Spectral reflectance indices as proxies for yield potential and heat stress tolerance in spring wheat: heritability estimates and marker-trait associations

Abstract

The application of spectral reflectance indices (SRIs) as proxies to screen for yield potential (YP) and heat stress (HS) is emerging in crop breeding programs. Thus, a comparison of SRIs and their associations with grain yield (GY) under YP and HS conditions is important. In this study, we assessed the usefulness of 27 SRIs for indirect selection for agronomic traits by evaluating an elite spring wheat association mapping initiative (WAMI) population comprising 287 elite lines under YP and HS conditions. Genetic and phenotypic analysis identified 11 and 9 SRIs in different developmental stages as efficient indirect selection indices for yield in YP and HS conditions, respectively. We identified enhanced vegetation index (EVI) as the common SRI associated with GY under YP at booting, heading and late heading stages, whereas photochemical reflectance index (PRI) and normalized difference vegetation index (NDVI) were the common SRIs under booting and heading stages in HS. Genome-wide association study (GWAS) using 18704 single nucleotide polymorphisms (SNPs) from Illumina iSelect 90K identified 280 and 43 marker-trait associations for efficient SRIs at different developmental stages under YP and HS, respectively. Common genomic regions for multiple SRIs were identified in 14 regions in 9 chromosomes: 1B (60–62 cM), 3A (15, 85–90, 101– 105 cM), 3B (132–134 cM), 4A (47–51 cM), 4B (71– 75 cM), 5A (43–49, 56–60, 89–93 cM), 5B (124–125 cM), 6A (80–85 cM), and 6B (57–59, 71 cM). Among them, SNPs in chromosome 5A (89–93 cM) and 6A (80–85 cM) were co-located for yield and yield related traits. Overall, this study highlights the utility of SRIs as proxies for GY under YP and HS. High heritability estimates and identification of marker-trait associations indicate that SRIs are useful tools for understanding the genetic basis of agronomic and physiological traits.

Keywords

genome-wide association study (GWAS) / heat tolerance / spectral reflectance / spring wheat

References

[1] Pereira J J. Climate change 2014—impacts, adaptation and vulnerability. Part B: regional aspects. In: Contribution of Working Group II to the Fifth Assessment Report of the IPCC. Cambridge: Cambridge University Press, 2014

[2] Prasad P, Boote K, Allen L Jr, Sheehy J, Thomas J. Species, ecotype and cultivar differences in spikelet fertility and harvest index of rice in response to high temperature stress. Field Crops Research, 2006, 95(2–3): 398–411

[3] Loss S P, Siddique K H M. Morchological and physiological traits associated with wheat yield increases in Mediterranean environment. In: Sparks D, ed. Advances in Agronomy. San Diego: Academic Press, 1994, 229–276

[4] Ferrio J, Villegas D, Zarco J, Aparicio N, Araus J, Royo C. Assessment of durum wheat yield using visible and near-infrared reflectance spectra of canopies. Field Crops Research, 2005, 94(2–3): 126–148

[5] Reynolds M, Rajaram S, Sayre K. Physiological and genetic changes of irrigated wheat in the post-green revolution period and approaches for meeting projected global demand. Crop Science, 1999, 39(6): 1611–1621

[6] Cossani C M, Reynolds M P. Physiological traits for improving heat tolerance in wheat. Plant Physiology, 2012, 160(4): 1710–1718

[7] Reynolds M, Pask A, Mullan D. Physiological breeding I: interdisciplinary approaches to improve crop adaptation. Mexico, D.F.: CIMMYT, 2012

[8] Tattaris M, Reynolds M P, Chapman S C. A direct comparison of remote sensing approaches for high-throughput phenotyping in plant breeding. Frontiers of Plant Science, 2016, 7: 1131

[9] Araus J L, Kefauver S C, Zaman-Allah M, Olsen M S, Cairns J E. Translating high-throughput phenotyping into genetic gain. Trends in Plant Science, 2018, 23(5): 451–466

[10] Knipling E B. Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sensing of Environment, 1970, 1(3): 155–159

[11] Shorter R, Lawn R, Hammer G. Improving genotypic adaptation in crops—a role for breeders, physiologists and modellers. Experimental Agriculture, 1991, 27(2): 155–175

[12] Babar M, Van Ginkel M, Klatt A, Prasad B, Reynolds M. The potential of using spectral reflectance indices to estimate yield in wheat grown under reduced irrigation. Euphytica, 2006, 150(1–2): 155–172

[13] Jordan C F. Derivation of leaf-area index from quality of light on the forest floor. Ecology, 1969, 50(4): 663–666

[14] Rouse J, Haas R, Schell J, Deerin D. Monitoring vegetation systems in the great plains with ERTS. In: NASA. Goddard Space Flight Center 3d ERTS-1 Symposium, College Station. Washington: NASA, 1974, 309–317

[15] Gamon J A, Serrano L, Surfus J S. The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels. Oecologia, 1997, 112(4): 492–501

[16] Penuelas J, Gamon J A, Griffin K L, Field C B. Assessing community type, plant biomass, pigment composition, and photosynthetic efficiency of aquatic vegetation from spectral reflectance. Remote Sensing of Environment, 1993, 46(2): 110–118

[17] Peñuelas J, Pinol J, Ogaya R, Filella I. Estimation of plant water concentration by the reflectance water index WI (R900/R970). International Journal of Remote Sensing, 1997, 18(13): 2869–2875

[18] Sellers P. Canopy reflectance, photosynthesis, and transpiration: II. The role of biophysics in the linearity of their interdependence. Remote Sensing of Environment, 1987, 21(2): 143–183

[19] Wiegand C L, Richardson A J. Use of spectral vegetation indices to infer leaf area, evapotranspiration and yield: I. Rationale. Agronomy Journal, 1990, 82(3): 623–629

[20] Baret F, Guyot G. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment, 1991, 35(2–3): 161–173

[21] Chappelle E W, Kim M S, McMurtrey J E III. Ratio analysis of reflectance spectra (RARS): an algorithm for the remote estimation of the concentrations of chlorophyll a, chlorophyll b, and carotenoids in soybean leaves. Remote Sensing of Environment, 1992, 39(3): 239–247

[22] Aparicio N, Villegas D, Araus J, Casadesus J, Royo C. Relationship between growth traits and spectral vegetation indices in durum wheat. Crop Science, 2002, 42(5): 1547–1555

[23] Babar M, Reynolds M, Van Ginkel M, Klatt A, Raun W, Stone M. Spectral reflectance to estimate genetic variation for in-season biomass, leaf chlorophyll, and canopy temperature in wheat. Crop Science, 2006, 46(3): 1046–1057

[24] Raun W R, Solie J B, Johnson G V, Stone M L, Lukina E V, Thomason W E, Schepers J S. In-season prediction of potential grain yield in winter wheat using canopy reflectance. Agronomy Journal, 2001, 93(1): 131–138

[25] Hassan M, Yang M, Rasheed A, Jin X, Xia X, Xiao Y, He Z. Time-series multispectral indices from unmanned aerial vehicle imagery reveal senescence rate in bread wheat. Remote Sensing, 2018, 10(6): 809

[26] Hassan M A, Yang M, Rasheed A, Yang G, Reynolds M, Xia X, Xiao Y, He Z. A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform. Plant Science, 2019, 282: 95–103

[27] Gizaw S A, Godoy J G V, Pumphrey M O, Carter A H. Spectral reflectance for indirect selection and genome-wide association analyses of grain yield and drought tolerance in North American spring wheat. Crop Science, 2018, 58(6): 2289–2301

[28] Gizaw S A, Godoy J G V, Garland-Campbell K, Carter A H. Using spectral reflectance indices as proxy phenotypes for genome-wide association studies of yield and yield stability in Pacific Northwest winter wheat. Crop Science, 2018, 58(3): 1232–1241

[29] Wang S, Wong D, Forrest K, Allen A, Chao S, Huang B E, Maccaferri M, Salvi S, Milner S G, Cattivelli L, Mastrangelo A M, Whan A, Stephen S, Barker G, Wieseke R, Plieske J, Lillemo M, Mather D, Appels R, Dolferus R, Brown-Guedira G, Korol A, Akhunova A R, Feuillet C, Salse J, Morgante M, Pozniak C, Luo M C, Dvorak J, Morell M, Dubcovsky J, Ganal M, Tuberosa R, Lawley C, Mikoulitch I, Cavanagh C, Edwards K J, Hayden M, Akhunov E. Characterization of polyploid wheat genomic diversity using a high-density 90000 single nucleotide polymorphism array. Plant Biotechnology Journal, 2014, 12(6): 787–796

[30] Lopes M S, Dreisigacker S, Peña R J, Sukumaran S, Reynolds M P. Genetic characterization of the wheat association mapping initiative (WAMI) panel for dissection of complex traits in spring wheat. Theoretical and Applied Genetics, 2015, 128(3): 453–464

[31] Sukumaran S, Dreisigacker S, Lopes M, Chavez P, Reynolds M P. Genome-wide association study for grain yield and related traits in an elite spring wheat population grown in temperate irrigated environments. Theoretical and Applied Genetics, 2015, 128(2): 353–363

[32] Sukumaran S, Reynolds M P, Lopes M S, Crossa J. Genome-wide association study for adaptation to agronomic plant density: a component of high yield potential in spring wheat. Crop Science, 2015, 55(6): 2609–2619

[33] Reynolds M, Balota M, Delgado M, Amani I, Fischer R. Physiological and morphological traits associated with spring wheat yield under hot, irrigated conditions. Functional Plant Biology, 1994, 21(6): 717–730

[34] Birth G S, McVey G R. Measuring the color of growing turf with a reflectance spectrophotometer 1. Agronomy Journal, 1968, 60(6): 640–643

[35] Dale M, Causton D. Use of the chlorophyll a/b ratio as a bioassay for the light environment of a plant. Functional Ecology, 1992, 6(2): 190–196

[36] Chapelle E, Kim M, McMurtrey I. Ratio analysis of reflectance spectra (RARS): an algorithm for the remote estimation of the concentrations of Chl a, b and carotenoids in soybean leaves. Remote Sensing of Environment, 1992, 39: 239–247

[37] Gitelson A, Merzlyak M N. Quantitative estimation of chlorophyll-a using reflectance spectra: experiments with autumn chestnut and maple leaves. Journal of Photochemistry and Photobiology B: Biology, 1994, 22(3): 247–252

[38] Liu H Q, Huete A. A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Transactions on Geoscience and Remote Sensing, 1995, 33(2): 457–465

[39] Dash J, Curran P. Evaluation of the MERIS terrestrial chlorophyll index (MTCI). Advances in Space Research, 2007, 39(1): 100–104

[40] Blackburn G A. Quantifying chlorophylls and caroteniods at leaf and canopy scales: an evaluation of some hyperspectral approaches. Remote Sensing of Environment, 1998, 66(3): 273–285

[41] Gitelson A A, Merzlyak M N, Chivkunova O B. Optical properties and nondestructive estimation of anthocyanin content in plant leaves. Photochemistry and Photobiology, 2001, 74(1): 38–45

[42] Rondeaux G, Steven M, Baret F. Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 1996, 55(2): 95–107

[43] Haboudane D, Miller J R, Tremblay N, Zarco-Tejada P J, Dextraze L. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 2002, 81(2–3): 416–426

[44] Gitelson A A, Zur Y, Chivkunova O B, Merzlyak M N. Assessing carotenoid content in plant leaves with reflectance spectroscopy. Photochemistry and Photobiology, 2002, 75(3): 272–281

[45] Peñuelas J, Filella I, Biel C, Serrano L, Save R. The reflectance at the 950–970 nm region as an indicator of plant water status. International Journal of Remote Sensing, 1993, 14(10): 1887–1905

[46] Merzlyak M N, Gitelson A A, Chivkunova O B, Rakitin V Y. Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiologia Plantarum, 1999, 106(1): 135–141

[47] Prasad B, Carver B F, Stone M L, Babar M, Raun W R, Klatt A R. Potential use of spectral reflectance indices as a selection tool for grain yield in winter wheat under great plains conditions. Crop Science, 2007, 47(4): 1426–1440

[48] Penuelas J, Filella I, Gamon J A. Assessment of photosynthetic radiation-use efficiency with spectral reflectance. New Phytologist, 1995, 131(3): 291–296

[49] Peñuelas J, Filella I, Lloret P, Mun Oz F, Vilajeliu M. Reflectance assessment of mite effects on apple trees. International Journal of Remote Sensing, 1995, 16(14): 2727–2733

[50] Vargas M, Combs E, Alvarado G, Atlin G, Mathews K, Crossa J. META: a suite of SAS programs to analyze multienvironment breeding trials. Agronomy Journal, 2013, 105(1): 11–19

[51] Yu J, Pressoir G, Briggs W H, Vroh Bi I, Yamasaki M, Doebley J F, McMullen M D, Gaut B S, Nielsen D M, Holland J B, Kresovich S, Buckler E S. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nature Genetics, 2006, 38(2): 203–208

[52] Turner S D. qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots. bioRxiv, 2014: 005165

[53] Valluru R, Reynolds M P, Davies W J, Sukumaran S. Phenotypic and genome-wide association analysis of spike ethylene in diverse wheat genotypes under heat stress. New Phytologist, 2017, 214(1): 271–283

[54] Sukumaran S, Lopes M, Dreisigacker S, Reynolds M. Genetic analysis of multi-environmental spring wheat trials identifies genomic regions for locus-specific trade-offs for grain weight and grain number. Theoretical and Applied Genetics, 2018, 131(4): 985–998

[55] Araus J L, Slafer G A, Reynolds M P, Royo C. Plant breeding and drought in C3 cereals: what should we breed for? Annals of Botany, 2002, 89(7): 925–940

[56] Prasad B, Carver B F, Stone M L, Babar M, Raun W R, Klatt A R. Genetic analysis of indirect selection for winter wheat grain yield using spectral reflectance indices. Crop Science, 2007, 47(4): 1416–1425

[57] Aparicio N, Villegas D, Casadesus J, Araus J L, Royo C. Spectral vegetation indices as nondestructive tools for determining durum wheat yield. Agronomy Journal, 2000, 92(1): 83–91

[58] Royo C, Aparicio N, Villegas D, Casadesus J, Monneveux P, Araus J. Usefulness of spectral reflectance indices as durum wheat yield predictors under contrasting Mediterranean conditions. International Journal of Remote Sensing, 2003, 24(22): 4403–4419

[59] Condorelli G E, Maccaferri M, Newcomb M, Andrade-Sanchez P, White J W, French A N, Sciara G, Ward R, Tuberosa R. Comparative aerial and ground based high throughput phenotyping for the genetic dissection of NDVI as a proxy for drought adaptive traits in durum wheat. Frontiers of Plant Science, 2018, 9: 893

[60] Liu C, Sukumaran S, Claverie E, Sansaloni C, Dreisigacker S, Reynolds M. Genetic dissection of heat and drought stress QTLs in phenology-controlled synthetic-derived recombinant inbred lines in spring wheat. Molecular Breeding, 2019, 39(3): 34

[61] Mittler R. Abiotic stress, the field environment and stress combination. Trends in Plant Science, 2006, 11(1): 15–19

[62] Jamsheer K M, Laxmi A. Expression of Arabidopsis FCS-Like Zinc finger genes is differentially regulated by sugars, cellular energy level, and abiotic stress. Frontiers of Plant Science, 2015, 6: 746

[63] Hwang J U, Song W Y, Hong D, Ko D, Yamaoka Y, Jang S, Yim S, Lee E, Khare D, Kim K, Palmgren M, Yoon H S, Martinoia E, Lee Y. Plant ABC transporters enable many unique aspects of a terrestrial plant’s lifestyle. Molecular Plant, 2016, 9(3): 338–355

[64] De Rienzo F, Gabdoulline R R, Menziani M C, Wade R C. Blue copper proteins: a comparative analysis of their molecular interaction properties. Protein Science, 2000, 9(8): 1439–1454

[65] Ambawat S, Sharma P, Yadav N R, Yadav R C. MYB transcription factor genes as regulators for plant responses: an overview. Physiology and Molecular Biology of Plants, 2013, 19(3): 307–321

[66] McHale L, Tan X, Koehl P, Michelmore R W. Plant NBS-LRR proteins: adaptable guards. Genome Biology, 2006, 7(4): 212

Supplementary materials

The online version of this article at https://doi.org/10.15302/J-FASE-2019269 contains supplementary materials (Tables S1–S4).

Acknowledgements

This work was implemented by the CIMMYT as part of the projects ARCADIA and MasAgro Trigo in collaboration with the CIMMYT, made possible by the generous support of SAGARPA MasAgro Trigo and ARCADIA. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of SAGARPA (SADER) and ARCADIA. Dr. Caiyun Liu’s stay at the CIMMYT was sponsored by the China Scholarship Council (CSC)–CIMMYT scholarship.

Compliance with ethics guidelines

Caiyun Liu, Francisco Pinto, C. Mariano Cossani, Sivakumar Sukumaran, and Matthew P. Reynolds declare that they have no conflicts of interest or financial conflicts to disclose.

This article does not contain any studies with human or animal subjects performed by any of the authors.

RIGHTS & PERMISSIONS

The Author(s) 2019. Published by Higher Education Press. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0)