Please use this identifier to cite or link to this item: https://hdl.handle.net/11000/34191
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dc.contributor.authorPérez Murcia, Mª Dolores-
dc.contributor.authorGarcía Rández, Ana-
dc.contributor.authorNetherton Marks, Evan Alexander-
dc.contributor.authorOrden, Luciano-
dc.contributor.authorMartínez Sabater, Encarnación-
dc.contributor.authorAndreu Rodríguez, Fco. Javier-
dc.contributor.authorSáez Tovar, Jose Antonio-
dc.contributor.authorBustamante Muñoz, María de los Ángeles-
dc.contributor.authorMoral, Raúl-
dc.contributor.otherDepartamentos de la UMH::Agroquímica y Medio Ambientees_ES
dc.date.accessioned2024-12-23T13:00:06Z-
dc.date.available2024-12-23T13:00:06Z-
dc.date.created2023-03-
dc.identifier.citationNutrient Cycling in Agroecosystems Vol 129, pag 427–443, (2024)es_ES
dc.identifier.issn1573-0867-
dc.identifier.issn1385-1314-
dc.identifier.urihttps://hdl.handle.net/11000/34191-
dc.description.abstractUnderstanding the extent and characteristics of biomass resources is important for managing it effectively within the bioeconomy and leveraging biomass towards the highest value uses or those which are most appropriate. To this end, a large regional study was conducted to characterize the main physicochemical characteristics of common biomasses and identify potential limitations to use or opportunities for. Valencia is fourth most populous autonomous region of Spain, having a large importance for the European vegetable and citrus product markets, among others. Across 164 municipalities, 625 samples were characterized for contents of organic matter, total nitrogen, total phosphorus, pH, electrical conductivity, and polyphenol contents, and 224 samples were characterized for metal and metalloid contents. The different biomass types included in the study were expert-classified into a total of 54 biomass subcategories. Overall, nutrient contents were the parameter most associated with waste type, while electrical conductivity had the highest variability within groups. Considering all the samples, nutrient contents were sufficient to reach established minimums for marking as an EU-labelled fertilizing product in 479/625 cases, and pertinent limits on heavy metal contents were exceeded in 20/224 cases. The highest polyphenol contents were found in the pomegranate and citric wastes, which were substantially higher than in the organic wastes from olive oil and wine production. Machine learning techniques (k-means and hierarchical clustering analysis) applied to the datasets showed that biomasses were best classified into two groups based on pH, electrical conductivity, organic matter, and N, P, and Na contents, and three groups based on metal and metalloid contents. The summary data are presented in appendices for regional and European nutrient budgeting and modelling use. Based on the analyzed properties, the most appropriate uses can be identified, whether for transformation in biological processes, energy generation, recovery of critical elements, or extraction of high value compounds.es_ES
dc.formatapplication/pdfes_ES
dc.format.extent17es_ES
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.rightsinfo:eu-repo/semantics/closedAccesses_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectCircular bioeconomyes_ES
dc.subjectNutrientses_ES
dc.subjectPolyphenolses_ES
dc.subjectHeavy metalses_ES
dc.subjectOlive mill wastees_ES
dc.subjectMachine learninges_ES
dc.titleIdentifying the circularization opportunities for organic wastes generated in a Mediterranean regiones_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://doi.org/10.1007/s10705-023-10292-yes_ES
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Artículos Agroquímica y Medio Ambiente


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