OWC/OWRF Use of Sensors and Spectral Reflectance Water Indices to Select for Grain Yield in Wheat Dr. Arthur Klatt Dr. Ali Babar Dr. B. Prasad Mr. Mario Gutierrez R.

Plant Breeding Methodology Selection in classical plant breeding is based on yield Breeding and release of new wheat cultivars or varieties have been based on grain yield measurements A large number of genotypes or advanced lines have to be evaluated for yield potential Promising high yielding genotypes require repeated testing across locations and years to make a final selection decision = costly and time consuming Yield has low heritability and a high genotype-environment interaction, which can lead to imprecise results Breeders are searching for new indirect selection tools to improve efficiency of selecting for yield (Richards, 1982)

Spectral Reflectance Technique Spectral reflectance from the canopy provides information about several physiological traits of the wheat plant Spectral reflectance measurements are convenient and can be used to screen a large number of genotypes, with limited cost and minimal time commitment Many indices have been developed from spectral reflectance measurements of the canopy Are there spectral reflectance indices (SRI) that differentiate wheat genotypes for yield potential, and do so consistently across years and environments?

THE LIGHT SPECTRUM Visible spectrum (400-700 nm) The human eye is sensitive to this region NIR (near infrared) (700-1300 nm) Human eye not able to see it

Scattered by atmosphere (i.e., clouds, particles, smoke) Energy source Absorbed by atmosphere (CO2, H2O, etc.) Reflected radiation by ground surface Plant canopy 1 Reflected radiation in Visible region (400-700 nm) NIR region (700-1300 nm) Absorbed radiation in the visible region (400-700 nm) Chlorophyll Xanthophylls Carotenoids 2 3 Transmitted radiation to the ground Absorbed by ground surface

Canopy reflectance Radiometer computer EReflected Refλ= EIncident Reflected radiation a) Visible region (400-700 nm) b) NIR region (700-1300 nm) Canopy reflectance EReflected EIncident Refλ= Radiometer computer

Canopy Reflectance Soil reflectance Visible Green Blue Red R900+R680 R900-R680 NDVI= Soil reflectance Visible Green Blue Red Near infrared (Lillesand et al., 2004)

Canopy Reflectance Visible Near infrared Leaf pigments Cell structure Chlorophyll Water absorption band at 970 nm Xanthophylls Green biomass Visible Near infrared (Lillesand et al., 2004)

Spectral Reflectance in Wheat Well irrigated Drought Water stress deficit Nitrogen status

Canopy Spectral Reflectance Chlorophyll strongly absorbs radiation in the visible region The absorbed radiation is influenced by Overall area of leaves Other photosynthetic organs (stem and spike) Pigment concentration Canopy spectral reflectance provides information to estimate other parameters Green biomass Leaf area index (photosynthetic area) Absorbed radiation (photosynthetic potential) Nutrient deficiencies Chlorophyll content (Knipling, 1970; Osborne et al., 2002)

Spectral reflectance indices Vegetation indices NDVI=900-680/900+680 RED-NDVI=780-670/780+670 GREEN-NDVI=780-550/780+550 NVI=555-680/555+680 NDVI-2=820-700/820+700 GNDVI=820-550/820+550 IVEST=780+550/680 VI 700=700-680/700+680 SR=900/680 NR=550/850 NRVI=850-550/850+550 NDVI-3=880-590/880+590 Grain yield Green Biomass Leaf area index Intercepted radiation Nitrogen content Photosynthetic capacity Chlorophyll indices RARSa=675/700 RARS2a=680/800 RARSb=675/650*700 RARSc=760/500) Datt1=780-710/780-680 Datt2=850-710/850-680 mND=750-705/750+705-2*445 mSR=750-445/705-445 Gitelson1=750-705/750+705 Gitelson2=750/700 NPQI=415-435/415+435 SIPI=800-435/415+435 Chlorophyll a, b Carotenoids SPAD readings Green biomass Water indices WI=900/970 NWI-1=970-900/970+900 NWI-2=970-850/970+850 NWI-3=970-880/970+880 NWI-4=970-920/970+920 Relative water content Canopy temperature (Aparicio et al., 2002; Araus et al., 2001; Babar et al., 2006; Knipling, 1970; Osborne et al., 2002; Prasad et al., 2007)

Water index Water index (WI) Peñuelas et al. (1993) established a water index based on the water absorption at 900 and 970 nm (as a reference) WI=R970/R900 It is related with relative water content in canopy, leaf water potential, and canopy temperature 970 nm wavelength is a water absorption band 900 nm is used as reference (Peñuelas et al., 1993)

Water index as selection criteria Babar et al. (2006) proposed two normalized water indices NWI-1=R970-R900/R970+R900 NWI-2=R970-R850/R970+R850 They showed high relationship with yield in spring wheat genotypes (r=-0.40 to -0.88) Prasad et al. (2007) proposed other two normalized water indices NWI-3=R970-R880/R970+R880 NWI-4=R970-R920/R970+R920 They found a strong correlation with yield (r=-0.40 to -0.86) in winter wheat (Babar et al., 2006; Prasad et al., 2007)

Water Index as Selection Criteria They compared the water indices at booting compared with heading-grain filling They determined that using the average SRI from the heading and grain filling stages can be used for predicting yield of the individual genotypes The normalization removes Soil interference Position of sun (illumination) Angle of view

Reflectance Calibration from a Radiometer Handheld sensor B) Maximum reflectance Foreoptic Growth stages Booting Heading (anthesis) Grain filling 50 cm 25o A) Portable spectroradiometer (UV/VNIR FieldSpec) No radiation (dark) Barium sulfate panel

HTWYT Yield (n=18) Grain yield (Kg ha-1) range 2006 2007 Well-irrigated 5700-8290 4610-7850 Drought - 1000-1800 High temperature 1870-3800 1110-3900

Heading-Grain filling HTWYT Well-irrigated (n=18) Relationship between spectral indices and grain yield 2006 2007 Booting Heading-Grain filling Vegetation indices RNDVI 0.10 0.39 0.38 0.27 GNDVI 0.21 0.42 0.19 SR 0.01 0.49 0.40 Water indices WI -0.18 -0.35 -0.69** -0.61** NWI1 -0.33 -0.62** NWI2 -0.16 -0.38 -0.65** -0.59** NWI3 -0.36 -0.64** -0.55* *Significant at p=0.05 **Significant at p=0.01

Relationship Between Spectral Indices and Grain Yield Low water index value means high Yield Water content in canopy Transpiration rate Photosynthesis rate Cooler canopies

Heading-Grain filling HTWYT-Drought Relationship between spectral indices and grain yield 2007 Booting Heading-Grain filling Vegetation indices RNDVI 0.62** -0.05 GNDVI 0.33 -0.12 SR 0.57** -0.03 Water indices WI -0.26 -0.57* NWI1 -0.52* NWI2 -0.37 -0.63** NWI3 -0.32 -0.61** *Significant at p=0.05 **Significant at p=0.01

HTWYT-High Temperature (n=18) Heading-Grain filling (> 39oC at midday) Relationship between spectral indices and grain yield 2006 2007 Booting Heading-Grain filling Vegetation indices RNDVI 0.69** 0.63** 0.75** 0.52* GNDVI 0.62** 0.78** 0.51* SR 0.68* 0.58** 0.56* 0.44 Water indices WI -0.76** -0.51* -0.85** -0.78** NWI1 -0.77** -0.50* NWI2 -0.42 -0.89** -0.71** NWI3 -0.75** -0.48* -0.88** *Significant at p=0.05 **Significant at p=0.01

SAWYT Yield (n=50) Grain yield (Kg ha-1) range 2006 2007 Well-irrigated 3330-12650 3830-9450 Drought 510-2490 490-3130

SAWYT-well irrigated (n=50) Heading-Grain filling Relationship between spectral indices and grain yield 2006 2007 Booting Heading-Grain filling Vegetation indices RNDVI -0.04 -0.09 -0.01 -0.10 GNDVI 0.04 0.03 -0.07 SR -0.02 -0.13 0.19 -0.20 Water indices WI -0.31* -0.43** -0.17 -0.55** NWI1 -0.16 NWI2 -0.33* -0.41** -0.19 -0.53** NWI3 -0.32* -0.42** -0.18 -0.52** *Significant at p=0.05 **Significant at p=0.01

Heading-Grain filling SAWYT-Drought (n=50) Relationship between spectral indices and grain yield 2006 2007 Booting Heading-Grain filling Vegetation indices RNDVI -0.08 0.26 -0.17 0.18 GNDVI -0.05 0.01 0.05 SR -0.16 0.06 Water indices WI -0.32* -0.41** -0.50** -0.51** NWI1 -0.40** -0.49** NWI2 -0.43** -0.48** NWI3 -0.44* *Significant at p=0.05 **Significant at p=0.01

CONCLUSIONS The potential of the water indices for predicting grain yield in wheat has been demonstrated repeatedly in diverse environments The water indices have an inverse relationship with grain yield The water indices have shown a consistently good association with yield under well-irrigated, drought and high temperature conditions There is essentially no difference in accuracy between the different water indices in predicting yield and total biomass These indices may prove to be an efficient and low cost indirect selection tool for wheat breeders May be especially effective in selecting at the preliminary yield trial level—testing is now being done!

THE NEWEST HAND-HELD SENSOR