Evidence map›Paper›PMID 36968404›Full record

ArticleFrontiers in plant science2023

Genetic trends for yield and key agronomic traits in pre-commercial and commercial maize varieties between 2008 and 2020 in Uganda.

Godfrey Asea, Daniel Bomet Kwemoi, Clay Sneller, Charles L Kasozi, Biswanath Das, Lennin Musundire, Dan Makumbi, Yoseph Beyene, Boddupalli M Prasanna

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Article in Frontiers in plant science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 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

9 authors.

Godfrey AseaNational Crops Resources Research Institute, National Agricultural Research Organization, Kampala, Uganda.
Daniel Bomet KwemoiNational Crops Resources Research Institute, National Agricultural Research Organization, Kampala, Uganda.
Clay SnellerDepartment of Horticulture and Crop Science, The Ohio State University, Wooster, OH, United States.
Charles L KasoziNational Crops Resources Research Institute, National Agricultural Research Organization, Kampala, Uganda.
Biswanath DasGlobal Maize Program, International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya.
Lennin MusundireGlobal Maize Program, International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya.
Dan MakumbiGlobal Maize Program, International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya.
Yoseph BeyeneGlobal Maize Program, International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya.
Boddupalli M PrasannaGlobal Maize Program, International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Estimating genetic gains is vital to optimize breeding programs for increased efficiency. Genetic gains should translate into productivity gains if returns to investments in breeding and impact are to be realized. The objective of this study was to estimate genetic gain for grain yield and key agronomic traits in pre-commercial and commercial maize varieties from public and private breeding programs tested in (i) national performance trials (NPT), (ii) era trial and, (iii) compare the trends with the national average. The study used (i) historical NPT data on 419 improved maize varieties evaluated in 23 trials at 6-8 locations each between 2008 and 2020, and (ii) data from an era trial of 54 maize hybrids released between 1999 and 2020. The NPT data was first analyzed using a mixed model and resulting estimate for each entry was regressed onto its first year of testing. Analysis was done over all entries, only entries from National Agricultural Research Organization (NARO), International Maize and Wheat Improvement Center (CIMMYT), or private seed companies. Estimated genetic gain was 2.25% or 81 kg ha

Indexed as

genetic gainsmaize breedingmaize yields and agronomic traitsproductivity gainsregression analysis

Identifiers

PMID36968404
PMCPMC10036907

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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.