[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128420-en":3,"doc-seo-128420-105":31,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128420,962084925290,"Ophelia","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Statistical and machine learning approaches to describe factors affecting preweaning mortality of piglets","High preweaning mortality (PWM) in piglets threatens global pork production by creating major economic losses and animal welfare challenges. The study analyzed 1,982 litters from 2016–2021 using historical production data from the U.S. Meat Animal Research Center, Nebraska. Generalized linear models assessed sow, litter, environment, and piglet parameters, followed by beta regression and random forest modeling to identify influential factors. Random forest best predicted PWM and overlays, showing strong agreement with observed values.","Translational Animal Science, 2023, 7, txad117 [https://doi.org/10.1093/tas/txad117](https://doi.org/10.1093/tas/txad117)  \nAdvance access publication 25 October 2023  \nAnimal Health and Well Being  \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 limited studies exploring multiple major contributing factors, this study provides valuable insights for breeding and production management, as well as further investigations on postural transitions and behavior analysis of sows during the lactation period.  \nLay Summary  \nCurrently, preweaning piglet mortality is a major problem for the global pork industry, causing economic losses, and animal welfare concerns. This research analyzed nearly 2,000 piglet litters born between 2016 and 2021 at the U. S. Meat Animal Research Center in Nebraska. The goal was to understand and predict the factors influencing piglet mortality using historical production data, statistical modeling, and machine learning. The study found that litter size, birth weight, number of stillborns, and sow parity order had a significant impact on piglet mortality. For the location of the farrowing crate, and seasonal variations did not show an impact. A machine ","cbCaipp45dvqKRLT","https://ap.wps.com/l/cbCaipp45dvqKRLT","pdf",1008878,2,1,12,"English","en",105,"# Abstract\n# Lay Summary\n# Introduction","[{\"question\":\"What data were used to study factors affecting preweaning mortality in piglets?\",\"answer\":\"The analysis used 1,982 litters collected at the U.S. Meat Animal Research Center in Nebraska between 2016 and 2021, based on historical production data.\"},{\"question\":\"Which modeling approaches were evaluated for predicting PWM?\",\"answer\":\"A generalized linear model was used to analyze contributing parameters, and predictive models including beta regression and a random forest were evaluated, with random forest providing the best fit.\"},{\"question\":\"What factors were most important for PWM and overlays according to the random forest results?\",\"answer\":\"PWM increased with larger litter size, lower birth weight, greater health diagnosis values, longer gestation length, and higher parity order. For overlays, parity order was highest importance, followed by litter size and mean birth weight.\"}]","Statistical and machine learning approaches to describe factors affecting preweaning mortality of piglets | PDF",1785947426,30,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"statistical-and-machine-learning-approaches-to-describe-factors-affecting-preweaning-mortality-of-piglets","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/statistical-and-machine-learning-approaches-to-describe-factors-affecting-preweaning-mortality-of-piglets/128420/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-30","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What data were used to study factors affecting preweaning mortality in piglets?","Question",{"text":76,"@type":77},"The analysis used 1,982 litters collected at the U.S. Meat Animal Research Center in Nebraska between 2016 and 2021, based on historical production data.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which modeling approaches were evaluated for predicting PWM?",{"text":81,"@type":77},"A generalized linear model was used to analyze contributing parameters, and predictive models including beta regression and a random forest were evaluated, with random forest providing the best fit.",{"name":83,"@type":74,"acceptedAnswer":84},"What factors were most important for PWM and overlays according to the random forest results?",{"text":85,"@type":77},"PWM increased with larger litter size, lower birth weight, greater health diagnosis values, longer gestation length, and higher parity order. 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