Combination of Sensor Data and Health Monitoring for Early Detection of Subclinical Ketosis in Dairy Cows

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Subclinical ketosis is a metabolic disease in early lactation. It contributes to economic losses because of reduced milk yield and may promote the development of secondary diseases. Thus, an early detection seems desirable as it enables the farmer to initiate countermeasures. To support early detection, we examine different types of data recordings and use them to build a flexible algorithm that predicts the occurence of subclinical ketosis. This approach shows promising results and can be seen as a step toward automatic health monitoring in farm animals.

Related collections

Most cited references 30

Record: found
Abstract: not found
Article: not found

Deep learning for time series classification: a review

Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber … (2019)

0 comments Cited 335 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Invited review: sensors to support health management on dairy farms.

C Rutten, A Velthuis, W. Steeneveld … (2013)

Since the 1980s, efforts have been made to develop sensors that measure a parameter from an individual cow. The development started with individual cow recognition and was followed by sensors that measure the electrical conductivity of milk and pedometers that measure activity. The aim of this review is to provide a structured overview of the published sensor systems for dairy health management. The development of sensor systems can be described by the following 4 levels: (I) techniques that measure something about the cow (e.g., activity); (II) interpretations that summarize changes in the sensor data (e.g., increase in activity) to produce information about the cow's status (e.g., estrus); (III) integration of information where sensor information is supplemented with other information (e.g., economic information) to produce advice (e.g., whether to inseminate a cow or not); and (IV) the farmer makes a decision or the sensor system makes the decision autonomously (e.g., the inseminator is called). This review has structured a total of 126 publications describing 139 sensor systems and compared them based on the 4 levels. The publications were published in the Thomson Reuters (formerly ISI) Web of Science database from January 2002 until June 2012 or in the proceedings of 3 conferences on precision (dairy) farming in 2009, 2010, and 2011. Most studies concerned the detection of mastitis (25%), fertility (33%), and locomotion problems (30%), with fewer studies (16%) related to the detection of metabolic problems. Many studies presented sensor systems at levels I and II, but none did so at levels III and IV. Most of the work for mastitis (92%) and fertility (75%) is done at level II. For locomotion (53%) and metabolism (69%), more than half of the work is done at level I. The performance of sensor systems varies based on the choice of gold standards, algorithms, and test sizes (number of farms and cows). Studies on sensor systems for mastitis and estrus have shown that sensor systems are brought to a higher level; however, the need to improve detection performance still exists. Studies on sensor systems for locomotion problems have shown that the search continues for the most appropriate indicators, sensor techniques, and gold standards. Studies on metabolic problems show that it is still unclear which indicator reflects best the metabolic problems that should be detected. No systems with integrated decision support models have been found.

0 comments Cited 113 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Prevalence of subclinical ketosis and relationships with postpartum diseases in European dairy cows.

V S Suthar, J Canelas-Raposo, Ashok A. Deniz … (2013)

Subclinical ketosis (SCK) is defined as concentrations of β-hydroxybutyrate (BHBA) ≥ 1.2 to 1.4 mmol/L and it is considered a gateway condition for other metabolic and infectious disorders such as metritis, mastitis, clinical ketosis, and displaced abomasum. Reported prevalence rates range from 6.9 to 43% in the first 2 mo of lactation. However, there is a dearth of information on prevalence rates considering the diversity of European dairy farms. The objectives of this study were to (1) determine prevalence of SCK, (2) identify thresholds of BHBA, and (3) study their relationships with postpartum metritis, clinical ketosis, displaced abomasum, lameness, and mastitis in European dairy farms. From May to October 2011, a convenience sample of 528 dairy herds from Croatia, Germany, Hungary, Italy, Poland, Portugal, Serbia, Slovenia, Spain, and Turkey was studied. β-Hydroxybutyrate levels were measured in 5,884 cows with a handheld meter within 2 to 15 d in milk (DIM). On average, 11 cows were enrolled per farm and relevant information (e.g., DIM, postpartum diseases, herd size) was recorded. Using receiver operator characteristic curve analyses, blood BHBA thresholds were determined for the occurrence of metritis, mastitis, clinical ketosis, displaced abomasum, and lameness. Multivariate binary logistic regression models were built for each disease, considering cow as the experimental unit and herd as a random effect. Overall prevalence of SCK (i.e., blood BHBA ≥ 1.2 mmol/L) within 10 countries was 21.8%, ranging from 11.2 to 36.6%. Cows with SCK had 1.5, 9.5, and 5.0 times greater odds of developing metritis, clinical ketosis, and displaced abomasum, respectively. Multivariate binary logistic regression models demonstrated that cows with blood BHBA levels of ≥ 1.4, ≥ 1.1 and ≥ 1.7 mmol/L during 2 to 15 DIM had 1.7, 10.5, and 6.9 times greater odds of developing metritis, clinical ketosis, and displaced abomasum, respectively, compared with cows with lower BHBA blood levels. Interestingly, a postpartum blood BHBA threshold ≥ 1.1 mmol/L increased the odds for lameness in dairy cows 1.8 (95% confidence interval: 1.3 to 2.5) times. Overall, prevalence of SCK was high between 2 to 15 DIM and SCK increased the odds of metritis, clinical ketosis, lameness, and displaced abomasum in European dairy herds.

0 comments Cited 99 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Journal

Journal ID (nlm-ta): Sensors (Basel)

Journal ID (iso-abbrev): Sensors (Basel)

Journal ID (publisher-id): sensors

Title: Sensors (Basel, Switzerland)

Publisher: MDPI

ISSN (Electronic): 1424-8220

Publication date (Electronic): 08 March 2020

Publication date Collection: March 2020

Volume: 20

Issue: 5

Electronic Location Identifier: 1484

Affiliations

[1 ]Linz Center of Mechatronics GmbH, 4040 Linz, Austria

[2 ]Institute of Stochastics, Johannes Kepler University Linz, 4040 Linz, Austria; dmitry.efrosinin@ 123456jku.at

[3 ]SMARTBOW GmbH, 4675 Weibern, Austria; manfred.oehlschuster@ 123456zoetis.com

[4 ]Clinical Unit for Herd Health Management in Ruminants, University Clinic for Ruminants, Department for Farm Animals and Veterinary Public Health, University of Veterinary Medicine Vienna, 1210 Vienna, Austria; Erika.Gusterer@ 123456vetmeduni.ac.at (E.G.); Marc.Drillich@ 123456vetmeduni.ac.at (M.D.); Michael.Iwersen@ 123456vetmeduni.ac.at (M.I.)

Author notes

[* ]Correspondence: valentin.sturm@ 123456lcm.at

Author information

Dmitry Efrosinin https://orcid.org/0000-0002-0902-6640

Marc Drillich https://orcid.org/0000-0002-2824-8185

Michael Iwersen https://orcid.org/0000-0001-7893-6050

Article

Publisher ID: sensors-20-01484

DOI: 10.3390/s20051484

PMC ID: 7085771

PubMed ID: 32182701

SO-VID: 76ad2abb-53c8-4ff4-8bf4-81ed850ab7f2

License:

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/).

Combination of Sensor Data and Health Monitoring for Early Detection of Subclinical Ketosis in Dairy Cows

Read this article at

Abstract

Related collections

REPO4EU

Most cited references 30

Deep learning for time series classification: a review

Invited review: sensors to support health management on dairy farms.

Prevalence of subclinical ketosis and relationships with postpartum diseases in European dairy cows.

Author and article information

Journal

Affiliations

Author notes

Author information

Article

History

Categories

Comments

Comment on this article

Similar content 333

Cited by 6

Most referenced authors 409