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Milk Quality and Carbon Footprint Indicators of Dairy Sheep Farms Depend on Grazing Level and Identify the Different Management Systems

SIMPLE SUMMARY: In order to assess the effect of grazing level on milk quality and indicators related to the carbon footprint of dairy sheep farms, monthly data collection was carried out for 1 year on 17 farms in the region of Castilla y León (Spain). These data were analysed using a multivariate s...

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Detalles Bibliográficos
Autores principales: Plaza, Javier, Revilla, Isabel, Nieto, Jaime, Hidalgo, Cristina, Sánchez-García, Mario, Palacios, Carlos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8156543/
https://www.ncbi.nlm.nih.gov/pubmed/34065724
http://dx.doi.org/10.3390/ani11051426
Descripción
Sumario:SIMPLE SUMMARY: In order to assess the effect of grazing level on milk quality and indicators related to the carbon footprint of dairy sheep farms, monthly data collection was carried out for 1 year on 17 farms in the region of Castilla y León (Spain). These data were analysed using a multivariate statistical procedure that allowed the association of the mentioned indicators with the grazing level and identifying the management system of the farms. It was shown that farms with higher grazing levels were more environmentally sustainable, as indirect gas emissions and energy consumption were much lower. Milk quality from these farms was higher in terms of total protein, fat, omega 3 fatty acids, conjugated linoleic acid and α-tocopherol levels. ABSTRACT: Currently, there are very few studies in the dairy sheep sector associating milk quality and indicators regarding carbon footprint and their link to grazing levels. For 1 year, monthly milk samples and records related to environmental emissions and management systems were collected through surveys from 17 dairy sheep farms in the region of Castilla y León (Spain), in order to relate this information to the use of natural pastures under free grazing. Indicators were constructed on the collected data and subjected to a multivariate statistical procedure that involved a factor analysis, a cluster analysis and a population canonical analysis. By applying multivariate statistical techniques on milk quality and carbon footprint indicators, it was possible to identify the management system of the farms. From an environmental point of view, farms with a higher grazing level (cluster 4) were more sustainable, as they had the lowest carbon footprint (lower CO(2), N(2)O and CO(2) equivalent emissions per sheep and year) and the lowest energy consumption levels, which were gradually lower than those of farms in cluster 3; both indicators were much lower than those of farms in clusters 1 and 2. The milk quality of cluster 1 and 2 farms was significantly lower in terms of total protein and fat content, dry extract, omega-3 fatty acid levels and α-tocopherol content than farms in clusters 3 and 4, which had higher accessibility to grazing resources. In sum, the higher the use of natural resources, the lower the external inputs the farms required and the lower environmental impact and energy costs they have.