ArticleJournal of animal science2019
Generalized additive mixed model on the analysis of total transport losses of market-weight pigs1.
Article in Journal of animal science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 22 citations in OpenAlex.
- Impact of increasing market pig weights in North America: a comprehensive review.Journal of animal science and biotechnology · 2026Review
- Time-Weighted Average Proteinuria and Renal Function Decline in IgA Nephropathy: A Retrospective Cohort Study.International journal of nephrology and renovascular disease · 2025Article
- Association between niacin intake and knee osteoarthritis pain and function: a longitudinal cohort study.Clinical rheumatology · 2024Article
- Field Implementation of Forecasting Models for Predicting Nursery Mortality in a Midwestern US Swine Production System.Animals : an open access journal from MDPI · 2023Article
- Association between Elevated Magnesium Intake and Reduced Risk of Recurrent Falls and Frailty in Osteoarthritis: Data from the Osteoarthritis Initiative.The journal of nutrition, health & aging · 2023Article
- Utilizing productivity and health breeding-to-market information along with disease diagnostic data to identify pig mortality risk factors in a U.S. swine production system.Frontiers in veterinary science · 2023Article
- Welfare of pigs during transport.EFSA journal. European Food Safety Authority · 2022Article
- Field Trial of Factors Associated With the Presence of Dead and Non-ambulatory Pigs During Transport Across Three Colombian Slaughterhouses.Frontiers in veterinary science · 2022Article
- A mixed-method evaluation of the relationship between Oxford classification scores and longitudinal changes in proteinuria in patients with immunoglobulin A nephropathy.Frontiers in endocrinology · 2022Article
- Beef production from cull dairy cows: a review from culling to consumption.Journal of animal science · 2021Review
- The Association of the Oxford Classification Score with Longitudinal Estimated Glomerular Filtration Rate Decline in Patients with Immunoglobulin A Nephropathy: A Mixed-Method Study.International journal of general medicine · 2021Article
- Forecasting beef production and quality using large-scale integrated data from Brazil.Journal of animal science · 2020Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Transportation losses of market-weight pigs are an animal welfare concern, and result in direct economic impact for producers and abattoirs. Such losses are related to multiple factors including pig genetics, human handling, management, and weather conditions. Understanding the factors associated with total transport losses (TTL) is important to the swine industry because it can aid decision-making, and help in the development of transportation strategies to minimize the risk of losses. Hence, the objective of this study was to investigate factors associated with TTL on market-weight pigs in typically field conditions for Midwestern United States using a generalized additive mixed model (GAMM). The final quasi-binomial GAMM included the fixed (main and interactions) effects of abattoir of destination, type of driver, average market weight, distance traveled, wind speed, precipitation, and temperature-humidity index (THI), as well as the random effects of truck companies and the combination of site of origin and period of the year. Results indicate significant associations between TTL and the main effect of all explanatory variables (P < 0.05), except for wind speed and precipitation. Interactions of average market weight × abattoir, and wind speed × precipitation were also significant. A complex nonlinear relationship between TTL and model covariates were observed for distance traveled, THI, and interaction terms. This study showed that TTL of market-weight pigs are caused by a complex system involving multiple interacting factors, which can be potentially managed to mitigate the risk of losses. In addition, the GAMM showed to be a simple and flexible approach to model TTL because it can capture nonlinear relationships, handle non-normal data, and can potentially accommodate data structure.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.