Developing regional soil micronutrient management strategies through ensemble learning based digital soil mapping

Abstract

Mapping of soil micronutrient variability is critical for improving agronomic biofortification. This study used 1778 surface soil samples collected from four agro-climatic regions of the Indo-Gangetic Plain of India to produce digital soil maps of available Zn, Cu, Fe, and Mn using 52 environmental covariates at a resolution of 150 m. The micronutrient prediction accuracy was compared for 14 machine learning approaches and their ensemble model. The hybrid ensemble model outperformed all 14 base learners and was subsequently used for producing micronutrient maps. All four micronutrients exhibited sufficient spatial variability. Both available Zn and Fe maps exhibited lower prediction uncertainties. Moreover, the inter-relationship between micronutrient concentration in soil and rice grain was explored to understand the Zn and Fe biofortification potential. The linear regression models revealed moderate agreement between soil available and grain micronutrient concentrations, with R2 values of 0.52–0.63 for Zn and Fe, respectively. The developed models were used to predict grain Zn and Fe content from their respective soil concentrations, indicating the potential of the tested approach to identify specific pockets where rice varieties with biofortification potential can be planted. In the future, the digital soil mapping approach tested herein can help policymakers with regional decision-making, encouraging nutrient-based subsidy and investment opportunities and sustainable micronutrient recommendations toward micronutrient-enriched food. Further research is needed to develop a digital soil intelligence platform using micronutrient DSM products in resource-poor countries.