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Thomann B, Kuntzer T, Schüpbach-Regula G, Rieder S. Investigating the use of machine learning algorithms to support risk-based animal welfare inspections of cattle and pig farms. Front Vet Sci 2024; 11:1401007. [PMID: 39193368 PMCID: PMC11347403 DOI: 10.3389/fvets.2024.1401007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 07/29/2024] [Indexed: 08/29/2024] Open
Abstract
In livestock production, animal-related data are often registered in specialised databases and are usually not interconnected, except for a common identifier. Analysis of combined datasets and the possible inclusion of third-party information can provide a more complete picture or reveal complex relationships. The aim of this study was to develop a risk index to predict farms with an increased likelihood for animal welfare violations, defined as non-compliance during on-farm welfare inspections. A data-driven approach was chosen for this purpose, focusing on the combination of existing Swiss government databases and registers. Individual animal-level data were aggregated at the herd level. Since data collection and availability were best for cattle and pigs, the focus was on these two livestock species. We present machine learning models that can be used as a tool to plan and optimise risk-based on-farm welfare inspections by proposing a consolidated list of priority holdings to be visited. The results of previous on-farm welfare inspections were used to calibrate a binary welfare index, which is the prediction goal. The risk index is based on proxy information, such as the participation in animal welfare programmes with structured housing and outdoor access, herd type and size, or animal movement data. Since transparency of the model is critical both for public acceptance of such a data-driven index and farm control planning, the Random Forest model, for which the decision process can be illustrated, was investigated in depth. Using historical inspection data with an overall low prevalence of violations of approximately 4% for both species, the developed index was able to predict violations with a sensitivity of 81.2 and 79.5% for cattle and pig farms, respectively. The study has shown that combining multiple and heterogeneous data sources improves the quality of the models. Furthermore, privacy-preserving methods are applied to a research environment to explore the available data before restricting the feature space to the most relevant. This study demonstrates that data-driven monitoring of livestock populations is already possible with the existing datasets and the models developed can be a useful tool to plan and conduct risk-based animal welfare inspection.
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Affiliation(s)
- Beat Thomann
- Veterinary Public Health Institute, Vetsuisse Faculty, University of Bern, Bern, Switzerland
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Lemmens L, Schodl K, Fuerst-Waltl B, Schwarzenbacher H, Egger-Danner C, Linke K, Suntinger M, Phelan M, Mayerhofer M, Steininger F, Papst F, Maurer L, Kofler J. The Combined Use of Automated Milking System and Sensor Data to Improve Detection of Mild Lameness in Dairy Cattle. Animals (Basel) 2023; 13:ani13071180. [PMID: 37048436 PMCID: PMC10093521 DOI: 10.3390/ani13071180] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 03/23/2023] [Accepted: 03/25/2023] [Indexed: 03/30/2023] Open
Abstract
This study aimed to develop a tool to detect mildly lame cows by combining already existing data from sensors, AMSs, and routinely recorded animal and farm data. For this purpose, ten dairy farms were visited every 30–42 days from January 2020 to May 2021. Locomotion scores (LCS, from one for nonlame to five for severely lame) and body condition scores (BCS) were assessed at each visit, resulting in a total of 594 recorded animals. A questionnaire about farm management and husbandry was completed for the inclusion of potential risk factors. A lameness incidence risk (LCS ≥ 2) was calculated and varied widely between farms with a range from 27.07 to 65.52%. Moreover, the impact of lameness on the derived sensor parameters was inspected and showed no significant impact of lameness on total rumination time. Behavioral patterns for eating, low activity, and medium activity differed significantly in lame cows compared to nonlame cows. Finally, random forest models for lameness detection were fit by including different combinations of influencing variables. The results of these models were compared according to accuracy, sensitivity, and specificity. The best performing model achieved an accuracy of 0.75 with a sensitivity of 0.72 and specificity of 0.78. These approaches with routinely available data and sensor data can deliver promising results for early lameness detection in dairy cattle. While experimental automated lameness detection systems have achieved improved predictive results, the benefit of this presented approach is that it uses results from existing, routinely recorded, and therefore widely available data.
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Affiliation(s)
- Lena Lemmens
- Department of Farm Animals and Veterinary Public Health, University Clinic for Ruminants, University of Veterinary Medicine Vienna, 1210 Vienna, Austria
| | - Katharina Schodl
- Department of Sustainable Agricultural Systems, Institute of Livestock Sciences, University of Natural Resources and Life Sciences Vienna, 1180 Vienna, Austria
| | - Birgit Fuerst-Waltl
- Department of Sustainable Agricultural Systems, Institute of Livestock Sciences, University of Natural Resources and Life Sciences Vienna, 1180 Vienna, Austria
| | | | | | - Kristina Linke
- ZuchtData EDV-Dienstleistungen GmbH, 1200 Vienna, Austria
| | | | | | | | | | - Franz Papst
- Institute of Technical Informatics, Graz University of Technology, 8010 Graz, Austria
- Austria and Complexity Science Hub Vienna, 1080 Vienna, Austria
| | - Lorenz Maurer
- Department of Sustainable Agricultural Systems, Institute of Livestock Sciences, University of Natural Resources and Life Sciences Vienna, 1180 Vienna, Austria
| | - Johann Kofler
- Department of Farm Animals and Veterinary Public Health, University Clinic for Ruminants, University of Veterinary Medicine Vienna, 1210 Vienna, Austria
- Correspondence:
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Dittrich I, Gertz M, Maassen-Francke B, Krudewig KH, Junge W, Krieter J. Estimating risk probabilities for sickness from behavioural patterns to identify health challenges in dairy cows with multivariate cumulative sum control charts. Animal 2022; 16:100601. [DOI: 10.1016/j.animal.2022.100601] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 06/30/2022] [Accepted: 07/01/2022] [Indexed: 11/16/2022] Open
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A systematic approach to analyse the impact of farm-profiles on bovine health. Sci Rep 2021; 11:21152. [PMID: 34707145 PMCID: PMC8551198 DOI: 10.1038/s41598-021-00469-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 07/23/2021] [Indexed: 11/21/2022] Open
Abstract
In this study we present systematic framework to analyse the impact of farm profiles as combinations of environmental conditions and management practices on common diseases in dairy cattle. The data used for this secondary data analysis includes observational data from 166 farms with a total of 5828 dairy cows. Each farm is characterised by features from five categories: husbandry, feeding, environmental conditions, housing, and milking systems. We combine dimension reduction with clustering techniques to identify groups of similar farm attributes, which we refer to as farm profiles. A statistical analysis of the farm profiles and their related disease risks is carried out to study the associations between disease risk, farm membership to a specific cluster as well as variables that characterise a given cluster by means of a multivariate regression model. The disease risks of five different farm profiles arise as the result of complex interactions between environmental conditions and farm management practices. We confirm previously documented relationships between diseases, feeding and husbandry. Furthermore, novel associations between housing and milking systems and specific disorders like lameness and ketosis have been discovered. Our approach contributes to paving a way towards a more holistic and data-driven understanding of bovine health and its risk factors.
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