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On exploring bivariate and trivariate maps as visualization tools for spatial associations in digital soil mapping: A focus on soil properties

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Abstract

The benefits of digital soil maps cannot be overemphasised. For many years, researchers have mapped different soil classes, properties and processes while identifying the spatial associations between soil properties using side-by-side visualization maps. Although this is acceptable, it may be difficult to identify complex spatial associations between the mapped soil properties. For some, the task may be challenging owing to multiple times of side-by-side placing of the maps and the possible application of none user-friendly colour palettes and or schemes. Innovative tools are proposed for visualizing and identifying spatial associations between digital soil maps (raster layers) using bivariate and trivariate maps. These tools are applied in a case study to identify the spatial interactions between pH and selected macro-nutrients [nitrogen (N) and potassium (K)] of similar locality (Czech Republic), resolution and scale. This study further gives a brief overview of the applicability of bivariate and trivariate maps following the digital soil mapping process. Results show that bivariate and trivariate maps are effective for visualizing complex associations between pH and macro-nutrients. However, precautionary measures should be taken while applying bivariate and trivariate maps to ensure they are self-explanatory and that the legend colour schemes applied are user-friendly. Also, the variables mapped should be related. In this case, pH is a key soil quality indicator that affects macro-nutrient availability in soils.

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Data availability

The LUCAS soil data used were obtained with written permission from the European Soil Data Centre (ESDAC).

Code availability

The R codes used in this study are openly available from the corresponding author.

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Acknowledgements

The European Soil Data Centre (ESDAC), https://esdac.jrc.ec.europa.eu/, European Commission, Joint Research Centre is greatly appreciated for the soil data used in this study.

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Authors and Affiliations

  1. Department of Geosciences, Chair of Soil Science and Geomorphology, University of Tübingen, Rümelinstr. 19-23, Tübingen, Germany

    Ndiye M. Kebonye

  2. DFG Cluster of Excellence “Machine Learning: New Perspectives for Science”, University of Tübingen, AI Research Building, Maria-von-Linden-Str. 6, 72076, Tübingen, Germany

    Ndiye M. Kebonye

  3. Department of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Kamýcká 129, 165 00, Prague-Suchdol, Czech Republic

    Prince C. Agyeman

  4. Department of Animal Science and Production, Faculty of Animal and Veterinary Sciences, Botswana University of Agriculture and Natural Resources, Private Bag 0027, Gaborone, Botswana

    Zibanani Seletlo

  5. Department of Earth and Environmental Science, Botswana International University of Science and Technology, Private Bag 16, Palapye, Botswana

    Peter N. Eze

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Kebonye, N.M., Agyeman, P.C., Seletlo, Z. et al. On exploring bivariate and trivariate maps as visualization tools for spatial associations in digital soil mapping: A focus on soil properties. Precision Agric 24, 511–532 (2023). https://doi.org/10.1007/s11119-022-09955-7

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