Spectral reflectance indices as a rapid and nondestructive phenotyping tool for estimating different morphophysiological traits of contrasting spring wheat germplasms under arid conditions

The aim of this study was to investigate whether spectral reflectance indices could be used to estimate different destructive morphophysiological traits of a wide and diverse range of spring wheat germplasm in a rapid and nondestructive manner. A total of 90 spring wheat germoplasms were evaluated under water shortage by applying only three irrigations during the growing cycle of germplasm with the amount of water totaling 2550 m3 ha-1. Ten selected spectral reflectance indices were related to the green leaf number per plant, green leaf area per plant, total dry weight per plant (TDW), grain yield per hectare (GY), leaf water content (LWC), leaf area index (LAI), and canopy temperature (CT). Significant genotypic variability was shown for all morphophysiological traits and the ten selected spectral reflectance indices. The broad-sense heritability of the normalized water index (NWI)-3, NWI-4, water band index (WBI), and normalized difference vegetation index (NDVI) was high to medium as reflected in the morphophysiological traits, while for other spectral reflectance indices it was low. The indices NWI-3 and NWI-4 proved to be better predictors for LWC, GY, and LAI than NWI-1 and NWI-2. Spectral indices based on combine visible and near-infrared wavelengths such as the NDVI, the ratio of WBI/NDVI, and the R940/R960/NDVI were viable options to estimate TDW, GY, and LAI, whereas the WBI and R1000/R1100 had the best fit to LWC. The R940/R960 index failed to capture the genotypic variability in any morphophysiological traits. The LAI was more correlated to and had more direct effects on all agronomic traits than CT. The overall results indicated that it is indeed possible to apply spectral reflectance tools in wheat breeding programs to estimate the destructive morphophysiological traits and assess genotypic variability of a large number of germplasms in a rapid and nondestructive manner.

Spectral reflectance indices as a rapid and nondestructive phenotyping tool for estimating different morphophysiological traits of contrasting spring wheat germplasms under arid conditions

The aim of this study was to investigate whether spectral reflectance indices could be used to estimate different destructive morphophysiological traits of a wide and diverse range of spring wheat germplasm in a rapid and nondestructive manner. A total of 90 spring wheat germoplasms were evaluated under water shortage by applying only three irrigations during the growing cycle of germplasm with the amount of water totaling 2550 m3 ha-1. Ten selected spectral reflectance indices were related to the green leaf number per plant, green leaf area per plant, total dry weight per plant (TDW), grain yield per hectare (GY), leaf water content (LWC), leaf area index (LAI), and canopy temperature (CT). Significant genotypic variability was shown for all morphophysiological traits and the ten selected spectral reflectance indices. The broad-sense heritability of the normalized water index (NWI)-3, NWI-4, water band index (WBI), and normalized difference vegetation index (NDVI) was high to medium as reflected in the morphophysiological traits, while for other spectral reflectance indices it was low. The indices NWI-3 and NWI-4 proved to be better predictors for LWC, GY, and LAI than NWI-1 and NWI-2. Spectral indices based on combine visible and near-infrared wavelengths such as the NDVI, the ratio of WBI/NDVI, and the R940/R960/NDVI were viable options to estimate TDW, GY, and LAI, whereas the WBI and R1000/R1100 had the best fit to LWC. The R940/R960 index failed to capture the genotypic variability in any morphophysiological traits. The LAI was more correlated to and had more direct effects on all agronomic traits than CT. The overall results indicated that it is indeed possible to apply spectral reflectance tools in wheat breeding programs to estimate the destructive morphophysiological traits and assess genotypic variability of a large number of germplasms in a rapid and nondestructive manner.

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  • Abd El-Kareem THA, Saidy AEA (2011). Evaluation of yield and grain quality of some bread wheat genotypes under normal irrigation and drought stress conditions in calcareous soil. J Bio Sci 11: 156–164.
  • Aparicio N, Villegas D, Araus JL, Casadesus J, Royo C (2002). Relationship between growth traits and spectral vegetation indices in durum wheat. Crop Sci 42: 1547–1555.
  • Araus JL,  Cairns J  (2014).  Field high-throughput phenotyping—The new crop breeding frontier. Trends Plant Sci 19: 52–61.
  • Araus JL, Casadesus J, Bort J (2001). Recent tools for the screening of physiological traits determining yield. In: Reynolds MP, Ortiz- Monasterio JI, McNab A, editors. Application of Physiology in Wheat Breeding. Mexico City, Mexico: CIMMYT, pp. 59–77.
  • Araus JL, Slafer GA, Royo C, Serret MD (2008). Breeding for yield potential and stress adaptation in cereals. Crit Rev Plant Sci 27: 377–412.
  • Babar MA, Reynolds MP, van Ginkel M, Klatt AR, Raun WR, Stone ML (2006). Spectral reflectance indices as a potential indirect selection criteria for wheat yield under irrigation. Crop Sci 46: 578–588.
  • Berger B, Parent B, Tester M (2010). High-throughput shoot imaging to study drought responses. J Exp Bot 6: 3519–3528.
  • Bürling K, Cerovic ZG, Cornic G, Ducruet JM, Noga G, Hunsche M (2013). Fluorescence-based sensing of drought-induced stress in the vegetative phase of four contrasting wheat genotypes. Enviro Exp Bot 89: 51–59.
  • Chaves MM, Pereira JS, Maroco J, Rodrigues ML, Ricardo CPP, Osorio ML, Carvalho I, Faria T, Pinheiro C (2002). How plants cope with drought in the field? Photosynthesis and growth. Ann Bot 89: 907–916.
  • Chen X, Mina D, Yasir TA, Hu Y (2012). Evaluation of 14 morphological, yield-related and physiological traits as indicators of drought tolerance in Chinese winter bread wheat revealed by analysis of the membership function value of drought tolerance (MFVD). Field Crops Res 137: 195–201.
  • Claudio HC, Cheng Y, Fuentes DA, Gamon JA, Luo H, Oechel W, Qiu HL, Rahman AF, Sims DA (2006). Monitoring drought effects in vegetation 20 water content and fluxes in chaparral with the 970 nm water band index. Remote Sens Environ 103: 304–311.
  • Cobb JN, DeClerck G, Greenberg A, Clark R, McCouch S (2013). Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype–phenotype relationships and its relevance to crop improvement.  Theor Appl Genet 126: 867–887.
  • Condon AG, Richards, RA, Rebetzke GJ, Farquhar GD (2004). Breeding for high water-use efficiency. J Exp Bot 55: 2447– 2460.
  • El-Hendawy S, Hu Y, Schmidhalter U (2005). Growth, ion content, gas exchange, and water relations of wheat genotypes differing in salt tolerance. Austr J Agr Res 56: 123–134.
  • Elsayed S, Mistele B, Schmidhalter U (2011). Can changes in leaf water potential be assessed spectrally? Funct Plant Biol 38: 523–533.
  • El-Shikha DM, Waller P, Hunsaker D, Clarke T, Barnes E (2007). Ground-based remote sensing for assessing water and nitrogen status of broccoli. Agric Water Manage 92: 183–193.
  • Erdle K, Mistele B, Schmidhalter U (2013a). Spectral high-throughput assessments of phenotypic differences in biomass and nitrogen partitioning during grain filling of wheat under high yielding Western European conditions. Field Crops Res 141: 16–21.
  • Erdle K, Mistele B, Schmidhalter U (2013b). Spectral assessments of phenotypic differences in spike development during grain filling affected by varying N supply in wheat. J Plant Nutr Soil Sci 6: 952–963.
  • Feng B, Yu H, Hu Y, Gao X, Gao J, Gao D, Zhang S (2009). The physiological characteristics of the low canopy temperature wheat (Triticum aestivum L.) genotypes under simulated drought condition. Acta Physiol Plant 31: 1229–1235.
  • Gutierrez M, Reynolds MP, Raun WR, Stone ML, Klatt AR (2010). Spectral water indices for assessing yield in elite bread wheat genotypes in well irrigated, moisture stressed, and high temperature conditions. Crop Sci 50: 197–214.
  • Hackl H, Hu Y, Schmidhalter U (2014). Evaluating growth platforms and stress scenarios to assess the salt tolerance of wheat plants. Funct Plant Biol 40: 409–424.
  • Heege HJ, Reusch S, Thiessen E (2008). Prospects and results for optical systems for site-specific on-the-go control of nitrogen- top-dressing in Germany. Precis Agric 9: 115–131.
  • Hicks SK, Lascano RJ (1995). Estimation of leaf area index for cotton canopies using the LI-COR LAI-2000 plant canopy analyzer. Agron J 87: 458–464.
  • Hura T, Grzesiak S, Hura K, Thiemt E, Tokarz K, Wedzones M (2007). Physiological and biochemical tools useful in drought-tolerance detection in genotypes of winter triticale: accumulation of ferulic acid correlates with drought tolerance. Ann Bot 100: 767–775.
  • Jin X, Diao W, Xiao C, Wang F, Chen B (2013). Estimation of wheat agronomic parameters using new spectral indices. PLoS ONE 8: e72736.
  • Jones HG (1999). The use of thermography for quantitative studies of spatial and temporal variation of stomatal conductance over leaf surfaces. Plant Cell Environ 22: 1043–1055.
  • Jongdee B, Fukai S, Cooper M (2002). Leaf water potential and osmotic adjustment as physiological traits to improve drought tolerance in rice. Field Crop Res 76: 153–163.
  • Kipp S, Mistele B, Baresel P, Schmidhalter U (2014a).  High- throughput phenotyping early plant vigour of winter wheat. Eur J Agron 52: 271–278.
  • Kipp S, Mistele B, Schmidhalter U (2014b). Identification of stay- green and early-senescence phenotypes in high-yielding winter wheat and their relationship to grain yield and grain protein concentration using high-throughput phenotyping techniques. Funct Plant Biol 41: 227–235.
  • Leon CT, Shaw DR, Cox MS, Abshire MJ, Ward B, Wardlaw MC (2003). Utility of remote sensing in predicting crop and soil characteristics. Preci Agri 4: 359–384.
  • Lopes  MS,  Reynolds  MP (2012).  Stay-green in spring wheat can be determined by spectral reflectance measurements (normalized difference vegetation index) independently from phenology. J Exp Bot 63: 3789–3798.
  • Loss SP, Siddique KHM (1994). Morphological and physiological traits associated with wheat yield increases in Mediterranean environments. Adv Agron 52: 229–276.
  • Mistele B, Schmidhalter U (2010). Tractor-based quadrilateral spectral reflectance measurements to detect biomass and total aerial nitrogen in winter wheat. Agron J 102: 499–506.
  • Munjal R, Dhanda SS (2005). Physiological evaluation of wheat (Triticum aestivum L.) genotypes for drought resistance. Indian J Genet Pl Br 65: 307–308.
  • Mutava RN, Prasad PVV, Tuinstra MR, Kofoid KD, Yu J (2011) Characterization of sorghum genotypes for traits related to drought tolerance. Field Crops Res 123: 10–18.
  • Pask AJD, Reynolds MP (2013). Breeding for yield potential has increased deep soil water extraction capacity. Crop Sci 53: 2090–2104.
  • Peñuelas J, Filella I, Biel C, Serrano L, Save R (1993). The reflectance at the 950–970 mm region as an indicator of plant water status. Int J Remote Sens 14: 1887–1905.
  • Peñuelas J, Inoue Y (1999). Reflectance indices indicative of changes in water and pigment contents of peanut and wheat leaves. Photosynthetica 36: 355–360.
  • Peñuelas J, Piñol J, Ogaya R, Filella I (1997). Estimation of plant water concentration by the reflectance water index WI (R900/R970). Int J Remote Sens 18: 2869–2875.
  • Prasad B, Carver BF, Stone ML, Babar MA, Raun WR, Klatt AR (2007). Genetic analysis of indirect selection for winter wheat grain yield using spectral reflectance indices. Crop Sci 47: 1416–1425.
  • Rashid A, Stark JC, Tanveer A, Mustafa T (1999). Use of canopy temperature measurements as a screening tool for drought tolerance in spring wheat. J Agron Crop Sci 182: 231–237.
  • 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 Sci 39: 1611–1621.
  • Royo C, Álvaro F, Martos V, Ramdani A, Isidro J, Villegas D, García del Moral LF (2007). Genetic changes in durum wheat yield components and associated traits in Italian and Spanish varieties during the 20th century. Euphytica 155: 259–270.
  • Royo C, Aparicio N, Blanco R, Villegas D (2004). Leaf and green area development of durum wheat genotypes grown under Mediterranean conditions. Eur J Agron 20: 419–430.
  • Royo C, García del Moral LF, Slafer G, Nachit MM, Araus JL (2005). Selection tools for improving yield-associated physiological traits. In: Royo C, Nachit MN, Di Fonzo N, Araus JL, Pfeiffer WH, Slafer GA, editors. Durum Wheat Breeding: Current Approaches and Future Strategies. Binghamton, NY, USA: Food Products Press, pp. 563–598.
  • Scotford IM, Miller PCH (2004). Estimating tiller density and leaf area index of winter wheat using spectral reflectance and ultrasonic sensing techniques. Biosys Engin 89: 395–408.
  • Sims DA, Gamon JA (2003). Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: a comparison of indices based on liquid water and chlorophyll absorption features. Remote Sens Environ 84: 526–537.
  • Teal RK, Tubana B, Girma K, Freeman KW, Arnall DB, Walsh O, Raun WR (2006). In-season prediction of corn grain yield potential using normalized difference vegetation index. Agron J 98: 1488–1494.
  • Winterhalter L, Mistele B, Jampatong S, Schmidhalter U (2011). High throughput phenotyping of canopy water mass and canopy temperature in well-watered and drought stressed tropical maize hybrids in the vegetative stage. Eur J Agron 35: 22–32.
  • Zadoks JC, Chang TT, Konzak CF (1974). A decimal code for the growth stages of cereals. Weeds Res 14: 412–415.
  • Zia S, Romano G, Spreer W, Sanchez C, Cairns J, Araus JL, Müller J (2013). Infrared thermal imaging as a rapid tool for identifying moisture stress tolerant maize genotypes of different phenology. J Agron Crop Sci 199: 75–84.
Turkish Journal of Agriculture and Forestry-Cover
  • ISSN: 1300-011X
  • Yayın Aralığı: Yılda 6 Sayı
  • Yayıncı: TÜBİTAK