Fertilizer Micro-dosing and Timing of Weeding for Enhancing Finger-Millet Production in Eastern Uganda
Bibliographic record
Abstract
Finger-millet response to micro-dosing with N and P fertilizer in the Semi-Arid areas of eastern Uganda is not fully known. Consequently, we conducted a field study at Kuju in eastern Uganda in 2016 long and short rains. The study determined the effect of fertilizer micro-dosing and weeding time on finger-millet performance. The experiment was laid out in a randomized complete block design with a split plot treatment structure. Weeding time constituted the main plot (weeding at 20, 30, 45 DAS (days after sowing) and 20 + 45 DAS while fertilizer rates (16.6 kg N ha-1 + 10.6 kg P2O5 ha-1; 10.6 kg P2O5 ha-1; 16.6 kg N ha-1; 0 N + 0 N and 83 kg N ha-1 + 52 kg P2O5 ha-1) constituted the sub-plots. Results of this study revealed that finger-millet grain yield was highest (2182 kg ha-1) at fertilizer micro-dosing (16.6 kg N ha-1 + 10.6 kg P2O5 ha-1) and lowest (950 kg ha-1) in plots with no fertilizer. Fertilizer micro-dose application (16.6 kg N ha-1 + 10.6 kg P2O5 ha-1) caused early flowering of finger-millet (52.2 days), whereas delayed flowering was recorded, where 16.6 kg N ha-1 (55.12 days) was applied. The interaction between time of weeding and fertilizer rates significantly (P < 0.05) increased finger millet growth and grain yield. The interaction of N and P fertilizer micro-dose with weeding once at 20 DAS had the highest finger-millet grain yield. Therefore, it is recommended that fertilizer micro-dosing can be used to enhance finger-millet productivity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".