Fatty acid composition of milk from Holstein-Friesian, Brown Swiss, Simmental and Alpine Grey cows predicted by mid-infrared spectroscopy

Gottardo, Paolo; Penasa, M.; Righi, Federico; López‐Villalobos, N.; Cassandro, Martino; Marchi, Massimo De

doi:10.1080/1828051x.2017.1298411

Cited by 49 publications

(62 citation statements)

References 34 publications

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“…Mean, standard deviation (SD), coefficient of variation (CV), minimum, and maximum of predicted blood metabolites, milk yield, and composition traits of early-lactation cows in multi-breed herds (43,201 observations). Bearing in mind that only the first month of lactation was investigated here, average milk yield was slightly greater, fat percentage was similar, and SCS and protein and casein percentages were slightly lower than those reported in multi-breed herds of northeast Italy [21][22][23]. As indicated in previous investigations [21,22], SCS was the most variable milk trait, followed by MUN and milk yield ( Table 2).…”

Section: Descriptive Statisticssupporting

confidence: 57%

Variation of Blood Metabolites of Brown Swiss, Holstein-Friesian, and Simmental Cows

Benedet

Franzoi

Manuelian

et al. 2020

Animals

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Serum metabolic profile is a common method to monitor health and nutritional status of dairy cows, but blood sampling and analysis are invasive, time-consuming, and expensive. Milk mid-infrared spectra have recently been used to develop prediction models for blood metabolites. The current study aimed to investigate factors affecting blood β-hydroxybutyrate (BHB), non-esterified fatty acids (NEFA), and urea nitrogen (BUN) predicted from a large milk mid-infrared spectra database. Data consisted of the first test-day record of early-lactation cows in multi-breed herds. Holstein-Friesian cows had the greatest concentration of blood BHB and NEFA, followed by Simmental and Brown Swiss. The greatest and the lowest concentrations of BUN were detected for Brown Swiss and Holstein-Friesian, respectively. The greatest BHB concentration was observed in the first two weeks of lactation for Brown Swiss and Holstein-Friesian. Across the first month of lactation, NEFA decreased and BUN increased for all considered breeds. The greatest concentrations of blood BHB and NEFA were recorded in spring and early summer, whereas BUN peaked in December. Environmental effects identified in the present study can be included as adjusting factors in within-breed estimation of genetic parameters for major blood metabolites.

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Section: Descriptive Statisticssupporting

confidence: 57%

Variation of Blood Metabolites of Brown Swiss, Holstein-Friesian, and Simmental Cows

Benedet

Franzoi

Manuelian

et al. 2020

Animals

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“…This is important because the Australian dairy industry is well recognized to have diverse feeding systems, which range from grazed pasture to TMR (Dairy Australia, 2016a). Differences in feeding and genetics have been reported to significantly affect milk composition and thus MIR spectra (Jenkins and McGuire, 2006;Gottardo et al, 2017;Tomassini et al, 2019). Figure 1 presents the conception rate to first service of the herds used in this study.…”

Section: Resultsmentioning

confidence: 99%

Classifying the fertility of dairy cows using milk mid-infrared spectroscopy

Bonfatti

Luke

et al. 2019

Journal of Dairy Science

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The objective of this study was to investigate the potential of milk mid-infrared (MIR) spectroscopy, MIR-derived traits including milk composition, milk fatty acids, and blood metabolic profiles (fatty acids, β-hydroxybutyrate, and urea), and other on-farm data for discriminating cows of good versus poor likelihood of conception to first insemination (i.e., pregnant vs. open). A total of 6,488 spectral and milk production records of 2,987 cows from 19 commercial dairy herds across 3 Australian states were used. Seven models, comprising different explanatory variables, were examined. Model 1 included milk production; concentrations of fat, protein, and lactose; somatic cell count; age at calving; days in milk at herd test; and days from calving to insemination. Model 2 included, in addition to the variables in model 1, milk fatty acids and blood metabolic profiles. The MIR spectrum collected before first insemination was added to model 2 to form model 3. Fat, protein, and lactose percentages, milk fatty acids, and blood metabolic profiles were removed from model 3 to create model 4. Model 5 and model 6 comprised model 4 and either fertility genomic estimated breeding value or principal components obtained from a genomic relationship matrix derived using animal genotypes, respectively. In model 7, all previously described sources of information, but not MIR-derived traits, were used. The models were developed using partial least squares discriminant analysis. The performance of each model was evaluated in 2 ways: 10-fold random cross-validation and herd-by-herd external validation. The accuracy measures were sensitivity (i.e., the proportion of pregnant cows that were correctly classified), specificity (i.e., the proportion of open cows that were correctly classified), and area under the curve (AUC) for the receiver operating curve. The results showed that in all models, prediction accuracy obtained through 10fold random cross-validation was higher than that of herd-by-herd external validation, with the difference in AUC ranging between 0.01 and 0.09. In the herd-byherd external validation, using basic on-farm information (model 1) was not sufficient to classify good-and poor-fertility cows; the sensitivity, specificity, and AUC were around 0.66. Compared with model 1, adding milk fatty acids and blood metabolic profiles (model 2) increased the sensitivity, specificity, and AUC by 0.01, 0.02, and 0.02 unit, respectively (i.e., 0.65, 0.63, and 0.678). Incorporating MIR spectra into model 2 resulted in sensitivity, specificity, and AUC values of 0.73, 0.63, and 0.72, respectively (model 3). The comparable prediction accuracies observed for models 3 and 4 mean that useful information from MIR-derived traits is already included in the spectra. Adding the fertility genomic estimated breeding value and animal genotypes (model 7) produced the highest prediction accuracy, with sensitivity, specificity, and AUC values of 0.75, 0.66, and 0.75, respectively. However, removing either the fertility estimated breeding value or ...

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“…The comprehensive analysis of FA via gas chromatography is costly and labourious. However, the FTIR technology provides a reliable estimate of FA groups: saturated fatty acid (SFA), monounsaturated fatty acid (MUFA) and polyunsaturated FA (Gottardo et al, 2017). In the past milk FA composition attracted manufacturers' and consumers' interest as it significantly influences nutritional, flavour and other physical-chemical properties such as oxidative stability during milk processing (Kay et al, 2005).…”

Section: Milk Fatty Acid Profilementioning

confidence: 99%

Review: Metabolic challenges in lactating dairy cows and their assessment via established and novel indicators in milk

Gross

Bruckmaier

2019

Animal

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The increasing lactational performance of dairy cows over the last few decades is closely related to higher nutritional requirements. The decrease in dry matter intake during the peripartal period results in a considerable mobilisation of body tissues (mainly fat reserves and muscle mass) to compensate for the prevailing lack of energy and nutrients. Despite the activation of adaptive mechanisms to mobilise nutrients from body tissues for maintenance and milk production, the increased metabolic load is still a risk factor for animal health. The prevalence of production diseases, particularly subclinical ketosis is high in the early lactation period. Increased β-hydroxybutyrate (BHB) concentrations further depress gluconeogenesis, feed intake and the immune system. Despite a variety of adaptation responses to nutrient and energy deficit that exists among dairy cows, an early and non-invasive detection of developing metabolic disorders in milk samples would be useful. The frequent and regular milking process of dairy cows creates the ability to obtain samples at any stage of lactation. Routine identification of biomarkers accurately characterising the physiological status of an animal is crucial for decisive strategies. The present overview recapitulates established markers measured in milk that are associated with metabolic health of dairy cows. Specifically, measurements of milk fat, protein, lactose and urea concentrations are evaluated. Changes in the ratio of milk fat to protein may indicate an increased risk for rumen acidosis and ketosis. The costly determination of individual fatty acids in milk creates barriers for grouping of fatty acids into saturated, mono- and polyunsaturated fatty acids. Novel approaches include the potential of mid-IR (MIR) based predictions of BHB and acetone in milk, although the latter are not directly measured, but only estimated via indirect associations of concomitantly altered milk composition during (sub)clinical ketosis. Although MIR-based ketone body concentrations in milk are not suitable to monitor the metabolic status of the individual cow, they provide an estimate of the overall herd or specific groups of animals earlier in a particular stage of lactation. Management decisions can be made earlier and animal health status improved by adjusting diet composition.

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Fatty acid composition of milk from Holstein-Friesian, Brown Swiss, Simmental and Alpine Grey cows predicted by mid-infrared spectroscopy

Cited by 49 publications

References 34 publications

Variation of Blood Metabolites of Brown Swiss, Holstein-Friesian, and Simmental Cows

Variation of Blood Metabolites of Brown Swiss, Holstein-Friesian, and Simmental Cows

Classifying the fertility of dairy cows using milk mid-infrared spectroscopy

Review: Metabolic challenges in lactating dairy cows and their assessment via established and novel indicators in milk

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