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Nitrous oxide and methane emissions from cattle manure heaps in Kenya

2020· article· en· W3089405484 on OpenAlexaff
Sonja Leitner, Dónal Ring, George N. Wanyama, Daniel Korir, David E. Pelster, J. P. Goopy, Lutz Merbold

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland Management and Livestock Ecology
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsManureLivestockGreenhouse gasManure managementAgricultureEnvironmental scienceLivelihoodAgricultural scienceAgronomyBiologyEcology

Abstract

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Agricultural greenhouse gas (GHG) emissions in Africa contribute 15 % to the global total agricultural emissions, which is in the same range as agricultural emissions from Europe. The majority of these agricultural GHG emissions is attributed to livestock farming (up to 80 % at national scale), of which 10-25 % originate from livestock manure. At the same time, livestock production is essential for the livelihoods of millions of people in Sub-Saharan Africa (SSA), where 45-80 % of livestock production occurs in smallholder systems. With the growing population in SSA, the demand for livestock products is expected to increase, and – without low-emission manure management – a rise in manure-borne GHG emissions will occur. However, reliable in situ measurements from SSA are scarce, leading to substantial uncertainties in agricultural GHG budgets and making assessments of potential mitigation options difficult. Here we present results from two cattle manure incubation experiments in Kenya, using manure from Boran (Bos indicus) cattle, a breed common in East Africa that were fed with typical feeds used in SSA smallholder farms. Manure was collected and piled in heaps (solid storage), the most common form of manure storage in Kenyan smallholder systems, and CH4 and N2O emissions were measured over 140 days. In the first trial, cattle were fed a diet that either met their maintenance-energy requirements (i.e. animals received enough food to support their metabolism), or a diet at sub-maintenance energy levels to simulate common conditions in smallholder farming systems, particularly during the dry seasons. Cumulative manure N2O emissions from the sub-maintenance diet (i.e. the “hungry” cows) were lower than from cattle fed at maintenance energy levels. These lower N2O emission likely resulted from lower N concentration and a wider C:N ratio in the manure than in the “better fed” animals. Furthermore, the urine-N:faecal-N ratio in the “hungry” cows decreased, indicating a shift from urine-N (mostly inorganic N) to faecal-N (mostly organic N), which further backs the lower observed N2O emissions. Both N2O as well as CH4 emissions from manure were lower than the IPCC default emission factors for solid storage in tropical regions across all diets tested. In the second trial, Boran cattle were fed with three different tropical forage grasses common in Kenya: Napier (Pennisetum purpureum), Rhodes (Gloris gayana), and Brachiaria (Brachiaria brizantha). Manure from the Rhodes grass diet had the lowest N concentration and also the lowest cumulative CH4 emissions, while N2O emissions did not differ between diets. Similar to the sub-maintenance feeding trial, total CH4 and N2O emissions were lower than the IPCC default factors. Taken together, these results are an important step towards reducing the uncertainties in GHG emissions from agriculture in SSA. Furthermore, if African nations use IPCC default values for their national GHG reporting on livestock, emissions are likely to be overestimated, highlighting the importance and benefits of localized data from Africa.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.011
GPT teacher head0.210
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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