Visible spectroscopy on lamb fat and muscle to authenticate the duration of pasture finishing.

Pasture-based livestock systems are considered environmentally-sustainable and welfare-friendly farming systems that can meet consumer demand for good-quality produce. However, trust in products labelled as 'grass-fed' depends on the ability to reliably authenticate pasture origin. The two objectives of this study were (i) to test the ability of visible spectroscopy combined with discriminant analysis on lamb perirenal fat (PF), dorsal fat (DF) and longissimus thoracis et lumborum muscle to discriminate different durations of pasture-finishing; and (ii) to determine the timing of appearance of the pasture signature and its stabilization in these tissues. Four groups of 35 lambs were used over two years, i.e. lambs fed concentrate in-stall (L0) and lambs grazing alfalfa for 21d (L21), 42d (L42) and 63d (L63) before slaughter. No one tissue satisfactorily discriminated the four treatment groups, with ≤75% of lambs correctly classified. However, visible spectroscopy discriminated L0 from L21 + L42 + L63 lambs with an accuracy of 92.8%, 92.0%, and 85.3% lambs correctly classified on PF, DF and muscle, respectively, and discriminated L0 + L21 from L42 + L63 lambs with an accuracy of 90.1%, 76.5% and 92.3% on PF, DF and muscle, respectively. The pasture fingerprint or signature on the spectrum appeared in most lambs between 0 and 21d in PF and DF and between 0 and 42d in muscle. Pasture signature gradually stabilized with increasing time on pasture but was not entirely stabilized in any tissue within the range of grazing durations explored. These promising results need to be confirmed on larger datasets with different breeds and grazing conditions.

Regression trees to identify combinations of farming practices that achieve the best overall intrinsic quality of milk.

Many studies over the last 30 years have shown the effects of farming practices on milk compounds. Combinations of practices may have antagonistic or synergistic effects on milk compounds, but these combination effects remain underinvestigated. Research needs to focus on overall intrinsic milk quality (including sensory, technological, health, and nutritional dimensions) and identify the combinations that can optimize it. The aim of this study was to identify which combinations of farming practices achieved the best scores for sensory, technological, health, and nutritional dimensions and for overall intrinsic milk quality. Ninety-nine private farms were visited once each to sample their bulk tank milk and survey their farming practices. The surveyed practices concerned herd characteristics, feeding management, housing conditions, and milking and milk storage conditions on the day of test. Analyses of bulk tank milk were designed to evaluate the overall intrinsic quality of the milk for 2 target products: raw milk cheese and semi-skimmed UHT milk. Regression trees were then used to identify the combinations of farming practices that achieved the best scores on each dimension and on overall intrinsic quality of the milk. Breed and diet (type of forage) were the most influential factors for sensory and health dimensions and for technological and nutritional dimension scores, respectively, in the cheese assessment. Overall cheese quality was highly positively correlated with these 4 dimension scores. Therefore, breed and diet emerged as the most influential practices in the regression tree for overall cheese quality. However, the combinations of practices that resulted in the best quality scores differed according to dimension studied and product targeted. This suggests that advice on farming practices to improve intrinsic milk quality needs to be adapted according to the end-purpose of the collected milk. This innovative approach combining on-farm data and regression trees provides farm managers with a valuable and practical tool to prioritize practices in terms of their role in shaping milk quality, and to identify the combinations of practices that promote good milk quality and practice thresholds or modalities needed to achieve it.

Co-construction of a method for evaluating the intrinsic quality of bovine milk in relation to its fate.

There are time-tested assessments for the environmental and economic aspects of sustainability. Its societal aspect has mainly been approached through the assessment of animal welfare. However, the intrinsic quality of milk is seldom taken into account. We developed a participatory construction method for the overall assessment of intrinsic milk quality in its different dimensions (sensory, technological, nutritional and health), according to the fate of the raw milk. Two assessment models were developed, for semi-skimmed standardized ultra-high temperature (UHT) milk and for pressed uncooked non-standardized raw milk cheese. They were constructed by a participatory approach involving experts in the dairy sector with the aim to obtain a diagnostic tool that could be used in the field to help farmers to manage the quality of their milk (by prioritizing improvements on major problems). They were shaped from prerequisite specifications (limited costs and time of application, desire to obtain a transparent tool with all the steps kept visible) and current technical and scientific knowledge. They were based on indicators obtained from raw bulk tank milk analyses (30 for UHT milk and 50 for cheese assessments), which were then aggregated into criteria, principles, dimensions and overall intrinsic quality at farm level. The assessment models had parts in common, for example, same four dimensions, common indicators for health and nutritional dimensions. They also had process-specific features: units chosen, criteria, indicators and weightings in relation to the final product specifications. For instance, sensory and technological dimensions are more complex and preponderant in the cheese assessment (three principles for cheese vs one for UHT milk in both dimensions). Another example is the lack of microbial pathogens (as potential health risk for consumer) in the UHT milk assessment because of pasteurization. The assessment models then underwent a sensitivity analysis and an application in 30 farms in indoor and grazing periods to finally obtain overall UHT milk and cheese quality scores at a 1-year level. The tool was found to be applicable at farm level. However, we observed low overall quality scores with a narrow dispersion, characteristic of a severe evaluation. Even so, the assessment models showed up seasonal differences of the UHT milk and cheese quality at both overall and dimensional levels. In the light of new scientific knowledge and future quality objectives, these are adaptable to other dairy products allowing for their specific features.