External validation of the GrazeIn model of pasture dry matter intake and milk yield prediction for cows managed at different calving dates and stocking rates

Ana I. Roca-Fernández, Antonio González-Rodríguez

Abstract


The aim was to evaluate the prediction accuracy of pasture dry matter intake (PDMI) and milk yield (MY) predicted by the GrazeIn model using a database representing 124 PDMI measurements at paddock level and 2232 MY measurements at cow level. External validation of the model was conducted using data collected from a trial carried out with Holstein-Friesian cows (n=72) while grazed 28 paddocks and were managed in a 2×2 factorial design by considering two calving dates (CD), with different number of days in milk (DIM), early (E, 29 DIM) vs. middle (M, 167 DIM), and two stocking rates (SR), medium (M, 3.9 cows ha-1) vs. high (H, 4.8 cows ha-1), under a rotational grazing system. Cows were randomly assigned to four grazing scenarios (EM, EH, MM and MH). The mean observed PDMI of the total database was 14.2 kg DM cow-1 day-1 while GrazeIn predicted a mean PDMI for the database of 13.8 kg DM cow-1 day-1. The mean bias was −0.4 kg DM cow-1 day-1. GrazeIn predicted PDMI for the total database with a relative prediction error (RPE) of 10.0% at paddock level. The mean observed MY of the database was 23.2 kg cow-1 day-1 while GrazeIn predicted a MY for the database of 23.1 kg cow-1 day-1. The mean bias was –0.1 kg cow-1 day-1. GrazeIn predicted MY for the total database with a mean RPE of 17.3% at cow level. For the scenarios investigated, GrazeIn predicted PDMI and MY with a low level of error which made it a suitable tool for decision support systems.


Keywords


grass intake; milk production; modeling; decision support system; lactation stage; grazing pressure; dairy cows

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References


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DOI: 10.5424/sjar/2017154-10380