Comparative Performance of Spectral Reflectance Indices and Multivariate Modeling for Assessing Agronomic Parameters in Advanced Spring Wheat Lines Under Two Contrasting Irrigation Regimes
Comparative Performance of Spectral Reflectance Indices and Multivariate Modeling for Assessing Agronomic Parameters in Advanced Spring Wheat Lines Under Two Contrasting Irrigation Regimes
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DOI：
10.3389/fpls.2019.01537
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发表时间：
2019-11-28
影响因子：
5.6
通讯作者：
Schmidhalter, Urs
中科院分区：
文献类型：
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作者：
El-Hendawy, Salah E.;Alotaibi, Majed;Schmidhalter, Urs
在遗传干旱研究中采用非破坏性和成本效益高的工具，并结合表现出高度遗传性和遗传相关性的可靠间接筛选标准，对于解决干旱条件下农业部门缺水的挑战和确保基因型开发的成功至关重要。本研究利用近端光谱反射率数据对30个重组F7和F8自交系（RILs）在充分灌溉（FL）和有限灌溉（LM）条件下的地上生物量干重（DW）、含水量（WC）和籽粒产量（戈伊）进行了分析。根据遗传力和遗传相关性，检验了不同光谱反射指数（SRI）作为间接评估工具的效用。SRI和不同的偏最小二乘回归（PLSR）和逐步多元线性回归（SMLR）模型在估计破坏性参数的性能被认为是。一般来说，所有组的SRI，以及不同的模型PLSR和SMLR，产生更好的估计LM和FL+LM下的破坏性参数比FL。即使大多数的SRI表现出低关联与破坏性参数下FL，他们表现出中度到高度的遗传相关性，也有很高的遗传力。基于近红外（NIR）/可见光（维斯）和近红外/近红外（NIR/NIR）的SRI，特别是本研究开发的SRI，在维斯、红边和近红外光谱范围内提取的光谱带间隔，或与绿色、红色、红边和中近红外光谱区域相关的单个有效波长，被发现在所有条件下估计破坏性参数更有效。五种模型的SMLR和PLSR的每种条件下解释了大部分的变异，在三个破坏性参数之间的基因型。这些模型分别解释了FL下基因型间DW、WC和戈伊变异的42%~ 46%、19%~ 30%和39%~ 46%，LM下基因型间DW、WC和GY变异的69%~ 72%、59%~ 61%和77%~ 81%，FL+LM下基因型间DW、WC和GY变异的71%~ 75%、61%~ 71%和74%~ 78%。总体而言，这些结果证实，高光谱反射率传感在育种计划中的应用不仅是重要的快速和具有成本效益的方式评估足够数量的基因型，但也可以利用开发间接育种性状，有助于加速基因型的发展，在不利的环境条件下应用。
The incorporation of nondestructive and cost-effective tools in genetic drought studies in combination with reliable indirect screening criteria that exhibit high heritability and genetic correlations will be critical for addressing the water deficit challenges of the agricultural sector under arid conditions and ensuring the success of genotype development. In this study, the proximal spectral reflectance data were exploited to assess three destructive agronomic parameters [dry weight (DW) and water content (WC) of the aboveground biomass and grain yield (GY)] in 30 recombinant F7 and F8 inbred lines (RILs) growing under full (FL) and limited (LM) irrigation regimes. The utility of different groups of spectral reflectance indices (SRIs) as an indirect assessment tool was tested based on heritability and genetic correlations. The performance of the SRIs and different models of partial least squares regression (PLSR) and stepwise multiple linear regression (SMLR) in estimating the destructive parameters was considered. Generally, all groups of SRIs, as well as different models of PLSR and SMLR, generated better estimations for destructive parameters under LM and combined FL+LM than under FL. Even though most of the SRIs exhibited a low association with destructive parameters under FL, they exhibited moderate to high genetic correlations and also had high heritability. The SRIs based on near-infrared (NIR)/visible (VIS) and NIR/NIR, especially those developed in this study, spectral band intervals extracted within VIS, red edge, and NIR spectral range, or individual effective wavelengths relevant to green, red, red edge, and middle NIR spectral region, were found to be more effective in estimating the destructive parameters under all conditions. Five models of SMLR and PLSR for each condition explained most of the variation in the three destructive parameters among genotypes. These models explained 42% to 46%, 19% to 30%, and 39% to 46% of the variation in DW, WC, and GY among genotypes under FL, 69% to 72%, 59% to 61%, and 77% to 81% under LM, and 71% to 75%, 61% to 71%, and 74% to 78% under FL+LM, respectively. Overall, these results confirmed that application of hyperspectral reflectance sensing in breeding programs is not only important for evaluating a sufficient number of genotypes in an expeditious and cost-effective manner but also could be exploited to develop indirect breeding traits that aid in accelerating the development of genotypes for application under adverse environmental conditions.