USING PROXIMAL HYPERSPECTRAL SENSING TO MEASURE SOIL OLSEN P AND pH

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dc.contributor.authorGrafton M
dc.contributor.authorKaul T
dc.contributor.authorPalmer A
dc.contributor.authorBishop P
dc.contributor.authorWhite M
dc.contributor.editorCurrie, L
dc.contributor.editorChristensen, C
dc.coverage.spatialMassey University, Palmerston North
dc.date.issued12/04/2019
dc.description.abstractThis paper reports on work undertaken to use a large data set of hyperspectral data measured on dry soil samples to obtain regression analysis which allows predictions of pH and Olsen P to be obtained from an independent data set. The large data set was obtained from 3,190 soil samples taken from the Ravensdown Primary Growth Partnership to a depth of 7.5cm. The spectra were measured using an Analytical Spectral Device which recorded 2,150 wavebands of 1nm resolution between 350nm and 2,500nm. Values for Olsen P and pH were provided from chemical analysis by Analytical Research Laboratories. The spectra were regressed using “R” statistical software which has the power to handle the data and report the wavebands with the most significance for the model. The data set for the prediction came from a stratified nested, grid soil sampling exercise which was used to find Olsen P stability at varying depths. This set had 400 samples from each of two data sets from different areas on Patitapu Station using a grid sample protocol. The 100 most significant wavebands from the PGP data set were used to regress the Patitapu data which were combined. These were regressed using “R” (Version 3.41, The R Foundation) and Statdata (Palisade, New York), which produced the same result. The partial least square regression of pH was very significant and was predicted well. Olsen P had a very significant correlation which was quite noisy, correlating the log10 of Olsen P was also undertaken and it would appear something is being measured that is associated with Olsen P. This work shows that it is possible to measure soil nutrient by proximal hyperspectral analysis which is transferable to an independent data set.
dc.identifier.citationOccasional Report No. 32. Fertilizer and Lime Research Centre, Massey University, Palmerston North, 2019, 32 (32)
dc.identifier.elements-id422646
dc.identifier.harvestedMassey_Dark
dc.identifier.issn2230-3944
dc.identifier.urihttps://hdl.handle.net/10179/14584
dc.relation.isPartOfOccasional Report No. 32. Fertilizer and Lime Research Centre, Massey University, Palmerston North
dc.relation.urihttp://flrc.massey.ac.nz/workshops/19/Manuscripts/Paper_Grafton_2019.pdf
dc.rightsThe Author(s); Fertilizer and Lime Research Centre; Massey University
dc.sourceNutrient loss mitigations for compliance in agriculture
dc.subjectsoil testing, partial least squares regression, hyperspectral sensing, big data
dc.titleUSING PROXIMAL HYPERSPECTRAL SENSING TO MEASURE SOIL OLSEN P AND pH
dc.typeconference
pubs.confidentialFALSE
pubs.finish-date14/02/2019
pubs.issue32
pubs.notesNot known
pubs.organisational-group/Massey University
pubs.organisational-group/Massey University/College of Sciences
pubs.organisational-group/Massey University/College of Sciences/School of Agriculture & Environment
pubs.start-date12/02/2019
pubs.volume32
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