当前位置: X-MOL 学术Renew. Energy › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Multivariate analysis in the selection of elephant grass genotypes for biomass production
Renewable Energy ( IF 9.0 ) Pub Date : 2020-11-01 , DOI: 10.1016/j.renene.2020.06.094
Lilia M. Gravina , Tâmara Rebecca A. de Oliveira , Rogério F. Daher , Geraldo A. Gravina , Ana Kessia F. Vidal , Wanessa F. Stida , Derivaldo P. Cruz , Camila Q.S.S. de Sant’Anna , Richardson S. Rocha , Antonio V. Pereira , Gustavo Hugo F. de Oliveira

Abstract The elephant grass has proved to be a great option for biomass production because it causes less damage to the environment, yet there is not a lot of information about its use as a renewable energy source. The objective of this study was to use multivariate analysis to check elephant grass genotypes for biomass production. Between 2016 and 2018, comprising four semiannual cutting, 12 entries were evaluated, and the Active Germplasm Bank of Elephant grass of North Fluminense State University Darcy Ribeiro presented better performances for dry matter yield. The experimental design was a randomized complete block design with three replications. ANOVA and biplot graphs were drawn for variables plant height, number of tillers, stem diameter, leaf blade length, leaf blade width, dry weight and percentage of dry matter. The variance analysis showed the existence of genetic variability between elephant grass genotypes and two first principal components, a biplot analysis of the genotype and the characteristics explained 70.07% of the total variation. The dry weight per linear meter and yield per hectare were the variables with greater discriminatory powers, thus being suitable for selecting elephant grass genotypes. The dry matter yield was positively correlated with dry weight and stem diameter. Genotypes 7, 11 and 12 presented the highest overall mean and were also the most stable.

中文翻译:

用于生物量生产的象草基因型选择的多元分析

摘要 象草已被证明是一种很好的生物质生产选择,因为它对环境的破坏较小,但关于其用作可再生能源的信息并不多。本研究的目的是使用多变量分析来检查用于生物量生产的象草基因型。2016年至2018年,包括四次半年度扦插,评估了12个条目,北弗鲁米嫩塞州立大学达西里贝罗象草活性种质库在干物质产量方面表现出更好的表现。实验设计是具有三个重复的随机完整区组设计。为变量植物高度、分蘖数、茎直径、叶片长度、叶片宽度、干重和干物质百分比绘制方差分析和双标图。方差分析表明象草基因型和两个第一主成分之间存在遗传变异,基因型和特征的双标分析解释了总变异的 70.07%。每延米干重和每公顷产量是判别力较大的变量,适用于选择象草基因型。干物质产量与干重和茎粗呈正相关。基因型 7、11 和 12 的总体平均值最高,也是最稳定的。每延米干重和每公顷产量是具有较大判别力的变量,因此适用于选择象草基因型。干物质产量与干重和茎粗呈正相关。基因型 7、11 和 12 的总体平均值最高,也是最稳定的。每延米干重和每公顷产量是判别力较大的变量,适用于选择象草基因型。干物质产量与干重和茎粗呈正相关。基因型 7、11 和 12 的总体平均值最高,也是最稳定的。
更新日期:2020-11-01
down
wechat
bug