Dataset Information

Normalization method for canopy temperature as an indirect indicator of yield potential in wheat breeding programs.

ABSTRACT: Canopy temperature (CT) is often related to potential yield and is a possible yield indicator in breeding programs. However, it is difficult to evaluate genetic variations of CT accurately in large-scale investigations, such as breeding programs, because CT is strongly affected by environmental conditions. In this study, to precisely evaluate these genetic variations, we determined the environmental factors that affect CT measurement and proposed a convenient normalization method to minimize their influence. We measured the CT of CT-high or CT-low cultivars in the field under various conditions. We found that as the sun and shade levels were alternated, the CT changed within seconds; the position in the field also critically affected the CT. However, even under these conditions, the differences between cultivars became clearer if CT was normalized by neighboring lines. Additionally, we revealed that CT measurements between 12:00 and 15:00 maximized the difference between cultivars. Using our normalization technique under the favorable conditions specified can help breeders select high-yield lines using CT in breeding programs.

SUBMITTER: Ohnishi S

PROVIDER: S-EPMC8784343 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Normalization method for canopy temperature as an indirect indicator of yield potential in wheat breeding programs.

Ohnishi Shizen S Kasuya Masashi M Sonoda Tatsuya T Jinno Hironobu H

Breeding science 20210928 5

Canopy temperature (CT) is often related to potential yield and is a possible yield indicator in breeding programs. However, it is difficult to evaluate genetic variations of CT accurately in large-scale investigations, such as breeding programs, because CT is strongly affected by environmental conditions. In this study, to precisely evaluate these genetic variations, we determined the environmental factors that affect CT measurement and proposed a convenient normalization method to minimize the ...[more]

PMID: 35087316

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Project description:BACKGROUND:Modern agriculture uses hyperspectral cameras to obtain hundreds of reflectance data measured at discrete narrow bands to cover the whole visible light spectrum and part of the infrared and ultraviolet light spectra, depending on the camera. This information is used to construct vegetation indices (VI) (e.g., green normalized difference vegetation index or GNDVI, simple ratio or SRa, etc.) which are used for the prediction of primary traits (e.g., biomass). However, these indices only use some bands and are cultivar-specific; therefore they lose considerable information and are not robust for all cultivars. RESULTS:This study proposes models that use all available bands as predictors to increase prediction accuracy; we compared these approaches with eight conventional vegetation indexes (VIs) constructed using only some bands. The data set we used comes from CIMMYT's global wheat program and comprises 1170 genotypes evaluated for grain yield (ton/ha) in five environments (Drought, Irrigated, EarlyHeat, Melgas and Reduced Irrigated); the reflectance data were measured in 250 discrete narrow bands ranging between 392 and 851 nm. The proposed models for the simultaneous analysis of all the bands were ordinal least square (OLS), Bayes B, principal components with Bayes B, functional B-spline, functional Fourier and functional partial least square. The results of these models were compared with the OLS performed using as predictors each of the eight VIs individually and combined. CONCLUSIONS:We found that using all bands simultaneously increased prediction accuracy more than using VI alone. The Splines and Fourier models had the best prediction accuracy for each of the nine time-points under study. Combining image data collected at different time-points led to a small increase in prediction accuracy relative to models that use data from a single time-point. Also, using bands with heritabilities larger than 0.5 only in Drought as predictor variables showed improvements in prediction accuracy.

Project description:Increased genetic gain for complex traits in plant breeding programs can be achieved through different selection strategies. The objective of this study was to compare potential gains for grain yield in a winter wheat breeding program through estimating response to selection R values across several selection approaches including phenotypic (PS), marker-based (MS), genomic (GS), and a combination of PS and GS (PS+GS). Ten populations of Washington State University (WSU) winter wheat breeding lines including a diversity panel and F5 and double haploid lines evaluated from 2015 to 2019 growing seasons for grain yield in Lind and Pullman, WA, USA were used in the study. Selection was conducted by selecting the top 20% of lines based on observed yield (PS strategy), genomic estimated breeding values (GS), presence of yield "enhancing" alleles of the most significant single nucleotide polymorphism (SNP) markers identified from genome-wide association mapping (MS), and high observed yield and estimated breeding values (PS+GS). Overall, PS compared to other individual selection strategies (MS and GS) showed the highest mean response (R = 0.61) within the same environment. When combined with GS, a 23% improvement in R for yield was observed, indicating that gains could be improved by complementing traditional PS with GS within the same environment. Validating selection strategies in different environments resulted in low to negative R values indicating the effects of genotype-by-environment interactions for grain yield. MS was not successful in terms of R relative to the other selection approaches; using this strategy resulted in a significant (P < 0.05) decrease in response to selection compared with the other approaches. An integrated PS+GS approach could result in optimal genetic gain within the same environment, whereas a PS strategy might be a viable option for grain yield validated in different environments. Altogether, we demonstrated that gains through increased response to selection for yield could be achieved in the WSU winter wheat breeding program by implementing different selection strategies either exclusively or in combination.

Dataset Information

Normalization method for canopy temperature as an indirect indicator of yield potential in wheat breeding programs.

Publications

Normalization method for canopy temperature as an indirect indicator of yield potential in wheat breeding programs.

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