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Record W4309000362 · doi:10.5539/jas.v14n12p37

Genotypic Yield Stability of Wild and Landrace Sorghum Species Under Drought Stress and Striga Infestation

2022· article· en· W4309000362 on OpenAlexvenueno aff
Kahiu Ngugi, Nicoleta Muchira, Grace Ochieng, Damaris A. Odeny, Eric Manyasa

Bibliographic record

VenueJournal of Agricultural Science · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Parasitism and Resistance
Canadian institutionsnot available
FundersFonds National de la Recherche LuxembourgGovernment of the Republic of Kenya
KeywordsStrigaBiologySorghumAgronomyDrought toleranceStriga hermonthicaCropGermplasmInfestationResistance (ecology)Population

Abstract

fetched live from OpenAlex

Recurring drought stress cycles and widespread striga (Striga hermonthica (Del.) Benth) infestations are two of the major constraints of sorghum production in sub-Saharan Africa (SSA) where they cause a crop loss of about 60 billion US dollars and affect a population of about 100 M people annually. Plant breeders continue to employ conventional and molecular crop breeding strategies in the search for durable genetic resistance/tolerance mechanisms or for germplasm with genes against these two constraints. Crop wild relatives and landraces remain valuable resources of resistance/tolerance genes and have been utilized in the past to improve tolerance to drought stress and resistance to striga. The aim of this study was to assess the stability of performance of 64 sorghum wild relatives, landraces and progenies from some generation of crosses under striga infested and drought stress conditions in agroecological environments endemic for these two stresses. The performance of the genotypes under drought stress was assessed in well-watered and in water stressed conditions at the Kenya Agricultural Livestock Research Organization (KALRO) Kiboko Research Centre whereas the same set was evaluated under striga artificially infested field and potted trials at the KALRO Alupe Research Centre during 2018/2019 rainy seasons. Genotypes, B35 × ICSV III N, Macia, N13, ICSV 111 IN, F6YQ212 × B35, SRN39, GENO47293, ICSV 111 IN × B35, IS9830, Framida, GENO 45827, F6YQ212, B35 × AKUOR ACHOT were found to maintain stable high yields in both striga and drought conditions. The results here, showed that Genotype, Genotype × Environment (GGE) interaction partitioned genotypes in two of the four mega-environments according to their stability and mean grain yield (GY) and identified representative genotypes of the two traits that could be exploited to develop superior sorghum varieties adapted to drought and striga prone environments.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.212
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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