelligence (AI) tools and remotely sensed observations in modeling non-wood forest products. Integrating such models into sustainable forest management plans is crucial for recognizing the ecosystem services provided by them.

Keywords: Lactarius deliciosus; remote sensing; non-wood forest products; wild edible mushrooms; Artificial Intelligence; neural networks; forest management (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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