White Lupin (Lupinus albus L. cv. Amiga) Increases Solubility of Minjingu Phosphate Rock, Phosphorus Balances and Maize Yields in Njoro Kenya
Bibliographic record
Abstract
Exudation of high amounts of citrate in white lupin (Lupinus albus L. cv. Amiga) has the advantage of being effective in mobilization of a wide range of sparingly soluble P sources. To improve cultivation system of maize, a field experiment was conducted to assess effectiveness of white lupin (Lupinus albus L. cv. Amiga) in increasing solubility of minjingu phosphate rock (MPR), phosphorus balances and maize yields in Njoro sub-County, Kenya. The randomized complete block design experiment was conducted for four seasons; short (October – February) and long rain seasons (March-September) of 2010 and 2011. The treatments were; (i) fallow (F) – maize (M) rotation with triple superphosphate (TSP) applied (MTSP- F), (ii) fallow - maize rotation with MPR applied (MMPR –F), (iii) lupin (L) – maize rotation with MPR applied (MMPR- L) and (iv) maize/lupin intercrop with MPR applied (M/LMPR – F). Soil and plant P and maize grain yield were higher in M/LMPR – F (with additional lupin grain yield) and MTSP– F treatments. All treatments resulted in positive P balances at the end of two years with highest values in MTSP– F treatment and lowest in M/LMPR – F. Intercropping lupin with maize amid application of MPR is recommended for enhanced maize performance in the farming systems of resource poor farmers. Measurement of available soil nitrogen and comparison of lupin with other legumes in solubilizing MPR is recommended.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".