Heat stress on breeding value prediction for milk yield and composition of a Brazilian Holstein cattle population.

Due to the high milk production of Holstein cows, many countries have chosen to import semen to improve local dairy herds. This strategy would be more effective if this semen was used in the same environment conditions in which the bulls were selected. If the effect of genotype by environment (G × E) interaction is not considered, the estimated breeding values (EBVs) may vary, potentially reducing the selection response. We evaluate the impact of heat stress on selection for milk yield and composition of Holstein cows using random regression models. To verify the interference of heat stress in milk yield (MY) and composition traits (fat, protein, total saturated, and total unsaturated fatty acids content in milk), temperature-humidity index (THI) on test-day milk records was used. The threshold value to divide the environments using test-day information from Brazilian Holstein cows was 72 units of THI, i.e., < 72 represented no heat stress and > 72 represented heat stress. Legendre polynomials of second-order (Leg 2) model and two lactation points (33 and 122 DIM) were used to estimate heritabilities and EBVs for five important dairy traits. The heritabilities of milk components and fatty acids were low (0.09-0.29), regardless of lactation period and degree of heat stress, with the exception of protein content (0.30-0.35). Fat content was the only milk component that was reduced according to the degree of heat stress and lactation period. The EBVs tended to decrease in heat stress conditions, thus animals with high genetic potential demonstrated evidence of G × E interaction. However, acclimatization of dairy cows to heat stress in the farm production systems may have been responsible for the low differences among genetic parameters and EBVs with and without heat stress found in this study.

Assessing the accuracy of prediction for milk fatty acids by using a small reference population of tropical Holstein cows.

Fatty acids (FA) have been related to effects on human health, sensory quality and shelf life of dairy products, cow's health and methane emission. However, despite their importance, they are not regularly measured in all dairy herds yet, which can affect the accuracy of estimated breeding values (EBV) for these traits. In this case, an alternative is to use genomic selection. Thus, the aim was to assess the use of genomic information in the genetic evaluation for milk traits in a tropical Holstein population. Monthly records (n = 36,457) of milk FA percentage, daily milk yield and quality traits from 4,203 cows as well as the genotypes of 755 of these cows for 57,368 single nucleotide polymorphisms (SNP) were used. Polygenic and genomic-polygenic models were applied for EBV prediction, and both models were compared through the EBV accuracy calculated from the prediction error and Spearman's correlation among EBV rankings. Prediction accuracy was assessed by using cross-validation. In this case, the accuracy was the correlation between the genomic breeding values (GEBV) obtained as the sum of SNP effects and the EBV obtained in the polygenic model in each validation group. For all traits, the use of the genomic-polygenic model did not alter the animals' ranking, with correlations higher than 0.87. Nevertheless, through this model, the accuracy increased from 1.5% to 6.8% compared to the polygenic model. The correlations between GEBV and EBV varied from 0.52 to 0.68. Therefore, the use of a small group of genotyped cows in the genetic evaluation can increase the accuracy of EBV for milk FA and other traditional milk traits.

Month-wise variation and prediction of bulk tank somatic cell count in Brazilian dairy herds and its impact on payment based on milk quality.

BACKGROUND: Payment programs based on milk quality (PPBMQ) are used in several countries around the world as an incentive to improve milk quality. One of the principal milk parameters used in such programs is the bulk tank somatic cell count (BTSCC). In this study, using data from an average of 37,000 farms per month in Brazil where milk was analyzed, BTSCC data were divided into different payment classes based on milk quality. Then, descriptive and graphical analyses were performed. The probability of a change to a worse payment class was calculated, future BTSCC values were predicted using time series models, and financial losses due to the failure to reach the maximum bonus for the payment based on milk quality were simulated. RESULTS: In Brazil, the mean BTSCC has remained high in recent years, without a tendency to improve. The probability of changing to a worse payment class was strongly affected by both the BTSCC average and BTSCC standard deviation for classes 1 and 2 (1000-200,000 and 201,000-400,000 cells/mL, respectively) and only by the BTSCC average for classes 3 and 4 (401,000-500,000 and 501,000-800,000 cells/mL, respectively). The time series models indicated that at some point in the year, farms would not remain in their current class and would accrue financial losses due to payments based on milk quality. CONCLUSION: The BTSCC for Brazilian dairy farms has not recently improved. The probability of a class change to a worse class is a metric that can aid in decision-making and stimulate farmers to improve milk quality. A time series model can be used to predict the future value of the BTSCC, making it possible to estimate financial losses and to show, moreover, that financial losses occur in all classes of the PPBMQ because the farmers do not remain in the best payment class in all months.

Adulteration identification in raw milk using Fourier transform infrared spectroscopy.

Adulteration of milk is a common practice that concerns regulatory agencies, industry, and the population. Despite the growing need for checking adulteration, the current methods employed generally have low performance and are highly dependent on manual labor. This study aims to calibrate and validate a compact equipment (MilkoScan FT1) that adopts a Fourier transform infrared spectroscopy methodology to monitor adulteration in raw milk. Almost 2500 milk samples were used for reference spectrum construction and 1650 samples were used to validate the identification of the following five most commonly used adulterants (at three different concentrations each): (1) cornstarch, (2) sodium bicarbonate, (3) sodium citrate, (4) formaldehyde, and (5) saccharose, plus the additions of two levels of water or whey. To define the calibration with the best performance in milk adulteration identification, 12 calibrations involving 8, 10, 12, 14, 16, or 18 factors, with one or two outlier eliminations, were developed. The results of sensitivity and specificity analyses, as well as Kruskal-Wallis and Dunn multiple comparison tests, revealed that the calibration that best identified the adulterants was the one involving 14 factors, with a single elimination of outliers, exhibiting for all adulterants simultaneously, 84% sensitivity and 100% specificity. The calibration showed excellent sensitivity to cornstarch (>98%), sodium bicarbonate (100%), sodium citrate (99%), and formaldehyde (>84%), indicating that this calibration has good capacity for adulteration detection. Thus, this methodology is a viable option for the dairy industry to identify adulteration of raw milk.

Do Milk Samples Stored for 12 Days after Collection Exhibit a Change in Composition Related to the Initial Bacterial Load?

Total bacterial count (TBC) is a tool used to assess milk quality and is associated with not only the initial sample contamination but also the sample storage time and temperature. Several countries have reported milk samples with a high TBC, and the influence of TBC on milk preservation remains unclear. Thus, the aim of this study was to evaluate the impact of the initial bacterial contamination level on the macrocomponents and somatic cell count (SCC) of raw milk samples preserved with bronopol and maintained at two storage temperatures (7 and 25°C) for up to 12 days. Thus, we collected milk samples from 51 dairy farms, which were divided into two groups according to the initial bacterial load: low TBC (<100,000 CFU/ml) and high TBC (≥100,000 CFU/ml). We analyzed the sample composition for protein, fat, total solids, lactose, milk urea nitrogen, and the SCC. We did not observe an effect from TBC and storage time and temperature on the concentration of protein, fat, total solids, and lactose. SCC changes were not observed for samples maintained under refrigeration (7°C); however, samples maintained at room temperature (25°C) exhibited a decrease in the SCC beginning on day 6 of storage. For milk urea nitrogen, values increased when the samples were maintained at room temperature, beginning on the ninth storage day. Samples with the preservative bronopol added and maintained under refrigeration may be analyzed up to 12 days after collection, regardless of the milk microbial load.

Aflatoxin M1 in raw milk from different regions of São Paulo state--Brazil.

A total of 635 raw milk samples from 45 dairy farms, from three regions of São Paulo state - Brazil, were evaluated during 15 months for aflatoxin M1 (AFM1). AFM1 was determined by high performance liquid chromatograph with fluorescence detection. AFM1 was detected (>0.003 µg kg(-1)) in 72.9%, 56.3% and 27.5% of the samples from Bauru, Araçatuba and Vale do Paraíba regions, respectively. The mean AFM1 contamination considering all the samples was 0.021 µg kg(-1). Furthermore, the concentration of AFM1 was quite different among Bauru (0.038 µg kg(-1)), Araçatuba (0.017 µg kg(-1)) and Vale do Paraíba (<0.01 µg kg(-1)) regions. Only three samples (0.5%) had higher contamination than the tolerated limit in Brazil (0.50 µg kg(-1)) and 64 samples (10.1%) had a higher contamination than the maximum limit as set by the European Union (0.050 µg kg(-1)). The estimated AFM1 daily intake was 0.358 and 0.120 ng kg(-1) body weight per day for children and adults, respectively.

Concordance analysis between estimation methods of milk fatty acid content.

Considering the milk fatty acid influence on human health, the aim of this study was to compare gas chromatography (GC) and Fourier transform infrared (FTIR) spectroscopy for the determination of these compounds. Fatty acid content (g/100g of fat) were obtained by both methods and compared through Pearson's correlation, linear Bayesian regression, and the Bland-Altman method. Despite the high correlations between the measurements (r=0.60-0.92), the regression coefficient values indicated higher measures for palmitic acid, oleic acid, unsaturated and monounsaturated fatty acids and lower values for stearic acid, saturated and polyunsaturated fatty acids estimated by GC in comparison to FTIR results. This inequality was confirmed in the Bland-Altman test, with an average bias varying from -8.65 to 6.91g/100g of fat. However, the inclusion of 94% of the samples into the concordance limits suggested that the variability of the differences between the methods was constant throughout the range of measurement. Therefore, despite the inequality between the estimates, the methods displayed the same pattern of milk fat composition, allowing similar conclusions about the milk samples under evaluation.

Determinaçäo do número mínimo de amostragens mensais de leite, para pagamento por qualidade / Determination of minimum samples of milk to the payment by quality

Devido à total ausência de trabalhos relativos ao número de amostragens necessárias, o objetivo foi determinar o número mínimo para que o pagamento de leite aos produtores no Brasil seja adequado. Para isto, foram coletadas semanalmente amostras de leite do tanque, proveniente do rebanho de vacas leiteiras do Departamento de Produçäo Animal, da ESALQ/USP, por 27 meses, totalizando 134 amostras, que foram analisadas, no mesmo dia da coleta, pela Clínica do Leite, para verificar as concentraçöes de gordura, proteína e sólidos totais e a contagem de células somáticas (CCS). O desvio na média dos componentes do leite, em funçäo do tamanho da amostra, foi calculado a partir da variância amostral. O maior ganho na precisäo ocorre quando da coleta de 4-5 amostras mensais, considerando o cálculo da média por mês, bimestral ou trimestral. Entretanto, com base nas classes utilizadas pela indústria, seriam necessárias 13 amostras mensais para uma estimativa precisa da média mensal, 9 para a média bimestral e 7 para a média trimestral, considerando que a porcentagem de gordura é a maior limitante para o pagamento do leite.