Articles | Volume 17, issue 6
https://doi.org/10.5194/bg-17-1393-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-17-1393-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Mar 2020
Research article |
| 20 Mar 2020
| 20 Mar 2020
DRIFTS band areas as measured pool size proxy to reduce parameter uncertainty in soil organic matter models
Moritz Laub
CORRESPONDING AUTHOR
Institute of Agricultural Sciences in the Tropics
(Hans-Ruthenberg-Institute), University of Hohenheim, Garbenstrasse 13, 70599 Stuttgart,
Germany
Michael Scott Demyan
School of Environment and Natural Resources, The Ohio State
University, 2021 Coffey Rd., Columbus, OH 43210, USA,
Yvonne Funkuin Nkwain
Institute of Agricultural Sciences in the Tropics
(Hans-Ruthenberg-Institute), University of Hohenheim, Garbenstrasse 13, 70599 Stuttgart,
Germany
Sergey Blagodatsky
Institute of Agricultural Sciences in the Tropics
(Hans-Ruthenberg-Institute), University of Hohenheim, Garbenstrasse 13, 70599 Stuttgart,
Germany
Institute of Physicochemical and Biological Problems in Soil Science,
Russian Academy of Sciences, 142290 Pushchino, Russia
Thomas Kätterer
Department of Ecology, Swedish University of Agricultural Sciences,
Ulls Väg 16, Uppsala, Sweden
Hans-Peter Piepho
Biostatistics Unit, Institute of Crop Science, University of Hohenheim, Fruwirthstr. 23, 70599 Stuttgart, Germany
Georg Cadisch
CORRESPONDING AUTHOR
Institute of Agricultural Sciences in the Tropics
(Hans-Ruthenberg-Institute), University of Hohenheim, Garbenstrasse 13, 70599 Stuttgart,
Germany
Related authors
Tobias K. D. Weber, Joachim Ingwersen, Petra Högy, Arne Poyda, Hans-Dieter Wizemann, Michael Scott Demyan, Kristina Bohm, Ravshan Eshonkulov, Sebastian Gayler, Pascal Kremer, Moritz Laub, Yvonne Funkiun Nkwain, Christian Troost, Irene Witte, Tim Reichenau, Thomas Berger, Georg Cadisch, Torsten Müller, Andreas Fangmeier, Volker Wulfmeyer, and Thilo Streck
Earth Syst. Sci. Data, 14, 1153–1181, https://doi.org/10.5194/essd-14-1153-2022, https://doi.org/10.5194/essd-14-1153-2022, 2022
Short summary
Short summary
Presented are measurement results from six agricultural fields operated by local farmers in southwestern Germany over 9 years. Six eddy-covariance stations measuring water, energy, and carbon fluxes between the vegetated soil surface and the atmosphere provided the backbone of the measurement sites and were supplemented by extensive soil and vegetation state monitoring. The dataset is ideal for testing process models characterizing fluxes at the vegetated soil surface and in the atmosphere.
Maria Regina Gmach, Martin Anders Bolinder, Lorenzo Menichetti, Thomas Kätterer, Heide Spiegel, Olle Åkesson, Jürgen Kurt Friedel, Andreas Surböck, Agnes Schweinzer, and Taru Sandén
SOIL, 10, 407–423, https://doi.org/10.5194/soil-10-407-2024, https://doi.org/10.5194/soil-10-407-2024, 2024
Short summary
Short summary
We evaluated the effect of soil management practices on decomposition at 29 sites (13 in Sweden and 16 in Austria) using long-term field experiments with the Tea Bag Index (TBI) approach. We found that the decomposition rate (k) and stabilization factor (S) were mainly governed by climatic conditions. In general, organic and mineral fertilization increased k and S, and reduced tillage increased S. Edaphic factors also affected k and S.
Moritz Laub, Sergey Blagodatsky, Marijn Van de Broek, Samuel Schlichenmaier, Benjapon Kunlanit, Johan Six, Patma Vityakon, and Georg Cadisch
Geosci. Model Dev., 17, 931–956, https://doi.org/10.5194/gmd-17-931-2024, https://doi.org/10.5194/gmd-17-931-2024, 2024
Short summary
Short summary
To manage soil organic matter (SOM) sustainably, we need a better understanding of the role that soil microbes play in aggregate protection. Here, we propose the SAMM model, which connects soil aggregate formation to microbial growth. We tested it against data from a tropical long-term experiment and show that SAMM effectively represents the microbial growth, SOM, and aggregate dynamics and that it can be used to explore the importance of aggregate formation in SOM stabilization.
Niel Verbrigghe, Niki I. W. Leblans, Bjarni D. Sigurdsson, Sara Vicca, Chao Fang, Lucia Fuchslueger, Jennifer L. Soong, James T. Weedon, Christopher Poeplau, Cristina Ariza-Carricondo, Michael Bahn, Bertrand Guenet, Per Gundersen, Gunnhildur E. Gunnarsdóttir, Thomas Kätterer, Zhanfeng Liu, Marja Maljanen, Sara Marañón-Jiménez, Kathiravan Meeran, Edda S. Oddsdóttir, Ivika Ostonen, Josep Peñuelas, Andreas Richter, Jordi Sardans, Páll Sigurðsson, Margaret S. Torn, Peter M. Van Bodegom, Erik Verbruggen, Tom W. N. Walker, Håkan Wallander, and Ivan A. Janssens
Biogeosciences, 19, 3381–3393, https://doi.org/10.5194/bg-19-3381-2022, https://doi.org/10.5194/bg-19-3381-2022, 2022
Short summary
Short summary
In subarctic grassland on a geothermal warming gradient, we found large reductions in topsoil carbon stocks, with carbon stocks linearly declining with warming intensity. Most importantly, however, we observed that soil carbon stocks stabilised within 5 years of warming and remained unaffected by warming thereafter, even after > 50 years of warming. Moreover, in contrast to the large topsoil carbon losses, subsoil carbon stocks remained unaffected after > 50 years of soil warming.
Tobias K. D. Weber, Joachim Ingwersen, Petra Högy, Arne Poyda, Hans-Dieter Wizemann, Michael Scott Demyan, Kristina Bohm, Ravshan Eshonkulov, Sebastian Gayler, Pascal Kremer, Moritz Laub, Yvonne Funkiun Nkwain, Christian Troost, Irene Witte, Tim Reichenau, Thomas Berger, Georg Cadisch, Torsten Müller, Andreas Fangmeier, Volker Wulfmeyer, and Thilo Streck
Earth Syst. Sci. Data, 14, 1153–1181, https://doi.org/10.5194/essd-14-1153-2022, https://doi.org/10.5194/essd-14-1153-2022, 2022
Short summary
Short summary
Presented are measurement results from six agricultural fields operated by local farmers in southwestern Germany over 9 years. Six eddy-covariance stations measuring water, energy, and carbon fluxes between the vegetated soil surface and the atmosphere provided the backbone of the measurement sites and were supplemented by extensive soil and vegetation state monitoring. The dataset is ideal for testing process models characterizing fluxes at the vegetated soil surface and in the atmosphere.
Helena Doležalová-Weissmannová, Stanislav Malý, Martin Brtnický, Jiří Holátko, Michael Scott Demyan, Christian Siewert, David Tokarski, Eliška Kameníková, and Jiří Kučerík
SOIL Discuss., https://doi.org/10.5194/soil-2021-109, https://doi.org/10.5194/soil-2021-109, 2021
Preprint withdrawn
Short summary
Short summary
Soil provides many ecosystem functions, which are orchestrated by the activity of soil microorganisms. For the assessment of the activity is necessary to employ many, sometimes, time-consuming, methods. We aim to replace all these methods by thermogravimetry, which registers mass losses induced by heating and previously appeared useful for analysis of soil carbon and nitrogen. Here, we show the potential of thermogravimetry to replace some methods and discuss its limits and problems.
Sergio Naranjo, Francelino A. Rodrigues Jr., Georg Cadisch, Santiago Lopez-Ridaura, Mariela Fuentes Ponce, and Carsten Marohn
Hydrol. Earth Syst. Sci., 25, 5561–5588, https://doi.org/10.5194/hess-25-5561-2021, https://doi.org/10.5194/hess-25-5561-2021, 2021
Short summary
Short summary
We integrate a spatially explicit soil erosion model with plot- and watershed-scale characterization and high-resolution drone imagery to assess the effect of spatial resolution digital terrain models (DTMs) on discharge and soil loss. Results showed reduction in slope due to resampling down of DTM. Higher resolution translates to higher slope, denser fluvial system, and extremer values of soil loss, reducing concentration time and increasing soil loss at the outlet. The best resolution was 4 m.
Sebastian Doetterl, Rodrigue K. Asifiwe, Geert Baert, Fernando Bamba, Marijn Bauters, Pascal Boeckx, Benjamin Bukombe, Georg Cadisch, Matthew Cooper, Landry N. Cizungu, Alison Hoyt, Clovis Kabaseke, Karsten Kalbitz, Laurent Kidinda, Annina Maier, Moritz Mainka, Julia Mayrock, Daniel Muhindo, Basile B. Mujinya, Serge M. Mukotanyi, Leon Nabahungu, Mario Reichenbach, Boris Rewald, Johan Six, Anna Stegmann, Laura Summerauer, Robin Unseld, Bernard Vanlauwe, Kristof Van Oost, Kris Verheyen, Cordula Vogel, Florian Wilken, and Peter Fiener
Earth Syst. Sci. Data, 13, 4133–4153, https://doi.org/10.5194/essd-13-4133-2021, https://doi.org/10.5194/essd-13-4133-2021, 2021
Short summary
Short summary
The African Tropics are hotspots of modern-day land use change and are of great relevance for the global carbon cycle. Here, we present data collected as part of the DFG-funded project TropSOC along topographic, land use, and geochemical gradients in the eastern Congo Basin and the Albertine Rift. Our database contains spatial and temporal data on soil, vegetation, environmental properties, and land management collected from 136 pristine tropical forest and cropland plots between 2017 and 2020.
Elisa Bruni, Bertrand Guenet, Yuanyuan Huang, Hugues Clivot, Iñigo Virto, Roberta Farina, Thomas Kätterer, Philippe Ciais, Manuel Martin, and Claire Chenu
Biogeosciences, 18, 3981–4004, https://doi.org/10.5194/bg-18-3981-2021, https://doi.org/10.5194/bg-18-3981-2021, 2021
Short summary
Short summary
Increasing soil organic carbon (SOC) stocks is beneficial for climate change mitigation and food security. One way to enhance SOC stocks is to increase carbon input to the soil. We estimate the amount of carbon input required to reach a 4 % annual increase in SOC stocks in 14 long-term agricultural experiments around Europe. We found that annual carbon input should increase by 43 % under current temperature conditions, by 54 % for a 1 °C warming scenario and by 120 % for a 5 °C warming scenario.
Lauric Cécillon, François Baudin, Claire Chenu, Bent T. Christensen, Uwe Franko, Sabine Houot, Eva Kanari, Thomas Kätterer, Ines Merbach, Folkert van Oort, Christopher Poeplau, Juan Carlos Quezada, Florence Savignac, Laure N. Soucémarianadin, and Pierre Barré
Geosci. Model Dev., 14, 3879–3898, https://doi.org/10.5194/gmd-14-3879-2021, https://doi.org/10.5194/gmd-14-3879-2021, 2021
Short summary
Short summary
Partitioning soil organic carbon (SOC) into fractions that are stable or active on a century scale is key for more accurate models of the carbon cycle. Here, we describe the second version of a machine-learning model, named PARTYsoc, which reliably predicts the proportion of the centennially stable SOC fraction at its northwestern European validation sites with Cambisols and Luvisols, the two dominant soil groups in this region, fostering modelling works of SOC dynamics.
Cited articles
Abramoff, R., Xu, X., Hartman, M., O'Brien, S., Feng, W., Davidson, E.,
Finzi, A., Moorhead, D., Schimel, J., Torn, M., and Mayes, M. A.: The
Millennial model: in search of measurable pools and transformations for
modeling soil carbon in the new century, Biogeochemistry, 137, 51–71,
https://doi.org/10.1007/s10533-017-0409-7, 2018.
Ahrens, B., Reichstein, M., Borken, W., Muhr, J., Trumbore, S. E., and
Wutzler, T.: Bayesian calibration of a soil organic carbon model using
Δ14C measurements of soil organic carbon and heterotrophic
respiration as joint constraints, Biogeosciences, 11, 2147–2168,
https://doi.org/10.5194/bg-11-2147-2014, 2014.
Ali, R. S., Ingwersen, J., Demyan, M. S., Funkuin, Y. N., Wizemann, H.-D.,
Kandeler, E., and Poll, C.: Modelling in situ activities of enzymes as a tool
to explain seasonal variation of soil respiration from agro-ecosystems, Soil
Biol. Biochem., 81, 291–303, https://doi.org/10.1016/j.soilbio.2014.12.001, 2015.
Ali, R. S., Kandeler, E., Marhan, S., Demyan, M. S., Ingwersen, J.,
Mirzaeitalarposhti, R., Rasche, F., Cadisch, G., and Poll, C.: Controls on
microbially regulated soil organic carbon decomposition at the regional
scale, Soil Biol. Biochem., 118, 59–68,
https://doi.org/10.1016/j.soilbio.2017.12.007, 2018.
Andrén, O. and Kätterer, T.: ICBM: The introductory carbon balance
model for exploration of soil carbon balances, Ecol. Appl., 7,
1226–1236, https://doi.org/10.1890/1051-0761(1997)007[1226:ITICBM]2.0.CO;2, 1997.
Bailey, V. L., Bond-Lamberty, B., DeAngelis, K., Grandy, A. S., Hawkes, C.
V., Heckman, K., Lajtha, K., Phillips, R. P., Sulman, B. N., Todd-Brown, K.
E. O., and Wallenstein, M. D.: Soil carbon cycling proxies: Understanding
their critical role in predicting climate change feedbacks, Glob. Change
Biol., 24, 895–905, https://doi.org/10.1111/gcb.13926, 2018.
Barré, P., Plante, A. F., Cécillon, L., Lutfalla, S., Baudin, F.,
Bernard, S., Christensen, B. T., Eglin, T., Fernandez, J. M., Houot, S.,
Kätterer, T., Le Guillou, C., Macdonald, A., van Oort, F., and Chenu, C.:
The energetic and chemical signatures of persistent soil organic matter,
Biogeochemistry, 130, 1–12, https://doi.org/10.1007/s10533-016-0246-0, 2016.
Blair, N., Faulkner, R. D., Till, A. R., Korschens, M., and Schulz, E.:
Long-term management impacts on soil C, N and physical fertility. Part II:
Bad Lauchstadt static and extreme FYM experiments, Soil Till. Res.,
91, 39–47, https://doi.org/10.1016/j.still.2005.11.001, 2006.
Bruun, S. and Jensen, L. S.: Initialisation of the soil organic matter pools
of the Daisy model, Ecol. Modell., 153, 291–295,
https://doi.org/10.17665/1676-4285.20155108, 2002.
Bruun, S., Christensen, B. T., Hansen, E. M., Magid, J., and Jensen, L. S.:
Calibration and validation of the soil organic matter dynamics of the Daisy
model with data from the Askov long-term experiments, Soil Biol. Biochem.,
35, 67–76, https://doi.org/10.1016/S0038-0717(02)00237-7, 2003.
Calderón, F. J., Reeves, J. B., Collins, H. P., and Paul, E. A.: Chemical
Differences in Soil Organic Matter Fractions Determined by
Diffuse-Reflectance Mid-Infrared Spectroscopy, Soil Sci. Soc. Am. J., 75,
568–579, https://doi.org/10.2136/sssaj2009.0375, 2011.
Campbell, E. E. E. and Paustian, K.: Current developments in soil organic
matter modeling and the expansion of model applications: a review, Environ.
Res. Lett., 10, 123004, https://doi.org/10.1088/1748-9326/10/12/123004, 2015.
Cécillon, L., Baudin, F., Chenu, C., Houot, S., Jolivet, R.,
Kätterer, T., Lutfalla, S., Macdonald, A., van Oort, F., Plante, A. F.,
Savignac, F., Soucémarianadin, L. N., and Barré, P.: A model based on
Rock-Eval thermal analysis to quantify the size of the centennially
persistent organic carbon pool in temperate soils, Biogeosciences, 15,
2835–2849, https://doi.org/10.5194/bg-15-2835-2018, 2018.
Clifford, D., Pagendam, D., Baldock, J., Cressie, N., Farquharson, R.,
Farrell, M., Macdonald, L., and Murray, L.: Rethinking soil carbon modelling:
a stochastic approach to quantify uncertainties, Environmetrics, 25,
265–278, https://doi.org/10.1002/env.2271, 2014.
Coleman, K. and Jenkinson, D. S.: RothC-26.3 – A Model for the turnover of
carbon in soil, in Evaluation of Soil Organic Matter Models,
Springer Berlin Heidelberg, Berlin, Heidelberg, 237–246, 1996.
Cotrufo, M. F., Wallenstein, M. D., Boot, C. M., Denef, K., and Paul, E.: The
Microbial Efficiency-Matrix Stabilization (MEMS) framework integrates plant
litter decomposition with soil organic matter stabilization: do labile plant
inputs form stable soil organic matter?, Glob. Change Biol., 19,
988–995, https://doi.org/10.1111/gcb.12113, 2013.
Demyan, M. S., Rasche, F., Schulz, E., Breulmann, M., Müller, T., and
Cadisch, G.: Use of specific peaks obtained by diffuse reflectance Fourier
transform mid-infrared spectroscopy to study the composition of organic
matter in a Haplic Chernozem, Eur. J. Soil Sci., 63, 189–199,
https://doi.org/10.1111/j.1365-2389.2011.01420.x, 2012.
Demyan, M. S., Rasche, F., Schütt, M., Smirnova, N., Schulz, E., and
Cadisch, G.: Combining a coupled FTIR-EGA system and in situ DRIFTS for
studying soil organic matter in arable soils, Biogeosciences, 10,
2897–2913, https://doi.org/10.5194/bg-10-2897-2013, 2013.
Ellerbrock, R. H. and Gerke, H. H.: Explaining soil organic matter
composition based on associations between OM and polyvalent cations, J.
Plant Nutr. Soil Sci., 181, 721–736, https://doi.org/10.1002/jpln.201800093, 2018.
Franko, U. and Merbach, I.: Modelling soil organic matter dynamics on a bare
fallow Chernozem soil in Central Germany, Geoderma, 303, 93–98,
https://doi.org/10.1016/j.geoderma.2017.05.013, 2017.
Gelman, A. and Rubin, D. B.: Inference from Iterative Simulation Using
Multiple Sequences, Stat. Sci., 7, 457–472, https://doi.org/10.1214/ss/1177011136,
1992.
Giacometti, C., Demyan, M. S., Cavani, L., Marzadori, C., Ciavatta, C., and
Kandeler, E.: Chemical and microbiological soil quality indicators and their
potential to differentiate fertilization regimes in temperate
agroecosystems, Appl. Soil Ecol., 64, 32–48,
https://doi.org/10.1016/j.apsoil.2012.10.002, 2013.
Good, W. D. and Smith, N. K.: Enthalpies of combustion of toluene, benzene,
cyclohexane, cyclohexene, methylcyclopentane, 1-methylcyclopentene, and
n-hexane, J. Chem. Eng. Data, 14, 102–106, https://doi.org/10.1021/je60040a036,
1969.
Hansen, S., Jensen, L. S., Nielsen, N. E., and Svendsen, H.: The Soil Plant System Model Daisy – Basic Principles and Modelling Approach, The Royal Veterinary and Agricultural University, Copenhagen, 39–50, 1993.
Hansen, S., Abrahamsen, P., Petersen, C. T., and Styczen, M.: Daisy: Model Use,
Calibration, and Validation, Trans. ASABE, 55, 1317–1335,
https://doi.org/10.13031/2013.42244, 2012.
Hararuk, O., Shaw, C., and Kurz, W. A.: Constraining the organic matter decay
parameters in the CBM-CFS3 using Canadian National Forest Inventory data and
a Bayesian inversion technique, Ecol. Modell., 364, 1–12,
https://doi.org/10.1016/j.ecolmodel.2017.09.008, 2017.
Heinlein, F., Biernath, C., Klein, C., Thieme, C., and Priesack, E.:
Evaluation of Simulated Transpiration from Maize Plants on Lysimeters,
Vadose Zone J., 16, 1–16, https://doi.org/10.2136/vzj2016.05.0042, 2017.
Herbst, M., Welp, G., Macdonald, A., Jate, M., Hädicke, A., Scherer, H.,
Gaiser, T., Herrmann, F., Amelung, W., and Vanderborght, J.: Correspondence
of measured soil carbon fractions and RothC pools for equilibrium and
non-equilibrium states, Geoderma, 314, 37–46,
https://doi.org/10.1016/j.geoderma.2017.10.047, 2018.
Jacobs, A., Flessa, H., Don, A., Heidkamp, A., Prietz, R., Dechow, R.,
Gensior, A., Poeplau, C., Riggers, C., Schneider, F., Tiemeyer, B., Vos, C.,
Wittnebel, M., Müller, T., Säurich, A., Fahrion-Nitschke, A.,
Gebbert, S., Hopfstock, R., Jaconi, A., Kolata, H., Lorbeer, M.,
Schröder, J., Laggner, A., Weiser, C., and Freibauer, A.:
Landwirtschaftlich genutzte Böden in Deutschland – Ergebnisse der
Bodenzustandserhebung – Thünen Report 64, Johann Heinrich von
Thünen-Institut, Bundesallee 50, 38116 Braunschweig, Germany, 2018.
Jensen, L. S., Mueller, T., Nielsen, N. E., Hansen, S., Crocker, G. J.,
Grace, P. R., Klír, J., Körschens, M., and Poulton, P. R.:
Simulating trends in soil organic carbon in long-term experiments using the
soil-plant-atmosphere model DAISY, Geoderma, 81, 5–28,
https://doi.org/10.1016/S0016-7061(97)88181-5, 1997.
Joergensen, R. G. and Mueller, T.: The fumigation-extraction method to
estimate soil microbial biomass: Calibration of the kEC value, Soil Biol.
Biochem., 28, 25–31, https://doi.org/10.1016/0038-0717(95)00102-6, 1996.
Kätterer, T., Bolinder, M. A., Andrén, O., Kirchmann, H., and
Menichetti, L.: Roots contribute more to refractory soil organic matter than
above-ground crop residues, as revealed by a long-term field experiment,
Agr. Ecosyst. Environ., 141, 184–192,
https://doi.org/10.1016/j.agee.2011.02.029, 2011.
Kirchmann, H., Haberhauer, G., Kandeler, E., Sessitsch, A., and Gerzabek, M.
H.: Effects of level and quality of organic matter input on carbon storage
and biological activity in soil: Synthesis of a long-term experiment, Global
Biogeochem. Cy., 18, 1–9, https://doi.org/10.1029/2003GB002204, 2004.
Klein, C., Biernath, C., Heinlein, F., Thieme, C., Gilgen, A. K., Zeeman, M.,
and Priesack, E.: Vegetation Growth Models Improve Surface Layer Flux
Simulations of a Temperate Grassland, Vadose Zone J., 16, 1–19,
https://doi.org/10.2136/vzj2017.03.0052, 2017.
Klein, C. G.: Modeling fluxes of energy and water between land surface and
atmosphere for grass- and cropland system, Fakultät Wissenschaftszentrum
Weihenstephan, 11–20, 2018.
Kozak, M. and Piepho, H. P.: What's normal anyway? Residual plots are more
telling than significance tests when checking ANOVA assumptions, J. Agron.
Crop Sci., 204, 86–98, https://doi.org/10.1111/jac.12220, 2018.
Laub, M., Blagodatsky, S., Nkwain, Y. F., and Cadisch, G.: Soil sample drying
temperature affects specific organic mid-DRIFTS peaks and quality indices,
Geoderma, 355, 113897, https://doi.org/10.1016/j.geoderma.2019.113897, 2019.
Lefèvre, R., Barré, P., Moyano, F. E., Christensen, B. T., Bardoux,
G., Eglin, T., Girardin, C., Houot, S., Kätterer, T., van Oort, F., and
Chenu, C.: Higher temperature sensitivity for stable than for labile soil
organic carbon – Evidence from incubations of long-term bare fallow soils,
Glob. Change Biol., 20, 633–640, https://doi.org/10.1111/gcb.12402, 2014.
Lu, D., Ye, M., and Hill, M. C.: Analysis of regression confidence intervals
and Bayesian credible intervals for uncertainty quantification, Water
Resour. Res., 48, 1–20, https://doi.org/10.1029/2011WR011289, 2012.
Lu, D., Ye, M., Hill, M. C., Poeter, E. P., and Curtis, G. P.: A computer
program for uncertainty analysis integrating regression and Bayesian
methods, Environ. Model. Softw., 60, 45–56,
https://doi.org/10.1016/j.envsoft.2014.06.002, 2014.
Luo, Z., Wang, E., Shao, Q., Conyers, M. K., and Liu, D. L.: Confidence in
soil carbon predictions undermined by the uncertainties in observations and
model parameterisation, Environ. Model. Softw., 80, 26–32,
https://doi.org/10.1016/j.envsoft.2016.02.013, 2016.
Margenot, A. J., Calderón, F. J., Bowles, T. M., Parikh, S. J., and
Jackson, L. E.: Soil Organic Matter Functional Group Composition in Relation
to Organic Carbon, Nitrogen, and Phosphorus Fractions in Organically Managed
Tomato Fields, Soil Sci. Soc. Am. J., 79, 772–782,
https://doi.org/10.2136/sssaj2015.02.0070, 2015.
Menichetti, L., Kätterer, T., and Leifeld, J.: Parametrization
consequences of constraining soil organic matter models by total carbon and
radiocarbon using long-term field data, Biogeosciences, 13, 3003–3019,
https://doi.org/10.5194/bg-13-3003-2016, 2016.
Minasny, B., Malone, B. P., McBratney, A. B., Angers, D. A., Arrouays, D.,
Chambers, A., Chaplot, V., Chen, Z.-S., Cheng, K., Das, B. S., Field, D. J.,
Gimona, A., Hedley, C. B., Hong, S. Y., Mandal, B., Marchant, B. P., Martin,
M., McConkey, B. G., Mulder, V. L., O'Rourke, S., Richer-de-Forges, A. C.,
Odeh, I., Padarian, J., Paustian, K., Pan, G., Poggio, L., Savin, I.,
Stolbovoy, V., Stockmann, U., Sulaeman, Y., Tsui, C.-C., Vågen, T.-G.,
van Wesemael, B., and Winowiecki, L.: Soil carbon 4 per mille, Geoderma, 292,
59–86, https://doi.org/10.1016/j.geoderma.2017.01.002, 2017.
.
Monteith, J. L.: Evaporation and surface temperature, Q. J. R. Meteorol.
Soc., 12, 513–522, https://doi.org/10.1002/qj.49710745102, 1976.
Mualem, Y.: A new model for predicting the hydraulic conductivity of
unsaturated porous media, Water Resour. Res., 12, 513–522,
https://doi.org/10.1029/WR012i003p00513, 1976.
Mueller, T., Jensen, L. S. S., Magid, J., and Nielsen, N. E. E.: Temporal
variation of C and N turnover in soil after oilseed rape straw incorporation
in the field: simulations with the soil-plant-atmosphere model DAISY, Ecol.
Modell., 99, 247–262,
https://doi.org/10.1016/S0304-3800(97)01959-5, 1997.
Mueller, T., Magid, J., Jensen, L. S., Svendsen, H., and Nielsen, N. E.: Soil
C and N turnover after incorporation of chopped maize, barley straw and blue
grass in the field: Evaluation of the DAISY soil–organic-matter submodel,
Ecol. Modell., 111, 1–15, https://doi.org/10.1016/S0304-3800(98)00094-5, 1998.
Nguyen, T., Janik, L., and Raupach, M.: Diffuse reflectance infrared fourier
transform (DRIFT) spectroscopy in soil studies, Soil Res., 29, 49–67,
https://doi.org/10.1071/SR9910049, 1991.
Nkwain, F. N., Demyan, M. S., Rasche, F., Dignac, M.-F., Schulz, E.,
Kätterer, T., Müller, T., and Cadisch, G.: Coupling pyrolysis with
mid-infrared spectroscopy (Py-MIRS) to fingerprint soil organic matter bulk
chemistry, J. Anal. Appl. Pyrol., 133, 176–184,
https://doi.org/10.1016/j.jaap.2018.04.004, 2018.
Nocita, M., Stevens, A., van Wesemael, B., Aitkenhead, M., Bachmann, M.,
Barthès, B., Ben Dor, E., Brown, D. J., Clairotte, M., Csorba, A.,
Dardenne, P., Demattê, J. A. M., Genot, V., Guerrero, C., Knadel, M.,
Montanarella, L., Noon, C., Ramirez-Lopez, L., Robertson, J., Sakai, H.,
Soriano-Disla, J. M., Shepherd, K. D., Stenberg, B., Towett, E. K., Vargas,
R., and Wetterlind, J.: Soil Spectroscopy: An Alternative to Wet Chemistry
for Soil Monitoring, Adv. Agron., 132, 139–159, 2015.
O'Leary, G. J., Liu, D. L., Ma, Y., Li, F. Y., McCaskill, M., Conyers, M.,
Dalal, R., Reeves, S., Page, K., Dang, Y. P., and Robertson, F.: Modelling
soil organic carbon 1. Performance of APSIM crop and pasture modules against
long-term experimental data, Geoderma, 264, 227–237,
https://doi.org/10.1016/j.geoderma.2015.11.004, 2016.
Parton, W. J., Schimel, D. S., Cole, C. V., and Ojima, D. S.: Analysis of
Factors Controlling Soil Organic Matter Levels in Great Plains Grasslands1,
Soil Sci. Soc. Am. J., 51, 1173,
https://doi.org/10.2136/sssaj1987.03615995005100050015x, 1987.
Parton, W. J., Scurlock, J. M. O., Ojima, D. S., Gilmanov, T. G., Scholes,
R. J., Schimel, D. S., Kirchner, T., Menaut, J.-C., Seastedt, T., Garcia
Moya, E., Kamnalrut, A., and Kinyamario, J. I.: Observations and modeling of
biomass and soil organic matter dynamics for the grassland biome worldwide,
Global Biogeochem. Cy., 7, 785–809, https://doi.org/10.1029/93GB02042, 1993.
Pengerud, A., Cécillon, L., Johnsen, L. K., Rasse, D. P., and Strand, L.
T.: Permafrost Distribution Drives Soil Organic Matter Stability in a
Subarctic Palsa Peatland, Ecosystems, 16, 934–947,
https://doi.org/10.1007/s10021-013-9652-5, 2013.
Piepho, H. P., Büchse, A., and Richter, C.: A Mixed Modelling Approach
for Randomized Experiments with Repeated Measures, J. Agron. Crop Sci.,
190, 230–247, https://doi.org/10.1111/j.1439-037X.2004.00097.x, 2004.
Poeplau, C., Don, A., Dondini, M., Leifeld, J., Nemo, R., Schumacher, J.,
Senapati, N., and Wiesmeier, M.: Reproducibility of a soil organic carbon
fractionation method to derive RothC carbon pools, Eur. J. Soil Sci., 64,
735–746, https://doi.org/10.1111/ejss.12088, 2013.
Poeter, E. P., Hill, M. C., Banta, E. R., Mehl, S., and Christensen, S.:
UCODE_2005 and six other computer codes for universal
sensitivity analysis, inverse modeling, and uncertainty evaluation, U.S.
Geological Survey Techniques and Methods 6-A11, 283 pp., 2005 (updated in February 2008).
Poeter, E. P., Hill, M. C., Lu, D., Tiedeman, C. R., and Mehl, S.:
UCODE_2014, with New Capabilities to Define Parameters Unique
to Predictions, Calculate Weights using Simulated Values, Estimate
Parameters with SVD, Evaluate Uncertainty with MCMC, and More, Integrated
Groundwater Modeling Center Report Number: GWMI 2014-02, 2014.
Poyda, A., Wizemann, H.-D., Ingwersen, J., Eshonkulov, R., Högy, P.,
Demyan, M. S., Kremer, P., Wulfmeyer, V., and Streck, T.: Carbon fluxes and
budgets of intensive crop rotations in two regional climates of southwest
Germany, Agr. Ecosyst. Environ., 276, 31–46,
https://doi.org/10.1016/j.agee.2019.02.011, 2019.
Segoli, M., De Gryze, S., Dou, F., Lee, J., Post, W. M., Denef, K., and Six,
J.: AggModel: A soil organic matter model with measurable pools for use in
incubation studies, Ecol. Modell., 263, 1–9,
https://doi.org/10.1016/j.ecolmodel.2013.04.010, 2013.
Sohi, S. P., Mahieu, N., Arah, J. R. M., Powlson, D. S., Madari, B., and
Gaunt, J. L.: A Procedure for Isolating Soil Organic Matter Fractions
Suitable for Modeling, Soil Sci. Soc. Am. J., 65, 1121,
https://doi.org/10.2136/sssaj2001.6541121x, 2001.
Stevenson, F. J.: Humus chemistry: genesis, composition, reactions, John
Wiley & Sons, New York, 308–317, 1994.
Sulman, B. N., Phillips, R. P., Oishi, A. C., Shevliakova, E., and Pacala, S.
W.: Microbe-driven turnover offsets mineral-mediated storage of soil carbon
under elevated CO2, Nat. Clim. Change, 4, 1099–1102,
https://doi.org/10.1038/nclimate2436, 2014.
Sulman, B. N., Moore, J. A. M., Abramoff, R., Averill, C., Kivlin, S.,
Georgiou, K., Sridhar, B., Hartman, M. D., Wang, G., Wieder, W. R.,
Bradford, M. A., Luo, Y., Mayes, M. A., Morrison, E., Riley, W. J., Salazar,
A., Schimel, J. P., Tang, J., and Classen, A. T.: Multiple models and
experiments underscore large uncertainty in soil carbon dynamics,
Biogeochemistry, 141, 109–123, https://doi.org/10.1007/s10533-018-0509-z, 2018.
Tinti, A., Tugnoli, V., Bonora, S., and Francioso, O.: Recent applications of
vibrational mid-infrared (IR) spectroscopy for studying soil components: A
review, J. Cent. Eur. Agric., 16, 1–22, https://doi.org/10.5513/JCEA01/16.1.1535,
2015.
van Genuchten, M. T.: A comparison of numerical solutions of the
one-dimensional unsaturated – saturated flow and mass transport equations,
Adv. Water Resour., 5, 47–55, https://doi.org/10.1016/0309-1708(82)90028-8, 1982.
Vrugt, J. A.: Markov chain Monte Carlo simulation using the DREAM software
package: Theory, concepts, and MATLAB implementation, Environ. Model.
Softw., 75, 273–316, https://doi.org/10.1016/j.envsoft.2015.08.013, 2016.
Wattenbach, M., Gottschalk, P., Hatterman, C., Rachimow, C., Flechsig, M., and Smith, P.: A framework for assessing uncertainty in ecosystem models, in Proceedings of the iEMSs Third Biennial Meeting, Paper 373, International Environmental Modeling and Software Society, available at: https://abdn.pure.elsevier.com/en/publications/a-framework-for-assessing-uncertainty-in-ecosystem-models (last access: 17 March 2020), 2006.
Wizemann, H.-D., Ingwersen, J., Högy, P., Warrach-Sagi, K., Streck, T.,
and Wulfmeyer, V.: Three year observations of water vapor and energy fluxes
over agricultural crops in two regional climates of Southwest Germany,
Meteorol. Zeitschrift, 24, 39–59, https://doi.org/10.1127/metz/2014/0618, 2015.
Yeasmin, S., Singh, B., Johnston, C. T., and Sparks, D. L.: Evaluation of
pre-treatment procedures for improved interpretation of mid infrared spectra
of soil organic matter, Geoderma, 304, 83–92,
https://doi.org/10.1016/j.geoderma.2016.04.008, 2017.
Zimmermann, M., Leifeld, J., Schmidt, M. W. I., Smith, P., and Fuhrer, J.:
Measured soil organic matter fractions can be related to pools in the RothC
model, Eur. J. Soil Sci., 58, 658–667,
https://doi.org/10.1111/j.1365-2389.2006.00855.x, 2007.
Short summary
Loss of soil carbon to the atmosphere represents a global challenge. We tested an innovative way to reduce the high uncertainty related to turnover of carbon stored in soils. With the use of infrared spectra of soils from model bare fallow systems, we were able to better assess the current state of soil carbon and predict its behavior in overdecadal time spans. In agreement with recent studies, carbon turnover seems faster than earlier assumed, with potential for high loss under mismanagement.
Loss of soil carbon to the atmosphere represents a global challenge. We tested an innovative way...
Altmetrics
Final-revised paper
Preprint