Short communication: Projecting milk yield using best prediction and the MilkBot lactation model.
The accuracy and precision of 3 lactation models was estimated by summarizing means and variability in projection error for next-test milk and actual 305-d milk yield (M305) for 50-d intervals in a large Dairy Herd Improvement Association data set. Lactations were grouped by breed (Holstein, Jersey, and crossbred) and parity (first vs. later). A smaller, single-herd data set with both Dairy Herd Improvement Association data and daily milk weights was used to compare M305 calculated from test-day data with M305 computed by summing daily milk weights. The lactation models tested were best prediction (BP), the nonlinear MilkBot (MB) model, and a null model (NM) based on a stepwise function. The accuracy of the models was ranked (best to worst) MB, BP, and NM for later-parity cows and MB, NM, and BP for first-parity cows, with MB achieving accuracy in projecting daily milk of 0.5 kg or better in most groups. The models generally showed better accuracy after 50 d in milk. Best prediction and NM had low accuracy for crossbred cows and first-parity Holstein and Jersey cows. The MB model appears to be more precise than BP, and NM had low precision, especially for M305. Regression of model-generated M305 on summed M305 showed BP and MB to be equally efficient in ranking lactations, but MB was better at quantifying differences.
Animal Improvement Programs Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705-2350, USA. john.cole@ars.usda.gov
J B
JB
Cole
J L
JL
Ehrlich
D J
DJ
Null
D000818:Animals; D002417:Cattle / Q000502:physiology; D005260:Female; D007774:Lactation / Q000502:physiology*; D008892:Milk / Q000378:metabolism; D008954:Models, Biological; D010298:Parity