Quantitative Prediction of Biochar Soil Amendments by near-Infrared Reflectance Spectroscopy.

Any sale of C credits in a cap and trade market system or government green payments based on soil biochar applications will require an effective and inexpensive audit system.  We investigated NIR spectroscopy’s ability to predict biochar amendment levels, total carbon, and C:N (m/m) ratios within and between two independent sample sets of Clarion (Typic Hapludoll) soils collected in Boone County, Iowa.  One set had high intrinsic diversity while the other set had low diversity.  The calibration cross-validation procedure showed the ability of NIRS to model % total C and % biochar C along with their normalized values quantitatively with R²s > 0.82 and RPDs > 2.2, except when modeling total C of the isolated controls (R² = 0.69, RPD = 1.56) and normalized total C (R² = 0.51, RPD = 1.31) from the low diversity sample set.  Validation using the independent PLSR models for the high diversity sample set to predict results for the low diversity sample set showed predictions of % total C, % biochar C, and normalized % biochar C with bias corrected RPDs > 2.1 and R²s > 0.87.  Not surprisingly, poor predictions (R² < 0.55, (RPD < 1.32) were obtained using the PLSR models for the low diversity sample set to predict results for the high diversity sample set.  Analysis of variance for predicted C:N ratios showed significant differences (alpha < 0.05) between biochar treatments and, independently, NIR’s ability to respond to biochar treatments.  The model validation R² (0.92) between the measured normalized biochar C and model-predicted normalized biochar C was significantly greater (alpha<0.05) than the autocorrelation R² between the measured total C and biochar C (0.89), thus demonstrating that NIRS responds to biochar C apart from total C. The results indicate that inexpensive NIRS analyses can be used to audit soil biochar applications.