Welfare Health and Productivity in Commercial Pig Herds.

health herd management monitoring technologies pigs welfare

Journal

Animals : an open access journal from MDPI
ISSN: 2076-2615
Titre abrégé: Animals (Basel)
Pays: Switzerland
ID NLM: 101635614

Informations de publication

Date de publication:
20 Apr 2021
Historique:
received: 15 03 2021
revised: 16 04 2021
accepted: 17 04 2021
entrez: 30 4 2021
pubmed: 1 5 2021
medline: 1 5 2021
Statut: epublish

Résumé

In recent years, there have been very dynamic changes in both pork production and pig breeding technology around the world. The general trend of increasing the efficiency of pig production, with reduced employment, requires optimisation and a comprehensive approach to herd management. One of the most important elements on the way to achieving this goal is to maintain animal welfare and health. The health of the pigs on the farm is also a key aspect in production economics. The need to maintain a high health status of pig herds by eliminating the frequency of different disease units and reducing the need for antimicrobial substances is part of a broadly understood high potential herd management strategy. Thanks to the use of sensors (cameras, microphones, accelerometers, or radio-frequency identification transponders), the images, sounds, movements, and vital signs of animals are combined through algorithms and analysed for non-invasive monitoring of animals, which allows for early detection of diseases, improves their welfare, and increases the productivity of breeding. Automated, innovative early warning systems based on continuous monitoring of specific physiological (e.g., body temperature) and behavioural parameters can provide an alternative to direct diagnosis and visual assessment by the veterinarian or the herd keeper.

Identifiants

pubmed: 33924224
pii: ani11041176
doi: 10.3390/ani11041176
pmc: PMC8074599
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Subventions

Organisme : Faculty of Veterinary Medicine and Animal Science University of Life Sciences
ID : 506.569.05.00

Références

J Neurosci Methods. 2005 Apr 30;143(2):123-32
pubmed: 15814144
Sensors (Basel). 2016 May 02;16(5):
pubmed: 27144572
Sensors (Basel). 2019 Apr 17;19(8):
pubmed: 30999637
Sensors (Basel). 2018 Feb 24;18(2):
pubmed: 29495290
Anim Behav. 1998 Aug;56(2):265-274
pubmed: 9787017
Sensors (Basel). 2019 Feb 19;19(4):
pubmed: 30791377
Physiol Behav. 2015 Jan;138:37-51
pubmed: 25447478
J Dairy Sci. 2008 Sep;91(9):3439-53
pubmed: 18765602
J Anim Sci. 2009 Dec;87(12):4173-80
pubmed: 19684272
Sensors (Basel). 2013 Sep 25;13(10):12929-42
pubmed: 24072029
Animals (Basel). 2020 Oct 01;10(10):
pubmed: 33019558
Sensors (Basel). 2019 Jan 31;19(3):
pubmed: 30709013
J Dairy Sci. 2016 Sep;99(9):7714-7725
pubmed: 27320661
Animal. 2014 Nov;8(11):1881-8
pubmed: 25075605
Sensors (Basel). 2019 Mar 08;19(5):
pubmed: 30857169
PLoS One. 2019 Dec 23;14(12):e0226669
pubmed: 31869364
Sensors (Basel). 2019 Aug 29;19(17):
pubmed: 31470571
Animals (Basel). 2020 Aug 28;10(9):
pubmed: 32872206
Vet J. 2015 Jul;205(1):38-43
pubmed: 25986130
Rev Sci Tech. 2012 Aug;31(2):605-17, 591-604
pubmed: 23413736
J Anim Sci. 2000 Nov;78(11):2821-31
pubmed: 11063304
Contemp Top Lab Anim Sci. 1998 May;37(3):51-55
pubmed: 12456161
Rev Sci Tech. 2014 Apr;33(1):189-96
pubmed: 25000791
Sensors (Basel). 2018 Oct 24;18(11):
pubmed: 30352969
Lancet. 2008 Apr 19;371(9621):1364-74
pubmed: 18424325
Vet J. 2016 Nov;217:43-51
pubmed: 27810210
Animals (Basel). 2019 Mar 31;9(4):
pubmed: 30935123
Acta Vet Scand Suppl. 2003;98:21-32
pubmed: 15259777
Vet Rec. 2006 Mar 11;158(10):331-4
pubmed: 16531581
Transbound Emerg Dis. 2017 Apr;64(2):364-373
pubmed: 25955521
Sensors (Basel). 2017 Nov 29;17(12):
pubmed: 29186060
ALTEX. 2003;20(2):65-70
pubmed: 12764542
Acta Vet Scand. 2010 May 05;52:29
pubmed: 20444254
Acta Vet Scand. 2015 Feb 03;57:5
pubmed: 25644397
Behav Processes. 2020 Dec;181:104262
pubmed: 33049377
Sensors (Basel). 2018 Jan 10;18(1):
pubmed: 29320395
Animal. 2017 Jan;11(1):131-139
pubmed: 27353419
J Anim Sci. 2014 Sep;92(9):3925-36
pubmed: 25057024

Auteurs

Przemysław Racewicz (P)

Laboratory of Veterinary Public Health Protection, Department of Animal Breeding and Product Quality Assessment, Poznan University of Life Sciences, Słoneczna 1, 62-002 Suchy Las, Poland.

Agnieszka Ludwiczak (A)

Department of Animal Breeding and Product Quality Assessment, Poznan University of Life Sciences, Słoneczna 1, 62-002 Suchy Las, Poland.

Ewa Skrzypczak (E)

Department of Animal Breeding and Product Quality Assessment, Poznan University of Life Sciences, Słoneczna 1, 62-002 Suchy Las, Poland.

Joanna Składanowska-Baryza (J)

Department of Animal Breeding and Product Quality Assessment, Poznan University of Life Sciences, Słoneczna 1, 62-002 Suchy Las, Poland.

Hanna Biesiada (H)

Laboratory of Veterinary Public Health Protection, Department of Animal Breeding and Product Quality Assessment, Poznan University of Life Sciences, Słoneczna 1, 62-002 Suchy Las, Poland.

Tomasz Nowak (T)

Department of Genetics and Animal Breeding, Animal Reproduction Laboratory, Poznan University of Life Sciences, 60-637 Poznan, Poland.

Sebastian Nowaczewski (S)

Department of Animal Breeding and Product Quality Assessment, Poznan University of Life Sciences, Słoneczna 1, 62-002 Suchy Las, Poland.

Maciej Zaborowicz (M)

Institute of Biosystems Engineering, Poznan University of Life Sciences, 60-637 Poznan, Poland.

Marek Stanisz (M)

Department of Animal Breeding and Product Quality Assessment, Poznan University of Life Sciences, Słoneczna 1, 62-002 Suchy Las, Poland.

Piotr Ślósarz (P)

Department of Animal Breeding and Product Quality Assessment, Poznan University of Life Sciences, Słoneczna 1, 62-002 Suchy Las, Poland.

Classifications MeSH