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Predicting grass dry matter intake, milk yield and milk fat and protein yield of spring calving grazing dairy cows during the grazing season

Published online by Cambridge University Press:  10 April 2013

B. F. O'Neill ,
E. Lewis ,
M. O'Donovan ,
L. Shalloo ,
N. Galvin ,
F. J. Mulligan ,
T. M. Boland  and
R. Delagarde
B. F. O'Neill
Affiliation:
Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy, Co. Cork, Ireland School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, Ireland
E. Lewis*
Affiliation:
Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy, Co. Cork, Ireland
M. O'Donovan
Affiliation:
Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy, Co. Cork, Ireland
L. Shalloo
Affiliation:
Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy, Co. Cork, Ireland
N. Galvin
Affiliation:
Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy, Co. Cork, Ireland
F. J. Mulligan
Affiliation:
School of Veterinary Medicine, University College Dublin, Belfield, Dublin 4, Ireland
T. M. Boland
Affiliation:
School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, Ireland
R. Delagarde
Affiliation:
INRA, UMR1348, INRA-Agrocampus Ouest, PEGASE, F-35590 Saint-Gilles, France
*
†E-mail: [email protected]
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Abstract

Predicting the grass dry matter intake (GDMI), milk yield (MY) or milk fat and protein yield (milk solids yield (MSY)) of the grazing dairy herd is difficult. Decisions with regard to grazing management are based on guesstimates of the GDMI of the herd, yet GDMI is a critical factor influencing MY and MSY. A data set containing animal, sward, grazing management and concentrate supplementation variables recorded during weeks of GDMI measurement was used to develop multiple regression equations to predict GDMI, MY and MSY. The data set contained data from 245 grazing herds from 10 published studies conducted at Teagasc, Moorepark. A forward stepwise multiple regression technique was used to develop the multiple regression equations for each of the dependent variables (GDMI, MY, MSY) for three periods during the grazing season: spring (SP; 5 March to 30 April), summer (SU; 1 May to 31 July) and autumn (AU; 1 August to 31 October). The equations generated highlighted the importance of different variables associated with GDMI, MY and MSY during the grazing season. Peak MY was associated with an increase in GDMI, MY and MSY during the grazing season with the exception of GDMI in SU when BW accounted for more of the variation. A higher body condition score (BCS) at calving was associated with a lower GDMI in SP and SU and a lower MY and MSY in all periods. A higher BCS was associated with a higher GDMI in SP and SU, a higher MY in SU and AU and a higher MSY in all periods. The pre-grazing herbage mass of the sward (PGHM) above 4 cm was associated with a quadratic effect on GDMI in SP, on MY in SP and SU and on MSY in SU. An increase in daily herbage allowance (DHA) above 4 cm was associated with an increase in GDMI in AU, an increase in MY in SU and AU and MSY in AU. Supplementing grazing dairy cows with concentrate reduced GDMI and increased MY and MSY in all periods. The equations generated can be used by the Irish dairy industry during the grazing season to predict the GDMI, MY and MSY of grazing dairy herds.

Keywords

dairy cowmultiple regression equationgrass dry matter intakemilk yieldmilk solids yield
Type
Farming systems and environment
Information
animal , Volume 7 , Issue 8 , August 2013 , pp. 1379 - 1389
Copyright
Copyright © The Animal Consortium 2013 

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