在雨养和丰水条件下小麦基因型的形态比色鉴别、分类和产量预测

IF 1.8 4区 农林科学 Q2 AGRICULTURE, MULTIDISCIPLINARY
Ehsan Rabieyan, M. Bihamta, Mohsen Esmaeilzadeh Moghaddam, V. Mohammadi, H. Alipour
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引用次数: 6

摘要

抽象的上下文。植物种子形态计量学数字分析可以实现基于形态比色特征的物种分类,并可用于小麦(Triticum aestivum L.)的基因型分类。目标本文主要研究了伊朗小麦品种和地方品种的形态比色分离和分类,并根据这些性状预测产量和幼苗活力。方法。采用α晶格设计,在2018-19和2019-20两个作物年(旱作和旱作条件下)对133个小麦基因型(91个地方品种和42个栽培品种)进行了评价。对小麦种子收获后的40个形态比色性状进行了成像测定。种子颜色、种子形态、种子活力和产量也进行了评估。关键的结果。采用基于形态比色性状的线性判别分析,分离小麦品种和地方品种的验证率较高(水分充足条件下为90%,旱作条件下为98.6%)。形态比色性状L、白度指数、色度、a、Feret和Rectang是旱地最具判别性的变量。在基于种子颜色的分析中,根据国际植物新品种保护联盟和国际植物遗传资源委员会的描述符,采用线性判别分析将小麦基因型划分为4类,准确率较高。其中,黄色小麦占97.3%,中红色小麦占99.7%。结论。我们的观察结果表明,种子数字分析是在大量小麦基因型中评估表型品种的一种负担得起且有价值的方法。品种和本地居群的形态计量学分析是进行基因型分类、预测产量和幼苗活力的有效手段。的影响。形态计量数据库将有助于植物育种者在育种计划中选择基因型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Morpho-colorimetric seed traits for the discrimination, classification and prediction of yield in wheat genotypes under rainfed and well-watered conditions
ABSTRACT Context. Morphometric digital analysis of plant seeds enables taxonomic discrimination of species based on morpho-colorimetric traits, and may be used to classify genotypes of wheat (Triticum aestivum L.). Aims. This study was focused on the isolation and classification of cultivars and landraces of Iranian wheat based on morpho-colorimetric traits, and the prediction of yield and seedling vigour based on these traits. Methods. In total, 133 wheat genotypes (91 native landraces and 42 cultivars) were evaluated by alpha lattice design in two crop years (2018–19 and 2019–20) under rainfed and conditions. After seed harvesting, 40 morpho-colorimetric traits of wheat seeds were measured by imaging. Seed colour, morphometric seed, seed vigour and yield were also assessed. Key results. Using linear discriminant analysis based on morpho-colorimetric traits, wheat cultivars and landraces were separated with high validation percentage (90% in well-watered and 98.6% in rainfed conditions). Morpho-colorimetric traits L, Whiteness index, Chroma, a, Feret and Rectang were found to be the most discriminant variables in the rainfed field. In analysis based on seed colour according to descriptors of the International Union for the Protection of New Varieties of Plants and International Board for Plant Genetic Resources, wheat genotypes were classified into four groups with high accuracy by using linear discriminant analysis. Specifically, 97.3% could be identified as yellow and 99.7% as medium-red wheat groups. Conclusions. Our observations suggest that seed digital analysis is an affordable and valuable approach for evaluating phenotypic variety among a large number of wheat genotypes. Morphometric analysis of cultivars and native populations can provide an effective step in classifying genotypes and predicting yield and seedling vigour. Implications. Morphometric databases will help plant breeders when selecting genotypes in breeding programs.
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来源期刊
Crop & Pasture Science
Crop & Pasture Science AGRICULTURE, MULTIDISCIPLINARY-
CiteScore
4.20
自引率
15.80%
发文量
111
审稿时长
3 months
期刊介绍: Crop and Pasture Science (formerly known as Australian Journal of Agricultural Research) is an international journal publishing outcomes of strategic research in crop and pasture sciences and the sustainability of farming systems. The primary focus is broad-scale cereals, grain legumes, oilseeds and pastures. Articles are encouraged that advance understanding in plant-based agricultural systems through the use of well-defined and original aims designed to test a hypothesis, innovative and rigorous experimental design, and strong interpretation. The journal embraces experimental approaches from molecular level to whole systems, and the research must present novel findings and progress the science of agriculture. Crop and Pasture Science is read by agricultural scientists and plant biologists, industry, administrators, policy-makers, and others with an interest in the challenges and opportunities facing world agricultural production. Crop and Pasture Science is published with the endorsement of the Commonwealth Scientific and Industrial Research Organisation (CSIRO) and the Australian Academy of Science.
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