English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Changes in feeding behavior and intake have been used to identify bovine respiratory disease in individual animals, but not at the pen level. Correctly identifying high morbidity pens early in the feeding period could facilitate interventions to improve health and economic outcomes. The objective was to determine the ability of feed delivery data from the first 15 d on feed to predict total feeding period morbidity. Data consisted of 518 pens (10 feedlots, 56,796 animals). Overall morbidity was classified into high (&amp;gt;15% total morbidity) or low categories with 18.5% of pens having high morbidity. Five predictive algorithms (advanced perceptron, decision forest, logistic regression, neural network, and boosted decision tree) were utilized to predict overall morbidity given arrival (body weight, timing, gender) and feeding characteristics. The dataset was split into training (75%) and testing (25%) subsets. Algorithms were generated in Microsoft Azure and evaluated based on accuracy, sensitivity (Sn, the ability to accurately detect high morbidity pens), and specificity (Sp, the ability to accurately detect low morbidity pens). The decision forest had the highest Sp (97%) with the greatest ability to accurately identify low morbidity lots (103 of 106 identified correctly), but this model had low Sn (33%). The logistic regression and neural network had good Sn (both 63%) with the best ability to correctly identify high morbidity lots (15 of 24 correctly identified), but a decreased Sp (69 and 72%, respectively). Three models provided feature importance data demonstrating that percent change in feed delivery between days and over 4-d moving averages were of great importance. The most frequent variable with a high level of importance among models was the percent change from d 2 to 3 after arrival. In conclusion, feed delivery data during the first 15 d on feed was an important predictor of total pen-level morbidity.

journal of animal science/journal of animal science ... and asas reference compendium

9 Evaluation of Predictive Models to Determine Total Morbidity Outcome of Feedlot Cattle Based on Pen-Level Feed Delivery Data During the First 15 Days on Feed