Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Field Implementation of Forecasting Models for Predicting Nursery Mortality in a Midwestern US Swine Production System

Edison S. Magalhaes
* ,
Danyang Zhang
,
Chong Wang
ORCID logo ,
Pete Thomas
,
Cesar A. A. Moura
,
Derald J. Holtkamp
,
Giovani Trevisan
,
Chris Rademacher
,
Gustavo S. Silva
ORCID logo ,
Daniel C.L. Linhares
Version 1 : Received: 7 July 2023 / Approved: 10 July 2023 / Online: 10 July 2023 (10:40:52 CEST)

A peer-reviewed article of this Preprint also exists.

Magalhaes, E.S.; Zhang, D.; Wang, C.; Thomas, P.; Moura, C.A.A.; Holtkamp, D.J.; Trevisan, G.; Rademacher, C.; Silva, G.S.; Linhares, D.C.L. Field Implementation of Forecasting Models for Predicting Nursery Mortality in a Midwestern US Swine Production System. Animals 2023, 13, 2412. Magalhaes, E.S.; Zhang, D.; Wang, C.; Thomas, P.; Moura, C.A.A.; Holtkamp, D.J.; Trevisan, G.; Rademacher, C.; Silva, G.S.; Linhares, D.C.L. Field Implementation of Forecasting Models for Predicting Nursery Mortality in a Midwestern US Swine Production System. Animals 2023, 13, 2412.

Abstract

The performance of 5 forecasting models was investigated for predicting nursery mortality using the master table built for 3,242 groups of pigs (~ 13 million animals) and 42 variables, which concerned the pre-weaning phase of production and conditions at placement in growing sites. After training and testing each model’s performance through cross-validation, the model with the best overall prediction results was the Support Vector Machine model in terms of Root Mean Squared Error (RMSE=0.406), Mean Absolute Error (MAE=0.284), and Coefficient of Determination (R2=0.731). Subsequently, the forecasting performance of the SVM model was tested on a new dataset containing 72 new groups, simulating ongoing and near real-time forecasting analysis. Despite a decrease in R2 values on the new dataset (R2=0.554), the model demonstrated high accuracy (77.78%) for predicting groups with high (5>%) or low (5<%) nursery mortality. This study demonstrated the capability of forecasting models to predict the nursery mortality of commercial groups of pigs using pre-weaning information and stocking conditions variables collected post-placement in nursery sites.

Keywords

Swine; mortality; data-wrangling; forecasting; machine-learning

Subject

Biology and Life Sciences, Animal Science, Veterinary Science and Zoology

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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