The cost‐efficiency and reliability of two methods for soil organic C accounting

Rossel, Raphael A. Viscarra; Brus, D.J.

doi:10.1002/ldr.2887

Cited by 23 publications

(16 citation statements)

References 36 publications

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“…Forests and production forestry were excluded because we lack adequate data to support simulations under these land uses. The C fractions, clay content and AWC were estimated with visible-near-infrared spectra (Viscarra Rossel and Webster, 2012;Viscarra Rossel et al, 2015). Maximum air temperature, minimum air temperature, precipitation and pan evaporation are also required to run the model.…”

Section: Data Compilation and Synthesismentioning

confidence: 99%

“…In this context, the development of robust frameworks for soil organic C modelling and simulation to synthesise and integrate measurements and datasets with models is critical (Harden et al, 2018;Ogle et al, 2010;Paustian et al, 1997;Smith et al, 2020). Their development should also allow for their efficient updating, with new measurements, data and models, as they become available (Viscarra Rossel and Brus, 2018;England and Viscarra Rossel, 2018;Smith et al, 2020) and enable a more systematic approach for calibration and validation, making simulations more reliable and reproducible.…”

Section: Introductionmentioning

confidence: 99%

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Assessing the response of soil carbon in Australia to changing inputs and climate using a consistent modelling framework

et al. 2021

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Abstract. Land use and management practices affect the response of soil organic carbon (C) to global change. Process-based models of soil C are useful tools to simulate C dynamics, but it is important to bridge any disconnect that exists between the data used to inform the models and the processes that they depict. To minimise that disconnect, we developed a consistent modelling framework that integrates new spatially explicit soil measurements and data with the Rothamsted carbon model (Roth C) and simulates the response of soil organic C to future climate change across Australia. We compiled publicly available continental-scale datasets and pre-processed, standardised and configured them to the required spatial and temporal resolutions. We then calibrated Roth C and ran simulations to estimate the baseline soil organic C stocks and composition in the 0–0.3 m layer at 4043 sites in cropping, modified grazing, native grazing and natural environments across Australia. We used data on the C fractions, the particulate, mineral-associated and resistant organic C (POC, MAOC and ROC, respectively) to represent the three main C pools in the Roth C model's structure. The model explained 97 %–98 % of the variation in measured total organic C in soils under cropping and grazing and 65 % in soils under natural environments. We optimised the model at each site and experimented with different amounts of C inputs to simulate the potential for C accumulation under constant climate in a 100-year simulation. With an annual increase of 1 Mg C ha−1 in C inputs, the model simulated a potential soil C increase of 13.58 (interquartile range 12.19–15.80), 14.21 (12.38–16.03) and 15.57 (12.07–17.82) Mg C ha−1 under cropping, modified grazing and native grazing and 3.52 (3.15–4.09) Mg C ha−1 under natural environments. With projected future changes in climate (+1.5, 2 and 5.0 ∘C) over 100 years, the simulations showed that soils under natural environments lost the most C, between 3.1 and 4.5 Mg C ha−1, while soils under native grazing lost the least, between 0.4 and 0.7 Mg C ha−1. Soil under cropping lost between 1 and 2.7 Mg C ha−1, while those under modified grazing showed a slight increase with temperature increases of 1.5 ∘C, but with further increases of 2 and 5 ∘C the median loss of TOC was 0.28 and 3.4 Mg C ha−1, respectively. For the different land uses, the changes in the C fractions varied with changes in climate. An empirical assessment of the controls on the C change showed that climate, pH, total N, the C : N ratio and cropping were the most important controls on POC change. Clay content and climate were dominant controls on MAOC change. Consistent and explicit soil organic C simulations improve confidence in the model's estimations, facilitating the development of sustainable soil management under global change.

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Section: Data Compilation and Synthesismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing the response of soil carbon in Australia to changing inputs and climate using a consistent modelling framework

et al. 2021

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“…et al (2016b) showed that the 5 sensing system can be used to accurately baseline soil C stocks for accounting purposes. The system was use to derive baseline estimates of soil organic C stocks (Viscarra Rossel, R.A. et al, 2016b) and its efficiency and reliability for soil C accounting was assessed by Viscarra Rossel, R. A. and Brus (2018). They found that compared to more conventional methods that use composite sampling and laboratory analysis, sensing with the SCANS is more cost-efficient in that it provides a good balance between accuracy and cost.…”

Section: Aga Backscatter Bulk Densitymentioning

confidence: 99%

“…Their measurements are accurate and cost-efficient (Viscarra Rossel, R. A. and Brus, 2018). While there are several reviews on the use of sensors for measuring soil organic C concentration (e.g Bellon-Maurel and McBratney, 2011;Izaurralde et al, 2013;Reeves et al, 2012;Stenberg et al, 2010;Viscarra Rossel, R.A. et al, 2011), few studies report on sensors for measuring bulk density or gravel (Lobsey and Viscarra Rossel, 2016;Fouinat et al, 2017), or report on the integration of 5 sensing methods for the purpose of soil organic C accounting.…”

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confidence: 99%

Proximal sensing for soil carbon accounting

England¹,

Rossel²

2018

Preprint

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Abstract. Maintaining or increasing soil organic carbon (C) is important for securing food production, and for mitigating greenhouse gas (GHG) emissions, climate change and land degradation. Some land management practices in cropping, grazing, horticultural and mixed farming systems can be used to increase organic C in soil, but to assess their effectiveness, we need accurate and cost-efficient methods for measuring and monitoring the change. To determine the stock of organic C in soil, one needs measurements of soil organic C concentration, bulk density and gravel content, but using conventional laboratory-based 5 analytical methods is expensive. Our aim here is to review the current state of proximal sensing for the development of new soil C accounting methods for emissions reporting and in emissions reduction schemes. We evaluated sensing techniques in terms of their rapidity, cost, accuracy, safety, readiness and their state of development. The most suitable technique for measuring soil organic C concentrations appears to be vis-NIR spectroscopy and for bulk density active gamma-ray attenuation. Sensors for measuring gravel have not been developed, but an interim solution with rapid wet-sieving and automated measurement appears 10 useful. Field-deployable, multi-sensor systems are needed for cost-efficient soil C accounting. Proximal sensing can be used for soil organic C accounting, but the methods need to be standardised and procedural guidelines need to be developed to ensure proficient measurement and accurate reporting and verification. This is particularly important if the schemes use financial incentives for landholders to adopt management practices to sequester soil organic C. We list and discuss the requirements for the development of new soil C accounting methods that are based on proximal sensing, including requirements for recording, 15 verification and auditing.

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“…There is a wide range of statistical tools available for multivariate modelling of soil properties. Ongoing research is continuously evaluating new tools at the same time striving to clarify the viability of application of VIS-NIR-SWIR soil spectroscopy in specific scenarios (e.g., Gholizadeh, Saberioon, Carmon, Boruvka, & Ben-Dor, 2018;Ogen, Neumann, Chabrillat, Goldshleger, & Ben-Dor, 2018;Ostovari et al, 2018;Terra, Demattê, & Viscarra Rossel, 2018;Viscarra Rossel & Brus, 2018).…”

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Soil organic matter and texture estimation from visible–near infrared–shortwave infrared spectra in areas of land cover changes using correlated component regression

et al. 2019

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Land use changes due to natural and human‐related factors, which include wildfires and crop abandonment, are among the most important drivers of soil degradation and demand regular monitoring. Proximal soil sensing in visible–near infrared–shortwave infrared spectral regions could offer a solution. However, to become operational, optimal combination of data and technique has to be defined. Thus, the purpose of this study was (a) to predict the soil organic matter (SOM) content and soil texture in areas of wildfire burns and crop abandonment in Aragón Province, Northern Spain, from their laboratory reflectance spectra using novel correlated components regression with a step‐down variable selection algorithm (CCR‐SD) and (b) to compare the CCR‐SD and the partial least squares regression (PLSR) methods. The results obtained by the tested methods were similar. CCR‐SD models showed high predictive capacity with coefficients of determination (R2) in the range of 0.80–0.86 and 0.70–0.87 for calibration and validation data sets, respectively, and the highest R2 value was attained in the SOM estimation. Moreover, the CCR‐SD models stand out for the superior accuracy–parsimony relationship: the number of predictors varied from 16 (silt models) to 49 (SOM models). On average, the CCR‐SD calibrations needed less than a half of the predictors employed in PLSR models. This research confirmed that CCR‐SD can be used for monitoring SOM content and texture of soils from visible–near infrared–shortwave infrared spectra in the study area and, probably, in other areas of land use/land cover change and that CCR‐SD can create highly parsimonious models that achieve results comparable with the commonly used PLSR method.

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The cost‐efficiency and reliability of two methods for soil organic C accounting

Cited by 23 publications

References 36 publications

Assessing the response of soil carbon in Australia to changing inputs and climate using a consistent modelling framework

Assessing the response of soil carbon in Australia to changing inputs and climate using a consistent modelling framework

Proximal sensing for soil carbon accounting

Soil organic matter and texture estimation from visible–near infrared–shortwave infrared spectra in areas of land cover changes using correlated component regression

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