A5006576169- Genetics and Plant Breeding
- Genetic Mapping and Diversity in Plants and Animals
- Genetic and phenotypic traits in livestock
- Crop Yield and Soil Fertility
- Plant nutrient uptake and metabolism
- Rice Cultivation and Yield Improvement
- Plant Pathogens and Resistance
- Banana Cultivation and Research
- Potato Plant Research
- Plant Virus Research Studies
- Chromosomal and Genetic Variations
- Plant tissue culture and regeneration
- Wheat and Barley Genetics and Pathology
- Genetic and Environmental Crop Studies
- Genetic diversity and population structure
- Agricultural and Food Sciences
- Nematode management and characterization studies
- Growth and nutrition in plants
- Plant Micronutrient Interactions and Effects
- Gene expression and cancer classification
- Plant-Microbe Interactions and Immunity
- Agricultural pest management studies
- Sugarcane Cultivation and Processing
- Remote Sensing in Agriculture
- Plant Taxonomy and Phylogenetics
Louisiana State University
2022-2025
Louisiana State University Agricultural Center
2022-2025
Universidade de São Paulo
2015-2024
Cornell University
2024
International Rice Research Institute
2020-2023
Centro Internacional de Mejoramiento de Maíz Y Trigo
2023
ORCID
2021
Universidade Brasil
2014-2020
Secretaria de Agricultura e Abastecimento
2017
Universidade Metodista de São Paulo
2014
OPINION article Front. Plant Sci., 16 April 2021Sec. Breeding https://doi.org/10.3389/fpls.2021.651480
Abstract Modern whole-genome prediction (WGP) frameworks that focus on multi-environment trials (MET) integrate large-scale genomics, phenomics, and envirotyping data. However, the more complex statistical model, longer computational processing times, which do not always result in accuracy gains. We investigated use of new kernel methods modeling structures involving genomics nongenomic sources variation two MET maize data sets. Five WGP models were considered, advancing complexity from a...
Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare prediction accuracy of four developed genomic-enabled models: (1) single-environment, main genotypic effect model (SM); (2) multi-environment, effects (MM); (3) single variance G×E deviation (MDs); and (4) environment-specific (MDe). Each these models were fitted using two kernel methods: a linear Genomic Best Linear Unbiased Predictor, GBLUP (GB),...
The purpose of breeding programs is to obtain sustainable gains in multiple traits while controlling the loss genetic variation. decisions at each cycle involve multiple, usually competing, objectives; these complex can be supported by insights that are gained applying multi-objective optimization principles breeding. discussion this manuscript includes definition several optimized approaches within phenotypic or genomic frameworks and comparison with standard multi-trait schemes such as...
Abstract Envirotyping is an essential technique used to unfold the nongenetic drivers associated with phenotypic adaptation of living organisms. Here, we introduce EnvRtype R package, a novel toolkit developed interplay large-scale envirotyping data (enviromics) into quantitative genomics. To start user-friendly pipeline, this package offers: (1) remote sensing tools for collecting (get_weather and extract_GIS functions) processing ecophysiological variables (processWTH function) from raw...
The selection of hybrids is an essential step in maize breeding. However, evaluating a large number field trials can be extremely costly. genomic models used to predict the expected performance un-tested genotypes. Bayesian offer very flexible framework for hybrid prediction. methodology with parametric and semi-parametric assumptions additive non-additive effects. Furthermore, samples from posterior distribution estimate variance due general specific combining abilities even cases where...
Estimating genetic trends using historical data is an important parameter to check the success of breeding programs. The estimated can act as a guideline target appropriate strategies and optimize program for improved gains. In this study, 17 years from IRRI's rice drought was used estimate assess program's success. We also identified top-performing lines based on grain yield values elite panel implementing future population improvement-based schemes. A two-stage approach pedigree-based...
The objective of this study was to update the ranking coefficients variation (CVs) from maize experiments and evaluate accuracy data latest Brazilian publications. We rank-ordered CVs for grain yield, plant ear heights, number ears per plant, weight commercial ears, except 100 grains. were obtained 143 scientific papers published 2005 2010. classification based on average (m) standard deviation (SD) ranked as low, intermediate, high very high. All random variables had normally distributed....
One of the major issues in plant breeding is occurrence genotype × environment (GE) interaction. Several models have been created to understand this phenomenon and explore it. In genomic era, several were employed improve selection by using markers account for GE interaction simultaneously. Some these use special genetic covariance matrices. addition, scale multi-environment trials getting larger, increases computational challenges. context, we propose an R package that, general, allows...
Quantitative genetics states that phenotypic variation is a consequence of the interaction between genetic and environmental factors. Predictive breeding based on this statement, because this, ways modeling effects are still evolving. At same time, refinement must be used for processing information. Here, we present an "enviromic assembly approach," which includes using ecophysiology knowledge in shaping relatedness into whole-genome predictions (GP) plant (referred to as enviromic-aided...
Abstract Linking high-throughput environmental data (enviromics) to genomic prediction (GP) is a cost-effective strategy for increasing selection intensity under genotype-by-environment interactions (G × E). This study developed data-driven approach based on Environment–Phenotype Association (EPA) aimed at recycling important G E information from historical breeding data. EPA was in two applications: (1) scanning secondary source of genetic variation, weighted the shared reaction-norms...
This study aimed to verify the relationship between breeding for tolerance low levels of soil nutrients and nutrient use efficiency in tropical maize. Fifteen inbred lines were evaluated two greenhouse experiments under contrasting N P. The nutritional stress was estimated by Spearman ranking correlation genotypes traits related P phenotypic plasticity indices. lack traits, magnitude as well significance, indicates that these characters are controlled different gene groups. Consequently,...
Objetivou-se com este trabalho comparar o sistema de produção milho, recomendado nos anos 40, atualmente empregado. Para isso, utilizou-se como base artigo publicado por Antônio Secundino São José, na Revista Ceres, em 1944, comparando-se as práticas agrícolas recomendadas para a cultura do milho época empregadas. Naquela época, não havia preocupação direta os aspectos conservacionistas solo e água. Todavia, iniciava-se processo elevação da produtividade grãos, no uso mais insumos, todos...
ABSTRACT Nitrogen is essential for sustaining life on the planet, and it most important nutrient obtaining high agricultural production. However, their use leads to release of nitrous oxide with a global warming potential 296 times higher than CO2 molecule, making challenge reduce in agriculture. The objective this research was identify efficient popcorn inbred lines responsive nitrogen exhibit good expansion volume. For this, 29 from Germplasm Collection Darcy Ribeiro North Fluminense State...
In this study, we compared the prediction accuracy of main genotypic effect model (MM) without G×E interactions, multi-environment single variance deviation (MDs), and environment-specific (MDe) where random genetic effects lines are modeled with markers (or pedigree). With objective further modeling residual lines, incorporated intercepts ([Formula: see text]) generated another three models. Each these 6 models were fitted a linear kernel method (Genomic Best Linear Unbiased Predictor, GB)...