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Mastitis is a common disease that prevails in cattle owing mainly to environmental pathogens; they are also the most expensive disease for cattle in dairy farms. Several prevention and treatment methods are available, although most of these options are quite expensive, especially for small farms. In this study, we utilized a dataset of 6600 cattle along with several of their sensory parameters (collected via inexpensive sensors) and their prevalence to mastitis. Supervised machine learning approaches were deployed to determine the most effective parameters that could be utilized to predict the risk of mastitis in cattle. To achieve this goal, 26 classification models were built, among which the best performing model (the highest accuracy in the shortest time) was selected. Hyper parameter tuning and K-fold cross validation were applied to further boost the top model’s performance, while at the same time avoiding bias and overfitting of the model. The model was then utilized to build a GUI application that could be used online as a web application. The application can predict the risk of mastitis in cattle from the inhale and exhale limits of their udder and their temperature with an accuracy of 98.1% and sensitivity and specificity of 99.4% and 98.8%, respectively. The full potential of this application can be utilized via the standalone version, which can be easily integrated into an automatic milking system to detect the risk of mastitis in real time.

Mastitis, a multifactorial disease influenced by both cow and herd-level factors, results in significant losses throughout the dairy chain. We aimed to evaluate the relationship between milking frequency (MF), parity order (PO), days in milk (DIM), and milk yield (MY) on somatic cell count (SCC) and the odds of a cow having subclinical mastitis (SCM) in Brazilian Holstein and Jersey dairy cows. Our dataset consisted of 747,520 test-day records from 52,954 cows, including 49,089 Holstein cows and 3865 Jersey cows and 498 herds. The SCC was evaluated using a generalized linear mixed model, whereas SCM occurrence was evaluated using a logistic regression model. A case of SCM was defined when a cow had >200×103 cells/mL. Our results indicated that the SCC increases with higher PO and DIM and decreases in cows milked three times a day and those with higher MY in both breeds (>40 and >25 L/d for Holstein and Jersey, respectively). Increasing MF from two to three times a day reduced the chances of a Holstein and Jersey cow having SCM by 10 and 20 %, respectively. For Holstein and Jersey cows, those with ≥quadriparous had 3.9 times and 2.2 times higher chances, respectively, of having SCM compared to primiparous cows. Cows with >305 DIM had 2.0 times greater chances of having SCM for both, Holstein and Jersey cows, compared to cows with ≤105 DIM. Holstein cows yielding ≥40 L/d had a 75 % lower chance of having SCM compared to those yielding <20 L/d, while Jersey cows with ≥25 L/d had a 60 % lower chance compared to those yielding <15 L/d. In conclusion, higher PO and DIM pose risks, whereas a MF of three times a day and higher MY are protective factors against increases in SCC and SCM occurrence in Brazilian Holstein and Jersey cows. • We analyzed large dataset from 52,954 Holstein and Jersey cows in 498 herds. • MF of 3× vs. 2× reduces SCC and SCM risk in 10 % for Holstein and 20 % for Jersey cows. • Multiparous cows had higher SCC and SCM risk than primiparous in both breeds. • Cows with >305 DIM had higher SCC and a doble SCM risk compared to ≤105 DIM. • Higher MY is associated with reduction of SCC and SCM risk in both breeds. [ABSTRACT FROM AUTHOR]

Mastitis is a common disease that prevails in cattle owing mainly to environmental pathogens; they are also the most expensive disease for cattle in dairy farms. Several prevention and treatment methods are available, although most of these options are quite expensive, especially for small farms. In this study, we utilized a dataset of 6600 cattle along with several of their sensory parameters (collected via inexpensive sensors) and their prevalence to mastitis. Supervised machine learning approaches were deployed to determine the most effective parameters that could be utilized to predict the risk of mastitis in cattle. To achieve this goal, 26 classification models were built, among which the best performing model (the highest accuracy in the shortest time) was selected. Hyper parameter tuning and K-fold cross validation were applied to further boost the top model’s performance, while at the same time avoiding bias and overfitting of the model. The model was then utilized to build a GUI application that could be used online as a web application. The application can predict the risk of mastitis in cattle from the inhale and exhale limits of their udder and their temperature with an accuracy of 98.1% and sensitivity and specificity of 99.4% and 98.8%, respectively. The full potential of this application can be utilized via the standalone version, which can be easily integrated into an automatic milking system to detect the risk of mastitis in real time. [ABSTRACT FROM AUTHOR]

This study was aimed to investigate the effect of heat stress and seasons on diurnal body temperature, milk yield, and physicochemical properties of dromedary camel in southeastern rangelands Ethiopia. Twenty lactating camels with 2–3 months postmortem and 3–4 parities were selected for evaluation of milk yield, physicochemical properties, and body temperature measurements. The current finding shows diurnal body temperature was significantly higher in hot weather conditions (dry season). Temperature-humidity index (THImax) showed a strong positive correlation to rectal body temperatures (r = 0.72) and daily milk yield (r = 0.6) of a camel. The mean daily milk yield observed in this study was 6.02, 4.99, 2.38, and 2.57 Liters (L) during autumn, spring, summer, and winter, respectively, and showed higher (P 0.05) across the seasons. The body temperature, milk physicochemical properties, and yield of dromedary camels were varied (p

Payment programs based on milk quality (PPBMQ) are important in the dairy sector as they enable farmers to improve profitability upon reaching payment based on milk quality (PBMQ). We used data submitted to a PPBMQ from a dairy farm referring to a four-year period (January 2013 - December 2016). Correlation, multiple regression, and principal component analysis were performed. We found significant correlations between PBMQ and fat (r = 0.32), protein (r = 0.51), and total bacterial count (TBC) (r = -0.66), as well as an effect of all studied variables on PBMQ using multiple regression analysis (with somatic cell count [SCC] also affecting PBMQ). Thus, protein and fat positively and SCC and TBC negatively affected PBMQ value. Principal component analysis revealed an inverse relationship between summer and winter months. In summer months, the PBMQ was affected by the increase of TBC and SCC and decrease protein, whereas in winter months, protein increase and TBC and SCC decrease were relevant. A varied behaviour was detected for the remaining months. Milk components (fat, protein, SCC, and TBC) significantly affected the final value the PBMQ paid to the farmer. Moreover, there was seasonal effect on PBMQ, with PBMQ being higher in winter months and lower in summer months. Variation in milk composition and payment due to the seasonality should be considered by farmers to reach higher values of bonuses, and by the dairy sector to plane adequate payment throughout the year. [ABSTRACT FROM AUTHOR]

Abstract 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.

ABSTRACT In this study, a young Cheddar curd was used to produce two types of surface-ripened cheese, using two commercial smear-culture mixes of yeasts and bacteria. Whole-metagenome shotgun sequencing was used to screen the microbial population within the smear-culture mixes and on the cheese surface, with comparisons of microorganisms at both the species and the strain level. The use of two smear mixes resulted in the development of distinct microbiotas on the surfaces of the two test cheeses. In one case, most of the species inoculated on the cheese established themselves successfully on the surface during ripening, while in the other, some of the species inoculated were not detected during ripening and the most dominant bacterial species, Glutamicibacter arilaitensis, was not a constituent of the culture mix. Generally, yeast species, such as Debaryomyces hansenii and Geotrichum candidum, were dominant during the first stage of ripening but were overtaken by bacterial species, such as Brevibacterium linens and G. arilaitensis, in the later stages. Using correlation analysis, it was possible to associate individual microorganisms with volatile compounds detected by gas chromatography-mass spectrometry in the cheese surface. Specifically, D. hansenii correlated with the production of alcohols and carboxylic acids, G. arilaitensis with alcohols, carboxylic acids and ketones, and B. linens and G. candidum with sulfur compounds. In addition, metagenomic sequencing was used to analyze the metabolic potential of the microbial populations on the surfaces of the test cheeses, revealing a high relative abundance of metagenomic clusters associated with the modification of color, variation of pH, and flavor development. IMPORTANCE Fermented foods, in particular, surface-ripened cheese, represent a model to explain the metabolic interactions which regulate microbial succession in complex environments. This study explains the role of individual species in a heterogeneous microbial environment, i.e., the exterior of surface-ripened cheese. Through whole-metagenome shotgun sequencing, it was possible to investigate the metabolic potential of the resident microorganisms and show how variations in the microbial populations influence important aspects of cheese ripening, especially flavor development. Overall, in addition to providing fundamental insights, this research has considerable industrial relevance relating to the production of fermented food with specific qualities.

An acute case of clinical mastitis in a Holstein cow from second lactation is reported here. A milk sample from the affected quarter was cultured on 5% bovine blood agar and incubated at 37°C for 72 h. After 24 h of incubation, numerous colonies of yeast were observed: the Candida characteristic was not detected by CHROMagar Candida (Difco, Franklin Lakes, NJ). The DNA extraction of the isolate was performed, and DNA was subjected to amplification and sequencing of the D1/D2 region of the large subunit rRNA gene. The sequences were aligned using Mega 7.0 and used for searching GenBank by BLASTn (Basic Local Alignment Search Tool for nucleotides), revealing 98% of identity with Cyberlindnera rhodanensis. To date, this is the first report of this yeast associated with clinical bovine mastitisAn acute case of clinical mastitis in a Holstein cow from second lactation is reported here. A milk sample from the affected quarter was cultured on 5% bovine blood agar and incubated at 37°C for 72 h. After 24 h of incubation, numerous colonies of yeast were observed: the Candida characteristic was not detected by CHROMagar Candida (Difco, Franklin Lakes, NJ). The DNA extraction of the isolate was performed, and DNA was subjected to amplification and sequencing of the D1/D2 region of the large subunit rRNA gene. The sequences were aligned using Mega 7.0 and used for searching GenBank by BLASTn (Basic Local Alignment Search Tool for nucleotides), revealing 98% of identity with Cyberlindnera rhodanensis. To date, this is the first report of this yeast associated with clinical bovine mastitis. [ABSTRACT FROM AUTHOR]

A goat project was developed in the province of Cartago, Costa Rica, from September 2007 through January 2009, with the objective of implementing a practical system to determine productive and financial data for dairy production under a given integrated system. This involved the design and application of techniques related to livestock and environmental management, animal resources, feed, agronomy, administration and financial projections. The economic results showed a investment of ¢ 14 305 000 (570 colones per dollar) for a 60 female goat unit with a production cost per liter of milk of ¢ 459, with utilities of 18%. Financially, the projection generated a net present value (NPV) of ¢ 13 700 000, with a lifetime estimated in ten years. Simultaneously, a value of Internal Rate of Return (IR) of 26.23% from the sale of milk was determined. Regarding production costs, the most important item represented the acquisition of the concentrate, with 41% of the total production cost, followed by labor (22%) and the monthly payment for the credit requested (17%). The economic profitability was 15%. [ABSTRACT FROM AUTHOR]

The aim of this study was to analyse the relationship between different small ruminant livestock production systems with different levels of specialization. The analysis is carried out by using the self-organizing map. This tool allows high-dimensional input spaces to be mapped into much lower-dimensional spaces, thus making it much more straightforward to understand any set of data. These representations enable the visual extraction of qualitative relationships among variables (visual data mining), converting the data to maps. The data used in this study were obtained from surveys completed by farmers who are principally dedicated to goat and sheep production. With the self-organizing map we found a relationship between qualitative and quantitative variables showing that more specialized farms have greater milk incomes per goat, highlighting farms that have a greater number of animals, better facilities (including milking machines) or animals fed with elaborated diets. The use of self-organizing maps for the analysis of this kind of data has proven to be highly valuable in extracting qualitative conclusions and in guiding improvements in farm performance. [ABSTRACT FROM AUTHOR]

Characterization of the bovine leukocyte antigen (BoLA) DRB3 gene has shown that specific alleles associate with susceptibility or resilience to the progression of bovine leukemia virus (BLV), measured by proviral load (PVL). Through surveillance of multi-farm BLV eradication field trials, we observed differential phenotypes within seropositive cows that persist from months to years. We sought to develop a multiplex next-generation sequencing workflow (NGS-SBT) capable of genotyping 384 samples per run to assess the relationship between BLV phenotype and two BoLA genes. We utilized longitudinal results from milk ELISA screening and subsequent blood collections on seropositive cows for PVL determination using a novel BLV proviral load multiplex qPCR assay to phenotype the cows. Repeated diagnostic observations defined two distinct phenotypes in our study population, ELISA-positive cows that do not harbor detectable levels of provirus and those who do have persistent proviral loads. In total, 565 cows from nine Midwest dairy farms were selected for NGS-SBT, with 558 cows: 168 BLV susceptible (ELISA-positive/PVL-positive) and 390 BLV resilient (ELISA-positive/PVL-negative) successfully genotyped. Three BoLA-DRB3 alleles, including one novel allele, were shown to associate with disease resilience, *009:02, *044:01, and *048:02 were found at rates of 97.5%, 86.5%, and 90.3%, respectively, within the phenotypically resilient population. Alternatively, DRB3*015:01 and *027:03, both known to associate with disease progression, were found at rates of 81.1% and 92.3%, respectively, within the susceptible population. This study helps solidify the immunogenetic relationship between BoLA-DRB3 alleles and BLV infection status of these two phenotypic groupings of US dairy cattle. [ABSTRACT FROM AUTHOR]

Genetically modified (GM) corn hybrids have been recently compared against their isogenic reference counterparts in order to establish proof of safety as feedstuffs for dairy cattle. Most such studies have been based on the classical hypothesis test, whereby the null hypothesis is that of equivalence. Because the null hypothesis cannot be accepted, bioequivalence-testing procedures in which the alternative hypothesis is specified to be the equivalence hypothesis are proposed for these trials. Given a Type I error rate of 5%, this procedure is simply based on determining whether the 90% confidence interval on the GM vs. reference hybrid mean difference falls between two limits defining equivalence. Classical and bioequivalence power of test are determined for 4 × 4 Latin squares and double-reversal designs, the latter of which are ideally suited to bioequivalence studies. Although sufficient power likely exists for classical hypothesis testing in recent GM vs. reference hybrid studies, the same may not be true for bioequivalence testing depending on the equivalence limits chosen. The utility of observed or retrospective power to provide indirect evidence of bioequivalence is also criticized. Design and analysis issues pertain to Latin square and crossover studies in dairy nutrition studies are further reviewed. It is recommended that future studies should place greater emphasis on the use of confidence intervals relative to P-values to unify inference in both classical and bioequivalence-testing frameworks. [ABSTRACT FROM AUTHOR]

Neosporosis, an infectious disease caused by the protozoan Neospora caninum, has been associated with economic losses in cattle rearing worldwide. However, previous studies have not presented any evidence regarding the association between serological status of neosporosis and alteration of the reproductive parameters. Thus, this study aimed to determine whether N. caninum is associated with reproductive disorders and to evaluate the possible risk factors of the infection. Blood samples from 202 dairy cows, 51 with a history of reproductive disorders (case group) and 151 without (control group), were collected from different farms in Brazil. Epidemiological questionnaires were conducted with all the farmers. Serum samples were subjected to an indirect fluorescent antibody test to detect antibodies against the parasite. In total, 28.22% (57/202) of the cows were seropositive: 47.06% (24/51) from the case group and 21.85% (33/151) from the control group. By logistic regression, cows aged ≥48 months and cows with history of abortion were 4.8 (95% confidence interval [CI] = 1.91-12.05; p = 0.001) and 2.3 (95% CI = 1.06-5.1; p = 0.034) times more likely to be seropositive, respectively. Furthermore, our results show an association between N. caninum seropositivity and abortion in dairy cows from Brazil with poor management conditions and N. caninum seropositivity risk factors for reproductive disorders., (Copyright © 2021 Elsevier B.V. All rights reserved.)

In this study, a young Cheddar curd was used to produce two types of surface-ripened cheese, using two commercial smear-culture mixes of yeasts and bacteria. Whole-metagenome shotgun sequencing was used to screen the microbial population within the smear-culture mixes and on the cheese surface, with comparisons of microorganisms at both the species and the strain level. The use of two smear mixes resulted in the development of distinct microbiotas on the surfaces of the two test cheeses. In one case, most of the species inoculated on the cheese established themselves successfully on the surface during ripening, while in the other, some of the species inoculated were not detected during ripening and the most dominant bacterial species, Glutamicibacter arilaitensis , was not a constituent of the culture mix. Generally, yeast species, such as Debaryomyces hansenii and Geotrichum candidum , were dominant during the first stage of ripening but were overtaken by bacterial species, such as Brevibacterium linens and G. arilaitensis , in the later stages. Using correlation analysis, it was possible to associate individual microorganisms with volatile compounds detected by gas chromatography-mass spectrometry in the cheese surface. Specifically, D. hansenii correlated with the production of alcohols and carboxylic acids, G. arilaitensis with alcohols, carboxylic acids and ketones, and B. linens and G. candidum with sulfur compounds. In addition, metagenomic sequencing was used to analyze the metabolic potential of the microbial populations on the surfaces of the test cheeses, revealing a high relative abundance of metagenomic clusters associated with the modification of color, variation of pH, and flavor development. IMPORTANCE Fermented foods, in particular, surface-ripened cheese, represent a model to explain the metabolic interactions which regulate microbial succession in complex environments. This study explains the role of individual species in a heterogeneous microbial environment, i.e., the exterior of surface-ripened cheese. Through whole-metagenome shotgun sequencing, it was possible to investigate the metabolic potential of the resident microorganisms and show how variations in the microbial populations influence important aspects of cheese ripening, especially flavor development. Overall, in addition to providing fundamental insights, this research has considerable industrial relevance relating to the production of fermented food with specific qualities.

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.