Predictive modeling of nutrients in Conilon coffee leaves using portable X-ray fluorescence spectrometry

Ivne Franco  Pires; Ademir Fontana; Beatriz César Maestá; Higor Moreira Mendonça; Fabio Luiz Partelli; Wenceslau Geraldes Teixeira; Lucas Benedet; Marcelo Mancini; Nilton Curi

doi:10.25186/.v20i.2386

Authors

Ivne Franco Pires Prefeitura de Boa Esperança, Boa Esperança, ES, Brasil. https://orcid.org/0000-0003-0113-0846
Ademir Fontana Empresa Brasileira de Pesquisa Agropecuária/Embrapa Gado de Corte, Campo Grande, MS, Brasil. https://orcid.org/0000-0001-7624-8676
Beatriz César Maestá Rua Jagoroaba, 179, Vila Valqueire, Rio de Janeiro, RJ, Brasil. https://orcid.org/0000-0001-5830-0936
Higor Moreira Mendonça Produtor de café, Santa Maria, ES, Brasil. https://orcid.org/0009-0006-4504-7904
Fabio Luiz Partelli Universidade Federal do Espírito Santo, São Mateus, ES, Brasil. https://orcid.org/0000-0002-8830-0846
Wenceslau Geraldes Teixeira Empresa Brasileira de Pesquisa Agropecuária/Embrapa Solos, Rio de Janeiro, RJ, Brasil. https://orcid.org/0000-0002-2010-6078
Lucas Benedet Universidade Federal de Lavras/UFLA, Departamento de Ciência do Solo, Lavras, MG, Brasil. https://orcid.org/0000-0002-0560-8790
Marcelo Mancini Universidade Federal de Lavras/UFLA, Departamento de Ciência do Solo, Lavras, MG, Brasil. https://orcid.org/0000-0003-4118-7943
Nilton Curi Universidade Federal de Lavras/UFLA, Departamento de Ciência do Solo, Lavras, MG, Brasil. https://orcid.org/0000-0002-2604-0866

DOI:

https://doi.org/10.25186/.v20i.2386

Abstract

Portable X-ray fluorescence (pXRF) spectrometers can offer accurate assessment of total elemental contents in the assessment of crop leaves. pXRF is a fast, low-cost, and accurate method of determining the total elemental contents in leaf samples without generating chemical waste, contributing to sustainable agribusiness practices. The objective of this work was to create prediction models capable of predicting nutrient contents in coffee leaves determined by ICP-OES from pXRF data. Leaf samples were collected in coffee plantations located north of the Espírito Santo state and subjected to macro and micronutrient analyses by wet chemistry and pXRF. pXRF analyses of leaf samples used two calibration methods: Mode 1 (“Soil”) and mode 2 (“Geochem”). Mode 1 was not capable of determining P contents. Nutrient contents determined by pXRF, in both modes, were higher than those determined by wet chemistry, except for Ca. High correlations were found between contents determined by wet chemistry and those determined by pXRF analysis, especially for mode 2 (r between 0.93 and 0.97), except for Zn (r = 0.65). With the calibration of linear equations, it was possible to predict P, K, Ca, Fe, and Mn contents in Conilon coffee leaves from pXRF data using mode 2 (R² between 0.89 and 0.95). Results showed that the prediction of ICP-OES contents in leaves using pXRF is an accurate, fast, and ecofriendly method.

Key words: pXRF; predictive modeling; plant nutrition; nutritional monitoring; Coffea canephora.

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Predictive modeling of nutrients in Conilon coffee leaves using portable X-ray fluorescence spectrometry

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