Analysis of Pig Growth from Birth to Sixty Days of Age

Schinckel, A. P.; Ferrel, Jon; Einstein, M.E.; Pearce, S.M.; Boyd, R. D.

doi:10.15232/s1080-7446(15)31276-6

Cited by 31 publications

(21 citation statements)

References 4 publications

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“…This method has been used in sev- Table 6. Heritabilities (on diagonal) and genetic (below diagonal) and phenotypic (above diagonal) correlations, with SE, between random regression growth rate traits eral growth studies published in recent years (e.g., Schinckel et al, 2003Schinckel et al, , 2004Wang and Zuidhof, 2004) and should be considered for future analyses to estimate growth variables for sheep populations, given the fact that the current results suggest that selection on growth variables may be possible.…”

Section: Discussionmentioning

confidence: 89%

“…However, all relevant models were not tested in this study. For example, the Bridges, Brody, and Michaelis-Menten models have been used in other studies to describe growth in livestock species (e.g., López et al, 2000;McManus et al, 2003;Schinckel et al, 2004). In general, the goodness-of-fit of these models for growth data for various species tended to be similar to, or poorer than, that of the Richards or Gompertz models, and correlations between variables estimated by the different models were high.…”

Section: Discussionmentioning

confidence: 99%

“…This method allowed plots for individual lambs to be observed to check for unusual growth patterns or incorrect fitting of the models. In recent years an alternative method, using mixed model nonlinear analysis (such as the NLMIXED procedure in SAS), has been proposed for modeling serial BW data, which may be less labor-intensive and may better model the variance-covariance structure of growth data and reduce the residual variance compared with traditional methods such as NLIN (Schinckel et al, 2004;Wang and Zuidhof, 2004). This method has been used in sev- Table 6.…”

Section: Discussionmentioning

confidence: 99%

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A genetic investigation of various growth models to describe growth of lambs of two contrasting breeds1

Lambe

Navajas

Simm

et al. 2006

Journal of Animal Science

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This study compared the use of various models to describe growth in lambs of 2 contrasting breeds from birth to slaughter. Live BW records (n = 7559) from 240 Texel and 231 Scottish Blackface (SBF) lambs weighed at 2-wk intervals were modeled. Biologically relevant variables were estimated for each lamb from modified versions of the logistic, Gompertz, Richards, and exponential models, and from linear regression. In both breeds, all nonlinear models fitted the data well, with an average coefficient of determination (R2) of > 0.98. The linear model had a lower average R2 than any of the nonlinear models (< 0.94). The variables used to describe the best 3 models (logistic, Gompertz, and Richards) included estimated final BW (A); maximum ADG (B); age at maximum ADG (C); position of point of inflection in relation to A (D, for Richards only). The Richards and Gompertz models provided the best fit (average R2 = 0.986 to 0.989) in both breeds. Richards estimated an extra variable, allowing increased flexibility in describing individual growth patterns, but the Akaike's information criteria value (which weighs log-likelihood by number of parameters estimated) was similar to that of the Gompertz model. Variables A, B, C, and D were moderately to highly heritable in Texel lambs (h2 = 0.33 to 0.87), and genetic correlations between variables within-model ranged from -0.80 to 0.89, suggesting some flexibility to change the shape of the growth curve when selecting for different variables. In SBF lambs, only variables from the logistic and Gompertz models had moderate heritabilities (0.17 to 0.56), but with high genetic correlations between variables within each model (< -0.88 or > 0.92). Selection on growth variables seems promising (in Texel more than SBF), but high genetic correlations between variables may restrict the possibilities to change the growth curve shape. A random regression model was also fitted to the data to allow predictions of growth rates at relevant time points. Heritabilities for growth rates differed markedly at various stages of growth and between the 2 breeds (Texel: 0.14 to 0.74; SBF: 0.07 to 0.34), with negative correlations between growth rate at 60 d of age and growth rate at finishing. Following these results, future studies should investigate genetic relationships between relevant growth curve variables and other important production traits, such as carcass composition and meat quality.

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Section: Discussionmentioning

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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A genetic investigation of various growth models to describe growth of lambs of two contrasting breeds1

Lambe

Navajas

Simm

et al. 2006

Journal of Animal Science

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“…Although the common practice is to sort pigs by size at the entry of the nursery and/or GF barn (Deen, 1997;Tokach, 2004), research has shown that this is not effective in decreasing weight variation (O'Quinn et al, 2001), while increasing aggressive behavior during the 2-day period after regrouping (O'Connell et al, 2005). Schinckel et al (2004) showed that pigs in the smallest 20th weight percentile at birth grow slower after weaning and are responsible for the majority of variation in pig weights after weaning. Pigs sourced from our LBW litters are likely to overlap to a great extent with the overall 20th percentile of lowest birth weight pigs in all litters born.…”

Section: Discussionmentioning

confidence: 99%

Consequences of a low litter birth weight phenotype for postnatal lean growth performance and neonatal testicular morphology in the pig

Smit

Spencer

Almeida

et al. 2013

Animal

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The consequences of a low litter average birth weight phenotype for postnatal growth performance and carcass quality of all progeny, and testicular development in male offspring, were investigated. Using data from 25 sows with one, and 223 sows with two consecutive farrowing events, individual birth weight (BW) was measured and each litter between 9 and 16 total pigs born was classified as low (LBW), medium (MBW) or high (HBW) birth weight: low and high BW being defined as .1 standard deviation below or above, respectively, the population mean for each litter size. Litter average BW was repeatable within sows. At castration, testicular tissue was collected from 40 male pigs in LBW and HBW litters with individual BW close to their litter average BW and used for histomorphometric analysis. LBW piglets had a lower absolute number of germ cells, Sertoli cells and Leydig cells in their testes and a higher brain : testis weight ratio than HBW piglets. Overall, LBW litters had lower placental weight and higher brain : liver, brain : intestine and brain : Semitendinosus muscle weight ratios than MBW and HBW litters. In the nursery and grow-finish (GF) phase, pigs were kept in pens by BW classification (9 HBW, 17 MBW and 10 LBW pens) with 13 males and 13 females per pen. Average daily gain tended to be lower in LBW than HBW litters in lactation (P 5 0.06) and throughout the nursery and GF phases (P , 0.01), resulting in an increasing difference in body weight between LBW, MBW and HBW litters (P , 0.05). Average daily feed intake was lower (P , 0.001) in LBW than HBW litters in the nursery and GF phases. Feed utilization efficiency (feed/gain) was similar for LBW and HBW litters in the nursery, but was lower (P , 0.001) in HBW than LBW litters in the GF phase. By design, slaughter weight was similar between BW classifications; however, LBW litters needed 9 more days to reach the same slaughter weight than HBW litters (P , 0.001). BW classification did not affect carcass composition traits. In conclusion, LBW litters showed benchmarks of intrauterine growth retardation, LBW had a negative impact on testicular development and germ and somatic cell populations, and was associated with decreased postnatal growth during all phases of production; however, no measurable effect on carcass composition traits was established.

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“…For example in pig production, we want to know how BW changes with age (Schinckel et al, 2004), during a disease challenge or after applying a therapy . However, so far diurnal BW variation has been neglected.…”

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confidence: 99%

Analyses of body weight patterns in growing pigs: a new view on body weight in pigs for frequent monitoring

Stygar

Dolecheck

Kristensen

2018

Animal

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Frequent BW monitoring of growing pigs can be useful for identifying production (e.g. feeding), health and welfare problems. However, in order to construct a tool which will properly recognize abnormalities in pigs' growth a precise description of the growth process should be used. In this study we proposed a new model of pig growth accounting for daily fluctuations in BW. Body weight measurements of 1710 pigs (865 gilts and 843 barrows) originating from five consecutive batches from a Danish commercial farm were collected. Pigs were inserted into a large pen (maximum capacity = 400) between November 2014 and September 2015. On average, each pig was observed for 42 days and weighed 3.6 times a day when passing from the resting to feeding area. Altogether, 243,160 BW measurements were recorded. A multilevel model of pig growth was constructed and fitted to available data. The BW of pigs was modeled as a quadratic function of time. A diurnal pattern was incorporated into the model by a cosine wave with known length (24 h). The model included pig effect which was defined as a random autoregressive process with exponential correlation. Variance of within-pigs error was assumed to increase with time. Because only five batches were observed, it was not possible to obtain the random effect for batch. However, in order to account for the batch effect the model included interactions between batch and fixed parameters: intercept, time, square value of time and cosine wave. The gender effect was not significant and was removed from the final model. For all batches, morning and afternoon peaks in the frequency of visits to the feeding area could be distinguished. According to results, pigs were lighter in the morning and heavier in the evening (minimum BW was reached around 1000 h and maximum around 2200 h). However, the exact time of obtaining maximum and minimum BW during the day differed between batches. Pigs had access to natural light and, therefore, existing differences could be explained by varying daylight level during observations periods. Because the diurnal amplitude for pig growth varied between batches from 0.9 to 1.4 kg, BW monitoring tools based on frequent measurements should account for diurnal variation in BW of pigs. This proposed description of growth will be built into a monitoring tool (a dynamic linear model) and applied to farm data in future studies.

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Analysis of Pig Growth from Birth to Sixty Days of Age

Cited by 31 publications

References 4 publications

A genetic investigation of various growth models to describe growth of lambs of two contrasting breeds1

A genetic investigation of various growth models to describe growth of lambs of two contrasting breeds1

Consequences of a low litter birth weight phenotype for postnatal lean growth performance and neonatal testicular morphology in the pig

Analyses of body weight patterns in growing pigs: a new view on body weight in pigs for frequent monitoring

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