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The objective of this study was to explore how linear body measurements are related to body weight and can be used to predict calf body weight using linear and machine learning models. To meet these objectives, a total of 69 Holstein calves from a commercial dairy farm were enrolled in the study from wk 2 - 8 of age. Calves were weighed and linear measurements were collected weekly. Nineteen linear measurements were obtained each week, including: poll to nose, width across the eyes, width across the right ear, neck length (NL), wither height (WH), heart girth (HG), midpiece height (MH), midpiece circumference (MC), midpiece width (MW), midpiece depth (MD), midpiece width across the 13th rib (MW13), hook height, hook width, pin height, top of pin bones width (PW), nose to tail body length, the length between the withers and pins (WPL), forearm to hoof, cannon bone to hoof. These measurements were taken using a commercial soft tape measure and calipers. Using a machine learning approach, models were generated to predict BW from calf linear measurements using Weka software 3.8.5 (University of Waikato, New Zealand) using a 10-fold cross-validation method. Both linear regression (LR) and random forest (RF) models were evaluated. Across all weeks the LR model derived 12 of the 19 traits to fit the BW model (r2 = 0.93). These included: PN, NL, WH, HG, MC, MW, MD, HW, PW, MW13, WPL. The RF model slightly reduced BW predictions (r2= 0.92). The results of this study suggest that linear models built on linear measurements can accurately estimate body weight in dairy calves. These data and models generated are important to further the development of visualized weighing systems for young dairy calves and may be used to accurately predict BW without a scale. [ABSTRACT FROM AUTHOR]

The objective of this study is to investigate how linear body measurements relate to and can be used to predict calf body weight using linear and machine learning models. To meet these objectives, a total of 103 Angus cross calves were enrolled in the study from wk 2 - 8. Calves were weighed and linear measurements were collected weekly, such as: poll to nose, width across the eyes (WE), width across the right ear, neck length, wither height, heart girth (HG), midpiece height (MH), midpiece circumference, midpiece width (MW), midpiece depth (MD), hook height, hook width, pin height, top of pin bones width (PW), width across the ends of pin bones, nose to tail body length, the length between the withers and pins, forearm to hoof, cannon bone to hoof. These measurements were taken using a commercial soft tape measure and calipers. To assess relationships between traits and to fit a model to predict BW, data were analyzed using the Weka (The University of Waikato, New Zealand) software using both linear regression (LR) and random forest (RF) machine learning models. The models were trained using a 10-fold cross-validation approach. The automatically derived LR model used 11 traits to fit the data to weekly BW (r2 = 0.97), where the traits with the highest coefficients were HG, PW and WE. The RF model improved further the BW predictions (r2= 0.98). Additionally, sex differences were examined. Although the BW model continued to fit well (r2 0.97), some of the top linear traits differed. The results of this study suggest that linear models built on linear measurements can accurately estimate body weight in beef calves, and that machine learning can further improve the model fit. [ABSTRACT FROM AUTHOR]

Tryptophan (Trp) is an indispensable dietary amino acid that supports the modulation of mood and behavior in mammalian species through its role in the serotonergic pathway. This study assessed the behavior patterns of 36 healthy, female adult mixed-breed hound dogs fed a control diet (tryptophan = 0.18% as-fed) or 1 of several experimental diets with graded concentrations of tryptophan (0.05%, 0.1%, and 0.15% of the total diet) supplemented on top of the 0.18% basal level. At baseline, and every 8 weeks throughout the 24-week period, behavioral parameters were evaluated for each dog in response to the approach of an individual familiar to the dogs and another individual who was unfamiliar to the dogs. Differences in behavior scores (activity, P = 0.0197; distance, P = 0.0358; confidence, P < 0.0001; and ear position, P < 0.0001) between the unfamiliar and familiar individuals supported the efficacy of the behavioral ethogram used. No consistent and significant differences in behavior were observed, however, among dogs fed the control diet and those fed an experimental diet with any level of tryptophan supplementation. Future research should consider the tryptophan-to-large-neutral-amino-acid ratio and not just tryptophan concentrations, seek to understand the variation in tryptophan requirements among breeds, and look to utilize additional markers of serotonin status., Competing Interests: Author disclosure: James R. Templeman, John P. Cant, and Vern R. Osborne have no conflicts of interest. Gary M. Davenport and Anna-Kate Shoveller were previously employed by Procter and Gamble — Pet Care and have had financial and personal interest in the company. Gary M. Davenport is now an employee of Archer-Daniel Mills and Anna-Kate Shoveller is employed by the University of Guelph.