Micronutrients prediction via pXRF spectrometry in Brazil: Influence of weathering degree

• Published in: Geoderma Regional Vol. 27; p. e00431
• Main Authors: Andrade, Renata, Silva, Sérgio Henrique Godinho, Weindorf, David C., Chakraborty, Somsubhra, Faria, Wilson Missina, Guilherme, Luiz Roberto Guimarães, Curi, Nilton
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2021
• Subjects: Brazil; data collection; Entisols; fluorescence; Micronutrients; nutrient availability; Oxisols; Plant nutrition; precision agriculture; prediction; Prediction models; rapid methods; regression analysis; Soil fertility; Soils of the tropics; support vector machines; toxicity; Weathering-leaching; X-radiation; Brazil; Micronutrients; Prediction models; Soil fertility; Plant nutrition; Soils of the tropics; Weathering-leaching
• ISSN: 2352-0094; 2352-0094
• DOI: 10.1016/j.geodrs.2021.e00431

Management of micronutrient levels in soils must be done carefully to avoid their deficiency or toxicity to plants. The laboratory determination of micronutrient contents is time-consuming, expensive and generates chemical wastes, making it difficult for soil surveys required in precision agriculture, especially in tropical countries. While proximal sensors like portable X-ray fluorescence (pXRF) spectrometry have been successfully used to predict contents of soil available macronutrient, little effort has focused on micronutrients, especially involving a large dataset, soils weathering degree and a practical application of the predictions. This study aimed to use pXRF data for the prediction of available micronutrients in 1514 samples from variable soil classes (from Entisols to Oxisols) from seven Brazilian states using machine learning algorithms and to assess the influence of soil weathering degree on such prediction models. The soil samples were collected from both surface (A) and subsurface (B or C) horizons of various soil classes under several land uses, and with varying parent materials. Available B, Cu, Fe, Mn, and Zn were predicted via stepwise multiple linear regression (SMLR), support vector machine (SVM), extreme gradient boosting (XGB), and random forest (RF) algorithms and subsequently validated. The best prediction models were classified according to micronutrient availability classes (categorical validation). Adequate predictions were achieved for Cu: R2 = 0.80; RPD = 2.28; Mn: 0.68; 1.76; and Zn: 0.68; 1.70. Predictions of B, Cu, Fe, Mn, and Zn availability classes yielded overall accuracy of 0.90, 0.65, 0.67, 0.73, and 0.53, respectively. Summarily, pXRF data in conjunction with prediction models can be an effective and rapid method to determine available Cu, Mn, and Zn. Soil weathering degree must be considered on such predictions as they strongly influence model accuracy. •PXRF and machine learning algorithms predicted content of micronutrients.•PXRF elemental data were used to calculate geochemical indices.•Available Cu, Mn and Zn were rapidly predicted via pXRF data.•Soil weathering degree strongly influences micronutrients prediction accuracy.•Random forest generated the most accurate micronutrient prediction models.