Geostatistical Determination of Soil Noise and Soil Phosphorus Spatial Variability

Abstract

This research studies the effect of stratifying soil samples to try and find a suitable depth to establish a geospatial relationship for a practical soil sampling grid in New Zealand hill country. Cores were collected from 200 predetermined sites in grids at two trial sites at "Patitapu" hill country farm in the Wairarapa, New Zealand. Trial 1 was a 200 m × 100 m grid located in a gently undulating paddock. Trial 2 was a 220 m × 80 m grid located on a moderately sloped paddock. Each grid had cores taken at intervals of 5 m, 10 m, or 20 m. Core sites were mapped out prior to going into the field; these points were found using a Leica Geo Systems GS15 (real time kinematic GPS) and marked with pigtail pegs and spray-paint on the ground. Cores were taken using a 50 mm-diameter soil core sampler. Cores were cut into three sections according to depth: A-0-30 mm, B-30-75 mm, and C-75-150 mm. Olsen P lab results were obtained for half of the total 1400 samples due to financial constraints. The results indicate that there was a significant decrease in variability from Section A to Section B for both trials. Section B and C for Trial 1 had similar variability, whereas there was another significant drop in variability from Section B to C in Trial 2. Measuring samples below the top 3 cm appeared to effectively reduce noise when sampled from 3 to 15 cm. However, measuring from 7.5 cm to 15 cm on the slope in Trial 2 reduced variability so much that all results were almost identical, which may mean that there is no measurable representation of plant available P. The reduction in noise by removing the top 3 cm of soil samples is significant for improving current soil nutrient testing methods by allowing better geospatial predictions for whole paddock soil nutrient variability mapping.

Technical Note: Regression Analysis of Proximal Hyperspectral Data to Predict Soil pH and Olsen P

“…References [7,14]. The pH measurements were taken using a digital probe in 10 g samples in 25 mL of distilled water.…”

“…A PLSR assumes the data to be normally distributed, the mean and standard deviations of the data sets are presented in Table 1. The shape of the distributions are shown in Figure 3a The second data set was from two stratified nested grid samples from different fields on the same farm used to look at geospatial variance with soil depth [6,7]. The samples were tested for Olsen P using the chemical extraction methods and photo-spectrometer was used for the geospatial analysis [6,7].…”

“…The shape of the distributions are shown in Figure 3a The second data set was from two stratified nested grid samples from different fields on the same farm used to look at geospatial variance with soil depth [6,7]. The samples were tested for Olsen P using the chemical extraction methods and photo-spectrometer was used for the geospatial analysis [6,7]. Those that had not already been measured were completed using the method employed in References [7,14].…”

“…The samples were tested for Olsen P using the chemical extraction methods and photo-spectrometer was used for the geospatial analysis [6,7]. Those that had not already been measured were completed using the method employed in References [7,14]. The pH measurements were taken using a digital probe in 10 g samples in 25 mL of distilled water.…”

“…Combining the samples eliminates the variation, especially for Olsen P. This test is the most important, in hill country fertilizer recommendations. The variation within transects is much greater than the 10 m sampling distance, therefore, there is no geospatial interpolation that is meaningful between sampling points [5][6][7]. Hyperspectral analysis has the potential to be a useful tool for proxy measurements of soil nutrients that could increase sampling density and reduce cost.…”