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Up to now, little has been known about backfat thickness (BFT) in dairy cattle. The objective of this study was to investigate the lactation curve and genetic parameters for BFT as well as its relationship with body condition score (BCS) and milk yield (MKG). For this purpose, a dataset was analysed including phenotypic observations of 1929 German Holstein cows for BFT, BCS and MKG recorded on a single research dairy farm between September 2005 and December 2022. Additionally, pedigree and genomic information was available. Lactation curves were predicted and genetic parameters were estimated for all traits in first to third lactation using univariate random regression models. For BCS, lactation curves had nadirs at 94 DIM, 101 DIM and 107 DIM in first, second and third lactation. By contrast, trajectories of BFT showed lowest values later in lactation at 129 DIM, 117 DIM and 120 DIM in lactation numbers 1 to 3, respectively. Although lactation curves of BCS and BFT had similar shapes, the traits showed distinct sequence of curves for lactation number 2 and 3. Cows in third lactation had highest BCS, whereas highest BFT values were found for second parity animals. Average heritabilities were 0.315 ± 0.052, 0.297 ± 0.048 and 0.332 ± 0.061 for BCS in lactation number 1 to 3, respectively. Compared to that, BFT had considerably higher heritability in all lactation numbers with estimates ranging between 0.357 ± 0.028 and 0.424 ± 0.034. Pearson correlation coefficients between estimated breeding values for the 3 traits were negative between MKG with both BCS (r = −0.245 to −0.322) and BFT (r = −0.163 to −0.301). Correlation between traits BCS and BFT was positive and consistently high (r = 0.719 to 0.738). Overall, the results of this study suggest that BFT and BCS show genetic differences in dairy cattle, which might be due to differences in depletion and accumulation of body reserves measured by BFT and BCS. Therefore, routine recording of BFT on practical dairy farms could provide valuable information beyond BCS measurements and might be useful, for example, to better assess the nutritional status of cows. [ABSTRACT FROM AUTHOR]

The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. Research in a variety of species including cattle has suggested energy required for maintenance may be affected by body condition. The objective of this study was to use indirect calorimetry and total fecal and urine collections to estimate maintenance energy and fasting heat production (FHP) of cows differing in BCS. Twelve multiparous nonpregnant and nonlactating Jersey cows were randomly assigned to one of 2 treatment groups. To construct these groups, cows were fed 2 different TMR in a preexperimental period for 84 d resulting in a BCS of >5 (HiBCS) and a BCS <3 (LoBCS), and no difference was observed in initial BW or BCS between treatments, averaging 509 ± 26.0 kg and 4.1 ± 0.23. To begin the experiment all animals were fed at maintenance [NE L maint, (Mcal/d) = 0.10 × metabolic BW (BW0.75)] for 24 d followed by 4 d of data collection for energy balance, cows were then fasted of 96 h with data collection for energy balance once again taken over the final 24 h. During the maintenance collection period, differences in BW and BCS existed (439 and 566 ± 19.0 kg BW, and 3.0 and 5.0 ± 0.13 BCS) for LoBCS and HiBCS, respectively. Heat production increased with increasing BCS (13.1 to 16.2 ± 0.55 Mcal/d), but when expressed per unit of BW0.75 no difference was observed (0.14 ± 0.002 Mcal/d per BW0.75). When fasted, BW loss did not differ averaging 28.9 ± 0.181 kg. The FHP did not differ, averaging 0.10 ± 0.004 Mcal/d per BW0.75 and resulted in the following representation of maintenance: [NE L maint, (Mcal/d) = 0.10 ± 0.004 × BW0.75]. During fasting the nitrogen free respiratory quotient tended to differ (0.69 and 0.73 ± 0.014) and O 2 consumption and CO 2 production for protein oxidation differed for LoBCS and HiBCS (5.44 and 2.35 ± 0.988 O 2 and 4.52 and 1.95 ± 0.821 CO 2 L/BW0.75). Overall, FHP increased with increasing BCS, but FHP per unit of BW0.75 did not differ. Although BW change was similar during fasting, differences O 2 consumption and CO 2 production per unit of BW0.75 used for protein oxidation may indicate differences in the nature of body tissue utilization in cows differing in BCS. [ABSTRACT FROM AUTHOR]

Introduction: High testosterone levels might be associated with coarser fiber in alpacas, a [ABSTRACT FROM AUTHOR]

Background: Body condition score (BCS) is a subjective tool and the deposition of subcutaneous fat differs from the deposition of abdominal fat. Objective: The aim of this study was to evaluate by multivariate regression models and ultrasonography the amount of fat accumulated in different areas of the abdominal cavity in Jersey non‐pregnant non‐lactating cows and its association with their BCS. Methods: From a commercial farm, 21 non‐pregnant non‐lactating Jersey cows were selected at random. Cows were placed in a headlock line, and BCS was evaluated (scale 1–5 with a 0.25 unit increment) by the same assessor. Ultrasonographic evaluation was performed using a Sonosite machine in duplicate, considering key anatomical points of the body to predict total abdominal fat (AT), retroperitoneal fat (RT), omental fat (OT) and mesenteric fat (MT). A regression analysis for each abdominal fat depot and the BCS was run using SAS. Results: Models from the lowest to the largest r2 are reported. The r2 for the models were MT r2 = 0.023; RT r2 = 0.1047; OT r2 = 0.323 and AT r2 = 0.369. Correlation between BCS and abdominal fat depots was positive, lower for mesenteric and retroperitoneal fat, but higher for omental and total abdominal fat. Cows were divided on the basis of the median of BCS distribution to high (≥3.5) and low (≤3.25). Those with high BCS had significantly larger amounts of fat in different anatomical areas of abdominal cavity than cows with low BCS. Conclusion: BCS has a low‐to‐moderate association with abdominal fat depots, but a high variability exists within each BCS punctuation, which supports the notion that fat accumulation patterns and metabolic turnover between abdominal and subcutaneous fat differ from each other. [ABSTRACT FROM AUTHOR]

Simple Summary: This study proposes an automatic classification system for determining body condition score in dairy cows using a deep learning architecture. An original dataset was created by categorizing images of different breeds from different farms into five body condition score classes: Emaciated, Poor, Good, Fat, and Obese. In the experimental analysis, the proposed deep learning model accurately classified 102 out of 126 cow images in the test set, achieving an average accuracy of 0.81 for all classes in Holstein and Simmental cows and an average area under the precision–recall curve of 0.87. The proposed body condition score classification system can help to accurately monitor rapid declines in body condition in dairy cows and serve as a tool for production decision-makers to reduce negative energy balance during early lactation. Body condition score (BCS) is a common tool used to assess the welfare of dairy cows and is based on scoring animals according to their external appearance. If the BCS of dairy cows deviates from the required value, it can lead to diseases caused by metabolic problems in the animal, increased medication costs, low productivity, and even the loss of dairy cows. BCS scores for dairy cows on farms are mostly determined by observation based on expert knowledge and experience. This study proposes an automatic classification system for BCS determination in dairy cows using the YOLOv8x deep learning architecture. In this study, firstly, an original dataset was prepared by dividing the BCS scale into five different classes of Emaciated, Poor, Good, Fat, and Obese for images of Holstein and Simmental cow breeds collected from different farms. In the experimental analyses performed on the dataset prepared in this study, the BCS values of 102 out of a total of 126 cow images in the test set were correctly classified using the proposed YOLOv8x deep learning architecture. Furthermore, an average accuracy of 0.81 was achieved for all BCS classes in Holstein and Simmental cows. In addition, the average area under the precision–recall curve was 0.87. In conclusion, the BCS classification system for dairy cows proposed in this study may allow for the accurate observation of animals with rapid declines in body condition. In addition, the BCS classification system can be used as a tool for production decision-makers in early lactation to reduce the negative energy balance. [ABSTRACT FROM AUTHOR]

ABSTRACT: Research in a variety of species including cattle has suggested energy required for maintenance may be affected by body condition. The objective of this study was to use indirect calorimetry and total fecal and urine collections to estimate maintenance energy and fasting heat production (FHP) of cows differing in BCS. Twelve multiparous nonpregnant and nonlactating Jersey cows were randomly assigned to one of 2 treatment groups. To construct these groups, cows were fed 2 different TMR in a preexperimental period for 84 d resulting in a BCS of >5 (HiBCS) and a BCS

Objective Three experiments were conducted to evaluate the effects of Saccharomyces yeast postbiotics (SYP) in feeds for sows on the growth of offspring (Exp. 1), for nursery pigs on their growth (Exp. 2), and for nursery and finishing pigs on their growth (Exp. 3). Methods Exp. 1 had 80 sows at breeding assigned to 4 groups with SYP at 0, 0.050, 0.175, and 0.500 g/kg. Offspring were fed a common diet for 126 d. Exp. 2 had 144 barrows at 8 kg body weight (BW) allotted to CON (no SYP); YPC (SYP at 0.175 g/kg; d 0 to 42); and YPD (SYP at 1.25, 0.75, and 0 g/kg; d 0 to 7, d 8 to 21, and d 22 to 42, respectively) with 8 pens/treatment (6 pigs/pen). Exp. 3 had 96 barrows at 8 kg BW allotted to CON (no SYP); YPN (SYP at 0.175 g/kg; d 0 to 42); YPF (SYP at 0.100 g/kg; d 43 to 119); and YPA (SYP at 0.175 and 0.100 g/kg; d 0 to 42 and d 43 to 119, respectively) with 8 pens/treatment (3 pigs/pen). Results In Exp. 1, increasing SYP increased (p

This study aims to analyze the linear size of the bodies of participated cow used in the progeny test of dairy cows in KAN Jabung, Malang, East Java. The material used consisted of 180 lactating cows emplyed as participated cows. The research employed was a survey with data collection throught purposive sampling, selecting cows that are lactating based on specific criteria. The data were analyzed using correlation analysis and simple linear regression. The results of the study, revealed correlation values between body weight, chest circumference, body length, height with body condition score (BCS), as follows: 0,3542; 0,3523; 0,1926; 0,3198 respectively. Meanwhile, the correlation values of chest circumference, body length, and height with body were found to be: 0,9990; 0,5381; 0,4528, respectively. In conclusion, this study determined that the highest correlation of participated cow linear body size was observed in the relationship between chest circumference and body weight, while the lowest correlation was found in the relationship between body length and BCS.

ABSTRACT: The objectives of this retrospective observational study were to investigate the association between BCS at 21 d before calving with prepartum and postpartum DMI, energy balance (EB), and milk yield. Data from 427 multigravid cows from 11 different experiments conducted at the University of Florida (Gainesville, FL) were used. Cows were classified according to their BCS at 21 d before calving as fat (BCS ≥ 4.00; n = 83), moderate (BCS = 3.25–3.75; n = 287), and thin (BCS ≤ 3.00; n = 57). Daily DMI from −21 to −1 and from +1 to +28 DIM was individually recorded. Energy balance was calculated as the difference between net energy for lactation consumed and required. Dry matter intake in fat cows was lower than that in moderate and thin cows both prepartum (fat = 9.97 ± 0.21, moderate = 11.15 ± 0.14, and thin = 11.92 ± 0.22 kg/d) and postpartum (fat = 14.35 ± 0.49, moderate = 15.47 ± 0.38, and thin = 16.09 ± 0.47 kg/d). Dry matter intake was also lower for moderate cows compared with thin cows prepartum, but not postpartum. Energy balance in fat cows was lower than in moderate and thin cows both prepartum (fat = −4.16 ± 0.61, moderate = −1.20 ± 0.56, and thin = 0.88 ± 0.62 Mcal/d) and postpartum (fat = −12.77 ± 0.50, moderate = −10.13 ± 0.29, and thin = −6.14 ± 0.51 Mcal/d). Energy balance was also lower for moderate cows compared with thin cows both prepartum and postpartum. There was a quadratic association between BCS at 21 d before calving and milk yield. Increasing BCS from 2.5 to 3.5 was associated with an increase in daily milk yield of 6.0 kg and 28 d cumulative milk of 147 kg. Increasing BCS from 3.5 to 4.5 was associated with a decrease in daily milk yield of 4.4 kg and 28 d cumulative milk of 116 kg. In summary, a moderate BCS at 21 d before calving was associated with intermediate DMI and EB pre- and postpartum but greater milk yield compared with thinner and fatter cows. Our findings indicate that a moderate BCS is ideal for ensuring a successful lactation.

Abstract Background The rumen is a crucial digestive organ for dairy cows. The rumen microbiota assists in the digestion of plant feed through microbe-mediated fermentation, during which the plant feed is transformed into nutrients for the cow's use. Variations in the composition and function of the rumen microbiome affect the energy utilization efficiency of dairy cows, which is one of the reasons for the varying body condition scores (BCSs). This study focused on prepartum Holstein dairy cows to analyze differences in rumen microbiota and metabolites among cows with different BCSs. Twelve prepartum dairy cows were divided into two groups, low BCS (LBCS, BCS = 2.75, n = 6) and high BCS (HBCS, BCS = 3.5, n = 6), to explore differences in microbial composition and metabolites. Results In the HBCS group, the genera within the phylum Firmicutes exhibited stronger correlations and greater abundances. Phyla such as Firmicutes, Patescibacteria, Acidobacteriota, Euryarchaeota, and Desulfobacterota, in addition to most of their constituent microbial groups, were significantly more abundant in the HBCS group than in the LBCS group. At the genus level, the abundances of Anaerovibrio, Veillonellaceae_UCG_001, Ruminococcus_gauvreauii_group, Blautia, Eubacterium, Prevotellaceae_YAB2003_group, Schwartzia, and Halomonas significantly increased in the HBCS group. The citrate cycle, involved in carbohydrate metabolism, exhibited a significant enrichment trend, with a notable increase in the abundance of its key substrate, citrate, in the HBCS group. This increase was significantly positively correlated with the differential bacterial genera. Conclusion In this study, prepartum dairy cows with higher BCS exhibited greater abundance of Firmicutes. This study provides theoretical support for microbiological research on dairy cows with different BCSs and suggests that regulating the rumen microbiome could help maintain prepartum dairy cows within an optimal BCS range.

: [Objective) The study aimed to analyze the single nucleotide polymorphisms (SNPs) of LEP and LEPR genes and their correlation with body condition score (BCS) in Texel x Kazak hybrid (Teha) ewes. (Method) The missense mutations in exon 18 of LEP and LEPR were screened by PCR sequencing technology, and the SNPs were genotyped by KASP typing method, and the association between the genotyping results and their BCS was analyzed. (Result) (i) The results showed that one SNP site, rs1093355763, was found in the LEP gene, and three SNPs sites, rs428867159, rs412929474 and g. 2770 G > A, were found in the LEPR gene. All of them belong to exon missense mutations, rs428867159 and rs412929474 belonged to moderate polymorphism (0.25 A be-longed to low polymorphism (PIC≤0.25). All of them were in Hard-Weinberg equilibrium (P<0.05). (ii) In the genotype effect of rs1093355763, the body weight of CA genotype was significantly higher than that of CC genotype at the middle stage of lactation (P<0.05, the samw as below). Among the genotype effects of rs428867159, the BCS of TT genotype was significantly higher than that of CC genotype and CT genotype at lambing stage. In the genotype effect of g. 2770G > A locus, the BCS of GA genotype was significantly higher than that of GG genotype at 120 days of pregnancy, and the BCS of GG genotype was significantly higher than that of GA genotype at weaning stage. The body weight of GA genotype was significantly higher than that of GG genotype at 120 days of pregnancy. (Conclusion) The rs1093355763, rs428867159 and g. 2770 G > A loci in LEP and LEPR genes can be used as molecular markers for BCS and body weight of Teha ewes in sheep breeding. [ABSTRACT FROM AUTHOR]

Summary: Background: Impaired oxygenation during anaesthesia can be detrimental. Bodyweight and body shape influence arterial oxygen tension (PaO2) but the effect of body condition score (BCS) has not been investigated. Objectives: To examine the effect of BCS on respiratory indices. Study design: Retrospective. Methods: In total, 524 anaesthetic records were reviewed. Records from healthy adult horses under isoflurane anaesthesia in dorsal recumbency, receiving controlled mechanical ventilation, with complete ventilation and arterial blood gas data and BCS assessment by one person (KL) were included in data analysis. Data were analysed using student's t‐test, Mann–Whitney U test, chi‐squared analysis and logistic regression. Results: In total, 135 records for 85 horses with BCS <6/9 (Group N) and 50 horses with BCS ≥6/9 (Group O) met the inclusion criteria. Overall, there was a strong negative correlation between BCS and PaO2 (Spearman's r = 0.72, p < 0.001). Group O had significantly lower PaO2 (112 [50–546 mmHg]) compared to Group N (380 [65–658 mmHg], p < 0.001) which remained when horses were further categorised by bodyweight >500 kg (118 mmHg vs. 390 mmHg, p < 0.001) and <500 kg (160 mmHg vs. 451 mmHg, p < 0.04). Group O were 34 times more likely to be hypoxaemic (PaO2 < 100 mmHg) compared to Group N (χ2 = 34.39, p = 0.03). Alveolar dead space fraction was greater in Group O (0.27 [0.10–0.41]) versus Group N (0.24 [0.12–0.35]), p < 0.001. Multivariate logistic regression analysis identified a significant association between PaO2 and bodyweight (p = 0.01), alveolar dead space fraction (p = 0.005) and BCS (p < 0.001). Main limitations: Retrospective design. Conclusions: Body condition score ≥6/9 has a detrimental effect on PaO2, alveolar dead space fraction and is a risk factor for hypoxaemia in healthy anaesthetised horses in dorsal recumbency. [ABSTRACT FROM AUTHOR]

Measuring trace element concentrations in tissue can be a valuable approach to monitor animal health status. Temporal variation in the absorption, transport, and storage of elements between different tissues can, however, complicate the assessment of element-health relationships. Here, we measured concentrations of selected essential (copper (Cu), zinc (Zn), selenium (Se)) and non-essential (arsenic (As), cadmium (Cd), lead (Pb)) trace elements within blood, liver, kidney, and hair of fallow deer (Dama dama; N=20) and red deer (Cervus elaphus; N=21). Using multivariate regression and structural equation models, we estimated direct and indirect linkages between tissue-specific trace element profiles and long- (body condition) and short-term (serum protein biomarkers for acute inflammation, infection, and malnutrition) health indicators. Trace element concentrations varied markedly and were weakly correlated among tissues, with the exception of Se. After accounting for sex- and site-differences in trace element concentrations, body condition of red deer was directly, and positively, associated to trace element status in liver and hair, but not in kidney. For both deer species, trace element status in blood was directly linked to serum protein status with an indirect positive association to deer body condition. For fallow deer, no direct association between trace element status and body condition was detected in any of the tissues, possibly because of elemental homeostasis, and because all individuals were in good clinical health. This study shows that hair can serve as an effective, non-invasive, biomarker in deer health assessments, yet, to fully uncover trace element-health relationships a variety of sample matrices is preferred. [ABSTRACT FROM AUTHOR]

The objectives of this retrospective observational study were to investigate the association between BCS at 21 d before calving with prepartum and postpartum DMI, energy balance (EB), and milk yield. Data from 427 multigravid cows from 11 different experiments conducted at the University of Florida (Gainesville, FL) were used. Cows were classified according to their BCS at 21 d before calving as fat (BCS ≥ 4.00; n = 83), moderate (BCS = 3.25–3.75; n = 287), and thin (BCS ≤ 3.00; n = 57). Daily DMI from −21 to −1 and from +1 to +28 DIM was individually recorded. Energy balance was calculated as the difference between net energy for lactation consumed and required. Dry matter intake in fat cows was lower than that in moderate and thin cows both prepartum (fat = 9.97 ± 0.21, moderate = 11.15 ± 0.14, and thin = 11.92 ± 0.22 kg/d) and postpartum (fat = 14.35 ± 0.49, moderate = 15.47 ± 0.38, and thin = 16.09 ± 0.47 kg/d). Dry matter intake was also lower for moderate cows compared with thin cows prepartum, but not postpartum. Energy balance in fat cows was lower than in moderate and thin cows both prepartum (fat = −4.16 ± 0.61, moderate = −1.20 ± 0.56, and thin = 0.88 ± 0.62 Mcal/d) and postpartum (fat = −12.77 ± 0.50, moderate = −10.13 ± 0.29, and thin = −6.14 ± 0.51 Mcal/d). Energy balance was also lower for moderate cows compared with thin cows both prepartum and postpartum. There was a quadratic association between BCS at 21 d before calving and milk yield. Increasing BCS from 2.5 to 3.5 was associated with an increase in daily milk yield of 6.0 kg and 28 d cumulative milk of 147 kg. Increasing BCS from 3.5 to 4.5 was associated with a decrease in daily milk yield of 4.4 kg and 28 d cumulative milk of 116 kg. In summary, a moderate BCS at 21 d before calving was associated with intermediate DMI and EB pre- and postpartum but greater milk yield compared with thinner and fatter cows. Our findings indicate that a moderate BCS is ideal for ensuring a successful lactation. [ABSTRACT FROM AUTHOR]

The assessment of animal body condition has important practical and management implications for endangered wildlife populations. The nutritional condition of a population can be evaluated in a non-invasive way using photogrammetry techniques, avoiding direct manipulation. This study evaluates the utility of using body condition scoring (BCS) based on the visual assessment of subcutaneous fat and muscle from the body contour as a non-invasive method to quantify body condition in free ranging bears from camera trap photographs. Photographs of Apennine brown bears (Ursus arctos marsicanus), taken between 2007 and 2009 in the Abruzzo, Lazio and Molise National Park (PNALM, Italy), were used to evaluate the potential of this technique. BCS assessment was performed on 754 photographs representing 71 independent observations. Forty-eight of these photographs were selected to also score quantitative body ratios using a standardised measure of torso height. BCS varied seasonally, as expected by food availability and brown bear nutritional physiology, and it was also positively correlated to all three body ratios. Our findings indicate that BCS assessment is a good proxy for body condition, and that camera trap data can be effectively used to assess and monitor the nutritional condition of bear populations, such as the critically endangered one in central Italy. [ABSTRACT FROM AUTHOR]

Background: The rumen is a crucial digestive organ for dairy cows. The rumen microbiota assists in the digestion of plant feed through microbe-mediated fermentation, during which the plant feed is transformed into nutrients for the cow's use. Variations in the composition and function of the rumen microbiome affect the energy utilization efficiency of dairy cows, which is one of the reasons for the varying body condition scores (BCSs). This study focused on prepartum Holstein dairy cows to analyze differences in rumen microbiota and metabolites among cows with different BCSs. Twelve prepartum dairy cows were divided into two groups, low BCS (LBCS, BCS = 2.75, n = 6) and high BCS (HBCS, BCS = 3.5, n = 6), to explore differences in microbial composition and metabolites. Results: In the HBCS group, the genera within the phylum Firmicutes exhibited stronger correlations and greater abundances. Phyla such as Firmicutes, Patescibacteria, Acidobacteriota, Euryarchaeota, and Desulfobacterota, in addition to most of their constituent microbial groups, were significantly more abundant in the HBCS group than in the LBCS group. At the genus level, the abundances of Anaerovibrio, Veillonellaceae_UCG_001, Ruminococcus_gauvreauii_group, Blautia, Eubacterium, Prevotellaceae_YAB2003_group, Schwartzia, and Halomonas significantly increased in the HBCS group. The citrate cycle, involved in carbohydrate metabolism, exhibited a significant enrichment trend, with a notable increase in the abundance of its key substrate, citrate, in the HBCS group. This increase was significantly positively correlated with the differential bacterial genera. Conclusion: In this study, prepartum dairy cows with higher BCS exhibited greater abundance of Firmicutes. This study provides theoretical support for microbiological research on dairy cows with different BCSs and suggests that regulating the rumen microbiome could help maintain prepartum dairy cows within an optimal BCS range. [ABSTRACT FROM AUTHOR]

The objective of the study was to quantify the association between the birth weight of a calf and the subsequent performance of its dairy dam in the absence of any recorded calving assistance. A total of 11,592 lactation records from 4,549 spring-calving dairy cows were used. The association between a series of quantitative cow performance metrics (dependent variable) and calf birth weight (independent variable) was determined using linear mixed models; logistic regression was used where the dependent variable was binary. Nuisance factors in the models were calf sex, heterosis coefficient of both the cow and calf, dry period length immediately before the birth of the calf, cow age at calving relative to the median cow age per parity, breed proportion of the cow, cow live weight between 100 and 200 d of lactation relative to the mean cow weight per parity, and contemporary group. Calf birth weight was included in the model as either a continuous or a categorical variable. Primiparous and multiparous cows were analyzed separately. Mean (SD) calf birth weight was 36.2 (6.8) kg. In primiparous cows, calf birth weight was associated with milk yield in the first 60 d of lactation, calving to first service interval, calving body weight (BW), and both nadir BW and body condition score (BCS). In multiparous cows, calf birth weight was associated with total milk, fat, and protein yield in the first 60 and 305 d of lactation, peak milk yield, total milk solids, both calving and nadir BW, and BCS loss from calving to nadir. Relative to primiparous cows that gave birth to calves weighing 34 to 37 kg (i.e., population mean), their contemporaries who gave birth to calves that weighed 15 to 29 kg produced 9.82 kg more milk in the first 60 d of lactation, had a 2-d shorter interval to first service, and were 8.08 kg and 5.51 kg lighter at calving and nadir BW, respectively; the former was also 0.05 units lower in BCS (5-point scale, 1 = emaciated and 5 = obese) at nadir. Relative to multiparous cows that gave birth to calves that were 34 to 37 kg birth weight, multiparous cows that gave birth to calves that were 15 to 29 kg yielded 59.63 kg, 2.44 kg, and 1.76 kg less milk, fat, and protein, respectively, in the first 60 d of lactation; produced 17.69 kg less milk solids throughout the 305-d lactation; and were also 10.49 kg lighter at nadir and lost 0.01 units more BCS to nadir. In a separate series of analyses, sire breed was added to the model as a fixed effect with and without calf birth weight. When calf birth weight was not adjusted for, 60-d milk yield for multiparous cows who gave birth to calves sired by a traditional beef breed (i.e., Angus, Hereford) produced 59.63 kg more milk than multiparous cows who gave birth to calves sired by a Holstein-Friesian. Hence, calf birth weight is associated with some subsequent dam performance measures; however, where associations do exist, the effect is biologically small. [ABSTRACT FROM AUTHOR]

Abstract Background Body condition score (BCS) is a subjective tool and the deposition of subcutaneous fat differs from the deposition of abdominal fat. Objective The aim of this study was to evaluate by multivariate regression models and ultrasonography the amount of fat accumulated in different areas of the abdominal cavity in Jersey non‐pregnant non‐lactating cows and its association with their BCS. Methods From a commercial farm, 21 non‐pregnant non‐lactating Jersey cows were selected at random. Cows were placed in a headlock line, and BCS was evaluated (scale 1–5 with a 0.25 unit increment) by the same assessor. Ultrasonographic evaluation was performed using a Sonosite machine in duplicate, considering key anatomical points of the body to predict total abdominal fat (AT), retroperitoneal fat (RT), omental fat (OT) and mesenteric fat (MT). A regression analysis for each abdominal fat depot and the BCS was run using SAS. Results Models from the lowest to the largest r2 are reported. The r2 for the models were MT r2 = 0.023; RT r2 = 0.1047; OT r2 = 0.323 and AT r2 = 0.369. Correlation between BCS and abdominal fat depots was positive, lower for mesenteric and retroperitoneal fat, but higher for omental and total abdominal fat. Cows were divided on the basis of the median of BCS distribution to high (≥3.5) and low (≤3.25). Those with high BCS had significantly larger amounts of fat in different anatomical areas of abdominal cavity than cows with low BCS. Conclusion BCS has a low‐to‐moderate association with abdominal fat depots, but a high variability exists within each BCS punctuation, which supports the notion that fat accumulation patterns and metabolic turnover between abdominal and subcutaneous fat differ from each other.

ABSTRACT: Our objective was to compare insemination rate and pregnancies per artificial insemination (P/AI) of lactating Jersey cows inseminated at first service with sexed Jersey or conventional beef semen after submission to a Double-Ovsynch protocol for timed artificial insemination (TAI) versus a protocol to synchronize estrus at similar days in milk (DIM). Secondary objectives were to determine the effect of protocol synchrony and postpartum body condition score (BCS) change on P/AI. Lactating Jersey cows (n = 1,272) were allocated by odd versus even ear tag number, which was randomly allocated within the herd, within parity and semen type for submission to a Double-Ovsynch protocol (DO; n = 707) or a protocol to synchronize estrus (ED; n = 565). All ED cows detected in estrus were inseminated (EDAI; n = 424), with undetected cows receiving TAI after an Ovsynch protocol (EDTAI; n = 141). There was a treatment by parity interaction on insemination rate with 100% of DO cows receiving TAI, but a tendency for fewer primiparous ED cows to be detected in estrus and AI than multiparous cows (69.5% ± 0.04% vs. 77.1% ± 0.02%, respectively). For cows inseminated with sexed Jersey or conventional beef semen, DO cows tended to have and had more P/AI than EDAI cows (sexed, 49.2% ± 0.03% vs. 43.6% ± 0.03%; beef, 64.2% ± 0.04% vs. 56.3% ± 0.05%, respectively) and had more P/AI than EDAI+EDTAI cows (sexed, 49.1% ± 0.03% vs. 40.6% ± 0.03%; beef, 65.5% ± 0.04% vs. 56.2% ± 0.04%, respectively). Overall, 29.1% of DO cows expressed estrus with 5.0% and 24.2% of cows detected in estrus ≥24 h before and at TAI, respectively, and there was no difference in P/AI 61 ± 4 d after AI based on expression of estrus at TAI. The synchronization rate was greater for DO than EDAI cows (92.1% ± 0.01% vs. 79.2% ± 0.02%, respectively); however, synchronized DO cows had more P/AI than synchronized EDAI cows (55.0% ± 0.02% vs. 49.2% ± 0.03%, respectively). There was an interaction between BCS change from 7 to 39 ± 2 DIM and treatment on P/AI 61 ± 4 d after AI with no difference between DO and EDAI cows that lost = 0.25 (49.8% ± 0.04% vs. 51.0% ± 0.05%, respectively) or maintained or gained (55.6% ± 0.04% vs. 50.8% ± 0.05%, respectively) BCS, but within cows that lost ≥0.5 BCS, DO cows had more P/AI than EDAI cows (54.1% ± 0.04% vs. 36.1% ± 0.04%, respectively). In conclusion, submission of lactating Jersey cows to a Double-Ovsynch protocol for first insemination increased insemination rate and fertility to first insemination compared with AI after a detected estrus regardless of semen type and expression of estrus, particularly for cows with excessive postpartum BCS loss.

ABSTRACT: Monitoring body condition score (BCS) is a useful management tool to estimate the energy reserves of an individual cow or a group of cows. The aim of this study was to develop and evaluate the performance of a fully automated 2-dimensional imaging system using a machine learning algorithm to generate real-time BCS for dairy cows. Two separate datasets were used for training and testing. The training dataset included 34,150 manual BCS (MAN_BCS) assigned by 5 experienced veterinarians during 35 visits at 7 dairy farms. Ordinal regression methods and deep learning architecture were used when developing the algorithm. Subsequently, the testing dataset was used to evaluate the developed BCS prediction algorithm on 4 of the participating farms. An experienced human assessor (HA1) visited these farms and performed 8 whole-milking-herd BCS sessions. Each farm was visited twice, allowing for 30 d (±2 d) to pass between visits. The MAN_BCS assigned by HA1 were considered the ground truth data. At the end of the validation study, MAN_BCS were merged with the stored automated BCS (AI_BCS), resulting in a testing dataset of 9,657 single BCS. A total of 3,817 cows in the testing dataset were scored twice 30 d (±2 d) apart, and the change in their BCS (ΔBCS) was calculated. A subset of cows at one farm were scored twice on consecutive days to evaluate the within-observer agreement of both the human assessor and the system. The manual BCS of 2 more assessors (HA2 and HA3) were used to assess the interobserver agreement between humans. Finally, we also collected ultrasound measurements of backfat thickness (BFT) from 111 randomly selected cows with available MAN_BCS and AI_BCS. Using the testing dataset, intra- and interobserver agreement for single BCS and ΔBCS were estimated by calculating the simple percentage agreement (PA) at 3 error levels and the weighted kappa (κw) for the exact agreement. A Bland-Altman plot was constructed to visualize the systematic and proportional bias. The association between MAN_BCS and AI_BCS and the BFT was assessed with Passing-Bablok regressions. The system had an almost perfect repeatability with a κw of 0.99. The agreement between MAN_BCS and AI_BCS was substantial, with an overall κw of 0.69. The overall PA at the exact, ± 0.25-unit, and ± 0.50-unit BCS error range between MAN_BCS and AI_BCS was 44.4%, 84.6%, and 94.8%, respectively, and greater than the PA obtained between HA1 and HA3. The Bland-Altman plot revealed a minimal systematic bias of −0.09 with a proportional bias at the extreme scores. Furthermore, despite the low κw of 0.20, the overall PA at the exact and ± 0.25-unit of BCS error range between MAN_BCS and AI_BCS regarding the ΔBCS was 45.7 and 88.2%, respectively. A strong linear relationship was observed between BFT and AI_BCS (ρ = 0.75), although weaker than that between BFT and MAN_BCS (ρ = 0.91). The system was able to predict single BCS and ΔBCS with satisfactory accuracy, comparable to that obtained between trained human scorers.

Objectives: The aims of the present study were to generate the first life tables for the UK companion cat population overall as well as broken down by sex and breed status, and to quantify associations between mortality and traits such as sex, neuter status, breed status and body weight in relation to mortality. Methods: Life table construction and modelling included data on 7936 confirmed deaths in cats under primary veterinary care at clinics participating in the VetCompass Programme in 2019. The life tables were built for cats overall, female and male cats, and crossbred and purebred cats. Multivariable generalised linear regression models were generated to explore the risk factors for a shortened lifespan. Results: Life expectancy at age 0 for UK companion cats overall was 11.74 years (95% confidence interval [CI] 11.61−11.87). The probability of death at each year interval increased with age from year interval 3–4, with the probability value not exceeding 0.05 before year 9. Female cats (12.51 years; 95% CI 12.32–12.69) had a 1.33-year longer life expectancy than male cats (11.18 years; 95% CI 11.01–11.38) at age 0. Among the 12 breeds (including crossbred) analysed, Burmese and Birman had the longest life expectancy at year 0, showing 14.42 years (95% CI 12.91–15.93) and 14.39 years (95% CI 12.87–15.91), respectively. Sphynx had the shortest life expectancy at year 0 among the analysed breeds at 6.68 years (95% CI 4.53–8.83). Being entire, purebred and with a non-ideal body weight were significantly linked to a decreased lifespan. Conclusions and relevance: The life tables presented here for companion cats in the UK overall, by sex, and by crossbred and purebred cats can contribute to a better understanding of the life trajectory of cats, helping with evidence-based decision-making for cat owners and the veterinary profession. We have also provided an updated life expectancy at age 0 for various cat breeds for 2019 and showed evidence of the association between non-ideal weight and a decreased lifespan. [ABSTRACT FROM AUTHOR]

Effective management of lactating sows significantly influences various aspects of swine production. This study compared the impact of an hourly feeding regime and a five-times-daily feeding regime on the productive performance, body condition, and welfare of lactating sows, as well as on the growth performance of their offspring. Twenty-eight lactating sows (Landrace × Yorkshire) were divided into two groups: Group 1 was fed five times a day, and Group 2 was fed according to an hourly regime. The data were analyzed using independent-samples T-tests and the Mann–Whitney U test using Statistical Analysis System (SAS, 2011, Version 9.3) software. An hourly feeding regime positively affected (p < 0.05) sows' feed intake and body condition, significantly reducing the days from the weaning-to-estrus interval. Group 1 exhibited significantly higher (p < 0.05) reductions in backfat thickness (BFT) and body condition score (BCS) during the weaning period compared to Group 2. Additionally, significant differences (p < 0.05) were observed in regard to sow body weight loss, feed intake, piglet livability and mortality rate at weaning, sow index, and calculated milk yield. Feeding sows according to an hourly regime positively impacted their productive performance compared to those fed five times daily. No significant differences (p > 0.05) were recorded in regard to the total number of piglets born, live births, mummified piglets, stillbirths, piglet mortality, litter size at weaning, and sow feed conversion ratio (FCR). However, the number of piglets weaned per sow per year (PSY) was numerically higher in Group 2 (p > 0.05). The piglets from Group 2 had significantly higher (p < 0.05) weaning weights and exhibited lower feed intake, greater weight gain, improved average daily gain, and greater litter size weight gain than those from Group 1. Statistically, sows from Group 2 exhibited a higher frequency of standing (p < 0.05), which potentially contributed to the reduction in shoulder skin lesions in sows (p > 0.05). In conclusion, an hourly feeding regime could optimize sow productive performance, body condition, milk yield, welfare, and piglet growth in swine production. [ABSTRACT FROM AUTHOR]

Monitoring body condition score (BCS) is a useful management tool to estimate the energy reserves of an individual cow or a group of cows. The aim of this study was to develop and evaluate the performance of a fully automated 2-dimensional imaging system using a machine learning algorithm to generate real-time BCS for dairy cows. Two separate datasets were used for training and testing. The training dataset included 34,150 manual BCS (MAN_BCS) assigned by 5 experienced veterinarians during 35 visits at 7 dairy farms. Ordinal regression methods and deep learning architecture were used when developing the algorithm. Subsequently, the testing dataset was used to evaluate the developed BCS prediction algorithm on 4 of the participating farms. An experienced human assessor (HA1) visited these farms and performed 8 whole-milking-herd BCS sessions. Each farm was visited twice, allowing for 30 d (±2 d) to pass between visits. The MAN_BCS assigned by HA1 were considered the ground truth data. At the end of the validation study, MAN_BCS were merged with the stored automated BCS (AI_BCS), resulting in a testing dataset of 9,657 single BCS. A total of 3,817 cows in the testing dataset were scored twice 30 d (±2 d) apart, and the change in their BCS (ΔBCS) was calculated. A subset of cows at one farm were scored twice on consecutive days to evaluate the within-observer agreement of both the human assessor and the system. The manual BCS of 2 more assessors (HA2 and HA3) were used to assess the interobserver agreement between humans. Finally, we also collected ultrasound measurements of backfat thickness (BFT) from 111 randomly selected cows with available MAN_BCS and AI_BCS. Using the testing dataset, intra- and interobserver agreement for single BCS and ΔBCS were estimated by calculating the simple percentage agreement (PA) at 3 error levels and the weighted kappa (κ w) for the exact agreement. A Bland-Altman plot was constructed to visualize the systematic and proportional bias. The association between MAN_BCS and AI_BCS and the BFT was assessed with Passing-Bablok regressions. The system had an almost perfect repeatability with a κ w of 0.99. The agreement between MAN_BCS and AI_BCS was substantial, with an overall κ w of 0.69. The overall PA at the exact, ± 0.25-unit, and ± 0.50-unit BCS error range between MAN_BCS and AI_BCS was 44.4%, 84.6%, and 94.8%, respectively, and greater than the PA obtained between HA1 and HA3. The Bland-Altman plot revealed a minimal systematic bias of −0.09 with a proportional bias at the extreme scores. Furthermore, despite the low κ w of 0.20, the overall PA at the exact and ± 0.25-unit of BCS error range between MAN_BCS and AI_BCS regarding the ΔBCS was 45.7 and 88.2%, respectively. A strong linear relationship was observed between BFT and AI_BCS (ρ = 0.75), although weaker than that between BFT and MAN_BCS (ρ = 0.91). The system was able to predict single BCS and ΔBCS with satisfactory accuracy, comparable to that obtained between trained human scorers. [ABSTRACT FROM AUTHOR]

Our objective was to compare insemination rate and pregnancies per artificial insemination (P/AI) of lactating Jersey cows inseminated at first service with sexed Jersey or conventional beef semen after submission to a Double-Ovsynch protocol for timed artificial insemination (TAI) versus a protocol to synchronize estrus at similar days in milk (DIM). Secondary objectives were to determine the effect of protocol synchrony and postpartum body condition score (BCS) change on P/AI. Lactating Jersey cows (n = 1,272) were allocated by odd versus even ear tag number, which was randomly allocated within the herd, within parity and semen type for submission to a Double-Ovsynch protocol (DO; n = 707) or a protocol to synchronize estrus (ED; n = 565). All ED cows detected in estrus were inseminated (EDAI; n = 424), with undetected cows receiving TAI after an Ovsynch protocol (EDTAI; n = 141). There was a treatment by parity interaction on insemination rate with 100% of DO cows receiving TAI, but a tendency for fewer primiparous ED cows to be detected in estrus and AI than multiparous cows (69.5% ± 0.04% vs. 77.1% ± 0.02%, respectively). For cows inseminated with sexed Jersey or conventional beef semen, DO cows tended to have and had more P/AI than EDAI cows (sexed, 49.2% ± 0.03% vs. 43.6% ± 0.03%; beef, 64.2% ± 0.04% vs. 56.3% ± 0.05%, respectively) and had more P/AI than EDAI+EDTAI cows (sexed, 49.1% ± 0.03% vs. 40.6% ± 0.03%; beef, 65.5% ± 0.04% vs. 56.2% ± 0.04%, respectively). Overall, 29.1% of DO cows expressed estrus with 5.0% and 24.2% of cows detected in estrus ≥24 h before and at TAI, respectively, and there was no difference in P/AI 61 ± 4 d after AI based on expression of estrus at TAI. The synchronization rate was greater for DO than EDAI cows (92.1% ± 0.01% vs. 79.2% ± 0.02%, respectively); however, synchronized DO cows had more P/AI than synchronized EDAI cows (55.0% ± 0.02% vs. 49.2% ± 0.03%, respectively). There was an interaction between BCS change from 7 to 39 ± 2 DIM and treatment on P/AI 61 ± 4 d after AI with no difference between DO and EDAI cows that lost = 0.25 (49.8% ± 0.04% vs. 51.0% ± 0.05%, respectively) or maintained or gained (55.6% ± 0.04% vs. 50.8% ± 0.05%, respectively) BCS, but within cows that lost ≥0.5 BCS, DO cows had more P/AI than EDAI cows (54.1% ± 0.04% vs. 36.1% ± 0.04%, respectively). In conclusion, submission of lactating Jersey cows to a Double-Ovsynch protocol for first insemination increased insemination rate and fertility to first insemination compared with AI after a detected estrus regardless of semen type and expression of estrus, particularly for cows with excessive postpartum BCS loss. [ABSTRACT FROM AUTHOR]

This study evaluated the effects of dose of equine chorionic gonadotropin (eCG) and its splitting in different days of the synchronization protocol on reproductive performance of primiparous and multiparous Nellore cows. In the present study, 2,536 Nellore cows (1,634 primiparous and 902 multiparous) were assigned to receive in a 2 × 2 factorial design 1) an intravaginal progesterone (P4) device and 2.0 mg of estradiol benzoate (EB) on day −11, 12.5 mg (i.m.) of dinoprost tromethamine (PGF), 300 IU (i.m.) of eCG, 0.6 mg (i.m.) of estradiol cypionate (ECP), and P4 device withdrawal on day −2, followed by TAI on day 0 (n = 632 cows, being 409 primiparous and 223 multiparous; 300 – 2), 2) 300 IU (i.m) of eCG administered on days −4 and −2 (150 IU of eCG/day; n = 637 cows, being 412 primiparous and 225 multiparous; 300-4-2), 3) 400 IU (i.m.) of eCG administered on day −2 (n = 633 cows, being 406 primiparous and 227 multiparous; 400 – 2), and 4) 400 IU (i.m) of eCG administered on days −4 and −2 (200 IU of eCG/day; n = 634 cows, being 407 primiparous and 227 multiparous; 400-4-2). Individual cow BCS was assessed on days −11, 0 (timed-AI), and 31 of the study. Body condition score of the animals was classified into LOW or HIGH using the threshold of 2.75 (≤2.75 = LOW; >2.75 = HIGH). For primiparous cows, an eCG splitting effect was observed on follicle size, as cows receiving eCG on days −4 and −2 of the synchronization protocol had a larger follicle than cows administered eCG only on day −2. For day 31 P/AI, primiparous cows receiving 400-4-2, regardless of BCS, had a greater P/AI than cows from other treatments. Administering 400-4-2 to LOW BCS cows also resulted in greater P/AI than all other treatments assigned to LOW BCS cows. For multiparous cows, no treatment effect was observed for follicle size, estrus expression, and day 31 P/AI (P ≥ 0.21). In summary, increasing the dose and splitting the dose of eCG positively impacted the pregnancy rates of primiparous cows under a BCS ≤2.75, but no effects were detected on multiparous cows. • Low BCS during the breeding season limits reproductive performance of Bos indicus females. • Splitting the eCG dose has improved follicle development in beef cows. • Increasing the dose and its splitting benefited P/AI in low BCS primiparous cows. [ABSTRACT FROM AUTHOR]

Assessing the seasonal changes in body condition (BC) of animals is important for understanding their reproduction and survival, as well as human–animal interaction. Visual or photographic assessment of BC has, however, been limited to species in open habitats or to specific times and locations. To noninvasively assess the seasonal changes in the entire local population of brown bears (Ursus arctos) inhabiting the forest habitat in eastern Hokkaido, Japan, we evaluated their BC using camera-trapping during 2015–2017. We assigned a 5-point BC score (BCS) for each individual based on the images, and examined seasonal changes in BCS by sex–age classes. Out of the 1,714 events in which brown bears were recorded, sex–age classes including adult males, subadults, females with offspring, and solitary adult females, could be determined in 887 events. Overall, BCS tended to decrease from spring to summer, with the lowest value in July, and tended to increase toward autumn, with the highest value in November. The BCS of adult male bears showed the highest seasonal variation, whereas that of subadults showed the least variation. Adult females, both solitary and those with offspring, showed intermediate seasonal variation in BCS compared with adult males and subadults. These seasonal changes were considered to be a general pattern and point to the amount of accumulated body fat. Adult males have a larger home range and, because of their social dominance, can exploit higher quality resources than can be exploited by other sex–age classes, which results in them being able to accumulate relatively more fat during hyperphagia. Females with offspring tended to have a lesser increase in BCS after August and lower BCS in autumn than did solitary adults. We were able to demonstrate the possibility of evaluating the BC of individuals and their seasonal changes by examining BCS based on the videos taken by camera traps. [ABSTRACT FROM AUTHOR]

Background: The Rembi sheep breed is renowned for its exceptional meat quality and significant economic profit. Nevertheless, there is a gap in research on the hepatic parameters of this breed in Algeria despite the considerable value of the liver in the Algerian population's diet. Objectives: The research aimed to determine the impact of physiological factors (age, physiological stage, and body condition score) on biochemical sheep liver parameters. Methods: Sixty clinically healthy female Rembi breed sheep, aged between 3 months and 7 years, raised at the Technical Institute of Livestock (ITELV) Ksar Chellala Farm in Tiaret Province, Algeria, were selected for biochemical analysis and assessing aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyl transferase (GGT), alkaline phosphatase (ALP), total bilirubin (TBIL), direct bilirubin (DBIL), albumin (ALB), and total protein (TP). Results: The concentrations of AST, ALP, and GGT increased significantly (P≤0.001) in young sheep, while ALT and TP levels decreased (P≤0.05). However, a high level of GGT (P<0.001) was also observed in lactating ewes, and during pregnancy, females have a low level of ALP (P<0.001). Furthermore, the body condition score does not affect any of the measurements. Conclusion: Considering the animal's age and physiological stage is critical before interpreting the results. [ABSTRACT FROM AUTHOR]

South American camelids (SACs) play an increasing role in veterinary care in Europe. Many alpacas or llamas presented to veterinarians suffer from anaemia, regularly with a packed cell volume (PCV) below 0.10 l/l, which is a life-threatening condition for the animals. This review article presents clinical and laboratory diagnostic tools for the diagnosis of anaemia in SACs. Clinical identification of anaemic animals can be performed by assessing the FAMACHA© score and the Body Condition Score (BCS), since anaemia in alpacas and llamas correlates with pale mucous membranes and a lowered BCS. Haematological examination of a blood sample can provide a more differentiated diagnosis of anaemia in SACs. A common finding is regenerative anaemia with an increased number of reticulocytes that is often caused by blood loss due to Haemonchus contortus. Changes in a blood smear from an alpaca or llama with regenerative anaemia may include normoblasts (nucleated red blood cells), anisocytosis, poikilocytosis, polychromasia, Howell-Jolly bodies or basophilic stippling. Furthermore, non-regenerative anaemia, often caused by trace element deficiency or cachexia, can also occur. [ABSTRACT FROM AUTHOR]

Simple Summary: Climate change presents significant nutritional challenges to animal breeding, demanding resilience and efficiency for survival and productivity. In this study, we investigated the effects of body reserves dynamics (mobilization and accretion) on the production and reproduction parameters of fat-tailed Barbarine and thin-tailed Queue Fine de l'Ouest ewes over two production cycles under semi-intensive conditions. The results show that all reproductive traits were similar among breeds; however, they depend on ewes' body reserves. Similarly, lambs' growth parameters depend on ewes' body condition scores (BCSs) at lambing and weaning, with higher growth for ewes with BCS > 2 at lambing. In conclusion, regardless of the breed, ewes have evolved adaptive mechanisms by mobilizing body reserves to effectively cope with environmental challenges and successfully rear their offspring. Additionally, to sustain a high growth rate in offspring, it is advisable to carefully plan the flock nutritional needs during the suckling period. Under climate change, animal breeding faces severe nutritional challenges, exhibiting their resilience and efficiency to survive and produce. The animals' robustness can be evaluated through the use and reconstitution of body reserves (BR). This study investigated the dynamics of ewes' BR, measured through body weight (BW) and the body condition score (BCS), and their effect on production performances of 480 ewes belonging to fat-tailed Barbarine (BB) and thin-tailed Queue Fine de l'Ouest (QFO) ewes over two consecutive years. The BW and BCS were recorded across physiological stages. Reproductive parameters and lambs' growth were calculated. For both years and breeds, the decrease in the BCS between mating and lambing showed BR mobilization to balance nutritional restrictions, which continues until suckling; after weaning, a BR accretion was noted. The lambs' birth weight was similar regardless of the dams' BCS at lambing, though it was lower in the second year (3.5 vs. 4 kg). Lambs' growth parameters depended on ewes' BCS at lambing and weaning. Fertility rates changed with BCS changes and the higher values (80%) were recorded for ewes with BCSs between 2 and 2.5. However, prolificacy was similar for both breeds and years (p > 0.05). In conclusion, ewes have developed adaptative capacities through BR mobilization to cope with environmental challenges and successfully rear their offspring. To maintain a high offspring growth rate, it is recommended to plan correct flock nutrition during suckling. [ABSTRACT FROM AUTHOR]

Simple Summary: Fatness is linked to reproductive performance in sheep, and it is realistic to expect that higher body condition scores at breeding would have positive effects on the reproductive rate of ewe lambs, over and above liveweight. Data were analysed from over 17,000 records from Merino and non-Merino ewe lambs from 22 different flocks across Australia. There were significant curvilinear relationships between liveweight or body condition score prior to breeding and reproductive rate for both Merino and non-Merino ewe lambs. When analysed together, there was a significant quadratic effect of body condition score on reproductive rate, independent of correlated changes in liveweight. The results indicated that if only a proportion of ewe lambs were selected for breeding, selection based on both liveweight and body condition scores may only improve overall reproductive rate by 1 to 2% compared to selection based on liveweight alone. Ewe lambs that are heavier due to improved nutrition pre- and post-weaning achieve puberty at a younger age, are more fertile, and have a higher reproductive rate. Fatness is intimately linked to reproduction, and we hypothesised that higher body condition scores at breeding would have positive effects on the reproductive rate of ewe lambs over and above liveweight. We also expected that if only a proportion of ewe lambs were presented for breeding, then it would be more effective to select them based on both liveweight and body condition score. To test these hypotheses, we analysed data from over 17,000 records from Merino and non-Merino ewe lambs from 22 different flocks across Australia. Non-Merino ewe lambs were more fertile (69.4% vs. 48.7%) and achieved a higher reproductive rate than Merino ewe lambs (96.9% vs. 60.7%). There were significant curvilinear relationships between liveweight (p < 0.001) or body condition score (p < 0.001) prior to breeding and reproductive rate for both Merino and non-Merino ewe lambs. For both breeds, there was a significant (p < 0.001) quadratic effect of body condition score prior to breeding on reproductive rate, independent of the correlated changes in liveweight, and at the same liveweight, an extra 0.5 of a body condition score up to 3.3 improved reproductive rate by about 20%. Nevertheless, the results indicated that if only a proportion of ewe lambs were selected for breeding, then selection based on both liveweight and body condition scores may only improve the overall reproductive rate by 1 to 2% compared to selection based on liveweight alone. We conclude that liveweight is a more effective method than body condition score for selecting ewe lambs for breeding. [ABSTRACT FROM AUTHOR]

Background: Equine metabolic syndrome is a collection of risk factors associated with an increased risk of endocrinopathic laminitis. All affected animals display insulin dysregulation and some may show adiponectin dysregulation and/or excessive adiposity. However, the relationship between obesity and hypoadiponectinaemia in equids remains unclear. Objectives: To investigate the relationship between obesity and circulating plasma total adiponectin (TA) concentrations in native‐breed ponies in England. Study design: Secondary data analysis. Methods: Data collected for three previous studies were retrospectively analysed and cohorts were pooled where possible (maximum sample size: n = 734 ponies). Correlations between [TA], age, and morphometric measures were assessed using Spearman's correlation coefficient. [TA] was compared between animals of different body condition score (BCS) classification (ideal‐weight, overweight, and obese), breed, and body shape using Kruskal–Wallis with Dunn's post hoc tests, and sex using Mann–Whitney U test. The proportions of obese and ideal‐weight ponies with basal hyperinsulinaemia and/or hypoadiponectinaemia were compared using a Chi‐square test of homogeneity and post hoc z‐test. Logistic regression was used to identify factors that may discriminate ponies with hypoadiponectinaemia. Results: [TA] was weakly positively correlated with BCS, height, weight, and weight:height ratio (Spearman's ρ = 0.14–0.29, p < 0.05). There were significant differences in [TA] in ponies with different BCS group classification, body shape, and breed. A greater percentage of obese (54.6%) than ideal‐weight ponies (33.1%, p < 0.001) had both normal [TA] and [basal insulin], and a greater percentage of ideal‐weight (38.6%) than obese ponies (16.5%, p < 0.001) showed hypoadiponectinaemia. Weight:height and BCS group were significant variables in a logistic regression of hypoadiponectinaemia but model fit and predictive accuracy were poor. Main limitations: Retrospective study design, only native‐breed ponies included. Conclusions: Morphometric measures such as BCS do not closely reflect [TA]. Circulating [TA] and [basal insulin] should be determined in all animals with predisposing factors, regardless of obesity status. [ABSTRACT FROM AUTHOR]

IntroductionHigh testosterone levels might be associated with coarser fiber in alpacas, affecting fiber quality. In the husbandry systems employed in the Andes, males with higher libido might have an increased frequency of successful matings than males with lower libido. This study aimed to determine some of the factors affecting fiber quality in Peruvian alpacas and to evaluate the association between fiber quality and male mating behavior.MethodsThe study population consisted of 189 adult male alpacas at La Raya, Cusco, Peru, at 4,400 m above sea level, belonging to the National University of San Marcos, Lima, and the National University of San Antonio Abad of Cusco. Fiber samples were collected from male alpacas in September 2015; body condition score (BCS) was evaluated. After washing and drying, the fiber samples were analyzed using an Optical Fiber Diameter Analyser 2000; linear regression analysis between predictor variables (location of alpacas, age, BCS, and fiber color) and fiber quality outcomes was conducted. Fertility data were available only for some males belonging to San Marcos University, consisting of hand-written records from the breeding season January to April 2015; individual fertility quotients were calculated for each male.ResultsAge was associated with fiber quality, young alpacas having the finest fibers (p < 0.05). An increased BCS was associated with increased fiber length, fiber diameter and spinning fineness, but decreased comfort factor (p < 0.05). White fibers were thinner (p = 0.05) than colored fibers, with lower comfort factor and spinning fineness. A significant association between the fertility quotient and fiber curvature was observed (p = 0.018).DiscussionThese results suggest that careful selection of breeding individuals and attention to husbandry could result in improved fiber quality among alpaca herds in Peru. However, it would be advisable to increase the number of males studied, using more reliable methods for evaluating male fertility and pregnancy diagnosis than were available for this study.

Modern livestock farming systems face the challenge of meeting the growing demand for dairy and meat products while ensuring the well-being of animals. Body Condition Scoring serves as a vital process for assessing the body reserves in animals, impacting their health, welfare, and productivity. However, traditional body condition score (BCS) evaluation methods via observation and palpation of specific anatomical regions are labor-intensive and subjective, hindering their widespread adoption. To address this issue, Precision Livestock Farming (PLF) techniques, particularly those involving Internet of Things (IoT) devices and artificial intelligence (AI), have emerged as promising solutions. In this work, we explore the use of AI, specifically Convolutional Neural Networks (CNNs), to automate the assessment of BCS in goats utilizing imagery data. Our model was trained on 5000 images illustrating the dorsal view of the backside of goats achieving an overall accuracy of 97.94 % which was the highest compared to other popular deep learning architectures from literature (e.g. VGG16, ResNet34, ResNet50, DenseNet, GoogleNet). The proposed custom CNN model for goat-specific BCS estimation overcomes the limitations of manual sketching, providing automatic region identification for BCS assessment. Moreover, it is a lightweight model specifically designed for seamless integration with IoT devices, allowing for efficient on-board processing via cameras. The model's pose-independent nature and adaptability to environmental constraints make it a valuable tool for efficient and sustainable goat farming. This research advances the application of AI as a precision livestock farming tool, contributing to the reinforcement of the animal welfare and productivity, and supporting evidence-based decision-making processes to increase farms' resilience.

This study aimed to investigate the relationship between measures of body condition score (BCS) during the transition period and milk yield, reproductive performance, and occurrence of metritis and ketosis in cows. A total of 5026 lactations of 2424 Holstein cows from a single dairy farm were assessed for BCS (5-point scale; 0.25-point increments) 15 days precalving, at calving, and first service. ΔBCS were classed as 0.75 from 15 days precalving to parturition and from calving to first artificial insemination. BCS classes at parturition were 3.75. 305-d milk yield was the highest in cows with BCS change (ΔBCS) 0.75. The overall pregnancy rate was least [281/836 (33.6%); p0.75 units of BCS, and greatest for cows that lost 0.75 BCS between calving and the first artificial insemination (AI) [175/836 (20.9 %); p

The use of computer vision to estimate the Body Condition Score for cow has demonstrated to be feasible. However, most research has been limited to fixed camera positions, which restricts the technique’s usefulness. This research acquired cow data at various distances and angles to investigate the impact of distance and different depth images restoration method on scoring accuracy. A U-Net neural network-based model was employed for background segmentation. The model proposed in this study performs best among SegNet, UNet, UNet++ and DeeplabV3, with significant performance improvement and superior overall segmentation accuracy, and has stronger foreground object identification capability. Additionally, we utilize the concept of human feature point localization to pinpoint the positions of cow feature points. The results show that compared to Hourglass, CPN, and Hrnet, the model in this study has significant advantages in three core indicators: accuracy, recall, and mAP. Moreover, we presented an unsupervised depth image reconstruction model based on the Denoising Diffusion Probabilistic Model and Unet++ Mode, facilitating the measurement and scoring of cow characteristics. Finally, the measurement results of cow body size using the Lerp model, autoregressive model, GAN model, and the depth image completion model proposed in this study were compared. The method proposed in this study was found to be effective and feasible for meeting actual production requirements at a camera distance of 1–2 m from the cow, achieving high accuracy with a coefficient of determination above 0.9. Additionally, the three models used were effective in handling small-scale depth deficits, with a high degree of agreement between machine and manual measurements. The DDPM-based depth image reconstruction model was found to produce the most accurate results when the camera distance from the cow was between 1-3m. Therefore, this research makes it possible to make flexible measurements in the near range using existing methods. Accurate measurements at longer ranges are influenced by depth image quality and feature point localization accuracy, which require more advanced techniques for further investigation.

ABSTRACT. The aim of the study is to determine the relationship between body condition score with reproductive disorders, reproductive performance and estrus performance in beef cattle. Sixty beef cattle were used in this research. Body condition score (BCS), reproductive disorders, reproductive performance and estrus performance were measured in this research. Data were analyzed using correlation and simple linear regression. The results in this research showed that the relationship between BCS and reproductive disorders, condition of vulva, behavior, intensity of mucus and uterine erection had no significant effect (p 0.05) with the value of the regression equation and the correlation coefficient respectively Y = 4,964 - 0,57X and 0,12; Y = 2.235 + 0.14X and 0.26; Y = 1.788 + 0.89X and 0.153; Y = 2.343 - 0.12X and 0.23; Y = 1.352 + 0.1X and 0.198. BCS with Service per conception, Days open, Conception rate and Estrus post partum showed significant results (P 0.05) with the value of the regression equation and the correlation coefficient Y=2.266-0.201X and 0.35; Y = 107.35-3.56X and 0.33 and Y = 96.923-4.385X and 0.499; Y = 87,891 - 4,189X and 0,508. The conclusion of this study is that Body condition score has a significant effect on Service per conception, Days open, Conception rate and Estrus post partum.

ABSTRACT: Directly measuring individual cow energy balance is not trivial. Other traits such as 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 worldwide to estimate cow body reserves, but 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. The ΔBCS records were merged with milk MIR spectra recorded on the same week. The dataset 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. The ΔBCS data in both the first 120 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 dataset and of 3.63 × 10−3 BCS units in the 305-d dataset. A 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 dataset). 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 dataset and of the prediction model used, combining DIM information with MIR spectral data as prediction variables reduced the RMSE compared with the inclusion of DIM alone, albeit the benefit was small (the RMSE from cross-validation reduced by 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 were 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.

Body condition score (BCS) is a common tool used to assess the welfare of dairy cows and is based on scoring animals according to their external appearance. If the BCS of dairy cows deviates from the required value, it can lead to diseases caused by metabolic problems in the animal, increased medication costs, low productivity, and even the loss of dairy cows. BCS scores for dairy cows on farms are mostly determined by observation based on expert knowledge and experience. This study proposes an automatic classification system for BCS determination in dairy cows using the YOLOv8x deep learning architecture. In this study, firstly, an original dataset was prepared by dividing the BCS scale into five different classes of Emaciated, Poor, Good, Fat, and Obese for images of Holstein and Simmental cow breeds collected from different farms. In the experimental analyses performed on the dataset prepared in this study, the BCS values of 102 out of a total of 126 cow images in the test set were correctly classified using the proposed YOLOv8x deep learning architecture. Furthermore, an average accuracy of 0.81 was achieved for all BCS classes in Holstein and Simmental cows. In addition, the average area under the precision–recall curve was 0.87. In conclusion, the BCS classification system for dairy cows proposed in this study may allow for the accurate observation of animals with rapid declines in body condition. In addition, the BCS classification system can be used as a tool for production decision-makers in early lactation to reduce the negative energy balance.

Our aims were to evaluate changes in body characteristics, milk yield and milk constituents as well as to determine the relationship between the thermal environment and production characteristics during the first lactation of dairy Gyr cows managed on pasture. Between 2013 and 2015, forty-five primiparous dairy Gyr cows were evaluated from prepartum to 10 months of lactation in Southeast of Brazil. Body weight, body condition score (BCS), subcutaneous fat thickness (SFT), milk yield (305 d), and milk constituents were collected monthly and progesterone was collected weekly. Additionally, we determined the temperature humidity index (THI) based on microclimate data. Overall, the cows lost body weight until six months of lactation and there was a progressive decrease in BCS, SFT, milk yield and milk lactose as the months in lactation progressed. In contrast, there was an increase in milk fat, milk protein and milk solids. The thermal environment did not pose a consistent heat challenge, nevertheless, we found a positive correlation between the average THI two days before milk collection with milk yield, fat and lactose contents, but in contrast a negative correlation was found with total solids and protein. In conclusion, the THI and months of lactation affected the yield and constituents of milk. However, more studies are necessary to understand the impacts of body characteristics and thermal environment on yield and milk constituents throughout the productive life of Gyr dairy cows. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the relationship between measures of body condition score (BCS) during the transition period and milk yield, reproductive performance, and occurrence of metritis and ketosis in cows. A total of 5026 lactations of 2424 Holstein cows from a single dairy farm were assessed for BCS (5-point scale; 0.25-point increments) 15 days precalving, at calving, and first service. ΔBCS were classed as <0.25, 0.25-0.5, 0.5-0.75, and >0.75 from 15 days precalving to parturition and from calving to first artificial insemination. BCS classes at parturition were <3.5, 3.5-3.75, and >3.75. 305-d milk yield was the highest in cows with BCS change (ΔBCS) <0.25 between 15-d precalving and parturition (11526±1088 kg), and the lowest (10878±1114) in cows with precalving ΔBCS <0.75. The total milk yield was the highest in cows with ΔBCS <0.25 between 15-d precalving and parturition (13861±2541 kg), and lowest (13214±3174) in cows with precalving ΔBCS >0.75. The overall pregnancy rate was least [281/836 (33.6%); p<0.01] for cows that lost >0.75 units of BCS, and greatest for cows that lost <0.25 BCS [477/615 (77.6 %)]. Puerperal metritis occurred more often in cows that lost >0.75 BCS between calving and the first artificial insemination (AI) [175/836 (20.9 %); p<0.01). Cows with reduced BCS loss between calving and first AI were less likely to present metritis [67/615 (10.9 %); p<0.01], whereas cows with ≥ 0.75 BCS loss between calving and first AI were 2.3 times more likely [175/836 (20.9 %)] to suffer metritis. In conclusion, greater changes in BCS between calving and first AI increased 305-d and the total milk yield, and occurrence of puerperal metritis and clinical ketosis, although fertility was reduced. [ABSTRACT FROM AUTHOR]

This study aimed to assess the variations in serum Anti-müllerian hormone (AMH) levels during the estrus and diestrus phases of the estrous cycle and its effect on fertility in cross-bred Hamdani ewes. In the study, 21 non-prolific cross-bred Hamdani ewes (aged 2-3 years with BCS 2.75-3.25) were subjected to estrous synchronization using intra-vaginal sponges. Following synchronization, ewes underwent natural mating and pregnancy examination was carried out on the 45th day after mating through trans-rectal ultrasonography. Jugular blood samples were collected on day of estrus and 8 days later and analyzed for serum AMH and progesterone concentrations. Results showed that serum AMH levels were 100-480 and 95-520 pg/ml during estrus and diestrus phases, respectively. There was no change in serum AMH levels due to age, BCS and during estrus and diestrus phases of the estrous cycle (P>0.05). Estrus and diestrus serum AMH levels were significantly higher in pregnant ewes compared to non-pregnant ewes (P<0.05). Receiver-operating characteristic (ROC) analysis revealed that serum AMH cut-off value of >270 pg/mL was feasible to predict fertility in cross-bred Hamdani ewes. In conclusion, serum AMH can be used as a marker of fertility in non-prolific cross-bred Hamdani ewes. [ABSTRACT FROM AUTHOR]

The importance of backfat thickness in sows lies in its correlation with nutritional status, reproductive performance, and overall health. Identifying the optimum backfat thickness is crucial for determining the ideal energy reserves needed to support successful reproduction and lactation. This research aimed to determine optimal backfat thickness (BFT) of sows in relation to reproductive and lactation performance. In this study, 32 lactating sows were housed in a controlled environment and assigned to four groups based on their BFT before farrowing: <17.00 mm, 17.00–17.99 mm, 18.00–18.99 mm, and ≥19.00 mm. The data were analyzed with One-way analysis of variance, and the association between backfat thickness and sow reproductive performance was examined through Spearman's correlation analysis using SAS software. The results revealed no significant difference between the groups in total born, total born alive, and litter size weaned, but the piglets' survival rate during the lactation period is lower from sows with BFT < 17.00. Moreover, piglet birth weight and body weight at Day 3 were significantly lower in sows with BFT < 17.00 mm. The BFT of sows at weaning showed significant differences among the groups associated with the backfat thickness before farrowing. No significant difference was found in the duration of farrowing. The return-to-estrus interval was longer in sows with <17.00 mm BFT than in those with 17.00–17.99 mm, 18.00–18.99 mm, and ≥19.00 mm backfat thickness, with estrus intervals of 7.17, 6.25, 5.31, and 5 days after weaning, respectively. Numerically, calculated milk yield (MY) is lowest in sows with BFT < 17.00, and the highest MY was obtained from sows with BFT 18.00–18.99 mm. In conclusion, sows with at least 17.00 mm BFT before farrowing are ideal for increasing the lifetime productivity of sows. This study provides valuable insights into the importance of sow management during gestation for subsequent reproductive success. [ABSTRACT FROM AUTHOR]

Genetic selection for higher productivity increased dairy sheep susceptibility to diseases and environmental stressors, challenging their health and welfare status and production efficiency. Improving resilience to such stressors can enhance their ability to face these challenges without compromising productivity. Our objective was to estimate genomic heritability and perform genome-wide association studies (GWAS) to detect SNPs and candidate genes associated with three proxy traits for resilience (milk somatic cell count—SCC, lactation persistency—LP, body condition score—BCS) of Chios and Frizarta dairy ewes. We used genome-wide genotypes of 317 Chios and 346 Frizarta ewes. Individual records of milk yield and BCS, and milk samples were collected monthly for two consecutive milking periods; samples were analyzed to determine SCC. The LP was calculated as the regression coefficient of daily milk yield on days from lambing. Within breed, variance components analyses and GWAS were performed using genomic relatedness matrices in single-trait animal linear mixed models. Genomic-based heritability estimates were relatively high (BCS: h2 = 0.54 and 0.55, SCC: h2 = 0.25 and 0.38, LP: h2 = 0.43 and 0.45, for Chios and Frizarta ewes, respectively), compared to previous pedigree-based studies. The GWAS revealed 7 novel SNPs associated with the studied traits; one genome-wide and two suggestive significant SNPs for SCC (Frizarta: rs403061409, rs424064526 and rs428540973, on chromosomes 9, 1 and 12, respectively), one suggestive significant SNP for BCS (Chios: rs424834097 on chromosome 4) and three suggestive significant SNPs for LP (Frizarta: rs193632931 and rs412648955 on chromosomes 1 and 6, Chios: rs428128299 on chromosome 3). Nineteen candidate genes were detected: two for BCS (Chios: POT1, TMEM229A), thirteen for SCC (Frizarta: NTAQ1, ZHX1, ZHX2, LOC101109545, HAS2, DERL1, FAM83A, ATAD2, RBP7, FSTL1, CD80, HCLS1, GSK3B) and four for LP (Frizarta: GRID2, FAIM, CEP70—Chios: GRIP1). Present results show that resilience in the studied dairy sheep breeds is heritable and advance existing knowledge on the genomic background of SCC, LP, and BCS. Future research will quantify effects of different alleles of significant SNPs on the studied traits and search for possible correlations among traits to facilitate their effective incorporation in breeding programs aiming to improve resilience. [ABSTRACT FROM AUTHOR]

Directly measuring individual cow energy balance is not trivial. Other traits such as 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 worldwide to estimate cow body reserves, but 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. The ΔBCS records were merged with milk MIR spectra recorded on the same week. The dataset 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. The ΔBCS data in both the first 120 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 dataset and of 3.63 × 10−3 BCS units in the 305-d dataset. A 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 dataset). 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 dataset and of the prediction model used, combining DIM information with MIR spectral data as prediction variables reduced the RMSE compared with the inclusion of DIM alone, albeit the benefit was small (the RMSE from cross-validation reduced by 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 were 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. [ABSTRACT FROM AUTHOR]

The transition period for dairy cows is stressful, and if this occurs during heat stress conditions, it will become more challenging for them. This study aimed to evaluate the effect of sustained-release bolus (Each bolus consisted of a mixture of mineral salts including copper sulfate (8 g), sodium selenite (0.17 g), manganese sulfate (3.9 g), zinc sulfate (2.4 g), and vitamin A (0.47 g) on body condition score (BCS) change, serum metabolites, uterine health, and some reproductive parameters in transition cows with moderate or high pre-calving BCS. Four experimental treatments were (1) moderate BCS without bolus consumption (MB-Bo, n = 35), (2) moderate BCS with bolus consumption (MB + Bo, n = 35), (3) high BCS without bolus consumption (HB-Bo, n = 35), and (4) high BCS with bolus consumption (HB + Bo, n = 35). Results showed that after calving, negative energy balance occurred in all experimental groups. However, cows with high BCS (HB-Bo and HB + Bo) had greater (P = 0.02) BCS change during the postpartum period (0–40 days). Bolus administration decreased white blood cells count 14 days after calving (P = 0.02). Cows with moderate BCS (MB-BO and MB + Bo) had higher (P < 0.01) red blood cell count than cows with high BCS (HB-Bo and HB + Bo) on 14 days after calving. The cows in MB + Bo group had higher (P < 0.05) serum glucose and albumin and lower (P < 0.01) non-esterified fatty acids and beta-hydroxybutyrate. Moreover, this group of cows had higher (P < 0.05) serum total antioxidant capacity, glutathione peroxidase and superoxide dismutase, and lower malondialdehyde (P = 0.03) than other groups. In this regard, the increase in antioxidant capacity with the consumption of blues caused the HB-Bo group to have more incidence of metritis (P = 0.08) and endometritis (P = 0.08). The HB-Bo group had about 12 days longer (P < 0.01) days open than MB + Bo group. It was concluded that consumption of slow-release bolus containing antioxidant elements had positive effect on the metabolic and reproductive status of high-producing dairy cows under heat stress condition. [ABSTRACT FROM AUTHOR]

This research aims to determine the pregnancy success of SimPO cows artificially inseminated (AI) with Simmental and Brahman frozen semen. The research was carried out using a survey method, namely in January–April 2023. The research material was 214 SimPO cows inseminated with Simmental and Brahman frozen semen, which was taken using purposive sampling based on complete data on IB implementers. The research parameters were the body condition score (BCS) of the SimPO cows and the intensity of cervical erection during AI and pregnancy. Data analysis was carried out descriptively using percentages, histograms, and polygon graphs. The results of the study showed that the cows who had a BCS of 7, 6, and 5 had mostly moderate quality (++) cervical erection scores of 47.22%, 42.42%, and 40.11%, respectively, while the cows who had a BCS of 4 and 3 were mostly of low quality (+) 53.33% and 53.13%, respectively. The pregnancy percentage of SimPO cows inseminated with Brahman frozen semen was highest at BCS 7 (50.00%), while Simmental frozen semen was at BCS 5 (31.86%). This research concludes that the pregnancy rate of SimPO cows inseminated with Brahman frozen semen is higher than Simmental frozen semen.