Analysis of the Factors Influencing Body Weight Variation in Hanwoo Steers Using an Automated Weighing System

Cho, Hyun-jin; Jeon, Seoyoung; Lee, Mingyung; Kang, Kyewon; Kang, Hamin; Park, EunKyu; Kim, Minkook; Hong, Seokman; Seo, Seongwon

doi:10.3390/ani10081270

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…Artificial Intelligence Techniques for the Prediction of Body Weights in Sheep consideration for the prediction of body weight at a later age (Cho et al, 2020). ML models can take all these factors into consideration for more accurate predictions.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Techniques for the Prediction of Body Weights in Sheep

Hamadani

Ganai

Alam

et al. 2022

IJAR

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Background: Artificial intelligence (AI) is transforming all spheres of life and it has the potential to revolutionize animal husbandry as well. In this regard, an attempt was made to compare two AI techniques for predicting 12-month body weights of animals; viz. Principal Component regression (PCR) and Ordinary Least Squares (OLS) for datasets of Corriedale sheep spanning 11 years. Methods: PCR models were trained by varying proportions of training and testing datasets. The dataset was subject to PCR before analysis and tested (PCA dataset). A separate dataset was also created by feature selection of the PCA (PCA+FS dataset) variables. Result: The highest correlation coefficients between test and predicted variables for two datasets (PCA dataset and PCA+FS dataset) created among the multiple models trained using PCR were 0.982 and 0.9741. In terms of error, R2 or correlation coefficient, the PCA dataset performed better than the PCA+FS dataset. The second principal component had the highest explained variance in OLS (86.16%) and the highest coefficient of determination (R2) using OLS was obtained for the PCA dataset viz. 0.980. It is concluded that both the algorithms tested in this study were satisfactorily trained in their prediction of the body weights with OLS performing better than PCA in terms of R2 value.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Techniques for the Prediction of Body Weights in Sheep

Hamadani

Ganai

Alam

et al. 2022

IJAR

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“…), high accuracy in weight measurements cannot be expected unless the experiment is of sufficient duration. It is also important to consider individual genetic differences in animals, as they significantly contribute to the variability in animal weight, as noted by other researchers (Cho 2020).…”

Section: Discussionmentioning

confidence: 98%

Automatic cattle weighing on pastures with behavioral analysis during drinking

Uskenov,

Mirmanov,

Tretyakov

et al. 2023

J. Anim. Behav. Biometeorol.

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Livestock body weight (BW) and average daily weight gain (ADG) are primary indicators of beef cattle productivity. The conventional method of weighing involves moving the cattle to a weighing location, which is labor-intensive, stressful for the animals and has a negative impact on their growth. An alternative approach is to use special weighing platforms attached to the drinkers to weigh the animals. This method enables daily monitoring of BW and ADG without incurring additional labor costs or stress. In this study, an experimental weighing platform, previously developed at KazATU and named after S. Seifullin, was employed to measure livestock's partial body weight (PBW). The weighing platform recorded the weights of the animals on the front legs at one-second intervals, allowing for subsequent calculation of the animals' total weight. However, due to significant weight fluctuations observed when the animals were on the platform, the accuracy of calculating the weight based on a simple average of the one-second measurements was questionable. Hence, an algorithm was developed to determine live weight by analyzing the primary data from the scales and identifying moments of animal immobility during drinking. The calculated results were compared with both mean and median values and data from Kazakhstan's information base of selection and breeding work (IBSBW). The experimental method exhibited a stronger correlation (r = 0.925) with the actual IBSBW data compared to the mean method (r = 0.887) or the median method (r = 0.921).

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“…We cannot provide information on the exact age of the GPs we used in this study as the vendor did not have the birth record of their animals -a common problem in the conventional animal farming system. GPs weighing 250-350 g are approximately 2-3 months old [39][40][41]. GPs can be bred when their body weight reaches 400-450 g [17,41,42].…”

Section: Body Weight and Body Temperaturementioning

confidence: 99%

Health comparison between guinea pigs raised in uncontrolled and controlled environments

et al. 2022

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Background and Aim: Guinea pigs (GPs) (Cavia porcellus) are not only kept as pets but also widely used in biological and biomedical research. At present, GPs are also used as a species for animal-assisted therapy (AAT). Consequently, assessing their health status is vital to determining their quality of life, usability for research, and prevention of spread of potential zoonotic diseases to patients using them for AAT. GPs are mainly sourced from animal markets supplied by traditional farms, where environmental factors and sanitation are not properly controlled. This study aimed to compare health status between GPs raised in uncontrolled (conventional farm) and controlled (animal facility) environments. Materials and Methods: Sample animals were obtained from a local animal market and transported to an animal facility. After 1 week of acclimatization, the health status of the animals, including general health condition, body weight, body temperature, complete blood count, liver function (alanine aminotransferase and bilirubin), renal function (blood urea nitrogen and creatinine), and presence of ectoparasites and endoparasites, was assessed. Then, the animals were maintained in the animal facility following the standard procedure for laboratory animals. After 2 months, the animals' health status was re-examined, assessing the same parameters. Results: Based on the evaluated parameters, GPs raised in an uncontrolled environment were found to have poorer health status than those raised in a controlled environment. There were significant differences in almost all parameters between GPs raised in controlled and uncontrolled environments. We found that the populations of two ectoparasites, Gyropus ovalis and Gliricola porcelli, and one endoparasite, Eimeria caviae>, decreased significantly following the movement of the animals from an uncontrolled to a controlled environment. Conclusion: GPs raised in an uncontrolled environment have poor health status. However, a controlled environment with better care management can improve the health status of GPs.

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Analysis of the Factors Influencing Body Weight Variation in Hanwoo Steers Using an Automated Weighing System

Cited by 6 publications

References 11 publications

Artificial Intelligence Techniques for the Prediction of Body Weights in Sheep

Artificial Intelligence Techniques for the Prediction of Body Weights in Sheep

Automatic cattle weighing on pastures with behavioral analysis during drinking

Health comparison between guinea pigs raised in uncontrolled and controlled environments

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