Behaviors and body weight of suckling piglets in different social environments

Hong, Ji‐Yeon; Kim, Ki‐Hyun; Hwang, Hyun-Su; Lee, Jae-Kang; Eom, Tae-Kyung; Rhim, Shin‐Jae

doi:10.5713/ajas.16.0653

Cited by 8 publications

(2 citation statements)

References 26 publications

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“…Moreover, the inclusion of SNP effects with SGEs in the model could provide better predictions [40]. For successful realization of TBV, it is also important to consider social environments, such as the mixing method of suckling piglets [41].…”

Section: Resultsmentioning

confidence: 99%

Application of single-step genomic evaluation using social genetic effect model for growth in pig

Hong

Kim

Cho

et al. 2019

Asian-Australas J Anim Sci

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ObjectiveSocial genetic effects (SGE) are an important genetic component for growth, group productivity, and welfare in pigs. The present study was conducted to evaluate i) the feasibility of the single-step genomic best linear unbiased prediction (ssGBLUP) approach with the inclusion of SGE in the model in pigs, and ii) the changes in the contribution of heritable SGE to the phenotypic variance with different scaling ω constants for genomic relationships.MethodsThe dataset included performance tested growth rate records (average daily gain) from 13,166 and 21,762 pigs Landrace (LR) and Yorkshire (YS), respectively. A total of 1,041 (LR) and 964 (YS) pigs were genotyped using the Illumina PorcineSNP60 v2 BeadChip panel. With the BLUPF90 software package, genetic parameters were estimated using a modified animal model for competitive traits. Giving a fixed weight to pedigree relationships (τ: 1), several weights (ωxx, 0.1 to 1.0; with a 0.1 interval) were scaled with the genomic relationship for best model fit with Akaike information criterion (AIC).ResultsThe genetic variances and total heritability estimates (T2) were mostly higher with ssGBLUP than in the pedigree-based analysis. The model AIC value increased with any level of ω other than 0.6 and 0.5 in LR and YS, respectively, indicating the worse fit of those models. The theoretical accuracies of direct and social breeding value were increased by decreasing ω in both breeds, indicating the better accuracy of ω0.1 models. Therefore, the optimal values of ω to minimize AIC and to increase theoretical accuracy were 0.6 in LR and 0.5 in YS.ConclusionIn conclusion, single-step ssGBLUP model fitting SGE showed significant improvement in accuracy compared with the pedigree-based analysis method; therefore, it could be implemented in a pig population for genomic selection based on SGE, especially in South Korean populations, with appropriate further adjustment of tuning parameters for relationship matrices.

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Section: Resultsmentioning

confidence: 99%

Application of single-step genomic evaluation using social genetic effect model for growth in pig

Hong

Kim

Cho

et al. 2019

Asian-Australas J Anim Sci

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“…In animal production, livestock body weight (BW) is a very important and widely used feature that has a significant impact on feed consumption ( Putnam et al, 1964 ), breeding potential ( Buckley et al, 2003 ; Ghotbaldini et al, 2019 ), social behavior ( Bouissou, 1972 ; Hong et al, 2017 ), energy balance ( Thorup et al, 2012 ), and overall farm management ( Halachmi et al, 2019 ). It may be used indirectly in the assessment of health and welfare status ( Dikmen et al, 2012 ), and in the determination of time-to-market for animals ( Mc Hugh et al, 2011 ).…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Shadpour

Chan

et al. 2021

Journal of Animal Science

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Monitoring, recording, and predicting livestock body weight (BW) allows for timely intervention in diets and health, greater efficiency in genetic selection, and identification of optimal times to market animals because animals that have already reached the point of slaughter represent a burden for the feedlot. There are currently two main approaches (direct and indirect) to measure the BW in livestock. Direct approaches include partial-weight or full-weight industrial scales placed in designated locations on large farms that measure passively or dynamically the weight of livestock. While these devices are very accurate, their acquisition, intended purpose and operation size, repeated calibration and maintenance costs associated with their placement in high-temperature variability, and corrosive environments are significant and beyond the affordability and sustainability limits of small and medium size farms and even of commercial operators. As a more affordable alternative to direct weighing approaches, indirect approaches have been developed based on observed or inferred relationships between biometric and morphometric measurements of livestock and their BW. Initial indirect approaches involved manual measurements of animals using measuring tapes and tubes and the use of regression equations able to correlate such measurements with BW. While such approaches have good BW prediction accuracies, they are time consuming, require trained and skilled farm laborers, and can be stressful for both animals and handlers especially when repeated daily. With the concomitant advancement of contactless electro-optical sensors (e.g., 2D, 3D, infrared cameras), computer vision (CV) technologies, and artificial intelligence fields such as machine learning (ML) and deep learning (DL), 2D and 3D images have started to be used as biometric and morphometric proxies for BW estimations. This manuscript provides a review of CV-based and ML/DL-based BW prediction methods and discusses their strengths, weaknesses, and industry applicability potential.

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Review on image-based animals weight weighing

Zhao,

Xiao,

et al. 2023

Computers and Electronics in Agriculture

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Behaviors and body weight of suckling piglets in different social environments

Cited by 8 publications

References 26 publications

Application of single-step genomic evaluation using social genetic effect model for growth in pig

Application of single-step genomic evaluation using social genetic effect model for growth in pig

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

Review on image-based animals weight weighing

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