Validity of Weight Estimation Models in Pigs Reared under Different Management Conditions

Abstract

A study was carried out to determine the relationship between linear body measurements and live weight in Landrace and Large White pigs reared under different management conditions in Zimbabwe. Data was collected for body length, heart girth, and live weight in 358 pigs reared under intensive commercial conditions. The stepwise multiple linear regression method was done to develop a model using a random selection of 202 records of pigs. The model showed that age, body length, and heart girth were useful predictors of live weight in these pigs with significantly high positive correlations observed. The model was internally validated using records of the remaining 156 pigs and there was a significantly high positive correlation between the actual and predicted weights. The model was then externally validated using 40 market age pigs reared under communal conditions and there was a significantly low positive correlation between the actual and predicted weights. The results of the study show that while linear measurements can be useful in predicting pig weights the appropriateness of the model is also influenced by the management of the pigs. Models can only be applicable to pigs reared under similar conditions of management.

Effect of feeding three lysine to energy diets on growth, body composition and age at puberty in replacement gilts

“…Weighing gilts is labor intensive, and correlated measures have been developed that do not require moving gilts to a scale (Iwasawa et al, 2004;Sungirai et al, 2014). Flank to flank measurement is correlated with weight as previously reported, however, if 84 cm is used to ensure that all gilts are at or greater than the weight threshold, 80% of gilts that did not meet the flank to flank threshold were actually at a weight greater than the minimum threshold.…”

Lectin-Functionalized Magnetic Iron Oxide Nanoparticles for Reproductive Improvement

“…These data were then used to estimate the gilt weights which appeared comparable between the control (260 ± 0 kg) and the nanopurified (249 ± 32 kg) groups (means ± SD; P>0.05). This method has been reported to be highly accurate for body weight estimations [24,30]. The observed results on dams lead us to consider that any potential differences at the piglet level would be attributable to the semen quality, unless there are undetectable issues associated with the bred gilts.…”

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.…”

“…Some of the most popular morphometric measurements include heart girth circumference, wither height, hip width/height, and body length. These measurements are manually selected and used as features for traditional regression models, which result in predictive equations with one or more variables based on the number of selected measurements in various species, such as cattle ( Heinrichs et al, 1992 ; Franco et al, 2017 ; Goopy et al, 2018 ), pigs ( Groesbeck et al, 2002 ; Mutua et al, 2011 ; Sungirai et al, 2014 ; Al Ard Khanji et al, 2018 ), sheep ( Sowande and Sobola, 2008 ; Kunene et al, 2009 ; Chay-Canul et al, 2019 ; Canul-Solis et al, 2020 ), goats ( Sebolai et al, 2012 ), camels ( Fadlelmoula et al, 2020 ; Meghelli et al, 2020 ), and yaks ( Yan et al, 2019 ).…”