Genotypic Yield Stability of Wild and Landrace Sorghum Species Under Drought Stress and Striga Infestation
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".