Small for Gestational Age Calves: Part I-Concept and Definition, Contributing Prenatal Factors and Neonatal Body Morphometrics in Holstein Friesian Calves.

Low birth weight (BW) calves exhibit higher mortality rates, reduced body weights at parturition, lower first-lactation milk yields, and longer parturition to first insemination intervals. In human medicine, small for gestational age (SGA) births are associated with increased perinatal morbidity and long-term metabolic risks. This study aimed to define SGA in Holstein Friesian (HF) calves, evaluate their body measurements and proportions, and identify its prenatal risk factors. Four linear regression models were built with weight as a function for gestation length for bull and heifer calves born from nulli- or multiparous dams. Calves with a BW below the 10th percentile were classified as SGA. Differences in body measurements were analyzed using ANOVA, and logistic regression models identified prenatal risk factors to be born SGA. Gestation length, calf sex, and dam parity were crucial variables in defining SGA. SGA calves had significantly smaller body measurements (p < 0.001) and larger body proportions (p < 0.001) compared to average and large calves. For nulliparous dams, a higher 2nd trimester temperature-humidity index (p = 0.032) and older age at parturition (>26 months, p = 0.026) significantly increased the birth of SGA calves. For multiparous dams, both low (<5800 kg, p = 0.049) and high (6700-8600 kg, p = 0.027) milk yields during gestation lead to more SGA births, although very high-yielding dams (>8600 kg) did not birth more SGA calves. This study establishes SGA in HF calves, suggests SGA calves are asymmetrical with evidence of "brain sparing", and highlights the impact of prenatal factors on calf size at birth. Further research is needed to determine the long-term effects of being born SGA on growth, reproductive performance, and productivity.

Small for Gestational Age Calves: Part II-Reduced Fertility, Productive Performance, and Survival in Holstein Friesian Heifers Born Small for Their Gestational Age.

Recently: more attention has been given to low-birth-weight calves, often without considering gestation length. Calves can be classified as small for gestational age (SGA) when their birth weight is below the 10th percentile, similar to the definition in human medicine. While SGA babies face various health risks, it remains unclear if SGA calves face similar long-term health consequences. This study aimed to investigate the long-term effects on fertility, productive performance, and overall survival in Holstein Friesian (HF) heifers born SGA. Chi-squared analysis assessed culling and survival rates, and linear mixed-effect models evaluated the impact of gestational age group (small, average, or large for gestational age, respectively, SGA, AGA, and LGA) on growth, fertility, milk yield, and lifespan. SGA calves showed catch-up growth at six months but weighed significantly less at twelve months (p = 0.003). Age at first insemination and calving did not differ significantly, although SGA heifers required more inseminations (2.3 ± 1.50) compared to AGA and LGA heifers (1.7 ± 0.98 and 1.5 ± 0.89, respectively, p = 0.006). SGA calves tended to be culled more during the first lactation than AGA calves (25.0% vs. 11.9%, p = 0.078) and showed lower survival to second calving (p = 0.019) compared to AGA and LGA heifers. The Kaplan-Meier analysis indicated a tendency for gestational age to affect overall survival (p = 0.1), with SGA heifers having a higher risk of leaving the herd prematurely (p = 0.035, hazard ratio = 1.53). Milk yield per productive day was significantly lower in SGA heifers (21.2 ± 8.73 kg) compared to AGA and LGA heifers (26.9 ± 5.01 kg and 26.3 ± 3.38 kg, respectively, p = 0.006). This study reveals that HF calves born SGA suffer long-term consequences, although further research is needed to understand the economic impact of rearing SGA heifers.

Twin management in the mare: A review.

Twin gestation in the mare is undesirable and can have disastrous consequences. As in many cases, the key to success in twin management lies in a thorough follow-up and accurate recording of clinical findings in the pre-breeding examination. A pregnancy diagnosis in the mobility phase is imperative for a good outcome in the event of twin reduction. If a twin gestation is not diagnosed during this early pregnancy stage, several other procedures exist for managing post-fixation twins (>16 days) with varying degrees of success. Most twin pregnancies are the result of multiple ovulations (dizygotic twins). However, monozygotic twins are also sporadically diagnosed, due to the increasing number of transferred in vitro produced equine embryos. In these cases, the most optimal treatment strategy still needs to be determined. This review provides an overview of the various twin reduction techniques described with the expected prognosis as well as of some less reported techniques with their results. In addition, physiological events and the reduction techniques are demonstrated to the user in virtual 3-dimensional illustrations.

Automatic early detection of induced colic in horses using accelerometer devices.

BACKGROUND: To seek appropriate veterinary attention for horses with colic, owners must recognise early signs. Direct observation of horse behaviour has several drawbacks: it is time-consuming, hard to see subtle and common behavioural signs, and is based on intuition and subjective decisions. Due to recent advances in wearables and artificial intelligence, it may be possible to develop diagnostic software that can automatically detect colic signs. OBJECTIVES: To develop a software algorithm to aid in the detection of colic signs and levels of pain. STUDY DESIGN: In vivo experiments. METHODS: Transient colic was induced in eight experimental mares with luteolytic doses of prostaglandin. Veterinarians observed the horses before and throughout the interventions and assigned pain scores which were used to separate colic episodes into none (pain score ≤5), level 1 (pain score 6-10) or level 2 (pain score ≥11). Accelerometric data and videos were collected throughout the experiments and using accelerometric data, the horse's behaviour was classified into normal and 10 pain-related behaviours and an activity index was calculated. Models were designed that utilised behaviour and activity index characteristics both detecting the presence of colic and assessing its severity. To determine the accuracy of the model, the ground truth, that is the veterinarians' observation of colic signs and assessment of pain level, was compared with the automatic detection system. RESULTS: The cross-validation analysis demonstrated an accuracy of 91.2% for detecting colic and an accuracy of 93.8% in differentiating between level 1 colic and level 2 colic. The model was able to accurately classify 10 pain-related behaviours and distinguish them from normal behaviour with a high accuracy. MAIN LIMITATIONS: We included a limited number of horses with severe pain related behaviours in the dataset. This constraint affects the accuracy of categorising colic severity rather than limiting the algorithms' capacity to identify early colic signs. CONCLUSIONS: Our system for early detection of colic in horses is unique and innovative, and it can distinguish between colic of varying severity.