Effect of Soil Water Environment and Simulated Savanna Climate on Growth and Mineral Nutrition in Jatropha curcas L.
Bibliographic record
Abstract
This study aimed to determine the optimal soil water conditions for J. curcas L. cultivation under the bi-annual rain seasons of the African Savannah climate. Changes in J. curcas L. seedling biomass and mineral nutrient uptake and movement patterns were examined under soil water matric potentials of-2.5,-2.9,-3.4 and-4.0kPa, and two rain seasons separated by a dry spell. The bi-annual savannah rain conditions were simulated using automated buried-type tensiometer system. During the first rain season (irrigation),-3.4kPa soil water matric potential showed the highest water-use efficiency (2.44 g dry weight L-) and biomass yield (57.14g). Seedlings were variously affected by the subsequent dry spell, including total defoliation and stunting. On re-irrigation, seedling recovery (biomass and water use efficiency) was less as compared to the first rain season. Among the nutrients monitored, N, P and K were the most predominantly uptaken, with N showing the closest correlation with seedling growth. Soil matric water potential of －3.4 kPa is the optimum soil water environment for J. curcas cultivation in the savannah conditions; additionally,
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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".