Subtropical grassland root-to-shoot ratios and plant organic carbon estimates: why global averages fall short

Old-growth subtropical grasslands are often underestimated in their capacity to store carbon. Growing recognition of belowground plant biomass as an important measure of ecosystem health and organic carbon storage potential has led to the application of allometric equations to predict root biomass from aboveground biomass measurements, commonly expressed as a root-to-shoot ratio (RSR). Furthermore, the carbon storage potential of ecosystems is often estimated using a standard carbon conversion factor (CCF), typically set to 0.45 x dry biomass. However, studies validating these estimates for their application in grasslands are mostly from temperate and alpine ecosystems, making RSR- and subsequent CCF-based carbon estimates unreliable in subtropical grasslands.

Biomass sampling was conducted at two subtropical grassland sites in South Africa representing contrasting rainfall regions (semi-arid vs. moist grasslands). Root and shoot biomass were destructively harvested to calculate site-specific RSRs. Elemental organic carbon content was measured and compared with the 0.45 CCF used in global carbon estimation approaches.

The mean RSR across our study for subtropical grasslands was 14.9, approximately five times higher than the global average of 3.22. RSR varied with rainfall, with 19.7 in moist grasslands and 8.9 in semi-arid grasslands. The default CCF of 0.45 overestimated carbon content in both above- and belowground biomass, with a proposed adjusted CCF of 0.39 reflecting a more accurate estimate. However, when both RSR and the adjusted CCF are applied, subtropical grasslands store 10 tC ha-1 more carbon than would be predicted using global defaults.

Applying global conversion factors without accounting for regional variability leads to inaccurate carbon stock estimates, particularly in subtropical grassland systems where belowground allocation dominates. Accurate RSRs and CCFs are therefore critical for accurate carbon modelling and promoting conservation in these underrepresented ecosystems.