Rapid prediction of particulate, humus and resistant fractions of soil organic carbon in reforested lands using infrared spectroscopy.

Reforestation of agricultural lands with mixed-species environmental plantings can effectively sequester C. While accurate and efficient methods for predicting soil organic C content and composition have recently been developed for soils under agricultural land uses, such methods under forested land uses are currently lacking. This study aimed to develop a method using infrared spectroscopy for accurately predicting total organic C (TOC) and its fractions (particulate, POC; humus, HOC; and resistant, ROC organic C) in soils under environmental plantings. Soils were collected from 117 paired agricultural-reforestation sites across Australia. TOC fractions were determined in a subset of 38 reforested soils using physical fractionation by automated wet-sieving and 13C nuclear magnetic resonance (NMR) spectroscopy. Mid- and near-infrared spectra (MNIRS, 6000-450 cm-1) were acquired from finely-ground soils from environmental plantings and agricultural land. Satisfactory prediction models based on MNIRS and partial least squares regression (PLSR) were developed for TOC and its fractions. Leave-one-out cross-validations of MNIRS-PLSR models indicated accurate predictions (R2 > 0.90, negligible bias, ratio of performance to deviation > 3) and fraction-specific functional group contributions to beta coefficients in the models. TOC and its fractions were predicted using the cross-validated models and soil spectra for 3109 reforested and agricultural soils. The reliability of predictions determined using k-nearest neighbour score distance indicated that >80% of predictions were within the satisfactory inlier limit. The study demonstrated the utility of infrared spectroscopy (MNIRS-PLSR) to rapidly and economically determine TOC and its fractions and thereby accurately describe the effects of land use change such as reforestation on agricultural soils.

Mid-infrared spectroscopy for rapid assessment of soil properties after land use change from pastures to Eucalyptus globulus plantations.

There is an increasing demand for rapid and cost effective techniques to accurately measure the effects of land use change on soil properties. This study evaluated the ability of mid-infrared spectroscopy (MIRS) coupled with partial least squares regression (PLSR) to rapidly predict soil properties affected by land use change from agriculture (mainly pasture) to Eucalyptus globulus plantations in south-western Australia. We measured total organic carbon (TOC), total nitrogen (Total N), TOC/Total N (C/N ratio), microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), and total phosphorus (Total P). The PLSR calibration models were developed using mid-infrared (MIR) spectra (4000 to 450 cm(-1)) and square root transformed measured soil data (n = 180) from 23 paired pasture and E. globulus plantation sites representing the soils and climate of E. globulus plantation estates in south-western Australia. The calibration models for TOC, Total N, C/N ratio and Total P showed excellent correlations between measured and predicted data with coefficient of determination (R(2)) exceeding 0.91 and minimum root-mean-square error (RMSE) of calibration [TOC (R(2) = 0.95, RMSE = 0.36), Total N (R(2) = 0.96, RMSE = 0.10), C/N ratio (R(2) = 0.92, RMSE = 0.14) and Total P (R(2) = 0.91, RMSE = 0.06)]. The calibration models had reasonable predictions for MBC (R(2) = 0.66, RMSE = 0.07) and MBN (R(2) = 0.63, RMSE = 0.06). The calibrated models were validated using soils from 8 independent paired pasture and E. globulus sites (n = 64). The validated predictions were excellent for TOC (R(2) = 0.92, RMSE = 0.40) and Total N (R(2) = 0.91, RMSE = 0.12), but less so for C/N ratio (R(2) = 0.80, RMSE = 0.35), MBC (R(2) = 0.70, RMSE = 0.08) and Total P (R(2) = 0.75, RMSE = 0.12). The results demonstrate the potential of MIRS-PLSR to rapidly, accurately and simultaneously determine several properties in land use change affected soils.

Using measured stocks of biomass and litter carbon to constrain modelled estimates of sequestration of soil organic carbon under contrasting mixed-species environmental plantings.

Reforestation of agricultural land with mixed-species environmental plantings of native trees and shrubs contributes to abatement of greenhouse gas emissions through sequestration of carbon, and to landscape remediation and biodiversity enhancement. Although accumulation of carbon in biomass is relatively well understood, less is known about associated changes in soil organic carbon (SOC) following different types of reforestation. Direct measurement of SOC may not be cost effective where rates of SOC sequestration are relatively small and/or highly spatially-variable, thereby requiring intensive sampling. Hence, our objective was to develop a verified modelling approach for determining changes in SOC to facilitate the inclusion of SOC in the carbon accounts of reforestation projects. We measured carbon stocks of biomass, litter and SOC (0-30cm) in 125 environmental plantings (often paired to adjacent agricultural sites), representing sites of varying productivity across the Australian continent. After constraining a carbon accounting model to observed measures of growth, allocation of biomass, and rates of litterfall and litter decomposition, the model was calibrated to maximise the efficiency of prediction of SOC and its fractions. Uncertainties in both measured and modelled results meant that efficiencies of prediction of SOC across the 125 contrasting plantings were only moderate, at 39-68%. Data-informed modelling nonetheless improved confidence in outputs from scenario analyses, confirming that: (i) reforestation on agricultural land highly depleted in SOC (i.e. previously under cropping) had the highest capacity to sequester SOC, particularly where rainfall was relatively high (>600mmyear-1), and; (ii) decreased planting width and increased stand density and the proportion of eucalypts enhanced rates of SOC sequestration. These results improve confidence in predictions of SOC following environmental reforestation under varying conditions. The calibrated model will be a useful tool for informing land managers and policy makers seeking to understand the dynamics of SOC following such reforestation.