06 Jul 2022
06 Jul 2022

Mapping soil micronutrient concentration at national-scale: an illustration of a decision process framework

Christopher Chagumaira1,2,3,4, Joseph G. Chimungu4, Patson C. Nalivata4, Martin R. Broadley2,3, Madlene Nussbaum5, Alice E. Milne3, and R. Murray Lark1,2 Christopher Chagumaira et al.
  • 1Future Food Beacon of Excellence, University of Nottingham, Sutton Bonington Campus, Loughborough, LE12 5RD, United Kingdom
  • 2School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, LE12 5RD, United Kingdom
  • 3Rothamsted Research, Harpenden, AL5 2JQ, United Kingdom
  • 4Lilongwe University of Agriculture and Natural Resources, Bunda College, P.O. Box 219, Lilongwe, Malawi
  • 5Bern University of Applied Sciences (BFH), School of Agricultural, Forest and Food Sciences (HAFL), Switzerland

Abstract. Mineral micronutrient deficiencies (MND), prevalent in many countries, are linked to soil type. Stakeholders in Malawi, with different information needs, require spatial information about soil micronutrients in order to design efficient interventions. These stakeholders require reliable evidence for them to act, in most cases the outcome of their decisions involves financial costs and implications for farmers' livelihoods, food security and public health. They would not want to intervene where it is unnecessary to do so or not fail to intervene where it is needed. Information about the concentration of micronutrient in soil is needed by stakeholders for decision-making. In practice this information is uncertain. Geostatistical methods and those based on algorithmically driven machine learning (ML) generate predictions of soil properties with measures of uncertainty, these measures are rarely linked to the decision-making process for which spatial information is required and it may not be clear to the stakeholders how to make use of the uncertainty information in decision-making. In this study we start from an analysis of how stakeholders, in Malawi, may use uncertain spatial information to support decisions, providing the decisions about the acceptable quality of the information and how it should be collected. We then use this analysis as a framework to compare options for spatial prediction of micronutrients in soil by ML (e.g. random forest) and geostatistical methods (e.g. linear mixed models).

Christopher Chagumaira 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 egusphere-2022-583', Anonymous Referee #1, 29 Jul 2022
    • AC1: 'Reply on RC1', Christopher Chagumaira, 29 Jul 2022
  • RC2: 'Comment on egusphere-2022-583', Anonymous Referee #2, 19 Sep 2022
    • AC2: 'Reply on RC2', Christopher Chagumaira, 27 Oct 2022

Christopher Chagumaira et al.

Christopher Chagumaira et al.


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Short summary
Our study examines different quantitative methods to predict concentrations of micronutrients in the soil from field samples. However, we emphasize the concerns of stakeholders, who use such information to make decisions, in this case in relation to the study and management of micronutrient deficiency risk in the human population. We propose a framework to think about these concerns then compare common approaches for digital soil mapping within this framework.