Literature DB >> 15259234

Prediction of body lipid change in pregnancy and lactation.

N C Friggens1, K L Ingvartsen, G C Emmans.   

Abstract

A simple method to predict the genetically driven pattern of body lipid change through pregnancy and lactation in dairy cattle is proposed. The rationale and evidence for genetically driven body lipid change have their basis in evolutionary considerations and in the homeorhetic changes in lipid metabolism through the reproductive cycle. The inputs required to predict body lipid change are body lipid mass at calving (kg) and the date of conception (days in milk). Body lipid mass can be derived from body condition score and live weight. A key assumption is that there is a linear rate of change of the rate of body lipid change (dL/dt) between calving and a genetically determined time in lactation (T') at which a particular level of body lipid (L') is sought. A second assumption is that there is a linear rate of change of the rate of body lipid change (dL/dt) between T' and the next calving. The resulting model was evaluated using 2 sets of data. The first was from Holstein cows with 3 different levels of body fatness at calving. The second was from Jersey cows in first, second, and third parity. The model was found to reproduce the observed patterns of change in body lipid reserves through lactation in both data sets. The average error of prediction was low, less than the variation normally associated with the recording of condition score, and was similar for the 2 data sets. When the model was applied using the initially suggested parameter values derived from the literature the average error of prediction was 0.185 units of condition score (+/- 0.086 SD). After minor adjustments to the parameter values, the average error of prediction was 0.118 units of condition score (+/- 0.070 SD). The assumptions on which the model is based were sufficient to predict the changes in body lipid of both Holstein and Jersey cows under different nutritional conditions and parities. Thus, the model presented here shows that it is possible to predict genetically driven curves of body lipid change through lactation in a simple way that requires few parameters and inputs that can be derived in practice. It is expected that prediction of the cow's energy requirements can be substantially improved, particularly in early lactation, by incorporating a genetically driven body energy mobilization.

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Year:  2004        PMID: 15259234     DOI: 10.3168/jds.S0022-0302(04)73244-0

Source DB:  PubMed          Journal:  J Dairy Sci        ISSN: 0022-0302            Impact factor:   4.034


  10 in total

1.  Periparturition alterations to liver ultrasonographic echo-texture and fat mobilization parameters in clinically healthy Holstein cows.

Authors:  Saman Rafia; Taghi Taghipour-Bazargani; Farzad Asadi; Alireza Vajhi; Saied Bokaie
Journal:  Vet Res Commun       Date:  2011-09-02       Impact factor: 2.459

2.  Genetic analysis of robustness in meat sheep through body weight and body condition score changes over time.

Authors:  Tiphaine Macé; Eliel González-García; Julien Pradel; Sara Parisot; Fabien Carrière; Sebastien Douls; Didier Foulquié; Dominique Hazard
Journal:  J Anim Sci       Date:  2018-11-21       Impact factor: 3.159

3.  A dynamic model as a tool to describe the variability of lifetime body weight trajectories in livestock females.

Authors:  L Puillet; O Martin
Journal:  J Anim Sci       Date:  2017-11       Impact factor: 3.159

4.  Reproductive robustness differs between generalist and specialist maternal rabbit lines: the role of acquisition and allocation of resources.

Authors:  Davi Savietto; Nicolas C Friggens; Juan José Pascual
Journal:  Genet Sel Evol       Date:  2015-01-17       Impact factor: 4.297

Review 5.  Reproductive management in dairy cows - the future.

Authors:  Mark A Crowe; Miel Hostens; Geert Opsomer
Journal:  Ir Vet J       Date:  2018-01-08       Impact factor: 2.146

6.  Association between body energy content in the dry period and post-calving production disease status in dairy cattle.

Authors:  G L Smith; N C Friggens; C J Ashworth; M G G Chagunda
Journal:  Animal       Date:  2017-02-15       Impact factor: 3.240

7.  Functional confirmation of PLAG1 as the candidate causative gene underlying major pleiotropic effects on body weight and milk characteristics.

Authors:  Tania Fink; Kathryn Tiplady; Thomas Lopdell; Thomas Johnson; Russell G Snell; Richard J Spelman; Stephen R Davis; Mathew D Littlejohn
Journal:  Sci Rep       Date:  2017-03-21       Impact factor: 4.379

8.  ASN-ASAS SYMPOSIUM: FUTURE OF DATA ANALYTICS IN NUTRITION: Mathematical modeling in ruminant nutrition: approaches and paradigms, extant models, and thoughts for upcoming predictive analytics1,2.

Authors:  Luis O Tedeschi
Journal:  J Anim Sci       Date:  2019-04-29       Impact factor: 3.159

9.  The cost of host genetic resistance on body condition: Evidence from divergently selected sheep.

Authors:  Frédéric Douhard; Andrea B Doeschl-Wilson; Alexander Corbishley; Adam D Hayward; Didier Marcon; Jean-Louis Weisbecker; Sophie Aguerre; Léa Bordes; Philippe Jacquiet; Tom N McNeilly; Guillaume Sallé; Carole Moreno-Romieux
Journal:  Evol Appl       Date:  2022-07-12       Impact factor: 4.929

10.  Body condition score and its correlation with ultrasonographic back fat thickness in transition crossbred cows.

Authors:  Randhir Singh; S N S Randhawa; C S Randhawa
Journal:  Vet World       Date:  2015-03-07
  10 in total

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