Articles | Volume 7, issue 2
SOIL, 7, 525–546, 2021
https://doi.org/10.5194/soil-7-525-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
SOIL, 7, 525–546, 2021
https://doi.org/10.5194/soil-7-525-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Original research article
18 Aug 2021
Original research article
| 18 Aug 2021
Developing the Swiss mid-infrared soil spectral library for local estimation and monitoring
Philipp Baumann et al.
Related authors
Philipp Baumann, Juhwan Lee, Emmanuel Frossard, Laurie Paule Schönholzer, Lucien Diby, Valérie Kouamé Hgaza, Delwende Innocent Kiba, Andrew Sila, Keith Sheperd, and Johan Six
SOIL, 7, 717–731, https://doi.org/10.5194/soil-7-717-2021, https://doi.org/10.5194/soil-7-717-2021, 2021
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This work delivers openly accessible and validated calibrations for diagnosing 26 soil properties based on mid-infrared spectroscopy. These were developed for four regions in Burkina Faso and Côte d'Ivoire, including 80 fields of smallholder farmers. The models can help to site-specifically and cost-efficiently monitor soil quality and fertility constraints to ameliorate soils and yields of yam or other staple crops in the four regions between the humid forest and the northern Guinean savanna.
Laura Summerauer, Philipp Baumann, Leonardo Ramirez-Lopez, Matti Barthel, Marijn Bauters, Benjamin Bukombe, Mario Reichenbach, Pascal Boeckx, Elizabeth Kearsley, Kristof Van Oost, Bernard Vanlauwe, Dieudonné Chiragaga, Aimé Bisimwa Heri-Kazi, Pieter Moonen, Andrew Sila, Keith Shepherd, Basile Bazirake Mujinya, Eric Van Ranst, Geert Baert, Sebastian Doetterl, and Johan Six
SOIL, 7, 693–715, https://doi.org/10.5194/soil-7-693-2021, https://doi.org/10.5194/soil-7-693-2021, 2021
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We present a soil mid-infrared library with over 1800 samples from central Africa in order to facilitate soil analyses of this highly understudied yet critical area. Together with an existing continental library, we demonstrate a regional analysis and geographical extrapolation to predict total carbon and nitrogen. Our results show accurate predictions and highlight the value that the data contribute to existing libraries. Our library is openly available for public use and for expansion.
Anatol Helfenstein, Philipp Baumann, Raphael Viscarra Rossel, Andreas Gubler, Stefan Oechslin, and Johan Six
SOIL, 7, 193–215, https://doi.org/10.5194/soil-7-193-2021, https://doi.org/10.5194/soil-7-193-2021, 2021
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In this study, we show that a soil spectral library (SSL) can be used to predict soil carbon at new and very different locations. The importance of this finding is that it requires less time-consuming lab work than calibrating a new model for every local application, while still remaining similar to or more accurate than local models. Furthermore, we show that this method even works for predicting (drained) peat soils, using a SSL with mostly mineral soils containing much less soil carbon.
Tegawende Léa Jeanne Ilboudo, Lucien NGuessan Diby, Delwendé Innocent Kiba, Tor Gunnar Vågen, Leigh Ann Winowiecki, Hassan Bismarck Nacro, Johan Six, and Emmanuel Frossard
EGUsphere, https://doi.org/10.5194/egusphere-2022-209, https://doi.org/10.5194/egusphere-2022-209, 2022
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Our results showed that at landscape level SOC stock variability was mainly explained by clay content. We found significant linear positive relationships between VC and SOC stocks for the land uses annual croplands, perennial croplands, grasslands and bushlands without soil depth restrictions until 110 cm. We concluded that in the forest-savanna transition zone, soil properties and topography determine land use, which in turn affects the stocks of SOC and TN and to some extent the VC stocks.
Yuanyuan Yang, Zefang Shen, Andrew Bissett, and Raphael A. Viscarra Rossel
SOIL, 8, 223–235, https://doi.org/10.5194/soil-8-223-2022, https://doi.org/10.5194/soil-8-223-2022, 2022
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We present a new method to estimate the relative abundance of the dominant phyla and diversity of fungi in Australian soil. It uses state-of-the-art machine learning with publicly available data on soil and environmental proxies for edaphic, climatic, biotic and topographic factors, and visible–near infrared wavelengths. The estimates could serve to supplement the more expensive molecular approaches towards a better understanding of soil fungal abundance and diversity in agronomy and ecology.
Zefang Shen, Raphael A. Viscarra Rossel, Haylee D'Agui, Lewis Walden, Mingxi Zhang, Tsoek Man Yiu, Kingsley Dixon, Paul Nevill, Adam Cross, Mohana Matangulu, and Yang Hu
SOIL Discuss., https://doi.org/10.5194/soil-2021-138, https://doi.org/10.5194/soil-2021-138, 2022
Revised manuscript accepted for SOIL
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Post-mining rehabilitation relies on measuring soil properties that are critical soil health indicators. We evaluated miniaturised visible and near-infrared spectrometers to model soil physical, chemical and biological properties related to soil health. We show there is potential to develop rapid, accurate, cost-effective diagnostic capacity to support mine site rehabilitation based on miniaturised spectrometers and deliver significant positive economic and environmental outcomes.
Kristof Van Oost and Jo Six
Biogeosciences Discuss., https://doi.org/10.5194/bg-2022-1, https://doi.org/10.5194/bg-2022-1, 2022
Revised manuscript under review for BG
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The direction and magnitude of the net erosion-induced land-atmosphere C exchange have been the topic of a big scientific debate for more than a decade now. Many have assumed that erosion leads to a loss of soil carbon to the atmosphere, whereas others have shown that erosion ultimately leads to a carbon sink. Here, we show that the soil carbon erosion source-sink paradox is reconciled when the broad range of temporal and spatial scales at which the underlying processes operate are considered.
Philipp Baumann, Juhwan Lee, Emmanuel Frossard, Laurie Paule Schönholzer, Lucien Diby, Valérie Kouamé Hgaza, Delwende Innocent Kiba, Andrew Sila, Keith Sheperd, and Johan Six
SOIL, 7, 717–731, https://doi.org/10.5194/soil-7-717-2021, https://doi.org/10.5194/soil-7-717-2021, 2021
Short summary
Short summary
This work delivers openly accessible and validated calibrations for diagnosing 26 soil properties based on mid-infrared spectroscopy. These were developed for four regions in Burkina Faso and Côte d'Ivoire, including 80 fields of smallholder farmers. The models can help to site-specifically and cost-efficiently monitor soil quality and fertility constraints to ameliorate soils and yields of yam or other staple crops in the four regions between the humid forest and the northern Guinean savanna.
Laura Summerauer, Philipp Baumann, Leonardo Ramirez-Lopez, Matti Barthel, Marijn Bauters, Benjamin Bukombe, Mario Reichenbach, Pascal Boeckx, Elizabeth Kearsley, Kristof Van Oost, Bernard Vanlauwe, Dieudonné Chiragaga, Aimé Bisimwa Heri-Kazi, Pieter Moonen, Andrew Sila, Keith Shepherd, Basile Bazirake Mujinya, Eric Van Ranst, Geert Baert, Sebastian Doetterl, and Johan Six
SOIL, 7, 693–715, https://doi.org/10.5194/soil-7-693-2021, https://doi.org/10.5194/soil-7-693-2021, 2021
Short summary
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We present a soil mid-infrared library with over 1800 samples from central Africa in order to facilitate soil analyses of this highly understudied yet critical area. Together with an existing continental library, we demonstrate a regional analysis and geographical extrapolation to predict total carbon and nitrogen. Our results show accurate predictions and highlight the value that the data contribute to existing libraries. Our library is openly available for public use and for expansion.
Juhwan Lee, Raphael A. Viscarra Rossel, Mingxi Zhang, Zhongkui Luo, and Ying-Ping Wang
Biogeosciences, 18, 5185–5202, https://doi.org/10.5194/bg-18-5185-2021, https://doi.org/10.5194/bg-18-5185-2021, 2021
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We performed Roth C simulations across Australia and assessed the response of soil carbon to changing inputs and future climate change using a consistent modelling framework. Site-specific initialisation of the C pools with measurements of the C fractions is essential for accurate simulations of soil organic C stocks and composition at a large scale. With further warming, Australian soils will become more vulnerable to C loss: natural environments > native grazing > cropping > modified grazing.
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
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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.
Mario Reichenbach, Peter Fiener, Gina Garland, Marco Griepentrog, Johan Six, and Sebastian Doetterl
SOIL, 7, 453–475, https://doi.org/10.5194/soil-7-453-2021, https://doi.org/10.5194/soil-7-453-2021, 2021
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In deeply weathered tropical rainforest soils of Africa, we found that patterns of soil organic carbon stocks differ between soils developed from geochemically contrasting parent material due to differences in the abundance of organo-mineral complexes, the presence/absence of chemical stabilization mechanisms of carbon with minerals and the presence of fossil organic carbon from sedimentary rocks. Physical stabilization mechanisms by aggregation provide additional protection of soil carbon.
Sophie F. von Fromm, Alison M. Hoyt, Markus Lange, Gifty E. Acquah, Ermias Aynekulu, Asmeret Asefaw Berhe, Stephan M. Haefele, Steve P. McGrath, Keith D. Shepherd, Andrew M. Sila, Johan Six, Erick K. Towett, Susan E. Trumbore, Tor-G. Vågen, Elvis Weullow, Leigh A. Winowiecki, and Sebastian Doetterl
SOIL, 7, 305–332, https://doi.org/10.5194/soil-7-305-2021, https://doi.org/10.5194/soil-7-305-2021, 2021
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We investigated various soil and climate properties that influence soil organic carbon (SOC) concentrations in sub-Saharan Africa. Our findings indicate that climate and geochemistry are equally important for explaining SOC variations. The key SOC-controlling factors are broadly similar to those for temperate regions, despite differences in soil development history between the two regions.
Anatol Helfenstein, Philipp Baumann, Raphael Viscarra Rossel, Andreas Gubler, Stefan Oechslin, and Johan Six
SOIL, 7, 193–215, https://doi.org/10.5194/soil-7-193-2021, https://doi.org/10.5194/soil-7-193-2021, 2021
Short summary
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In this study, we show that a soil spectral library (SSL) can be used to predict soil carbon at new and very different locations. The importance of this finding is that it requires less time-consuming lab work than calibrating a new model for every local application, while still remaining similar to or more accurate than local models. Furthermore, we show that this method even works for predicting (drained) peat soils, using a SSL with mostly mineral soils containing much less soil carbon.
Simon Baumgartner, Marijn Bauters, Matti Barthel, Travis W. Drake, Landry C. Ntaboba, Basile M. Bazirake, Johan Six, Pascal Boeckx, and Kristof Van Oost
SOIL, 7, 83–94, https://doi.org/10.5194/soil-7-83-2021, https://doi.org/10.5194/soil-7-83-2021, 2021
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We compared stable isotope signatures of soil profiles in different forest ecosystems within the Congo Basin to assess ecosystem-level differences in N cycling, and we examined the local effect of topography on the isotopic signature of soil N. Soil δ15N profiles indicated that the N cycling in in the montane forest is more closed, whereas the lowland forest and Miombo woodland experienced a more open N cycle. Topography only alters soil δ15N values in forests with high erosional forces.
Zhongkui Luo, Raphael A. Viscarra-Rossel, and Tian Qian
Biogeosciences, 18, 2063–2073, https://doi.org/10.5194/bg-18-2063-2021, https://doi.org/10.5194/bg-18-2063-2021, 2021
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Using the data from 141 584 whole-soil profiles across the globe, we disentangled the relative importance of biotic, climatic and edaphic variables in controlling global SOC stocks. The results suggested that soil properties and climate contributed similarly to the explained global variance of SOC in four sequential soil layers down to 2 m. However, the most important individual controls are consistently soil-related, challenging current climate-driven framework of SOC dynamics.
Simon Baumgartner, Matti Barthel, Travis William Drake, Marijn Bauters, Isaac Ahanamungu Makelele, John Kalume Mugula, Laura Summerauer, Nora Gallarotti, Landry Cizungu Ntaboba, Kristof Van Oost, Pascal Boeckx, Sebastian Doetterl, Roland Anton Werner, and Johan Six
Biogeosciences, 17, 6207–6218, https://doi.org/10.5194/bg-17-6207-2020, https://doi.org/10.5194/bg-17-6207-2020, 2020
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Soil respiration is an important carbon flux and key process determining the net ecosystem production of terrestrial ecosystems. The Congo Basin lacks studies quantifying carbon fluxes. We measured soil CO2 fluxes from different forest types in the Congo Basin and were able to show that, even though soil CO2 fluxes are similarly high in lowland and montane forests, the drivers were different: soil moisture in montane forests and C availability in the lowland forests.
Long Ho, Ruben Jerves-Cobo, Matti Barthel, Johan Six, Samuel Bode, Pascal Boeckx, and Peter Goethals
Biogeosciences Discuss., https://doi.org/10.5194/bg-2020-311, https://doi.org/10.5194/bg-2020-311, 2020
Revised manuscript not accepted
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Rivers are being polluted by human activities, especially in urban areas. We studied the greenhouse gas (GHG) emissions from an urban river system. The results showed a clear trend between water quality and GHG emissions in which the more polluted the sites were, the higher were their emissions. When river water quality worsened, its contribution to global warming can go up by 10 times. Urban rivers emitted 4-times more than of the amount of GHGs compared to rivers in natural sites.
Marijn Van de Broek, Shiva Ghiasi, Charlotte Decock, Andreas Hund, Samuel Abiven, Cordula Friedli, Roland A. Werner, and Johan Six
Biogeosciences, 17, 2971–2986, https://doi.org/10.5194/bg-17-2971-2020, https://doi.org/10.5194/bg-17-2971-2020, 2020
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Four wheat cultivars were labeled with 13CO2 to quantify the effect of rooting depth and root biomass on the belowground transfer of organic carbon. We found no clear relation between the time since cultivar development and the amount of carbon inputs to the soil. Therefore, the hypothesis that wheat cultivars with a larger root biomass and deeper roots promote carbon stabilization was rejected. The amount of root biomass that will be stabilized in the soil on the long term is, however, unknown.
Stephen J. Harris, Jesper Liisberg, Longlong Xia, Jing Wei, Kerstin Zeyer, Longfei Yu, Matti Barthel, Benjamin Wolf, Bryce F. J. Kelly, Dioni I. Cendón, Thomas Blunier, Johan Six, and Joachim Mohn
Atmos. Meas. Tech., 13, 2797–2831, https://doi.org/10.5194/amt-13-2797-2020, https://doi.org/10.5194/amt-13-2797-2020, 2020
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The latest commercial laser spectrometers have the potential to revolutionize N2O isotope analysis. However, to do so, they must be able to produce trustworthy data. Here, we test the performance of widely used laser spectrometers for ambient air applications and identify instrument-specific dependencies on gas matrix and trace gas concentrations. We then provide a calibration workflow to facilitate the operation of these instruments in order to generate reproducible and accurate data.
Andre Carnieletto Dotto, Jose A. M. Demattê, Raphael A. Viscarra Rossel, and Rodnei Rizzo
SOIL, 6, 163–177, https://doi.org/10.5194/soil-6-163-2020, https://doi.org/10.5194/soil-6-163-2020, 2020
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The objective of this study was to develop a soil grouping system based on spectral, climate, and terrain variables with the aim of developing a quantitative way to classify soils. To derive the new system, we applied the above-mentioned variables using cluster analysis and defined eight groups or "soil environment groupings" (SEGs). The SEG system facilitated the identification of groups with similar characteristics using not only soil but also environmental variables for their distinction.
Karl Voglmeier, Johan Six, Markus Jocher, and Christof Ammann
Biogeosciences, 16, 1685–1703, https://doi.org/10.5194/bg-16-1685-2019, https://doi.org/10.5194/bg-16-1685-2019, 2019
Tino Colombi, Florian Walder, Lucie Büchi, Marlies Sommer, Kexing Liu, Johan Six, Marcel G. A. van der Heijden, Raphaël Charles, and Thomas Keller
SOIL, 5, 91–105, https://doi.org/10.5194/soil-5-91-2019, https://doi.org/10.5194/soil-5-91-2019, 2019
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The role of soil aeration in carbon sequestration in arable soils has only been explored little, especially at the farm level. The current study, which was conducted on 30 fields that belong to individual farms, reveals a positive relationship between soil gas transport capability and soil organic carbon content. We therefore conclude that soil aeration needs to be accounted for when developing strategies for carbon sequestration in arable soil.
Elizabeth Verhoeven, Matti Barthel, Longfei Yu, Luisella Celi, Daniel Said-Pullicino, Steven Sleutel, Dominika Lewicka-Szczebak, Johan Six, and Charlotte Decock
Biogeosciences, 16, 383–408, https://doi.org/10.5194/bg-16-383-2019, https://doi.org/10.5194/bg-16-383-2019, 2019
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This study utilized state-of-the-art measurements of nitrogen isotopes to evaluate nitrogen cycling and to assess the biological sources of the potent greenhouse gas, N2O, in response to water-saving practices in rice systems. Water-saving practices did emit more N2O, and high N2O production had a lower 15N isotope signature. Modeling and visual interpretation indicate that these emissions mostly came from denitrification or nitrifier denitrification, controlled upstream by nitrification rates.
Juhwan Lee, Gina M. Garland, and Raphael A. Viscarra Rossel
SOIL, 4, 213–224, https://doi.org/10.5194/soil-4-213-2018, https://doi.org/10.5194/soil-4-213-2018, 2018
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Soil nitrogen (N) is an essential element for plant growth, but its plant-available forms are subject to loss from the environment by leaching and gaseous emissions. Still, factors controlling soil mineral N concentrations at large spatial scales are not well understood. We determined and discussed primary soil controls over the concentrations of NH4+ and NO3− at the continental scale of Australia while considering specific dominant land use patterns on a regional basis.
Lucie Greiner, Madlene Nussbaum, Andreas Papritz, Stephan Zimmermann, Andreas Gubler, Adrienne Grêt-Regamey, and Armin Keller
SOIL, 4, 123–139, https://doi.org/10.5194/soil-4-123-2018, https://doi.org/10.5194/soil-4-123-2018, 2018
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To maintain the soil resource, spatial information on soil multi-functionality is key. Soil function (SF) maps rate soils potentials to fulfill a certain function, e.g., nutrient regulation. We show how uncertainties in predictions of soil properties generated by digital soil mapping propagate into soil function maps, present possibilities to display this uncertainty information and show that otherwise comparable SF assessment methods differ in their behaviour in view of uncertainty propagation.
Jacqueline R. England and Raphael A. Viscarra Rossel
SOIL, 4, 101–122, https://doi.org/10.5194/soil-4-101-2018, https://doi.org/10.5194/soil-4-101-2018, 2018
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Proximal sensing can be used for soil C accounting, but the methods need to be standardized and procedural guidelines developed to ensure proficient measurement and accurate reporting. This is particularly important if there are financial incentives for landholders to adopt practices to sequester C. We review sensing for C accounting and discuss the requirements for the development of new soil C accounting methods based on sensing, including requirements for reporting, auditing and verification.
R. Hüppi, R. Felber, A. Neftel, J. Six, and J. Leifeld
SOIL, 1, 707–717, https://doi.org/10.5194/soil-1-707-2015, https://doi.org/10.5194/soil-1-707-2015, 2015
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Biochar is considered an opportunity to tackle major environmental issues in agriculture. Adding pyrolised organic residues to soil may sequester carbon, increase yields and reduce nitrous oxide emissions from soil. It is unknown, whether the latter is induced by changes in soil pH. We show that biochar application substantially reduces nitrous oxide emissions from a temperate maize cropping system. However, the reduction was only achieved with biochar but not with liming.
C. Decock, J. Lee, M. Necpalova, E. I. P. Pereira, D. M. Tendall, and J. Six
SOIL, 1, 687–694, https://doi.org/10.5194/soil-1-687-2015, https://doi.org/10.5194/soil-1-687-2015, 2015
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Further progress in understanding and mitigating N2O emissions from soil lies within transdisciplinary research that reaches across spatial scales and takes an ambitious look into the future.
M. S. Torn, A. Chabbi, P. Crill, P. J. Hanson, I. A. Janssens, Y. Luo, C. H. Pries, C. Rumpel, M. W. I. Schmidt, J. Six, M. Schrumpf, and B. Zhu
SOIL, 1, 575–582, https://doi.org/10.5194/soil-1-575-2015, https://doi.org/10.5194/soil-1-575-2015, 2015
B. Wolf, L. Merbold, C. Decock, B. Tuzson, E. Harris, J. Six, L. Emmenegger, and J. Mohn
Biogeosciences, 12, 2517–2531, https://doi.org/10.5194/bg-12-2517-2015, https://doi.org/10.5194/bg-12-2517-2015, 2015
S. Doetterl, J.-T. Cornelis, J. Six, S. Bodé, S. Opfergelt, P. Boeckx, and K. Van Oost
Biogeosciences, 12, 1357–1371, https://doi.org/10.5194/bg-12-1357-2015, https://doi.org/10.5194/bg-12-1357-2015, 2015
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We link the mineralogy of soils affected by erosion and deposition to the distribution of soil carbon fractions, their turnover and microbial activity. We show that the weathering status of soils and their history are controlling the stabilization of carbon with minerals. After burial, aggregated C is preserved more efficiently while non-aggregated C can be released and younger C re-sequestered more easily. Weathering changes the effectiveness of stabilization mechanism limiting this C sink.
E. C. Brevik, A. Cerdà, J. Mataix-Solera, L. Pereg, J. N. Quinton, J. Six, and K. Van Oost
SOIL, 1, 117–129, https://doi.org/10.5194/soil-1-117-2015, https://doi.org/10.5194/soil-1-117-2015, 2015
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This paper provides a brief accounting of some of the many ways that the study of soils can be interdisciplinary, therefore giving examples of the types of papers we hope to see submitted to SOIL.
V. Haverd, M. R. Raupach, P. R. Briggs, J. G. Canadell, P. Isaac, C. Pickett-Heaps, S. H. Roxburgh, E. van Gorsel, R. A. Viscarra Rossel, and Z. Wang
Biogeosciences, 10, 2011–2040, https://doi.org/10.5194/bg-10-2011-2013, https://doi.org/10.5194/bg-10-2011-2013, 2013
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Yuanyuan Yang, Zefang Shen, Andrew Bissett, and Raphael A. Viscarra Rossel
SOIL, 8, 223–235, https://doi.org/10.5194/soil-8-223-2022, https://doi.org/10.5194/soil-8-223-2022, 2022
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We present a new method to estimate the relative abundance of the dominant phyla and diversity of fungi in Australian soil. It uses state-of-the-art machine learning with publicly available data on soil and environmental proxies for edaphic, climatic, biotic and topographic factors, and visible–near infrared wavelengths. The estimates could serve to supplement the more expensive molecular approaches towards a better understanding of soil fungal abundance and diversity in agronomy and ecology.
Brieuc Hardy, Nils Borchard, and Jens Leifeld
SOIL Discuss., https://doi.org/10.5194/soil-2021-146, https://doi.org/10.5194/soil-2021-146, 2022
Revised manuscript accepted for SOIL
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Soil amendment with artificial black carbon (BC, biomass transformed by incomplete combustion) has the potential to mitigate climate change. Nevertheless, the accurate quantification of BC in soil remains a critical issue. Here, we successfully used dynamic thermal analysis (DTA) to quantify centennial BC in soil. We demonstrate that DTA is largely under-exploited despite providing rapidly and at low cost quantitative information all over the range of soil organic matter.
Elad Levintal, Yonatan Ganot, Gail Taylor, Peter Freer-Smith, Kosana Suvocarev, and Helen E. Dahlke
SOIL, 8, 85–97, https://doi.org/10.5194/soil-8-85-2022, https://doi.org/10.5194/soil-8-85-2022, 2022
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Do-it-yourself hardware is a new approach for improving measurement resolution in research. Here we present a new low-cost, wireless underground sensor network for soil monitoring. All data logging, power, and communication component cost is USD 150, much cheaper than other available commercial solutions. We provide the complete building guide to reduce any technical barriers, which we hope will allow easier reproducibility and open new environmental monitoring applications.
Ali Sakhaee, Anika Gebauer, Mareike Ließ, and Axel Don
SOIL Discuss., https://doi.org/10.5194/soil-2021-107, https://doi.org/10.5194/soil-2021-107, 2021
Revised manuscript under review for SOIL
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The demand on high resolution maps is increasing drastically with soil carbon becoming key component of climate-smart agriculture. Meanwhile, machine learning algorithms get into wide application and open up new solutions in soil mapping. This paper shows which algorithms are performing best and how soil inventory data can be used for digital soil mapping most efficiently. This paper explores the different options and methods to derive high resolution soil carbon data at large regional scale.
Philipp Baumann, Juhwan Lee, Emmanuel Frossard, Laurie Paule Schönholzer, Lucien Diby, Valérie Kouamé Hgaza, Delwende Innocent Kiba, Andrew Sila, Keith Sheperd, and Johan Six
SOIL, 7, 717–731, https://doi.org/10.5194/soil-7-717-2021, https://doi.org/10.5194/soil-7-717-2021, 2021
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This work delivers openly accessible and validated calibrations for diagnosing 26 soil properties based on mid-infrared spectroscopy. These were developed for four regions in Burkina Faso and Côte d'Ivoire, including 80 fields of smallholder farmers. The models can help to site-specifically and cost-efficiently monitor soil quality and fertility constraints to ameliorate soils and yields of yam or other staple crops in the four regions between the humid forest and the northern Guinean savanna.
Laura Summerauer, Philipp Baumann, Leonardo Ramirez-Lopez, Matti Barthel, Marijn Bauters, Benjamin Bukombe, Mario Reichenbach, Pascal Boeckx, Elizabeth Kearsley, Kristof Van Oost, Bernard Vanlauwe, Dieudonné Chiragaga, Aimé Bisimwa Heri-Kazi, Pieter Moonen, Andrew Sila, Keith Shepherd, Basile Bazirake Mujinya, Eric Van Ranst, Geert Baert, Sebastian Doetterl, and Johan Six
SOIL, 7, 693–715, https://doi.org/10.5194/soil-7-693-2021, https://doi.org/10.5194/soil-7-693-2021, 2021
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We present a soil mid-infrared library with over 1800 samples from central Africa in order to facilitate soil analyses of this highly understudied yet critical area. Together with an existing continental library, we demonstrate a regional analysis and geographical extrapolation to predict total carbon and nitrogen. Our results show accurate predictions and highlight the value that the data contribute to existing libraries. Our library is openly available for public use and for expansion.
Ulrich Weller, Lukas Albrecht, Steffen Schlüter, and Hans-Jörg Vogel
SOIL Discuss., https://doi.org/10.5194/soil-2021-96, https://doi.org/10.5194/soil-2021-96, 2021
Revised manuscript under review for SOIL
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Soil structure is of central importance for soil functions. It is, however, ill defined. With the increasing availability of X-ray CT scanners more and more soils are scanned and an undisturbed image of the soil's structure is produced. Often a qualitative description is all that is derived from these images. We provide now a web based soil structure library where these images can be evaluated in a standardised quantitative way, and can be compared to a world-wide data set.
Kpade O. L. Hounkpatin, Johan Stendahl, Mattias Lundblad, and Erik Karltun
SOIL, 7, 377–398, https://doi.org/10.5194/soil-7-377-2021, https://doi.org/10.5194/soil-7-377-2021, 2021
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Forests store large amounts of carbon in soils. Implementing suitable measures to improve the sink potential of forest soils would require accurate data on the carbon stored in forest soils and a better understanding of the factors affecting this storage. This study showed that the prediction of soil carbon stock in Swedish forest soils can increase in accuracy when one divides a big region into smaller areas in combination with information collected locally and derived from satellites.
Hana Beitlerová, Jonas Lenz, Jan Devátý, Martin Mistr, Jiří Kapička, Arno Buchholz, Ilona Gerndtová, and Anne Routschek
SOIL, 7, 241–253, https://doi.org/10.5194/soil-7-241-2021, https://doi.org/10.5194/soil-7-241-2021, 2021
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This study presents transfer functions for a calibration parameter of the Green–Ampt infiltration module of the EROSION-2D/3D model, which are significantly improving the model performance compared to the current state. The relationships found between calibration parameters and soil parameters however put the Green–Ampt implementation in the model and the state-of-the-art parametrization method in question. A new direction of the infiltration module development is proposed.
Anatol Helfenstein, Philipp Baumann, Raphael Viscarra Rossel, Andreas Gubler, Stefan Oechslin, and Johan Six
SOIL, 7, 193–215, https://doi.org/10.5194/soil-7-193-2021, https://doi.org/10.5194/soil-7-193-2021, 2021
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In this study, we show that a soil spectral library (SSL) can be used to predict soil carbon at new and very different locations. The importance of this finding is that it requires less time-consuming lab work than calibrating a new model for every local application, while still remaining similar to or more accurate than local models. Furthermore, we show that this method even works for predicting (drained) peat soils, using a SSL with mostly mineral soils containing much less soil carbon.
Istvan Dunkl and Mareike Ließ
SOIL Discuss., https://doi.org/10.5194/soil-2020-102, https://doi.org/10.5194/soil-2020-102, 2021
Revised manuscript under review for SOIL
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Legacy soil data provides a valuable data basis to generate high-resolution soil maps by digital soil mapping (DSM). DSM allows to regionalize soil properties by relating them to environmental covariates with the help of an empirical model. We studied the usefulness of data clustering methods to tackle potential sampling bias in the used legacy soil data while applying DSM for soil texture regionalization. Clustering has proved to be useful in various steps of the DSM procedure.
Jennifer M. Rhymes, Irene Cordero, Mathilde Chomel, Jocelyn M. Lavallee, Angela L. Straathof, Deborah Ashworth, Holly Langridge, Marina Semchenko, Franciska T. de Vries, David Johnson, and Richard D. Bardgett
SOIL, 7, 95–106, https://doi.org/10.5194/soil-7-95-2021, https://doi.org/10.5194/soil-7-95-2021, 2021
Matthew A. Belanger, Carmella Vizza, G. Philip Robertson, and Sarah S. Roley
SOIL, 7, 47–52, https://doi.org/10.5194/soil-7-47-2021, https://doi.org/10.5194/soil-7-47-2021, 2021
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Soil health is often assessed by re-wetting a dry soil and measuring CO2 production, but the potential bias introduced by soils of different moisture contents is unclear. Our study found that wetter soil tended to lose more carbon during drying than drier soil, thus affecting soil health interpretations. We developed a correction factor to account for initial soil moisture effects, which future studies may benefit from adapting for their soil.
Wartini Ng, Budiman Minasny, Wanderson de Sousa Mendes, and José Alexandre Melo Demattê
SOIL, 6, 565–578, https://doi.org/10.5194/soil-6-565-2020, https://doi.org/10.5194/soil-6-565-2020, 2020
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The number of samples utilised to create predictive models affected model performance. This research compares the number of samples needed by a deep learning model to outperform the traditional machine learning models using visible near-infrared spectroscopy data for soil properties predictions. The deep learning model was found to outperform machine learning models when the sample size was above 2000.
José Padarian, Alex B. McBratney, and Budiman Minasny
SOIL, 6, 389–397, https://doi.org/10.5194/soil-6-389-2020, https://doi.org/10.5194/soil-6-389-2020, 2020
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In this paper we introduce the use of game theory to interpret a digital soil mapping (DSM) model to understand the contribution of environmental factors to the prediction of soil organic carbon (SOC) in Chile. The analysis corroborated that the SOC model is capturing sensible relationships between SOC and climatic and topographical factors. We were able to represent them spatially (map) addressing the limitations of the current interpretation of models in DSM.
Yosra Ellili-Bargaoui, Brendan Philip Malone, Didier Michot, Budiman Minasny, Sébastien Vincent, Christian Walter, and Blandine Lemercier
SOIL, 6, 371–388, https://doi.org/10.5194/soil-6-371-2020, https://doi.org/10.5194/soil-6-371-2020, 2020
Anders Bjørn Møller, Amélie Marie Beucher, Nastaran Pouladi, and Mogens Humlekrog Greve
SOIL, 6, 269–289, https://doi.org/10.5194/soil-6-269-2020, https://doi.org/10.5194/soil-6-269-2020, 2020
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Decision trees have become a widely adapted tool for mapping soil properties in geographic space. However, it is problematic to implement spatial relationships in the models. We present a new method which uses geographic coordinates along several axes tilted at oblique angles in the models. We test this method on four spatial datasets. The results show that the new method is at least as accurate as other proposed alternatives, has a computational advantage and is flexible and interpretable.
Anika Gebauer, Monja Ellinger, Victor M. Brito Gomez, and Mareike Ließ
SOIL, 6, 215–229, https://doi.org/10.5194/soil-6-215-2020, https://doi.org/10.5194/soil-6-215-2020, 2020
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Pedotransfer functions (PTFs) for soil water retention were developed for two tropical soil landscapes using machine learning. The models corresponding to these PTFs had to be adjusted by tuning their parameters. The standard tuning approach was compared to mathematical optimization. The latter resulted in much better model performance. The PTFs derived are of particular importance for soil process and hydrological models.
Dominika Lewicka-Szczebak and Reinhard Well
SOIL, 6, 145–152, https://doi.org/10.5194/soil-6-145-2020, https://doi.org/10.5194/soil-6-145-2020, 2020
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This study aimed at comparison of various experimental strategies for incubating soil samples to determine the N2 flux. Such experiments require addition of isotope tracer, i.e. nitrogen fertilizer enriched in heavy nitrogen isotopes (15N). Here we compared the impact of soil homogenization and mixing with the tracer and tracer injection to the intact soil cores. The results are well comparable: both techniques would provide similar conclusions on the magnitude of N2 flux.
José Padarian and Alex B. McBratney
SOIL, 6, 89–94, https://doi.org/10.5194/soil-6-89-2020, https://doi.org/10.5194/soil-6-89-2020, 2020
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Data sharing and collaboration are critical to solving large-scale problems. The prevailing soil data-sharing model is of a centralized nature and, consequently, results in the participants ceding control and governance over their data to the lead party. Here we explore the use of a distributed ledger (blockchain) to solve the aforementioned issues. We also describe the potential use case of developing a global soil spectral library between multiple, international institutions.
José Padarian, Budiman Minasny, and Alex B. McBratney
SOIL, 6, 35–52, https://doi.org/10.5194/soil-6-35-2020, https://doi.org/10.5194/soil-6-35-2020, 2020
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The application of machine learning (ML) has shown an accelerated adoption in soil sciences. It is a difficult task to manually review all papers on the application of ML. This paper aims to provide a review of the application of ML aided by topic modelling in order to find patterns in a large collection of publications. The objective is to gain insight into the applications and to discuss research gaps. We found 12 main topics and that ML methods usually perform better than traditional ones.
Sören Thiele-Bruhn, Michael Schloter, Berndt-Michael Wilke, Lee A. Beaudette, Fabrice Martin-Laurent, Nathalie Cheviron, Christian Mougin, and Jörg Römbke
SOIL, 6, 17–34, https://doi.org/10.5194/soil-6-17-2020, https://doi.org/10.5194/soil-6-17-2020, 2020
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Soil quality depends on the functioning of soil microbiota. Only a few standardized methods are available to assess this as well as adverse effects of human activities. So we need to identify promising additional methods that target soil microbial function. Discussed are (i) molecular methods using qPCR for new endpoints, e.g. in N and P cycling and greenhouse gas emissions, (ii) techniques for fungal enzyme activities, and (iii) field methods on carbon turnover such as the litter bag test.
Jeroen H. T. Zethof, Martin Leue, Cordula Vogel, Shane W. Stoner, and Karsten Kalbitz
SOIL, 5, 383–398, https://doi.org/10.5194/soil-5-383-2019, https://doi.org/10.5194/soil-5-383-2019, 2019
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A widely overlooked source of carbon (C) in the soil environment is organic C of geogenic origin, e.g. graphite. Appropriate methods are not available to quantify graphite and to differentiate it from other organic and inorganic C sources in soils. Therefore, we examined Fourier transform infrared spectroscopy, thermogravimetric analysis and the smart combustion method for their ability to identify and quantify graphitic C in soils. The smart combustion method showed the most promising results.
Monja Ellinger, Ines Merbach, Ulrike Werban, and Mareike Ließ
SOIL, 5, 275–288, https://doi.org/10.5194/soil-5-275-2019, https://doi.org/10.5194/soil-5-275-2019, 2019
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Vis–NIR spectrometry is often applied to capture soil organic carbon (SOC). This study addresses the impact of the involved data and modelling aspects on SOC precision with a focus on the propagation of input data uncertainties. It emphasizes the necessity of transparent documentation of the measurement protocol and the model building and validation procedure. Particularly, when Vis–NIR spectrometry is used for soil monitoring, the aspect of uncertainty propagation becomes essential.
José Padarian and Ignacio Fuentes
SOIL, 5, 177–187, https://doi.org/10.5194/soil-5-177-2019, https://doi.org/10.5194/soil-5-177-2019, 2019
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A large amount of descriptive information is available in geosciences. Considering the advances in natural language it is possible to
rescuethis information and transform it into a numerical form (embeddings). We used 280764 full-text scientific articles to train a language model capable of generating such embeddings. Our domain-specific embeddings (GeoVec) outperformed general domain embedding tasks such as analogies, relatedness, and categorisation, and can be used in novel applications.
Cathelijne R. Stoof, Jasper H. J. Candel, Laszlo A. G. M. van der Wal, and Gert Peek
SOIL, 5, 159–175, https://doi.org/10.5194/soil-5-159-2019, https://doi.org/10.5194/soil-5-159-2019, 2019
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Teaching and outreach of soils is often done with real-life snapshots of soils and sediments in lacquer or glue peels. While it may seem hard, anyone can make such a peel. Illustrated with handmade drawings and an instructional video, we explain how to capture soils in peels using readily available materials. A new twist to old methods makes this safer, simpler, and more successful, and thus a true DIY (do-it-yourself) activity, highlighting the value and beauty of the ground below our feet.
Alexandre M. J.-C. Wadoux, José Padarian, and Budiman Minasny
SOIL, 5, 107–119, https://doi.org/10.5194/soil-5-107-2019, https://doi.org/10.5194/soil-5-107-2019, 2019
José Padarian, Budiman Minasny, and Alex B. McBratney
SOIL, 5, 79–89, https://doi.org/10.5194/soil-5-79-2019, https://doi.org/10.5194/soil-5-79-2019, 2019
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Digital soil mapping has been widely used as a cost-effective method for generating soil maps. DSM models are usually calibrated using point observations and rarely incorporate contextual information of the landscape. Here, we use convolutional neural networks to incorporate spatial context. We used as input a 3-D stack of covariate images to simultaneously predict organic carbon content at multiple depths. In this study, our model reduced the error by 30 % compared with conventional techniques.
Mario Guevara, Guillermo Federico Olmedo, Emma Stell, Yusuf Yigini, Yameli Aguilar Duarte, Carlos Arellano Hernández, Gloria E. Arévalo, Carlos Eduardo Arroyo-Cruz, Adriana Bolivar, Sally Bunning, Nelson Bustamante Cañas, Carlos Omar Cruz-Gaistardo, Fabian Davila, Martin Dell Acqua, Arnulfo Encina, Hernán Figueredo Tacona, Fernando Fontes, José Antonio Hernández Herrera, Alejandro Roberto Ibelles Navarro, Veronica Loayza, Alexandra M. Manueles, Fernando Mendoza Jara, Carolina Olivera, Rodrigo Osorio Hermosilla, Gonzalo Pereira, Pablo Prieto, Iván Alexis Ramos, Juan Carlos Rey Brina, Rafael Rivera, Javier Rodríguez-Rodríguez, Ronald Roopnarine, Albán Rosales Ibarra, Kenset Amaury Rosales Riveiro, Guillermo Andrés Schulz, Adrian Spence, Gustavo M. Vasques, Ronald R. Vargas, and Rodrigo Vargas
SOIL, 4, 173–193, https://doi.org/10.5194/soil-4-173-2018, https://doi.org/10.5194/soil-4-173-2018, 2018
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We provide a reproducible multi-modeling approach for SOC mapping across Latin America on a country-specific basis as required by the Global Soil Partnership of the United Nations. We identify key prediction factors for SOC across each country. We compare and test different methods to generate spatially explicit predictions of SOC and conclude that there is no best method on a quantifiable basis.
Louis-Pierre Comeau, Derrick Y. F. Lai, Jane Jinglan Cui, and Jenny Farmer
SOIL, 4, 141–152, https://doi.org/10.5194/soil-4-141-2018, https://doi.org/10.5194/soil-4-141-2018, 2018
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To date, there are still many uncertainties and unknowns regarding the soil respiration partitioning procedures. This study compared the suitability and accuracy of five different respiration partitioning methods. A qualitative evaluation table of the partition methods with five performance parameters was produced. Overall, no systematically superior or inferior partition method was found and the combination of two or more methods optimizes assessment reliability.
Jacqueline R. England and Raphael A. Viscarra Rossel
SOIL, 4, 101–122, https://doi.org/10.5194/soil-4-101-2018, https://doi.org/10.5194/soil-4-101-2018, 2018
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Proximal sensing can be used for soil C accounting, but the methods need to be standardized and procedural guidelines developed to ensure proficient measurement and accurate reporting. This is particularly important if there are financial incentives for landholders to adopt practices to sequester C. We review sensing for C accounting and discuss the requirements for the development of new soil C accounting methods based on sensing, including requirements for reporting, auditing and verification.
Madlene Nussbaum, Kay Spiess, Andri Baltensweiler, Urs Grob, Armin Keller, Lucie Greiner, Michael E. Schaepman, and Andreas Papritz
SOIL, 4, 1–22, https://doi.org/10.5194/soil-4-1-2018, https://doi.org/10.5194/soil-4-1-2018, 2018
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This paper presents an extensive evaluation of digital soil mapping (DSM) tools. Recently, large sets of environmental covariates (e.g. from analysis of terrain on multiple scales) have become more common for DSM. Many DSM studies, however, only compared DSM methods using less than 30 covariates or tested approaches on few responses. We built DSM models from 300–500 covariates using six approaches that are either popular in DSM or promising for large covariate sets.
R. Murray Lark, Elliott M. Hamilton, Belinda Kaninga, Kakoma K. Maseka, Moola Mutondo, Godfrey M. Sakala, and Michael J. Watts
SOIL, 3, 235–244, https://doi.org/10.5194/soil-3-235-2017, https://doi.org/10.5194/soil-3-235-2017, 2017
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An advantage of geostatistics for mapping soil properties is that, given a statistical model of the variable of interest, we can make a rational decision about how densely to sample so that the map is sufficiently precise. However, uncertainty about the statistical model affects this process. In this paper we show how Bayesian methods can be used to support decision making on sampling with an uncertain model, ensuring that the probability of meeting certain levels of precision is high enough.
Madlene Nussbaum, Lorenz Walthert, Marielle Fraefel, Lucie Greiner, and Andreas Papritz
SOIL, 3, 191–210, https://doi.org/10.5194/soil-3-191-2017, https://doi.org/10.5194/soil-3-191-2017, 2017
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Digital soil mapping (DSM) relates soil property data to environmental data that describe soil-forming factors. With imagery sampled from satellites or terrain analysed at multiple scales, large sets of possible input to DSM are available. We propose a new statistical framework (geoGAM) that selects parsimonious models for DSM and illustrate the application of geoGAM to two study regions. Straightforward interpretation of the modelled effects likely improves end-user acceptance of DSM products.
Hannes Keck, Bjarne W. Strobel, Jon Petter Gustafsson, and John Koestel
SOIL, 3, 177–189, https://doi.org/10.5194/soil-3-177-2017, https://doi.org/10.5194/soil-3-177-2017, 2017
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Several studies have shown that the cation adsorption sites in soils are heterogeneously distributed in space. In many soil system models this knowledge is not included yet. In our study we proposed a new method to map the 3-D distribution of cation adsorption sites in undisturbed soils. The method is based on three-dimensional X-ray scanning with a contrast agent and image analysis. We are convinced that this approach will strongly aid the development of more realistic soil system models.
Laura Arata, Katrin Meusburger, Alexandra Bürge, Markus Zehringer, Michael E. Ketterer, Lionel Mabit, and Christine Alewell
SOIL, 3, 113–122, https://doi.org/10.5194/soil-3-113-2017, https://doi.org/10.5194/soil-3-113-2017, 2017
Christopher Poeplau, Cora Vos, and Axel Don
SOIL, 3, 61–66, https://doi.org/10.5194/soil-3-61-2017, https://doi.org/10.5194/soil-3-61-2017, 2017
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This paper shows that three out of four frequently used methods to calculate soil organic carbon stocks lead to systematic overestimation of those stocks. Stones, which can be assumed to be free of carbon, have to be corrected for in both bulk density and layer thickness. We used data of the German Agricultural Soil Inventory to illustrate the potential bias and suggest a unified and unbiased calculation method for stocks of soil organic carbon, which is the largest terrestrial carbon pool.
Jan M. van Mourik, Thomas V. Wagner, J. Geert de Boer, and Boris Jansen
SOIL, 2, 299–310, https://doi.org/10.5194/soil-2-299-2016, https://doi.org/10.5194/soil-2-299-2016, 2016
Ranjith P. Udawatta, Clark J. Gantzer, Stephen H. Anderson, and Shmuel Assouline
SOIL, 2, 211–220, https://doi.org/10.5194/soil-2-211-2016, https://doi.org/10.5194/soil-2-211-2016, 2016
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Soil compaction degrades soil structure and affects water, heat, and gas exchange as well as root penetration and crop production. The objective of this study was to use X-ray computed microtomography (CMT) techniques to compare differences in geometrical soil pore parameters as influenced by compaction of two different aggregate size classes.
B. Reidy, I. Simo, P. Sills, and R. E. Creamer
SOIL, 2, 25–39, https://doi.org/10.5194/soil-2-25-2016, https://doi.org/10.5194/soil-2-25-2016, 2016
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This study reviews pedotransfer functions from the literature for different soil and horizon types. It uses these formulae to predict bulk density (ρb) per horizon using measured data of other soil properties. These data were compared to known pb per horizon and recalibrated. These calculations were used to fill missing horizon data in the Irish soil database. This allowed the generation of a pb map to 50 cm. These pb data are at horizon level allowing more accurate estimation of C with depth.
J. J. Keizer, M. A. S. Martins, S. A. Prats, L. F. Santos, D. C. S. Vieira, R. Nogueira, and L. Bilro
SOIL, 1, 641–650, https://doi.org/10.5194/soil-1-641-2015, https://doi.org/10.5194/soil-1-641-2015, 2015
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In this study, a novel plastic optical fibre turbidity sensor was exhaustively tested with a large set of runoff samples, mainly from a recently burnt area. The different types of samples from the distinct study sites revealed without exception an increase in normalized light loss with increasing sediment concentrations that agreed (reasonably) well with a power function. Nevertheless, sensor-based predictions of sediment concentration should ideally involve site-specific calibrations.
C. Rasmussen, R. E. Gallery, and J. S. Fehmi
SOIL, 1, 631–639, https://doi.org/10.5194/soil-1-631-2015, https://doi.org/10.5194/soil-1-631-2015, 2015
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There is a need to understand the response of soil systems to predicted climate warming for modeling soil processes. Current experimental methods for soil warming include expensive and difficult to implement active and passive techniques. Here we test a simple, inexpensive in situ passive soil heating approach, based on easy to construct infrared mirrors that do not require automation or enclosures. Results indicated that the infrared mirrors yielded significant heating and drying of soils.
E. Nadal-Romero, J. Revuelto, P. Errea, and J. I. López-Moreno
SOIL, 1, 561–573, https://doi.org/10.5194/soil-1-561-2015, https://doi.org/10.5194/soil-1-561-2015, 2015
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Geomatic techniques have been routinely applied in erosion studies, providing the opportunity to build high-resolution topographic models.The aim of this study is to assess and compare the functioning of terrestrial laser scanner and close range photogrammetry techniques to evaluate erosion and deposition processes in a humid badlands area.
Our results demonstrated that north slopes experienced more intense and faster dynamics than south slopes as well as the highest erosion rates.
L. M. Thomsen, J. E. M. Baartman, R. J. Barneveld, T. Starkloff, and J. Stolte
SOIL, 1, 399–410, https://doi.org/10.5194/soil-1-399-2015, https://doi.org/10.5194/soil-1-399-2015, 2015
B. A. Miller, S. Koszinski, M. Wehrhan, and M. Sommer
SOIL, 1, 217–233, https://doi.org/10.5194/soil-1-217-2015, https://doi.org/10.5194/soil-1-217-2015, 2015
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There are many different strategies for mapping SOC, among which is to model the variables needed to calculate the SOC stock indirectly or to model the SOC stock directly. The purpose of this research was to compare these two approaches for mapping SOC stocks from multiple linear regression models applied at the landscape scale via spatial association. Although the indirect approach had greater spatial variation and higher R2 values, the direct approach had a lower total estimated error.
W. Eugster and L. Merbold
SOIL, 1, 187–205, https://doi.org/10.5194/soil-1-187-2015, https://doi.org/10.5194/soil-1-187-2015, 2015
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The eddy covariance (EC) method has become increasingly popular in soil science. The basic concept of this method and its use in different types of experimental designs in the field are given, and we indicate where progress in advancing and extending the field of applications is made. The greatest strengths of EC measurements in soil science are (1) their uninterrupted continuous measurement of gas concentrations and fluxes and (2) spatial integration over
small-scale heterogeneity in the soil.
Cited articles
Ambroise, C. and McLachlan, G. J.: Selection Bias in Gene Extraction on the
Basis of Microarray Gene-Expression Data, P. Natl. Acad. Sci. USA, 99, 6562–6566, https://doi.org/10.1073/pnas.102102699, 2002. a
Angelopoulou, T., Balafoutis, A., Zalidis, G., and Bochtis, D.: From
Laboratory to Proximal Sensing Spectroscopy for Soil Organic Carbon
Estimation – A Review, Sustainability, 12, 443,
https://doi.org/10.3390/su12020443, 2020. a
Awiti, A. O., Walsh, M. G., Shepherd, K. D., and Kinyamario, J.: Soil condition classification using infrared spectroscopy: A proposition for assessment of soil condition along a tropical forest-cropland chronosequence, Geoderma, 143, 73–84, https://doi.org/10.1016/j.geoderma.2007.08.021, 2008. a
Baumann, P.: philipp-baumann/simplerspec: Beta release simplerspec 0.1.0 for
zenodo, Zenodo [code], https://doi.org/10.5281/zenodo.3303637, 2019. a
Bellman, R.: Adaptive Control Processes: A Guided Tour, Princeton University
Press, available at: http://www.jstor.org/stable/j.ctt183ph6v (last access: 16 August 2021), 1961. a
Bellon-Maurel, V., Fernandez-Ahumada, E., Palagos, B., Roger, J.-M., and
McBratney, A.: Critical review of chemometric indicators commonly used for
assessing the quality of the prediction of soil attributes by NIR
spectroscopy, TrAC-Trend. Anal. Chem., 29, 1073–1081,
https://doi.org/10.1016/j.trac.2010.05.006, 2010. a
Briedis, C., Baldock, J., de Moraes Sá, J. C., dos Santos, J. B., and
Milori, D. M. B. P.: Strategies to Improve the Prediction of Bulk Soil and
Fraction Organic Carbon in Brazilian Samples by Using an Australian
National Mid-Infrared Spectral Library, Geoderma, 373, 114401,
https://doi.org/10.1016/j.geoderma.2020.114401, 2020. a
Bui, E. N., Henderson, B. L., and Viergever, K.: Knowledge Discovery from
Models of Soil Properties Developed through Data Mining, Ecol. Model., 191, 431–446, https://doi.org/10.1016/j.ecolmodel.2005.05.021, 2006. a
Bundesamt für Umwelt (BAFU): Biodiversitätsmonitoring Schweiz BDM, Koordinationsstelle BDM 2014: Biodiversitätsmonitoring Schweiz
BDM. Beschreibung der Methoden und Indikatoren, Umwelt-Wissen Nr. 1410, 104 pp., Bundesamt für Umwelt, Bern, Switzerland, available at: https://www.bafu.admin.ch/bafu/de/home/themen/biodiversitaet/publikationen-studien/publikationen/biodiversitaetsmonitoring.html (last access: 16 August 2021), 2014. a
Clairotte, M., Grinand, C., Kouakoua, E., Thébault, A., Saby, N. P. A.,
Bernoux, M., and Barthès, B. G.: National Calibration of Soil Organic
Carbon Concentration Using Diffuse Infrared Reflectance Spectroscopy,
Geoderma, 276, 41–52, https://doi.org/10.1016/j.geoderma.2016.04.021, 2016. a, b
Dangal, S. R. S., Sanderman, J., Wills, S., and Ramirez-Lopez, L.: Accurate and Precise Prediction of Soil Properties from a Large Mid-Infrared Spectral Library, Soil Syst., 3, 11, https://doi.org/10.3390/soilsystems3010011,
2019. a, b, c
Deng, F., Minasny, B., Knadel, M., McBratney, A., Heckrath, G., and Greve,
M. H.: Using Vis-NIR Spectroscopy for Monitoring Temporal Changes
in Soil Organic Carbon, Soil Sci., 178, 389–399,
https://doi.org/10.1097/SS.0000000000000002, 2013. a
Desaules, A., Ammann, S., and Schwab, P.: Advances in long-term soil-pollution monitoring of Switzerland, J. Plant Nutr. Soil Sc., 173, 525–535, https://doi.org/10.1002/jpln.200900269, 2010. a, b
Dietterich, T. G., Wettschereck, D., Atkeson, C. G., and Moore, A. W.:
Memory-Based Methods for Regression and Classification, in: Advances in
Neural Information Processing Systems 6, 7th NIPS Conference, 29 November–2 December 1993, Denver, Colorado, USA, edited by: Cowan, J. D., Tesauro, G., and Alspector, J., 1165–1166, Morgan Kaufmann, 1993. a
Dokuchaev, V.: Report to the Transcaucasian Statistical Committee on Land
Evaluation in General and Especially for the Transcaucasia.
Horizontal and Vertical Soil Zones, Off. Press
Civ, Affairs Commander-in-Chief Cacasus, Tiflis, Russia, 1899 (in Russian). a
Dowle, M. and Srinivasan, A.: data.table: Extension of “data.frame”, r package version 1.12.8,
available at: https://CRAN.R-project.org/package=data.table (last access: 16 August 2021), 2019. a
England, J. R. and Viscarra Rossel, R. A.: Proximal sensing for soil carbon accounting, SOIL, 4, 101–122, https://doi.org/10.5194/soil-4-101-2018, 2018. a
Friedman, J., Hastie, T., and Tibshirani, R.: The elements of statistical
learning, Springer series in statistics Springer, Berlin, 2nd edn., available at: http://statweb.stanford.edu/~tibs/book/preface.ps (last access: 16 August 2021),
2008. a
Grêt-Regamey, A., Kool, S., Bühlmann, L., and Kissling, S.: Eine
Bodenagenda für die Raumplanung, Thematische Synthese TS3 des Nationalen
Forschungsprogramms “Nachhaltige Nutzung der Ressource
Boden” (NFP 68), Swiss National Science Foundation (SNF),
Bern, Switzerland, 2018. a
Gubler, A., Wächter, D., and Schwab, P.: Homogenisation of Series of Soil
Organic Carbon: Harmonising Results by Wet Oxidation (Swiss Standard
Method) and Dry Combustion, Agroscope Science, 62, 1–9, available at: https://ira.agroscope.ch/en-US/publication/37689
(last access: 16 August 2021), 2018. a
Guerrero, C., Zornoza, R., Gómez, I., and Mataix-Beneyto, J.: Spiking of NIR Regional Models Using Samples from Target Sites: Effect of Model Size on Prediction Accuracy, Geoderma, 158, 66–77,
https://doi.org/10.1016/j.geoderma.2009.12.021, 2010. a
Guerrero, C., Stenberg, B., Wetterlind, J., Viscarra Rossel, R. A., Maestre,
F. T., Mouazen, A. M., Zornoza, R., Ruiz-Sinoga, J. D., and Kuang, B.:
Assessment of Soil Organic Carbon at Local Scale with Spiked NIR
Calibrations: Effects of Selection and Extra-Weighting on the Spiking Subset:
Spiking and Extra-Weighting to Improve Soil Organic Carbon Predictions
with NIR, Eur. J. Soil Sci., 65, 248–263,
https://doi.org/10.1111/ejss.12129, 2014. a, b
Guerrero, C., Wetterlind, J., Stenberg, B., Mouazen, A. M., Gabarrón-Galeote,
M. A., Ruiz-Sinoga, J. D., Zornoza, R., and Viscarra Rossel, R. A.: Do We
Really Need Large Spectral Libraries for Local Scale SOC Assessment with
NIR Spectroscopy?, Soil Till. Res., 155, 501–509,
https://doi.org/10.1016/j.still.2015.07.008, 2016. a, b
Guyon, I., Weston, J., Barnhill, S., and Vapnik, V.: Gene Selection for
Cancer Classification Using Support Vector Machines, Mach.
Learn., 46, 389–422, https://doi.org/10.1023/A:1012487302797, 2002. a
Hand, D. J. and Vinciotti, V.: Local Versus Global Models for
Classification Problems: Fitting Models Where it Matters,
Am. Stat., 57, 124–131, https://doi.org/10.1198/0003130031423, 2003. a, b
Helfenstein, A., Baumann, P., Viscarra Rossel, R., Gubler, A., Oechslin, S., and Six, J.: Quantifying soil carbon in temperate peatlands using a mid-IR soil spectral library, SOIL, 7, 193–215, https://doi.org/10.5194/soil-7-193-2021, 2021. a
Helfenstein, A., Baumann, P., Viscarra Rossel, R., Gubler, A., Oechslin, S., and Six, J.: Quantifying soil carbon in temperate peatlands using a mid-IR soil spectral library, SOIL, 7, 193–215, https://doi.org/10.5194/soil-7-193-2021, 2021. a
Hong, Y., Chen, S., Liu, Y., Zhang, Y., Yu, L., Chen, Y., Liu, Y., Cheng, H.,
and Liu, Y.: Combination of Fractional Order Derivative and Memory-Based
Learning Algorithm to Improve the Estimation Accuracy of Soil Organic Matter
by Visible and near-Infrared Spectroscopy, CATENA, 174, 104–116,
https://doi.org/10.1016/j.catena.2018.10.051, 2019. a
Hubert, M. and Debruyne, M.: Minimum Covariance Determinant, WIREs
Computational Statistics, 2, 36–43, https://doi.org/10.1002/wics.61, 2010. a
Janik, L. J. and Skjemstad, J. O.: Characterization and analysis of soils using mid-infrared partial least-squares .2. Correlations with some laboratory data, Aust. J. Soil Res., 33, 637–650,
https://doi.org/10.1071/sr9950637, 1995. a, b, c
Janik, L. J., Skjemstad, J. O., and Merry, R. H.: Can mid infrared diffuse
reflectance analysis replace soil extractions?, Aust. J.
Exp. Agr., 38, 681, https://doi.org/10.1071/EA97144, 1998. a
Jenny, H.: Factors of Soil Formation, McGraw-Hill Book Co., New York, USA, 1941. a
Keller, A., Franzen, J., Knüsel, P., Papritz, A., and Zürrer, M.:
Bodeninformations-Plattform Schweiz (BIP-CH), Thematische Synthese TS4 des
Nationalen Forschungsprogramms “Nachhaltige Nutzung der
Ressource Boden” (NFP 68), Swiss National Science Foundation
(SNF), Bern, Switzerland, 2018. a
Kuhn, M.: caret: Classification and Regression Training, r package version
6.0-85, available at: https://CRAN.R-project.org/package=caret (last access: 16 August 2021), 2020. a
Lin, J.-H. and Vitter, J. S.: A Theory for Memory-Based Learning,
Mach. Learn., 17, 143–167, https://doi.org/10.1023/A:1022667616941, 1994. a, b
Liu, L., Ji, M., and Buchroithner, M.: Transfer Learning for Soil
Spectroscopy Based on Convolutional Neural Networks and Its
Application in Soil Clay Content Mapping Using Hyperspectral Imagery,
Sensors, 18, 3169, https://doi.org/10.3390/s18093169, 2018. a
Lobsey, C. R., Viscarra Rossel, R. A., Roudier, P., and Hedley, C. B.:
Rs-Local Data-Mines Information from Spectral Libraries to Improve
Local Calibrations: Rs-Local Improves Local Spectroscopic
Calibrations, Eur. J. Soil Sci., 68, 840–852, https://doi.org/10.1111/ejss.12490,
2017. a, b, c, d, e, f, g
Madari, B. E., Reeves, J. B., Machado, P. L., Guimarães, C. M., Torres, E., and McCarty, G. W.: Mid- and near-Infrared Spectroscopic Assessment of Soil Compositional Parameters and Structural Indices in Two Ferralsols,
Geoderma, 136, 245–259, https://doi.org/10.1016/j.geoderma.2006.03.026, 2006. a, b
Meuli, R. G., Wächter, D., Schwab, P., Kohli, L., and Zimmermann, R.:
Connecting biodiversity monitoring with soil inventory data – a Swiss case study, BGS Bulletin, 38, 65–69, 2017. a
Miller, B. A., Koszinski, S., Wehrhan, M., and Sommer, M.: Comparison of spatial association approaches for landscape mapping of soil organic carbon stocks, SOIL, 1, 217–233, https://doi.org/10.5194/soil-1-217-2015, 2015. a
Nguyen, T., Janik, L., and Raupach, M.: Diffuse Reflectance Infrared Fourier
Transform (DRIFT) Spectroscopy in Soil Studies, Soil Res., 29, 49,
https://doi.org/10.1071/SR9910049, 1991. a
Nocita, M., Stevens, A., van Wesemael, B., Brown, D. J., Shepherd, K. D.,
Towett, E., Vargas, R., and Montanarella, L.: Soil Spectroscopy: An
Opportunity to Be Seized, Glob. Change Biol., 21, 10–11,
https://doi.org/10.1111/gcb.12632, 2015. a
Ogen, Y., Zaluda, J., Francos, N., Goldshleger, N., and Ben-Dor, E.:
Cluster-Based Spectral Models for a Robust Assessment of Soil Properties,
Geoderma, 340, 175–184, https://doi.org/10.1016/j.geoderma.2019.01.022, 2019. a
Padarian, J., Minasny, B., and McBratney, A. B.: Transfer Learning to Localise a Continental Soil Vis-NIR Calibration Model, Geoderma, 340, 279–288, https://doi.org/10.1016/j.geoderma.2019.01.009, 2019a. a, b, c
Padarian, J., Minasny, B., and McBratney, A. B.: Using Deep Learning to Predict Soil Properties from Regional Spectral Data, Geoderma Regional, 16, e00198, https://doi.org/10.1016/j.geodrs.2018.e00198, 2019b. a, b, c, d
Pan, S. J. and Yang, Q.: A Survey on Transfer Learning, IEEE
T. Knowl. Data En., 22, 1345–1359,
https://doi.org/10.1109/TKDE.2009.191, 2010. a, b
Parikh, S. J., Goyne, K. W., Margenot, A. J., Mukome, F. N. D., and
Calderón, F. J.: Chapter One – Soil Chemical Insights Provided
through Vibrational Spectroscopy, in: Advances in Agronomy, edited by: Sparks, D. L., Academic Press, vol. 126, 1–148,
https://doi.org/10.1016/B978-0-12-800132-5.00001-8, 2014. a
Peng, Y., Xiong, X., Adhikari, K., Knadel, M., Grunwald, S., and Greve, M. H.: Modeling Soil Organic Carbon at Regional Scale by Combining
Multi-Spectral Images with Laboratory Spectra, 10, e0142295,
https://doi.org/10.1371/journal.pone.0142295, 2015. a
Pratt, L. and Thrun, S.: Guest Editors' Introduction, Mach. Learn., 28, 5–5, https://doi.org/10.1023/A:1007322005825, 1997. a
Pratt, L. Y., Pratt, L. Y., Hanson, S. J., Giles, C. L., and Cowan, J. D.:
Discriminability-Based Transfer between Neural Networks, Adv. Neur. In., 5, 204–211, 1993. a
Quinlan, J.: Combining Instance-Based and Model-Based Learning,
Machine Learning Proceedings, 1993, 236–243,
https://doi.org/10.1016/B978-1-55860-307-3.50037-X, 1993. a
Quinlan, J. R.: Learning with Continuous Classes, Proceedings of Australian Joint Conference on Artificial Intelligence, 16–18 November 1992, Hobart, Australia, 343–348, 1992. a
R Core Team: R: A Language and Environment for Statistical Computing, R
Foundation for Statistical Computing, Vienna, Austria,
available at: https://www.R-project.org/ (last access: 16 August 2021), 2019. a
Ramirez-Lopez, L., Behrens, T., Schmidt, K., Stevens, A., Demattê, J. A. M.,
and Scholten, T.: The Spectrum-Based Learner: A New Local Approach for
Modeling Soil Vis–NIR Spectra of Complex Datasets, Geoderma, 195–196,
268–279, https://doi.org/10.1016/j.geoderma.2012.12.014, 2013. a, b, c, d, e, f, g
Rehbein, K., Sprecher, C., and Keller, A.: Übersicht Stand
Bodenkartierung in Der Schweiz. Ergänzung Des
Bodenkartierungskataloges Schweiz Um Bodeninformationen Aus
Meliorationsprojekten, Servicestelle NABODAT, Agroscope, Zürich, Switzerland,
2020. a
Rousseeuw, P. J.: Least Median of Squares Regression, J. Am. Stat. Assoc., 79, 871–880, https://doi.org/10.1080/01621459.1984.10477105, 1984. a
Savitzky, A. and Golay, M. J. E.: Smoothing and Differentiation of Data by Simplified Least Squares Procedures, Anal. Chem., 36,
1627–1639, https://doi.org/10.1021/ac60214a047, 1964. a
Seidel, M., Hutengs, C., Ludwig, B., Thiele-Bruhn, S., and Vohland, M.:
Strategies for the Efficient Estimation of Soil Organic Carbon at the Field
Scale with Vis-NIR Spectroscopy: Spectral Libraries and Spiking vs.
Local Calibrations, Geoderma, 354, 113856,
https://doi.org/10.1016/j.geoderma.2019.07.014, 2019. a
Sila, A. M., Shepherd, K. D., and Pokhariyal, G. P.: Evaluating the Utility of Mid-Infrared Spectral Subspaces for Predicting Soil Properties, Chemometr. Intell. Lab., 153, 92–105,
https://doi.org/10.1016/j.chemolab.2016.02.013, 2016. a, b, c, d
Skjemstad, J. and Dalal, R.: Spectroscopic and Chemical Differences in Organic Matter of Two Vertisols Subjected to Long Periods of Cultivation, Soil Res., 25, 323, https://doi.org/10.1071/SR9870323, 1987. a
Solomatine, D.: Combining Machine Learning and Domain Knowledge in
Modular Modelling, in: Practical Hydroinformatics: Computational
Intelligence and Technological Developments in Water Applications,
edited by: Abrahart, R. J., See, L. M., and Solomatine, D. P., Water Science and Technology Library, Springer, Berlin, Heidelberg, 333–345,
https://doi.org/10.1007/978-3-540-79881-1_24, 2008. a
Soriano-Disla, J. M., Janik, L. J., Viscarra Rossel, R. A., Macdonald, L. M., and McLaughlin, M. J.: The Performance of Visible, Near-, and Mid-Infrared Reflectance Spectroscopy for Prediction of Soil Physical, Chemical, and Biological Properties, Appl. Spectrosc. Rev., 49, 139–186, https://doi.org/10.1080/05704928.2013.811081, 2014. a
Stanfill, C. and Waltz, D.: Toward Memory-Based Reasoning, Commun. ACM, 29, 1213–1228, https://doi.org/10.1145/7902.7906, 1986. a
Stenberg, B. and Rossel, R. V.: Diffuse Reflectance Spectroscopy for
High-Resolution Soil Sensing, in: Proximal Soil Sensing, edited
by: Viscarra Rossel, R. A., McBratney, A. B., and Minasny, B., Progress in
Soil Science, Springer Netherlands, Dordrech, the Netherlands, 29–47,
https://doi.org/10.1007/978-90-481-8859-8_3, 2010. a, b
Stevens, A. and Ramirez-Lopez, L.: An introduction to the prospectr package, r package version 0.1.3, available at: https://cran.r-project.org/web/packages/prospectr/vignettes/prospectr.html (last access: 16 August 2021), 2013. a
Stevens, A., Nocita, M., Tóth, G., Montanarella, L., and van Wesemael, B.:
Prediction of Soil Organic Carbon at the European Scale by
Visible and Near InfraRed Reflectance Spectroscopy, PLoS ONE, 8,
e66409, https://doi.org/10.1371/journal.pone.0066409, 2013. a
Thrun, S. and Pratt, L. (Eds.): Learning to Learn, Springer US, Boston, MA, https://doi.org/10.1007/978-1-4615-5529-2, 1998. a
Tsakiridis, N. L., Keramaris, K. D., Theocharis, J. B., and Zalidis, G. C.:
Simultaneous prediction of soil properties from VNIR-SWIR spectra using a
localized multi-channel 1-D convolutional neural network, Geoderma, 367,
114208, https://doi.org/10.1016/j.geoderma.2020.114208, 2020. a, b, c, d
Tziolas, N., Tsakiridis, N., Ben-Dor, E., Theocharis, J., and Zalidis, G.: A
Memory-Based Learning Approach Utilizing Combined Spectral Sources and
Geographical Proximity for Improved VIS-NIR-SWIR Soil Properties
Estimation, Geoderma, 340, 11–24, https://doi.org/10.1016/j.geoderma.2018.12.044,
2019. a
Varmuza, K. and Filzmoser, P.: Introduction to Multivariate Statistical
Analysis in Chemometrics, CRC Press, https://doi.org/10.1201/9781420059496,
2016. a
Viscarra Rossel, R., Walvoort, D., McBratney, A., Janik, L., and Skjemstad, J.: Visible, near Infrared, Mid Infrared or Combined Diffuse Reflectance
Spectroscopy for Simultaneous Assessment of Various Soil Properties,
Geoderma, 131, 59–75, https://doi.org/10.1016/j.geoderma.2005.03.007, 2006. a
Viscarra Rossel, R., Behrens, T., Ben-Dor, E., Brown, D., Demattê, J.,
Shepherd, K., Shi, Z., Stenberg, B., Stevens, A., Adamchuk, V., Aïchi,
H., Barthès, B., Bartholomeus, H., Bayer, A., Bernoux, M., Böttcher,
K., Brodský, L., Du, C., Chappell, A., Fouad, Y., Genot, V., Gomez, C.,
Grunwald, S., Gubler, A., Guerrero, C., Hedley, C., Knadel, M., Morrás,
H., Nocita, M., Ramirez-Lopez, L., Roudier, P., Campos, E. R., Sanborn, P.,
Sellitto, V., Sudduth, K., Rawlins, B., Walter, C., Winowiecki, L., Hong, S.,
and Ji, W.: A Global Spectral Library to Characterize the World's Soil,
Earth-Sci. Rev., 155, 198–230, https://doi.org/10.1016/j.earscirev.2016.01.012,
2016. a, b, c
Viscarra Rossel, R. A. and McBratney, A. B.: Soil chemical analytical accuracy and costs: implications from precision agriculture, Aust. J. Exp. Agr., 38, 765, https://doi.org/10.1071/EA97158, 1998. a, b
Viscarra Rossel, R. A., Lobsey, C. R., Sharman, C., Flick, P., and McLachlan,
G.: Novel Proximal Sensing for Monitoring Soil Organic C Stocks
and Condition, Environ. Sci. Technol., 51, 5630–5641,
https://doi.org/10.1021/acs.est.7b00889, 2017. a
Wang, Y. and Witten, I. H.: Induction of model trees for predicting continuous classes, Working Paper 96/23, University of Waikato, Department of Computer Science, Hamilton, New Zealand, available at: https://researchcommons.waikato.ac.nz/handle/10289/1183 (last access: 16 August 2021), 1996. a
Wickham, H.: tidyverse: Easily Install and Load the “Tidyverse”, r package
version 1.3.0, available at: https://CRAN.R-project.org/package=tidyverse (last access: 16 August 2021), 2019. a
Wold, S., Martens, H., and Wold, H.: The Multivariate Calibration Problem in
Chemistry Solved by the PLS Method, in: Matrix Pencils, edited by:
Kågström, B. and Ruhe, A., Springer Berlin Heidelberg, vol. 973, 286–293, https://doi.org/10.1007/BFb0062108, 1983. a
Wolpert, D. and Macready, W.: No Free Lunch Theorems for Optimization, IEEE
T. Evolut. Comput., 1, 67–82, https://doi.org/10.1109/4235.585893, 1997. a
Wolpert, D. H.: The Lack of A Priori Distinctions Between Learning
Algorithms, Neural Comput., 8, 1341–1390,
https://doi.org/10.1162/neco.1996.8.7.1341, 1996. a
Short summary
We developed the Swiss mid-infrared spectral library and a statistical model collection across 4374 soil samples with reference measurements of 16 properties. Our library incorporates soil from 1094 grid locations and 71 long-term monitoring sites. This work confirms once again that nationwide spectral libraries with diverse soils can reliably feed information to a fast chemical diagnosis. Our data-driven reduction of the library has the potential to accurately monitor carbon at the plot scale.
We developed the Swiss mid-infrared spectral library and a statistical model collection across...
