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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

This study aimed at predicting of live body weight from body measurements using stepwise regression analysis. Body measurements data of 212 animals, Sohagi sheep flock (64 male and 148 female) were used. Body weight (BW) and four body measurements were measured: heart girth (HG), height at withers (HW), height at rump (HR) and body length (BL). The stepwise regression analysis was performed in order to retain the X variable(s) (the body measurements) that contribute significantly (P < 0.05) to the variability in the dependent variable (BW). Results indicated that, there were high and positive correlation coefficients between the body weight and all body measurements. The highest correlation coefficient (r=0.93) was obtained between BW and HG and the lowest correlation coefficient (r= 0.88) was between BW and BL. All the studied body measurements were entered into the model and through stepwise elimination procedure two of them were considered unfit in the model (HR) and (BL). The two body measurements that best fit the model are heart girth (HG) and height at withers (HW), accounting for 92% of the live weight in Sohagi sheep. Changes of R from the first model (R=0.86, this model included HG only) to the third model (R=0.92), explained that, the most important variable in predicting BW is HG. The standardized coefficient (Beta) is used to explain the contribution of each independent variable in the model. So, the most important variable is HG (Beta = 0.92), this variable is the most important variable to explain the variability in BW. The prediction equation explained that regression coefficient of BW/HG = 0.35, this means that when the heart girth increases by one unit (1cm), the live body weight increases by 0.35 kg in sohagi sheep.

Prediction of Live Body Weight from Body Measurements Using Stepwise Regression Analysis in Sohagi Sheep