Farm-specific estimates of forage variability result in larger optimal control limits for forage quality monitoring

Variation in forage composition decreases the accuracy of diets delivered to dairy cows. However, variability of forages can be managed using a renewal reward model (RRM) and genetic algorithm (GA) to optimize sampling and monitoring practices for farm conditions. Specifically, use of quality control charts to monitor forage composition can identify changes in composition for which adjustment in the formulated diet will result in a better match of the nutrients delivered to cows. The objectives of this study were (1) to assess the use of a clustering algorithm to estimate the mean time (d) the process is stable or in control (T_Stable) and the magnitude of the change in forage composition between stable periods (Δ_Forage) for corn silage and alfalfa-grass silage, which are input parameters for the RRM; (2) to compare optimized farm-specific sampling practices (number of samples, sampling interval, and control limits [Δ_Limit]) using previously proposed defaults and our estimates for the T_Stable and Δ_Forage input parameters; and (3) to conduct a simulation study to compare the number of recommended diet changes under the proposed sampling and monitoring protocols. We estimated the T_Stable and Δ_Forage parameters for corn silage NDF and starch and alfalfa-grass silage NDF and CP using a k-means clustering approach applied to forage samples collected from 8 farms, 3×/wk during a 16-wk period. We compared 4 sampling and monitoring protocols that resulted from the 2 methods for estimating T_Stable and Δ_Forage (default values and our proposed method) and either optimizing only the control limit or optimizing the control limits, the number of samples, and the number of days between sampling. We simulated the outcomes of implementing the optimized monitoring protocols using a quality control chart for corn silage and alfalfa-grass silage of each farm. Estimates of T^_Stable and Δ^_Forage from the k-means clustering analysis were, respectively, shorter and larger than previously proposed default values. In the simulated quality control monitoring, larger Δ^_Forage estimates increased the optimized Δ_Limit, resulting in fewer detected shifts in composition of forages, a lower frequency of false alarms, and a lower quality control cost ($/d). Recommended diet reformulation intervals from the simulated quality control analysis were specific for the type of forage and farm management practices. The median of the diet reformulation intervals for all farms using our optimal protocols was 14 d (quartile [Q]₁ = 8, Q₃ = 26) for corn silage and 16 d (Q₁ = 8, Q₃ = 26) for alfalfa-grass silage.