An approach to prediction of feed intake in growing pigs using simple body measurements

Whittemore, C. T.; Kerr, J. C.; Cameron, N. D.

doi:10.1016/0308-521x(94)p4413-v

Cited by 16 publications

(13 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The PDm is an indicator of the average growth potential during the period of calibration, instead of the protein mass at maturity used in the Gompertz function (Emmans and Fisher, 1986). It is closely linked to ADG in the present study (r 5 0.94) because of model assumptions, illustrating the role of PD as a driving force of BW growth (Whittemore et al, 1995;van Milgen et al, 2008).…”

Section: Discussionsupporting

confidence: 64%

Accounting for variability among individual pigs in deterministic growth models

Vautier

Quiniou

Milgen

et al. 2013

Animal

View full text Add to dashboard Cite

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...

show abstract

Section: Discussionsupporting

confidence: 64%

Accounting for variability among individual pigs in deterministic growth models

Vautier

Quiniou

Milgen

et al. 2013

Animal

View full text Add to dashboard Cite

show abstract

“…Piétrain crosses have a low voluntary feed intake. 17 Amino acid content of the growing and early finishing diet highly influenced performance in the late finishing phase. Feed efficiency in the late finishing phase was clearly higher in the previously restricted pigs.…”

Section: Discussionmentioning

confidence: 99%

Effect of amino acid level in the pig diet during growing and early finishing on growth response during the late finishing phase of lean meat type gilts

Millet¹,

Langendries²,

Aluwé³

et al. 2011

J Sci Food Agric

View full text Add to dashboard Cite

show abstract

“…Maximum protein retention was derived from the Gompertz growth function (Whittemore et al, 2001). Mature size protein mass was taken to be 40 kg, whereas current protein was determined as described by Whittemore et al (1995). Protein turnover was estimated from protein retention and degree of maturity (Millward et al, 1974).…”

Section: Economic Value Derivationmentioning

confidence: 99%

“…LEAN was calculated by first determining the amount of fat deposited using the relationship with BW (Whittemore et al, 1995). Then carcass fat was subtracted from carcass weight to obtain LEAN (McManus, 2007).…”

Section: Economic Value Derivationmentioning

confidence: 99%

See 1 more Smart Citation

Integrating economic parameters into genetic selection for Large White pigs

Dube

Mulugeta

Dzama

2013

Animal

View full text Add to dashboard Cite

The objective of the study was to integrate economic parameters into genetic selection for sow productivity, growth performance and carcass characteristics in South African Large White pigs. Simulation models for sow productivity and terminal production systems were performed based on a hypothetical 100-sow herd, to derive economic values for the economically relevant traits. The traits included in the study were number born alive (NBA), 21-day litter size (D21LS), 21-day litter weight (D21LWT), average daily gain (ADG), feed conversion ratio (FCR), age at slaughter (AGES), dressing percentage (DRESS), lean content (LEAN) and backfat thickness (BFAT). Growth of a pig was described by the Gompertz growth function, while feed intake was derived from the nutrient requirements of pigs at the respective ages. Partial budgeting and partial differentiation of the profit function were used to derive economic values, which were defined as the change in profit per unit genetic change in a given trait. The respective economic values (ZAR) were: 61. 26, 38.02, 210.15, 33.34, 221.81, 268.18, 5.78, 4.69 and 21.48. These economic values indicated the direction and emphases of selection, and were sensitive to changes in feed prices and marketing prices for carcasses and maiden gilts. Economic values for NBA, D21LS, DRESS and LEAN decreased with increasing feed prices, suggesting a point where genetic improvement would be a loss, if feed prices continued to increase. The economic values for DRESS and LEAN increased as the marketing prices for carcasses increased, while the economic value for BFAT was not sensitive to changes in all prices. Reductions in economic values can be counterbalanced by simultaneous increases in marketing prices of carcasses and maiden gilts. Economic values facilitate genetic improvement by translating it to proportionate profitability. Breeders should, however, continually recalculate economic values to place the most appropriate emphases on the respective traits during genetic selection.

show abstract

An approach to prediction of feed intake in growing pigs using simple body measurements

Cited by 16 publications

References 7 publications

Accounting for variability among individual pigs in deterministic growth models

Accounting for variability among individual pigs in deterministic growth models

Effect of amino acid level in the pig diet during growing and early finishing on growth response during the late finishing phase of lean meat type gilts

Integrating economic parameters into genetic selection for Large White pigs

Contact Info

Product

Resources

About