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Etchsmall Etch-A-Lac dynamic graph for lactation curves.


Recent Publications

Cole, John. Ehrlich, Jim. Null, Dan. Predicting Milk Yield Using Best Prediction and the MilkBot Lactation Model. Journal of Dairy Science, July 2012.
Abstract 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 0.5kg or better in most groups. The models generally showed better accuracy after 50d 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.

Hostens, Miel, Ehrlich, Jim, Van Ranst, Bonny, Opsomer, Geert. On-farm Evaluation of the Effect of Metabolic Diseases on the Shape of the Lactation Curve in Dairy Cows through the Milkbot Lactation Model Journal of Dairy Science, June 2012.
Abstract The effects of metabolic diseases (MD) occurring during the transition period on milk production of dairy cows have been evaluated in many different ways, often with conflicting conclusions. The present study used a fitted lactation model to analyze specific aspects of lactation curve shape and magnitude in cows that avoided culling or death in the first 120 d in milk (DIM). Production and health records of 1,946 lactations in a 1-yr follow-up study design were collected from a transition management facility in Germany to evaluate both short- and long-term effects of MD on milk production. Milk production data were fitted with the nonlinear MilkBot lactation model, and health records were used to classify cows as healthy (H), affected by one MD (MD), or by multiple MD (MD+). The final data set contained 1,071H, 348 MD, and 136 MD+ cows, with distinct incidences of 3.7% twinning, 4.8% milk fever, 3.6% retained placenta, 15.4% metritis, 8.3% ketosis, 2.0% displaced abomasum, and 3.7% mastitis in the first 30 DIM. The model containing all healthy and diseased cows showed that lactations classified as H had milk production that increased faster (lower ramp) and also declined faster (lower persistence) compared with cows that encountered one or more metabolic problems. The level of production (scale) was only lowered in MD+ cows compared with H and MD cows. Although the shape of the lactation curve changed when cows encounter uncomplicated (single) MD or complicated MD (more than one MD), the slower increase to a lower peak seemed to be compensated for by greater persistency, resulting in the overall 305-d milk production only being lowered in MD+ cows. In the individual disease models, specific changes in the shape of the lactation curve were found for all MD except twinning. Milk fever, retained placenta, ketosis, and mastitis mainly affected the lactation curve when accompanied by another MD, whereas metritis and displaced abomasum affected the lactation curve equally with or without another MD. Overall, 305-d milk production was decreased in complicated metritis (10,603±50kg vs. 10,114±172kg). Although care should be taken in generalizing conclusions from a highly specialized transition management facility, the current study demonstrated that lactation curve analysis may contribute substantially to the evaluation of both short- and long-term effects of metabolic diseases on milk production by detecting changes in the distribution of production that are not apparent when only totals are analyzed.