[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127751-en":3,"doc-seo-127751-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127751,1099523882182,"Eliana","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Statistical and machine learning approaches to describe factors affecting preweaning mortality of piglets","High preweaning mortality (PWM) in piglets remains a major concern for global pork industries, driving both economic losses and animal welfare impacts. This study identifies key factors behind PWM and develops predictive models using historical production records. Data from 1,982 litters (2016–2021) at the US Meat Animal Research Center were analyzed with generalized linear models, beta regression, and a random-forest model. RF best predicted PWM, with observed–predicted agreement (r=0.89), and feature importance highlighted litter size, birth weight, health diagnosis, gestation length, and parity effects.","Department of Biological Systems Engineering: Papers and Publications  \nBiological Systems Engineering, Department of  \n10-13-2023  \nStatistical and machine learning approaches to describe factors affecting preweaning mortality of piglets  \nMd Towfiqur Rahman  \nTami M. Brown-Bandl  \nGary A. Rohrer  \nSudhendu R. Sharma  \nVamsi Manthena  \nSee next page for additional authors  \nFollow this and additional works at: [https://digitalcommons.unl.edu/biosysengfacpub](https://digitalcommons.unl.edu/biosysengfacpub)  \n Part of the Bioresource and Agricultural Engineering Commons, Environmental Engineering Commons, and the Other Civil and Environmental Engineering Commons  \nThis Article is brought to you for free and open access by the Biological Systems Engineering, Department of at DigitalCommons@University of Nebraska-Lincoln. It has been accepted for inclusion in Department of Biological Systems Engineering: Papers and Publications by an authorized administrator of DigitalCommons@University of Nebraska-Lincoln.  \nAuthors  \nMd Towfiqur Rahman, Tami M. Brown-Bandl, Gary A. Rohrer, Sudhendu R. Sharma, Vamsi Manthena, and Yeyin Shi  \nStatistical and machine learning approaches to describe factors affecting preweaning mortality of piglets  \nMd Towfiqur Rahman,†,Tami M. Brown-Brandl,†, 1, Gary A. Rohrer,‡ Sudhendu R. Sharma,† Vamsi Manthena,|| andYeyin Shi†  \n†Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE 68503, USA  \nUSDA-ARS, US Meat Animal Research Center, Clay Center, NE 68933, USA Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE 68503, USA 1Corresponding author: [tami.brownbrandl@unl.edu](tami.brownbrandl@unl.edu)  \nAbstract  \nHigh preweaning mortality (PWM) rates for piglets are a significant concern for the worldwide pork industries, causing economic loss and well-being issues. This study focused on identifying the factors affecting PWM, overlays, and predicting PWM using historical production data with statistical and machine learning models. Data were collected from 1,982 litters from the U. S. Meat Animal Research Center, Nebraska, over the years 2016 to 2021. Sows were housed in a farrowing building with three rooms, each with 20 farrowing crates, and taken care of by well-trained animal caretakers. A generalized linear model was used to analyze the various sow, litter, environment, and piglet parameters on PWM. Then, different models (beta-regression and machine learning model: a random forest [RF]) were evaluated. Finally, the RF model was used to predict PWM and overlays for all listed contributing factors. On average, the mean birth weight was 1.44 kg, and the mean mortality was 16. 1% where 5. 55% was for stillbirths and 6.20% was contributed by overlays. No significant effect was found for seasonal and location variations on PWM. Significant differences were observed in the effects of litter lines on PWM ( P \u003C 0.05) . Landrace-sired litters had a PWM of 16.26%(±0 . 13), whereas Yorkshire-sired litters had 15.91%(±0 . 13) . PWM increased with higher parity orders ( P \u003C 0.05) due to larger litter sizes. The RF model provided the best fit for PWM prediction with a root mean squared errors of 2.28 and a correlation coefficient (r) of 0.89 between observed and predicted values. Features’ importance from the RF model indicated that, PWM increased with the increase of litter size (mean decrease accuracy (MDA) = 93.17), decrease in mean birth weight (MDA = 22.72), increase in health diagnosis (MDA = 15.34), longer gestation length (MDA = 11.77), and at older parity (MDA = 10.86) . However, in this study, the location of the farrowing crate, seasonal differences, and litter line turned out to be the least important predictors for PWM. For overlays, parity order was the highest importance predictor (MDA = 7.68) followed by litter size and mean birth weight. Considering the challenges to reducing the PWM in the larger litters produced in modern swine industry and the li","cbCaimhoM5W8mbtd","https://ap.wps.com/l/cbCaimhoM5W8mbtd","pdf",1013381,1,14,"English","en",105,"# Abstract\n# Lay Summary\n# Introduction","[{\"question\":\"What problem does the study address regarding piglets?\",\"answer\":\"The study addresses high preweaning mortality (PWM) rates in piglets, which cause economic losses and animal welfare concerns in pork industries.\"},{\"question\":\"Which data source and time period were used to build the models?\",\"answer\":\"The analysis used historical production data from 1,982 litters collected at the US Meat Animal Research Center in Nebraska over 2016 to 2021.\"},{\"question\":\"Which modeling approach performed best for predicting PWM?\",\"answer\":\"The random forest model provided the best fit for PWM prediction, with a correlation coefficient of r=0.89 between observed and predicted values.\"}]","Statistical and machine 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problem does the study address regarding piglets?","Question",{"text":76,"@type":77},"The study addresses high preweaning mortality (PWM) rates in piglets, which cause economic losses and animal welfare concerns in pork industries.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which data source and time period were used to build the models?",{"text":81,"@type":77},"The analysis used historical production data from 1,982 litters collected at the US Meat Animal Research Center in Nebraska over 2016 to 2021.",{"name":83,"@type":74,"acceptedAnswer":84},"Which modeling approach performed best for predicting PWM?",{"text":85,"@type":77},"The random forest model provided the best fit for PWM prediction, with a correlation coefficient of r=0.89 between observed and predicted 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