World's Best Scientists 2026 revealed!

D-Index & Metrics

Plant Science and Agronomy

D-Index
34
Citations
3770
World Ranking
5471
National Ranking
1332

Best Publications

  • The Modern Plant Breeding Triangle: Optimizing the Use of Genomics, Phenomics, and Enviromics Data.

    Jose Crossa;Roberto Fritsche-Neto;Osval A. Montesinos-Lopez;Germano Costa-Neto

  • Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials.

    Germano Costa-Neto;Roberto Fritsche-Neto;José Crossa

  • snpReady: a tool to assist breeders in genomic analysis

    Unknown

  • EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture.

    Germano Costa-Neto;Giovanni Galli;Humberto Fanelli Carvalho;José Crossa

  • Multi-objective optimized genomic breeding strategies for sustainable food improvement

    Unknown

  • Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction.

    Massaine Bandeira e Sousa;Jaime Cuevas;Evellyn Giselly de Oliveira Couto;Paulino Pérez-Rodríguez

  • Updating the ranking of the coefficients of variation from maize experiments

    Roberto Fritsche-Neto;Rafael Augusto Vieira;Carlos Alberto Scapim;Glauco Vieira Miranda

  • Sete décadas de evolução do sistema produtivo da cultura do milho

    Unknown

  • Accuracy of genomic selection to predict maize single-crosses obtained through different mating designs

    Roberto Fristche-Neto;Deniz Akdemir;Jean-Luc Jannink

  • Genome and Environment Based Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction.

    Unknown

  • Herança de caracteres associados à eficiência de utilização do fósforo em milho

    Unknown

  • Bayesian analysis and prediction of hybrid performance

    Unknown

  • BGGE: A New Package for Genomic-Enabled Prediction Incorporating Genotype × Environment Interaction Models

    Italo Granato;Jaime Cuevas;Francisco Luna-Vázquez;Jose Crossa

  • Multi-trait genomic prediction for nitrogen response indices in tropical maize hybrids

    Unknown

  • Enviromic Assembly Increases Accuracy and Reduces Costs of the Genomic Prediction for Yield Plasticity in Maize.

    Germano Costa-Neto;Germano Costa-Neto;Jose Crossa;Roberto Fritsche-Neto;Roberto Fritsche-Neto

  • Machine learning algorithms translate big data into predictive breeding accuracy

    Unknown

  • Increasing accuracy and reducing costs of genomic prediction by marker selection

    Unknown

  • Association mapping for traits related to nitrogen use efficiency in tropical maize lines under field conditions

    Unknown

  • Plant breeding for abiotic stress tolerance

    Unknown

  • The difference between breeding for nutrient use efficiency and for nutrient stress tolerance

    Unknown

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