Evidence map›Paper›PMID 38773324›Full record

ArticleScientific reports2024

Envirotype-based delineation of environmental effects and genotype × environment interactions in Indian soybean (Glycine max, L.).

Vennampally Nataraj, Sanjay Gupta, K H Singh, Prince Choyal, Raghavendra Nargund, M Shivakumar, Nisha Agrawal, Giriraj Kumawat, Vangala Rajesh, Rakesh Kumar Verma and 3 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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
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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

13 authors.

Vennampally NatarajICAR-Indian Institute of Soybean Research, Indore, Madhya Pradesh, 452001, India.
Sanjay GuptaICAR-Indian Institute of Soybean Research, Indore, Madhya Pradesh, 452001, India. sanitaishu@gmail.com.
K H SinghICAR-Indian Institute of Soybean Research, Indore, Madhya Pradesh, 452001, India.
Prince ChoyalICAR-Indian Institute of Soybean Research, Indore, Madhya Pradesh, 452001, India.
Raghavendra NargundICAR-Indian Institute of Soybean Research, Indore, Madhya Pradesh, 452001, India.
M ShivakumarICAR-Indian Institute of Soybean Research, Indore, Madhya Pradesh, 452001, India.
Nisha AgrawalICAR-Indian Institute of Soybean Research, Indore, Madhya Pradesh, 452001, India.
Giriraj KumawatICAR-Indian Institute of Soybean Research, Indore, Madhya Pradesh, 452001, India.
Vangala RajeshICAR-Indian Institute of Soybean Research, Indore, Madhya Pradesh, 452001, India.
Rakesh Kumar VermaICAR-Indian Institute of Soybean Research, Indore, Madhya Pradesh, 452001, India.
Gyanesh K SatputeICAR-Indian Institute of Soybean Research, Indore, Madhya Pradesh, 452001, India.
Bairi SrikanthICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi, 110012, India.
Savita KolheICAR-Indian Institute of Soybean Research, Indore, Madhya Pradesh, 452001, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Soybean is a rainfed crop grown across a wide range of environments in India. Its grain yield is a complex trait governed by many minor genes and influenced by environmental effects and genotype × environment interactions. In the current investigation, grain yield data of different sets of 41, 30 and 48 soybean genotypes evaluated during 2019, 2020 and 2021, respectively across 19 locations and twenty years' data on 19 different climatic parameters at these locations was used to study the environmental effects on grain yield, to understand the genotype × environment interactions and to identify the mega-environments. Through analysis of variance (ANOVA), it was found that predominant portion of the variation was explained by environmental effects (E) (53.89, 54.86 and 60.56% during 2019, 2020 and 2021, respectively), followed by genotype × environment interactions (GEI) (31.29, 33.72 and 28.82% during 2019, 2020 and 2021, respectively). Principal Component Analysis (PCA) revealed that grain yield was positively associated with RH (Relative humidity at 2 m height), FRUE (Effect of temperature on radiation use efficiency), WSM (Wind speed at 2 m height) and RTA (Global solar radiation based on latitude and Julian day) and negatively associated with VPD (Deficit of vapour pressure), Trange (Daily temperature range), ETP (Evapotranspiration), SW (Insolation incident on a horizontal surface), n (Actual duration of sunshine) and N (Daylight hours). Identification of mega-environments is critical in enhancing the selection gain, productivity and varietal recommendation. Through envirotyping and genotype main effect plus genotype by environment interaction (GGE) biplot methods, nineteen locations across India were grouped into four mega-environments (MEs). ME1 included five locations viz., Bengaluru, Pune, Dharwad, Kasbe Digraj and Umiam. Eight locations-Anand, Amreli, Lokbharti, Bidar, Parbhani, Ranchi, Bhawanipatna and Raipur were included in ME2. Kota and Morena constitutes ME3, while Palampur, Imphal, Mojhera and Almora were included in ME4. Locations Imphal, Bidar and Raipur were found to be both discriminative and representative; these test locations can be utilized in developing wider adaptable soybean cultivars. Pune and Amreli were found to be high-yielding locations and can be used in large scale breeder seed production.

Indexed as

Gene-Environment InteractionGenotypeGlycine maxEnvironmentIndiaPrincipal Component AnalysisEnvirotypingGGE biplotGrain yieldMega-environment and Soybean

Identifiers

PMID38773324
PMCPMC11109282

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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.