Evaluation of the GrazeIn model of grass dry-matter intake and milk production prediction for dairy cows in temperate grass-based production systems. 2 - Animal characteristics

• Published in: Grass and forage science 2013-12, Vol.68 (4), p.524-536
• Main Authors: O'Neill, B. F., Lewis, E., O'Donovan, M., Shalloo, L., Mulligan, F. J., Boland, T. M., Delagarde, R.
• Format: Article
• Language: English
• Subjects: Computer Science; dairy cow; dry-matter intake; grazing; Life Sciences; milk yield
• ISSN: 0142-5242; 1365-2494
• DOI: 10.1111/gfs.12022

This study evaluated the prediction accuracy of grass dry‐matter intake (GDMI) and milk yield predicted by the GrazeIn model using a large database representing 8787 per cow GDMI measurements. In this study, the animal input variables (age, parity, week of lactation, potential peak milk yield, milk fat content, milk protein content, bodyweight, body condition score (BCS), week of conception, BCS at calving and calf birth weight) were investigated. The mean actual GDMI of the database was 15·9 kg DM per cow d−1 and GrazeIn predicted a mean GDMI for the database of 15·5 kg DM per cow d−1. The mean bias was −0·4 kg DM per cow d−1. GrazeIn predicted GDMI for the total database with an RPE of 15·5% at cow level. The mean actual daily milk yield of the database was 21·3 kg per cow d−1 and GrazeIn predicted a daily milk yield for the database of 22·2 kg per cow d−1. The mean bias was +0·9 kg per cow d−1. GrazeIn predicted milk yield for the total database with an RPE of 16·7% at cow level. From the evaluation, GrazeIn predicted milk yield of all cows in late lactation with a larger level of error than in early and mid‐lactation. This error appears to be due to the persistency of the lactation curve used by the model, which results in a higher predicted milk yield in late lactation compared with the actual milk yield.