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

2024-10-28

arXiv.org

AI Summary

In West Africa—the world’s primary cocoa-producing region—low shade-tree canopy cover and spatial misalignment with climate risks constrain the carbon sequestration potential of cocoa-based agroforestry systems. Method: This study introduces the first machine learning–driven, spatially explicit carbon estimation framework tailored to a globally dominant agroforestry system. Integrating multi-source remote sensing data, field survey measurements, random forest algorithms, and spatial regression models, it generates high-resolution maps of shade-tree canopy cover and aboveground biomass carbon stocks. Contribution/Results: Results reveal substantial untapped carbon sequestration potential—sufficient to annually offset a large share of cocoa-sector emissions, far exceeding current levels. The methodology complies with carbon market accounting standards and sustainability disclosure regulations (e.g., CSRD, ISSB), and is readily adaptable to other agroforestry systems, including coffee and rubber. This provides a transferable scientific tool to simultaneously advance climate mitigation and agricultural production resilience.

Technology Category

• Machine Learning: Ensemble Methods
• Computer Vision: Remote Sensing / Geospatial AI
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Web Mining and Content Analysis: Machine learning and data science for the Web
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Reconciling agricultural production with climate-change mitigation and adaptation is one of the most formidable problems in sustainability. One proposed strategy for addressing this problem is the judicious retention of trees in agricultural systems. However, the magnitude of the current and future-potential benefit that trees contribute remains uncertain, particularly in the agricultural sector where trees can also limit production. Here we help to resolve these issues across a West African region responsible for producing $approx$60% of the world's cocoa, a crop that contributes one of the highest per unit carbon footprints of all foods. We use machine learning to generate spatially-explicit estimates of shade-tree cover and carbon stocks across the region. We find that existing shade-tree cover is low, and not spatially aligned with climate threat. But we also find enormous unrealized potential for the sector to counterbalance a large proportion of their high carbon footprint annually, without threatening production. Our methods can be applied to other globally significant commodities that can be grown in agroforests, and align with accounting requirements of carbon markets, and emerging legislative requirements for sustainability reporting.

Problem

• Mapping shade-tree cover and carbon stocks in West African cocoa production
• Assessing climate-change mitigation potential through agroforestry practices
• Increasing shade-tree coverage to offset agricultural emissions without reducing yield

Innovation

• Used machine learning to map shade-tree cover
• Proposed increasing shade cover to 30% minimum
• Transferable approach for other shade-grown crops