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a Department of Plant and Soil Sciences, 368 Ag. Hall, Oklahoma State University, Stillwater, OK 74078, USA
b International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera Mexico, El Batan, Texcoco, Mexico
c Department of Primary Industries (DPI), Private Bag 260, Horsham, Victoria, Postcode: 3401, DX Number: 216515, Australia
d Department of Biosystems and Agricultural Engineering, Oklahoma State University, Stillwater, OK 74078, USA
* Corresponding author (mreynolds{at}cgiar.org)
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ABSTRACT |
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 TOPNOTES ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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Abbreviations: CT, canopy temperature • CTD, canopy temperature depression • GHIST, global historic • NDVI, normalized difference vegetation index • NIR, near infrared radiation • NWI-1, normalized water index-1 • NWI-2, normalized water index-2 • PSSRa, pigment specific simple ratio-chlorophyll a • RARSa, ratio analysis of reflectance spectra-chlorophyll a • RARSb, ratio analysis of reflectance spectra-chlorophyll b • RARSc, ratio analysis of reflectance spectra-carotenoids • RILs, recombinant inbred lines • SR, simple ratio • SRI, spectral reflectance indices • WI, water index
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INTRODUCTION |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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Direct estimation of biomass is a time- and labor-intensive undertaking. Moreover, destructive in-season sampling involves large sampling errors (Whan et al., 1991) and reduces the final area for estimation of grain yield and final biomass. Regan et al. (1992) demonstrated a method to select superior genotypes of spring wheat for early vigor under rainfed conditions using a destructive sampling technique, but such sampling is impossible for breeding programs where a large number of genotypes are being screened for various desirable traits. Spectral reflectance indices are a potentially rapid technique that could assess biomass at the genotypic level without destructive sampling (Elliott and Regan, 1993; Smith et al., 1993; Bellairs et al., 1996; Peñuelas et al., 1997).
Canopy light reflectance properties based mainly on the absorption of light at a specific wavelength are associated with specific plant characteristics. The spectral reflectance in the visible (VIS) wavelengths (400–700 nm) depends on the absorption of light by leaf chlorophyll and associated pigments such as carotenoid and anthocyanins. The reflectance of the VIS wavelengths is relatively low because of the high absorption of light energy by these pigments. In contrast, the reflectance of the NIR wavelengths (700–1300 nm) is high, since it is not absorbed by plant pigments and is scattered by plant tissue at different levels in the canopy, such that much of it is reflected back rather than being absorbed by the soil (Knipling, 1970). Spectral reflectance indices were developed on the basis of simple mathematical formula, such as ratios or differences between the reflectance at given wavelengths (Araus et al., 2001). Simple ratio (SR = NIR/VIS) and the normalized difference vegetation index [NDVI = (NIR – VIS)/(NIR + VIS)] were the first developed SRI, and they combined information from the VIS and NIR wavelengths. These indices were used to predict different vegetative parameters, such as green biomass and green leaf area index. Spectral reflectance indices have been developed based only on VIS, such as the photochemical reflectance index [PRI = (R531 – R570)/(R531 + R570)] to assess radiation use efficiency by the plants (Peñuelas et al., 1995), and also only on NIR, such as the water index (WI = R970/R900) to assess water status of the canopy (Peñuelas et al., 1993).
Spectral reflectance indices have proven to be useful in the assessment of early biomass and vigor of different wheat genotypes (Elliott and Regan, 1993; Bellairs et al., 1996). Their studies were performed mainly under water-limiting conditions and with measurements taken at early growth stages to measure early vigor. The most widely used SRI, the NDVI and the SR, were not able to predict the variations in biomass successfully when estimated at later growth stages in durum wheat (Aparicio et al., 2000, 2002). Therefore, it is necessary to investigate the reflectance at other wavelengths to verify if there is a relationship between biomass and SRI, which may provide an indirect selection tool to differentiate spring wheat genotypes for biomass production.
Spectral reflectance indices based on visible wavelengths have also been developed to estimate the concentration of different leaf pigments such as chlorophyll and carotenoids. Chappelle et al. (1992) developed ratio analysis of reflectance spectra indices (RARS) to estimate chlorophyll-a, chlorophyll-b, and carotenoids based on the reflectance of light at different visible wavelengths (500, 650, 675, 700 nm) in soybean leaves. Later Blackburn (1998) used reflectance at 680 and 800 nm to estimate chlorophyll-a concentration, and showed that the combination of these wavelengths significantly improved the relationship with chlorophyll-a compared to the indices developed by Chappelle et al. (1992). A handheld portable SPAD chlorophyll meter has also been used to estimate chlorophyll concentration. SPAD chlorophyll meter readings have been shown to be strongly associated with extracted chlorophyll from the plants (Yadava, 1986; Dwyer et al., 1991). The relationship between spectral indices (RARS) and the SPAD chlorophyll meter values were never verified at the genotypic level. It is worthwhile to verify the relationship between the RARS indices and SPAD chlorophyll meter values in different growth stages at the genotypic level, so that breeders can quantitatively assess important physiological traits like stay-green to improve the productivity of the genotypes.
The WI, calculated from the reflected light at 900 and 970 nm, is an indicator of plant water status at both the leaf and canopy level (Peñuelas et al., 1993, 1997). It can assess the changes of relative water content, leaf water potential, stomatal conductance, and canopy temperature depression (CTD) when water stress becomes considerable (Peñuelas et al., 1993). Wheat genotypes with higher grain yield have been demonstrated to have a cooler canopy under irrigated conditions (Fischer et al., 1998). A significant positive correlation between WI and CTD was reported in spring wheat under irrigation by Gutierrez-Rodriguez et al. (2004), but the study was conducted with a small number of unrelated genotypes. The correlation between WI and CTD in different generations of the breeding population and with stable adapted genotypes may provide a true genetic relationship between these two physiological parameters, and the information could be useful for plant breeders in selecting genotypes with higher productive capacity.
Thus, the objectives of the present study were to (i) evaluate the correlation of the most widely used SRI, NDVI and SR, with the biomass production of a large number of bread wheat genotypes under near optimum N and irrigation levels at different stages of development; (ii) derive new SRI that better differentiate genotypes with higher biomass production capacity; (iii) calculate the association of chlorophyll concentration estimated by the SPAD chlorophyll meter and the spectral indices based on the visible wavelengths; and (iv) determine the relationship between CT and the WI at the genotypic level.
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MATERIALS AND METHODS |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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The seeding rate for each experiment was 78 kg ha–1. Nitrogen and P were applied to the plots at the rate of 200 kg ha–1 and 26 kg ha–1, respectively. During the first 2 yr, 150 kg N and all of the P were applied during land preparation, and 50 kg N was applied in the second week of January, coinciding with the first node growth stage and the second supplementary irrigation. In the third year, the same procedure was followed, but half of the total dosage of N was applied before planting and half with the second supplementary irrigation. A total of five supplementary irrigations were applied in the first and third years, and four supplementary irrigations in the second year. Folicur 250EW (25% tebuconazole) was applied twice in every crop cycle, in the second week of February and in the second week of March at the rate of 0.5 L ha–1 to protect the experimental materials from leaf rust (caused by Puccinia triticina Eriks.).
Experimental Materials
Experiment 1 (GHIST): This experiment contained 15 widely adapted and unrelated spring bread wheat genotypes developed by the CIMMYT wheat-breeding program over the past 40 yr, released by national programs around the world, and widely grown by the farmers. The genotypes represent the historical success achieved by CIMMYT's breeding program. While all are high yielding, they vary widely in morphological traits. The genotypes were planted in a 5 by 3 
-lattice design with two replications. In this paper, this experiment is referred to as "GHIST," since it studies a historical set of commercial genotypes.
Experiment 2 (RILs1): This experiment had 25 genotypes, comprising 23 RILs and the two parents (‘Sonalika’ and ‘Attila’). The RILs were random F3–derived F6 and F7 lines. All plants in the F2 were bulked. The experiment was planted in a 5 by 5 -lattice design with two replications. In this paper, this experiment is designated as "RILs1."
Experiment 3 (RILs2): This experiment consisted of a total of 36 genotypes, comprising 34 RILs and the two parents (‘Bacanora88’ and ‘Cndo/R143//Ente/Mexi2/3/Ae. sq.(Taus)/4/Weaver). The RILs were developed in the same procedure as described for RILs1. The experiment was planted in a 6 by 6 -lattice design with two replications. In this paper, this experiment is called "RILs2."
Radiometric Measurements
The spectral reflectance measurements were taken by a portable narrow-bandwidth Spectroradiometer (Model FieldSpec UV/VNIR, Analytical Spectral Devices, Boulder, CO) with 25° field of view. This instrument can detect reflected light from the canopy ranging from 350 to 1100 nm. Therefore, it covers the visible and part of the NIR portion of the spectrum. The Spectroradiometer gave 512 continuous bands with a sampling interval of 1.43 nm. The Spectroradiometer was connected to a computer, which stored the individual scans for subsequent processing. Each reflectance measurement was the average of 10 scans from an area of 18.94 cm2 of the plot. The sensor was mounted with the help of a pistol grip 40 to 50 cm above the canopy facing the center of the bed. The Spectroradiometer was calibrated against a white reference plate (BaSO4) after collecting spectral reflectance readings from every 10th plot. The reflectance measurements were taken between 1030 to 1330 h under sunny conditions, and from four different places randomly within each plot. The mean of those four readings were used to calculate the spectral indices.
The spectral reflectance measurements were taken at booting (Zadoks stage 39–47), heading (Zadoks stage 55–69), and grainfilling (Zadoks stage 75–83) in the GHIST experiment for 3 yr (years 2001–2002, 2002–2003, and 2003–2004) and in RILs1 and RILs2 for 1 yr (year 2003–2004) (Zadoks et al., 1974). For the RILs1 and RILs2 in year 2002–2003 the readings were only taken at booting and heading, and not at grainfilling. The genotypes varied among themselves for their growth stages. So the reflectance measurements were taken in different genotypes in different times coinciding with closest possible day to the respective growth stages.
Harvest of Biomass
For harvesting biomass, all the plants in a 0.5-m-long area were cut at soil level from one of the two beds of each plot, and harvested area for biomass was 0.4 m2 (0.5 by 0.8 m). The spectral reflectance data were taken randomly before harvesting biomass. The biomass cut was done randomly from the middle 3 m of the plot (1-m areas were excluded from both ends of the plot to avoid any border effect) on the same or the closest possible day coinciding with the spectral reflectance measurements. After harvesting of biomass, total fresh weight was taken and a representative sample was oven-dried at 75°C for 48 h. The oven-dried weight of biomass was recorded and total dry weight of biomass was estimated from total fresh weight and converted into grams per square meter. Biomass was sampled three times at booting, heading, and grainfilling in the GHIST experiments in both years (2002–2003 and 2003–2004), and two times (booting and heading) and three times (booting, heading, and grainfilling) for the other two experiments (RILs1 and RILs2) in the years 2002–2003 and 2003–2004, respectively.
Chlorophyll Meter Measurements
A handheld portable SPAD-502 chlorophyll meter (Minolta, Tokyo, Japan) was used to estimate chlorophyll concentration. This instrument provides a convenient means of assessing relative leaf chlorophyll concentration. Fifteen flag leaves were used to take chlorophyll meter readings from each plot at three growth stages (booting, heading, and grainfilling) in the GHIST experiment for 3 yr, and the data presented are the means of the 15 readings for each plot. Chlorophyll meter data were taken on the same day or the closest possible day coinciding with the spectral reflectance measurements.
Calculation and Selection of Indices
Initially, different ratios and normalized SRI were calculated using a combination of different visible and NIR wavelengths. Based on these results, two indices (based on NIR) were selected for presentation in this paper. The two indices combined information from 850, 900, and 970 nm. The 970 nm has been reported as a weak water absorption band (Peñuelas et al., 1993), and the other two bands (850 and 900 nm) were used as reference bands to normalize 970 nm. We have referred to these two indices as normalized water index-1 (NWI-1) and normalized water index-2 (NWI-2). Seven other reference indices, including the most widely used NDVI and SR to assess different physiological parameters, were also calculated. The different SRI, which were calculated based on different references, are provided in the Table 1. The above mentioned normalized water indices were calculated as follows:
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Statistical Analysis
Alpha-lattice analyses for biomass, different SRI, SPAD-chlorophyll meter readings, and RARS and pigment-specific simple ratio chlorophyll-a (PSSRa) indices (combined across different years) were performed using the MIXED procedure of the SAS software (SAS Institute, 2001). Phenotypic and genetic correlations were used to estimate the relationship between biomass and different spectral indices, between SPAD chlorophyll meter readings and RARS and PSSRa indices, and between CT and NIR-based indices at different growth stages within the year and averaged over different growth stages within the year. These correlations were estimated using the SAS software (SAS Institute, 2001).
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RESULTS |
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Correlations between Dry Biomass and SRI
Spectral reflectance indices showed a significant phenotypic correlation with dry biomass at the heading and grainfilling stages (Table 5). Near infrared radiation–based indices were negatively correlated with dry biomass, while NDVI and SR were positively correlated. NDVI and SR were uncorrelated with dry biomass at the booting stage, but NIR-based indices (WI, NWI-1, and NWI-2) were consistently correlated with dry biomass with a single exception.
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The relationships (phenotypic) between dry biomass and NDVI, SR and one of the NIR-based indices (NWI-2) are presented in a simple linear model (Fig. 3 ), where correlation coefficients were calculated from the values averaged over all the growth stages and years for biomass and SRI. The NIR-based index (NWI-2, Fig. 3c) explained a greater proportion of the variability than the other two indices (NDVI and SR) (Fig. 3a,3b). Considering all the phenotypic and genetic correlations in all experiments in all growth stages, NIR-based indices gave a higher predictive capacity to differentiate genotypes for total dry biomass than either NDVI or SR.
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DISCUSSION |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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Large genotypic variation for biomass production, which is evident in our study at various growth stages, has also been reported by other authors in spring wheat (Calderini et al., 1997) and in durum wheat (Triticum turgidum L. var. durum) (Aparicio et al., 2002).
Effect of Growth Stages
Younger wheat plants generally absorb more photosynthetically active radiation, and therefore, reflect more NIR. As the plants progress in growth stage, new tissue is formed (until all tillers have reached flag leaf emergence stage), but also older green tissue loses chlorophyll concentration, turns chlorotic and then necrotic. The latter increases the reflectance of the visible wavelengths and decreases reflectance at the NIR wavelengths. Normalized difference vegetation index and SR, which were developed based on the red (visible) and NIR wavelengths, give higher values at the early growth stages, but their values decrease with the advancement in growth cycle because the plants are losing photosynthetically active plant parts. This phenomenon is evident in our study and has also been reported by other researchers (Aparicio et al., 2000). Modern wheat genotypes reach maximum leaf area index at booting under irrigated conditions (Calderini et al., 1997) and subsequently the lower leaves senesce because of chlorophyll loss. Fischer et al. (1998) also found a decreasing photosynthetic rate with advanced growth stage under the same environmental conditions where the present study was conducted.
The NIR-based indices (WI, NWI-1 and NWI-2) were calculated combining information from 850, 900, and 970 nm. The 970-nm wavelength is a water absorption band and the sensitivity of this band depends on the extent of penetration of radiation into the canopy, thus reflectance is more dependent on the total water content (Bull, 1991). Moreover, WI is influenced by leaf area index, and therefore by the total biomass (Royo et al., 2003). Jackson and Pinter (1986) found that the architecture of the plant canopy affects the direction of reflection of incident radiation. Near infrared radiation reflectance decreases with a change in leaf orientation, predominantly from horizontal to vertical at certain stages in the growth cycle (Jackson and Erza, 1985). The three NIR-based indices used in this study incorporated the water status of the canopy, and the index values decreased from booting to heading and increased again from heading to grainfilling. The lower the value, the higher the amount of water that is retained by the canopy. The NIR penetrated more into the canopy at the heading stage because of the fully extended flag leaf. Moreover because of the higher total water content (fresh weight minus dry weight, Table 4) at that growth stage, more energy was absorbed from the radiation that penetrated into the canopy. The combination of these two factors contributed to the reduction in indices values from the booting to the heading stage. The indices values increased as the total water content decreased (Table 4), but the penetration of the NIR wavelength was still high because of the fully extended flag leaf at the grainfilling stage, which contributed to a lower value for the NIR-based indices at the grainfilling stage compared to the booting stage.
The SPAD and other visible wavelength based spectral indices (PSSRa, RARSb and RARSc) gave the highest values at the booting stage and maintained a similar value at the heading stage. However, the values decreased sharply at the grainfilling stage. RARSa gave the lowest value at the booting stage and the index was calculated by using the formula R675/R700. Because of the division of R675 by R700, lower values reflect a high concentration of chlorophyll-a. Therefore, chlorophyll-a values decrease with plant development. Modern wheat varieties possess the highest leaf area index at or near the booting stage (Calderini et al., 1997), and afterward the lower leaves start to senesce. The decrease in indices values (based on different leaf pigments absorption bands) after booting is largely due to the loss of green plant tissue.
Biomass accumulation increased with advances in the growth cycle. The maximum accumulated dry biomass was at the grainfilling stage in all experiments. The increase in biomass under irrigated conditions as growth stage progresses is logical and was also reported by Jackson and Pinter (1986) in spring wheat and Aparicio et al. (2000) in durum wheat.
Correlation between Dry Biomass and SRI
The spectral indices based on NIR (WI, NWI-1, and NWI-2) generally showed a negative significant correlation with the dry biomass, and the correlation coefficients increased in value with an advance in growth stage. The highest correlation occurred either at the heading or the grainfilling stage. These indices are indicators of water status in the canopy; the lower the values, the greater the amount of water retained by the canopy. Therefore, negative correlations indicate a canopy with higher amounts of water and this corresponds to higher biomass production. The strong genetic correlation (RILs1 and RILs2) and correlation among genotypes (GHIST) between dry biomass production and the NIR-based indices (averaged over three growth stages) indicate that this relationship can be mainly attributed to the genotypes, and not to environmental factors.
A further correlation analysis was done to better understand the relationship between biomass and NIR-based indices. Canopy water content was estimated by deducting dry biomass from fresh biomass values (Ahlrichs and Bauer, 1983). The correlations between water content and dry biomass, and between water content and the NIR-based indices were subsequently calculated (data not presented). In general, a strong correlation was found between water content and dry biomass (r = 0.708–0.952 in the different experiments). The correlation between water content and NIR-based indices ranged from –0.582 to –0.904 when estimated at heading and at grainfilling in the different experiments. Generally, the relationship between water content and NIR-based indices was higher at the reproductive growth stages than at the vegetative growth stages. These three-way associations between water content and NIR-based indices, and between water content and dry biomass has contributed indirectly to the association between biomass and NIR-based indices, and to the higher association in the reproductive growth stages than in the vegetative growth stages.
The superiority in performance among these three NIR-based indices was indistinguishable. Normalizing the WI (NWI-1 and NWI-2) did not improve the relationship. However, Tucker (1979) showed the superiority of a normalized index over a ratio index under water-stressed conditions. The normalization partially removed the disturbance caused by external factors such as soil interference, position of sun, illumination, and angle of view. Our study was done under irrigated conditions where the plots were more uniform and complete ground cover was achieved in the early growth stages, which minimized the interference from the soil. A constant field of view (25°) was used throughout the experimental cycle and the data were taken in the middle of the day (1030 to 1330 h) under cloud-free conditions. These circumstances resulted in a minimal advantage for the normalized value over the ratio index, and a high correlation with all three indices made it difficult to determine that any specific one was superior. Similarly Royo et al. (2003) reported similar correlation values between grain yield and NDVI and SR under irrigated conditions in durum wheat when the spectral reflectance data were taken at the grainfilling stage by using 900 and 680 nm to develop the NDVI and the SR.
Normalized difference vegetation index and SR gave significant positive correlations with dry biomass when estimated at the heading and grainfilling stages in almost all occasions. Aparicio et al. (2000) reported a positive significant correlation between crop dry matter and NDVI and SR in durum wheat under irrigated conditions at the grainfilling stage. Correlation values generally increased with growth stage, which was also supported by Aparicio et al. (2000) and Aparicio et al. (2002) in durum wheat.
All indices studied explained the largest amount of variation when taken at the heading and grainfilling stages. Hence, those two stages appear to be the most appropriate time to utilize the spectral indices to discriminate the genotypes for biomass production, and thus crop productivity.
Correlations between Dry Biomass and SPAD Values, RARSa, PSSRa, RARSb, RARSc
Estimation of green biomass from the measurements of spectral reflectance at the visible wavelengths is mainly based on the absorption of electromagnetic wavelengths by chlorophyll and associated pigments. A strong negative correlation between visible wavelengths and dry biomass was reported by Ahlrichs and Bauer (1983) and Elliott and Regan (1993) in wheat under water-limiting conditions at low levels of biomass production. The light at visible wavelengths is highly absorbed by the green vegetation and at a very high concentration of pigments, the absorption of visible wavelengths (especially red light) is saturated by the pigments (Gitelson et al., 1996). In this study a low correlation was noted between indices based on visible wavelengths and SPAD values with biomass at the early growth stages. This may have been caused by a very high concentration of different pigments at the early growth stages, which made the indices insensitive to any differences among genotypes for biomass production. The correlation levels increased with advanced growth stages, when the plants started to lose their green photosynthetically active parts, thereby increasing the variability among the genotypes for the reflection of light at visible wavelengths, which was more effectively detected by the indices. The strong correlation among genotypes between biomass and the visible wavelength–based indices indicate the higher contribution of genotypic effects rather than environmental effects to these associations.
Even though RARSa and PSSRa are indicators of the chlorophyll-a concentration in the plant, a differential performance was observed between these indices to distinguish genotypes for biomass production. Blackburn (1998) developed the PSSRa index by using 680- and 800-nm wavelengths, which significantly improved the capacity to predict chlorophyll-a concentration over the RARSa index that was developed by Chappelle et al. (1992) using the 675 and 700 nm wavelengths. PSSRa combined information from both the red and NIR wavelengths, while RARSa combines information only from the red bands. Combining the information from the NIR wavelengths may have contributed to the improved correlation between biomass and PSSRa over RARSa. This study demonstrates the effectiveness of using PSSRa over RARSa at the genotypic level as well. It also demonstrates the possibility of using indices based on visible wavelengths to differentiate genotypes with high biomass production levels under irrigated conditions at reproductive growth stages when the accumulated biomass contributes to the total yield by partitioning assimilates to the grains.
Correlation between SPAD Values and RARSa, PSSRa, RARSb, and RARSc
The SPAD-502 chlorophyll meter is a handheld portable instrument, which estimates chlorophyll, based on the amount of transmitted light in the chlorophyll absorption region of the spectrum. The SPAD chlorophyll meter uses light transmittance at 650 and 940 nm to estimate leaf chlorophyll concentration (Yadava, 1986; Dwyer et al., 1991), and thus monitors N status in corn leaves (Blackmer et al., 1994; Blackmer and Schepers, 1995). SPAD chlorophyll meter readings have been reported to be well correlated with different wavelengths (550, 650, 710 nm) under different N levels in corn (Blackmer et al., 1994; Schepers et al., 1996). RARSa, RARSb, and RARSc were developed to estimate chlorophyll-a, chlorophyll-b, and carotenoids in soybean (Chappelle et al., 1992), and PSSRa was developed to estimate chlorophyll-a in a range of plant species (Blackburn, 1998). Blackburn (1998) showed the optimum individual wavelengths for the estimation of different leaf pigments concentration such as chlorophyll-a, chlorophyll-b, and carotenoid were 680, 635, and 470 nm, respectively.
The SPAD chlorophyll meter uses single leaves to estimate chlorophyll concentration, but reflectance measurements can be made using the entire canopy. The advantage of reflectance measurements is that when made from above the canopy, they represent a more representative area relative to a single leaf. In this study, we have observed strong relationships between SPAD readings and PSSRa, RARSb, and RARSc when estimated at the heading and grainfilling stages, but the relationship was poor at booting. The relationship of these indices at booting was not well understood. At the later growth stages when the plants started to loose their pigments, both devices (chlorophyll meter and spectrometer) were equally effective, especially at the grainfilling stage. Our results verify the "greenness" as a factor in biomass production and the ability to accurately measure these differences should have an important impact on breeding progress where green leaf area duration (also known as "stay-green") is considered an important empirical trait in improving the productivity of genotypes.
Correlation between Canopy Temperature and NIR-Based Indices
Canopy temperature or CTD (CT minus air temperature), which is sensed remotely using an infrared thermometer, has been shown to be closely associated with grain yield of wheat cultivars (Reynolds et al., 1994; Fischer et al., 1998), as well as that of RILs and advanced breeding materials (Reynolds et al., 1998, 1999) in irrigated, high radiation environments. Leaf temperature is depressed below air temperature when water evaporates from the surface of leaves, thus keeping the canopy cooler. The WI assesses canopy water status and depends on the absorption of light by water at certain NIR wavelengths. The higher the water content of the tissue, the greater the absorption, and consequently the lower the reflectance. Peñuelas et al. (1997) showed that WI and CTD was well correlated under irrigated conditions across different salinity gradients at the genotypic level. Babar et al. (2006) found a strong association between grain yield of wheat and WI under the same environmental conditions where the present study was conducted.
In this study, strong positive correlations were observed between CT and NIR-based indices when the data were taken at heading and at the grainfilling stages. These strong positive correlations indicate that genotypes with higher water content (represented by lower NIR-based indices values) had lower CT. Thus, the canopy with higher water content are indicative of genotypes with higher biomass resulting from larger rates of carbon fixation associated with greater stomatal conductance and therefore, cooler canopies. Gutierrez-Rodriguez et al. (2004) suggested that the WI could be an alternative to CTD. Our results have reconfirmed that finding across a broad spectrum of genotypes. The combined information on these two traits can help plant breeders to select genotypes with higher water content and canopies with lower temperatures, thus identifying genotypes with higher productive capacity.
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CONCLUSIONS |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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NOTES |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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Received for publication March 11, 2005.
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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