Evidence map›Paper›PMID 38879711›Full record

ArticleScientific reports2024

Climate-smart rice (Oryza sativa L.) genotypes identification using stability analysis, multi-trait selection index, and genotype-environment interaction at different irrigation regimes with adaptation to universal warming.

Muhammad Ashraful Habib, Mohammad Golam Azam, Md Ashraful Haque, Lutful Hassan, Mst Suhana Khatun, Swati Nayak, Hasan Muhammad Abdullah, Riaz Ullah, Essam A Ali, Nazmul Hossain and 2 more

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

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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

12 authors.

Muhammad Ashraful HabibDepartment of Genetics and Plant Breeding, Faculty of Agriculture, Bangladesh Agricultural University, Mymensingh, 2202, Bangladesh.
Mohammad Golam AzamPulses Research Centre, Bangladesh Agricultural Research Institute, Ishurdi, Pabna, 6620, Bangladesh.
Md Ashraful HaqueDepartment of Genetics and Plant Breeding, Faculty of Agriculture, Bangladesh Agricultural University, Mymensingh, 2202, Bangladesh.
Lutful HassanDepartment of Genetics and Plant Breeding, Faculty of Agriculture, Bangladesh Agricultural University, Mymensingh, 2202, Bangladesh.
Mst Suhana KhatunDepartment of Genetics and Plant Breeding, Faculty of Agriculture, Bangladesh Agricultural University, Mymensingh, 2202, Bangladesh.
Swati NayakRice Breeding Innovation Platform, International Rice Research Institute, 4031, Los Banos, Laguna, Philippines.
Hasan Muhammad AbdullahDepartment of Remote Sensing and GIS, Faculty of Forestry and Environment, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, 1706, Bangladesh.
Riaz UllahDepartment of Pharmacognosy, College of Pharmacy, King Saud University, 11451, Riyadh, Saudi Arabia.
Essam A AliDepartment of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, 11451, Riyadh, Saudi Arabia.
Nazmul HossainDepartment of Agronomy, Iowa State University, Ames, IA, 50010, USA.
Sezai ErcisliDepartment of Horticulture, Faculty of Agriculture, Ataturk University, 25240, Erzurum, , Turkey.
Umakanta SarkerDepartment of Genetics and Plant Breeding, Faculty of Agriculture, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, 1706, Bangladesh. umakanta@bsmrau.edu.bd.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Climate change has brought an alarming situation in the scarcity of fresh water for irrigation due to the present global water crisis, climate variability, drought, increasing demands of water from the industrial sectors, and contamination of water resources. Accurately evaluating the potential of future rice genotypes in large-scale, multi-environment experiments may be challenging. A key component of the accurate assessment is the examination of stability in growth contexts and genotype-environment interaction. Using a split-plot design with three replications, the study was carried out in nine locations with five genotypes under continuous flooding (CF) and alternate wet and dry (AWD) conditions. Utilizing the web-based warehouse inventory search tool (WIST), the water status was determined. To evaluate yield performance for stability and adaptability, AMMI and GGE biplots were used. The genotypes clearly reacted inversely to the various environments, and substantial interactions were identified. Out of all the environments, G3 (BRRI dhan29) had the greatest grain production, whereas G2 (Binadhan-8) had the lowest. The range between the greatest and lowest mean values of rice grain output (4.95 to 4.62 t ha

Indexed as

Agricultural IrrigationClimate ChangeGene-Environment InteractionGenotypeOryzaAdaptation, PhysiologicalDroughtsAMMIGGE biplot modelMGIDIRiceweb-based WISTYield

Identifiers

PMID38879711
PMCPMC11180187

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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.