The effects of agronomic practices on soil greenhouse gas emissions in maize production systems in Buea, Cameroon
Bibliographic record
Abstract
With a specific focus on zero tillage and organic fertilization, this study examines the effects of agronomic practices on soil greenhouse gas (GHGs—CO 2 , N 2 O, and CH 4 ) emissions, global warming potential (GWP), maize productivity and greenhouse gas intensity (GHGI) over two growing seasons (2020 minor and 2021 main season) in Buea, Cameroon. Two tillage practices–i.e., zero-tillage and conventional tillage with ridge formation and three fertilizer treatments—i.e., no fertilizer, synthetic fertilizer (urea), and organic fertilizer (composted municipal solid waste), were factorially combined in a split-plot design with three replications. Fertilizer was applied at a rate of 100 kg N ha - ¹. The hybrid maize cultivar CMS 8704 was used. GHG emissions were measured using the static flux chamber method, and flux rates were calculated with the HMR package in R software. Results showed that tillage and fertilizer types significantly (p<0.05) influenced seasonal cumulative CO 2 , N 2 O, and CH 4 emissions. Synthetic fertilizer treatments produced the highest cumulative N 2 O emissions, particularly under zero-tillage in 2020 and conventional tillage in 2021. Conventional tillage paired with organic fertilizer yielded the highest CO 2 emissions across both seasons, while methane fluxes were low and largely negative across treatments, indicating that the volcanic upland soils acted as CH 4 sinks. Application of synthetic fertilizer increased GWP by 20% and 322% under zero tillage in the 2020 and 2021 seasons, respectively. Under conventional tillage, GWP decreased by 15% in 2020 but sharply increased by 295% in 2021, highlighting season-specific effects. Although treatment effects were not significant (P>0.05) on maize yields in 2020, the highest yield (3.06 t/ha) occurred under conventional tillage without fertilization. Fertilizer type and its interaction with tillage significantly (P<0.05) influenced yields in 2021, with the highest yield under conventional tillage with synthetic fertilization (6.15 tons/ha). However, conventional tillage treatment without fertilization produced the highest yield (3.06 t/ha) in 2020 and the lowest GHGI (12.04 kg CO 2 -eq t - ¹). In 2021, zero tillage treatment without fertilization resulted in a high yield (5.56 t/ha) with the lowest GHGI (2.15 kg CO 2 -eq t - ¹). The results suggest that in Buea’s minor growing season, conventional tillage with or without organic fertilization reduced GHG emissions without compromising yields, while in main seasons, zero tillage without fertilization offered the most favorable yield-emission balance. This study highlights the importance of context-specific soil and nutrient management strategies for sustainable agriculture and climate change mitigation. Findings provide valuable data for national GHG inventory reporting and inform agronomic practices in tropical upland agricultural systems.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".