Evidence map›Paper›PMID 35671336›Full record

ArticleJournal of animal science2022

Application of a hand-held laser methane detector for measuring enteric methane emissions from cattle in intensive farming.

Kyewon Kang, Hyunjin Cho, Sinyong Jeong, Seoyoung Jeon, Mingyung Lee, Seul Lee, Yulchang Baek, Joonpyo Oh, Seongwon Seo

Open access · greenAbstract readRandomized Controlled Trial, Veterinary
In one paragraph

Article in Journal of animal science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
2.0field-weighted citation impact, top 15% 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

12 citing papers in PubMed, 17 citations in OpenAlex.

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  12. Oral administration ofFrontiers in veterinary science · 2023
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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

9 authors at 2 institutions in 1 country.

Kyewon KangDivision of Animal and Dairy Sciences, Chungnam National University, Daejeon 34134, Republic of Korea.
Hyunjin ChoDivision of Animal and Dairy Sciences, Chungnam National University, Daejeon 34134, Republic of Korea.ORCID 0000-0001-8676-1481
Sinyong JeongDivision of Animal and Dairy Sciences, Chungnam National University, Daejeon 34134, Republic of Korea.
Seoyoung JeonDivision of Animal and Dairy Sciences, Chungnam National University, Daejeon 34134, Republic of Korea.
Mingyung LeeDivision of Animal and Dairy Sciences, Chungnam National University, Daejeon 34134, Republic of Korea.
Seul LeeNational Institute of Animal Science, 1500, Kongjwipatjwi-ro, Iseo-myeon, Wanju-Gun, Jeollabuk-do 55365, Republic of Korea.
Yulchang BaekNational Institute of Animal Science, 1500, Kongjwipatjwi-ro, Iseo-myeon, Wanju-Gun, Jeollabuk-do 55365, Republic of Korea.
Joonpyo OhCargill Animal Nutrition Korea, Seongnam 13630, Republic of Korea.
Seongwon SeoDivision of Animal and Dairy Sciences, Chungnam National University, Daejeon 34134, Republic of Korea.ORCID 0000-0002-4131-0545
Chungnam National University · KRNational Institute of Animal Science · KR

Funding

Cooperative Research Program for Agriculture Science and Technology Development PJ01477801
6 · The paper itself

Abstract

The hand-held laser methane detector (LMD) technique has been suggested as an alternative method for measuring methane (CH4) emissions from enteric fermentation of ruminants in the field. This study aimed to establish a standard procedure for using LMD to assess CH4 production in cattle and evaluate the efficacy of the protocol to detect differences in CH4 emissions from cattle fed with diets of different forage-to-concentrate (FC) ratios. Experiment 1 was conducted with four Hanwoo steers (584 ± 57.4 kg body weight [BW]) individually housed in metabolic cages. The LMD was installed on a tripod aimed at the animal's nostril, and the CH4 concentration in the exhaled gas was measured for 6 min every hour for 2 consecutive days. For the data processing, the CH4 concentration peaks were identified by the automatic multi-scale peak detection algorithm. The peaks were then separated into those from respiration and eructation by fitting combinations of two of the four distribution functions (normal, log-normal, gamma, and Weibull) using the mixdist R package. In addition, the most appropriate time and number of consecutive measurements to represent the daily average CH4 concentration were determined. In experiment 2, 30 Hanwoo growing steers (343 ± 24.6 kg BW), blocked by BW, were randomly divided into three groups. Three different diets were provided to each group: high FC ratio (35:65) with low-energy concentrate (HFC-LEC), high FC ratio with high-energy concentrate (HFC-HEC), and low FC ratio (25:75) with high-energy concentrate (LFC-HEC). After 10 d of feeding the diets, the CH4 concentrations for all steers were measured and analyzed in duplicate according to the protocol established in experiment 1. In experiment 1, the mean correlation coefficient between the CH4 concentration from respiration and eructation was highest when a combination of two normal distributions was assumed (r = 0.79). The most appropriate measurement times were as follows: 2 h and 1 h before, and 1 h and 2 h after morning feeding. Compared with LFC-HEC, HFC-LEC showed 49% and 57% higher CH4 concentrations in exhaled gas from respiration and eructation (P < 0.01). In conclusion, the LMD method can be applied to evaluate differences in CH4 emissions in cattle using the protocol established in this study.

Indexed as

EructationMethaneAgricultureAnimal FeedAnimalsBody WeightCattleDietLasersRumenMethanecattleeructationforage-to-concentrate ratiolaser methane detectormethane emissionrespiration

Identifiers

PMID35671336
PMCPMC9387598
OpenAlexW4281659566

What OpenQuestion holds

Textmetadata
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.