Effect of Inorganic Fertilizer Micro-dosing and Weed Management Regimes on Finger Millet Productivity in Uganda
Bibliographic record
Abstract
Finger millet yields in farmers’ fields are low primarily due to low soil fertility and poor weed management. Fertilizer application could be one of the key strategies used to address the problem. However, due to high cost of fertilizer, its utilization has been limited to very few farmers in the country. Consequently, a study was conducted in Kuju sub-county, Amuria district in Eastern Uganda during 2016 long and short rains to determine the effect of micro dosing and weeding time on finger millet performance and weed growth. The experiment was laid out in a randomized complete block design in a split plot treatment structure. Weeding times constituted the main plot [weeding at 20 DAS (days after sowing), 30 DAS, 45 DAS, and 20 & 45 DAS (double weeding)], while fertilizer rates (N 16.6 kg ha-1 + P2O5 10.6 kg ha-1; P2O5 10.6 kg ha-1; N 16.6 kg ha-1; N0 + P0, and N 83 kg ha-1 + P2O5 52 kg ha-1) constituted the sub plots. Results of this study revealed that the interaction between weeding at 45 DAS and fertilizer micro-dosing significantly (P < 0.05) reduced weed biomass (7.7 × 10-4 kg ha-1) without significant increase in finger-millet grain yield. Interaction of fertilizer micro-dosing (N 16.6 kg ha-1 + P2O5 10.6 kg ha-1) with weeding once at 20 DAS recorded the highest (2292 kg ha-1) finger millet grain yield with significant (P < 0.05) reduction in days to finger millet flowering. Therefore, it is recommended that fertilizer micro-dosing at N 16.6 kg ha-1 + P2O5 10.6 kg ha-1 and weeding once at 20 DAS can be used to control weeds and enhance finger-millet productivity.
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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 | 0.001 |
| 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".