Selection based on the phenomenic approach and agronomic ideotic of white oat

Author:

Pradebon Leonardo Cesar12ORCID,Carvalho Ivan Ricardo12ORCID,Da Silva José Antonio Gonzalez12ORCID,Loro Murilo Veiera3ORCID,Pettenon Adriano Lucas12ORCID,Roza João Pedro Dalla12ORCID,Schulz Adriano Dietterle12ORCID,Da Silva Thayane Beck12ORCID

Affiliation:

1. Departamento de Estudos Agrários Universidade Regional do Noroeste do Rio Grande do Sul Ijuí Rio Grande do Sul Brazil

2. PMG Ijuí Brazil

3. Centro de Ciências Rurais, Departamento de Fitotecnia Universidade Federal de Santa Maria Santa Maria Rio Grande do Sul Brazil

Abstract

AbstractThis work aimed to propose multivariate selection strategies reconciling the agronomic ideotype and vegetation indices to obtain genotypes with high tillering, early cycle, tolerant to lodging, and foliar diseases. The sowing of white oat lines was carried out on April 27, 2023, allocated in an experimental design of blocks augmented with interspersed controls. The regular treatments correspond to 593 lines of white oat and the common treatments were represented by the cultivars URS Olada, URS Altaneira, and IPR Artêmis arranged in four replications. The measurements of variables of agronomic interest took place in each experimental unit as follows: percentage of tillering, days to flowering, percentage of lodging, percentage of leaf rust, percentage of stem rust, and percentage of leaf spots. The phenomic analyses took place through flights carried out with a specialized drone, equipped with a 20 MP resolution camera and standard height (80 m). The best proposed indices were index for reducing the percentage of lodging with normalized green (NG), green–red ratio (IGR), soil color index (SCI), green leaf index (GLI), visible atmospheric resistant index (VARI), green leaf index 2 (GLI2), red–blue (RB), blue green pigment (BGI), normalized red–blue difference index (NRBDI), green–blue ratio, BIM, normalized green–blue difference index (NGBDI), and ExGR; index for reducing days to flowering with brightness index (BI), NRBDI, days to flowering (DF), gray (GRAY), IGR, average intensity (L), normalized red (NR), RGRI, SCI, BGI, BIM, GLI, NGBDI, and NG; and index for reducing the percentage of stem rust with NG, BI, Gray2, GLI2, RGRI, GLI, VARI, BGI, L, NR, RB, and coloration index. Only two lines were selected concomitantly with multivariate indices, line L167 (M5, SA) and L426 (M6, ETMS), that combine lodging tolerance and leaf rust genes.

Publisher

Wiley

Reference54 articles.

1. Seleção de variáveis stepwise aplicadas em redes neurais artificiais para previsão de demanda de cargas elétricas

2. Implications of the environment on grain yield in oats and their influence on estimates of genetic parameters;Benin G.;Current Agricultural Science and Technology,2003

Cited by 1 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

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3