Estimation of body weight using body measurements in female water buffaloes reared in southeastern Mexico

Ruiz-Ramos, Jorge; Torres-Chablé, Oswaldo Margarito; Peralta-Torres, Jorge Alonso; Ojeda-Robertos, Nadia Florencia; Luna-Palomera, Carlos; Portillo-Salgado, Rodrigo; Tyasi, Thobela Louis; Gurgel, Antônio Leandro Chaves; Ítavo, Luís Carlos Vinhas; Chay-Canúl, Alfonso Juventino

doi:10.1007/s11250-023-03549-9

Cited by 5 publications

(3 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…BW and body measurements were taken in 130 Murrah buffaloes aged 6 to 10 months (78 females and 52 males). The body measurements recorded were: (1) hearth girth (HG), (2) thorax width (TW), (3) hip width (HW), (4) body length (BL) and ( 5) diagonal body length (BDL), (6) withers height (WH), (7) rump height (RH) and ( 8) rib depth (RD), respectively. BW was recorded by weighing the animals on a fixed platform scale with a capacity of 2000 kg and an accuracy of 0.5 kg (Revuelta, Torreon, Coahuila, Mexico), while body measurements were recorded using a flexible fibreglass tape measure (Truper ® , Truper, S. A. de C. V., San Lorenzo, Mexico) and a 65 cm forcipule, as previously described by [6].…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Body Weight by Using PCA-Supported Gradient Boosting and Random Forest Algorithms in Water Buffaloes (Bubalus bubalis) Reared in South-Eastern Mexico

Gomez-Vazquez,

Tırınk,

Cruz-Tamayo

et al. 2024

Animals

Self Cite

View full text Add to dashboard Cite

This study aims to use advanced machine learning techniques supported by Principal Component Analysis (PCA) to estimate body weight (BW) in buffalos raised in southeastern Mexico and compare their performance. The first stage of the current study consists of body measurements and the process of determining the most informative variables using PCA, a dimension reduction method. This process reduces the data size by eliminating the complex structure of the model and provides a faster and more effective learning process. As a second stage, two separate prediction models were developed with Gradient Boosting and Random Forest algorithms, using the principal components obtained from the data set reduced by PCA. The performances of both models were compared using R2, RMSE and MAE metrics, and showed that the Gradient Boosting model achieved a better prediction performance with a higher R2 value and lower error rates than the Random Forest model. In conclusion, PCA-supported modeling applications can provide more reliable results, and the Gradient Boosting algorithm is superior to Random Forest in this context. The current study demonstrates the potential use of machine learning approaches in estimating body weight in water buffalos, and will support sustainable animal husbandry by contributing to decision making processes in the field of animal science.

show abstract

Section: Methodsmentioning

confidence: 99%

“…For this reason, body weight (BW) appears as the most critical information in production systems, as it will vary depending on many financial characteristics [6,7]. Accurate BW prediction is a basis in animal science studies, such as animal healthcare management, animal husbandry, and determining drug doses and feeding optimization [8].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Body Weight by Using PCA-Supported Gradient Boosting and Random Forest Algorithms in Water Buffaloes (Bubalus bubalis) Reared in South-Eastern Mexico

Gomez-Vazquez,

Tırınk,

Cruz-Tamayo

et al. 2024

Animals

Self Cite

View full text Add to dashboard Cite

show abstract

“…Body weight (BW) is an important parameter because it provides important information within production systems and is associated with several economic traits (Ruiz-Ramos et al, 2023;Ramos Zapata et al, 2023). However, in the major buffalo producing regions of the world, the systems are marked by low investment in infrastructure and regular weighing of animals is uncommon because of the lack of animal weighbridges (Ruiz-Ramos et al, 2023;Ramos-Zapata et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Reliability and Concordance of Visual Weight Assessment in Water Buffalo Females

Vázquez-Bolaina,

Salazar-Cuytun,

Cruz-Tamayo

et al. 2024

Trop Subtrop Agroecosyst

View full text Add to dashboard Cite

Background. For the development of reproductive, nutritional and health programmes, estimation of animal body weight (BW) is a fundamental tool in herd management. Objective. To evaluate of the reliability and concordance of visual weight assessment in water buffalo females. Methodology. Data on visually estimated body weight (VM) and actual body weight (BW) were recorded for 229 female water buffaloes. BW was recorded using a digital scale and visual estimation BW was taken as the average of three observations made by three observers. The measurements obtained by each of the different estimation methods were also compared with the observed weights by inspecting the paired Bland-Altman plots prior to logarithmic transformation. Results. Correlations between observed BW and mean predicted BW for visual methods showed a positive and significant relationship (P<0.001), with an r value of 0.95. According to the intraclass correlation coefficient (ICC), there was a high degree of reliability in the visual estimates of buffalo weight, due to the similarity in the mean and dispersion of each observer's estimates. Both the Bland-Altman plot and the ICC show that there is a high level of concordance between the buffalo weights obtained by the visual and real methods. Implications. The result of the present study showed that visual assessment highlights the ability of livestock handlers to have a very accurate estimation of BW in female water buffaloes. Conclusion. The results of this study show that visual assessment highlights the ability of livestock handlers to visually estimate BW in female water buffaloes with high reliability and concordance with the animal weighbridge method.

show abstract

Predicting body weight through biometric measurements in growing hair sheep using data mining and machine learning algorithms

Vázquez-Martínez,

Tırınk,

Salazar-Cuytun

et al. 2023

Trop Anim Health Prod

View full text Add to dashboard Cite

Estimation of body weight using body measurements in female water buffaloes reared in southeastern Mexico

Cited by 5 publications

References 26 publications

Prediction of Body Weight by Using PCA-Supported Gradient Boosting and Random Forest Algorithms in Water Buffaloes (Bubalus bubalis) Reared in South-Eastern Mexico

Prediction of Body Weight by Using PCA-Supported Gradient Boosting and Random Forest Algorithms in Water Buffaloes (Bubalus bubalis) Reared in South-Eastern Mexico

Evaluation of the Reliability and Concordance of Visual Weight Assessment in Water Buffalo Females

Predicting body weight through biometric measurements in growing hair sheep using data mining and machine learning algorithms

Contact Info

Product

Resources

About