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Spectral data modelling for tropical soil fertility analysis: association of vis-NIR and XRF techniques for the modernization of the traditional methods of analysis

Grant number: 20/16670-9
Support Opportunities:Scholarships in Brazil - Post-Doctoral
Effective date (Start): August 01, 2021
Effective date (End): August 01, 2025
Field of knowledge:Agronomical Sciences - Agronomy - Soil Science
Principal Investigator:José Lavres Junior
Grantee:Tiago Rodrigues Tavares
Host Institution: Centro de Energia Nuclear na Agricultura (CENA). Universidade de São Paulo (USP). Piracicaba , SP, Brazil
Associated scholarship(s):22/09678-9 - Optimizing the prediction of local soil fertility parameters using Brazilian national and regional spectral libraries comprising XRF and vis-NIR data, BE.EP.PD

Abstract

The modernization of soil fertility diagnostics using sensor systems is a current and prominent topic of research in the context of Soil Science and Precision Agriculture. The application of non-destructive techniques in this diagnosis makes it possible to reduce the use of chemical reagents and reduces the number of operations involved in the analytical procedure, making it faster. This has a positive impact on the environment and has the potential to optimize the management of fertilizers for crop production. Our research proposal aims to develop a method for using X-Ray Fluorescence (XRF) spectrometry and its integration with visible and Near-Infrared (vis-NIR) diffuse reflectance spectroscopy as an analysis tool for soil fertility diagnosis. For this, three steps with well-defined objectives will be conducted. In the first one, the temporal stability of XRF models for predicting fertility attributes will be assessed, as well as the influence of the change in fertilization management on this temporal stability. In the second step, the ability of vis-NIR and XRF spectra to group samples with similar performance for predicting fertility attributes via XRF sensor will be assessed by multivariate statistics. Finally, the third step aims to evaluate the potential and applicability of data fusion techniques to exploit the synergy between vis-NIR and XRF sensors to improve the accuracy of soil fertility prediction. We also highlight that the evaluation of the second and third steps will be conducted using a massive group of data, which will give a comprehensive coverage and confidence in the results. With the execution of this project it is expected to evolve the Technology Readiness Level (TRL) of the application of XRF and vis-NIR sensors as an analytical method of soil fertility, subsidizing the integration of these sensors in agricultural analysis laboratories or in on-board systems for on-site analysis. (AU)

News published in Agência FAPESP Newsletter about the scholarship:
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Scientific publications (10)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
TAVARES, TIAGO RODRIGUES; MOLIN, JOSE PAULO; ALVES, ELTON EDUARDO NOVAIS; MELQUIADES, EABIO LUIZ; DE CARVALHO, HUDSON WALLACE PEREIRA; MOUAZEN, ABDUL MOUNEM. Towards rapid analysis with XRF sensor for assessing soil fertility attributes: Effects of dwell time reduction. SOIL & TILLAGE RESEARCH, v. 232, p. 10-pg., . (20/16670-9)
TAVARES, TIAGO RODRIGUES; MOLIN, JOSE PAULO; NUNES, LIDIANE CRISTINA; ALVES, ELTON EDUARDO NOVAIS; KRUG, FRANCISCO JOSE; DE CARVALHO, HUDSON WALLACE PEREIRA. Spectral data of tropical soils using dry-chemistry techniques (VNIR, XRF, and LIBS): A dataset for soil fertility prediction. DATA IN BRIEF, v. 41, p. 7-pg., . (20/16670-9)
PINHEIRO JUNIOR, CARLOS ROBERTO; TAVARES, TIAGO RODRIGUES; PEREIRA, MARCOS GERVASIO; FURQUIM, SHEILA APARECIDA CORREIA; TERRA, FABRICIO DA SILVA; DOS ANJOS, LUCIA HELENA CUNHA; DEMATTE, JOSE ALEXANDRE MELO; DE AZEVEDO, ANTONIO CARLOS; DE OLIVEIRA, FABIO SOARES. Pedogenesis on Jurassic formations in the Araripe Basin, northeastern Brazil: Weathering and parent material. CATENA, v. 223, p. 18-pg., . (14/22262-0, 20/16670-9)
DE CAMARGO, RACHEL FERRAZ; TAVARES, TIAGO RODRIGUES; DA SILVA, NICOLAS GUSTAVO DA CRUZ; DE ALMEIDA, EDUARDO; DE CARVALHO, HUDSON WALLACE PEREIRA. Soybean sorting based on protein content using X-ray fluorescence spectrometry. Food Chemistry, v. 412, p. 7-pg., . (20/16670-9)
TAVARES, TIAGO R.; MOUAZEN, ABDUL M.; NUNES, LIDIANE C.; DOS SANTOS, FELIPE R.; MELQUIADES, FABIO L.; DA SILVA, THAINARA R.; KRUG, FRANCISCO J.; MOLIN, JOSE P.. Laser-Induced Breakdown Spectroscopy (LIBS) for tropical soil fertility analysis. SOIL & TILLAGE RESEARCH, v. 216, . (04/15965-2, 17/21969-0, 20/16670-9)
WEI, MARCELO CHAN FU; CANAL FILHO, RICARDO; TAVARES, TIAGO RODRIGUES; MOLIN, JOSE PAULO; VIEIRA, AFRANIO MARCIO CORREA. Dimensionality Reduction Statistical Models for Soil Attribute Prediction Based on Raw Spectral Data. AI, v. 3, n. 4, p. 11-pg., . (20/16670-9)
PINHEIRO JUNIOR, CARLOS R.; SALVADOR, CONAN A.; TAVARES, TIAGO R.; ABREU, MARCEL C.; FAGUNDES, HUGO S.; ALMEIDA, WILK S.; SILVA NETO, EDUARDO C.; ANJOS, LUCIA H. C.; PEREIRA, MARCOS G.. ithic soils in the semi-arid region of Brazil: edaphic characterization and susceptibility to erosio. JOURNAL OF ARID LAND, v. 14, n. 1, p. 56-69, . (20/16670-9)
MARTELLO, MAURICIO; MOLIN, JOSE PAULO; BAZAME, HELIZANI COUTO; TAVARES, TIAGO RODRIGUES; MALDANER, LEONARDO FELIPE. Use of Active Sensors in Coffee Cultivation for Monitoring Crop Yield. AGRONOMY-BASEL, v. 12, n. 9, p. 16-pg., . (20/16670-9)
CHERUBIN, MAURICIO ROBERTO; DAMIAN, JUNIOR MELO; TAVARES, TIAGO RODRIGUES; TREVISAN, RODRIGO GONCALVES; COLACO, ANDRE FREITAS; EITELWEIN, MATEUS TONINI; MARTELLO, MAURICIO; INAMASU, RICARDO YASSUSHI; PIAS, OSMAR HENRIQUE DE CASTRO; MOLIN, JOSE PAULO. Precision Agriculture in Brazil: The Trajectory of 25 Years of Scientific Research. AGRICULTURE-BASEL, v. 12, n. 11, p. 29-pg., . (20/16670-9)
TAVARES, TIAGO RODRIGUES; DE ALMEIDA, EDUARDO; JUNIOR, CARLOS ROBERTO PINHEIRO; GUERRERO, ANGELA; FIORIO, PETERSON RICARDO; DE CARVALHO, HUDSON WALLACE PEREIRA. Analysis of Total Soil Nutrient Content with X-ray Fluorescence Spectroscopy (XRF): Assessing Different Predictive Modeling Strategies and Auxiliary Variables. AGRIENGINEERING, v. 5, n. 2, p. 18-pg., . (20/16670-9)

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