Path analysis of body weight in grower and finisher pigs

Machebe, N. S.; Ezekwe, A. G.; Okeke, G. C.; Banik, S.

doi:10.18805/ijar.11319

Cited by 8 publications

(11 citation statements)

References 4 publications

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“…The records of 394 piglets comprising of 218 male and 176 female, maintained at the farm during 2017-2019, were used for heart girth (circumference around barrel behind fore legs), pounch girth (circumference around barrel before hind legs), height at shoulder (ground to point of shoulder), height at back (ground to point of pin bone), height at fore leg (ground to barrel at fore leg), height at hind leg (ground to barrel at hind leg) and body length (point of wither to starting point of tail) (Banik et al, 2012;Machebe et al 2016). Weight (kg) and other body measurement traits (cm) of all the animals were recorded on birth, at 15 d, 30 d and 42 d (weaning).…”

Section: Measurementsmentioning

confidence: 99%

Nonlinear Prediction Models for Estimation of Pre-weaning Body Weight of Pigs using Morphometric Traits

Banik

Naskar

Barman

et al. 2020

IJAR

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Background: In the present study, a set of non-linear prediction equation was developed using records of body measurement traits, namely heart girth, punch girth, height at shoulder, height at back, height at fore leg, height at back leg and body length of pigs. Repeated measurement of traits at birth, 15 d, 30 d and weaning (42 d) of 394 piglets were used. Methods: The regression of body weight on body measurement traits showed non-linear relationship based on equation, Y = aXb. Correlation of heart girth with body weight (r2 = 0.968 for male and 0.969 for females; P less than 0.01) was highest among the traits studied. Result: Heart girth was found to be most suited among the traits to predict body weight in an exponential equation, Y = 0.00058 H2.49 and Y = 0.00061 H2.47 for male and females, respectively, explaining 96 percent of variation.

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Section: Measurementsmentioning

confidence: 99%

Nonlinear Prediction Models for Estimation of Pre-weaning Body Weight of Pigs using Morphometric Traits

Banik

Naskar

Barman

et al. 2020

IJAR

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“…A Path model, which is the application of structural equation modeling without latent variables, could provide greater flexibility to test the structure coefficients on a theoretical basis. Although different models were used, the application of this technique is limited and reported in Karacabey Merino sheep (4), Yankasa sheep (33), Sahiwal cattle (17) and Landrace × Large White pigs (13).…”

Section: Introductionmentioning

confidence: 99%

İvesi koyunlarında bazı vücut ölçüleri yardımıyla canlı ağırlığın tahmin edilmesinde kullanılabilecek rekursif bir path modeli

Özen¹,

Kocakaya²,

Ünal³

et al. 2019

Ankara Üniversitesi Veteriner Fakültesi Dergisi

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Aim of this study was to investigate direct, indirect and total effects of body measurements on live weight in Awassi sheep and to derive prediction equations for estimation of the live weight. Live weight, head length, body length, withers height, chest depth, chest width and rump height measurements of 270 Awassi sheep at the age of 2-4 years raised at a private farm in Eskişehir Province, Turkey was used. A recursive path model was established to estimate the live weight of the Awassi sheep using these body measurements. Results showed that there was a significant indirect effect of withers height, rump height and body length and significant direct effect of chest depth, chest width and head length on live weight. The strongest predictor of live weight was chest depth, followed by chest width and head length. In conclusion, the equations given in this study could serve as a practical tool to predict the live weight of Awassi sheep. İvesi koyunlarda bazı vücut ölçüleri yardımıyla canlı ağırlığın tahmin edilmesinde kullanılabilecek rekursif bir path modeliÖzet: Bu çalışmanın amacı, İvesi ırkı koyunlarda çeşitli vücut ölçülerininin canlı ağırlık üzerine doğrudan, dolaylı ve toplam etkilerini incelemek ve bu ölçülerden yararlanarak canlı ağırlığı tahmin edecek bir model oluşturmaktır. Bu amaçla, Eskişehir ilinde özel bir çiftlikte yetiştirilen 2-4 yaşlarındaki toplam 270 İvesi ırkı koyunun canlı ağırlığı, baş uzunluğu, gövde uzunluğu, cidago yüksekliği, göğüs derinliği, göğüs genişliği ve sağrı yüksekliği ölçümleri elde edilmiştir. Bu ölçümler yardımıyla canlı ağırlığı tahmin etmek için rekursif bir path modeli oluşturulmuştur. Elde edilen sonuçlar, cidago yüksekliği, sağrı yüksekliği ve vücut uzunluğunun vücut ağırlığına dolaylı; göğüs genişliği, göğüs derinliği ve baş uzunluğunun ise doğrudan etkisinin olduğunu göstermiştir. Canlı ağırlığın en güçlü belirleyicisi göğüs derinliği, ardından göğüs genişliği ve baş uzunluğu olarak belirlenmiştir. Sonuç olarak, bu çalışmada verilen denklemlerin, İvesi koyunlarının canlı ağırlığını tahmin etmek için pratik bir araç olarak kullanılabileceği ortaya konmuştur. Anahtar sözcükler: Canlı ağırlık, İvesi koyunu, path analizi, yapısal eşitlik modeli.

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“…Machebe and Ezekwe (2010) estimated BW that included growing (8-15 wk of age) to finishing (16-30 wk of age) gilts using simple linear body measurement of HG (R 2 = 0.95), L (R 2 = 0.91), and FF (R 2 = 0.93). For gilts in these stages of production, Machebe et al (2016) found that HG was the major contributor to BW (R 2 = 0.95), whereas L and FF mostly affected BW indirectly via HG. For gilts at the time of first insemination, the present study's equations included FF and BCS with lower R 2 (R 2 = 0.53).…”

Section: Physiological State Amentioning

confidence: 95%

Using body measurements to estimate body weight in gilts

et al. 2018

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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.

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Path analysis of body weight in grower and finisher pigs

Cited by 8 publications

References 4 publications

Nonlinear Prediction Models for Estimation of Pre-weaning Body Weight of Pigs using Morphometric Traits

Nonlinear Prediction Models for Estimation of Pre-weaning Body Weight of Pigs using Morphometric Traits

İvesi koyunlarında bazı vücut ölçüleri yardımıyla canlı ağırlığın tahmin edilmesinde kullanılabilecek rekursif bir path modeli

Using body measurements to estimate body weight in gilts

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