Statistical and machine learning approaches to describe factors affecting preweaning mortality of piglets

Q: What data and timeframe were used to study piglet preweaning mortality (PWM)?

The analysis used historical production records from 1,982 litters born between 2016 and 2021 at the U.S. Meat Animal Research Center in Nebraska.

Q: Which models were used to analyze and predict PWM?

A generalized linear model was used to analyze contributing factors, and predictive performance was evaluated using beta-regression and a random forest model.

Q: What factors were most important for PWM prediction according to the random forest model?

Feature importance indicated that PWM increased with larger litter sizes, lower mean birth weight, more health diagnoses, longer gestation length, and higher parity order.

Aug 5, 2026

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High preweaning mortality (PWM) in piglets poses major concerns for global pork industries, creating economic losses and animal-welfare challenges. This study analyzed factors affecting PWM and overlays by using historical production data from 1,982 litters collected from the U.S. Meat Animal Research Center between 2016 and 2021. A generalized linear model assessed sow, litter, environment, and piglet parameters, followed by comparisons of beta-regression and a random forest model. The random forest best predicted PWM and identified key drivers such as litter size, birth weight, health status, gestation length, and parity, while season and farrowing location showed limited importance.

LanguageEnglish

CategoryResearch & Report

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Pages14

Reading Time35 min

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Biological Systems Engineering: Papers and Publications Biological Systems Engineering 10-13-2023 Statistical and machine learning approaches to describe factors affecting preweaning mortality of piglets Md Towfiqur Rahman Tami M. Brown-Bandl Gary A. Rohrer Sudhendu R. Sharma Vamsi Manthena See next page for additional authors Follow this and additional works at: [https://digitalcommons.unl.edu/biosysengfacpub](https://digitalcommons.unl.edu/biosysengfacpub) Part of the Bioresource and Agricultural Engineering Commons, Environmental Engineering Commons, and the Other Civil and Environmental Engineering Commons This 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. Authors Md Towfiqur Rahman, Tami M. Brown-Bandl, Gary A. Rohrer, Sudhendu R. Sharma, Vamsi Manthena, and Yeyin Shi Statistical and machine learning approaches to describe factors affecting preweaning mortality of piglets Md Towfiqur Rahman,†,Tami M. Brown-Brandl,†, 1, Gary A. Rohrer,‡ Sudhendu R. Sharma,† Vamsi Manthena,|| andYeyin Shi† †Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE 68503, USA USDA-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) Abstract High 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 < 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 < 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 factor

FAQs

What data and timeframe were used to study piglet preweaning mortality (PWM)?

The analysis used historical production records from 1,982 litters born between 2016 and 2021 at the U.S. Meat Animal Research Center in Nebraska.

Which models were used to analyze and predict PWM?

A generalized linear model was used to analyze contributing factors, and predictive performance was evaluated using beta-regression and a random forest model.

What factors were most important for PWM prediction according to the random forest model?

Feature importance indicated that PWM increased with larger litter sizes, lower mean birth weight, more health diagnoses, longer gestation length, and higher parity order.

Aug 5, 2026

Upload by Violet