Longitudinal analysis of bovine mammary gland development.

Many studies on bovine mammary glands focus on one stage of development. Often missing in those studies are repeated measures of development from the same animals. As milk production is directly affected by amount of parenchymal tissue within the udder, understanding mammary gland growth along with visualization of its structures during development is essential. Therefore, analysis of ultrasound and histology data from the same animals would result in better understanding of mammary development over time. Thus, this research aimed to describe mammary gland development using non-invasive and invasive tools to delineate growth rate of glandular tissue responsible for potential future milk production. Mammary gland ultrasound images, biopsy samples, and blood samples were collected from 36 heifer dairy calves beginning at 10 weeks of age, and evaluated at 26, 39, and 52 weeks. Parenchyma was quantified at 10 weeks of age using ultrasound imaging and histological evaluation, and average echogenicity was utilized to quantify parenchyma at later stages of development. A significant negative correlation was detected between average echogenicity of parenchyma at 10 weeks and total adipose as a percent of histological whole tissue at 52 weeks. Additionally, a negative correlation between average daily gain at 10 and 26 weeks and maximum echogenicity at 52 weeks was present. These results suggest average daily gain and mammary gland development prior to 39 weeks of age is associated with development of the mammary gland after 39 weeks. These findings could be predictors of future milk production, however this must be further explored.

Near-infrared spectroscopy analysis of blood plasma for predicting nonesterified fatty acid concentrations in dairy cows.

During the transition period, dairy cows are often exposed to negative energy balance (NEB), leading to lipid mobilization from adipose tissue into nonesterified fatty acids (NEFA), a common indicator of heightened illness risk. This study aimed to use blood near-infrared (NIR) spectra data to classify NEB into high or low categories, based on early-lactation cow NEFA thresholds. We collected a total of 186 plasma samples from 100 Holstein cows. The samples were categorized into critical thresholds, based on previous literature, of ≥0.60 and ≥0.70 mEq/L for identifying high NEB. Spectral data were preprocessed before the development of the predictive modes, which included the implementation of multiplicative scatter correction, standard normal variate (SNV), and first and second derivatives. The classification was performed using partial least square discriminant analyses (PLS-DA), and predictive performance was assessed using leave-one-out cross-validation. Predictive quality for each class was evaluated through specificity, precision, sensitivity, and F1 score. The study showed promising results, with the SNV technique achieving higher F1 scores. The model found 72.7% specificity, 78.9% precision, 80.8% sensitivity, and 79.8% F1 score to classify animals with NEFA levels of ≥0.60 mEq/L, and 82.1% specificity, 78.7% precision, 80.8% sensitivity, and 79.7% F1 score to classify animals with NEFA levels ≥0.70 mEq/L. These results indicate that NIR spectroscopy could serve as a tool for detecting cows under severe NEB, also showing potential for broader application across the entire transition period, as the spectral signal carried relevant information regarding cow metabolism. Furthermore, the combination of predictors derived from plasma spectra and other cow-level information can lead to more accurate disease alerts, given their relationship with the NEB.

Exploring associations among morphometric measurements, genetic group of sire, and performance of beef on dairy calves.

Sire selection for beef on dairy crosses plays an important role in livestock systems as it may affect future performance and carcass traits of growing and finishing crossbred cattle. The phenotypic variation found in beef on dairy crosses has raised concerns from meat packers due to animals with dairy-type carcass characteristics. The use of morphometric measurements may help to understand the phenotypic structures of sire progeny for selecting animals with greater performance. In addition, due to the relationship with growth, these measurements could be used to early predict the performance until the transition from dairy farms to sales. The objectives of this study were 1) to evaluate the effect of different beef sires and breeds on the morphometric measurements of crossbred calves including cannon bone (CB), forearm (FA), hip height (HH), face length (FL), face width (FW) and growth performance; and (2) to predict the weight gain from birth to transition from dairy farms to sale (WG) and the body weight at sale (BW) using such morphometric measurements obtained at first days of animals' life. CB, FA, HH, FL, FW, and weight at 7 ±â€…5 d (BW7) (Table 1) were measured on 206 calves, from four different sire breeds [Angus (AN), SimAngus (SA), Simmental (SI), and Limousin (LI)], from five farms. To evaluate the morphometric measurements at the transition from dairy farms to sale and animal performance 91 out of 206 calves sourced from four farms, and offspring of two different sires (AN and SA) were used. To predict the WG and BW, 97 calves, and offspring of three different sires (AN, SA, and LI) were used. The data were analyzed using a mixed model, considering farm and sire as random effects. To predict WG and BW, two linear models (including or not the morphometric measurements) were used, and a leave-one-out cross-validation strategy was used to evaluate their predictive quality. The HH and BW7 were 7.67% and 10.7% higher (P < 0.05) in SA crossbred calves compared to AN, respectively. However, the ADG and adjusted body weight to 120 d were 14.3% and 9.46% greater (P < 0.05) in AN compared to SA. The morphometric measurements improved the model's predictive performance for WG and BW. In conclusion, morphometric measurements at the first days of calves' life can be used to predict animals' performance in beef on dairy. Such a strategy could lead to optimized management decisions and greater profitability in dairy farms.