Using body measurements to estimate body weight in gilts

Abstract

The absence of a scale on pig farms has led to indirect body weight (BW) estimation using regression models based on body measurements. The objectives of the present study were to (1) develop prediction equations for weight estimation in gilts using body measurements (FF: flank-to-flank distance; L: length; HG: heart girth; BF2: ultrasound backfat measurement; LD: loin depth; and BCS: body condition score) and (2) validate the use of an existing prediction equation for BW in gilts (HG2 × L × 69.3 = HGLW), only used for finishing pigs. Data set A (derivation, Large White × Landrace) included 42 gilts at first insemination, 45 gilts at the end of first gestation, and 58 gilts at weaning. Data set B (validation, Large White × Landrace) comprised of 14 gilts at first insemination, 15 gilts at the end of first gestation, and 19 gilts at weaning. Models were developed for each physiological state but a better BW prediction was obtained from an overall model, including an adjustment for physiological state (S1 and S2): −168.89 +1.06L +1.28HG +58.02S1 +33.03S2 +10.92BCS −1.10BF2 (adjusted R2 = 0.90). This model was validated under conditions found in the present study. Estimations using HGLW showed greater residual means than regression models.

Gilt Management for Fertility and Longevity

“…Although information on gilt weight at either the onset of boar stimulation, or at the time of pubertal estrus, is a critical step in meeting target gilt breeding weights [49], these records are typically not available across the production industry today [90]. Although a weigh scale is the most accurate way to determine body weight, estimating body weight using a weight tape that is established on the basis allometric growth curves which take advantage of the high correlation between heart girth circumference and body weight, is an objective and simple to manage alternative [90,91].…”

Precision Livestock Farming in Swine Welfare: A Review for Swine Practitioners

“…An animal-based measure of CSIA includes a spatial (mass) allowance such that 90% of pigs can lie down at once in group pens and that sows can lie down fully in stalls. Studies have shown promise and commercialization in the area of extracting the 3D shape of pigs for automatic mass and weight estimations [35,114,115,116].…”

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 ).…”