Effect of Manure and Urea Fertilization on Yield, Carbon Speciation and Greenhouse Gas Emissions from Vegetable Production Systems of Nigeria and Republic of Benin: A Phytotron Study
Bibliographic record
Abstract
Fertility management techniques being promoted in sub-Saharan Africa (SSA) seek to grow indigenous vegetables economically and sustainably. This study was conducted in a phytotron chamber and compared yield, soil carbon (C) speciation and greenhouse gas (nitrous oxide (N2O) and carbon dioxide (CO2)) emissions from SSA soils of two ecoregions; the dry savanna (lna, Republic of Benin) and rainforest (Ife, Nigeria) cultivated with local amaranth (Amaranthus cruentus) under manure (5 t/ha) and/or urea (80 kg N/ha) fertilization. Vegetable yield ranged from 4331 kg/ha to 7900 kg/ha in the rainforest, RF, soils and 3165 kg/ha to 4821 kg/ha in the dry savanna, DS, soils. Yield in the urea treatment was slightly higher compared to the manure, and manure+urea treatment, but the difference was not statistically significant. Cumulative CO2 emissions over 21 days ranged from 497.06 to 579.47 g CO2-C/kg soil/day in the RF, and 322.96 to 624.97 g CO2-C/kg soil/day in the DS, while cumulative N2O emissions ranged from 60.53 to 220.86 mg N2O-N/kg soil/day in the RF, and 24.78 to 99.08 mg N2O-N/kg soil/day in the DS. In the RF samples, when compared to the use of urea alone, the combined use of manure and urea reduced N2O emissions but led to an increase in the DS samples. ATR-FTIR analysis showed that the combined use of manure and manure+urea increased the rate of microbial decomposition in the soils of the DS, but no such effect was observed in soils of the RF. We conclude that combining manure and urea fertilization has different effects on soils of the two ecoregions, and that RF farmers can reduce agricultural N2O emissions without compromising soil productivity and yield potential.
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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 | 0.000 | 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 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".