A new mathematical model for combining growth and energy intake in animals: The case of the growing pig

Strathe, A. B.; Sørensen, Helle; Danfær, A.

doi:10.1016/j.jtbi.2009.07.039

Cited by 24 publications

(20 citation statements)

References 24 publications

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“…However, Vincek et al (2012) estimated final weights of 233 kg for barrows and 180 kg for gilts, using a sigmoidal function. Literature values of the final live weights indicate differences between breeds (Wellock et al, 2004), sex types and the prediction equations (Strathe et al, 2009). Strathe et al (2009) reported MBW of 406, 471 and 354 kg for barrows, boars and gilts, respectively.…”

Section: Allometric Growthmentioning

confidence: 99%

“…Literature values of the final live weights indicate differences between breeds (Wellock et al, 2004), sex types and the prediction equations (Strathe et al, 2009). Strathe et al (2009) reported MBW of 406, 471 and 354 kg for barrows, boars and gilts, respectively. These estimates are much larger than the present results and the ones previously reported by Knap (2000), which was partly due to the duration of the experimental period.…”

Section: Allometric Growthmentioning

confidence: 99%

“…These estimates are much larger than the present results and the ones previously reported by Knap (2000), which was partly due to the duration of the experimental period. Strathe et al (2009) caution that when growth data below 200 kg BW are analysed by sigmoid growth functions, asymptotic values are unreliable. To our knowledge, the final weight (biological maximum) of IM have not been reported, therefore we attempted to estimate this parameter.…”

Section: Allometric Growthmentioning

confidence: 99%

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Growth of total fat and lean and of primal cuts is affected by the sex type

Carabús

Sainz

Oltjen

et al. 2017

Animal

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Knowledge of tissue and cuts growth depending on the sex could be used to improve performance and efficiency. Computed tomography (CT) is a non-invasive technology that enables the study of the body composition of live animals during growth. The aims of the present study were (1) to evaluate variation in the body composition of four sex types (SEX) of pigs (castrated males (CM), immunocastrated males (IM), entire males (EM) and females (FE)) at the live weight of 30, 70, 100 and 120 kg, assessed using CT; (2) to model the growth of the main tissues and cuts; and (3) to predict the mature BW (MBW) of the four SEX and establish the relationships between the growth models and the MBW. There were significant phenotypic differences in the allometric growth of fat and lean among SEX. For the lean tissue, FE and EM showed higher values of the b coefficient than CM and IM (1.07 and 1.07 v. 1.00 and 1.02, respectively) all of them close to unity, indicating a proportional growth rate similar to live weight and that this tissue developed faster in FE and EM than in CM and IM. However, these differences were not related to differences in estimated MBW. There were significant differences in estimated MBW among SEX, being higher in IM and EM than in CM and FE (303 and 247 v. 219 and 216 kg), however, the MBW may have been overestimated, especially for the IM. The poorer accuracy of the MBW estimate for the IM could be due to a maximum live weight of 120 kg in the experiment, or to the fact that this particular SEX presented two clear behaviours, being more similar to EM from birth to the second injection of the vaccine (130 days) and comparable with CM from that point to the final BW.

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Section: Allometric Growthmentioning

confidence: 99%

Section: Allometric Growthmentioning

confidence: 99%

Section: Allometric Growthmentioning

confidence: 99%

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Growth of total fat and lean and of primal cuts is affected by the sex type

Carabús

Sainz

Oltjen

et al. 2017

Animal

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“…This variability is due to variation among individuals (related to genetic differences), or to variation of a given process over time for an individual (response to environmental or sanitary issues). The latter approach has been used by Strathe et al (2009) to build a model that includes daily variation in feed intake (DFI) and BW. However, calibration of such a model is complicated, and random processes make it difficult to obtain realistic ranges of parameters (Knap and Schrama, 1996).…”

Section: Introductionmentioning

confidence: 99%

Accounting for variability among individual pigs in deterministic growth models

Vautier

Quiniou

Milgen

et al. 2013

Animal

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Inclusion of variation in deterministic nutritional models for growth by repeating simulations using different sets of parameters has been performed in literature without or with only hypothetic consideration of the covariance structure among parameters. However, a description of the structure of links among parameters describing individuals is required to generate realistic sets of parameters. In this study, the mean and covariance structure of model parameters describing feed intake and growth were analyzed from 10 batches of crossbred gilts and barrows. Data were obtained from different crossbreeds, originating from Large White 3 Landrace sows and nine sire lines. Pigs were group-housed (12 pigs/pen) and performance testing was carried out from 70 days of age to ,110 kg BW. Daily feed intake (DFI) was recorded using automatic feeding stations and BW was measured at least every 3 weeks. A growth model was used to characterize individual pigs based on the observed DFI and BW. In this model, a Gompertz function was used to describe protein deposition and the resulting BW gain. A gamma function (expressing DFI as multiples of maintenance) was used to express the relationship between DFI and BW. Each pig was characterized through a set of five parameters: BW 70 (BW at 70 days of age), B Gompertz (a precocity parameter) PDm (mean protein deposition rate) and DFI 50 and DFI 100 (DFI at 50 and 100 kg BW, respectively). The data set included profiles for 1288 pigs for which no eating or growth disorders were observed (e.g. because of disease). All parameters were affected by sex (except for BW 70 ) and batch, but not by the crossbreed (except for PDm). An interaction between sex and crossbreed was observed for PDm (P , 0.01) and DFI 100 (P 5 0.05). Different covariance matrices were computed according to the batch, sex, crossbreed, or their combinations, and the similarity of matrices was evaluated using the Flury hierarchy. As covariance matrices were all different, the unit of covariance (subpopulation) corresponded to the combination of batch, sex and crossbreed. Two generic covariance matrices were compared afterwards, with (median matrix) or without (raw matrix) taking into account the size of subpopulations. The most accurate estimation of observed covariance was obtained with the median covariance matrix. The median covariance matrix can be used, in combination with average parameters obtained on-farm, to generate virtual populations of pigs that account for a realistic description of mean performances and their variability.Keywords: pig, growth, modeling, variability, Flury hierarchy ImplicationsMost pig growth models are deterministic and predicted performance and derived nutritional recommendations do not account for variation of performance within a group. Adding stochasticity to these models requires not only knowledge of the variation in model parameters but also the covariance among parameters. This study focused on this covariance and the extent to which this information can be generalized across popula...

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“…The porcine population examined contained weaners (piglets up to 10 weeks of age with body weights of approximately 25-30 kg), "human-like" pigs that are often used for surgical experiments (body weight of 45-60 kg), and fatteners (body weights of approximately 110 kg). In future studies, it would be interesting to determine the ontogenetic changes in VSMC orientation and compare those results to the metabolic rate, body mass, and other postnatal (Casas-Carrillo et al 1997;Strathe et al 2009;van Essen et al 2011) or even prenatal (Vallet and Freking 2006;McPherson et al 2004) growth characteristics. Information regarding the changes in SMC distribution during arterial remodeling (Thorne et al 2011;Valentin and Holzapfel 2012), arterial damage due to pressure overload, or angiological intervention by insertion of stents (Weisbecker et al 2012) remains limited.…”

Section: Applicability Of the Results For Further Researchmentioning

confidence: 99%

Segmental differences in the orientation of smooth muscle cells in the tunica media of porcine aortae

Tonar

Kochová

Cimrman

et al. 2014

Biomech Model Mechanobiol

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The orientation of vascular smooth muscle cells of porcine aortae was assessed to test the widely accepted assumption that these smooth muscle cells are arranged in two helices. We used tangential histological sections of 82 samples of five anatomical segments of thoracic and abdominal porcine aortae and three age groups in animals ranging in age from 5 to 210 days. The distribution of the orientation of smooth muscle cell nuclei in five proximodistal segments of the porcine aortae was determined using an algorithm that fitted a mixture of one to five von Mises probability distributions of the data retrieved from histological micrographs. Automated tracking of the nuclei was confirmed by and consistent with manual histological analysis. The orientation of the vascular smooth muscle cells was successfully fitted using two von Mises distributions in most of the samples with different ages, wall thicknesses, and anatomical positions, which corresponds to two populations of vascular smooth muscle cells. A minor fraction of samples also required a tertiary von Mises distribution to describe the orientation of the smooth muscle cell nuclei. The distribution of vascular smooth muscle cells in five aortic segments ranging from the thoracic ascending aorta to the abdominal intrarenal aorta exhibited similar main directions but different shapes. These results are consistent with the widely used model of two muscular helices intermingling in the arterial wall. Furthermore, we calculated the central angles of symmetry and the mean value of angles between the two assumed smooth muscle directions. We also successfully approximated the orientation of the smooth muscle cells using a mixture of von Mises distributions and our open-source software named dist_mixtures. This method is openly available to researchers who are interested in mathematically assessing the orientation of cell nuclei in various tissues.

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A new mathematical model for combining growth and energy intake in animals: The case of the growing pig

Cited by 24 publications

References 24 publications

Growth of total fat and lean and of primal cuts is affected by the sex type

Growth of total fat and lean and of primal cuts is affected by the sex type

Accounting for variability among individual pigs in deterministic growth models

Segmental differences in the orientation of smooth muscle cells in the tunica media of porcine aortae

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