Evaluation of the GrazeIn model of grass dry-matter intake and milk production prediction for dairy cows in temperate grass-based production systems. 1-Sward characteristics and grazing management factors
2013
O'Neill, B. F. | Lewis, E. | O'Donovan, M. | Shalloo, L. | Mulligan, F. J. | Boland, T. M. | Delagarde, Remy | Teagasc Agriculture and Food Development Authority (Teagasc) | University College Dublin [Dublin] (UCD) | Production du lait (PL) ; Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST | Irish Department of Agriculture, Fisheries and Food Research Stimulus Fund [RSF 07 526]
International audience
Mostrar más [+] Menos [-]Inglés. This study evaluated the prediction accuracy of grass dry-matter intake (GDMI) and milk yield predicted by the model GrazeIn using a database representing 522 grazing herds. The GrazeIn input variables under consideration were fill value (FV), grass energy content [Unite Fourragere Lait (UFL)], grass protein value [true protein absorbable in the small intestine when rumen fermen energy is limiting microbial protein synthesis in the rumen (PDIE)], pre-grazing herbage mass (PGHM), daily herbage allowance (DHA) and concentrate supplementation. GrazeIn was evaluated using the relative prediction error (RPE). The mean actual GDMI and milk yields of grazing herds in the database ranged from 9<bold>9</bold>-22<bold>0</bold>kg DM per cowd(-1) and 8<bold>9</bold>-41<bold>8</bold>kg per cowd(-1), respectively. The accuracy of predictions for the total database estimated by RPE was 12<bold>2</bold> and 12<bold>8</bold>% for GDMI and milk yield, respectively. The mean bias (predicted minus actual) for GDMI was -0<bold>3</bold>kg DM per cowd(-1) and for milk yield was +0<bold>9</bold>kg per cowd(-1). GrazeIn predicted GDMI with a level of error <13<bold>4</bold>% RPE for spring, summer and autumn. GrazeIn predicted milk yield in autumn (RPE=17<bold>6</bold>%) with a larger error in comparison with spring (RPE=10<bold>4</bold>%) and summer (RPE=11<bold>0</bold>%). Future studies should focus on the adaptation of GrazeIn to correct and improve the prediction of milk yield in autumn.
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