Carbon footprints and CO<sub>2</sub> removal in primary production of coffee: a meta-analytical review
Bibliographic record
Abstract
Producing countries and value-chain participants are seeking to minimize the effect of coffee production on global warming. In support of this goal, we present a global review of carbon footprints and atmospheric-CO2 removal rates in primary production of coffee, based on 21 studies of carbon footprint and 16 with data on CO2 removal rate. We aimed to characterize typical values, identify determinants, compare arabica and robusta coffee, compare cropping systems, and suggest ways of reducing footprints and increasing CO2 removal rates. The median ± interquartile range of carbon footprints of coffee production were 2954 ± 3636 CO2e ha−1 year−1 (area carbon footprint, ACF) and 2.18 ± 2.04 CO2e kg−1 green beans (product carbon footprint, PCF). Manufacture and application of fertilizers, particularly nitrogenous fertilizers, were almost always the main emissions sources (65%–100% of the total). ACF was significantly higher in arabica than robusta, while ACFs of agroforestry and organic systems were <50% of those of unshaded and non-organic systems; these differences were also significant. PCF of organic production was significantly lower than that of non-organic production, but marginally so; no other significant differences in PCF were found. The median CO2 removal rate of unshaded production was 6990 ± 3405 kg CO2 ha−1 year−1. For agroforestry production the median was much higher (17 676 ± 3971 kg CO2 ha−1 year−1). Removal rates of organic and non-organic production did not differ significantly. We present a generalized approach for reducing the overall global-warming effect of coffee production, consisting of 10 options for interventions within an overarching principle of zero deforestation. Wider use of agroforestry production and use of organic nitrogen sources are features of eight of the interventions. Implementation of the interventions will also require the building of enabling frameworks that allow farmers to meet climate-change mitigation goals without sacrificing productivity or profitability.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.024 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".