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The Importance of Low Daily Risk for the Prediction of Treatment Events of Individual Dairy Cows with Sensor Systems

The prediction of health disorders is the goal of many sensor systems in dairy farming. Although mastitis and lameness are the most common health disorders in dairy cows, these diseases or treatments are a rare event related to a single day and cow. A number of studies already developed and evaluate...

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Detalles Bibliográficos
Autores principales: Post, Christian, Rietz, Christian, Büscher, Wolfgang, Müller, Ute
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7922278/
https://www.ncbi.nlm.nih.gov/pubmed/33671216
http://dx.doi.org/10.3390/s21041389
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author Post, Christian
Rietz, Christian
Büscher, Wolfgang
Müller, Ute
author_facet Post, Christian
Rietz, Christian
Büscher, Wolfgang
Müller, Ute
author_sort Post, Christian
collection PubMed
description The prediction of health disorders is the goal of many sensor systems in dairy farming. Although mastitis and lameness are the most common health disorders in dairy cows, these diseases or treatments are a rare event related to a single day and cow. A number of studies already developed and evaluated models for classifying cows in need of treatment for mastitis and lameness with machine learning methods, but few have illustrated the effects of the positive predictive value (PPV) on practical application. The objective of this study was to investigate the importance of low-frequency treatments of mastitis or lameness for the applicability of these classification models in practice. Data from three German dairy farms contained animal individual sensor data (milkings, activity, feed intake) and were classified using machine learning models developed in a previous study. Subsequently, different risk criteria (previous treatments, information from milk recording, early lactation) were designed to isolate high-risk groups. Restricting selection to cows with previous mastitis or hoof treatment achieved the highest increase in PPV from 0.07 to 0.20 and 0.15, respectively. However, the known low daily risk of a treatment per cow remains the critical factor that prevents the reduction of daily false-positive alarms to a satisfactory level. Sensor systems should be seen as additional decision-support aid to the farmers’ expert knowledge.
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spelling pubmed-79222782021-03-03 The Importance of Low Daily Risk for the Prediction of Treatment Events of Individual Dairy Cows with Sensor Systems Post, Christian Rietz, Christian Büscher, Wolfgang Müller, Ute Sensors (Basel) Article The prediction of health disorders is the goal of many sensor systems in dairy farming. Although mastitis and lameness are the most common health disorders in dairy cows, these diseases or treatments are a rare event related to a single day and cow. A number of studies already developed and evaluated models for classifying cows in need of treatment for mastitis and lameness with machine learning methods, but few have illustrated the effects of the positive predictive value (PPV) on practical application. The objective of this study was to investigate the importance of low-frequency treatments of mastitis or lameness for the applicability of these classification models in practice. Data from three German dairy farms contained animal individual sensor data (milkings, activity, feed intake) and were classified using machine learning models developed in a previous study. Subsequently, different risk criteria (previous treatments, information from milk recording, early lactation) were designed to isolate high-risk groups. Restricting selection to cows with previous mastitis or hoof treatment achieved the highest increase in PPV from 0.07 to 0.20 and 0.15, respectively. However, the known low daily risk of a treatment per cow remains the critical factor that prevents the reduction of daily false-positive alarms to a satisfactory level. Sensor systems should be seen as additional decision-support aid to the farmers’ expert knowledge. MDPI 2021-02-17 /pmc/articles/PMC7922278/ /pubmed/33671216 http://dx.doi.org/10.3390/s21041389 Text en © 2021 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Post, Christian
Rietz, Christian
Büscher, Wolfgang
Müller, Ute
The Importance of Low Daily Risk for the Prediction of Treatment Events of Individual Dairy Cows with Sensor Systems
title The Importance of Low Daily Risk for the Prediction of Treatment Events of Individual Dairy Cows with Sensor Systems
title_full The Importance of Low Daily Risk for the Prediction of Treatment Events of Individual Dairy Cows with Sensor Systems
title_fullStr The Importance of Low Daily Risk for the Prediction of Treatment Events of Individual Dairy Cows with Sensor Systems
title_full_unstemmed The Importance of Low Daily Risk for the Prediction of Treatment Events of Individual Dairy Cows with Sensor Systems
title_short The Importance of Low Daily Risk for the Prediction of Treatment Events of Individual Dairy Cows with Sensor Systems
title_sort importance of low daily risk for the prediction of treatment events of individual dairy cows with sensor systems
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7922278/
https://www.ncbi.nlm.nih.gov/pubmed/33671216
http://dx.doi.org/10.3390/s21041389
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