Articles | Volume 4, issue 3
https://doi.org/10.5194/soil-4-173-2018
© Author(s) 2018. 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-4-173-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Original research article
| 
01 Aug 2018
Original research article |
| 01 Aug 2018
| 01 Aug 2018
No silver bullet for digital soil mapping: country-specific soil organic carbon estimates across Latin America
Mario Guevara
University of Delaware, Department of Plant and Soil Sciences, Newark, DE, USA
Guillermo Federico Olmedo
INTA EEA Mendoza, San Martín 3853, Luján de Cuyo, Mendoza, Argentina
FAO, Vialle de Terme di Caracalla, Rome, Italy
Emma Stell
University of Delaware, Department of Plant and Soil Sciences, Newark, DE, USA
Yusuf Yigini
FAO, Vialle de Terme di Caracalla, Rome, Italy
Yameli Aguilar Duarte
Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias, Mérida, Mexico
Carlos Arellano Hernández
Instituto Nacional de Estadísitica y Geografía, Aguascalientes, Mexico
Gloria E. Arévalo
Zamorano University of Honduras and Asociación Hondureña de la Ciencia del Suelo, Tegucigalpa, Honduras
Carlos Eduardo Arroyo-Cruz
National Commission for the Knowledge and Use of Biodiversity, Mexico City, Mexico
Adriana Bolivar
Subdirección Agrología, Instituto Geográfico Agustín Codazzi, Bogotá, Colombia
Sally Bunning
Oficina Regional de la FAO para América Latina y el Caribe, Santiago de Chile, Chile
Nelson Bustamante Cañas
Servicio Agrícola y Ganadero, Santiago de Chile, Chile
Carlos Omar Cruz-Gaistardo
Instituto Nacional de Estadísitica y Geografía, Aguascalientes, Mexico
Fabian Davila
Direccion General de Recursos Naturales, Ministerio de Ganaderia, Agricultura y Pesca, Montevideo, Uruguay
Martin Dell Acqua
Direccion General de Recursos Naturales, Ministerio de Ganaderia, Agricultura y Pesca, Montevideo, Uruguay
Arnulfo Encina
Facultad de Ciencias Agrarias de la Universidad Nacional de Asunción, Asunción, Paraguay
Hernán Figueredo Tacona
Land Viceministry, Ministry of Rural Development and Land, La Paz, Bolivia
Fernando Fontes
Direccion General de Recursos Naturales, Ministerio de Ganaderia, Agricultura y Pesca, Montevideo, Uruguay
José Antonio Hernández Herrera
Universidad Autónoma Agraria Antonio Narro Unidad Laguna, Torreón, Mexico
Alejandro Roberto Ibelles Navarro
Instituto Nacional de Estadísitica y Geografía, Aguascalientes, Mexico
Veronica Loayza
Ministerio de Agricultura y Ganaderia, Quito, Ecuador
Alexandra M. Manueles
Zamorano University of Honduras and Asociación Hondureña de la Ciencia del Suelo, Tegucigalpa, Honduras
Fernando Mendoza Jara
Universidad Nacional Agraria, Managua, Nicaragua
Carolina Olivera
Oficina Regional de la FAO para América Latina y el Caribe, Bogotá, Colombia
Rodrigo Osorio Hermosilla
Servicio Agrícola y Ganadero, Santiago de Chile, Chile
Gonzalo Pereira
Direccion General de Recursos Naturales, Ministerio de Ganaderia, Agricultura y Pesca, Montevideo, Uruguay
Pablo Prieto
Direccion General de Recursos Naturales, Ministerio de Ganaderia, Agricultura y Pesca, Montevideo, Uruguay
Iván Alexis Ramos
Instituto de Investigación Agropecuaria de Panamá, Panamá, Panama
Juan Carlos Rey Brina
Sociedad Venezolana de la Ciencia del Suelo, Caracas, Venezuela
Rafael Rivera
Ministerio de Medio Ambiente, Santo Domingo, Dominican Republic
Javier Rodríguez-Rodríguez
National Commission for the Knowledge and Use of Biodiversity, Mexico City, Mexico
Ronald Roopnarine
Department of Natural and Life Sciences, COSTAATT, Port of Spain, Trinidad and Tobago
University of the West Indies, St. Augustine Campus, St. Augustine, Trinidad and Tobago
Albán Rosales Ibarra
Instituto de Innovación en Transferencia y Tecnología Agropecuaria, San José, Costa Rica
Kenset Amaury Rosales Riveiro
Ministerio de Ambiente y Recursos Naturales de Guatemala, Ciudad Guatemala, Guatemala
Guillermo Andrés Schulz
INTA CNIA, Buenos Aires, Argentina
Adrian Spence
International Centre for Environmental and Nuclear Sciences, University of the West Indies, Kingston, Jamaica
Gustavo M. Vasques
Embrapa Solos, Rio de Janeiro, Brazil
Ronald R. Vargas
FAO, Vialle de Terme di Caracalla, Rome, Italy
University of Delaware, Department of Plant and Soil Sciences, Newark, DE, USA
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Mario Guevara, Michela Taufer, and Rodrigo Vargas
Earth Syst. Sci. Data, 13, 1711–1735, https://doi.org/10.5194/essd-13-1711-2021, https://doi.org/10.5194/essd-13-1711-2021, 2021
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Soil moisture is key for understanding soil–plant–atmosphere interactions. We provide a machine learning approach to increase the spatial resolution of satellite-derived soil moisture information. The outcome is a dataset of gap-free global mean annual soil moisture predictions and associated uncertainty for 28 years (1991–2018) across 15 km grids. This dataset has higher agreement with in situ soil moisture and precipitation measurements. Results show a decline of global annual soil moisture.
Flavio Lopes Ribeiro, Mario Guevara, Alma Vázquez-Lule, Ana Paula Cunha, Marcelo Zeri, and Rodrigo Vargas
Nat. Hazards Earth Syst. Sci., 21, 879–892, https://doi.org/10.5194/nhess-21-879-2021, https://doi.org/10.5194/nhess-21-879-2021, 2021
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The main objective of this paper was to analyze differences in soil moisture responses to drought for each biome of Brazil. For that we used satellite data from the European Space Agency from 2009 to 2015. We found an overall soil moisture decline of −0.5 % yr−1 at the country level and identified the most vulnerable biomes of Brazil. This information is crucial to enhance the national drought early warning system and develop strategies for drought risk reduction and soil moisture conservation.
Marco Pfeiffer, José Padarian, Rodrigo Osorio, Nelson Bustamante, Guillermo Federico Olmedo, Mario Guevara, Felipe Aburto, Francisco Albornoz, Monica Antilén, Elías Araya, Eduardo Arellano, Maialen Barret, Juan Barrera, Pascal Boeckx, Margarita Briceño, Sally Bunning, Lea Cabrol, Manuel Casanova, Pablo Cornejo, Fabio Corradini, Gustavo Curaqueo, Sebastian Doetterl, Paola Duran, Mauricio Escudey, Angelina Espinoza, Samuel Francke, Juan Pablo Fuentes, Marcel Fuentes, Gonzalo Gajardo, Rafael García, Audrey Gallaud, Mauricio Galleguillos, Andrés Gomez, Marcela Hidalgo, Jorge Ivelic-Sáez, Lwando Mashalaba, Francisco Matus, Francisco Meza, Maria de la Luz Mora, Jorge Mora, Cristina Muñoz, Pablo Norambuena, Carolina Olivera, Carlos Ovalle, Marcelo Panichini, Aníbal Pauchard, Jorge F. Pérez-Quezada, Sergio Radic, José Ramirez, Nicolás Riveras, Germán Ruiz, Osvaldo Salazar, Iván Salgado, Oscar Seguel, Maria Sepúlveda, Carlos Sierra, Yasna Tapia, Francisco Tapia, Balfredo Toledo, José Miguel Torrico, Susana Valle, Ronald Vargas, Michael Wolff, and Erick Zagal
Earth Syst. Sci. Data, 12, 457–468, https://doi.org/10.5194/essd-12-457-2020, https://doi.org/10.5194/essd-12-457-2020, 2020
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The CHLSOC database is the biggest soil organic carbon (SOC) database that has been compiled for Chile yet, comprising 13 612 data points. This database is the product of the compilation of numerous sources including unpublished and difficult-to-access data, allowing us to fill numerous spatial gaps where no SOC estimates were publicly available before. The values of SOC compiled in CHLSOC have a wide range, reflecting the variety of ecosystems that exists in Chile.
Mario Guevara, Michela Taufer, and Rodrigo Vargas
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2019-191, https://doi.org/10.5194/essd-2019-191, 2019
Revised manuscript not accepted
M. F. Adame, N. S. Santini, C. Tovilla, A. Vázquez-Lule, L. Castro, and M. Guevara
Biogeosciences, 12, 3805–3818, https://doi.org/10.5194/bg-12-3805-2015, https://doi.org/10.5194/bg-12-3805-2015, 2015
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Riverine wetlands of the south Pacific coast of Mexico had large ecosystem C stocks (784.5 MgC ha-1 for mangroves, 722.2 MgC ha-1 for peat swamps, and 336.5 MgC ha-1 for marshes). Long-term soil C sequestration values in mangroves were 1.3 ± 0.2 MgC ha-1yr-1. C stocks, and soil nitrogen stocks were in general larger for mangroves in the upper than in the lower estuary.
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Human activities have disrupted the global carbon cycle, increasing CO2 levels. Soils are the largest carbon stores on land, making it essential to understand how much carbon they hold to fight climate change. Our study improved estimates of soil carbon in peninsular Spain by integrating historical soil data and using machine-learning methods to create detailed maps of carbon content. These maps will help manage soil carbon better and support efforts to track carbon emissions globally.
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Earth Syst. Sci. Data, 16, 1229–1246, https://doi.org/10.5194/essd-16-1229-2024, https://doi.org/10.5194/essd-16-1229-2024, 2024
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In this work, the Latin America and Caribbean Soil Information System (SISLAC) database (https://54.229.242.119/sislac/es) was revised to generate an improved version of the data. Rules for data enhancement were defined. In addition, other datasets available in the region were included. Subsequently, through a principal component analysis (PCA), the main soil characteristics for the region were analyzed. We hope this dataset can help mitigate problems such as food security and global warming.
Josué Delgado-Balbuena, Henry W. Loescher, Carlos A. Aguirre-Gutiérrez, Teresa Alfaro-Reyna, Luis F. Pineda-Martínez, Rodrigo Vargas, and Tulio Arredondo
Biogeosciences, 20, 2369–2385, https://doi.org/10.5194/bg-20-2369-2023, https://doi.org/10.5194/bg-20-2369-2023, 2023
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Daphne Armas, Mario Guevara, Fernando Bezares, Rodrigo Vargas, Pilar Durante, Víctor Osorio, Wilmer Jiménez, and Cecilio Oyonarte
Earth Syst. Sci. Data, 15, 431–445, https://doi.org/10.5194/essd-15-431-2023, https://doi.org/10.5194/essd-15-431-2023, 2023
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The global need for updated soil datasets has increased. Our main objective was to synthesize and harmonize soil profile information collected by two different projects in Ecuador between 2009 and 2015.The main result was the development of the Harmonized Soil Database of Ecuador (HESD) that includes information from 13 542 soil profiles with over 51 713 measured soil horizons, including 92 different edaphic variables, and follows international standards for archiving and sharing soil data.
Rodrigo Vargas and Van Huong Le
Biogeosciences, 20, 15–26, https://doi.org/10.5194/bg-20-15-2023, https://doi.org/10.5194/bg-20-15-2023, 2023
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Quantifying the role of soils in nature-based solutions requires accurate estimates of soil greenhouse gas (GHG) fluxes. We suggest that multiple GHG fluxes should not be simultaneously measured at a few fixed time intervals, but an optimized sampling approach can reduce bias and uncertainty. Our results have implications for assessing GHG fluxes from soils and a better understanding of the role of soils in nature-based solutions.
Margaret Capooci and Rodrigo Vargas
Biogeosciences, 19, 4655–4670, https://doi.org/10.5194/bg-19-4655-2022, https://doi.org/10.5194/bg-19-4655-2022, 2022
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Tidal salt marsh soil emits greenhouse gases, as well as sulfur-based gases, which play roles in global climate but are not well studied as they are difficult to measure. Traditional methods of measuring these gases worked relatively well for carbon dioxide, but less so for methane, nitrous oxide, carbon disulfide, and dimethylsulfide. High variability of trace gases complicates the ability to accurately calculate gas budgets and new approaches are needed for monitoring protocols.
Kyle B. Delwiche, Sara Helen Knox, Avni Malhotra, Etienne Fluet-Chouinard, Gavin McNicol, Sarah Feron, Zutao Ouyang, Dario Papale, Carlo Trotta, Eleonora Canfora, You-Wei Cheah, Danielle Christianson, Ma. Carmelita R. Alberto, Pavel Alekseychik, Mika Aurela, Dennis Baldocchi, Sheel Bansal, David P. Billesbach, Gil Bohrer, Rosvel Bracho, Nina Buchmann, David I. Campbell, Gerardo Celis, Jiquan Chen, Weinan Chen, Housen Chu, Higo J. Dalmagro, Sigrid Dengel, Ankur R. Desai, Matteo Detto, Han Dolman, Elke Eichelmann, Eugenie Euskirchen, Daniela Famulari, Kathrin Fuchs, Mathias Goeckede, Sébastien Gogo, Mangaliso J. Gondwe, Jordan P. Goodrich, Pia Gottschalk, Scott L. Graham, Martin Heimann, Manuel Helbig, Carole Helfter, Kyle S. Hemes, Takashi Hirano, David Hollinger, Lukas Hörtnagl, Hiroki Iwata, Adrien Jacotot, Gerald Jurasinski, Minseok Kang, Kuno Kasak, John King, Janina Klatt, Franziska Koebsch, Ken W. Krauss, Derrick Y. F. Lai, Annalea Lohila, Ivan Mammarella, Luca Belelli Marchesini, Giovanni Manca, Jaclyn Hatala Matthes, Trofim Maximov, Lutz Merbold, Bhaskar Mitra, Timothy H. Morin, Eiko Nemitz, Mats B. Nilsson, Shuli Niu, Walter C. Oechel, Patricia Y. Oikawa, Keisuke Ono, Matthias Peichl, Olli Peltola, Michele L. Reba, Andrew D. Richardson, William Riley, Benjamin R. K. Runkle, Youngryel Ryu, Torsten Sachs, Ayaka Sakabe, Camilo Rey Sanchez, Edward A. Schuur, Karina V. R. Schäfer, Oliver Sonnentag, Jed P. Sparks, Ellen Stuart-Haëntjens, Cove Sturtevant, Ryan C. Sullivan, Daphne J. Szutu, Jonathan E. Thom, Margaret S. Torn, Eeva-Stiina Tuittila, Jessica Turner, Masahito Ueyama, Alex C. Valach, Rodrigo Vargas, Andrej Varlagin, Alma Vazquez-Lule, Joseph G. Verfaillie, Timo Vesala, George L. Vourlitis, Eric J. Ward, Christian Wille, Georg Wohlfahrt, Guan Xhuan Wong, Zhen Zhang, Donatella Zona, Lisamarie Windham-Myers, Benjamin Poulter, and Robert B. Jackson
Earth Syst. Sci. Data, 13, 3607–3689, https://doi.org/10.5194/essd-13-3607-2021, https://doi.org/10.5194/essd-13-3607-2021, 2021
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Methane is an important greenhouse gas, yet we lack knowledge about its global emissions and drivers. We present FLUXNET-CH4, a new global collection of methane measurements and a critical resource for the research community. We use FLUXNET-CH4 data to quantify the seasonality of methane emissions from freshwater wetlands, finding that methane seasonality varies strongly with latitude. Our new database and analysis will improve wetland model accuracy and inform greenhouse gas budgets.
Mario Guevara, Michela Taufer, and Rodrigo Vargas
Earth Syst. Sci. Data, 13, 1711–1735, https://doi.org/10.5194/essd-13-1711-2021, https://doi.org/10.5194/essd-13-1711-2021, 2021
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Soil moisture is key for understanding soil–plant–atmosphere interactions. We provide a machine learning approach to increase the spatial resolution of satellite-derived soil moisture information. The outcome is a dataset of gap-free global mean annual soil moisture predictions and associated uncertainty for 28 years (1991–2018) across 15 km grids. This dataset has higher agreement with in situ soil moisture and precipitation measurements. Results show a decline of global annual soil moisture.
Flavio Lopes Ribeiro, Mario Guevara, Alma Vázquez-Lule, Ana Paula Cunha, Marcelo Zeri, and Rodrigo Vargas
Nat. Hazards Earth Syst. Sci., 21, 879–892, https://doi.org/10.5194/nhess-21-879-2021, https://doi.org/10.5194/nhess-21-879-2021, 2021
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The main objective of this paper was to analyze differences in soil moisture responses to drought for each biome of Brazil. For that we used satellite data from the European Space Agency from 2009 to 2015. We found an overall soil moisture decline of −0.5 % yr−1 at the country level and identified the most vulnerable biomes of Brazil. This information is crucial to enhance the national drought early warning system and develop strategies for drought risk reduction and soil moisture conservation.
Jinshi Jian, Rodrigo Vargas, Kristina Anderson-Teixeira, Emma Stell, Valentine Herrmann, Mercedes Horn, Nazar Kholod, Jason Manzon, Rebecca Marchesi, Darlin Paredes, and Ben Bond-Lamberty
Earth Syst. Sci. Data, 13, 255–267, https://doi.org/10.5194/essd-13-255-2021, https://doi.org/10.5194/essd-13-255-2021, 2021
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Field soil-to-atmosphere CO2 flux (soil respiration, Rs) observations were compiled into a global database (SRDB) a decade ago. Here, we restructured and updated the database to the fifth version, SRDB-V5, with data published through 2017 included. SRDB-V5 aims to be a data framework for the scientific community to share seasonal to annual field Rs measurements, and it provides opportunities for the scientific community to better understand the spatial and temporal variability of Rs.
Rosa M. Poch, Lucia H. C. dos Anjos, Rafla Attia, Megan Balks, Adalberto Benavides-Mendoza, Martha M. Bolaños-Benavides, Costanza Calzolari, Lydia M. Chabala, Peter C. de Ruiter, Samuel Francke-Campaña, Fernando García Préchac, Ellen R. Graber, Siosiua Halavatau, Kutaiba M. Hassan, Edmond Hien, Ke Jin, Mohammad Khan, Maria Konyushkova, David A. Lobb, Matshwene E. Moshia, Jun Murase, Generose Nziguheba, Ashok K. Patra, Gary Pierzynski, Natalia Rodríguez Eugenio, and Ronald Vargas Rojas
SOIL, 6, 541–547, https://doi.org/10.5194/soil-6-541-2020, https://doi.org/10.5194/soil-6-541-2020, 2020
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Humanity depends on the existence of healthy soils, both for the production of food and for ensuring a healthy, biodiverse environment. In the face of global crises like the COVID-19 pandemic, a sustainable soil management strategy is essential to ensure food security based on more diverse, locally oriented, and resilient food production systems through improving access to land, sound land use planning, sustainable soil management, enhanced research, and investment in education and extension.
Marco Pfeiffer, José Padarian, Rodrigo Osorio, Nelson Bustamante, Guillermo Federico Olmedo, Mario Guevara, Felipe Aburto, Francisco Albornoz, Monica Antilén, Elías Araya, Eduardo Arellano, Maialen Barret, Juan Barrera, Pascal Boeckx, Margarita Briceño, Sally Bunning, Lea Cabrol, Manuel Casanova, Pablo Cornejo, Fabio Corradini, Gustavo Curaqueo, Sebastian Doetterl, Paola Duran, Mauricio Escudey, Angelina Espinoza, Samuel Francke, Juan Pablo Fuentes, Marcel Fuentes, Gonzalo Gajardo, Rafael García, Audrey Gallaud, Mauricio Galleguillos, Andrés Gomez, Marcela Hidalgo, Jorge Ivelic-Sáez, Lwando Mashalaba, Francisco Matus, Francisco Meza, Maria de la Luz Mora, Jorge Mora, Cristina Muñoz, Pablo Norambuena, Carolina Olivera, Carlos Ovalle, Marcelo Panichini, Aníbal Pauchard, Jorge F. Pérez-Quezada, Sergio Radic, José Ramirez, Nicolás Riveras, Germán Ruiz, Osvaldo Salazar, Iván Salgado, Oscar Seguel, Maria Sepúlveda, Carlos Sierra, Yasna Tapia, Francisco Tapia, Balfredo Toledo, José Miguel Torrico, Susana Valle, Ronald Vargas, Michael Wolff, and Erick Zagal
Earth Syst. Sci. Data, 12, 457–468, https://doi.org/10.5194/essd-12-457-2020, https://doi.org/10.5194/essd-12-457-2020, 2020
Short summary
Short summary
The CHLSOC database is the biggest soil organic carbon (SOC) database that has been compiled for Chile yet, comprising 13 612 data points. This database is the product of the compilation of numerous sources including unpublished and difficult-to-access data, allowing us to fill numerous spatial gaps where no SOC estimates were publicly available before. The values of SOC compiled in CHLSOC have a wide range, reflecting the variety of ecosystems that exists in Chile.
Mario Guevara, Michela Taufer, and Rodrigo Vargas
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2019-191, https://doi.org/10.5194/essd-2019-191, 2019
Revised manuscript not accepted
M. F. Adame, N. S. Santini, C. Tovilla, A. Vázquez-Lule, L. Castro, and M. Guevara
Biogeosciences, 12, 3805–3818, https://doi.org/10.5194/bg-12-3805-2015, https://doi.org/10.5194/bg-12-3805-2015, 2015
Short summary
Short summary
Riverine wetlands of the south Pacific coast of Mexico had large ecosystem C stocks (784.5 MgC ha-1 for mangroves, 722.2 MgC ha-1 for peat swamps, and 336.5 MgC ha-1 for marshes). Long-term soil C sequestration values in mangroves were 1.3 ± 0.2 MgC ha-1yr-1. C stocks, and soil nitrogen stocks were in general larger for mangroves in the upper than in the lower estuary.
A. W. King, R. J. Andres, K. J. Davis, M. Hafer, D. J. Hayes, D. N. Huntzinger, B. de Jong, W. A. Kurz, A. D. McGuire, R. Vargas, Y. Wei, T. O. West, and C. W. Woodall
Biogeosciences, 12, 399–414, https://doi.org/10.5194/bg-12-399-2015, https://doi.org/10.5194/bg-12-399-2015, 2015
P. C. Stoy, M. C. Dietze, A. D. Richardson, R. Vargas, A. G. Barr, R. S. Anderson, M. A. Arain, I. T. Baker, T. A. Black, J. M. Chen, R. B. Cook, C. M. Gough, R. F. Grant, D. Y. Hollinger, R. C. Izaurralde, C. J. Kucharik, P. Lafleur, B. E. Law, S. Liu, E. Lokupitiya, Y. Luo, J. W. Munger, C. Peng, B. Poulter, D. T. Price, D. M. Ricciuto, W. J. Riley, A. K. Sahoo, K. Schaefer, C. R. Schwalm, H. Tian, H. Verbeeck, and E. Weng
Biogeosciences, 10, 6893–6909, https://doi.org/10.5194/bg-10-6893-2013, https://doi.org/10.5194/bg-10-6893-2013, 2013
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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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As soil carbon has become a key component of climate-smart agriculture, the demand for high-resolution maps has increased drastically. Meanwhile, machine learning algorithms are becoming more widely used and are opening up new solutions in soil mapping. This paper shows which algorithms perform best, how soil inventory data can be most efficiently used for digital soil mapping, and the different available options and methods to derive high-resolution soil carbon data at the large regional scale.
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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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 standardized quantitative way and can be compared to a world-wide data set.
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
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.
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.
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.
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.
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
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68, 575–591, https://doi.org/10.1111/ejss.12446, 2017. a, b
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Short summary
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.
We provide a reproducible multi-modeling approach for SOC mapping across Latin America on a...
