Spatial modelling approach and accounting method affects soil carbon estimates and derived farm-scale carbon payments

Abstract

Improved farm management of soil organic carbon (SOC) is critical if national governments and agricultural businesses are to achieve net-zero targets. There are opportunities for farmers to secure financial benefits from carbon trading, but field measurements to establish SOC baselines for each part of a farm can be prohibitively expensive. Hence there is a potential role for spatial modelling approaches that have the resolution, accuracy, and estimates to uncertainty to estimate the carbon levels currently stored in the soil. This study uses three spatial modelling approaches to estimate SOC stocks, which are compared with measured data to a 10 cm depth and then used to determine carbon payments. The three approaches used either fine- (100 m × 100 m) or field-scale input soil data to produce either fine- or field-scale outputs across nine geographically dispersed farms. Each spatial model accurately predicted SOC stocks (range: 26.7-44.8 t ha^-1 ) for the five case study farms where the measured SOC was lowest (range: 31.6-48.3 t ha^-1). However, across the four case study farms with the highest measured SOC (range: 56.5-67.5 t ha^-1), both models underestimated the SOC with the coarse input model predicting lower values (range: 39.8-48.2 t ha-1) than those using fine inputs (range: 43.5-59.2 t ha^-1). Hence the use of the spatial models to establish a baseline, from which to derive payments for additional carbon sequestration, favoured farms with already high SOC levels, with that benefit greatest with the use of the coarse input data. Developing a national approach for SOC sequestration payments to farmers is possible but the economic impacts on individual businesses will depend on the approach and the accounting method.