Evidence map›Paper›PMID 41764445›Full record

ArticleBMC plant biology2026

Multivariate analysis and machine learning prediction of Sorghum cultivar traits under nitrogen regulation.

Muhammad Tanveer Altaf, Waqas Liaqat, Mehmet Bedir, Gönül Cömertpay, Seyid Amjad Ali, Muhammad Aasim, Muhammad Azhar Nadeem, Faheem Shehzad Baloch

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Article in BMC plant biology, 2026. 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

8 authors.

Muhammad Tanveer AltafDepartment of Field Crops, Faculty of Agriculture, Recep Tayyip Erdoğan University, Pazar, Rize, 53300, Türkiye. muhammadtanveer.altaf@erdogan.edu.tr.
Waqas LiaqatDepartment of Field Crops, Faculty of Agriculture, Recep Tayyip Erdoğan University, Pazar, Rize, 53300, Türkiye. waqasliaqat0043@gmail.com.
Mehmet BedirFaculty of Agricultural Sciences and Technologies, Sivas University of Science and Technology, Sivas, 58140, Türkiye.
Gönül CömertpayEastern Mediterranean Agricultural Research Institute, Adana, 01370, Türkiye.
Seyid Amjad AliDepartment of Information Systems and Technologies, Bilkent University, Ankara, 06800, Türkiye.
Muhammad AasimFaculty of Agricultural Sciences and Technologies, Sivas University of Science and Technology, Sivas, 58140, Türkiye.
Muhammad Azhar NadeemDepartment of Biotechnology, Faculty of Science, Mersin University, Yenişehir, Mersin, 33343, Türkiye.
Faheem Shehzad BalochDepartment of Biotechnology, Faculty of Science, Mersin University, Yenişehir, Mersin, 33343, Türkiye.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGenotypic differences in nitrogen use efficiency strongly influence sorghum growth and yield, highlighting the need for precise and reliable prediction of cultivar responses to nitrogen (N) availability. This study investigates the impact of two N treatments on sorghum cultivars, using artificial intelligence (AI) models for prediction.

resultsA randomized complete block design with two treatments: 0 kg N ha

conclusionLightGBM consistently achieved R

Indexed as

Machine LearningNitrogenSorghumBoosting Machine Learning AlgorithmsGenotypeMultivariate AnalysisPrediction AlgorithmsPredictive Learning ModelsRandom ForestNitrogenChlorophyll fluorescenceGrain yieldLight Gradient-Boosting MachineRandom ForestSPADStem diameterStomatal conductance

Identifiers

PMID41764445
PMCPMC13059567

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.