[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125922-en":3,"doc-seo-125922-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125922,2336474459895,"Aria","https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916",8,"Research & Report","Assessment of Mycological Possibility Using Machine Learning Models for Effective Inclusion in Sustainable Forest Management","Wild fungi play essential ecosystem roles, yet estimating and forecasting wild mushroom yields remains difficult because production varies across space and time. In Mediterranean forests, climate-change-driven droughts further affect fungal fruiting, which is shaped by climate, soil, topography, and forest structure. This study quantifies and predicts the mycological potential of Lactarius deliciosus in sustainably managed Mediterranean pine forests using machine learning and multisource environmental data.","sustainability   \nArticle  \nAssessment of Mycological Possibility Using Machine Learning Models for Effective Inclusion in Sustainable Forest Management  \nRaquel Martínez-Rodrigo 1,2, Beatriz Águeda 2,3, *, Teresa Ágreda 2,4, José Miguel Altelarrea 1, Luz Marina Fernández-Toirán 2 and Francisco Rodríguez-Puerta 2  \nCitation: Martínez-Rodrigo, R.;Águeda, B.; Ágreda, T.; Altelarrea, J.M.; Fernández-Toirán, L.M.; Rodríguez-Puerta, F. Assessment of Mycological Possibility Using Machine Learning Models for Effective Inclusion in Sustainable Forest Management. Sustainability 2024, 16, 5656. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/su16135656](10.3390/su16135656)  \nAcademic Editors: Jim Lynch and Ali Ayoub  \nReceived: 1 April 2024  \nRevised: 30 May 2024  \nAccepted: 29 June 2024  \nPublished: 2 July 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Fundación Cesefor, Calle C, E-42005 Soria, Spain; [raquel.martinez@cesefor.com](raquel.martinez@cesefor.com) (R.M.-R.); [josemiguel.altelarrea@cesefor.com](josemiguel.altelarrea@cesefor.com) (J.M.A.)  \n2 iuFOR-EiFAB, Campus de Soria, Universidad de Valladolid, E-42004 Soria, Spain;  \nteresanj.agreda@estudiantes.uva.es or [tagreda@almazan.es](tagreda@almazan.es) (T.Á .); [luzmarina.fernandez@uva.es](luzmarina.fernandez@uva.es) (L.M.F.-T.);  \n[francisco.rodriguez.puerta@uva.es](francisco.rodriguez.puerta@uva.es) (F.R.-P.)  \n3 föra forest technologies S.L.L., C/ . de la Universidad s/n, E-42004 Soria, Spain  \n4 Ayuntamiento de Almazán, Pza Mayor 1, E-42200 Almazán, Spain  \n* Correspondence: beatriz.agueda@uva.es  \nAbstract: 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, climatechange-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 (OAtraining = 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 remotel","cbCaif4V8Cn6oZaq","https://ap.wps.com/l/cbCaif4V8Cn6oZaq","pdf",878308,5,1,15,"English","en",105,"# Introduction\n## Role and challenges of non-wood forest products\n## Study objective and approach\n# Methods\n## Dataset and multisource predictors\n## Machine learning models and validation design\n# Results\n## Mycological exploitability index\n## Model performance and key variables\n# Discussion\n## Implications for sustainable forest management\n# Conclusion","[{\"question\":\"Why is predicting wild mushroom yields challenging in sustainable forest management?\",\"answer\":\"Yield prediction is constrained by strong spatial and temporal variability. Additional climate-driven droughts in Mediterranean forests can further shift fruiting outcomes.\"},{\"question\":\"What species and forest context does the study focus on?\",\"answer\":\"The study targets Lactarius deliciosus in sustainably managed Mediterranean pine forests. It uses long-term yield observations from 17 Pinus pinaster plots in Soria, Spain.\"},{\"question\":\"Which data sources and forest variables are used to build the models?\",\"answer\":\"The models integrate forest structural variables, NASA Landsat vegetation indices, and climatic data. Key predictors include precipitation and humidity variables, enhanced vegetation indices, and structural measures such as mean height, site index, and basal area.\"}]","Assessment of Mycological Possibility Using Machine Learning Models for Effective Inclusion in Sustainable Forest Management | PDF",1785902054,38,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"assessment-of-mycological-possibility-using-machine-learning-models-for-effective-inclusion-in-sustainable-forest-management","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/assessment-of-mycological-possibility-using-machine-learning-models-for-effective-inclusion-in-sustainable-forest-management/125922/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Why is predicting wild mushroom yields challenging in sustainable forest management?","Question",{"text":77,"@type":78},"Yield prediction is constrained by strong spatial and temporal variability. Additional climate-driven droughts in Mediterranean forests can further shift fruiting outcomes.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"What species and forest context does the study focus on?",{"text":82,"@type":78},"The study targets Lactarius deliciosus in sustainably managed Mediterranean pine forests. It uses long-term yield observations from 17 Pinus pinaster plots in Soria, Spain.",{"name":84,"@type":75,"acceptedAnswer":85},"Which data sources and forest variables are used to build the models?",{"text":86,"@type":78},"The models integrate forest structural variables, NASA Landsat vegetation indices, and climatic data. 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