Evidence map›Paper›PMID 39910447›Full record

ArticleBMC plant biology2025

Optimized framework for evaluating F3 transgressive segregants in cayenne pepper.

Ifayanti Ridwan, Muh Farid, Feranita Haring, Nuniek Widiayani, Ahmad Yani, Nirwansyah Amier, Muhammad Alfan Ikhlasul Amal, Jekvy Hendra, Nawab Ali, Mekhled M Alenazi and 3 more

Erratum issuedAbstract read
In one paragraph

Article in BMC plant biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. 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
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Ifayanti RidwanDepartment of Agronomy, Faculty of Agriculture, Hasanuddin University, Makassar, 90245, Indonesia.
Muh FaridDepartment of Agronomy, Faculty of Agriculture, Hasanuddin University, Makassar, 90245, Indonesia.
Feranita HaringDepartment of Agronomy, Faculty of Agriculture, Hasanuddin University, Makassar, 90245, Indonesia.
Nuniek WidiayaniDepartment of Agronomy, Faculty of Agriculture, Hasanuddin University, Makassar, 90245, Indonesia.
Ahmad YaniAgrotechnology Bachelor Study Program, Faculty of Agriculture, Hasanuddin University, Makassar, 90245, Indonesia.
Nirwansyah AmierAgrotechnology Master Study Program, Faculty of Agriculture, Hasanuddin University, Makassar, 90245, Indonesia.
Muhammad Alfan Ikhlasul AmalAgricultural Science Doctoral Program, Graduate School, Hasanuddin University, Makassar, 90245, Indonesia.
Jekvy HendraDirectorate of Horticultural Protection, Agricultural Ministry, Jakarta, 12520, Indonesia.
Nawab AliDepartment of Biosystems and Agricultural Engineering (BAE), College of Agriculture and Natural Resources, Michigan State University, East Lansing, USA.
Mekhled M AlenaziDepartment of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia.
Mahmoud F SeleimanDepartment of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia.
Willy Bayuardi SuwarnoDepartment of Agronomy and Horticulture, Faculty of Agriculture, IPB University, Bogor, 11680, Indonesia.
Muhammad Fuad AnshoriDepartment of Agronomy, Faculty of Agriculture, Hasanuddin University, Makassar, 90245, Indonesia. fuad.anshori@unhas.ac.id.

Funding

Researchers Supporting Project number, King Saud University, Riyadh, Saudi Arabia RSPD2024R751the Directorate of Research, Technology and Community Service from the Ministry of Education, Culture, Research, and Technology of the Republic of Indonesia with the scheme of Penelitian Fundamental Reguler BIMA 02035/UN4.22/PT.01.03/2024
6 · The paper itself

Abstract

Developing F3 transgenic segregants has significant potential to improve cayenne pepper varieties. However, current evaluation methods are often inconsistent and inaccurate, hindering the identification of effective traits. Traditional approaches only focus on a few aspects, thus not covering the full potential performance of the genotype. Utilizing morphometric image processing and categorical parameter assessment can fill the gap of traditional approaches to improving accuracy and objectivity in evaluation. In addition, environmental factors affecting the evaluation process are not adequately considered, making the results unreliable. Therefore, a systematic evaluation framework integrating morphometric analysis, categorical assessment, and environmental correction is essential for optimizing F3 cayenne transgressive segregants. The study aims to develop a synchronized assessment and selection approach based on agronomic, fruit morphometric, and categorical traits in evaluating F3 cayenne transgressive segregants. This research was designed with a randomized completed block design with 16 transgressive segregant genotypes and three check varieties. Each genotype was repeated three times, resulting in 57 experimental units. Based on the results of this study, quantitative and categorical indices could be used to selectively and systematically evaluate potential transgressive segregants in F3 cayenne peppers. The quantitative index is formed from outcome selection criteria, number of productive branches, area, and major axes weighted through an unbiased linear estimation approach, heritability, and best path analysis. Seven genotypes demonstrated superior transgressive performance based on quantitative indices, with G10.9.2, G10.7.1, and G6.8.5 excelling in both agronomic traits and categorical evaluations. These lines can be recommended for yield evaluation and hybrid cross-parents.

Indexed as

CapsicumPlant BreedingPlants, Genetically ModifiedFruitGenotypePhenotypeBLUECapsicum frutescensCayenne fruitImage processingSelection index

Identifiers

PMID39910447
PMCPMC11800620

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.