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Optimization Models to Improve Estimations and Reduce Nitrogen Excretion from Livestock Production

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Optimization Models to Improve Estimations and Reduce Nitrogen Excretion from Livestock Production

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dc.contributor.author Segura, Marina es_ES
dc.contributor.author Maroto Álvarez, Mª Concepción es_ES
dc.contributor.author Ginestar Peiro, Concepción Mª es_ES
dc.contributor.author Segura García Del Río, Baldomero es_ES
dc.date.accessioned 2019-06-08T20:01:40Z
dc.date.available 2019-06-08T20:01:40Z
dc.date.issued 2018 es_ES
dc.identifier.uri http://hdl.handle.net/10251/121814
dc.description.abstract [EN] Sustainable food production plays a key role at different levels, such as countries, producers and consumers worldwide. Commitments of countries to reducing environmental impact include livestock production due to its contribution to greenhouse gases and other pollutants. The purpose of this research is to design and validate optimization models to improve assessments of emissions from livestock. As feed consumed is the principal source of the emissions, we have developed an aggregated optimization model to assess feed intake and therefore emissions at country level, by using the best technical and statistical data. This model provides a common framework to assess livestock emissions for all countries and in particular in the European Union. The model has been validated in the Spanish intensive pig sector, which is the principal producer by head count of the European Union. Results from several scenarios, which differ in animal protein and energy needs, have been compared to those from other methodologies and the Spanish National Inventory System, which assesses greenhouse gases and pollutants annually. This model can be adapted to other species, and applied to other countries and at farm level. Finally, this model is a useful tool to evaluate the effects on the emissions related to changes in animal nutrition, price and supply of raw materials, as well as agricultural and environmental policies. es_ES
dc.description.sponsorship This research was funded by the Regional Ministry of Education, Research, Culture and Sport of the Autonomous Government of the Valencian Region, Spain, grant number AICO/2017/066. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Sustainability es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Livestock es_ES
dc.subject Animal feed es_ES
dc.subject Optimization models es_ES
dc.subject Emission inventory es_ES
dc.subject Sustainable food production es_ES
dc.subject Linear programming models es_ES
dc.subject Pollutant es_ES
dc.subject.classification ECONOMIA, SOCIOLOGIA Y POLITICA AGRARIA es_ES
dc.subject.classification ESTADISTICA E INVESTIGACION OPERATIVA es_ES
dc.title Optimization Models to Improve Estimations and Reduce Nitrogen Excretion from Livestock Production es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/su10072362 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GVA//AICO%2F2017%2F066/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Economía y Ciencias Sociales - Departament d'Economia i Ciències Socials es_ES
dc.description.bibliographicCitation Segura, M.; Maroto Álvarez, MC.; Ginestar Peiro, CM.; Segura García Del Río, B. (2018). Optimization Models to Improve Estimations and Reduce Nitrogen Excretion from Livestock Production. Sustainability. 10(7). https://doi.org/10.3390/su10072362 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://doi.org/10.3390/su10072362 es_ES
dc.description.upvformatpinicio 2362 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 10 es_ES
dc.description.issue 7 es_ES
dc.identifier.eissn 2071-1050 es_ES
dc.relation.pasarela S\370639 es_ES
dc.contributor.funder Generalitat Valenciana es_ES


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