Evaluation of the Accuracy of Horse Body Weight Estimation Methods

Relationships between body condition score, body weight and body measurements in alpacas

BACKGROUND

The nutritional status in alpacas is often masked by their dense fibre coat. Its assessment is commonly approached by different body condition scores (BCS) that rely on manual palpation of defined anatomical regions. However, BCS is an important diagnostic tool to aid recognition of diseased South American camelids (SACs) and low BCS has been associated with conditions like anaemia and neutrophilia. For dose-dependent veterinary treatment, body weight (BW), that should be as accurate as possible, is required. As on-site weighing with scales is often not possible, BW can mostly only be roughly estimated. To date, it remains unclear whether BCS in alpacas provides reliable information on BW or the ratios of BW to body length commonly known as Body Mass Index (BMI) or Ponderal Index (PI). Equations to estimate BW based on body measurements are available in the literature. Nonetheless, respective equations were developed in growing alpacas or adult llamas and BCS was not included.

RESULTS

To compare six different BCS approaches and to examine the relationship between BCS and BW, body measurements and BCS scores were recorded in a herd of 105 alpacas. The examined BCS approaches showed significant (p < 0.05) but poor to moderate positive correlations to BW, BMI or PI. A solely visual inspection of BCS, in contrast, was not correlated with BW, BMI or PI. Equations previously developed in other studies provided an accurate estimation of BW. Multiple linear regression showed that the accuracy in predicting BW could be further increased by adding BCS data and sex.

CONCLUSION

Our observations indicate that most selected BCS approaches are not only important measures of nutritional status but can also be used to create more accurate models for BW calculation in alpacas. The study also supports the claim that a purely visual inspection of alpacas is not an adequate method to evaluate the nutritional status of these animals.

Morphometric Measurements and Muscle Atrophy Scoring as a Tool to Predict Body Weight and Condition of Horses

Accurate estimation of body weight (BW) and condition (BCS) is important in the equine practice. The main goal of this research was to develop models for the prediction of BW and BCS of horses in the practice using both common morphometric measurements and measurements of Cresty Neck Score (CNS) and Muscle Atrophy Scoring System (MASS) as a measure of muscularity. Our model showed that the BW of horses could be predicted with high reproducibility (concordance correlation coefficient = 0.97), accuracy (0.99), and precision (0.97) using the morphometric measurements of the height at withers, circumference of the chest, cane circumference, body length, and body circumference as well as the BCS, CNS, and muscle atrophy score of the hindlimbs. The stepwise multiple regression analysis revealed that the BCS of horses can be predicted with the data of parameters such as age, body length and an index consisting of measurements of the body circumference to height of withers, and the atrophy of the neck. Future research should use larger cohorts of animals to validate the findings of this study.

Anthelmintic resistance of horse strongyle nematodes to ivermectin in São Paulo state, Brazil

The indiscriminate use of drugs to control gastrointestinal helminths in horses can lead to serious consequences for the animals, which represents a growing problem for health, animal welfare and productivity. Therefore, the present study aimed to evaluate the anthelmintic efficacy of ivermectin in naturally infected horses in the western region of the state of São Paulo. From May 2021 to April 2022, 123 naturally infected adult horses in 12 equine breeding farms (7 to 14 animals per farm) were evaluated with the fecal egg count reduction test. The horses had not been treated with anthelmintic drugs for at least 60 days before the start of the study. The animals were treated with ivermectin (0.2 mg/kg; Eqvalan®, Merial) administered orally according to recommended dosage by the manufacturer. Individual fecal samples were collected directly from the rectal ampulla and used to determine the eggs per gram of feces (EPG) and perform coproculture for identification of larvae on the day of anthelmintic treatment (D0) and 14 days later (D14). The fecal egg count reduction (FECR) on each property was calculated using the program Shiny-egg Counts R version 3.6.1, and the presence of anthelmintic resistance was confirmed when the FECR percentage was less than 95%, and when the lower confidence limit [LCI] was less than 90%. The pre-treatment average EPG count in the 12 properties was 991. In five properties the FECR was lower than 90%; in three properties between 90% and 95%; and in four properties equal to or greater than 95% after treatment with ivermectin. The occurrence of cyathostomins with resistance to ivermectin was recorded in the majority of the farms.

Factors Affecting Weigh Tape Reading in the Measurement of Equine Body Weight

Accurate measurement of equine body weight is important for evaluating medication dosages and feed quantities. Different methods exist for measuring body weight, including weigh tapes (WT), though accuracy varies. Measurements could be affected by external variables, such as time of day, human error, or uneven surfaces, and also horse-based variables, such as height and body condition score (BCS). The aim of this study was to investigate how different horse-based variables affect WT reading. A retrospective analysis was performed using anonymised data from feed company nutrition consultations (Baileys Horse Feeds). Data included a range of horse-based variables, a WT reading, and true body weight measured on a weighbridge. All horses were over two years of age. Likelihood ratio tests were used to assess whether adding different horse-based variables significantly improved the fit of the quadratic regression model. The variables included were height, BCS, breed, muscle top-line score, and bone type. Exploratory analysis showed that the WT generally underestimated body weight, particularly for horses with higher body weight. Adding height and muscle top-line scores did not significantly improve the fit of the model, suggesting no influence on WT reading over and above actual body weight. Adding breed groupings, BCS, and bone density did improve the fit. Each 0.5 unit increase in BCS increased the WT estimate by 1.24 kg (p < 0.001). These results confirm that a WT does not provide accurate body weight measurements, and generally underestimates body weight, though more so for heavier horses, being more accurate in pony breeds.

Anthelmintic efficacy in strongyles of horses in Northern Minas Gerais, Brazil

The intensive use of anthelmintics has resulted in resistant parasite populations in horses. The objective of this trial was to evaluate the anthelmintic efficacies of the anthelmintics fenbendazole, ivermectin and abamectin in 24 horse farms in Northern Minas Gerais. Egg counts per gram of faeces (EPG) were performed individually in 619 animals. Animals presenting EPG counts greater than or equal to 150 were used in the tests on faecal egg count reduction (FECR), totalling 436 equines. These animals received the anthelmintics, fenbendazole, ivermectin, and abamectin. Faeces were collected 14 days after the administration of anthelmintics to perform the EPG. Pre- and post-treatment EPG counts were used to calculate the FECR for each anthelmintic group, and faecal culture was used to identificy of the strongyles. The resistance status was evaluated based on the FECR and LCL95%. Fenbendazole was effective in 11 (45.8%) of the horse farms. Ivermectin was effective in 17 (77.3%) and abamectin in 17 (74%) of the farms; side-resistance was detected in 3 (12.5%) of the farms. Intestinal strongyle resistance to anthelmintics was observed in 14 (58.3%) of the farms. Cyathostomin larvae were found in 100% of the farms, Strongylus vulgaris in 13 (54.2%), and S. equinus in 3 (12.5%). Only cyathostomins larvae were detected post-treatment with ivermectin and abamectin.

Body Weight Prediction from Linear Measurements of Icelandic Foals: A Machine Learning Approach

Simple Summary

Knowing the body weight of a growing horse is important both for horse breeders and veterinarians because this information helps to identify abnormalities of the growing process, determine adequate feeding rations or choose an appropriate drug treatment regimen. It is not always possible to measure accurately a horse’s body weight using special scales, and a visual assessment, which is the easiest method for finding out a horse’s body weight, produces heavily biased results. Simple formulas are being sought to allow making accurate estimates of body weight in horses based on their body measurements. This study relates the estimation of body weight in Icelandic foals with the use of models relying on machine learning methods. Based on their evaluation, two of the models are recommended for use in practical applications.

Abstract

Knowledge of the body weight of horses permits breeders to provide appropriate feeding and care regimen and allows veterinarians to monitor the animals’ health. It is not always possible to perform an accurate measurement of the body weight of horses using horse weighbridges, and therefore, new body weight formulas based on biometric measurements are required. The objective of this study is to develop and validate models for estimating body weight in Icelandic foals using machine learning methods. The study was conducted using 312 data records of body measurements on 24 Icelandic foals (12 colts and 12 fillies) from birth to 404 days of age. The best performing model was the polynomial model that included features such as heart girth, body circumference and cannon bone circumference. The mean percentage error for this model was 4.1% based on cross-validation and 3.8% for a holdout dataset. The body weight of Icelandic foals can also be estimated using a less complex model taking a single trait defined as the square of heart girth multiplied by body circumference. The mean percentage error for this model was up to 5% both for the training and the holdout datasets. The study results suggest that machine learning methods can be considered a useful tool for designing models for the estimation of body weight in horses.