Combining field performance with controlled environment plant imaging to identify the genetic control of growth and transpiration underlying yield response to water-deficit stress in wheat

Parent, Boris; Shahinnia, Fahimeh; Maphosa, Lancelot; Berger, Bettina; Rabie, Huwaida; Chalmers, K. J.; Kovalchuk, Alex; Langridge, Peter; Fleury, Delphine

doi:10.1093/jxb/erv320

Cited by 68 publications

(57 citation statements)

References 41 publications

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“…The expectation is that image-based phenotyping methodologies allow for expanding the experiments, performing phenotypic evaluation quickly and precisely, contributing to increases in selection differential and the heritability coefficient, with a direct effect on genetic gain. When applied to breeding populations, the precise quantification of phenotype increments the proportion of variance due to genetic effects and genetic gain in the selection of superior genotypes (Honsdorf et al, 2014;Parent et al, 2015;Pauli et al, 2016). Thus, it can be used at several stages of papaya breeding programs such as for germplasm evaluation, inbred line development and yield trial evaluations in general.…”

Section: Phenotyping Of the Morpho-agronomic Fruit Traitsmentioning

confidence: 99%

Model-assisted phenotyping by digital images in papaya breeding program

Cortes

Santa-Catarina

Barros

et al. 2017

Sci. agric. (Piracicaba, Braz.)

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Manual phenotyping for papaya Carica papaya (L) breeding purposes limits the evaluation of a great number of plants and hampers selection of superior genotypes. This study aimed to validate two methodologies for the phenotyping of morpho-agronomic plant traits using image analysis and fruit traits through image processing. In plants of the 'THB' variety and 'UENF/ Caliman-01' hybrid two images (A and B) were analyzed to estimate commercial and irregularly shaped fruits. Image A was also used in the estimation of plant height, stem diameter and the first fruit insertion height. In 'THB' fruits, largest and smallest diameters, length, and volume were estimated by using a caliper and image processing (IP). Volume was obtained by water column displacement (WCD) and by the expression of ellipsoid approximation (EA). Correlations above 0.85 between manual and image measurements were obtained for all traits. The averages of the morpho-agronomic traits, estimated by using images, were similar when compared to the averages measured manually. In addition, the errors of the proposed methodologies were low compared to manual phenotyping. Bland-Altman's approach indicated agreement between the volume estimated by WCD and EA using caliper and IP. The strong association obtained between volume and fruit weight suggests the use of regression to estimate this trait. Thus, the expectation is that image-based phenotyping can be used to expand the experiments, thereby maintaining accuracy and providing greater genetic gains in the selection of superior genotypes.

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Section: Phenotyping Of the Morpho-agronomic Fruit Traitsmentioning

confidence: 99%

Model-assisted phenotyping by digital images in papaya breeding program

Cortes

Santa-Catarina

Barros

et al. 2017

Sci. agric. (Piracicaba, Braz.)

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“…As the Gladius variety originated from RAC875 and Kukri lines (Fleury et al, 2010), the two populations showed common QTLs, such as those targeted here on chromosomes 1B and 3B. The 1B QTL found on a Gladius 3 Drysdale recombinant inbred line (RIL) population controlled shoot expansion rate in a glasshouse platform and tiller number in the field in a way that appears constitutive (Parent et al, 2015). This QTL overlaps with QKpsl.aww-1B for grain per spikelet and yields QTL QYld.aww-1B on the second population, RAC875/ Kukri (Bennett et al, 2012a), but with lower effects than in Drysdale/Gladius.…”

mentioning

confidence: 90%

“…strong QTLs were identified previously. The first data set comprised 60 Drysdale/Gladius RILs cultivated in four environments in 2010 and segregating for a spike number QTL identified previously on chromosome 1B (marker wsnp_CAP11_c1902_1022590; Parent et al, 2015). This locus collocated with a QTL for growth that was identified in a phenotyping platform and seemed to be constitutive, with the Drysdale allele being advantageous under well-watered conditions and drought in cool environments (Parent et al, 2015).…”

Section: Dissecting the Qtl 3 E Interactions Observed Previously For mentioning

confidence: 99%

“…This strategy has been used to assess the variables related to growth and development and resulted in robust QTLs of plant dynamics such as plant growth rate (Malosetti et al, 2006;Parent et al, 2015) and plant responses to some environmental variables such as soil water potential or vapor pressure deficit (Welcker et al, 2011;Schoppach et al, 2016). However, growing conditions in controlled environments are far removed from those observed in the field (Poorter et al, 2016), resulting in trait values for biological variables such as yield or yield components that have little practical relevance.…”

mentioning

confidence: 99%

“…These lines provide interesting material for studying adaptive strategies to warm and dry environments. Mapping populations based on crosses between Drysdale and Gladius and between RAC875 and Kukri were studied for yield and other related traits (Bennett et al, 2012a;Maphosa et al, 2014;Parent et al, 2015). As the Gladius variety originated from RAC875 and Kukri lines (Fleury et al, 2010), the two populations showed common QTLs, such as those targeted here on chromosomes 1B and 3B.…”

mentioning

confidence: 99%

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Quantifying Wheat Sensitivities to Environmental Constraints to Dissect Genotype × Environment Interactions in the Field

et al. 2017

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ORCID IDs: 0000-0002-7600-9592 (B.P.); 0000-0003-3851-8617 (J.B.); 0000-0001-9494-400X (P.L.); 0000-0002-7077-4103 (D.F.).Yield is subject to strong genotype-by-environment (G 3 E) interactions in the field, especially under abiotic constraints such as soil water deficit (drought [D]) and high temperature (heat [H]). Since environmental conditions show strong fluctuations during the whole crop cycle, geneticists usually do not consider environmental measures as quantitative variables but rather as factors in multienvironment analyses. Based on 11 experiments in a field platform with contrasting temperature and soil water deficit, we determined the periods of sensitivity to drought and heat constraints in wheat (Triticum aestivum) and determined the average sensitivities for major yield components. G 3 E interactions were separated into their underlying components, constitutive genotypic effect (G), G 3 D, G 3 H, and G 3 H 3 D, and were analyzed for two genotypes, highlighting contrasting responses to heat and drought constraints. We then tested the constitutive and responsive behaviors of two strong quantitative trait loci (QTLs) associated previously with yield components. This analysis confirmed the constitutive effect of the chromosome 1B QTL and explained the G 3 E interaction of the chromosome 3B QTL by a benefit of one allele when temperature rises. In addition to the method itself, which can be applied to other data sets and populations, this study will support the cloning of a major yield QTL on chromosome 3B that is highly dependent on environmental conditions and for which the climatic interaction is now quantified.

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High‐fidelity detection of crop biomass quantitative trait loci from low‐cost imaging in the field

et al. 2018

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Field‐based, rapid, and nondestructive techniques for assessing plant productivity are needed to accelerate the discovery of genotype‐to‐phenotype relationships in next‐generation biomass grass crops. The use of hemispherical imaging and light attenuation modeling was evaluated against destructive harvest measures with respect to their ability to accurately capture phenotypic and genotypic relationships in a field‐grown grass crop. Plant area index (PAI) estimated from below‐canopy hemispherical images, as well as a suite of thirteen traits assessed by manual destructive harvests, were measured in a Setaria recombinant inbred line mapping population segregating for aboveground productivity and architecture. A significant correlation was observed between PAI and biomass production across the population at maturity (r2 = .60), as well as for select diverse genotypes sampled repeatedly over the growing season (r2 = .79). Twenty‐seven quantitative trait loci (QTL) were detected for manually collected traits associated with biomass production. Of these, twenty‐one were found in four clusters of colocalized QTL. Analysis of image‐based estimates of PAI successfully identified all four QTL hot spots for biomass production. QTL for PAI had greater overlap with those detected for traits associated with biomass production than with those for plant architecture and biomass partitioning. Hemispherical imaging is an affordable and scalable method, which demonstrates how high‐throughput phenotyping can identify QTL related to biomass production of field trials in place of destructive harvests that are labor, time, and material intensive.

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Combining field performance with controlled environment plant imaging to identify the genetic control of growth and transpiration underlying yield response to water-deficit stress in wheat

Abstract: HighlightWe describe new quantitative trait loci for growth and transpiration in wheat under two water regimes using an imaging platform, and co-location with loci for yield components in the field.

Cited by 68 publications

References 41 publications

Model-assisted phenotyping by digital images in papaya breeding program

Model-assisted phenotyping by digital images in papaya breeding program

Quantifying Wheat Sensitivities to Environmental Constraints to Dissect Genotype × Environment Interactions in the Field

High‐fidelity detection of crop biomass quantitative trait loci from low‐cost imaging in the field

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