<div style="text-align:justify"><strong>Background: </strong>Sheep farm data is often biased by extreme values which are generally introduced due to errors in manual measurement. These values interfere with the accuracy of estimations especially in state-of-the-art techniques like Machine Learning.&nbsp;<br />
<strong>Methods:</strong> Therefore, winsorization technique was attempted for the removal of outliers from sheep farm data data for 11 years (2011-2021) for body weights at different ages. Some outliers were deliberately introduced into the data to check the efficiency of the technique. This study was conducted during the year 2021.<br />
<strong>Result: </strong>Our results indicate that outlier values of 15.3, 42, 44, 60, 90 for birth weight, weaning weight, 6-month, 9 month and 12-month body weight which were far from the normal range were removed using this technique. The mean and standard deviation values were altered after winsorization. Winsorization technique works well for sheep farm data to remove the bias introduced by outliers and also removes, to a large extent, the need for manual outlier removal in data.</div>


<p class="ql-align-justify">Outlier Removal in Sheep Farm Datasets Using Winsorization</p>

<div style="text-align: justify;">Evaluation of veterinary medicinal products is one of the important areas where bioequivalence trials are conducted. One of the problems commonly encountered in bioequivalence trials is outlying subjects. These outlying data may occur either as unexpected observations or as the unusual subject who has extremely high or low bioavailability with respect to the reference formulation and may have dramatic effects on the bioequivalence test. Therefore, it is important to examine the data carefully for identification of outlying observations in bioequivalence studies. In this study we have developed another general approach, termed the Estimated Distance (ED), to the outliers&rsquo; detection problem.</div>

<div style="text-align: justify;">Detection of Outlying Subjects in Bioequivalence Trials</div>

<p><strong>Background:</strong> Understanding genetic and non-genetic factors that influence the quality and quantity of semen production plays a crucial role in determining the reproductive efficiency and genetic progress within the Sahiwal breed. The objective of this study was to assess the impact of genetic and non-genetic factors on the semen production potential of Sahiwal bulls.&nbsp;<br />
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<strong>Methods:</strong> Semen ejaculate data, including volume (VOL), sperm concentration per milliliter (CONC), total sperm count per ejaculate (TS), initial motility (INIT) and post-thaw motility (PT), as well as semen quality traits such as hypo-osmotic swelling test (HOST) and acrosome integrity of frozen semen (AIFS), were obtained from 52 Sahiwal bulls. The data spanned from January 2011 to December 2018 and were collected at BAIF&rsquo;s frozen semen station in Jind, Haryana, India. To ensure data reliability, observations beyond the mean&plusmn;4 standard deviations (S.D.) were identified as outliers and subsequently excluded from the study. Two key factors, namely the season of semen collection (winter, summer and monsoon) and the age of bulls at collection (categorized into 12-month intervals ranging from less than 36 months to 156 months), were considered influential factors affecting semen characteristics. To investigate the correlation between bulls (genetic + permanent environmental correlation) and semen parameters, a repeatability animal model was employed, treating the bull as a random effect. The statistical analysis was conducted using WOMBAT software.<br />
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<strong>Result: </strong>The findings revealed that the season of semen collection had a highly significant effect (P&lt;0.01) on VOL, CONC, TS and PT. However, INIT, HOST and AIFS were not influenced (P&gt;0.05) by the season of collection. Furthermore, age at semen collection significantly affected all semen characteristics. VOL and TS exhibited an increasing trend until 72 to 84 months, followed by a decline until 96 to 108 months.</p>

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Outlier Removal in Sheep Farm Datasets Using Winsorization

Detection of Outlying Subjects in Bioequivalence Trials

Evaluation of Genetic and Non-genetic Factors Influencing Semen Production Potential in Sahiwal Bulls