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 CO₂, N₂O and CO₂ 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.

It is necessary to study small-ruminant production systems in a spatial context, as different areas, depending on their natural, socioeconomic, and cultural context, will respond differently to the effects of such causal factors. The aim of this study was to analyze the relationships between technology use and farm and farmer characteristics in dairy sheep production by developing a farm typology using multivariate statistical analysis and comparing it with the multi-scale sustainability evaluation of the Natural Resource Management Systems (NRMS) method. Quantitative and qualitative data were evaluated and compared using the NRMS method and cluster analysis to identify groups of farms. Fourteen variables were considered for the analysis, resulting in four farm systems with the NRMS method, named Intensive, Semi-Intensive, Semi-Extensive, and Extensive; however, when analyzing the data by cluster analysis, the following clusters were used: Cluster 1, Semi-Extensive Dairy Systems (SEDSs); Cluster 2, Semi-Intensive Systems (SIDSs); Cluster 3, Semi-Extensive Cheese Systems (SECSs); and Cluster 4, Intensive Dairy Systems (IDSs). This study concludes that the traditional classification of dairy sheep farms based on their dependence on natural resources can be enriched by incorporating additional indicators that allow for a more comprehensive and accurate characterization of farms.

Eco-efficiency, which integrates environmental and economic variables, is crucial for decision-making in agriculture, particularly within the framework of European environmental policy. The SBM-DEA tool has been applied in several studies, and facilitates the optimization of greenhouse gas (GHG) emissions in the agricultural sector. Despite the significance of the Spanish agricultural economy at both European and international levels, the analysis of its eco-efficiency is limited and fragmented, and does not consider the regional disparities within the country. The objective of this study is therefore to assess the eco-efficiency of the Spanish regional agricultural sector during the period from 2004 to 2022, considering its regional and productive differences. This has been achieved using data from the Farm Accounting Network and the non-oriented SBM-DEA method with constant returns to scale. The eco-efficiency of the Spanish regional agricultural sector as a whole was estimated to range from 0.644 to 0.837, with an average value of 0.772 for the period analyzed. Forty-seven percent of the regions exceeded the average eco-efficiency. The data indicate significant opportunities to improve eco-efficiency and reduce GHG emissions at both the national (65.6%) and regional (ranging from 7.4 to 86.5%) levels. This paper discusses the need to develop regionalized strategies to optimize resource allocation and reduce greenhouse gas emissions within the Common Agricultural Policy.

ResumenEl objetivo del estudio es la evaluación de los efectos de las subvenciones percibidas por lo sectores ganaderos españoles recogidos en la Red Contable Agraria Nacional. El análisis parte de una función de producción tipo Cobb-Douglas, en la que se ha estimado su frontera de producción estocástica, obteniéndose los niveles de ineficiencia respecto al óptimo posible. La base de datos está constituida por un panel de 112 observaciones de datos económicos definitorios de una explotación tipo por cada orientación ganadera para el periodo 1993-2005AbstractThe aim of this study is to evaluate the subsidies perceived for the Spanish livestock sectors of the National Farm Accountancy Network. The analysis starts from a Cobb-Douglas function of production, estimating the stochastic frontier and their levels of inefficiency, which have been compared to the optimum. The database is constituted by a data economic panel of 112 observations of a medium farm for the period 1993-2005.

A livestock system is a productive unit, but in addition, if it pursues an optimal use of natural resources, it can increase overall sustainability. In order to evaluate the fulfillment of this objective, it is necessary to carry out a diagnosis of the system to describe and evaluate its degree of sustainability. One methodological option for this diagnosis is the construction of sustainability indicators. The MESMIS method is a methodological tool that analyses the interrelations between the results of the environmental, social and economic dimensions through a battery of indicators. The aim of this work is to assess the sustainability, using the MESMIS methodology, of different management systems of a sample of dairy sheep farms in Castilla y León (Spain). In general, the semi-extensive group obtained the highest overall score (8.40), and the intensive group achieved the highest volume of productivity. In conclusion, semi-extensive systems were more sustainable than intensive or semi-intensive systems in all attributes, especially those related to stability, adaptability and equity.