He has a degree in Agronomy from the Federal University of Santa Catarina (UFSC) (2006-2012), a master's degree in the Postgraduate Program in Agroecosystems at UFSC (2012-2014) and a PhD in Agroecosystems from UFSC (2014-2018), working mainly with soil management, conservation and fertility and contamination by trace elements. He was a member of the Núcleo de Ensino, Pesquisa e Extensão em Agroecologia at UFSC (NEPEA). Post-Doctorate in the Postgraduate Program in Soil Science (PPGCS) at the Federal University of Lavras (UFLA), working in the area of pedology and evaluating the use of proximal sensors for the characterization and prediction of soil attributes. He currently works at Agroteste Pesquisa e Desenvolvimento as a researcher in the area of fertilizers, inoculants and biostimulants, with an emphasis on soil fertility and plant nutrition.
EDUCATION
Agronomist Engineer - Fedeal University of Santa Catarina.
Master in Agroecosystems - Fedeal University of Santa Catarina.
PhD in Agroecosystems - Fedeal University of Santa Catarina.
Post-doctorate in the Soil Science Program - Federal University of Lavras
RESEARCH, TEACHING, or OTHER INTERESTS
Soil Science, Agricultural and Biological Sciences
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 Coffee Science, 2025 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.
Portable X-ray fluorescence sensor for ecofriendly, low-cost, and fast assessment of eucalypt charcoal attributes Renata Andrade, Lucas Benedet, Marcelo Mancini, Sérgio Henrique Godinho Silva, Camila da Silva Freitas, Marco Aurélio Carbone Carneiro, Nilton Curi Ciencia E Agrotecnologia, 2025 Brazilian steel industries require high-quality charcoal to produce pig iron. Desirable charcoal attributes include high elemental carbon content, large mean particle size (MPS), and high density, while producing low contents of ash and volatile matter, and presenting low contents of water and contaminants (e.g., phosphorous). These attributes are commonly determined by standardized laboratory analyses, which are time consuming and costly, besides generating chemical effluents. Portable X-ray fluorescence (pXRF) spectrometry can be used to avoid the downsides of laboratory analyses. The objective of this study was to evaluate the use of pXRF data in machine-learning models trained to predict attributes of eucalypt charcoal. pXRF data (elemental contents) from 276 charcoal samples were used to train predictive models using six machine-learning algorithms. Auxiliary explanatory variables (drying time, wood age, fine particle content, and friability) were included in the models. Models were trained to predict the following charcoal attributes: fixed C (%), ash content (%), volatile matter (%), MPS (mm), water content (%), density (kg/m3), and P contents (%). Satisfactory predictions were obtained for volatile matter, MPS, moisture, and density (R2 > 0.6), and very accurate predictions were obtained for ash and P contents (R2 > 0.75). The inclusion of auxiliary explanatory variables increased the prediction accuracy of MPS (R2 increased from 0.61 to 0.82), bulk density (from 0.56 to 0.73), and P contents (from 0.86 to 0.94). These results indicate that pXRF can be used as an ecofriendly alternative to assess the quality of eucalypt charcoal utilized in metallurgy.
Chemical and spectroscopic composition of humic substances in soil subjected to pig manure applications for ten years Lucas Benedet, Andria Paula Lima, Taís Morais Barbosa, Guilherme Wilbert Ferreira, Deborah Pinheiro Dick, Cledimar Rogério Lourenzi, Gustavo Brunetto, Arcângelo Loss, Jucinei José Comin Revista Brasileira De Ciencia do Solo, 2024 Application of pig manure (PM) in agriculture can influence the amount and composition of soil organic matter (SOM). This study evaluated the changes in contents and stock of C in chemical fractions of SOM and the chemical and spectroscopic composition of humic substances (HS) in a Typic Hapludult (Argissolo Vermelho-Amarelo) after ten years of PM application. Experimental area received 90 and 180 kg ha -1 of N in the form of pig slurry (PS90 and PS180) and pig deep litter (DL90 and DL180), in addition to the control, without application (SA). Soil samples were prepared, and the chemical fractioning of SOM, elemental analysis, and Fourier Transform Infrared Spectroscopy of HS were performed. Applications of PM favored the accumulation of C in soil (up to 53 %), and PS180 increased humic acids (HA) (up to 185 %), while applications of DL favored the increase of hydrophilic substances extracted with HCl and humin (HU) (up to 10 times and 60 %, respectively). Applications of PS180 and DL180 promoted an increase in the aromatic and carboxylic character of the HS, increasing cationic exchange capacity. Therefore, PM applications, especially with DL, contribute positively to C fixation in the soil and the chemical composition of organic material.
A Proximal Sensor-Based Approach for Clean, Fast, and Accurate Assessment of the Eucalyptus spp. Nutritional Status and Differentiation of Clones Renata Andrade, Sérgio Henrique Godinho Silva, Lucas Benedet, Elias Frank de Araújo, Marco Aurélio Carbone Carneiro, Nilton Curi Plants, 2023 Several materials have been characterized using proximal sensors, but still incipient efforts have been driven to plant tissues. Eucalyptus spp. cultivation in Brazil covers approximately 7.47 million hectares, requiring faster methods to assess plant nutritional status. This study applies portable X-ray fluorescence (pXRF) spectrometry to (i) distinguish Eucalyptus clones using pre-processed pXRF data; and (ii) predict the contents of eleven nutrients in the leaves of Eucalyptus (B, Ca, Cu, Fe, K, Mg, Mn, N, P, S, and Zn) aiming to accelerate the diagnosis of nutrient deficiency. Nine hundred and twenty samples of Eucalyptus leaves were collected, oven-dried, ground, and analyzed using acid-digestion (conventional method) and using pXRF. Six machine learning algorithms were trained with 70% of pXRF data to model conventional results and the remaining 30% were used to validate the models using root mean square error (RMSE) and coefficient of determination (R2). The principal component analysis clearly distinguished developmental stages based on pXRF data. Nine nutrients were accurately predicted, including N (not detected using pXRF spectrometry). Results for B and Mg were less satisfactory. This method can substantially accelerate decision-making and reduce costs for Eucalyptus foliar analysis, constituting an ecofriendly approach which should be tested for other crops.
Rapid soil fertility prediction using X-ray fluorescence data and machine learning algorithms Lucas Benedet, Salvador F. Acuña-Guzman, Wilson Missina Faria, Sérgio Henrique Godinho Silva, Marcelo Mancini, Anita Fernanda dos Santos Teixeira, Luiza Maria Pereira Pierangeli, Fausto Weimar Acerbi Júnior, Lucas Rezende Gomide, Alceu Linares Pádua Júnior, Igor Alexandre de Souza, Michele Duarte de Menezes, João José Marques, Luiz Roberto Guimarães Guilherme, Nilton Curi Catena, 2021
