Spatial variation of nitrate–N and related soil properties at the plot-scale

Abstract

Neglecting the spatial variation in soil nutrient status may result in unused yield potential and in environmental damage. Site-specific management has been suggested to reduce inappropriate fertilization that can adversely affect soil, ground and surface water. Decision criteria for determining variable-rate nitrogen fertilization are, however, lacking. This paper analyses the spatial variation of nitrate nitrogen (NO 3–N) and soil properties related to the N cycle at the plot-scale. Three 50×50 m plots were sampled in nested sampling designs of varying complexities. Classical statistics revealed a characteristic ranking in the variability of soil properties. Geostatistical analysis of the NO 3–N data from two plots showed that the small-scale variation found in one small subgrid was not typical for the small-scale variation in the entire plot, indicating bias in the sampling design. A trend component was found in the NO 3–N data and, consequently, the minimal requirement for the regionalized variable theory was not fulfilled. Problems due to design were overcome with a more complex nested sampling at the third plot. However, the spherical model fitted to the NO 3–N data of the first year explained only 21% of the total variance, whereas a pure nugget effect was observed in the second year. The water content data also showed a low structural variance, which was different in the two years. In contrast, two thirds of the variance of total carbon (C t) and total nitrogen (N t) could be explained by the fitted models. Seasonal variations, such as varying duration of snow cover, and extrinsic management effects, such as growing of a cover crop, may have contributed to the observed differences in variability between the years. Due to the low proportion of structural variance and the observation that spatial distribution was not stable with time, geostatistical analysis of NO 3–N and water contents data added only little information to classical statistical analysis. However, geostatistical analysis of total C and N contents provided a useful means to calculate spatial distribution patterns of these properties.