The unrealized potential of agroforestry for an emissions-intensive agricultural commodity

TL;DR

This study uses machine learning to assess how increasing shade-tree cover in West African cocoa farms can significantly enhance carbon sequestration, potentially offsetting the crop's emissions without reducing production.

Contribution

It quantifies the climate mitigation potential of agroforestry in cocoa production regions using innovative mapping and modeling techniques.

Findings

Current shade-tree cover is about 13%, below optimal levels.

Increasing cover to 30% could sequester 307 million tonnes of CO2e.

This sequestration could offset over 167% of cocoa-related emissions in Ghana and C extsuperscript{o}te d'Ivoire.

Abstract

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 C\^ote d'Ivoire--without reducing production. Our approach is transferable to other shade-grown crops and aligns with emerging carbon market and sustainability reporting frameworks.