Evidence map›Paper›PMID 36899660›Full record

ArticleAnimals : an open access journal from MDPI2023

The Development of Smart Dairy Farm System and Its Application in Nutritional Grouping and Mastitis Prediction.

Tingting Hu, Jinmen Zhang, Xinrui Zhang, Yidan Chen, Renlong Zhang, Kaijun Guo

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Article in Animals : an open access journal from MDPI, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Article
  2. Article
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.

Tingting HuCollege of Animal Science and Technology, Beijing University of Agriculture, Beijing 102206, China.ORCID 0000-0001-8819-5073
Jinmen ZhangDepartment of Computer and Information Engineering, Beijing University of Agriculture, Beijing 102206, China.
Xinrui ZhangCollege of Animal Science and Technology, Beijing University of Agriculture, Beijing 102206, China.
Yidan ChenCollege of Animal Science and Technology, Beijing University of Agriculture, Beijing 102206, China.
Renlong ZhangDepartment of Computer and Information Engineering, Beijing University of Agriculture, Beijing 102206, China.
Kaijun GuoCollege of Animal Science and Technology, Beijing University of Agriculture, Beijing 102206, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In order to study the smart management of dairy farms, this study combined Internet of Things (IoT) technology and dairy farm daily management to form an intelligent dairy farm sensor network and set up a smart dairy farm system (SDFS), which could provide timely guidance for dairy production. To illustrate the concept and benefits of the SDFS, two application scenarios were sampled: (1) Nutritional grouping (NG): grouping cows according to the nutritional requirements by considering parities, days in lactation, dry matter intake (DMI), metabolic protein (MP), net energy of lactation (NEL), etc. By supplying feed corresponding to nutritional needs, milk production, methane and carbon dioxide emissions were compared with those of the original farm grouping (OG), which was grouped according to lactation stage. (2) Mastitis risk prediction: using the dairy herd improvement (DHI) data of the previous 4 lactation months of the dairy cows, logistic regression analysis was applied to predict dairy cows at risk of mastitis in successive months in order to make suitable measurements in advance. The results showed that compared with OG, NG significantly increased milk production and reduced methane and carbon dioxide emissions of dairy cows (

Indexed as

dairy cowsgreenhouse gas reductionmastitis predictionnutritional groupingsmart dairy farm system

Identifiers

PMID36899660
PMCPMC10000150

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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.