Evidence map›Paper›PMID 31947639›Full record

ArticleSensors (Basel, Switzerland)2020

Assessment of Laying Hens' Thermal Comfort Using Sound Technology.

Xiaodong Du, Lenn Carpentier, Guanghui Teng, Mulin Liu, Chaoyuan Wang, Tomas Norton

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 1 pooled it
7.7field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed, 1 synthesis or guideline pooled it, 70 citations in OpenAlex.

  1. Pooled it
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  9. Article
  10. Laying Hens: Why Smothering and Not Surviving?-A Literature Review.Animals : an open access journal from MDPI · 2024
    Review
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  20. Transforming the Adaptation Physiology of Farm Animals through Sensors.Animals : an open access journal from MDPI · 2020
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 2 countries.

Xiaodong DuCollege of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, China.ORCID 0000-0002-9631-4201
Lenn CarpentierDivision Measure, Model & Mange Bioresponses, Department of Biosystems, KU Leuven, Kasteelpark Arenberg 30, 3001 Heverlee, Belgium.
Guanghui TengCollege of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, China.
Mulin LiuCollege of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, China.
Chaoyuan WangCollege of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, China.
Tomas NortonDivision Measure, Model & Mange Bioresponses, Department of Biosystems, KU Leuven, Kasteelpark Arenberg 30, 3001 Heverlee, Belgium.ORCID 0000-0002-0161-3189
China Agricultural University · CNKU Leuven · BE

Funding

National Key Research and Development Program of China 2016YFD0700204National Key Research and Development Program of China 2017YFD0701602
6 · The paper itself

Abstract

Heat stress is one of the most important environmental stressors facing poultry production and welfare worldwide. The detrimental effects of heat stress on poultry range from reduced growth and egg production to impaired health. Animal vocalisations are associated with different animal responses and can be used as useful indicators of the state of animal welfare. It is already known that specific chicken vocalisations such as alarm, squawk, and gakel calls are correlated with stressful events, and therefore, could be used as stress indicators in poultry monitoring systems. In this study, we focused on developing a hen vocalisation detection method based on machine learning to assess their thermal comfort condition. For extraction of the vocalisations, nine source-filter theory related temporal and spectral features were chosen, and a support vector machine (SVM) based classifier was developed. As a result, the classification performance of the optimal SVM model was 95.1 ± 4.3% (the sensitivity parameter) and 97.6 ± 1.9% (the precision parameter). Based on the developed algorithm, the study illustrated that a significant correlation existed between specific vocalisations (alarm and squawk call) and thermal comfort indices (temperature-humidity index, THI) (alarm-THI, R = -0.414, P = 0.01; squawk-THI, R = 0.594, P = 0.01). This work represents the first step towards the further development of technology to monitor flock vocalisations with the intent of providing producers an additional tool to help them actively manage the welfare of their flock.

Indexed as

Support Vector MachineAnimal HusbandryAnimalsAnimal WelfareChickensFemaleHeat Stress DisordersHousing, AnimalHumiditySignal Processing, Computer-AssistedSound SpectrographyTemperatureVocalization, Animalanimal vocalisationanimal welfarelaying hensSVMTHI

Identifiers

PMID31947639
PMCPMC7013866
OpenAlexW2999436028

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.