Preprints
https://doi.org/10.5194/soil-2020-99
https://doi.org/10.5194/soil-2020-99

  08 Jan 2021

08 Jan 2021

Review status: this preprint is currently under review for the journal SOIL.

Filling a key gap: a soil infrared library for central Africa

Laura Summerauer1, Philipp Baumann1, Leonardo Ramirez-Lopez2, Matti Barthel1, Marijn Bauters3,4, Benjamin Bukombe5, Mario Reichenbach5, Pascal Boeckx3, Elizabeth Kearsley4, Kristof Van Oost6, Bernard Vanlauwe7, Dieudonné Chiragaga7, Aimé Bisimwa Heri-Kazi6, Pieter Moonen8, Andrew Sila9, Keith Shepherd9, Basile Bazirake Mujinya10, Eric Van Ranst11, Geert Baert3, Sebastian Doetterl1,5, and Johan Six1 Laura Summerauer et al.
  • 1Department of Environmental Systems Science, ETH Zurich, Switzerland
  • 2NIR Data Analytics, BUCHI, Labortechnik AG, Flawil, Switzerland
  • 3Department of Green Chemistry and Technology, Ghent University, Ghent, Belgium
  • 4Department of Environment, Ghent University, Ghent, Belgium
  • 5Institute of Geography, University of Augsburg, Germany
  • 6Earth and Life Institute, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
  • 7International Institute of Tropical Agriculture, Nairobi, Kenya and Bukavu, Democratic Republic of Congo
  • 8Department of Earth and Environmental Sciences, KU Leuven, Leuven, Belgium
  • 9World Agroforestry Centre, Nairobi, Kenya
  • 10Department of General Agricultural Sciences, University of Lubumbashi, Lubumbashi, Democratic Republic of Congo
  • 11Department of Geology, Ghent University, Gent, Belgium

Abstract. Information on soil properties is crucial for soil preservation, improving food security, and the provision of ecosystem services. Especially, for the African continent, spatially explicit information on soils and their ability to sustain these services is still scarce. To address data gaps, infrared spectroscopy has gained great success as a cost-effective solution to quantify soil properties in recent decades. Here, we present a mid-infrared soil spectral library (SSL) for central Africa (CSSL) that can predict key soil properties allowing for future soil estimates with a minimal need for expensive and time-consuming wet chemistry. Currently, our CSSL contains over 1,800 soils from ten distinct geo-climatic regions throughout the Congo Basin and wider African Great Lakes region. We selected six hold-out core regions from our SSL, augmented them with the continental AfSIS SSL, which does not cover central African soils. We present three levels of geographical extrapolation, deploying Memory-based learning (MBL) to accurately predict carbon (TC) and nitrogen (TN) contents in the selected regions. The Root Mean Square Error of the predictions (RMSEpred) values were between 0.38–0.86 % and 0.04–0.17 % for TC and TN, respectively, when using the AfSIS SSL only to predict the six regions. Prediction accuracy could be improved for four out of six regions when adding central African soils to the AfSIS SSL. This reduction of extrapolation resulted in RMSEpred ranges of 0.41–0.89 % for TC and 0.03–0.12 % for TN. In general, MBL leveraged spectral similarity and thereby predicted the soils in each of the six regions accurately; the effect of avoiding geographical extrapolation and forcing regional samples in the local neighborhood (MBL-spiking) was small. We conclude that our CSSL adds valuable soil diversity that can improve predictions for the regions compared to using the continental scale AfSIS SSL alone; thus, analyses of other soils in central Africa will be able to profit from a more diverse spectral feature space. Given these promising results, the library comprises an important tool to facilitate economical soil analyses and predict soil properties in an understudied yet critical region of Africa. Our SSL is openly available for application and for enlargement with more spectral and reference data to further improve soil diagnostic accuracy and cost-effectiveness.

Laura Summerauer et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on soil-2020-99', Anonymous Referee #1, 02 Feb 2021
    • AC1: 'Reply on RC1', Laura Summerauer, 20 Mar 2021
  • RC2: 'Comment on soil-2020-99', Anonymous Referee #2, 04 Feb 2021
    • AC2: 'Reply on RC2', Laura Summerauer, 20 Mar 2021

Laura Summerauer et al.

Data sets

Codes and data for manuscript submission 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 https://doi.org/10.5281/zenodo.4351254

Model code and software

Codes and data for manuscript submission 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 https://doi.org/10.5281/zenodo.4351254

Laura Summerauer et al.

Viewed

Total article views: 509 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
388 109 12 509 5 4
  • HTML: 388
  • PDF: 109
  • XML: 12
  • Total: 509
  • BibTeX: 5
  • EndNote: 4
Views and downloads (calculated since 08 Jan 2021)
Cumulative views and downloads (calculated since 08 Jan 2021)

Viewed (geographical distribution)

Total article views: 444 (including HTML, PDF, and XML) Thereof 443 with geography defined and 1 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 19 Apr 2021
Download
Short summary
We present a soil mid-infrared library with over 1,800 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 highly accurate predictions and highlight the value that our library contributes to existing data. Our library is openly available for public use and for expansion.