Long term effects of reduced fertilizer rates on millet yields and soil properties in the West-African Sahel
Bibliographic record
Abstract
Microdosing, the point-source application of a reduced fertilizer rate within 10 days of sowing, has increased short-term crop yields across the Sahel and is being actively scaled up as an agronomic practice. However, there is no information on the long-term effects of the technique upon soil fertility. To rectify this, this study used soil samples from the International Crop Research Institute for the Semi-Arid Tropics in Sadore, Niger, to assess the effects of 16 years of a reduced fertilizer rate of 15 kg N and 4.4 kg P ha−1 compared to unfertilized soil and a recommended rate of 30 kg N and 13.2 kg P ha−1 upon millet yield trend, soil chemical properties, and soil organic matter quality. The interaction of fertilizer with crop residue and manure amendments at 300, 900, and 2700 kg ha−1 was also assessed. Compared to unfertilized soil, the reduced fertilizer rate improved yield by 116 % but did not increase total N or available P. The recommended rate doubled available P and increased total N by 27 %, but resulted in slightly lower pH compared to the reduced rate. Yield trends were negative for both fertilizer treatments, indicating mineral fertilizer alone is not sustainable at Sadore. Crop residue or manure addition at 2700 kg ha−1 with fertilizer did not improve SOC but buffered pH by 0.3 units, provided nutrients beyond N and P, and changed the forms C and N functional groups in soil organic matter.
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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".