Assessing Wheat Traits by Spectral Reflectance: Do We Really Need to Focus on Predicted Trait-Values or Directly Identify the Elite Genotypes Group?

Garriga, Miguel; Romero-Bravo, Sebastián; Estrada, Félix; Escobar, Alejandro; Matus, Iván; Pozo, Alejandro del; Astudillo, César; Lobos, Germán

doi:10.3389/fpls.2017.00280

Cited by 60 publications

(66 citation statements)

References 56 publications

(91 reference statements)

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“…Similarly, the robustness and superiority of these models over pedigree-based approaches for predicting milk yield, fat content, and somatic cell scores in dairy sheep has also been demonstrated [37]. PLS models have been used to predict grain yield [32]; yield, yield components and physiological traits [38]; and Septoria tritici blotch disease in wheat [39], among others. The feasibility of using these models for predicting yield in the presence of secondary traits in the model for Pacific Northwest winter wheat, nonetheless, has not been reported.…”

Section: Introductionmentioning

confidence: 99%

Accuracy of Single and Multi-Trait Genomic Prediction Models for Grain Yield in US Pacific Northwest Winter Wheat

2019

Crop Breed Genet Genom.

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Incorporating secondary correlated traits collected from high-throughput phenotyping in genomic selection (GS) models for complex traits has been demonstrated to improve accuracy. The prediction ability of different single and multiple trait partial least square (PLS) regression models for grain yield were assessed for winter wheat lines evaluated in US Pacific Northwest environments. Different populations including a diversity panel, F5, and double haploid breeding lines were evaluated in Lind and Pullman, WA between 2015 and 2018 and were genotyped with genotyping by sequencing-derived SNP markers. Prediction ability was assessed under cross-validations and independent predictions. Multi-trait covariate models were advantageous in obtaining optimal predictions for yield, especially when there is less genetic relatedness between the training and test populations. Adding multiple traits in the model improved predictions for environments with low heritability. Cross-validations resulted in the highest prediction ability (0.16) whereas independent predictions using the diversity panel to predict F5 and double haploid winter wheat breeding lines obtained the lowest (0.002). Relatedness between populations, heritability of the secondary traits, and the type of PLS model used were among the principal factors affecting prediction ability. Our results showed the relevance of using multi-trait GS models to achieve increased predictions. Genetic architecture of the target trait and genetic relatedness between populations should be taken into consideration when choosing which type of models to implement in the breeding program. An increased prediction ability for the multi-trait models indicates the potential to attain improved genetic gains for yield in wheat breeding programs through these GS approaches.

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Section: Introductionmentioning

confidence: 99%

Accuracy of Single and Multi-Trait Genomic Prediction Models for Grain Yield in US Pacific Northwest Winter Wheat

2019

Crop Breed Genet Genom.

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“…Methodologically, the first point of interest is the modeling criterion when the environmental conditions were evaluated together (All); usually performed better when higher coefficients of determination are looked for by the increment of the trait-range [20]. Interestingly, the results of the present study suggest that blueberry models, in general, had a better performance when the environments were considered separately, at least when a proximal approach (leaf clip with light source) is used.…”

Section: Discussionmentioning

confidence: 68%

“…Interestingly, the results of the present study suggest that blueberry models, in general, had a better performance when the environments were considered separately, at least when a proximal approach (leaf clip with light source) is used. When [17] and [20] estimated several physiological and productive traits by non-proximal spectral reflectance (80 cm above the canopy); it was concluded that the estimations were always improved when data from the contrasting water supply conditions (fully irrigated, mild and severe water deficit) were combined.…”

Section: Discussionmentioning

confidence: 99%

“…Reflectance spectroscopy has gained importance and is being widely used in eco-physiological studies for predicting traits of interest [10,16]. However, most of the studies have focused on the use of Spectral Reflectance Indices (SRIs) (denoting relationships between specific wavelengths or spectrum bands), paying less attention to the use of a wider proportion of wavelengths or the full spectrum [11,[17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

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Spectral Reflectance Modeling by Wavelength Selection: Studying the Scope for Blueberry Physiological Breeding under Contrasting Water Supply and Heat Conditions

et al. 2019

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To overcome the environmental changes occurring now and predicted for the future, it is essential that fruit breeders develop cultivars with better physiological performance. During the last few decades, high-throughput plant phenotyping and phenomics have been developed primarily in cereal breeding programs. In this study, plant reflectance, at the level of the leaf, was used to assess several physiological traits in five Vaccinium spp. cultivars growing under four controlled conditions (no-stress, water deficit, heat stress, and combined stress). Two modeling methodologies [Multiple Linear Regression (MLR) and Partial Least Squares (PLS)] with or without (W/O) prior wavelength selection (multicollinearity, genetic algorithms, or in combination) were considered. PLS generated better estimates than MLR, although prior wavelength selection improved MLR predictions. When data from the environments were combined, PLS W/O gave the best assessment for most of the traits, while in individual environments, the results varied according to the trait and methodology considered. The highest validation predictions were obtained for chlorophyll a/b (R2Val ≤ 0.87), maximum electron transport rate (R2Val ≤ 0.60), and the irradiance at which the electron transport rate is saturated (R2Val ≤ 0.59). The results of this study, the first to model modulated chlorophyll fluorescence by reflectance, confirming the potential for implementing this tool in blueberry breeding programs, at least for the estimation of a number of important physiological traits. Additionally, the differential effects of the environment on the spectral signature of each cultivar shows this tool could be directly used to assess their tolerance to specific environments.

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“…Instead, a more direct estimation is conceivable for spike density through the best-ranking pigment-specific simple ratio PSSR [57], which may detect the bright-colored spikes. In contrast, another study obtained good estimates only for spike density across environments but not for GNS and TKW [58]. Overall, likely indirect detections of most yield components must be carefully interpreted considering the growing conditions and contributing treatments.…”

Section: In-season Estimation Of Yield Componentsmentioning

confidence: 88%

Deep Phenotyping of Yield-Related Traits in Wheat

Prey

Schmidhalter

2020

Agronomy

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The complex formation of grain yield (GY) is related to multiple dry matter (DM) traits; however, due to their time-consuming determination, they are not readily accessible. In winter wheat (Triticum aestivum L.), both agronomic treatments and genotypic variation influence GY in interaction with the environment. Spectral proximal sensing is promising for high-throughput non-destructive phenotyping but was rarely evaluated systematically for dissecting yield-related variation in DM traits. Aiming at a temporal, spectral and organ-level optimization, 48 vegetation indices were evaluated in a high-yielding environment in 10 growth stages for the estimation of 31 previously compared traits related to GY formation—influenced by sowing time, fungicide, N fertilization, and cultivar. A quantitative index ranking was evaluated to assess the stage-independent index suitability. GY showed close linear relationships with spectral vegetation indices across and within agronomic treatments (R2 = 0.47–0.67 ***). Water band indices, followed by red edge-based indices, best used at milk or early dough ripeness, were better suited than the widely used normalized difference vegetation index (NDVI). Index rankings for many organ-level DM traits were comparable, but the relationships were often less close. Among yield components, grain number per spike (R2 = 0.24–0.34 ***) and spike density (R2 = 0.23–0.46 ***) were moderately estimated. GY was mainly estimated by detecting total DM rather than the harvest index. Across agronomic treatments and cultivars, seasonal index rankings were the most stable for GY and total DM, whereas traits related to DM allocation and translocation demanded specific index selection. The results suggest using indices with water bands, near infrared/red edge and visible light bands to increase the accuracy of in-season spectral phenotyping for GY, contributing organ-level traits, and yield components, respectively.

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Assessing Wheat Traits by Spectral Reflectance: Do We Really Need to Focus on Predicted Trait-Values or Directly Identify the Elite Genotypes Group?

Cited by 60 publications

References 56 publications

Accuracy of Single and Multi-Trait Genomic Prediction Models for Grain Yield in US Pacific Northwest Winter Wheat

Accuracy of Single and Multi-Trait Genomic Prediction Models for Grain Yield in US Pacific Northwest Winter Wheat

Spectral Reflectance Modeling by Wavelength Selection: Studying the Scope for Blueberry Physiological Breeding under Contrasting Water Supply and Heat Conditions

Deep Phenotyping of Yield-Related Traits in Wheat

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