Can statistical learning models make early selection among sugarcane families easier and still efficient?-Reference-Cited by-同舟云学术

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Can statistical learning models make early selection among sugarcane families easier and still efficient?

Published:2020-12-09 Issue:1 Volume:61 Page:456-465
ISSN:0011-183X
Container-title:Crop Science
language:en
Short-container-title:Crop Sci.

Author:

Moreira Édimo Fernando Alves¹,Barbosa Marcio Henrique Pereira²,Peternelli Luiz Alexandre³^ORCID

Affiliation:

1. Department of Agronomy Federal Institute of Triângulo Mineiro Uberaba MG 38064–790 Brazil

2. Department of Agronomy Federal University of Viçosa Viçosa MG 36570–900 Brazil

3. Department of Statistics Federal University of Viçosa Viçosa MG 36570–900 Brazil

Publisher

Wiley

Subject

Agronomy and Crop Science

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/csc2.20334

Reference31 articles.

1. Breeding Program and Cultivar Recommendations

2. Pattern Recognition and Machine Learning

3. Simulated Individual Best Linear Unbiased Prediction versus Mass Selection in Sugarcane Families

4. Consistency of the results of path analysis among sugarcane experiments;Brasileiro B. P.;Crop Breeding and Applied Biotechnology,2013

5. Importance of agronomic traits in the individual selection process in sugarcane determined using the logistic regression;Brasileiro B. P.;Acta Scientiarum. Agronomy,2016

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Study on the variation in and selection of Fraxinus mandshurica provenances and families in northeast China;Journal of Forestry Research;2022-05-28

2. Plant breeding in Brazil: Retrospective of the past 50 years;Crop Breeding and Applied Biotechnology;2021

3. Can statistical learning models make early selection among sugarcane families easier and still efficient?;Crop Science;2020-12-09

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