Using Moran's I and geostatistics to identify spatial patterns of soil nutrients in two different long‐term phosphorus‐application plots

Abstract

AbstractThe spatial variation of soil nutrients especially the soil test phosphorus (STP) in grassland soils is becoming important because of the use of soil‐nutrients information as a basis for policies such as the recently EU‐introduced Nitrates Directive. Up to now, the small‐scale spatial variation of soil nutrients in grassland has not been studied. The main aim of this study was to investigate the spatial patterns of soil nutrients in two grazed grassland plots with a long‐term (38 y) P‐application experiment, in order to better understand the spatial variation of soil nutrients and the correlation among soil nutrients in grasslands. Two small areas (one from a high‐P background and the other from a medium‐P background) were selected. Soil samples (304 per study area) were collected based on a 1 m × 1 m grid system. The samples were analyzed for STP, Mg, K, pH, and lime requirement (LR). The results were analyzed using conventional statistics, Moran's I, geostatistics, and a GIS.Based on the global Moran's I values, significant positive spatial autocorrelations were found for STP, Mg, pH, and LR in both study areas. Spatial clusters and spatial outliers were detected using the local Moran's I index. Clear linear‐shaped high‐high or low‐low value clusters of the studied variables except K were observed in the study areas due to long‐term usage of machine spreader or other agricultural‐management methods in the past. The corresponding linear patterns were further found in the spatial‐distribution maps. Small spatial patches were found for soil K revealing that it had a random spatial distribution related to the relatively uniform K fertilizer in the study areas. The spatial clusters revealed by local Moran's I were in line with the spatial patterns in the distribution maps.