Soil Greenhouse Gas Fluxes From Maize Production Under Different Soil Fertility Management Practices in East Africa
Bibliographic record
Abstract
Abstract In sub‐Saharan Africa (SSA), few studies have quantified greenhouse gas (GHG) emissions following application of soil amendments, for development of accurate national GHG inventories. Therefore, this study quantified soil GHG emissions using static chambers for two maize cropping seasons (one full year) of four different soil amendments in the central highlands of Kenya. The four treatments were (i) animal manure, (ii) inorganic fertilizer, (iii) combined animal manure and inorganic fertilizer, and (iv) a no‐N control (no amendment) laid out in a randomized complete block design. Cumulative annual soil fluxes (February 2017 to February 2018) ranged from −1.03 ± 0.19 kg CH 4 ‐C ha −1 yr −1 from the manure inorganic fertilizer treatment to −0.09 ± 0.03 kg CH 4 ‐C ha −1 yr −1 from the manure treatment, 1,391 ± 74 kg CO 2 ‐C ha −1 yr −1 from the control treatment to 3,574 ± 113 kg CO 2 ‐C ha −1 yr −1 from the manure treatment, and 0.13 ± 0.08 to 1.22 ± 0.12 kg N 2 O‐N ha −1 yr −1 in the control and manure treatments, respectively. Animal manure amendment produced the highest cumulative CO 2 emissions ( P < 0.001), N 2 O emissions ( P < 0.001), and maize yields ( P = 0.002) but the lowest N 2 O yield‐scaled emission (YSE) (0.5 g N 2 O–N kg −1 grain yield). Manure combined with inorganic fertilizer had the highest cumulative CH 4 uptake ( P < 0.001) and N 2 O YSE (2.2 g N 2 O–N kg −1 grain yield). Our results indicate that while the use of animal manure may increase total GHG emissions, the concurrent increase in maize yields results in reduced yield‐scaled GHG emissions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".