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Martínez-Peña F - Autor o Coautor
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Publicaciones > Artículo

Yield models for predicting aboveground ectomycorrhizal fungal productivity in Pinus sylvestris and Pinus pinaster stands of northern Spain

Publicado en:Forest Ecosystems. 6 (1): - 2019-12-01 6(1), doi: 10.1186/s40663-019-0211-1

Martínez-Peña F;

Afiliaciones

Agrotecnio Centre de Recerca en Agrotecnologia - Autor o Coautor
Centro de Investigaci?n y Tecnolog?a Agroalimentaria de Arag?n - Autor o Coautor
Centro de Investigación y Tecnología Agroalimentaria de Aragón - Entidad de origen
Ctr Invest & Tecnol Agroalimentaria Aragon CITA, Avda Montana 230, E-50059 Zaragoza, Spain - Autor o Coautor
Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria - Autor o Coautor
Joint Res Unit CTFC AGROTECNIO, Ave Alcalde Rovira Roure 191, E-25198 Lleida, Spain - Autor o Coautor
Ministerio de Economía y Competitividad - Financiador
Spanish Natl Inst Agr & Food Res & Technol INIA, Ctra La Coruna Km 7,5, E-28040 Madrid, Spain - Autor o Coautor
Univ Lleida, Dept Crop & Forest Sci, Ave Rovira Roure 191, E-25198 Lleida, Spain - Autor o Coautor
Univ Valladolid Palencia, Sustainable Forest Management Res Inst, Avda Madrid 44, E-34071 Palencia, Spain - Autor o Coautor
Universidad de Valladolid - Autor o Coautor
Universitat de Lleida - Autor o Coautor
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Resúmen

Resúmen: © 2019, The Author(s). Background: Predictive models shed light on aboveground fungal yield dynamics and can assist decision-making in forestry by integrating this valuable non-wood forest product into forest management planning. However, the currently existing models are based on rather local data and, thus, there is a lack of predictive tools to monitor mushroom yields on larger scales. Results: This work presents the first empirical models for predicting the annual yields of ectomycorrhizal mushrooms and related ecosystem services in Pinus sylvestris and Pinus pinaster stands in northern Spain, using a long-term dataset suitable to account for the combined effect of meteorological conditions and stand structure. Models were fitted for the following groups of fungi separately: all ectomycorrhizal mushrooms, edible mushrooms and marketed mushrooms. Our results show the influence of the weather variables (mainly precipitation) on mushroom yields as well as the relevance of the basal area of the forest stand that follows a right-skewed unimodal curve with maximum predicted yields at stand basal areas of 30–40 m2∙ha− 1. Conclusion: These models are the first empirical models for predicting the annual yields of ectomycorrhizal mushrooms in Pinus sylvestris and Pinus pinaster stands in northern Spain, being of the highest resolution developed to date and enable predictions of mushrooms productivity by taking into account weather conditions and forests’ location, composition and structure.

Palabras clave: Fungi; Hurdle models; Mixed models; Mushrooms; Non-wood forest products

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Best Categ.Forestry-
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