Yield Responses of Maize to Organic and Mineral Fertilizers at Different Inclinations in Tropical Smallholder Farming Systems
Bibliographic record
Abstract
A field study was conducted on the potential of Gliricidia (Gliricidia sepium (Jacq.) Kunth ex Walp.) to enhance productivity of degraded soils. Maize was cropped in a hilly region of Sri Lanka with and without the recommended mineral fertilization, in two major seasons, October-January in 2007/8 (Year 1) and in 2008/9 (Year 2) on 92 farms at two inclinations: Flat (0-10%) and Moderate (10-30%). On half the farms, green manure (Gliricidia leaves) was added (3 tonnes per hectare per season). NPK boosted production to a very respectable mean grain yield of 4.2 t/ha on Flat farms. At ZERO, the yield was lower by 60%, irrespective of the inclination. Gliricidia failed to replace the required nitrogen, even with an adequate supply of phosphorous and potassium (PK). In contrast, together with NPK, Gliricidia increased yields by 15-20% compared to NPK alone, while the gain was 35% at ZERO. Fields in the Moderate category were more responsive to green manure and mineral fertilizers. The high response to mineral fertilizers indicated that the degradation of the soils resulted to a greater extent in chemical rather than in physical deficits. But intensive cropping reduced the soil organic matter within two years, to some extent slowed down by Gliricidia green manure. Therefore an intense cropping for the sake of food security must be accompanied by soil conserving cropping systems.
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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".