Improving Rice Productivity and Profitability with a Single Fertilizer-Management Option over Two Cropping Seasons in the Senegal River Valley
Bibliographic record
Abstract
One of the main factors limiting the yield and productivity of irrigated rice in the Sahel of West Africa is the high cost of fertilizers and inefficient use of nutrients in the cropping systems. A two-year experiment was conducted over four consecutive seasons at Ndiaye (16°11'N, 16°15'W) and Fanaye (16°32'N, 15°11'W) along the Senegal River Valley to investigate alternative fertilizer management options in the double cropping system of rice. Eight fertilizer management options (FMO) were compared to the recommended seasonal application of NPK fertilizer based on yield data, the value-cost ratio (V/C) and the sustainabily of the recommendation. Rice yields increased from 2.5 t/ha without fertilizer application to 8 t/ha under the nine FMOs, which produced similar yields each season at both sites. The V/Cs of the recommended NPK fertilizer (applied each season) varied from 2.3 to 3.7. The V/Cs of FMO that supplied NPK during the hot dry season (HDS) and N during the wet season (WS), or conversely (NPK-N) varied from 3.2 to 5.7. The V/Cs of NPK-NP varied from 2.4 to 4.3. The V/Cs of NPK-NK varied from 2.4 to 4.4. The highest V/Cs ratios (5.2 to 6.3) were obtained by FMOs that supplied NP during the HDS and N during the WS, or conversely. It is concluded that when soil P-Bray1 is above 7 mg P ha-1, FMOs that supply NPK fertilizer in one season and only N fertilizer in the following season could reduce the cost of fertilization by 26% and improve rice productivity for sustainable management of the double cropping system of rice in the Senegal River Valley.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".