Are soil carbon stocks controlled by a soil’s capacity to protect carbon from decomposition?

Dr Miko Kirschbaum1, Dr Donna Glitrap1, Dr Sam McNally2, Dr Liyin Liang1, Dr Carolyn Hedley1, Dr Gabriel Moinet3, Dr Michael Blaschek1, Dr Michael Beare2, Dr Benny Theng1

1Landcare Research – Manaaki Whenua, Palmerston North, New Zealand, 2Plant and Food Research, Lincoln, New Zealand, 3Landcare Research – Manaaki Whenua, Lincoln, New Zealand

Greenhouse gas emissions can be off-set by increasing soil organic carbon (SOC), but the factors controlling changes in SOC storage must be understood to identify suitable management practices. Soils differ in their ability to stabilise SOC, but do soils have a maximum capacity to stabilise carbon, or does stabilisation simply act to reduce turn-over rates without limits? Here, we use observations from two specific NZ sites, and from national soils data to gain insights into the controls of SOC stabilisation. They showed:

  1. When other factors such as climate, soil fertility, and pasture management were the same, SOC was linearly correlated with soil specific mineral surface area (Sm).
  2. At each soil depth, SOC and Sm were linearly related, with the slopes and intercepts of the relationships decreasing with depth.
  3. Small intercept values at zero Sm implied that SOC was mostly protected by the soil matrix rather than biochemically, that mineral surface area was the functionally relevant measure of stabilisation capacity, and that its effectiveness was independent of carbon input rates.

We analysed New Zealand’s national soils data based for evidence of a maximum stabilisation capacity. We reasoned that if SOC was limited by maximum stabilisation capacity it should result in a skewed distribution of SOC around mean values. Some points could be much lower than the maximum stabilisation capacity, but points could not exceed that maximum capacity. SOC in the national data set, however, was normally distributed, thus being inconsistent with maximum stabilisation capacity as a SOC limitation.

Instead, our analysis suggested that protected SOC, Cp, could be described as:

Cp, = Cin Sm / f(T, W, …), where Cin is the carbon input rate, and f(T, W, …) is a SOC turn-over rate depending on temperature, soil moisture or any other factors able to affect decomposition rates.


Miko Kirschbaum is an ecophysiologist and modeller. He works for Landcare Research and is based in Palmerston North, New Zealand. He has had a long-standing interest in the effects of climate change on ecosystems and has extensively studied the effects of increasing temperature and CO2 concentration on biological productivity and soil carbon storage. His recent work has aimed to identify ways to modify farm management in order to mitigate climate change by increasing soil carbon storage.

Mechanistic modeling of managed grasslands: Model validation and projections of climate change effects on pasture productivity, GHG exchanges and soil carbon stocks

Dr Nicolas Puche1, Dr Nimai Senapati2, Dr Chris Flechard3, Dr Katja Klumpp4, Dr Miko Kirschbaum5, Dr Abad Chabbi6

1INRA, UMR ECOSYS, Thiverval-grignon, France, 2Rothamsted Research, Department of Plant Sciences, West Common/Harpenden, United Kingdom, 3INRA, UMR 1069 SAS, Rennes, France, 4INRA, VetAgro Sup, UMR 874 Ecosystème Prairial, Clermont Ferrand, France, 5Manaaki Whenua – Landcare Research, Palmerston North 4442, New Zealand, 6INRA, URP3F, Lusignan, France

The CenW ecosystem model simulates carbon, water, and nitrogen cycles following ecophysiological processes and management practices on a daily basis. In the first part of the study, we tested and evaluated the model using five years eddy covariance measurements from two adjacent but differently managed grasslands in France. The data were used to parameterize CenW for the two grassland sites until good agreements, i.e., high model efficiencies and correlations, between observed and modeled fluxes were achieved. The CenW model captured day-to-day, seasonal, and interannual variability observed in measured CO₂ and water fluxes. We also showed that following mowing and grazing, carbon gain was severely curtailed through a sharp and severe reduction in photosynthesizing biomass. We also identified large model/data discrepancies for carbon fluxes during grazing events caused by the noncapture by the eddy covariance system of large respiratory losses of C from dairy cows when they were present in the paddocks. The missing component of cows’ respiration in the net carbon budget of the grazed grassland can turn sites from being C sinks to being neutral or C sources, highlighting that extra care is needed in the processing of eddy covariance data from grazed grasslands to correctly calculate their annual CO₂ balances and carbon budgets. In the second part of the study, the calibrated CenW model was used to get a better understanding on how grasslands ecosystems and their soil carbon stocks will respond to future climate and to changes in management practices. We used 3 different sets of meteorological variables corresponding to possible future conditions at the study site according to RCP 2.6, 4.5 and 8.5 to run the model for long term. We showed that the long-term grassland productivity, milk production, carbon and water fluxes and soil carbon stocks were strongly modified by climate alteration.


I am currently doing a Postdoc at INRA (France) where I use ecosystem models to study the possible responses of grassland ecosystems and their SOC stocks under climate change.

A molecular-level perspective of soil water repellency in sand and clay

Mr Nicholas Daniel1, Assoc. Prof. David Henry1, Prof.  Richard Harper1

1Murdoch University, Swan View, Australia

Soil water repellency is estimated to affect over two million hectares of southern Australia,1 resulting in approximately $100 million in production losses.2 The cause is associated with soil organic matter, which can form a coating on the mineral grains.3 However, while speculative theories have been proposed to explain experimental data, little has been done to justify the theory at the molecular level. Thus, in this study, molecular dynamics simulations were carried out using mineral surface models of amorphous silica (weathered sand surface), kaolinite (Al-OH and Si-O) and quartz. The organo-mineral interactions between hexadecanoic acid on these surfaces was calculated using radial distribution functions, mean square displacements, torsion distributions, concentration profiles, equilibrium snapshots and interaction energies. Initial models were based on previous studies by Walden et al.,4 Uddin et al.,5  and Daniel et. al.,6 which excluded the presence of water; however, the current models now include the addition of water and charge effects. These more complex models show that both the organo-mineral interactions and structure/layering of the acid molecules on the weathered sand and kaolinite (Al-OH) surfaces differ, resulting in models that more accurately reflect experimental observations.

  1. Harper, R. J.; McKissock, I.; Gilkes, R. J.; Carter, D. J.; Blackwell, P. S. Journal of Hydrology. 2000, 231–232, 371–383.
  2. CSIRO Case Study: Water Repellent Soils. (accessed 29/03/2019).
  3. McGhie, D. A.; Posner, A. M. Aust. J. Soil Res. 1980, 18, 309 – 323.
  4. Walden, L. L.; Harper, R. J.; Mendham, D. S.; Henry, D. J.; Fontaine, J. B. Soil Research. 2015.
  5. Uddin, S. M. M.; Daniel, N. R. R.; Harper, R. J.; Henry, D. J. Biogeochemistry. 2017, 134 (1-2), 147-161.
  6. Daniel, N. R. R.; Uddin, S. M. M.; Harper, R. J.; Henry, D. J. Geoderma. 2019, 338, 56 – 66.

Biography: PhD student at Murdoch University, Western Australia. Studies involve the combination of computational chemistry with laboratory experiments to investigate the role of various organic compounds in inducing soil water repellency.


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