Assessing the effect of soil to water ratios and sampling strategies on the prediction of EC and pH using pXRF and Vis-NIR spectra

GÖZÜKARA G., ALTUNBAŞ S., Dengiz O., Adak A.

Computers and Electronics in Agriculture, vol.203, 2022 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 203
  • Publication Date: 2022
  • Doi Number: 10.1016/j.compag.2022.107459
  • Journal Name: Computers and Electronics in Agriculture
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, BIOSIS, CAB Abstracts, Communication Abstracts, Computer & Applied Sciences, Environment Index, Food Science & Technology Abstracts, INSPEC, Metadex, Veterinary Science Database, Civil Engineering Abstracts
  • Keywords: Alfisol, Digital soil mapping, Machine learning algorithms, Soil spectra
  • Akdeniz University Affiliated: Yes


© 2022 Elsevier B.V.Soil electrical conductivity (EC) and pH play a critical role in managing agricultural productivity. We investigated the effect of soil to water ratios (1:1, 1:2.5, 1:5) and sampling strategies (surface, profile wall, and surface + profile wall) on prediction accuracy using individual and combined visible near infrared (Vis-NIR) and portable X-ray fluorescence (pXRF) spectra with machine learning algorithms for EC and pH. In total, 200 soil samples were collected from the soil surface (100 soil samples) and profile wall (100 soil samples) in pasture lands in Eskisehir, Türkiye. The soil samples were analyzed by considering soil to water ratios (1:1, 1:2.5, 1:5) for EC and pH and scanned by Vis-NIR (350–2500 nm) and pXRF (0–45 keV). In total 54 different predictor models were tested to achieve the highest prediction accuracy for both EC and pH. The seven machine learning regressions (elastic net, k-nearest neighbors, lasso, partial least squares, random forest, ridge, and support vector machine-linear) were applied in modeling with calibration (70 % soil samples) and validation (30 % soil samples) datasets for each model. The results suggested that the EC1:2.5 and EC1:5 ratios had relatively higher prediction accuracy (r = 0.95, R2 = 0.93, RMSE = 0.58, MAE = 0.46, RPD = 3.57, and RPIQ = 5.33) using Vis-NIR spectra with partial least squares and support vector machine-linear models in profile wall compared to other sampling strategies and EC1:1 ratio. The pH1:2.5 ratio had relatively higher prediction accuracy (r = 0.90, R2 = 0.81, RMSE = 0.07, MAE = 0.06, RPD = 2.49, and RPIQ = 3.71) using Vis-NIR spectra with random forest model in profile wall compared to other sampling strategies and pH1:1 and pH1:5 ratios. In addition, combined Vis-NIR and pXRF spectra had no improvement in prediction accuracy. Finally, it can be concluded that the prediction accuracy is affected by soil to water ratios and sampling strategies. Individual Vis-NIR spectra can reach the highest prediction accuracy for EC and pH compared to combined pXRF and Vis-NIR spectra.