Growth of Rice Varieties in Different Kenyan Soil Types Under Water-Deficit Conditions
Bibliographic record
Abstract
The growth of 20 rice varieties, including both lowland and upland varieties, was evaluated in Kenya under well-watered and water-deficit conditions in four different soil types (black cotton, red clay, volcanic ash, and sandy clay) to compare the adaptability of these varieties to the two water regimes. Under water-deficit condition, after terminating irrigation, the reduction in soil water potential differed markedly according to soil type. The rice varieties tested were classified into four groups, which were largely consistent with differences in rice variety ecotype. We confirmed that lowland varieties such as Basmati 370 should not be considered as a candidate for cultivation under rain-fed upland conditions in Kenya. Furthermore, the results revealed that even potentially high-yielding varieties may not attain their yield potential if the cultivation environment is unsuitable. Although we found that five upland varieties (Dular, IRAT109, NERICA 1, NERICA 4, and Yumenohatamochi) presented relatively higher grain yield under water-deficit conditions in all soil types, individual variety’s grain yield depended on the soil type. Their higher yield is attributable to certain root traits and their yield variations to an interactive effect between root development and soil type. Our results indicated that the varieties Dular, IRAT109, NERICA 1, NERICA 4, and Yumenohatamochi are suitable for cultivation under rain-fed upland conditions across the major rice-growing areas in Kenya and that soil type should be considered when selecting varieties for an area’s maximum productivity.
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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) | 0.000 | 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".