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.

翻译：协调农业生产与气候变化减缓和适应是可持续发展中最棘手的问题之一。解决该问题的一项策略是在农业系统中明智地保留树木。然而，树木当前及未来潜在的贡献程度仍不确定，尤其是在树木也可能限制产量的农业部门。本研究致力于在约占全球可可产量60%的西非地区解决这些问题，可可是单位碳足迹最高的食品之一。我们利用机器学习生成该地区遮荫树覆盖率和碳储量的空间显式估计。我们发现现有遮荫树覆盖率较低，且与气候威胁的空间分布不一致。但我们也发现该行业每年有巨大潜力抵消其高碳足迹的很大一部分，且不会威胁生产。我们的方法可应用于其他可在农林复合系统中种植的全球重要商品，并符合碳市场的核算要求以及新兴的可持续发展报告立法要求。