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

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).