Effect of Total Solar Radiation and Rainfall on Yield of Different Tea (Camellia sinensis [L.] O. Kuntze) Clones at Two Sites in Kenya
Bibliographic record
Abstract
Tea cultivation is the leading cash crop in Kenya, making significant contribution to the economy. It is the single largest export commodity, accounting for about 26% of the country’s total export earnings. In 2017, the country got US$ 1.23 billion in foreign exchange earnings. However, tea production is affected by weather changes. Majority of research reporting the effects of weather on tea yields in Africa have mostly been carried out at single sites. This study investigated contribution of total solar radiation and rainfall to tea (Camellia sinensis) yields at two sites in Kenya. A split-plot layout study was conducted at two sites differing in altitude and climatic conditions in Kenya: Kangaita (0o30'S, 37o16'E, 2100 m.a.s.l.) and Kipkebe (0o17'S, 35o3'E, 1740 m.a.s.l.). Timbilil (0o22'S, 35o21'E, 2200 m.a.s.l.) was used as a reference site. Four tea clones of commercial and scientific interest in Kenya (AHP SC 31/37, EPK TN14-3, TRFK 301/5 and TRFK 31/8) were studied. Low radiation intensities at Timbilil in 2007 corresponded with low made tea yields at Kangaita (2.1 t ha-1 y-1) and Kipkebe (2.6 t ha-1 y-1) compared to 2008 (4.4 t ha-1 y-1 and 3.2 t ha-1 y-1) and 2009 (3.1 t ha-1 y-1 and 3.0 t ha-1 y-1) respectively when higher total radiation intensities were recorded. Statistical analysis done using two-way ANOVA (P = 0.05) for split plot design showed a strong positive correlation (r = 0.53) between total solar radiation, rainfall and mean made tea yield. Higher radiation and rainfall intensities yielded higher tea outputs.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".