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Open AccessArticle

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

1
School of Agriculture and Environment, Massey University, Private Bag, Palmerston North 4442, New Zealand
2
Ravensdown Fertiliser Ltd., P.O. Box 1049, Christchurch 8042, New Zealand
*
Author to whom correspondence should be addressed.
Agriculture 2019, 9(3), 55; https://doi.org/10.3390/agriculture9030055
Received: 27 January 2019 / Revised: 8 March 2019 / Accepted: 13 March 2019 / Published: 15 March 2019
(This article belongs to the Special Issue Soil Fertility)
This work examines two large data sets to demonstrate that hyperspectral proximal devices may be able to measure soil nutrient. One data set has 3189 soil samples from four hill country pastoral farms and the second data set has 883 soil samples taken from a stratified nested grid survey. These were regressed with spectra from a proximal hyperspectral device measured on the same samples. This aim was to obtain wavelengths, which may be proxy indicators for measurements of soil nutrients. Olsen P and pH were regressed with 2150 wave bands between 350 nm and 2500 nm to find wavebands, which were significant indicators. The 100 most significant wavebands for each proxy were used to regress both data sets. The regression equations from the smaller data set were used to predict the values of pH and Olsen P to validate the larger data set. The predictions from the equations from the smaller data set were as good as the regression analyses from the large data set when applied to it. This may mean that, in the future, hyperspectral analysis may be a proxy to soil chemical analysis; or increase the intensity of soil testing by finding markers of fertility cheaply in the field. View Full-Text
Keywords: regression analysis; soil fertility; correlation; statistical analysis; soil testing regression analysis; soil fertility; correlation; statistical analysis; soil testing
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Grafton, M.; Kaul, T.; Palmer, A.; Bishop, P.; White, M. Technical Note: Regression Analysis of Proximal Hyperspectral Data to Predict Soil pH and Olsen P. Agriculture 2019, 9, 55.

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