Practical challenges and potential approaches to predicting low-incidence diseases on farm using individual cow data: A clinical mastitis example.

Clinical mastitis (CM) incidence is considerable in terms of cows affected per year, but cases are much less common in terms of detections per cow per milking. From a modeling perspective, where predictions are made every time any cow is milked, low CM incidence per cow day makes training, evaluating, and applying CM prediction models a challenge. The objective of this study was to build models for predicting CM incidence using time-series sensor data and choose models that maximize net return based on a cost matrix. Data collected from 2 university dairy farms, the University of Florida and Virginia Polytechnic Institute and State University, were used to gather representative data, including 110,156 milkings and 333 CM cases. Variables used in the models were milk yield, protein, lactose, fat, electrical conductivity, days in milk, lactation number, and activity as the number of steps, lying time, lying bouts, and lying bout duration. Models that predicted either likelihood of CM caused by gram-negative (GN) or gram-positive (GP) bacteria on each day were derived using extreme gradient boosting with weighting favoring true-positive cases, logistic responses, and log-loss errors. Model accuracies were determined using data randomly held out from the training set on each run. All variables considered were in terms of change (slope) over previous days, including the day CM was visually detected. The GN models had a median sensitivity (Se) of 52.6% and specificity (Sp) of 99.8%, whereas the GP models had a median Se of 37.5% and Sp of 99.9% when tested on the held-out data. In our models optimized to reduce cost from predictions, the Se was much less than Sp, suggesting that CM models might benefit from greater model weighting placed on Sp. Results also highlight the importance of positive predictive value (true positive cases per predicted positive case) along with Sp and Se, as models built on sparse data tend to predict too many false-positive cases. The calculated partial net return of our GN and GP models were -$0.15 and -$0.10 per cow per lactation, respectively, whereas International Organization for Standardization (ISO) standard models with Se of 80% and Sp of 99% would return -$1.32 per cow per lactation. Models chosen that minimized the cost to the farmer differed markedly from models that met ISO guidelines, showing asymmetry in targets between Sp and Se when the disease incidence rate is low. Because of the unique challenges that low-incidence diseases like CM present, we recommend that future CM predictive models consider the economic and practical implications in addition to the traditional model evaluation metrics.

Algorithm development for individualized precision feeding of supplemental top dresses to influence feed efficiency of dairy cattle.

Individualized, precision feeding of dairy cattle may contribute to profitable and sustainable dairy production. Feeding strategies targeted at optimizing efficiency of individual cows, rather than groups of animals with similar characteristics, is a logical goal of individualized precision feeding. However, algorithms designed to make feeding recommendations for specific animals are scarce. The objective of this study was to develop and test 2 algorithms designed to improve feed efficiency of individual cows by supplementing total mixed rations (TMR) with varying types and amounts of top-dressed feedstuffs. Twenty-four Holstein dairy cows were assigned to 1 of 3 treatment groups as follows: a control group fed a common TMR ad libitum, a group fed individually according to algorithm 1, and a group fed individually according to algorithm 2. Algorithm 1 used a mixed-model approach with feed efficiency as the response variable and automated measurements of production parameters and top-dress type as dependent variables. Cow was treated as a random effect, and cow by top-dress interactions were included if significant. Algorithm 2 grouped cows based on top-dress response efficiency structure using a principal components and k-means clustering. Both algorithms were trained over a 36-d experimental period immediately before testing, and were updated weekly during the 35-d testing period. Production performance responses for dry matter intake (DMI), milk yield, milk fat percentage and yield, milk protein percentage and yield, and feed efficiency were analyzed using a mixed-effects model with fixed effects for feeding algorithm, top dress, week, and the 2- and 3-way interactions among these variables. Milk protein percentage and feed efficiency were significantly affected by the 3-way interaction of top dress, algorithm, and week, and DMI tended to be affected by this 3-way interaction. Feeding algorithm did not affect milk yield, milk fat yield, or milk protein yield. However, feeding costs were reduced, and hence milk revenue increased on the algorithm-fed cows. The efficacy of feeding algorithms differed by top dress and time, and largely relied on DMI shifts to modulate feed efficiency. The net result, for the cumulative feeding groups, was that cows in the algorithm 1 and 2 groups earned over $0.45 and $0.70 more per head per day in comparison to cows on the TMR control, respectively. This study yielded 2 candidate approaches for efficiency-focused, individualized feeding recommendations. Refinement of algorithm selection, development, and training approaches are needed to maximize production parameters through individualized feeding.

Technical note: Methodological and feed factors affecting measurement of protein A, B, and C fractions, degradation rate, and intestinal digestibility of rumen-undegraded protein.

When formulating dairy cow rations, characterization of protein in feeds requires estimation of protein degradation in the rumen and digestion in the intestine. The objective of this work was to evaluate experimental and feed-related factors that affect characterization using in situ, in vitro, or mobile bag techniques, of 0-h washout (A), potentially degradable (B), and undegradable (C) protein fractions, protein degradation rate (Kd), and digestibility of rumen undegradable protein (dRUP). Data sets of 136 studies on A, B, C, and Kd and 113 studies on dRUP were amassed from the literature. Mixed-effect linear models were used to relate these variables to methodological and feed factors while accounting for random differences among studies. Predictions of A, B, and C protein fractions were significantly influenced by crude protein and neutral detergent fiber interactions with bag pore size, incubation time, bag area, and sample-to-bag area ratio. For example, a 20.0% decrease in crude protein of a theoretical legume silage sample would increase A fraction prediction by 20.1%, but 34.7% with bag incubation time -1 standard deviation below the mean. Similarly, reported Kd values were significantly influenced by crude protein interactions with bag area and sample-to-bag area ratio and by neutral detergent fiber interaction with pore size. Feed variables and measurement variables influencing protein digestibility measures suggest that these analytical factors are likely associated with variance among differing methodologies and within unique samples of the same feed. When predicting dRUP, the use of mobile bag method produced significantly different estimates compared with the in vitro 3-step method. The use of mobile bag resulted in an 8.9% (±3.8%) higher estimate of dRUP compared with the in situ technique. In 618 and 977 samples, sample variation to sample mean ratio for acid detergent fiber and pepsin-acid incubation time was 63.0 and 58.0%, respectively. Variation in feedstuff content and lack of standardization of methods used to measure protein disappearance led to a lack of robustness in the measurements commonly employed.