Evidence map›Paper›PMID 41460447›Full record

ArticleTropical animal health and production2025

Predicting performance traits in Murrah buffaloes using machine learning: a comparative approach.

Rakesh Nehra, Yogesh C Bangar, C S Patil, Kamaldeep Dhundwal, Poonam Sevda

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in Tropical animal health and production, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Rakesh NehraDepartment of Animal Genetics and Breeding, Lala Lajpat Rai University of Veterinary and Animal Sciences, Hisar, Haryana, 125004, India.
Yogesh C BangarDepartment of Animal Genetics and Breeding, Lala Lajpat Rai University of Veterinary and Animal Sciences, Hisar, Haryana, 125004, India. yogeshbangar07@gmail.com.ORCID http://orcid.org/0000-0002-8861-161X
C S PatilDepartment of Animal Genetics and Breeding, Lala Lajpat Rai University of Veterinary and Animal Sciences, Hisar, Haryana, 125004, India.
Kamaldeep DhundwalDepartment of Animal Genetics and Breeding, Lala Lajpat Rai University of Veterinary and Animal Sciences, Hisar, Haryana, 125004, India.
Poonam SevdaDepartment of Animal Genetics and Breeding, Lala Lajpat Rai University of Veterinary and Animal Sciences, Hisar, Haryana, 125004, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study evaluated the comparative performance of nine machine learning (ML) algorithms for predicting 305-day first lactation milk yield (305FLMY) and total milk yield (TMY) in Murrah buffaloes. Data from 657 animals recorded over 24 years (2000-2023) were used, incorporating inputs such as animal details, year of calving, age at first calving (days), peak yield (kg), days to peak yield (DPY), and test day milk yields on the 6

Indexed as

BuffaloesDairyingLactationMachine LearningMilkAnimalsFemaleMachine learningMilk productionMurrah buffaloesPrediction

Identifiers

PMID41460447

What OpenQuestion holds

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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.