Geostatistical determination of soil noise and soil phosphorus spatial variability
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Date
2017-09-28
Open Access Location
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Publisher
Elsevier Masson
Rights
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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 theWairarapa, 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
Description
Keywords
soil phosphorus, geo-statistics, spatial variability, Olsen P, statistical noise
Citation
AGRICULTURE-BASEL, 2017, 7 (10)