Masters Theses

Date of Award

12-2024

Degree Type

Thesis

Degree Name

Master of Science

Major

Animal Science

Major Professor

Elizabeth A. Eckelkamp

Committee Members

Blair Downey, Charley Martinez, Katie Mason

Abstract

Automatic milking systems (AMS) can reduce dairy producers' labor burden and increase milk production. 88 robot naïve Holstein cows were grouped into an exposed group (n = 46) and a non-exposed group (n = 42) balanced for lactation and days in milk (DIM). Each group was assigned to a separate Lely Astronaut A5 AMS. The robot-exposed group underwent a pre-training protocol before the first robotic milking, while the non-exposed group was not acclimated before the first robotic milking. Cows experiencing pre-training (TRT) had higher milk yield and fat percentages compared to no pre-training in a 90d period (36.94 vs. 36.65 ± 0.2 kg/d; P = 0.29, 1.71 vs. 1.57 ± 0.007 kg/d; P < 0.01, respectively). Cows without pre-training had greater somatic cell count (SCC) (261,270 vs. 180,370 ± 8,000 cells/mL; P < 0.001).

Significant time is required for pre-training (40 ± 10 h/wk), and the economic implications of labor versus long-term benefits must be examined. A stochastic simulation tool was developed to consider the increased labor costs and the potential benefits of improved milk yield (MY), milk fat percentage, and decreased SCC. The Simetar© add-in for Microsoft Excel simulated 500 iterations across three herd sizes (60, 120, and 480 cows) and calculated the marginal return on investment across a 90-d period for milk production ranges. The GRKS distribution with minimum, mean, and maximum values were used to model increases in milk yield (0, 0.03, 1.0 kg), fat percentage (0, 0.03, 0.04), labor cost ($0.0011, $0.0016, $0.0020 per h) and decrease in SCC (0, 120,000, and 130,000 cells/mL) per 45.35 kg of milk produced. Baseline values for milk production ranged from 14.7 to 59.2 kg/cow/d in 4.4 kg increments with a fat percentage of 3.5% and an SCC of 312,000 cells/mL. The model assumed a minimum increase across milk yield, fat, and SCC of 0 (no change). Regardless of the starting milk production level, pre-training had a positive economic impact across all levels (30 to 50 h) and herd sizes. All simulations returned a positive marginal impact for pre-training, with at least 79.7% of all results being profitable across simulations.

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