Ecological Indicators (Nov 2021)
Large-extent spatial heterogeneity of soil bioavailable micronutrients and the relative roles of environmental indicators on them within maize fields
Abstract
Soil bioavailable micronutrients have a significant impact on soil fertility, soil quality, maize production, and even environmental quality. However, minimal research has been conducted to characterize the spatial patterns of soil bioavailable micronutrients at the large spatial extent within the maize filed and to reveal the relative control of environmental factors on them at the global and local scale. In the study, topsoil samples (0–20 cm of plough depth, totaling 4,448) were collected from maize field to determine the spatial heterogeneity of soil bioavailable micronutrients including iron (Fe), manganese (Mn), copper (Cu), zinc (Zn) and boron (B), and to assess the relative effect of environmental indicators on them. Based on the 2-dimensional empirical mode decomposition (2D-EMD), the spatial pattern of micronutrients at the residues and their relationships with influencing indicators were explored. The results demonstrated that the distribution of Fe and Mn had the dominant longitudinal zonality, the distribution of Cu and Zn had the prominent vertical zonality, and the spatial characteristic of B did not exhibit any particular pattern at the large spatial extent. Based on the stepwise multivariate linear regression with the residues, environmental indicators had less global effect on the distribution of soil bioavailable micronutrients. However, based on the combination of geographically weighted regression (GWR) and 2D-EMD, environmental indicators had a good and significant interpretation on the dynamics of micronutrients bioavailability, which ranged from 53% to 75% in the study area. Specifically, the impact of environmental indicators on Fe, Mn, and Zn were greater, and the impact of human activities on B was greater. Our findings indicated that the local and scale effects on the soil available micronutrients should be integrated into the prediction methods, and the combined method of GWR and 2D-EMD would be a good choice for the high-quality digital mapping at large spatial extent.
