Mr Matheus Massariol Suela |  Crop Science |  Best Researcher Award

PhD Candidate at  Federal University of Viçosa (UFV)/University of Florida (UF), United States

Profile:

• Orcid

Education:

• Ph.D. in Genetics and Breeding (July 2021 – Present), Federal University of Viçosa
  Project: Structural Equation Models for genome-wide studies in Glycine max and Coffea arabica.
• Sandwich Ph.D. Period (May 2024 – Present), University of Florida
  Focus: Quantitative genetics for blueberry and coffee breeding, with Ph.D. Patricio Muñoz and Ph.D. Felipe Ferrão.
• M.Sc. in Genetics and Breeding (March 2020 – June 2021), Federal University of Viçosa
• B.Sc. in Agronomy (March 2015 – December 2019), Federal University of Viçosa
• Agricultural Technician (March 2012 – December 2014), Federal Institute of Espírito Santo – Campus Itapina

Research and Professional Experience:

• Extensive experience in genome-wide association studies (GWAS) and genomic selection (GWS) using Structural Equation Models (SEM) and Bayesian networks.
• Currently researching and implementing advanced genetic approaches in blueberry and coffee breeding.
• Internship and project participation in agronomic and breeding-focused programs, including Agroconsult’s Harvest Rally and research on traits in rice, soy, and coffee.

Computational Skills:

• Programming: Advanced R, Intermediate C, Basic Python
• Data Analysis Tools: Power BI, Office Suite (Excel, Word, PowerPoint)
• Developed Software: ECERSEM-AdaptStab (Structural Equation Models for adaptability and stability analysis in crop breeding)

Awards and Recognitions:

• 2nd Place – GDM Innovation Award 40 years, 2023
• Silver Medal – Presidente Arthur da Silva Bernardes for academic performance in Agronomy, 2020
• 2nd Place – Brazilian Agricultural Olympics, 2014

Publication Top Notes 📚

1. Integrating Environmental Covariates into Adaptability and Stability Analyses: A Structural Equation Modeling Approach for Cotton Breeding
   Agriculture, 2024-10-28 | DOI: 10.3390/agriculture14111914
2. Updating knowledge in estimating the genetics parameters: Multi-trait and Multi-Environment Bayesian analysis in rice
   Scientia Agricola, 2023 | DOI: 10.1590/1678-992x-2022-0056
3. Genome-wide association study for morphological, physiological, and productive traits in Coffea arabica using structural equation models
   Tree Genetics & Genomes, 2023-06 | DOI: 10.1007/s11295-023-01597-8
4. Regional heritability mapping and genome‐wide association identify loci for rice traits
   Crop Science, 2022-03 | DOI: 10.1002/csc2.20706
5. Structural equation models for genome-wide association study in Coffea arabica
   2021-07-27 | DOI: 10.47328/ufvbbt.2021.164
6. Combined index of genomic prediction methods applied to productivity
   Ciência Rural, 2019 | DOI: 10.1590/0103-8478cr20181008
7. New insights into genomic selection through population-based non-parametric prediction methods
   Scientia Agricola, 2019-07 | DOI: 10.1590/1678-992x-2017-0351