Articles | Volume 7, issue 1
https://doi.org/10.5194/soil-7-95-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-7-95-2021
© 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
| 
26 Apr 2021
Original research article |
| 26 Apr 2021
| 26 Apr 2021
Are researchers following best storage practices for measuring soil biochemical properties?
Department of Earth and Environmental Sciences, Michael Smith
Building, University of Manchester, Oxford Road, Manchester, M13 9PT, UK
Environment Centre Wales, Bangor University, Deiniol Road, Bangor,
Gwynedd, LL57 2UW, UK
Irene Cordero
Department of Earth and Environmental Sciences, Michael Smith
Building, University of Manchester, Oxford Road, Manchester, M13 9PT, UK
Mathilde Chomel
Department of Earth and Environmental Sciences, Michael Smith
Building, University of Manchester, Oxford Road, Manchester, M13 9PT, UK
Jocelyn M. Lavallee
Department of Earth and Environmental Sciences, Michael Smith
Building, University of Manchester, Oxford Road, Manchester, M13 9PT, UK
Department of Soil and Crop Sciences, Colorado State University, Fort
Collins, CO 80523, USA
Angela L. Straathof
Department of Earth and Environmental Sciences, Michael Smith
Building, University of Manchester, Oxford Road, Manchester, M13 9PT, UK
Ontario Soil and Crop Improvement Association, 1 Stone Road West,
Guelph, ON N1G 4Y2, Canada
Deborah Ashworth
Department of Earth and Environmental Sciences, Michael Smith
Building, University of Manchester, Oxford Road, Manchester, M13 9PT, UK
Holly Langridge
Department of Earth and Environmental Sciences, Michael Smith
Building, University of Manchester, Oxford Road, Manchester, M13 9PT, UK
Marina Semchenko
Department of Earth and Environmental Sciences, Michael Smith
Building, University of Manchester, Oxford Road, Manchester, M13 9PT, UK
Institute of Ecology and Earth Sciences, University of Tartu, Lai 40,
Tartu, 51005, Estonia
Franciska T. de Vries
Department of Earth and Environmental Sciences, Michael Smith
Building, University of Manchester, Oxford Road, Manchester, M13 9PT, UK
Institute for Biodiversity and Ecosystem Dynamics, University of
Amsterdam, P.O. Box 94240, 1090 GE, Amsterdam, the Netherlands
David Johnson
Department of Earth and Environmental Sciences, Michael Smith
Building, University of Manchester, Oxford Road, Manchester, M13 9PT, UK
Richard D. Bardgett
Department of Earth and Environmental Sciences, Michael Smith
Building, University of Manchester, Oxford Road, Manchester, M13 9PT, UK
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Anne F. Van Loon, Sarra Kchouk, Alessia Matanó, Faranak Tootoonchi, Camila Alvarez-Garreton, Khalid E. A. Hassaballah, Minchao Wu, Marthe L. K. Wens, Anastasiya Shyrokaya, Elena Ridolfi, Riccardo Biella, Viorica Nagavciuc, Marlies H. Barendrecht, Ana Bastos, Louise Cavalcante, Franciska T. de Vries, Margaret Garcia, Johanna Mård, Ileen N. Streefkerk, Claudia Teutschbein, Roshanak Tootoonchi, Ruben Weesie, Valentin Aich, Juan P. Boisier, Giuliano Di Baldassarre, Yiheng Du, Mauricio Galleguillos, René Garreaud, Monica Ionita, Sina Khatami, Johanna K. L. Koehler, Charles H. Luce, Shreedhar Maskey, Heidi D. Mendoza, Moses N. Mwangi, Ilias G. Pechlivanidis, Germano G. Ribeiro Neto, Tirthankar Roy, Robert Stefanski, Patricia Trambauer, Elizabeth A. Koebele, Giulia Vico, and Micha Werner
Nat. Hazards Earth Syst. Sci., 24, 3173–3205, https://doi.org/10.5194/nhess-24-3173-2024, https://doi.org/10.5194/nhess-24-3173-2024, 2024
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Drought is a creeping phenomenon but is often still analysed and managed like an isolated event, without taking into account what happened before and after. Here, we review the literature and analyse five cases to discuss how droughts and their impacts develop over time. We find that the responses of hydrological, ecological, and social systems can be classified into four types and that the systems interact. We provide suggestions for further research and monitoring, modelling, and management.
Rebecca J. Even, Megan B. Machmuller, Jocelyn M. Lavallee, Tamara J. Zelikova, and M. Francesca Cotrufo
EGUsphere, https://doi.org/10.5194/egusphere-2024-1470, https://doi.org/10.5194/egusphere-2024-1470, 2024
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We conducted a service soil laboratory comparison study and tested the individual effect of common sieving, grinding, drying and quantification methods on total, inorganic, and organic soil carbon (C) measurements. We found that inter-lab variability is large and each soil processing step impacts C measurement accuracy and/or precision. Standardizing soil processing methods is needed to ensure C measurements are accurate and precise, especially for C credit allocation and model calibration.
Sam J. Leuthold, Jocelyn M. Lavallee, Bruno Basso, William F. Brinton, and M. Francesca Cotrufo
SOIL, 10, 307–319, https://doi.org/10.5194/soil-10-307-2024, https://doi.org/10.5194/soil-10-307-2024, 2024
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We examined physical soil organic matter fractions to understand their relationship to temporal variability in crop yield at field scale. We found that interactions between crop productivity, topography, and climate led to variability in soil organic matter stocks among different yield stability zones. Our results imply that linkages between soil organic matter and yield stability may be scale-dependent and that particulate organic matter may be an indicator of unstable areas within croplands.
Kailiang Yu, Johan van den Hoogen, Zhiqiang Wang, Colin Averill, Devin Routh, Gabriel Reuben Smith, Rebecca E. Drenovsky, Kate M. Scow, Fei Mo, Mark P. Waldrop, Yuanhe Yang, Weize Tang, Franciska T. De Vries, Richard D. Bardgett, Peter Manning, Felipe Bastida, Sara G. Baer, Elizabeth M. Bach, Carlos García, Qingkui Wang, Linna Ma, Baodong Chen, Xianjing He, Sven Teurlincx, Amber Heijboer, James A. Bradley, and Thomas W. Crowther
Earth Syst. Sci. Data, 14, 4339–4350, https://doi.org/10.5194/essd-14-4339-2022, https://doi.org/10.5194/essd-14-4339-2022, 2022
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We used a global-scale dataset for the surface topsoil (>3000 distinct observations of abundance of soil fungi versus bacteria) to generate the first quantitative map of soil fungal proportion across terrestrial ecosystems. We reveal striking latitudinal trends. Fungi dominated in regions with low mean annual temperature (MAT) and net primary productivity (NPP) and bacteria dominated in regions with high MAT and NPP.
Georgina Key, Mike G. Whitfield, Julia Cooper, Franciska T. De Vries, Martin Collison, Thanasis Dedousis, Richard Heathcote, Brendan Roth, Shamal Mohammed, Andrew Molyneux, Wim H. Van der Putten, Lynn V. Dicks, William J. Sutherland, and Richard D. Bardgett
SOIL, 2, 511–521, https://doi.org/10.5194/soil-2-511-2016, https://doi.org/10.5194/soil-2-511-2016, 2016
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Enhancing soil health is key to providing ecosystem services and food security. There are often trade-offs to using a particular practice, or it is not fully understood. This work aimed to identify practices beneficial to soil health and gaps in our knowledge. We reviewed existing research on agricultural practices and an expert panel assessed their effectiveness. The three most beneficial practices used a mix of organic or inorganic material, cover crops, or crop rotations.
Saskia D. Keesstra, Johan Bouma, Jakob Wallinga, Pablo Tittonell, Pete Smith, Artemi Cerdà, Luca Montanarella, John N. Quinton, Yakov Pachepsky, Wim H. van der Putten, Richard D. Bardgett, Simon Moolenaar, Gerben Mol, Boris Jansen, and Louise O. Fresco
SOIL, 2, 111–128, https://doi.org/10.5194/soil-2-111-2016, https://doi.org/10.5194/soil-2-111-2016, 2016
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Soil science, as a land-related discipline, has links to several of the UN Sustainable Development Goals which are demonstrated through the functions of soils and related ecosystem services. We discuss how soil scientists can rise to the challenge both internally and externally in terms of our relations with colleagues in other disciplines, diverse groups of stakeholders and the policy arena. To meet these goals we recommend the set of steps to be taken by the soil science community as a whole.
A. T. Nottingham, B. L. Turner, J. Whitaker, N. J. Ostle, N. P. McNamara, R. D. Bardgett, N. Salinas, and P. Meir
Biogeosciences, 12, 6071–6083, https://doi.org/10.5194/bg-12-6071-2015, https://doi.org/10.5194/bg-12-6071-2015, 2015
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We measured indices of soil microbial nutrient status in lowland, sub-montane and montane tropical forests along a natural gradient spanning 3400 m in elevation in the Peruvian Andes. We show that soil microorganisms shift investment in nutrient acquisition from P to N between lowland and montane tropical forests, suggesting that different nutrients regulate soil microbial metabolism and the soil carbon balance in these ecosystems.
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Ali Sakhaee, Anika Gebauer, Mareike Ließ, and Axel Don
SOIL, 8, 587–604, https://doi.org/10.5194/soil-8-587-2022, https://doi.org/10.5194/soil-8-587-2022, 2022
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István Dunkl and Mareike Ließ
SOIL, 8, 541–558, https://doi.org/10.5194/soil-8-541-2022, https://doi.org/10.5194/soil-8-541-2022, 2022
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Digital soil mapping (DSM) allows us to regionalize soil properties by relating them to environmental covariates with the help of an empirical model. Legacy soil data provide a valuable basis to generate high-resolution soil maps with DSM. We studied the usefulness of data-clustering methods to tackle potential sampling bias in legacy soil data while applying DSM for soil texture regionalization. Clustering has proved to be useful in various steps of the DSM process.
Ulrich Weller, Lukas Albrecht, Steffen Schlüter, and Hans-Jörg Vogel
SOIL, 8, 507–515, https://doi.org/10.5194/soil-8-507-2022, https://doi.org/10.5194/soil-8-507-2022, 2022
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Brieuc Hardy, Nils Borchard, and Jens Leifeld
SOIL, 8, 451–466, https://doi.org/10.5194/soil-8-451-2022, https://doi.org/10.5194/soil-8-451-2022, 2022
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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 rapid and low-cost quantitative information over the range of soil organic matter.
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.
Elad Levintal, Yonatan Ganot, Gail Taylor, Peter Freer-Smith, Kosana Suvocarev, and Helen E. Dahlke
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Philipp Baumann, Juhwan Lee, Emmanuel Frossard, Laurie Paule Schönholzer, Lucien Diby, Valérie Kouamé Hgaza, Delwende Innocent Kiba, Andrew Sila, Keith Sheperd, and Johan Six
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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.
Philipp Baumann, Anatol Helfenstein, Andreas Gubler, Armin Keller, Reto Giulio Meuli, Daniel Wächter, Juhwan Lee, Raphael Viscarra Rossel, and Johan Six
SOIL, 7, 525–546, https://doi.org/10.5194/soil-7-525-2021, https://doi.org/10.5194/soil-7-525-2021, 2021
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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.
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.
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
Bardgett, R. D., Leemans, D. K., Cook, R., and Hobbs, P. J.: Seasonality of
the soil biota of grazed and ungrazed hill grasslands, Soil Biol. Biochem.,
29, 1285–1294, https://doi.org/10.1016/S0038-0717(97)00019-9, 1997.
Barrios, E.: Soil biota, ecosystem services and land productivity, Ecol.
Econ., 64, 269–285, https://doi.org/10.1016/j.ecolecon.2007.03.004, 2007.
Bates, D., Maechler, M., Bolker, B., Walker, S., Christensen, R. H. B.,
Singmann, H., Dai, B., Scheipl, F., Grothendieck, G., and Green, P.: Package
“lme4”, Version, 1, 17, 2018.
Beck, T., Joergensen, R. G., Kandeler, E., Makeschin, F., Nuss, E.,
Oberholzer, H. R., and Scheu, S.: An inter-laboratory comparison of ten
different ways of measuring soil microbial biomass C, Soil Biol. Biochem.,
29, 1023–1032, https://doi.org/10.1016/S0038-0717(97)00030-8, 1997.
van Bochove, E., Prévost, D., and Pelletier, F.: Effects of Freeze-Thaw
and Soil Structure on Nitrous Oxide Produced in a Clay Soil, Soil Sci. Soc.
Am. J., 64, 1638–1643, https://doi.org/10.2136/sssaj2000.6451638x, 2000.
Brookes, P. C., Landman, A., Pruden, G., and Jenkinson, D. S.: Chloroform
fumigation and the release of soil nitrogen: A rapid direct extraction
method to measure microbial biomass nitrogen in soil, Soil Biol. Biochem.,
17, 837–842, https://doi.org/10.1016/0038-0717(85)90144-0, 1985.
Černohlávková, J., Jarkovský, J., Nešporová, M., and
Hofman, J.: Variability of soil microbial properties: Effects of sampling,
handling and storage, Ecotoxicol. Environ. Saf., 72, 2102–2108,
https://doi.org/10.1016/j.ecoenv.2009.04.023, 2009.
Cochran, W. G. and Cox, G. M.: Experimental designs, John Willey and Sons,
Inc., New York, 546–568, 1957.
Coe, S. and Downing, E.: The Agriculture Bill (2017–2019), House Commons
Libr., Number CBP(October), 1–93 [online] available at:
http://researchbriefings.files.parliament.uk/documents/CBP-8405/CBP-8405.pdf (last access: 12 October 2020),
2018.
Coleman, D. C., Crossley, J. A., and Hendrix, P. F.: Fundamentals of Soil
Ecology: Second Edition, Academic Press., 1–386, 2004.
Dangi, S. R.: Soil Ecology and Ecosystem Services, Oxford University Press.,
335–335, 2014.
Datta, R., Meena, R. S., Pathan, S. I., and Ceccherini, M. T.: Carbon and nitrogen cycling in soil, Springer, 2019.
DEFRA and EA: Farming rules for water Questions and Answers, [online]
available at:
https://www.gov.uk/government/publications/farming-rules-for-water-in-england (last access: 12 October 2020),
2018.
De Long, J. R., Jackson, B. G., Wilkinson, A., Pritchard, W. J., Oakley, S.,
Mason, K. E., Stephan, J. G., Ostle, N. J., Johnson, D., Baggs, E. M., and
Bardgett, R. D.: Relationships between plant traits, soil properties and
carbon fluxes differ between monocultures and mixed communities in temperate
grassland, J. Ecol., 107, 1704–1719, https://doi.org/10.1111/1365-2745.13160, 2019.
Forster, J. C.: Soil sampling, handling, storage and analysis, in: Methods in
Applied Soil Microbiology and Biochemistry, edited by: Kassem Alef and Paolo Nannipieri, 49–121, Academic Press, London,
1995.
Ghuneim, L. A. J., Jones, D. L., Golyshin, P. N., and Golyshina, O. V.:
Nano-sized and filterable bacteria and archaea: Biodiversity and function,
Front. Microbiol., 9(AUG), 1971, https://doi.org/10.3389/fmicb.2018.01971, 2018.
Guo, M.: Soil Sampling and Methods of Analysis, CRC press., 375–375, 2009.
Halbritter, A. H., De Boeck, H. J., Eycott, A. E., Reinsch, S., Robinson, D.
A., Vicca, S., Berauer, B., Christiansen, C. T., Estiarte, M.,
Grünzweig, J. M., Gya, R., Hansen, K., Jentsch, A., Lee, H., Linder, S.,
Marshall, J., Peñuelas, J., Kappel Schmidt, I., Stuart-Haëntjens,
E., Wilfahrt, P., Vandvik, V., Abrantes, N., Almagro, M., Althuizen, I. H.
J., Barrio, I. C., Te Beest, M., Beier, C., Beil, I., Carter Berry, Z.,
Birkemoe, T., Bjerke, J. W., Blonder, B., Blume-Werry, G., Bohrer, G.,
Campos, I., Cernusak, L. A., Chojnicki, B. H., Cosby, B. J., Dickman, L. T.,
Djukic, I., Filella, I., Fuchslueger, L., Gargallo-Garriga, A., Gillespie,
M. A. K., Goldsmith, G. R., Gough, C., Halliday, F. W., Hegland, S. J.,
Hoch, G., Holub, P., Jaroszynska, F., Johnson, D. M., Jones, S. B., Kardol,
P., Keizer, J. J., Klem, K., Konestabo, H. S., Kreyling, J.,
Kröel-Dulay, G., Landhäusser, S. M., Larsen, K. S., Leblans, N.,
Lebron, I., Lehmann, M. M., Lembrechts, J. J., Lenz, A., Linstädter, A.,
Llusià, J., Macias-Fauria, M., Malyshev, A. V., Mänd, P., Marshall,
M., Matheny, A. M., McDowell, N., Meier, I. C., Meinzer, F. C., Michaletz,
S. T., Miller, M. L., Muffler, L., Oravec, M., Ostonen, I., Porcar-Castell,
A., Preece, C., Prentice, I. C., Radujković, D., Ravolainen, V.,
Ribbons, R., Ruppert, J. C., Sack, L., Sardans, J., Schindlbacher, A.,
Scoffoni, C., Sigurdsson, B. D., Smart, S., Smith, S. W., Soper, F., Speed,
J. D. M., Sverdrup-Thygeson, A., Sydenham, M. A. K., et al.: The handbook
for standardized field and laboratory measurements in terrestrial climate
change experiments and observational studies (ClimEx), Methods Ecol. Evol.,
11, 22–37, https://doi.org/10.1111/2041-210X.13331, 2020.
Hassink, J.: Effects of soil texture and structure on carbon and nitrogen
mineralization in grassland soils, Biol. Fertil. Soils, 14, 126–134,
https://doi.org/10.1007/BF00336262, 1992.
Heffernan, B. J.: A handbook of methods of inorganic chemical analysis for
forest soils, foliage and water, CSIRO Division of Forest Research, 281 pp., 1985.
Islam, K. R., Weil, R. R., Mulchi, C. L., and Glenn, S. D.: Freeze-dried soil
extraction method for the measurement of microbial biomass C, Biol. Fertil.
Soils, 24, 205–210, https://doi.org/10.1007/s003740050232, 1997.
ISO18400-102:2017: Soil quality – Sampling – Part 102: Selection and
application of sampling techniques, 2017.
Jones, D. L. and Willett, V. B.: Experimental evaluation of methods to
quantify dissolved organic nitrogen (DON) and dissolved organic carbon (DOC)
in soil, Soil Biol. Biochem., 38, 991–999,
https://doi.org/10.1016/j.soilbio.2005.08.012, 2006.
Kachurina, O. M., Zhang, H., Raun, W. R., and Krenzer, E. G.: Simultaneous
determination of soil aluminum, ammonium- and nitrate- nitrogen using 1 M
potassium chloride, Commun. Soil Sci. Plant Anal., 31, 893–903,
https://doi.org/10.1080/00103620009370485, 2000.
Kaiser, K., Kaupenjohann, M., and Zech, W.: Sorption of dissolved organic
carbon in soils: effects of soil sample storage, soil-to-solution ratio, and
temperature, Geoderma, 99, 317–328,
https://doi.org/10.1016/S0016-7061(00)00077-X, 2001.
Keeney, D. R. and Nelson., D. W.: Nitrogen – Inorganic Forms 1, Methods
soil Anal. Part 2. Chem. Microbiol. Prop., 5(methodsofsoilan2), 643–698,
1982.
Kennedy, N. M., Gleeson, D. E., Connolly, J., and Clipson, N. J. W.: Seasonal
and management influences on bacterial community structure in an upland
grassland soil, FEMS Microbiol. Ecol., 53, 329–337,
https://doi.org/10.1016/j.femsec.2005.01.013, 2005.
Knowles, J. E., Frederick, C., Whitworth, A.: merTools: Tools for analyzing mixed effect
regression models. R package version 0.5.0, R package [online], available
at: https://cran.r-project.org/package=merTools (last access: 12 October 2020), 2019.
Lavahun, M. F. E., Joergensen, R. G., and Meyer, B.: Activity and biomass of
soil microorganisms at different depths, Biol. Fertil. Soils, 23, 38–42,
https://doi.org/10.1007/BF00335816, 1996.
Lee, Y. B., Lorenz, N., Dick, L. K., and Dick, R. P.: Cold Storage and
Pretreatment Incubation Effects on Soil Microbial Properties, Soil Sci. Soc.
Am. J., 71, 1299–1305, https://doi.org/10.2136/sssaj2006.0245, 2007.
Leff, J. W., Bardgett, R. D., Wilkinson, A., Jackson, B. G., Pritchard, W.
J., De Long, J. R., Oakley, S., Mason, K. E., Ostle, N. J., Johnson, D.,
Baggs, E. M., and Fierer, N.: Predicting the structure of soil communities
from plant community taxonomy, phylogeny, and traits, ISME J., 12,
1794–1805, https://doi.org/10.1038/s41396-018-0089-x, 2018.
Li, K. Y., Zhao, Y. Y., Yuan, X. L., Zhao, H. B., Wang, Z. H., Li, S. X., and Malhi, S. S.: Comparison of Factors Affecting Soil Nitrate Nitrogen
and Ammonium Nitrogen Extraction, Commun. Soil Sci. Plant Anal., 43,
571–588, https://doi.org/10.1080/00103624.2012.639108, 2012.
R Core Team: R: A language and environment for statistical computing. R
Foundation for Statistical Computing, Vienna, Austria, Platform: [online]
available at: https://www.r-project.org/ (last access: 12 October 2020), 2019.
Rees, R. M. and Parker, J. P.: Filtration increases the correlation between
water extractable organic carbon and soil microbial activity, Soil Biol.
Biochem., 37, 2240–2248, https://doi.org/10.1016/j.soilbio.2005.03.024, 2005.
Rita, H. and Ekholm, P.: Showing similarity of results given by two methods:
A commentary, Environ. Pollut., 145, 383–386,
https://doi.org/10.1016/j.envpol.2006.08.007, 2007.
Robinson, D. A., Fraser, I., Dominati, E. J., Davíðsdóttir, B.,
Jónsson, J. O. G., Jones, L., Jones, S. B., Tuller, M., Lebron, I.,
Bristow, K. L., Souza, D. M., Banwart, S.. and Clothier, B. E.: On the Value
of Soil Resources in the Context of Natural Capital and Ecosystem Service
Delivery, Soil Sci. Soc. Am. J., 78, 685–700,
https://doi.org/10.2136/sssaj2014.01.0017, 2014.
Rodwell, J. S.: British plant communities, grasslands and montane
communities, Br. plant communities, Vol. 3 grasslands Mont. communities,
https://doi.org/10.2307/2997013, 1992.
Rolston, D. E. and Liss, H. J.: Spatial and temporal variability of
water-soluble organic carbon in a cropped field, Hilgardia, 57, 1–19,
https://doi.org/10.3733/hilg.v57n03p019, 1989.
Romera-Castillo, C., Pinto, M., Langer, T. M., Álvarez-Salgado, X. A.,
and Herndl, G. J.: Dissolved organic carbon leaching from plastics
stimulates microbial activity in the ocean, Nat. Commun., 9, 1430,
https://doi.org/10.1038/s41467-018-03798-5, 2018.
Ros, G. H., Hoffland, E., van Kessel, C., and Temminghoff, E. J. M.:
Extractable and dissolved soil organic nitrogen - A quantitative assessment,
Soil Biol. Biochem., 41, 1029–1039, https://doi.org/10.1016/j.soilbio.2009.01.011,
2009.
Rousk, J. and Jones, D. L.: Loss of low molecular weight dissolved organic
carbon (DOC) and nitrogen (DON) in H2O and 0.5 M K2SO4 soil extracts, Soil
Biol. Biochem., 42, 2331–2335, https://doi.org/10.1016/j.soilbio.2010.08.017, 2010.
Schnecker, J., Wild, B., Takriti, M., Eloy Alves, R. J., Gentsch, N.,
Gittel, A., Hofer, A., Klaus, K., Knoltsch, A., Lashchinskiy, N., Mikutta,
R., and Richter, A.: Microbial community composition shapes enzyme patterns
in topsoil and subsoil horizons along a latitudinal transect in Western
Siberia, Soil Biol. Biochem., 83, 106–115,
https://doi.org/10.1016/j.soilbio.2015.01.016, 2015.
Stenberg, B., Johansson, M., Pell, M., Sjödahl-Svensson, K.,
Stenström, J., and Torstensson, L.: Microbial biomass and activities in
soil as affected by frozen and cold storage, Soil Biol. Biochem., 30,
393–402, https://doi.org/10.1016/S0038-0717(97)00125-9, 1998.
Tierney, G. L., Fahey, T. J., Groffman, P. M., Hardy, J. P., Fitzhugh, R. D.,
and Driscoll, C. T.: Soil freezing alters fine root dynamics in a northern
hardwood forest, Biogeochemistry, 56, 175–190,
https://doi.org/10.1023/A:1013072519889, 2001.
Tyler, K. B., Broadbent, F. E., and Hill, G. N.: Low-temperature effects on
nitrification in four California soils, Soil Sci., 87, 123–129,
https://doi.org/10.1097/00010694-195903000-00001, 1959.
Vance, E. D., Brookes, P. C., and Jenkinson, D. S.: An extraction method for
measuring soil microbial biomass C, Soil Biol. Biochem., 19, 703–707,
https://doi.org/10.1016/0038-0717(87)90052-6, 1987.
Wallenius, K., Rita, H., Simpanen, S., Mikkonen, A., and Niemi, R. M.: Sample
storage for soil enzyme activity and bacterial community profiles, J.
Microbiol. Methods, 81, 48–55, https://doi.org/10.1016/j.mimet.2010.01.021, 2010.
Wang, Y., Hammes, F., Boon, N., and Egli, T.: Quantification of the
filterability of freshwater bacteria through 0.45, 0.22, and 0.1 µm
pore size filters and shape-dependent enrichment of filterable bacterial
communities, Environ. Sci. Technol., 41, 7080–7086,
https://doi.org/10.1021/es0707198, 2007.
Zagal, E.: Measurement of microbial biomass in rewetted air-dried soil by
fumigation-incubation and fumigation-extraction techniques, Soil Biol.
Biochem., 25, 553–559, https://doi.org/10.1016/0038-0717(93)90193-F, 1993.
