The unrealized potential of agroforestry for an emissions-intensive agricultural commodity
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arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866914000149676032 |
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| author | Becker, Alexander Wegner, Jan D. Dawoe, Evans Schindler, Konrad Thompson, William J. Bunn, Christian Garrett, Rachael D. Castro-Llanos, Fabio Hart, Simon P. Blaser-Hart, Wilma J. |
| author_facet | Becker, Alexander Wegner, Jan D. Dawoe, Evans Schindler, Konrad Thompson, William J. Bunn, Christian Garrett, Rachael D. Castro-Llanos, Fabio Hart, Simon P. Blaser-Hart, Wilma J. |
| contents | Reconciling agricultural production with climate-change mitigation is a formidable sustainability problem. Retaining trees in agricultural systems is one proposed solution, but the magnitude of the current and future-potential benefit that trees contribute to climate-change mitigation remains uncertain. Here, we help to resolve these issues across a West African region that produces ~60% of the world's cocoa, a crop contributing one of the highest carbon footprints of all foods. Using machine learning, we mapped shade-tree cover and carbon stocks across the region and found that existing average cover is low (~13%) and poorly aligned with climate threats. Yet, increasing shade-tree cover to a minimum of 30% could sequester an additional 307 million tonnes of CO2e, enough to offset ~167% of contemporary cocoa-related emissions in Ghana and Côte d'Ivoire--without reducing production. Our approach is transferable to other shade-grown crops and aligns with emerging carbon market and sustainability reporting frameworks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_20882 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The unrealized potential of agroforestry for an emissions-intensive agricultural commodity Becker, Alexander Wegner, Jan D. Dawoe, Evans Schindler, Konrad Thompson, William J. Bunn, Christian Garrett, Rachael D. Castro-Llanos, Fabio Hart, Simon P. Blaser-Hart, Wilma J. Computer Vision and Pattern Recognition Reconciling agricultural production with climate-change mitigation is a formidable sustainability problem. Retaining trees in agricultural systems is one proposed solution, but the magnitude of the current and future-potential benefit that trees contribute to climate-change mitigation remains uncertain. Here, we help to resolve these issues across a West African region that produces ~60% of the world's cocoa, a crop contributing one of the highest carbon footprints of all foods. Using machine learning, we mapped shade-tree cover and carbon stocks across the region and found that existing average cover is low (~13%) and poorly aligned with climate threats. Yet, increasing shade-tree cover to a minimum of 30% could sequester an additional 307 million tonnes of CO2e, enough to offset ~167% of contemporary cocoa-related emissions in Ghana and Côte d'Ivoire--without reducing production. Our approach is transferable to other shade-grown crops and aligns with emerging carbon market and sustainability reporting frameworks. |
| title | The unrealized potential of agroforestry for an emissions-intensive agricultural commodity |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.20882 |