Agroforestry management in smallholder agriculture can provide climate change mitigation and adaptation benefits and has been promoted as 'climate-smart agriculture' (CSA), yet has generally been left out of international and voluntary carbon (C) mitigation agreements. A key reason for this omission is the cost and uncertainty of monitoring C at the farm scale in heterogeneous smallholder landscapes. A largely overlooked alternative is to monitor C at more aggregated scales and develop C contracts with groups of land owners, community organizations or C aggregators working across entire landscapes (e.g., watersheds, communities, municipalities, etc.). In this study we use a 100-km2 agricultural area in El Salvador to demonstrate how high-spatial resolution optical satellite imagery can be used to map aboveground woody biomass (AGWB) C at the landscape scale with very low uncertainty (95% probability of a deviation of less than 1%). Uncertainty of AGWB-C estimates remained low (<5%) for areas as small as 250 ha, despite high uncertainties at the farm and plot scale (34-99%). We estimate that CSA adoption could more than double AGWB-C stocks on agricultural lands in the study area, and that utilizing AGWB-C maps to target denuded areas could increase C gains per unit area by 46%. The potential value of C credits under a plausible adoption scenario would range from $38,270 to $354,000 yr-1 for the study area, or about $13 to $124 ha-1 yr-1, depending on C prices. Considering farm sizes in smallholder landscapes rarely exceed 1-2 ha, relying solely on direct C payments to farmers may not lead to widespread CSA adoption, especially if farm-scale monitoring is required. Instead, landscape-scale approaches to C contracting, supported by satellite-based monitoring methods such as ours, could be a key strategy to reduce costs and uncertainty of C monitoring in heterogeneous smallholder landscapes, thereby incentivizing more widespread CSA adoption.
Keywords: Aboveground biomass carbon; Ecosystem services; Landscape; Remote sensing; Smallholder agriculture; Uncertainty.
Copyright © 2017. Published by Elsevier Ltd.