Using path analysis to predict bodyweight from body measurements of goats and sheep of communal rangelands in Botswana

Abstract

The objective of this study was to determine the practicality of using linear body measurement traits to predict live weight of goats and sheep under communal grazing in three districts of Botswana, namely Central, Kweneng, and Kgalagadi. Pairwise (Pearson) correlations were estimated using bodyweight (BW) and morphological trait measurements, namely heart girth (HG), shoulder height (SH), and body condition score (BCS) for a sample of 1447 goats and 588 sheep. These ranged from 0.19 to 0.94 for goats and 0.44 to 0.94 for sheep, and were statistically significant. In both animals, regardless of sex, the highest positive correlations were found between BW and HG, followed by BW and SH, then BW and BCS. The direct and indirect relationship between BW and morphological traits using a path analysis approach were also estimated. This analysis suggested that the direct effect of HG on BW was higher at 0.761 for male and 0.662 for female goats, respectively. Similarly, the direct effect of HG on BW was higher among sheep at 0.764 for males and 0.882 for females. The direct effects of SH in all the animals were also positively influenced by BW. It was concluded that among these three morphological traits, the most valuable for estimating the BW for goats and sheep in Botswana is HG, followed by SH. BCS was found non-significant. Path analysis therefore has an advantage over previous empirical studies by providing more detailed information on the relationships (direct and indirect) between these variables. A further implication of this study is that a conversion table could be constructed to help smallholder farmers to estimate the live weight of their sheep and goats from linear measurements. This would aid in the management and marketing of their livestock.

Application of Biometric Traits for Predicting Weaning Weight of Dorper Sheep using Path Analysis

“…WH had a significant correlation with HG, BL, RH and BL was found to be significantly correlated to HG and RH. Other studies have been done on the prediction of weaning weight from body measurements of goats and sheep of communal rangelands in Botswana using path analysis and their descriptive statistics findings are in harmony with the current study results that males had higher average WW than females but same authors disagree with our results where average HG and SH of females were higher than of males (Temoso et al 2017), contrary may be due genetic factor such as type of breeds, furthermore reported similar results that positive statistical correlation was found between WW and HG but disagree with current study results where there was a statistical relationship between WW and SH. Moreover, a high correlation between various body measurements and live weight in sheep was reported and their results are similar with the current study findings on BL, WH and RH (Doğukan et al 2019).…”

“…Path analysis has an advantage in providing more detailed information on the relationships (direct and indirect) between these variables (Temoso et al 2017). The first objective of the current study was to examine the relationship between weaning weight and biometric traits (HG, RH, BL, WH, SH) of Dorper lamb at weaning using Person's correlation.…”

“…Other study has been done on Botswana communal sheep and goats using path analysis to predict weaning weight from body measurement traits (Temoso et al 2017). Path analysis was used to determine the relationship between weaning weight and some linear characteristics in Red Sokoto kids (Shuaibu et al 2020) and in some exotic sheep breeds (Costa et al 2020).…”

Prediction of Body Weight from Linear Body Measurement Traits of Boer Goats Raised at Farm Tivolie, Limpopo Province, South Africa

“…Berhe (2017), indicated that heart girth is the best body trait used to predict live body weight with reasonable accuracy. Temoso et al (2017) reported that in goats and sheep of communal rangelands in Botswana, heart girth and sternum height had a positive and significant effect on the bodyweight of both bucks and does.…”

ASAS-NANP SYMPOSIUM: Applications of machine learning for livestock body weight prediction from digital images

“…While animal biometrics is an emerging field focused on quantification and detection of the phenotypic appearance of species, individuals, behaviors, and morphological traits ( Kühl and Burghardt, 2013 ), animal morphometrics ( Rohlf, 1990 ; Adams et al, 2004 ; Doyle et al, 2018 ) is almost exclusively focused on landmark-based methods (and less on outline-based methods) using quantitative analysis of form relying on measuring the size and shape of animals, and the relation between size and shape (allometry). Estimation of livestock BW using biometric and morphometric measurements has been studied in detail for various species, such as cattle ( Taşdemir et al, 2011a , b ; Miller et al, 2019 ; Tasdemir and Ozkan, 2019 ; Gjergji et al, 2020 ; de Moraes Weber et al, 2020 ; Rudenko, 2020 ), pigs ( Brandl and Jørgensen, 1996 ; O’Connell et al, 2007 ; Mutua et al, 2011 ; Sungirai et al, 2014 ; Al Ard Khanji et al, 2018 ), sheep ( Eyduran et al, 2015 ; Huma and Iqbal, 2019 ), goats ( Sebolai et al, 2012 ; Eyduran et al, 2017 ; Temoso et al, 2017 ), camels ( Fadlelmoula et al, 2020 ; de Moraes Weber et al, 2020 ), yaks ( Yan et al, 2019 ), poultry ( Mendeş and Akkartal, 2009 ), and fish ( Fernandes et al, 2020b ). This process is typically applied to avoid drawbacks associated with manually performed individual animal weighing such as: 1) the animal and manual laborer stress associated with animal relocation, 2) the costs associated with this labor-intensive process, and 3) the significant cost associated with acquiring and maintaining industrial scales.…”