Heritability, correlated response, and indirect selection involving spectral reflectance indices and grain yield in wheat
M. A. Babar A , M. van Ginkel B , M. P. Reynolds C , B. Prasad D and A. R. Klatt D EA Department of Agronomy, 2004 Throckmorton Plant Sciences Center, Kansas State University, Manhattan, KS 66506, USA.
B Department of Primary Industries (DPI), Private Bag 260, Horsham, Vic. 3401, Australia.
C International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera Mexico, El Batan, Texcoco, Mexico.
D Department of Plant and Soil Sciences, 368 Ag. Hall, Oklahoma State University, Stillwater, OK 74078, USA.
E Corresponding author. Email: aklatt@mail.pss.okstate.edu
Australian Journal of Agricultural Research 58(5) 432-442 https://doi.org/10.1071/AR06270
Submitted: 19 August 2006 Accepted: 13 March 2007 Published: 11 May 2007
Abstract
The objectives of this study were to assess the potential of using spectral reflectance indices (SRIs) as an indirect selection tool for grain yield in wheat under irrigated conditions. This paper demonstrates the genetic correlation between grain yield and SRIs, heritability and expected response to selection for grain yield and SRIs, correlated response to selection for grain yield estimated from SRIs, and efficiency of indirect selection for grain yield using SRIs in different spring wheat populations. Four field experiments, GHIST (15 CIMMYT globally adapted genotypes), RLs1 (25 random F3-derived families), RLs2 (36 random F3-derived families), and RLs3 (64 random F5-derived families) were conducted under irrigated conditions at the CIMMYT research station in north-west Mexico in 3 different years. Spectral reflectance was measured at 3 growth stages (booting, heading, and grain filling) and 7 SRIs were calculated using average values of spectral reflectance at heading and grain filling. Five previously developed SRIs (PRI, WI, RNDVI, GNDVI, SR), and 2 newly calculated SRIs (NWI-1 and NWI-2) were evaluated in the experiments. In general, the within- and between-year genetic correlations between grain yield and SRIs were significant. Three NIR-based indices (WI, NWI-1, and NWI-2) showed higher genetic correlations (0.73–0.92) with grain yield than the other indices (0.35–0.67), and these observations were consistent in all populations. Broad-sense heritability estimates for all indices were in general moderate to high (0.60–0.80), and higher than grain yield (0.45–0.70). The realised heritability for the 3 NIR-based indices was higher than for the other indices and for grain yield itself. Expected response to selection for all indices was moderate to high (0.54–0.85). The correlated response for grain yield estimated from the 3 NIR-based indices (0.59–0.64) was much higher than the correlated response for grain yield estimated from the other indices (0.31–0.46), and the efficiency of indirect selection for these 3 NIR-based indices was 90–96% of the efficiency of direct selection for grain yield. These results demonstrate the potential for using the 3 NIR-based SRI tools in breeding programs for selecting for increased genetic gains for yield.
Additional keywords: water index, normalised water index, normalised difference vegetation index, simple ration, photochemical reflectance index.
Acknowledgments
This work was partially supported with funding from the Oklahoma Wheat Research Foundation, the International Maize and Wheat Improvement Center (CIMMYT), Mexico, and the Australian Centre of International Agricultural Research (ACIAR). The authors gratefully acknowledge the technical support of Eugenio Perez.
Aparicio N,
Villegas D,
Casadesus J,
Araus JL, Royo C
(2000) Spectral vegetation indices as nondestructive tools for determining durum wheat yield. Agronomy Journal 92, 83–91.
Austin RB,
Bingham J,
Blackwell RD,
Evans LT,
Ford MA,
Morgan CL, Tailor M
(1980) Genetic improvement in winter wheat since 1900 and associated physiological changes. Journal of Agricultural Science 94, 675–689.
Babar MA,
Reynolds MP,
Van Ginkel M,
Klatt AR,
Raun WR, Stone ML
(2006a) Spectral reflectance indices as a potential indirect selection criteria for wheat yield under irrigation. Crop Science 46, 578–588.
| Crossref | GoogleScholarGoogle Scholar |
Babar MA,
Reynolds MP,
Van Ginkel M,
Klatt AR,
Raun WR, Stone ML
(2006b) Spectral reflectance to estimate genetic variation for in-season biomass, leaf chlorophyll and canopy temperature in wheat. Crop Science 46, 1046–1057.
| Crossref | GoogleScholarGoogle Scholar |
Babar MA,
van Ginkel M,
Klatt AR,
Prasad B, Reynolds MP
(2006c) The potential of using spectral reflectance indices to estimate yield in wheat grown under reduced irrigation. Euphytica 150, 155–172.
| Crossref | GoogleScholarGoogle Scholar |
Ball ST, Konzak CF
(1993) Relationship between grain yield and remotely-sensed data in wheat breeding experiments. Plant Breeding 110, 277–282.
| Crossref | GoogleScholarGoogle Scholar |
Bohren BB,
McKean HE, Yamada Y
(1961) Relative efficiencies of heritability estimates based on regression of offspring on parent. Biometrics 17, 481–491.
| Crossref | GoogleScholarGoogle Scholar |
Collaku A, Harrison SA
(2005) Heritability of waterlogging tolerance in wheat. Crop Science 45, 722–727.
Condon AG, Richards RA
(1992) Broad-sense heritability and genotype × environment interaction for carbon isotope discrimination in field-grown wheat. Australian Journal of Agricultural Research 43, 921–934.
| Crossref | GoogleScholarGoogle Scholar |
Condon AG,
Richards RA,
Rebetzke GJ, Farquhar GD
(2004) Breeding for high water-use efficiency. Journal of Experimental Botany 55, 2447–2460.
| Crossref | GoogleScholarGoogle Scholar | PubMed |
Cooper M,
Stucker RE,
Delacy IH, Harch DD
(1997) Wheat breeding nurseries, target environments, and indirect selection for grain yield. Crop Science 37, 1168–1176.
Cox TS,
Shoryer RJ,
Ben-Hui L,
Sears RG, Martin TJ
(1988) Genetic improvement in agronomic traits of hard red winter wheat cultivars from 1919 to 1987. Crop Science 28, 756–760.
Fischer RA,
Rees D,
Sayre KD,
Lu ZM,
Condon AG, Larque-Saavedra A
(1998) Wheat yield progress associated with higher stomatal conductance and photosynthetic rate, and cooler canopies. Crop Science 38, 1467–1475.
Gitelson AA,
Kaufman YJ, Merzlyak MN
(1996) Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment 58, 289–298.
| Crossref | GoogleScholarGoogle Scholar |
Guthrie DA,
Smith EL, McNew RW
(1984) Selection for high and low grain yield protein in six winter wheat crosses. Crop Science 24, 1097–1100.
Knipling EB
(1970) Physical and physiological basis for the reflectance of visible and near- infrared radiation from vegetation. Remote Sensing of Environment 1, 155–159.
| Crossref | GoogleScholarGoogle Scholar |
Leroy AR,
Cianzio SR, Fehr WR
(1991) Direct and indirect selection for small seed of soybean in temperate and tropical environments. Crop Science 31, 697–699.
Loss SP, Siddique KHM
(1994) Morphological and physiological traits associated with wheat yield increases in Mediterranean environments. Advances in Agronomy 52, 229–276.
Ma BL,
Dwyer LM,
Costa C,
Cober ER, Morrison MJ
(2001) Early prediction of soybean yield from canopy reflectance measurements. Agronomy Journal 93, 1227–1234.
Peñuelas J,
Filella I,
Biel C,
Serrano L, Save R
(1993) The reflectance at the 950–970 nm region as an indicator of plant water status. International Journal of Remote Sensing 14, 1887–1905.
Peñuelas J,
Filella I, Gamon JA
(1995) Assessment of photosynthetic radiation-use efficiency with spectral reflectance. New Phytologist 131, 291–296.
| Crossref | GoogleScholarGoogle Scholar |
Raun WR,
Solie JB,
Johnson GV,
Stone ML,
Lukina EV,
Thomson WE, Schepers JS
(2001) In-season prediction of potential grain yield in winter wheat using canopy reflectance. Agronomy Journal 93, 131–138.
Reynolds MP,
Balota M,
Delgado MIB,
Amani I, Fischer RA
(1994) Physiological and morphological traits associated with spring wheat yield under hot, irrigated conditions. Australian Journal of Plant Physiology 21, 717–730.
Reynolds MP,
Rajaram S, Sayre KD
(1999) Physiological and genetic changes of irrigated wheat in the post-green revolution period and approaches for meeting projected global demand. Crop Science 39, 1611–1621.
Reynolds MP,
Singh RP,
Ibrahim A,
Ageeb OAA,
Larque-Saavedra A, Quick JS
(1998) Evaluating physiological traits to complete empirical selection for wheat in warm environments. Euphytica 100, 85–95.
| Crossref | GoogleScholarGoogle Scholar |
Richards RA
(1996) Defining selection criteria to improve yield under drought. Plant Growth Regulator 20, 157–166.
| Crossref | GoogleScholarGoogle Scholar |
Royo C,
Aparicio N,
Villegas D,
Casadesus J,
Monneveux P, Araus JL
(2003) Usefulness of spectral reflectance indices as durum wheat yield predictors under contrasting Mediterranean conditions. International Journal of Remote Sensing 24, 4403–4419.
| Crossref | GoogleScholarGoogle Scholar |
Sayre KD,
Rajaram S, Fischer RA
(1997) Yield potential progress in short bread wheat in Northern Mexico. Crop Science 37, 36–42.
Shearman VJ,
Sylvester-Bradley R,
Scout RK, Foulkes MJ
(2005) Physiological process associated with wheat yield progress in the UK. Crop Science 45, 175–185.
Shorter R,
Lawn RJ, Hammer GL
(1991) Improving genotypic adaptation in crops: a role for breeders, physiologists and modelers. Experimental Agriculture 27, 155–175.
Siddique KHM,
Belford RK,
Oerry MW, Tennant D
(1989) Growth, development and light interception of old and modern wheat cultivars in a Mediterranean-type environment. Australian Journal of Agricultural Research 43, 473–487.
Slafer GA, Andrare FH
(1991) Changes in physiological attributes of the dry matter economy of bread wheat (Triticum aestivum L.) through genetic improvement of grain yield potential at different regions of the world. A review. Euphytica 58, 37–49.
| Crossref | GoogleScholarGoogle Scholar |
Trethowan RM,
Van Ginkel M, Rajaram S
(2002) Progress in breeding wheat for yield and adaptation in global drought affected environments. Crop Science 42, 1441–1446.
Trethowan RM,
Van Ginkel M,
Ammar K,
Crossa J,
Payne TS,
Cukadar B,
Rajaram S, Hernandez E
(2003) Associations among twenty years of international bread wheat yield evaluation environments. Crop Science 43, 1698–1711.
Van Ginkel M,
Ortiz-Monasterio IJ,
Trethowan RM, Hernanadez E
(2001) Methodology for selecting segregating populations for improved nitrogen-use efficiency in bread wheat. Euphytica 119, 223–230.
| Crossref | GoogleScholarGoogle Scholar |
Zadoks JC,
Chang TT, Konzak CF
(1974) A decimal code for the growth stages of cereals. Weed Research 14, 415–421.
| Crossref | GoogleScholarGoogle Scholar |
