Guiding soil sampling strategies using classical and spatial statistics: A review

Abstract

Soil analysis is a key practice to increase the efficiency of nutrient management in agriculture. Since the early 20th century, increasingly sophisticated methods have been developed to describe and manipulate the inherent spatial variability in soil chemical properties within the realms of classical and spatial statistics. In this paper, we reviewed design‐based (classical) and model‐based (geostatistical) sampling to suggest field‐scale sampling strategies consistent with common agronomic management goals in annual crop production systems. To assess the relevance of common sampling methods in relation to practice, current extension recommendations across the United States were compared with results from peer‐reviewed literature. Despite decades of research, specific recommendations for sample sizes, sampling depths, numbers of soil cores, and layouts were highly variable for classical and geostatistical approaches. Mobile nutrients, such as NO3, are frequently lacking in spatial structure and rarely are recommended for site‐specific management. Nonmobile nutrients, such as P, are more spatially dependent and exhibit nested spatial structures that are inconsistent across fields. For these reasons, we recommend design‐based sampling in most situations for simplicity, cost, and objectivity. The common design‐based sampling protocol prescribes collection of individual cores in a zig‐zag pattern that are combined to produce a composite sample. This protocol should be amended because it is not sufficiently randomized and is inadequate for log‐normally distributed variables. To facilitate site‐specific management, we recommend structured approaches for delineating management zones or strata and for researchers to systematically enumerate confounding variables while explicitly defining the scope of inference for future soil sampling studies.

Complete Issue

“…Subsequent work by Franzen and Peck (1993) showed substantial field spatial variability for soil test P and K and suggested sampling intensities of one sample per acre would be required to characterize spatial variation in most fields in Illinois. However, spatial dependencies of soil test P are inconsistent across fields (Lawrence et al, 2020). Those with high soil test P values tend to have higher spatial variability (Mallarino, 1996;Clodfelter et al, 2005) associated with fields with a past applications of livestock and poultry manures.…”

Biochar and humus sediment mixture attenuates crude oil-derived PAHs in a simulated tropical ultisol

“…Soil sample without crude oil contamination was also analysed to serve as a control. All experiments were run in triplicates but analysed as composites following standard guidelines applicable to quantification of PAHs in soils [45][46][47][48].…”

“…The recoveries, limits of detection (LOD), and quantitation (LOQ) have been previously reported [37]. All experiments were run in triplicates but analysed as composites following standard guidelines applicable to quantification of PAHs in soils [45][46][47][48].…”

Sampling the spatial variability of grassland soil properties

“…El objetivo de esta revisión de literatura fue conocer el estado actual del diseño y análisis de muestreo de la variabilidad espacial de propiedades de suelos de pastizales y el comportamiento de la variación de estas. Esta revisión se basa en el hecho de que las propiedades del suelo varían principalmente por el manejo agronómico, clima, biota y tipo de suelo [19]. La medición de propiedades del suelo con predicciones precisas permite tomar decisiones fiables para su manejo y conservación.…”