[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127740-en":3,"doc-seo-127740-105":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},127740,962084926284,"Aurora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Statistical and Machine Learning Approaches to Describe Factors affecting Preweaning Mortality of Piglets","High preweaning mortality (PWM) in piglets represents a major concern for the global pork industry, driving both economic loss and animal-welfare issues. The study identifies key factors associated with PWM, overlays, and builds predictive models using historical production records. Data from 1,982 litters (2016–2021) at the US Meat Animal Research Center were analyzed using a generalized linear model and compared against beta-regression and a random-forest model. The random forest achieved the strongest PWM prediction, with feature-importance results highlighting litter size, birth weight, health diagnosis, gestation length, and parity-related effects.","University of Nebraska-Lincoln  \nDigitalCommons@University of Nebraska-Lincoln  \n\n| Biological Systems Engineering: Papers and\u003Cbr>Publications | Biological Systems Engineering |\n| --- | --- |\n| 10-25-2023\u003Cbr>Statistical and Machine Learning Approaches to Describe Factors affecting Preweaning Mortality of Piglets\u003Cbr>Md Towfiqur Rahman\u003Cbr>University of Nebraska-Lincoln, [mrahman8@huskers.unl.edu](mrahman8@huskers.unl.edu)\u003Cbr>[Tami M. Brown-Brandl](Tami M. Brown-Brandl)\u003Cbr>United States Department of Agriculture-Agricultural Research Service, Meat Animal Research Center, [tbrownbrandl2@unl.edu](tbrownbrandl2@unl.edu)\u003Cbr>Gary A. Rohrer\u003Cbr>United States Department of Agriculture-Agricultural Research Service, [gary.rohrer@usda.gov](gary.rohrer@usda.gov)\u003Cbr>Sudhendu R. Sharma\u003Cbr>University of Nebraska-Lincoln, [raj.sharma@unl.edu](raj.sharma@unl.edu)\u003Cbr>Vamsi Manthena\u003Cbr>University of Nebraska-Lincoln, [vamsi.manthena@gmail.com](vamsi.manthena@gmail.com)\u003Cbr>Follow this and additional works at: [https://digitalcommons.unl.edu/biosysengfacpub](https://digitalcommons.unl.edu/biosysengfacpub)\u003Cbr> Part of the Animal Sciences Commons, Applied Statistics Commons, Artificial Intelligence and\u003Cbr>ontei pCaogmeornasd, iitoiorol ctrnsd Agricultural Engineering Commons, Biostatistics Commons, Environmental Engineering Commons, Other Civil and Environmental Engineering Commons, Statistical Methodology Commons, and the Veterinary Preventive Medicine, Epidemiology, and Public Health Commons |  |\n\nRahman, Md Towfiqur; Brown-Brandl, Tami M.; Rohrer, Gary A.; Sharma, Sudhendu R.; Manthena, Vamsi; and Shi, Yeyin, \"Statistical and Machine Learning Approaches to Describe Factors affecting Preweaning Mortality of Piglets\" (2023) . Biological Systems Engineering: Papers and Publications. 889.  \n[https://digitalcommons.unl.edu/biosysengfacpub/889](https://digitalcommons.unl.edu/biosysengfacpub/889)  \nThis Article is brought to you for free and open access by the Biological Systems Engineering at DigitalCommons@University of Nebraska-Lincoln. It has been accepted for inclusion in Biological Systems Engineering: Papers and Publications by an authorized administrator of DigitalCommons@University of NebraskaLincoln.  \nAuthors  \nMd Towfiqur Rahman, Tami M. Brown-Brandl, Gary A. Rohrer, Sudhendu R. Sharma, Vamsi Manthena, and Yeyin Shi  \nThis article is available at DigitalCommons@University of Nebraska-Lincoln: [https://digitalcommons.unl.edu/](https://digitalcommons.unl.edu/)[ ](https://digitalcommons.unl.edu/)biosysengfacpub/889  \nTranslational 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 ","cbCaikbvC08lWWrh","https://ap.wps.com/l/cbCaikbvC08lWWrh","pdf",1055138,1,14,"English","en",105,"# Abstract\n## Study objective and data sources\n## Statistical and machine-learning methods\n## Key findings and predictive performance\n## Implications for breeding and production management","[{\"question\":\"What data were used to study preweaning mortality in piglets?\",\"answer\":\"The analysis used records from 1,982 litters collected from 2016 to 2021 at the US Meat Animal Research Center in Nebraska.\"},{\"question\":\"Which modeling approaches were evaluated for predicting PWM?\",\"answer\":\"A generalized linear model was used to assess sow, litter, environment, and piglet factors, and prediction performance was compared using beta-regression and a random forest model.\"},{\"question\":\"What factors were most influential for PWM and overlays?\",\"answer\":\"Random-forest importance indicated PWM increased with larger litter size, lower mean birth weight, worse health diagnosis, longer gestation length, and older parity; for overlays, parity order was the highest predictor followed by litter size and mean birth weight.\"}]","Statistical and Machine Learning Approaches to Describe Factors affecting Preweaning Mortality of Piglets | PDF",1785941347,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"statistical-and-machine-learning-approaches-to-describe-factors-affecting-preweaning-mortality-of-piglets","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/statistical-and-machine-learning-approaches-to-describe-factors-affecting-preweaning-mortality-of-piglets/127740/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-24","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 preweaning mortality in piglets?","Question",{"text":76,"@type":77},"The analysis used records from 1,982 litters collected from 2016 to 2021 at the US Meat Animal Research Center in Nebraska.","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 assess sow, litter, environment, and piglet factors, and prediction performance was compared using beta-regression and a random forest model.",{"name":83,"@type":74,"acceptedAnswer":84},"What factors were most influential for PWM and overlays?",{"text":85,"@type":77},"Random-forest importance indicated PWM increased with larger litter size, lower mean birth weight, worse health diagnosis, longer gestation length, and older parity; 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