Effect of feeding practices and manure quality on CH4 and N2O emissions from uncovered cattle manure heaps in Kenya
Bibliographic record
Abstract
Countries in sub-Saharan Africa (SSA) rely on IPCC emission factors (EF) for GHG emission reporting. However, these were derived for industrialized livestock farms and do not represent conditions of smallholder farms (small, low-producing livestock breeds, poor feed quality, feed scarcity). Here, we present the first measurements of CH4 and N2O emissions from cattle-manure heaps representing feeding practices typical for smallholder farms in the highlands of East Africa: 1) cattle fed below maintenance energy requirements to represent feed scarcity, and 2) cattle fed tropical forage grasses (Napier, Rhodes, Brachiaria). Sub-maintenance feeding reduced cumulative manure N2O emissions compared to cattle receiving sufficient feed but did not change EFN2O. Sub-maintenance feeding did not affect cumulative manure CH4 emissions or EFCH4. When cattle were fed tropical forage grasses, cumulative manure N2O emissions did not differ between diets, but manure EFN2O from Brachiaria and Rhodes diets were lower than the IPCC EFN2O for solid storage (1%, 2019 Refinement of IPCC Guidelines). Manure CH4 emissions were lower in the Rhodes grass diet than when feeding Napier or Brachiaria, and manure EFCH4 from all three grasses were lower than the IPCC default (4.4 g CH4 kg−1 VS, 2019 Refinement of IPCC Guidelines). Regression analysis revealed that manure N concentration and C:N were important drivers of N2O emissions, with low N concentrations and high C:N reducing N2O emissions. Our results show that IPCC EFs overestimate excreta GHG emissions, which calls for additional measurements to develop localized EFs for smallholder livestock systems in SSA.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".