Prediction of soil nutrient content via pXRF spectrometry and its spatial variation in a highly variable tropical area

Marcelo Henrique Procópio Pelegrino, Sérgio Henrique Godinho Silva, Álvaro José Gomes de Faria, Marcelo Mancini, Anita Fernanda dos Santos Teixeira, Somsubhra Chakraborty, David C. Weindorf, Luiz Roberto Guimarães Guilherme, Nilton Curi

Research output: Contribution to journalArticlepeer-review

Abstract

Precision agriculture provides detailed information on the spatial variability of soil properties, including nutrient content, allowing for local-specific decision making. Recently, proximal sensors have been used to accurately predict soil properties, contributing to reduce costs of conventional wet-chemistry analyses for soil characterization. However, further investigations on this approach in tropical soils are needed. This work aimed to use portable X-ray fluorescence (pXRF) spectrometry data for prediction of exchangeable Ca2+ and available K+ and P contents in soils of a highly heterogeneous tropical area and evaluating its practical applications. 90 samples from soil A horizon were collected in a regular grid design, and analyzed through pXRF and for nutrient contents. Such data were split into modeling (63 samples) and validation (27 samples) datasets. Linear regression (LR), polynomial regression (PR), power regression (PwR) and stepwise multiple linear regression (SMLR) were tested for predictions. The models were used to spatially represent nutrient contents across the area and to compare the practical effects of varying regression models. PXRF elemental data provided reliable predictions of exchangeable Ca2+ and available P via SMLR and PwR, respectively, reaching root mean square errors (RMSE) of 5.66 cmolc dm−3 for Ca2+ and 9.13 mg dm−3 for P. Available K+ predictions were not successful. Different models yielded contrasting maps showing the classes of soil fertility across the area, drawing attention to the importance of testing multiple prediction models and using the best one for precision agriculture. Fusion of data from different proximal sensors may enhance available K+ predictions.

Original languageEnglish
JournalPrecision Agriculture
DOIs
StateAccepted/In press - 2021

Keywords

  • Digital soil mapping
  • Proximal sensor
  • Random forest
  • Regression analysis
  • Soil fertility spatial prediction

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