Publication:
Fusion of gamma-rays and portable x-ray fluorescence spectral data to measure extractable potassium in soils

dc.contributor.authorNawar, Said
dc.contributor.authorRichard, Florence
dc.contributor.authorKassim, Anuar M.
dc.contributor.authorMouazen, Abdul M.
dc.contributor.buuauthorTekin, Yucel
dc.contributor.buuauthorTEKİN, YÜCEL
dc.contributor.departmentTeknik Bilimler Meslek Yüksekokulu
dc.contributor.researcheridGGM-1129-2022
dc.date.accessioned2024-11-08T13:29:41Z
dc.date.available2024-11-08T13:29:41Z
dc.date.issued2022-07-06
dc.description
dc.description.abstractThe detection and mapping of plant-extractable potassium (K-a) using proximal soil sensing and data fusion (DF) techniques are essential to optimise K2O fertiliser application, improve crop yield, and minimise environmental and financial costs. This work evaluates the potential of combined use of portable gamma ray and x-ray fluorescence spectroscopy for in field detection and mapping of K-a. After subjected to various pre-processing methods, spectral data were split into calibration (75%) and validation (25%) sets, and single sensor and DF models were developed using partial least squares regression (PLSR). Maps of Ka were used to present spatial variability of potassium across an 8.4 ha Voor de Hoeves target field, in Flanders, Belgium. Results showed that the gamma-ray PLSR model using wet soils had greater predictive ability with coefficient of determination (R-2) = 0.71, ratio of performance deviation (RPD) = 1.89, root mean square error (RMSE) = 31.7 mg kg(-1), and ratio of performance to interquartile range (RPIQ) = 2.36 than the corresponding wet-XRF PLSR model (R-2 = 0.61, RPD = 1.64, RMSE = 48.8 mg kg(-1) and RPIQ = 1.84). The DF PLSR model on wet soils, resulted in a more accurate Ka predictive ability (R-2 = 0.75, RPD = 2.03, RMSE = 31.3 mg kg(-1) and RPIQ = 2.79), compared to the individual gamma ray or XRF sensors in wet soils. The best accuracy was obtained with XRF spectrometer using dry samples (R-2 = 0.77, RPD = 2.14, RMSE = 26.5 mg kg(-1) and RPIQ = 3.39). All Ka prediction maps showed spatial similarity to the corresponding measured maps in the target field. In conclusion, since DF increased the Ka prediction accuracy compared to the single sensor models using wet soils, it is recommended to be adopted in future studies as a potential solution for Ka measurement, mapping, and ultimately for site-specific K2O fertilisation management. The XRF analysis of dry spectra is recommended as the best method for accurate measurement of K-a.
dc.description.sponsorshipFWO G0F9216N
dc.identifier.doi10.1016/j.still.2022.105472
dc.identifier.issn0167-1987
dc.identifier.urihttps://doi.org/10.1016/j.still.2022.105472
dc.identifier.urihttps://hdl.handle.net/11452/47666
dc.identifier.volume223
dc.identifier.wos000883425700002
dc.indexed.wosWOS.SCI
dc.language.isoen
dc.publisherElsevier
dc.relation.journalSoil & Tillage Research
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectPlant-available potassium
dc.subjectNear-infrared reflectance
dc.subjectIn-situ
dc.subjectQuality assessment
dc.subjectSpectroscopy
dc.subjectSpectrometry
dc.subjectRegression
dc.subjectCarbon
dc.subjectQuantification
dc.subjectFertilization
dc.subjectProximal soil sensing
dc.subjectData fusion
dc.subjectSoil potassium
dc.subjectPartial least squares regression
dc.subjectScience & technology
dc.subjectLife sciences & biomedicine
dc.subjectSoil science
dc.subjectAgriculture
dc.titleFusion of gamma-rays and portable x-ray fluorescence spectral data to measure extractable potassium in soils
dc.typeArticle
dspace.entity.typePublication
local.contributor.departmentTeknik Bilimler Meslek Yüksekokulu
relation.isAuthorOfPublication2e651285-91d3-4408-ab1a-c0292101c026
relation.isAuthorOfPublication.latestForDiscovery2e651285-91d3-4408-ab1a-c0292101c026

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