Field Implementation of Forecasting Models for Predicting Nursery Mortality in a Midwestern US Swine Production System
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Overview and Study Design
2.2. Data-Wrangling Pipeline
2.3. Comparing Forecasting Models Based on Training Data
2.4. Performance of the Selected Model on Independent Validation Data
3. Results
3.1. Data-Wrangling Pipeline
3.2. Comparing Forecasting Models
3.3. Performance of the Selected Model
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Morota, G.; Ventura, R.V.; Silva, F.F.; Koyama, M.; Fernando, S.C. Big Data Analytics and Precision Animal Agriculture Symposium: Machine Learning and Data Mining Advance Predictive Big Data Analysis in Precision Animal Agriculture. J. Anim. Sci. 2018, 96, 1540–1550. [Google Scholar] [CrossRef]
- Murphy, M.D.; O’Mahony, M.J.; Shalloo, L.; French, P.; Upton, J. Comparison of Modelling Techniques for Milk-Production Forecasting. J. Dairy Sci. 2014, 97, 3352–3363. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Nguyen, Q.T.; Fouchereau, R.; Frénod, E.; Gerard, C.; Sincholle, V. Comparison of Forecast Models of Production of Dairy Cows Combining Animal and Diet Parameters. Comput. Electron. Agric. 2020, 170, 105258. [Google Scholar] [CrossRef] [Green Version]
- Gebhardt, J.T.; Tokach, M.D.; Dritz, S.S.; DeRouchey, J.M.; Woodworth, J.C.; Goodband, R.D.; Henry, S.C. Postweaning Mortality in Commercial Swine Production II: Review of Infectious Contributing Factors. Transl. Anim. Sci. 2020, 4, 485–506. [Google Scholar] [CrossRef] [PubMed]
- Gebhardt, J.T.; Tokach, M.D.; Dritz, S.S.; DeRouchey, J.M.; Woodworth, J.C.; Goodband, R.D.; Henry, S.C. Postweaning Mortality in Commercial Swine Production. I: Review of Non-Infectious Contributing Factors. Transl. Anim. Sci. 2020, 4, 462–484. [Google Scholar] [CrossRef] [PubMed]
- Bush, E. Swine 2012 Part I: Baseline Reference of Swine Health and Management in the United States, 2012; United States Department of Agriculture: Washington, DC, USA, 2015. [Google Scholar]
- Agostini, P.D.S.; Manzanilla, E.G.; De Blas, C.; Fahey, A.G.; Da Silva, C.A.; Gasa, J. Managing Variability in Decision Making in Swine Growing-Finishing Units. Ir. Vet. J. 2015, 68, 20. [Google Scholar] [CrossRef] [Green Version]
- Goumon, S.; Faucitano, L. Influence of Loading Handling and Facilities on the Subsequent Response to Pre-Slaughter Stress in Pigs. Livest. Sci. 2017, 200, 6–13. [Google Scholar] [CrossRef]
- Passafaro, T.L.; Van De Stroet, D.; Bello, N.M.; Williams, N.H.; Rosa, G.J.M. Generalized Additive Mixed Model on the Analysis of Total Transport Losses of Market-Weight Pigs. J. Anim. Sci. 2019, 97, 2025–2034. [Google Scholar] [CrossRef]
- Magalhães, E.S.; Zimmerman, J.J.; Thomas, P.; Moura, C.A.A.; Trevisan, G.; Holtkamp, D.J.; Wang, C.; Rademacher, C.; Silva, G.S.; Linhares, D.C.L. Whole-Herd Risk Factors Associated with Wean-to-Finish Mortality under the Conditions of a Midwestern USA Swine Production System. Prev. Vet. Med. 2022, 198, 105545. [Google Scholar] [CrossRef]
- Kuhn, M. Building Predictive Models in R Using the Caret Package. J. Stat. Softw. 2008, 28, 1–26. [Google Scholar] [CrossRef] [Green Version]
- Arulmozhi, E.; Moon, B.E.; Basak, J.K.; Sihalath, T.; Park, J.; Kim, H.T. Machine Learning-Based Microclimate Model for Indoor Air Temperature and Relative Humidity Prediction in a Swine Building. Animals 2021, 11, 222. [Google Scholar] [CrossRef] [PubMed]
- Semakula, J.; Corner-thomas, R.A.; Morris, S.T.; Blair, H.T.; Kenyon, P.R. Application of Machine Learning Algorithms to Predict Body Condition Score from Liveweight Records of Mature Romney Ewes. Agriculture 2021, 11, 162. [Google Scholar] [CrossRef]
- Trevisan, G.; Linhares, L.C.M.; Crim, B.; Dubey, P.; Schwartz, K.J.; Burrough, E.R.; Main, R.G.; Sundberg, P.; Thurn, M.; Lages, P.T.F.; et al. Macroepidemiological Aspects of Porcine Reproductive and Respiratory Syndrome Virus Detection by Major United States Veterinary Diagnostic Laboratories over Time, Age Group, and Specimen. PLoS ONE 2019, 14, e0223544. [Google Scholar] [CrossRef] [Green Version]
- Trevisan, G.; Linhares, L.C.M.; Crim, B.; Dubey, P.; Schwartz, K.J.; Burrough, E.R.; Wang, C.; Main, R.G.; Sundberg, P.; Thurn, M.; et al. Prediction of Seasonal Patterns of Porcine Reproductive and Respiratory Syndrome Virus RNA Detection in the U.S. Swine Industry. J. Veter.-Diagn. Investig. 2020, 32, 394–400. [Google Scholar] [CrossRef] [PubMed]
- Almeida, M.N.; Rotto, H.; Schneider, P.; Robb, C.; Zimmerman, J.J.; Holtkamp, D.J.; Rademacher, C.J.; Linhares, D.C.L. Collecting Oral Fluid Samples from Due-to-Wean Litters. Prev. Vet. Med. 2020, 174, 104810. [Google Scholar] [CrossRef]
- Alvarez, J.; Sarradell, J.; Kerkaert, B.; Bandyopadhyay, D.; Torremorell, M.; Morrison, R.; Perez, A. Association of the Presence of Influenza A Virus and Porcine Reproductive and Respiratory Syndrome Virus in Sow Farms with Post-Weaning Mortality. Prev. Vet. Med. 2015, 121, 240–245. [Google Scholar] [CrossRef]
- Dong, J.G.; Yu, L.Y.; Wang, P.P.; Zhang, L.Y.; Liu, Y.L.; Liang, P.S.; Song, C.X. A New Recombined Porcine Reproductive and Respiratory Syndrome Virus Virulent Strain in China. J. Vet. Sci. 2018, 19, 89–98. [Google Scholar] [CrossRef]
- Fablet, C.; Rose, N.; Grasland, B.; Robert, N.; Lewandowski, E.; Gosselin, M. Factors Associated with the Growing-Finishing Performances of Swine Herds: An Exploratory Study on Serological and Herd Level Indicators. Porc. Health Manag. 2018, 4, 6. [Google Scholar] [CrossRef]
- Holtkamp, D.J.; Kliebenstein, J.B.; Neumann, E.J.; Zimmerman, J.J.; Rotto, H.F.; Yoder, T.K.; Wang, C.; Yeske, P.E.; Mowrer, C.L.; Haley, C.A. Assessment of the Economic Impact of Porcine Reproductive and Respiratory Syndrome Virus on United States Pork Producers. J. Swine Health Prod. 2013, 21, 72–84. [Google Scholar]
- Pomorska-Mól, M.; Kwit, K.; Czyżewska-Dors, E.; Pejsak, Z. Tulathromycin Enhances Humoral but Not Cellular Immune Response in Pigs Vaccinated against Swine Influenza. J. Vet. Pharmacol. Ther. 2019, 42, 318–323. [Google Scholar] [CrossRef]
- Silva, G.S.; Yeske, P.; Morrison, R.B.; Linhares, D.C.L. Benefit-Cost Analysis to Estimate the Payback Time and the Economic Value of Two Mycoplasma Hyopneumoniae Elimination Methods in Breeding Herds. Prev. Vet. Med. 2019, 168, 95–102. [Google Scholar] [CrossRef] [PubMed]
- Rosa, G.J.M.; Valente, B.D. Breeding and Genetics Symposium: Inferring Causal Effects from Observational Data in Livestock. J. Anim. Sci. 2013, 91, 553–564. [Google Scholar] [CrossRef] [PubMed]
- Bello, N.M.; Ferreira, V.C.; Gianola, D.; Rosa, G.J.M. Conceptual Framework for Investigating Causal Effects from Observational Data in Livestock. J. Anim. Sci. 2018, 96, 4045–4062. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Agostini, P.S.; Fahey, A.G.; Manzanilla, E.G.; O’Doherty, J.V.; De Blas, C.; Gasa, J. Management Factors Affecting Mortality, Feed Intake and Feed Conversion Ratio of Grow-Finishing Pigs. Animal 2014, 8, 1312–1318. [Google Scholar] [CrossRef] [Green Version]
- Oliveira, J.; Yus, E.; Guitián, F.J. Effects of Management, Environmental and Temporal Factors on Mortality and Feed Consumption in Integrated Swine Fattening Farms. Livest. Sci. 2009, 123, 221–229. [Google Scholar] [CrossRef]
- Larriestra, A.J.; Maes, D.G.; Deen, J.; Morrison, R.B. Mixed Models Applied to the Study of Variation of Grower-Finisher Mortality and Culling Rates of a Large Swine Production System. Can. J. Vet. Res. 2005, 69, 26–31. [Google Scholar]
- Aiken, V.C.F.; Fernandes, A.F.A.; Passafaro, T.L.; Acedo, J.S.; Dias, F.G.; Dórea, J.R.R.; de Magalhães Rosa, G.J. Forecasting Beef Production and Quality Using Large-Scale Integrated Data from Brazil. J. Anim. Sci. 2020, 98, 1–12. [Google Scholar] [CrossRef]
- Alonso, J.; Castañón, Á.R.; Bahamonde, A. Support Vector Regression to Predict Carcass Weight in Beef Cattle in Advance of the Slaughter. Comput. Electron. Agric. 2013, 91, 116–120. [Google Scholar] [CrossRef] [Green Version]
- Golden, C.E.; Rothrock, M.J.; Mishra, A. Comparison between Random Forest and Gradient Boosting Machine Methods for Predicting Listeria Spp. Prevalence in the Environment of Pastured Poultry Farms. Food Res. Int. 2019, 122, 47–55. [Google Scholar] [CrossRef] [Green Version]
- Kamphuis, C.; Mollenhorst, H.; Feelders, A.; Pietersma, D.; Hogeveen, H. Decision-Tree Induction to Detect Clinical Mastitis with Automatic Milking. Comput. Electron. Agric. 2010, 70, 60–68. [Google Scholar] [CrossRef]
- Shine, P.; Murphy, M.D.; Upton, J.; Scully, T. Machine-Learning Algorithms for Predicting on-Farm Direct Water and Electricity Consumption on Pasture Based Dairy Farms. Comput. Electron. Agric. 2018, 150, 74–87. [Google Scholar] [CrossRef]
- Zhang, F.; Upton, J.; Shalloo, L.; Murphy, M.D. Effect of Parity Weighting on Milk Production Forecast Models. Comput. Electron. Agric. 2019, 157, 589–603. [Google Scholar] [CrossRef]
- Lenzerini, M. Data Integration: A Theoretical Perspective. In Proceedings of the Twenty-First ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, Madison, WI, USA, 3–5 June 2002; pp. 233–246. [Google Scholar]
- Davis, M.E.; Sears, S.C.; Apple, J.K.; Maxwell, C.V.; Johnson, Z.B. Effect of Weaning Age and Commingling after the Nursery Phase of Pigs in a Wean-to-Finish Facility on Growth, and Humoral and Behavioral Indicators of Well-Being. J. Anim. Sci. 2006, 84, 743–756. [Google Scholar] [CrossRef] [PubMed]
- Leliveld, L.M.C.; Riemensperger, A.V.; Gardiner, G.E.; O’Doherty, J.V.; Lynch, P.B.; Lawlor, P.G. Effect of Weaning Age and Postweaning Feeding Programme on the Growth Performance of Pigs to 10 Weeks of Age. Livest. Sci. 2013, 157, 225–233. [Google Scholar] [CrossRef]
- Main, R.G.; Dritz, S.S.; Tokach, M.D.; Goodband, R.D.; Nelssen, J.L. Increasing Weaning Age Improves Pig Performance in a Multisite Production System. J. Anim. Sci. 2004, 82, 1499–1507. [Google Scholar] [CrossRef] [Green Version]
- Collins, C.L.; Pluske, J.R.; Morrison, R.S.; McDonald, T.N.; Smits, R.J.; Henman, D.J.; Stensland, I.; Dunshea, F.R. Post-Weaning and Whole-of-Life Performance of Pigs Is Determined by Live Weight at Weaning and the Complexity of the Diet Fed after Weaning. Anim. Nutr. 2017, 3, 372–379. [Google Scholar] [CrossRef]
Data Streams | Variable Type ‡ | Variables |
---|---|---|
(1) Breeding Herd Productivity and Health * | Rate | Service repeat rate; Abortion rate; Services per inventory; Proportion of gilts bred; Last week weaned sows bred rate; Proportion of sows pregnant at 105 days; Farrowing rate; Stillborn rate; Mummies rate; Pre-weaning mortality; Pre-natal losses; Sow death rate; Sow culls rate |
Count | Number of services; Number of farrows; Sows inventory | |
Average | Wean-to-service interval; Total born; Born alive; Parity at the farrow; Gestation length; Interval between farrows; Pigs weaned/sow; Piglet wean age; Non-productive days; Productive sow days; Litter/female/year; Mated inventory; Pigs/weaned/female/year | |
(2) Growing Phase Productivity † | Rate | Nursery mortality (mortality on the initial 60 days post placement in a growing site) |
(3) Closeouts Health Status * | Category | Group status for porcine reproductive and respiratory syndrome (PRRS) at placement; Group status for Mycoplasma hyopneumoniae (MhP) at placement |
(4) Pig Transportation * | Time | Weaning movement year; Weaning movement week |
Count | Number of animals transported | |
(5) Stocking Conditions * | Category | Type of flow; Type of ventilation; |
Count | Number of origins; Time to fill the site; Breeding herd origins | |
(6) Management Procedure * | Category | Type of PRRS vaccine; Type of piglet medication at weaning; Breeding herd type of mass medication protocol |
Data Type | Variable † | A * | B * | C * | D * | E * | F * |
---|---|---|---|---|---|---|---|
Breeding herd health | PRRS status | Epidemic | Endemic | Mixed | Negative | - | - |
MhP status | Epidemic | Endemic | Mixed | Negative | - | - | |
Stocking conditions * | Type of flow 3 | DS-M | DS-S | Y | S | - | - |
Type of ventilation | Tunnel Barn | Curtain Barn | - | - | - | - | |
Management procedure * | PRRS vaccine | Vaccine A | Vaccine B | - | - | - | - |
Piglet medication 1 | Enrofloxacin | Tulathromycin | Ceftiofur | Florfenicol | Mixed | None | |
Sow medication 2 | CTC 4 | Lincomycin | Tilmicosin | Mixed | None | - |
Model 1 | Parameters 2 | ||
---|---|---|---|
R2 | RMSE | MAE | |
MLR | 0.385 | 0.614 | 0.475 |
LASSO | 0.392 | 0.611 | 0.471 |
RF | 0.725 | 0.421 | 0.313 |
SVM | 0.731 | 0.406 | 0.284 |
NNet | 0.533 | 0.566 | 0.393 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
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. https://doi.org/10.3390/ani13152412
Magalhaes ES, Zhang D, Wang C, Thomas P, Moura CAA, Holtkamp DJ, Trevisan G, Rademacher C, Silva GS, Linhares DCL. Field Implementation of Forecasting Models for Predicting Nursery Mortality in a Midwestern US Swine Production System. Animals. 2023; 13(15):2412. https://doi.org/10.3390/ani13152412
Chicago/Turabian StyleMagalhaes, Edison S., Danyang Zhang, Chong Wang, Pete Thomas, Cesar A. A. Moura, Derald J. Holtkamp, Giovani Trevisan, Christopher Rademacher, Gustavo S. Silva, and Daniel C. L. Linhares. 2023. "Field Implementation of Forecasting Models for Predicting Nursery Mortality in a Midwestern US Swine Production System" Animals 13, no. 15: 2412. https://doi.org/10.3390/ani13152412