Statistical and Machine Learning Approaches to Describe Factors affecting Preweaning Mortality of Piglets

Authors

Md Towfiqur Rahman, University of Nebraska-LincolnFollow
Tami M. Brown-Brandl, United States Department of Agriculture-Agricultural Research Service, Meat Animal Research CenterFollow
Gary A. Rohrer, United States Department of Agriculture-Agricultural Research ServiceFollow
Sudhendu R. Sharma, University of Nebraska-LincolnFollow
Vamsi Manthena, University of Nebraska-LincolnFollow
Yeyin Shi, University of Nebraska-LincolnFollow

ORCID IDs

Rahman 0000-0001-9520-2753

Brown-Brandl 0000-0002-0874-8035

Rohrer 0000-0002-8252-9308

Sharma 0000-0003-1996-673X

Manthena 0000-0003-4535-0488

Shi 0000-0003-3964-2855

Document Type

Article

Date of this Version

10-25-2023

Citation

Translational Animal Science (October 25, 2023) 7(1): txad117

doi: 10.1093/tas/txad117

Comments

United States government work

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 United States 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 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.