Cowboy Research Articles

ОСОБЕННОСТИ ГЕМАТОЛОГИЧЕСКОГО ПРОФИЛЯ ГЛУБОКОСТЕЛЬНЫХ И НОВОТЕЛЬНЫХ КОРОВ НА ФОНЕ ПРИМЕНЕНИЯ БИОСТИМУЛЯТОРОВ (сообщение 2)

This article data on differentiated counting of the number of leukocytes are given, as well as the dynamics of total protein and protein fractions in the blood of animals, indicating the level of metabolic processes in a living organism, is determined. It has been established that intramuscular application of experimental biological preparations to cows 65-60, 45-40 and 25-20 days before calving stimulates the process of hematopoiesis, activates cellular factors of non-specific resistance (physiological leukocytosis, moderate eosinophilia and neutropenia with a right shift of the nucleus, lymphocytosis). The most pronounced effect was noted with three injections of the complex biological product Prevention-N-A-M. Biological preparations Prevention-N-A-M and PS-7 stimulate protein synthesis (albumins, α-, β-, γ-globulins) in the body before and after calving. The obtained results testify to the effectiveness of the tested biopreparations and make it possible to scientifically substantiate the feasibility of their use in animal husbandry as an effective way to increase the resistance of the body of cows, which can significantly improve their health and reduce the risk of diseases.

The effects of different feeding strategies providing different levels of vitamin A on animal performance, carcass traits, and the conversion rate of subcutaneous fat color in cull-cows

Abstract Cull cows represent a significant percentage of revenue received from the U.S. beef industry; however, cull cows are heavily price discounted at time of slaughter. This experiment’s objective is to evaluate different feeding strategies and their effects on body condition score, subcutaneous fat color, and carcass yield and quality traits in cull cows. The central hypothesis is feeding a high-energy diet, with low levels of vitamin A, for 56 d will improve animal performance, carcass yield, and quality traits in addition to capturing the point (rate) of the conversion of yellow to white subcutaneous fat. In the present experiment 98 Angus crossbreed cows were utilized. Cows were fed either low vitamin A (LVA) diet consisting of whole shelled corn, soybean hulls, soybean meal, and a mineral-vitamin supplement or high vitamin A (HVA) diet, formulated using whole shelled corn, fescue hay, dry distiller grains with soluble, and a mineral-vitamin supplement for 56 d. During the 56 d feeding period, body weights and condition scores, and subcutaneous adipose samples were collected every 14 d. On day 56, cattle were slaughtered; 48 h postmortem carcass characteristics and objective color scores (subcutaneous adipose tissue) were recorded and a sample of the longissimus dorsi lumborum was collected. Subcutaneous adipose tissue samples were utilized to record subjective color scores and then ground to be analyzed for β-carotene concentration. The longissimus dorsi lumborum samples (2.54 cm slices) were removed for Warner-Bratzler shear force (WBSF) and pH testing. Data were analyzed using the MIXED procedure of SAS. Feeding cull cows LVA resulted in differences in subcutaneous carcass fat color (P = 0.01) as well as b* values (P < 0.01) on day 56 compared with HVA. Subjective fat color scores were not different (P > 0.10) on day 0 or 14 but were different (P ≤ 0.05) on days 28, 42, and 56. Additionally, 9-cis-β-carotene concentration on day 56 were different (P = 0.05) between treatments. A trend was noticed for all-trans-β-carotene concentration (P = 0.10) on day 56 as well. Cull cow body weights were greater (P ≤ 0.04) when fed the LVA diet starting on days 14, 28, and 42; and a trend was noticed on day 56 (P = 0.09). Overall, cows fed the LVA treatment for 56 d exhibited decreased adipose yellowness and β-carotene concentrations as well as increased live weights.

A Method of Body Condition Scoring for Dairy Cows Based on Lightweight Convolution Neural Network

Highlights A cow body condition scoring model (ShuffleNet-SEH) based on a lightweight convolutional neural network using ShuffleNetV2 was proposed. Introducing the SE attention mechanism and H-Swish activation function enhances the feature extraction capability of the model. Achieved over 98% classification accuracy rate on the cow datasets. The proposed method can be applied to mobile devices. Abstract. Body condition scoring is an essential tool for nutrition management in large-scale dairy farming. The traditional method of manual scoring is inefficient and prone to subjective error. With the advancement of deep learning algorithms and machine vision technology, automatic scoring methods have been proposed. In response to the current problems of large parameter sizes and poor robustness in deep learning models, this study proposes a cow body condition scoring model (ShuffleNet-SEH) based on a lightweight convolutional neural network using ShuffleNetV2 as a foundation. Firstly, tail area images of cows were collected to construct a diverse dataset that includes complex variations such as motion blur and different levels of illumination. Secondly, the ShuffleNet-SEH model was developed by integrating a Squeeze-and-Excitation Networks (SENet) module into the main branch of ShuffleNetV2 after 1×1 convolution to enhance the representation ability of the network and improve its accuracy in identifying the cow body condition. Additionally, the nonlinear activation function ReLU in SENet was replaced by H-Swish to reduce the computational data required for the model on mobile devices, making it suitable for quantization operations. ShuffleNet-SEH was comprehensively evaluated, including ablation experiments, confusion matrix analysis, and feature map analysis. Multiple complex test sets were constructed for validation purposes. Throughout these evaluations, ShuffleNet-SEH exhibited remarkable performance. Furthermore, the efficacy and interpretability of ShuffleNet-SEH in cow body condition scoring tasks were further substantiated through meticulous feature map analysis. The findings underscore the robustness and reliability of ShuffleNet-SEH across diverse experimental scenarios and assessments. In the constructed motion blur test set, ShuffleNet-SEH demonstrated outstanding performance. Specifically, it achieved an accuracy rate of 98.2%, a precision rate of 98.5%, and a recall rate of 98.3%. These results represent significant improvements of 2.3%, 2.4%, and 3.2%, respectively, compared to the performance of the ShuffleNetV2 model. In addition, it is noteworthy that the ShuffleNet-SEH model has a size of approximately 5.64 MB, while the original ShuffleNet model has a size of 4.97 MB. This indicates a modest increase in model size of approximately 13.5% with the incorporation of the ShuffleNet-SEH enhancements. Moreover, superior overall performance was displayed by ShuffleNet-SEH model compared to mainstream convolutional neural network classification models, including MobileNetV2, EfficientNetV1, and ConvNeXt. The success of ShuffleNet-SEH in accurately identifying cow body condition while maintaining a lightweight architecture makes it suitable for deployment on mobile devices and, thus, has the potential to promote the commercialization of cow body condition scoring. Keywords: Attention mechanism, Body condition score, Deep learning, ShuffleNetV2.

The state of the body's immune system of beef cows with signs of endotoxicosis

During the development of endotoxicosis of various etiologies, secondary immunodeficiency occurs in animals, which progresses due to exposure to toxic metabolites. The work aimed to study the influence of endotoxicosis on the dynamics of indicators of the immune system of beef cows. The research was conducted on the Ukrainian black and spotted dairy breed beef cows. In cows with signs of endotoxicosis, clinical signs such as swelling of the external genital organs and mammary gland, anemia of the mucous membranes, depressed state, impaired appetite, and functional disorders of the antrum and intestines were characteristic. The main immunological tests characterizing the state of the immune system of beef cows are indicators of humoral immunity (bactericidal and lysozyme activity of blood serum), cellular immunity (T- and B-lymphocytes), indicators of non-specific resistance of the organism (phagocytic activity and phagocytic index). In beef cows with clinical manifestations of endotoxicosis, inhibition of cellular, humoral, and non-specific links of the immune system was established, which led to the development of the so-called secondary immunodeficiency. This is evidenced by a decrease in the bactericidal and lysozyme activity of blood serum, a decrease in the number of T- and B-lymphocytes, and a decrease in the phagocytic index and phagocytic activity of the blood of sick cows. In particular, it was established that the lowest indicator of bactericidal and lysozyme activity was observed in the 9th month of pregnancy in the experimental group of cows, where it decreased by 15.9 and 4.91 %, respectively, compared to the control. In the above study periods, a decrease in the phagocytic activity of neutrophils by 5.04 % and the phagocytic index by 33.3 % was established compared to the indicators of the control group. During the analysis of cellular immunity, similar changes were found in determining the number of T- and B-lymphocytes; they were lower by 12.5 % and 4.19 %, respectively. Changes in the indicators of the immune system are significant and objective markers of the state of the cow's body during the development of endotoxicosis, regardless of its causes.

Productive, reproductive and bioenergetic characteristics of cows with different body condition score

The aim of this work was to study the influence of fattening of dairy cows body condition score on reproductive traits, productivity and bioenergetic indicators. The research was conducted during May-August 2021 in the conditions of the Terezine of the Vilnotarasivske branch (Kyiv region). The values of average monthly temperatures were within the limits of thermoneutrality for cows of dairy breeds. Three groups of second-lactation cows were formed for the study. The first group (n=14) included animals with a fatness score of 2.0 to 3.0 points, and the second group (n=24) with a score of 3.0 to 3.75 points and to the III-rd group (n=15) – 4 and above points. Assessment of fatness in groups of animals was carried out during the period of deep body. The productivity of cows during the first 120 days of lactation was determined using computer records and the DelPro program. Cows of the II group had a somewhat shorter dry period (by 3 and 1 day) compared to animals of the I and III groups. The shortest service period was observed in the first group of cows – 164 days, which is 7 and 24 days less than in animals of the second and third groups, respectively. Cows of the II group had the highest average daily productivity during each of the first 4 months of lactation (120 days). At the same time, productivity was the lowest in cows of the III group, and intermediate values were observed in the animals of the I group. At the same time, cows of the III-rd group had the lowest productivity during the same period. Analyzing the bioenergetic characteristics of cows of different levels of fatness, it was established that the highest rate of net energy expenditure for the production of 1 kg of 4 % milk was in cows of the IIIrd group – 3.71 MJ, and the lowest in animals of the IInd group – 3.49 MJ. Research results showed that cows of different fattening levels before calving had high energy indices – from 50.25 to 53.99 %, productive – from 0.260 to 0.286 kg for specific net energy loss from 1.852 to 1.988 MJ per 1 MJ of milk energy. Key words: dairy cows, fatness, productivity, reproduction, bioenergetic characteristics.

Knowledge, attitudes and practices associated with giardiasis among cattle handlers in Jere, Borno State, Northeastern Nigeria

This study was designed to assess the level of knowledge, attitudes and practices (KAP) of cattle handlers (cattle owners, cattle attendants and veterinarians) concerning giardiasis in Jere Local Government Area (LGA) of Borno State, Northeastern Nigeria. Using a pre-validated questionnaire, 278 cattle handlers from six wards of Jere were interviewed, with a 84% (n = 252) response rate. The majority of the respondents were aged 27–38 years (62.3%), male (93.7%) and Muslim (96%). Regarding the control and prevention of giardiasis, 31.3%, 44.8% and 31.3% of the cattle handlers had good knowledge, attitudes and practices, respectively. The overall mean scores of 59.5%, 64.8% and 64.1% for knowledge, attitudes and practices were significantly (p < 0.05) lower than the ≥ 75% cut-off mark set for a good KAP score on giardiasis. Furthermore, there was a moderate, positive and significant (p < 0.05) correlation between the knowledge score and the attitudes and practices scores. The handlers’ knowledge and attitudes had significant associations (p < 0.05) with their levels of education and years of experience, signifying that high levels of education and years of experience were associated with improved awareness of giardiasis among cattle handlers. The study concluded that there is a potential risk of cattle handlers being infected with Giardia lamblia based on their overall suboptimal levels of knowledge, attitudes and practices toward giardiasis observed in the study area. Raising awareness among cattle handlers about the public health implications of giardiasis in the study area was recommended.

Depth video data-enabled predictions of longitudinal dairy cow body weight using thresholding and Mask R-CNN algorithms

Monitoring cow body weight is crucial to support farm management decisions due to its direct relationship with the growth, nutritional status, and health of dairy cows. Cow body weight is a repeated trait, however, the majority of previous body weight prediction research only used data collected at a single point in time. Furthermore, the utility of deep learning-based segmentation for body weight prediction using videos remains unanswered. Therefore, the objectives of this study were to predict cow body weight from repeatedly measured video data, to compare the performance of the thresholding and Mask R-CNN deep learning approaches, to evaluate the predictive ability of body weight regression models, and to promote open science in the animal science community by releasing the source code for video-based body weight prediction. An Intel RealSense D435 camera was installed on Virginia Tech dairy complex to collect top-view videos of 10 lactating Holstein cows twice a day for 28 days after milking sessions and 2 non-lactating, pregnant Jersey cows once a week upon calving. At the same time, the ground truth body weight records were collected using a walk-over weighing system. A total of 40,405 depth images and depth map files were obtained. Three approaches were investigated to segment the cow's body from the background, including single thresholding, adaptive thresholding, and Mask R-CNN. Four image-derived biometric features, such as dorsal length, abdominal width, height, and volume, were estimated from the segmented images and fitted using ordinary least squares, ridge regression, least absolute shrinkage and selection operator, and linear mixed models. Two cross-validation designs, forecasting and leave-three-cows-out, were used to evaluate prediction performance. The Pearson correlation between image-derived biometric features and scale-based body weight ranged from 0.74 to 0.95. On average, the Mask-RCNN approach combined with a linear mixed model resulted in the best prediction coefficient of determination and mean absolute percentage error of 0.98 and 2.03%, respectively, in the forecasting cross-validation. The Mask-RCNN approach was also the best in the leave-three-cows-out cross-validation, followed by adaptive and single thresholding. The prediction coefficients of determination and mean absolute percentage error of the Mask-RCNN coupled with the linear mixed model were 0.90 and 4.70%, respectively. Our results suggest that deep learning-based segmentation improves the prediction performance of cow body weight from longitudinal depth video data.

Automated Cow Body Condition Scoring Using Multiple 3D Cameras and Convolutional Neural Networks.

Body condition scoring is an objective scoring method used to evaluate the health of a cow by determining the amount of subcutaneous fat in a cow. Automated body condition scoring is becoming vital to large commercial dairy farms as it helps farmers score their cows more often and more consistently compared to manual scoring. A common approach to automated body condition scoring is to utilise a CNN-based model trained with data from a depth camera. The approaches presented in this paper make use of three depth cameras placed at different positions near the rear of a cow to train three independent CNNs. Ensemble modelling is used to combine the estimations of the three individual CNN models. The paper aims to test the performance impact of using ensemble modelling with the data from three separate depth cameras. The paper also looks at which of these three cameras and combinations thereof provide a good balance between computational cost and performance. The results of this study show that utilising the data from three depth cameras to train three separate models merged through ensemble modelling yields significantly improved automated body condition scoring accuracy compared to a single-depth camera and CNN model approach. This paper also explored the real-world performance of these models on embedded platforms by comparing the computational cost to the performance of the various models.

151 Depth Video Data-Enabled Predictions of Longitudinal Dairy Cow Body Weight Using Thresholding and Mask R-Cnn Algorithms

Abstract Cow body weight monitoring is critical in farm management because it is associated with the growth, nutritional status, and health of dairy cows. Regularly collecting body weight records often requires human labor or expensive equipment. Recently, a low-cost 3D depth sensor camera-enabled computer vision analysis has been proposed to address the body weight prediction challenges. Cow body weight is a repeated trait; however, past body weight prediction research only used data collected at a single time point. Furthermore, the utility of deep learning on body weight predictions remains unanswered. Therefore, the objectives of this study were to predict cow body weight from repeatedly measured video data and compare the performance of thresholding and Mask R-CNN deep learning approaches. We set up an Intel Realsense D435 camera at the Virginia Tech Kentland farm to collect top-view videos for 10 Holstein and 2 Jersey cows for 28 days, twice per day. Real body weight records were also collected by a walk-over-weighing system simultaneously. A total of 40,405 depth images and depth map files were obtained. We explored three approaches to segment the cow body from the background, including single thresholding, adaptive thresholding, and Mask R-CNN. Four image-derived biometric measurements, length, width, height, and volume, were estimated from segmented images and fitted in ordinary least squares. Two cross-validation designs, forecasting and leave-several-cow-out, were used to evaluate prediction performance. The correlation between image-derived biometric measurements and body weight ranged from 0.84 to 0.95. On average, the Mask-RCNN approach resulted in the best prediction coefficient of determination and mean absolute percentage error of 0.96 and 3.54% in the forecasting cross-validation, respectively. The predictive performance of the single and adaptive thresholding approaches was similar. The Mask-RCNN approach was also the best in the leave-several-cow-out cross-validation, followed by adaptive and single thresholding. The prediction coefficients of determination of Mask-RCNN for leave-one-cow-out, leave-two-cow-out, leave-three-cow-out, leave-four-cow-out were 0.05, 0.69, 0.85, and 0.90, respectively, while mean absolute percentage errors were 4.51%, 4.51%, and 4.59%, 4.70% respectively. Our results suggest predicting cow body weight from depth video data using deep learning is feasible.

Bodies on display: from parking pads to Agent Orange

What can a parking pad at the front of a house and agent orange, a defoliant used by the US military in its “Operation Ranch Hand” during the war in the Vietnam, possibly have in common? How unsafe does everything need to become to justify action? These are just a few of the questions at the centre of the stories I share in this piece; a piece that traces my/our experience trying to “get” something from our city to make access happen, and another story from decades earlier centred on restorative justice and the toxic chemical (dioxin) legacy of the American war in Vietnam.

Rancang Bangun Sistem Analisa Biaya Manfaat Ternak Sapi Berdasarkan Berat Badan Sapi

Cattle fattening proccess is very important to increase the weight of cattle before being sold. However, there are many traditional breeders looking for cows that have reached puberty, but the cow's body is still thin. This could be due to the absence of records in monitoring the weight growth of cows so that feeding becomes less precise. Based on these problems, a system was designed that helps farmers in managing their livestock management by recording the progress of each beef cattle that are kept, regulating cattle feeding so that it is more optimal, and knowing the cost benefits of cattle that are kept based on the growth of cow body weight. This system uses two load cells to weigh cows. The weight of the weighed cow can be stored in the web application database. The main function of this system is to be able to calculate and display the cost of benefits of cattle based on the growth of the cow's body weight. Based on the tests carried out, the system can weight cows using two 500kg load cells with an error value of 3.61%. The system can check cow data using keypad input with an average time of 3.99 seconds and store cattle data in the database with an average time of 4.99 seconds. The system can calculate the cost of cow feed needs in one month and income due to changes in cow weight with a 100% success rate.

Hind feet position score: A novel trait to genetically reduce lameness incidence

Lameness is an important health and welfare issue that causes considerable economic losses in dairy herds. The objective of this study was to investigate whether the hind feet position score (HFPS) can be used as an auxiliary trait for genetic evaluation of lameness. The HFPS is evaluated by visual scoring of the position of both the hind-digits to the mid-line of the cow's body. The higher the heel height of the lateral claw, the higher is the HFPS, and the higher is the risk for development of lameness. In total, 3,478 records from 1,064 Fleckvieh cows from 35 farms were obtained between September 1, 2021, and March 5, 2022. Data collection was carried out by the regional milk recording organizations. Hind feet position was scored visually by trained personnel during routine milk performance testing in the milking parlor using a 3-class scoring system: score 1 = 0° to <17° indicating a balanced heel height of both the medial and the lateral claw; score 2 = angle of 17° to 24°; score 3 = angle of >24°. After all cows had been milked, locomotion scoring was performed for each animal using a 5-class scoring system with locomotion scores ranging between 1 (normal) and 5 (severely lame). Using HFPS, sensitivity and specificity were 69.5% and 66.8%, respectively, for detecting lameness defined by locomotion score ≥2. For genetic analyses, a bivariate linear animal model was fitted with fixed effects of herd, parity, lactation stage, and classifier, and random effects of animal and permanent environment. Heritabilities for HFPS and locomotion score were 0.07 and 0.10, respectively, and the genetic correlation between the 2 traits studied was 0.80. These results suggest that the HFPS could be used for genetic evaluations to reduce lameness incidence in dairy cattle.

A Comparative Study of British Gentleman Culture and American Cowboy Culture

A Comparative Study of British Gentleman Culture and American Cowboy Culture

Semen quality of Nellore and Angus bulls classified by fertility indices and relations with field fertility in fixed-time artificial insemination

Despite being the most important form of biotechnology in animal reproduction, artificial insemination was used in about 23% of Brazilian bovine herds in 2021. This is due to the variability of results caused by varying bull fertility and body condition of the cows. This study aimed to correlate the fertility indices of bulls with qualitative attributes of the semen. Semen samples from 28 bulls (Nellore and Angus) were used to evaluate postthaw sperm morphology and kinetics using conventional analysis, image-based flow cytometry (IBFC) and computer-assisted semen analysis (CASA). The fertility index was effective in separating bulls into 4 different fertility classes (P < 0.001), and fertility rates in timed artificial insemination (TAI) remained constant between the cows' fertility categories (P < 0.001) and in the different grades of female body condition (P < 0.005). After partial least squares regression (PLS) analysis, four models were proposed with different variables. The coefficients of determination for the conventional analysis, CASA, and IBCFC models were 0.154, 0.380, and 0.259, respectively. The composite model, including select IBFC and CASA parameters, showed a greater R2 (0.481) with progressive motility, average speed (VAP, μm/s), membrane integrity, and mitochondrial potential, showing a positive effect. Linear speed (VSL, μm/s) and acrosomal integrity had a negative effect on bull fertility indices. Bulls classified by the fertility index attained dispersed pregnancy rates in different cow body condition score (BCS) classes, and the sperm quality pattern was consistent with this classification. In conclusion, this novel composite model including CASA and IBFC parameters improves the prediction of bull fertility used in TAI.

High yielding cows metabolism peculiarities under climate change conditions with the use of feed additive with protein protected from rumen degradation

The research relevance is determined by the problem of thermal load on the body of dairy cows by increasing the content of protein protected from breakdown in the rumen. The research aims to determine the metabolism and level of delivery of various forms of protein and energy to the body of highly productive cows in the diets of cows in the second half of lactation under conditions of climate change. Research methods include zootechnical (assessment of feed intake, rationing, selection of groups, organisation of the experiment, determination of milk production), biochemical (determination of the content of basic nutrients in feed, milk quality indicators), statistical, and mathematical. The compensation of protein in the diet of high-yielding cows under climate change conditions was carried out using the protein feed additive TEP-mix with protein protected from rumen breakdown. It has been proven that its inclusion in the cow’s diet can increase average daily milk production by 9.1% in the cold season (0-10°C) and by 28.0% in the summer (21-36.4°C). In addition, the fat and protein content in milk increased in cows receiving the high-protein feed additive TEP mix. It was found that the increase in protein nutrition of cows from 23.18% to 33.53% with the use of the feed additive TEP-mix with protein protected from scar breakdown ensures a reduction in the impact of climate change on the metabolism of dairy cows. Thus, it has been proved that the use of specific protein feed sources that regulate the level of non-cutting protein in the diet can be considered a factor in combating heat stress and increasing milk production of dairy cows throughout the physiological cycle of lactation, including in extreme conditions of high temperatures

Usefulness of mid-infrared spectroscopy as a tool to estimate body condition score change from milk samples in intensively fed dairy cows

Directly measuring individual cow energy balance is not trivial. Other traits, like body condition score (BCS) and BCS change (ΔBCS) can, however, be used as an indicator of cow energy status. Body condition score is a metric used world-wide to estimate cow body reserves and the estimation of ΔBCS was, until now, conditional on the availability of multiple BCS assessments. The aim of the present study was to estimate ΔBCS from milk mid-infrared (MIR) spectra and days in milk (DIM) in intensively-fed dairy cows using statistical prediction methods. Daily BCS was interpolated from cubic splines fitted through the BCS records and daily ΔBCS was calculated from these splines. Body condition score change records were merged with milk MIR spectra recorded on the same week. The data set comprised 37,077 ΔBCS phenotypes across 9,403 lactations from 6,988 cows in 151 herds based in Quebec (Canada). Partial least squares regression (PLSR) and a neural network (NN) were then used to estimate ΔBCS from 1) MIR spectra only, 2) DIM only, or 3) MIR spectra and DIM together. ΔBCS data in both the first 120 DIM and 305 DIM of lactation were used to develop the estimates. Daily ΔBCS had a standard deviation of 4.40*10-3 BCS units in the 120-d data set and of 3.63*10-3 BCS units in the 305-d data set. 4-fold cross-validation was used to calibrate and test the prediction equations. External validation was also conducted using more recent years of data. Irrespective of whether based on the first 120 or 305 DIM, or when MIR spectra only, DIM only or MIR spectra and DIM were jointly used as prediction variables, NN produced the lowest root mean square error (RMSE) of cross-validation (1.81*10-3 BCS units and 1.51*10-3 BCS units, respectively, using the 120-d and 305-d data set). Relative to predictions for the entire 305 DIM, the RMSE of cross-validation was 15.4% and 1.5% lower in the first 120 DIM when using PLSR and NN, respectively. Predictions from DIM only were more accurate than those using just MIR spectra data but, irrespective of the data set and of the prediction model used, the combining DIM information with MIR spectral data as prediction variables reduced the RMSE compared with inclusion of DIM alone, albeit the benefit was small (the RMSE from cross-validation was reduced up to 5.5% when DIM and spectral data were jointly used as model features instead of DIM only). However, when predicting extreme ΔBCS records, the MIR spectral data was more informative than DIM. Model performance when predicting ΔBCS records in future years was similar to that from cross-validation demonstrating the ability of MIR spectra of milk and DIM combined to estimate ΔBCS, particularly in early lactation. This can be used to routinely generate estimates of ΔBCS to aid in day-to-day individual cow management.

Comparison of deworming strategies for pre-weaned beef calves

While anthelmintic treatment in young calves at first vaccination has not proven repeatedly effective in improving weight gain, there is evidence that treatment 90 day prior to weaning can enhance weight gain. This is difficult in most California cow/calf operations as after initial vaccination, calves are not again handled until preconditioning just prior to weaning. In an attempt to increase weight gain without the need for anthelmintic retreatment prior to preconditioning, 130 cow/calf pairs were stratified and assigned to three treatments of eprinomectin extended release injection (150 d efficacy), doramectin conventional injection, and an untreated control. Dams and calves received the same treatment. All treatments were commingled on irrigated pasture in Northern California. Sampling included weight (d 0, 75, and 145), fecal parasite (d 0, 14, 75, and 145), cow body condition score (d 0, 75, and 145), and cow pregnancy detection (days bred). Calf weight gain was not impacted by anthelmintic treatment (P = 0.44). However, cow weight gain was impacted by treatment (P < 0.01), with eprinomectin extended release showing higher weight gain than both doramectin conventional and the control (ADG kg 0.29, 0.23, 0.22, respectively). Cow weight gain differences were expressed greater toward the end of the trial than early on. Fecal egg count sampling failed to explain the difference in cow weight gain. Cow body condition score (P = 0.15) and days bred (P = 0.50) were not significantly affected by treatment. Although cow weight gain increased with an extended release anthelmintic treatment, this effect did not seem to be evident in suckling calves on high quality irrigated pastures in Northern California when compared to a conventional injectable and control treatments. Neither anthelmintic treatment appeared beneficial over a control when administered to suckling calves during initial vaccination at approximately 90 days of age.

The Effect of Copper and Zinc Sources on Liver Copper and Zinc Concentrations and Performance of Beef Cows and Suckling Calves.

To determine the influence of the source of gestational and postnatal Cu and Zn supplementation on cow and calf performance, cows (n = 287) were assigned to one of the following two treatments: (1) inorganic (INORG) treatment, in which cows were supplemented with 15 mg of Cu (as CuSO4) and 15 mg of Zn (as ZnSO4) per kg of diet DM, or (2) organic (ORG) treatment, in which cows were supplemented with 15 mg of Cu (as Cu proteinate; Bioplex Cu, Alltech, Inc., Nicholasville, KY, USA) and 15 mg of Zn (as Zn proteinate; Bioplex Zn, Alltech, Inc., Nicholasville, KY, USA) per kg of diet DM. The treatments were initiated prior to breeding and continued throughout gestation until weaning. Liver biopsies were collected for analysis of mineral content. Cow body condition score (BCS), body weight (BW), pregnancy data, calf weaning weight (WW), and antibody response of the calves were recorded. The cows receiving the INORG treatment had a greater BW (p < 0.05) and BCS (p < 0.01) at breeding in Year 2, while the cows on the ORG treatment had a greater (p < 0.05) BW at weaning in Year 2. The cows that received the ORG mineral had improved (p < 0.05) conception rates in Year 1. The calves receiving the ORG treatment had heavier (p < 0.05) 205-day adjusted WWs.

Effect of Spraying Water on the Body of Holstein Cows under Heat Stress Conditions in Milk Production and Components

The aim of the current experiment is to reduce heat stress on imported Holstein cows in central Iraq during the months of August and September. Using 12 Holstein cows multiparous from milk-producing cows raised in the station, divided into three treatments each treatment containing 4 cows. C = cows without spraying (control treatment), T1 = cows sprayed once a day at 1 pm and T2 = cows sprayed twice a day at 11 am and 1 pm. The results of the statistical analysis indicated that there were significant differences between the treatments in the daily milk production during the second, fourth and eighth weeks of the experiment in the twice-daily spraying treatment, which recorded 13.25, 14.50 and 13.00 kg/cow/day, respectively, compared with the control treatment in the second and fourth weeks, where It recorded 9.50 and 11.00 kg/cow/day, respectively, and compared with the T1 treatment in the eighth week, which was 8.50 kg/cow/day, and it was noted that there were no significant differences between the treatments in the total milk production during the trial period. However, there was an arithmetical increase in the spraying treatment, which amounted to 796 kg/cow compared with control treatment which recorded 694 kg/cow, and in the treatment of spraying once a day it amounted to 722.75 kg/cow, and for the milk components that include fat, protein, lactose sugar, ash and density There were no significant differences between the treatments during the experiment period. We conclude from the current experiment that spraying Holstein cows with water twice a day during the period of heat stress had a positive effect in increasing milk production in some weeks of the experiment during the months of August and September.

Effect of pre- and postpartum supplementation of a pure glycerol product to dairy cows on feed intake, metabolic markers, and milk yield and components

The objective of this study was to quantify the effects of supplementing transition dairy cows with a low inclusion dry glycerol product in the pre- and postpartum periods on feed intake, metabolic markers, and milk yield and components. Multiparous Holstein dairy cows (n = 60) were enrolled in a 2-by-2 factorial design study. Starting 21 d before expected parturition, cows individually received a dry cow diet with (1) 250 g/d glycerol product supplementation [66% pure glycerol (United States Pharmacopeia grade); GLY], or (2) no supplementation (CON) mixed to their total mixed ration. After parturition, cows, again, were individually assigned to either GLY, or (2) no supplementation (CON) to their partial mixed ration for the first 21 d in milk (DIM). Cows were milked by an automated milking system and offered a target of 5.4 kg DM/d pellet (23% of target total dry matter intake, DMI) in the automated milking system and followed for 42 d into lactation. Blood samples were collected 6.3 ± 3.47 d before calving for all blood measures and 3, 7, 10, and 14 DIM for analysis of glucose and β-hydroxybutyrate, as well as 3 and 7 DIM for nonesterified fatty acids (NEFA) and haptoglobin. Initial dry cow body weight (BW), calf birth weight, previous 305-d milk, and month of parturition were used as covariates in the statistical model. Cows supplemented with GLY prepartum lost less BW and consumed more DMI pre- and postpartum, as well as had lower postpartum blood β-hydroxybutyrate and NEFA concentrations compared with those fed the CON treatment prepartum. Cows supplemented with GLY postpartum had lesser DMI in the first 42 DIM than cows fed CON postpartum, but also had reduced blood NEFA concentrations, odds of a high haptoglobin test, odds of a low blood glucose test, and lesser preformed fatty acid concentrations and yields in their milk. Cows supplemented glycerol both pre- and postpartum lost the least total BW from -21 to 21 DIM. No treatment effects were detected for milk yield; however, cows receiving GLY postpartum had lower milk fat. Overall, glycerol supplementation during the transition period, particularly during the 21 d before calving, was associated with markers of improved metabolic status.