Is the simple auger coring method reliable for below-ground standing biomass estimation in Eucalyptus forest plantations?

Background and Aims Despite their importance for plant production, estimations of below-ground biomass and its distribution in the soil are still difficult and time consuming, and no single reliable methodology is available for different root types. To identify the best method for root biomass estimations, four different methods, with labour requirements, were tested at the same location. Methods The four methods, applied in a 6-year-old Eucalyptus plantation in Congo, were based on different soil sampling volumes: auger (8 cm in diameter), monolith (25 x 25 cm quadrate), half Voronoi trench (1.5 m(3)) and a full Voronoi trench (3 m(3)), chosen as the reference method. Key Results With the reference method (0-1m deep), fine-root biomass (FRB, diameter <2 mm) was estimated at 1.8 t ha(-1), medium-root biomass (MRB diameter 2-10 mm) at 2.0 t ha(-1), coarse-root biomass (CRB, diameter >10 mm) at 5.6 t ha(-1) and stump biomass at 6.8 t ha(-1). Total below-ground biomass was estimated at 16.2 t ha(-1) (root : shoot ratio equal to 0.23) for this 800 tree ha(-1) eucalypt plantation density. The density of FRB was very high (0.56 t ha(-1)) in the top soil horizon (0-3 cm layer) and decreased greatly (0.3 t ha(-1)) with depth (50-100 cm). Without labour requirement considerations, no significant differences were found between the four methods for FRB and MRB; however, CRB was better estimated by the half and full Voronoi trenches. When labour requirements were considered, the most effective method was auger coring for FRB, whereas the half and full Voronoi trenches were the most appropriate methods for MRB and CRB, respectively. Conclusions As CRB combined with stumps amounted to 78% of total below-ground biomass, a full Voronoi trench is strongly recommended when estimating total standing root biomass. Conversely, for FRB estimation, auger coring is recommended with a design pattern accounting for the spatial variability of fine-root distribution.