Novel Proximal Sensing for Monitoring Soil Organic C Stocks and Condition

Abstract Aims Plant-derived carbon (C) inputs via foliar litter, root litter and root exudates are key drivers of soil organic C stocks. However, the responses of these three input pathways to climate warming have rarely been studied in alpine shrublands. Methods By employing a three-year warming experiment (increased by1.3 ℃), we investigated the effects of warming on the relative C contributions from foliar litter, root litter and root exudates from Sibiraea angustata, a dominant shrub species in an alpine shrubland on the eastern Qinghai-Tibetan Plateau. Important Findings The soil organic C inputs from foliar litter, root litter and root exudates were 77.45, 90.58 and 26.94 g C m -2, respectively. Warming only slightly increased the soil organic C inputs from foliar litter and root litter by 8.04 and 11.13 g C m -2, but significantly increased the root exudate C input by 15.40 g C m -2. Warming significantly increased the relative C contributions of root exudates to total C inputs by 4.6% but slightly decreased those of foliar litter and root litter by 2.5% and 2.1%, respectively. Our results highlight that climate warming may stimulate plant-derived C inputs into soils mainly through root exudates rather than litter in alpine shrublands on the Qinghai-Tibetan Plateau.

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Proximal sensing for soil carbon accounting

10.5194/soil-2017-36 ◽

2018 ◽

Author(s):

Jacqueline R. England ◽

Raphael Armando Viscarra Rossel

Keyword(s):

Bulk Density ◽

Financial Incentives ◽

Management Practices ◽

Ghg Emissions ◽

Carbon Accounting ◽

Soil Organic C ◽

Proximal Sensing ◽

Organic C ◽

Soil C ◽

Cost Efficient

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 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 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, verification and auditing.

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Evaluation of terrestrial pan-Arctic carbon cycling using a data-assimilation system

Earth System Dynamics ◽

10.5194/esd-10-233-2019 ◽

2019 ◽

Vol 10 (2) ◽

pp. 233-255 ◽

Cited By ~ 4

Author(s):

Efrén López-Blanco ◽

Jean-François Exbrayat ◽

Magnus Lund ◽

Torben R. Christensen ◽

Mikkel P. Tamstorf ◽

...

Keyword(s):

The Arctic ◽

Soil Organic C ◽

Area Index ◽

Organic C ◽

Soil C ◽

Transit Times ◽

C Cycle ◽

C Stocks ◽

Data Assimilation System ◽

Assimilation System

Abstract. There is a significant knowledge gap in the current state of the terrestrial carbon (C) budget. Recent studies have highlighted a poor understanding particularly of C pool transit times and of whether productivity or biomass dominate these biases. The Arctic, accounting for approximately 50 % of the global soil organic C stocks, has an important role in the global C cycle. Here, we use the CARbon DAta MOdel (CARDAMOM) data-assimilation system to produce pan-Arctic terrestrial C cycle analyses for 2000–2015. This approach avoids using traditional plant functional type or steady-state assumptions. We integrate a range of data (soil organic C, leaf area index, biomass, and climate) to determine the most likely state of the high-latitude C cycle at a 1∘ × 1∘ resolution and also to provide general guidance about the controlling biases in transit times. On average, CARDAMOM estimates regional mean rates of photosynthesis of 565 g C m−2 yr−1 (90 % confidence interval between the 5th and 95th percentiles: 428, 741), autotrophic respiration of 270 g C m−2 yr−1 (182, 397) and heterotrophic respiration of 219 g C m−2 yr−1 (31, 1458), suggesting a pan-Arctic sink of −67 (−287, 1160) g Cm−2 yr−1, weaker in tundra and stronger in taiga. However, our confidence intervals remain large (and so the region could be a source of C), reflecting uncertainty assigned to the regional data products. We show a clear spatial and temporal agreement between CARDAMOM analyses and different sources of assimilated and independent data at both pan-Arctic and local scales but also identify consistent biases between CARDAMOM and validation data. The assimilation process requires clearer error quantification for leaf area index (LAI) and biomass products to resolve these biases. Mapping of vegetation C stocks and change over time and soil C ages linked to soil C stocks is required for better analytical constraint. Comparing CARDAMOM analyses to global vegetation models (GVMs) for the same period, we conclude that transit times of vegetation C are inconsistently simulated in GVMs due to a combination of uncertainties from productivity and biomass calculations. Our findings highlight that GVMs need to focus on constraining both current vegetation C stocks and net primary production to improve a process-based understanding of C cycle dynamics in the Arctic.

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

Biogeosciences ◽

10.5194/bg-18-5185-2021 ◽

2021 ◽

Vol 18 (18) ◽

pp. 5185-5202

Author(s):

Juhwan Lee ◽

Raphael A. Viscarra Rossel ◽

Mingxi Zhang ◽

Zhongkui Luo ◽

Ying-Ping Wang

Keyword(s):

Global Change ◽

Future Climate Change ◽

Natural Environments ◽

Soil Organic C ◽

Total N ◽

Organic C ◽

Soil C ◽

Continental Scale ◽

Modelling Framework ◽

C Stocks

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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Simulation of soil carbon dynamics in Australia with {\sc Roth C}

10.5194/ismc2021-91 ◽

2021 ◽

Author(s):

Raphael Viscarra Rossel ◽

Juhwan Lee ◽

Mingxi Zhang ◽

Zhongkui Luo ◽

YingPing Wang

Keyword(s):

C:N Ratio ◽

Clay Content ◽

Natural Environments ◽

Soil Organic C ◽

Total N ◽

Organic C ◽

Continental Scale ◽

C Stocks ◽

C Decomposition ◽

Soil Measurements

We simulated soil organic carbon (C) dynamics across Australia with the Rothamsted carbon model ({\sc Roth C}) by connecting new spatially-explicit soil measurements and data with the model. This helped us to bridge the disconnection that exists between datasets used to inform the model and the processes that it depicts. We compiled publicly available continental-scale datasets and pre-processed, standardised and configured them to the required spatial and temporal resolutions. We then calibrated {\sc Roth C} and run simulations to estimate the baseline soil organic C stocks and composition in the 0--0.3~m layer at 4,043 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 {\sc Roth C} model's structure.&#160; 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 and chainging climate in a 100-year simulation. Soils under native grazing were the most potentially vulnerable to C decomposition and loss, while soils under natural environments were the least vulnerable. 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, contributing to the development of sustainable soil management under global change.&#160;

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Peer Review #1 of "Rebuilding soil organic C stocks in degraded grassland by grazing exclusion: a linked decline in soil inorganic C (v0.1)"

10.7287/peerj.8986v0.1/reviews/1 ◽

2020 ◽

Keyword(s):

Peer Review ◽

Soil Organic C ◽

Organic C ◽

Grazing Exclusion ◽

C Stocks ◽

Degraded Grassland ◽

Soil Inorganic

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Simulation of soil carbon dynamics in Australia under a framework that better connects spatially explicit data with Rᴏᴛʜ C

10.5194/bg-2020-150 ◽

2020 ◽

Author(s):

Juhwan Lee ◽

Raphael A. Viscarra Rossel ◽

Zhongkui Luo ◽

Ying Ping Wang

Keyword(s):

C:N Ratio ◽

Clay Content ◽

Spatially Explicit ◽

Natural Environments ◽

Soil Organic C ◽

Total N ◽

Organic C ◽

Soil C ◽

Continental Scale ◽

C Stocks

Abstract. We simulated soil organic carbon (C) dynamics across Australia with the Rothamsted carbon model (Rᴏᴛʜ C) under a framework that connects new spatially-explicit soil measurements and data with the model. Doing so helped to bridge the disconnection that exists between datasets used to inform the model and the processes that it depicts. Under this framework, we compiled continental-scale datasets and pre-processed, standardised and configured them to the required spatial and temporal resolutions. We then calibrated Rᴏᴛʜ C and run simulations to predict the baseline soil organic C stocks and composition in the 0–0.3 m layer at 4,043 sites in cropping, modified grazing, native grazing, and natural environments across Australia. The Rᴏᴛʜ C model uses measured C fractions, the particulate, humus, and resistant organic C (POC, HOC and ROC, respectively) to represent the three main C pools in its 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 predict the potential for C accumulation in a 100-year simulation. With an annual increase of 1 Mg C ha−1 in C inputs, the model predicted 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. Soils under native grazing were the most potentially vulnerable to C decomposition and loss, while soils under natural environments were the least vulnerable. 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 HOC change. Consistent and explicit soil organic C simulations improve confidence in the model's predictions, contributing to the development of sustainable soil management under global change.

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Farming systems’ productivity and soil organic carbon stocks following fertilisers, no-tillage or legumes on a fertility-depleted soil in a semi-arid subtropical region

Soil Research ◽

10.1071/sr17228 ◽

2018 ◽

Vol 56 (4) ◽

pp. 429 ◽

Cited By ~ 3

Author(s):

R. C. Dalal ◽

W. M. Strong ◽

E. J. Weston ◽

J. E. Cooper ◽

K. J. Lehane ◽

...

Keyword(s):

Soil Fertility ◽

Grain Legume ◽

No Tillage ◽

Soil Organic C ◽

Management Options ◽

N Application ◽

Organic C ◽

Grain Yields ◽

C Stocks

Depleted soil nitrogen supplies in long-term continuously cultivated soil for cereal grain cropping have resulted in reduced cereal yields, low grain proteins and hence low economic returns. This has necessitated the development of alternative management practices to sustain crop yields, as well as to restore and maintain soil fertility. In the present study we examined the comparative performance of several management options over a 12-year period, including: a 4-year rotation of grass + legume pasture followed by wheat (GL–wheat); 2-year rotations of lucerne–wheat, annual medic–wheat and chickpea–wheat; and continuous conventional tillage (CT) or no-tillage (NT), without or with fertiliser N application (0, 25 and 75 kg N ha–1 for each crop). Average wheat grain yields were highest in the chickpea–wheat rotation, followed by the NT wheat with 75 kg N ha–1; the lowest grain yields were in the CT or NT wheat treatment without fertiliser N application. Crop water use and gross margin were strongly correlated. However, there was an increasing potential for the deep leaching of nitrate-N at 75 kg N ha–1 application, as well as from the GL pasture initiated in 1987, but not from that initiated in 1986, emphasising the effect of variability in growing seasons. Soil organic C stocks increased under the 4-year GL pasture in the 0–0.1 m depth only, then decreased steadily following the cropping phase. The rotation of 4-year GL pasture followed by wheat cropping for 4–6 years may maintain initial soil organic C stock, but a shorter cropping phase is required to increase soil organic C and N stocks and soil fertility in the long term. Partial economic analysis of the treatments suggested that restoring or maintaining soil N fertility, either through legume-based pastures, grain legume and/or N fertiliser, provides long-term positive economic return.

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Proximal sensing for soil carbon accounting

SOIL ◽

10.5194/soil-4-101-2018 ◽

2018 ◽

Vol 4 (2) ◽

pp. 101-122 ◽

Cited By ~ 9

Author(s):

Jacqueline R. England ◽

Raphael A. Viscarra Rossel

Keyword(s):

Bulk Density ◽

Financial Incentives ◽

Management Practices ◽

Near Infrared ◽

Carbon Accounting ◽

Soil Organic C ◽

Proximal Sensing ◽

Organic C ◽

Soil C ◽

Cost Efficient

Abstract. Maintaining or increasing soil organic carbon (C) is vital 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 requires measurements of soil organic C concentration, bulk density, and gravel content, but using conventional laboratory-based 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 method for measuring soil organic C concentrations appears to be visible–near-infrared (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 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 standardized and procedural guidelines need to be developed to ensure proficient measurement and accurate reporting and verification. These are particularly important if the schemes use financial incentives for landholders to adopt management practices to sequester soil organic C. We list and discuss requirements for developing new soil C accounting methods based on proximal sensing, including requirements for recording, verification, and auditing.

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