The first goal of this work was the description of a model addressed to quantify the carbon footprint in Spanish autochthonous dairy sheep farms (Manchega group), foreign dairy sheep farms (foreigners group: Lacaune and Assaf breeds), and Spanish autochthonous dairy goat farms (Florida group). The second objective was to analyze the GHG emission mitigation potential of 17 different livestock farming practices that were implemented by 36 different livestock farms, in terms of CO₂e per hectare (ha), CO₂e per livestock unit (LU), and CO₂e per liter of fat- and protein-corrected milk (FPCM). The study showed the following results: 1.655 kg CO₂e per ha, 6.397 kg CO₂e per LU, and 3.78 kg CO₂e per liter of FPCM in the Manchega group; 12.634 kg CO₂e per ha, 7.810 CO₂e kg per LU, and 2.77 kg CO₂e per liter of FPCM in the Foreigners group and 1.198 kg CO₂e per ha, 6.507 kg CO₂e per LU, and 3.06 kg CO₂e per liter of FPCM in Florida group. In summary, purchasing off-farm animal feed would increase emissions by up to 3.86%. Conversely, forage management, livestock inventory, electrical supply, and animal genetic improvement would reduce emissions by up to 6.29%, 4.3%, 3.52%, and 0.8%, respectively; finally, an average rise of 2 °C in room temperature would increase emissions by up to 0.62%.

This study analyzes 174 tank milk samples taken from 89 commercial farms located all along the Cantabrian Coast (Green Spain). Sampling was performed in two periods: autumn 2016 and spring 2017. A survey was carried out for every day of sampling to record the average lactating dairy cow production and its diet composition. For each sample, the fatty acid (FA) profile (49 FA plus its main relationships) and nine fat-soluble antioxidant (FSA) profiles (retinol (vitamin A), α- and Υ-tocopherol (vitamin E), all-trans-β-carotene, 9-cis-β-carotene, 13-cis-β-carotene, lutein, zeaxanthin, and β-cryptoxanthin) were determined. The milk production varied between 7.3 and 45.9 liters per cow per day, highlighting the diversity found among production systems. The milk fat content ranged from 2.64% to 4.38% and the protein content from 2.87% to 3.56%. Regarding the fatty acids profile, the percentage of saturated fat varied between 59.95% and 75.99%. The linolenic acid content fluctuated between 0.21 and 1.31 and rumenic acid ranged from 0.20 to 2.47 (g 100 g⁻¹ total FA). The most important correlations between diet and milk FA were always related to the content of fresh grass and total forage (which is defined by both fresh and conserved forage derived from fresh grass (GCF)) in the diet. The content of vaccenic acid, linolenic acid, total omega-3, rumenic acid, and total CLA isomers showed the highest correlation with the proportion of fresh grass in the diet. The antioxidant contents were also highly variable, although correlations with dietary components were lower. The highest correlations were between total forage content (fresh grass (FG) plus GCF) and lutein, all-trans-β-carotene, and 13-cis-β-carotene. Diets without fresh grass had lower omega-3 content, CLA, vaccenic acid, lutein, all-trans-β-carotene, and 13-cis-β-carotene.

Understanding the composition of a cow’s diet through the analysis of its milk is very useful in the linking of the product consumed with the systems involved in its production. The aim of this study is to show the diet–milk composition relationship using correspondence analysis and multiple linear regression analysis. This study analyzed 174 tank milk samples taken from 89 commercial farms located in “Green Spain”. Sampling was performed in two different periods: autumn 2016 and spring 2017. The correspondence analysis allowed for study into the general relationships between diet components and their relationship with the composition of milk (chemical composition, fatty acid profile (FA), and fat-soluble antioxidants (FSA)). The model used to estimate the percentage of fresh grass (FG) in the diet had a high predictive power (R_aj² > 0.7), and the explanatory variables included in the model were linolenic acid (C18:3-n3), vaccenic acid (trans11-C18:1), and cis12-C18:1. The regression equation was applied to the 174 tank milk samples individually. To evaluate the equation’s predictive capacity, different thresholds for the dry matter percentage of fresh grass in the ration were marked (15%, 20%, 25%, and 30%), above which milk could be considered “grass-fed milk”, and below which, “not grass-fed milk”. The equation is considered valid when it correctly classifies the sample. The highest percentage of success (89.7%) was obtained by marking a threshold of 25% FG. When analyzing the misclassified milk samples, that is, where the equation did not classify the milk sample well according to its fresh grass composition, it was observed that the majority of cases corresponded to milk samples that came from herds fed with fresh grass above the marked threshold (>25%) but with a high content of concentrate in the ration. The conclusion is that the percentage of concentrate in the diet has a very important influence on the fatty acid profile of milk, particularly with respect to fresh grass. This is in such a way that anywhere above a concentrate content of >30%, the equation’s capacity to estimate the percentage of fresh grass decreases.

Asociación Interprofesional para el Desarrollo Agrario AIDA. Información Técnica Económica Agraria ITEA.

Asociación Interprofesional para el Desarrollo Agrario AIDA. Información Técnica Económica Agraria ITEA.