Carbon footprint of primary production of cacao: a meta-analytical review
Bibliographic record
Abstract
Cacao production occupies ≈120 000 km2 globally. Cacao beans are the most valuable agricultural export of four African countries and of importance in several other tropical countries. Greenhouse-gas emissions from cacao production are therefore of interest, as is the potential of the crop for mitigation of emissions by carbon sequestration. We present a global review of carbon footprints and CO2 removal rates in primary production of cacao. We aim to characterize typical overall values, identify determinants of footprints and removal rates, compare cropping systems, and suggest ways of reducing carbon footprints and increasing CO2 removal rates. The median ± interquartile range carbon footprints of cacao production, excluding any associated deforestation, were 732 ± 1318.5 kg CO2e ha−1 (area carbon footprint, ACF) and 1.55 ± 2.703 kg CO2e kg−1 dry beans (product carbon footprint, PCF). The typical ranges of ACF and PCF, defined as those from the first to the eighth decile, were 26–1691 kg CO2e ha−1 year−1 and 0.05–3.74 kg CO2e kg−1 dry beans, respectively. Nitrogenous fertilizers and harvest residues were the most important emissions sources. The ACF of agroforestry production was significantly lower than that of unshaded production, as were yields. The PCF of agroforestry systems did not differ significantly from unshaded systems. The mean ACF and PCFs of organic production were significantly lower than those of nonorganic production. CO2 removal rates were considerably larger than carbon emissions (ACF values), particularly for agroforestry production (median ± interquartile range of 3445 ± 1952 kg CO2 ha−1 year−1 and 10 237 ± 4178 kg CO2 ha−1 year−1 for unshaded and agroforestry production, respectively). CO2 removal rates in organic and nonorganic production did not differ significantly. We present a generalized approach for reducing the overall global-warming effect of cacao production, consisting of 12 specific intervention options within an overarching principle of zero deforestation. Improved management of harvest residues, wider use of agroforestry production, and use of organic nitrogen sources are central to enhancing the interventions. Implementation of 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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.021 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".