Integrating Soybean Residues With Nitrogen Fertilizer for Improved Maize Production in Eastern Uganda
Bibliographic record
Abstract
Soybean has become one of the dominant crops in Ugandan farming systems; however the contribution of its residues to improve soil and crop productivity is less known. To investigate the role of soybean residues to enhance crop productivity, researcher-managed experiments were conducted for two seasons (2011B and 2012A) in Namayingo and Tororo districts, representing the L. Victoria crescent, and South-eastern L. Kyoga basin agro-ecological zones, respectively. Factorial treatments of three levels of soybean residues (0, 2 and 4 t ha-1) and four levels of N fertilizer (0, 30, 60 and 120 kg ha-1 N) in form of urea were applied in maize fields in a randomized complete block design so as to; determine the optimum nutrient combination for maize, and establish the added yield benefits, if any, of using soybean residues with N fertilizer in maize production. Site yields varied significantly (p < 0.001), with higher maize yields obtained in Namayingo compared to Tororo. Significantly (p < 0.05) higher maize grain yields were obtained with the highest nutrient input combinations of 2 t ha-1 residue and 60 kg ha-1 N (yield increment of 71.72% above the control) in Namayingo district and 4 t ha-1 residue combined with120 kg ha-1 N increased maize grain yield by 140.69% above the control in Tororo. The added maize grain yield benefits ranged from 2540 kg grain ha-1 to 3250 kg grain ha-1 in Namayingo and from 2000 kg grain ha-1 to 2310 kg grain ha-1 in Tororo. Combined use of soybean residue with N fertilizer has been found to have agronomic yield benefits to maize production.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".