Indian Journal of Agricultural Research

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Research Article

Indian Journal of Agricultural Research, volume 55 issue 2 (april 2021) : 151-156

Indirect Selection of Tolerant Barley (Hordeum vulgare L.) Genotypes under Semi Arid Conditions Based on the Numerical Images Analysis Indices

Hocine Bendada1,*, Ali Guendouz2, Ramdane Benniou1, Nasreddine Louahdi3
1Department of Agronomy University of M’sila 28000 Algeria.
2National Institute of the Agronomic Research of Algeria, Setif, Algeria.
3Technical Institute of Field Crops, Setif, Algeria.
Cite article:- Bendada Hocine, Guendouz Ali, Benniou Ramdane, Louahdi Nasreddine (2020). Indirect Selection of Tolerant Barley (Hordeum vulgare L.) Genotypes under Semi Arid Conditions Based on the Numerical Images Analysis Indices . Indian Journal of Agricultural Research. 55(2): 151-156. doi: 10.18805/IJARe.A-448.

ABSTRACT

Image analysis systems have been increasingly utilized for the assessment of plant growth and health for decades. We used in this study the software Mesurim Pro to evaluate the variation of the leaf reflectance at Red, Green and Blue and the variation of the senescence parameters. The analysis of variance revealed that the reflectance at different wavelengths (Red, Blue and Green) was highly significant genotypes effects (P < 0.001); for this parameter the good genotypes are those we have the lowest values such as G19. In addition, the preferable genotypes were those which have low values for the mean senescence and senescence velocity; based on this raison the best genotype was the introduce genotype G12. The genotypes effect was significant for the grain yield and thousand-kernel weight, for the chlorophyll content and the analysis of variance showed a significant effect of genotypes, the highest values registered by the introduced genotype G5 this one was in the same homogenize group of G2, G4, G8 and G18. The ranking of genotypes based on all parameters suggested that the genotypes G11, G12, G5, G15 and G18, respectively (introduce genotypes) were the ideal genotypes under these conditions.      

INTRODUCTION

Barley (Hordeum vulgare L.) is the one of the most important cereal grain crops is cultivated all over the world; after wheat, rice, corn and potato has the fifth rank in production point of view in world. Also, barley is the main food resource for human beings and livestock in Middle East. The adaptation of barley is better than wheat and other crops in environmental stresses condition; nevertheless, abiotic stresses are a major factor limiting barley production in many Mediterranean environments (Ceccarelli and Grando, 1996). Drought stress is a significant abiotic factor that can diminish photosynthesis efficiency by reducing leaf expansion, hence, causing premature leaf senescence. Senescence is defined as the gradual deterioration of its functions with age, as leaves change color because chlorophyll is broken down, water content is reduced and membranes break down (Hafsi and Guendouz, 2012). Pigments are integrally related to the physiological function of leaves. Chlorophylls absorb light energy and transfer it into the photosynthetic apparatus. Carotenoids (yellow pigments) can also contribute energy to the photosynthetic system. Changes in leaf reflectance of green leaves with maturation and senescence have been attributed to changes in chlorophyll and mesophyll arrangement (Grant, 1987).
 
Variations in leaf chlorophyll content detectable by Spectral reflectance have also been shown to be related to leaf development and senescence (Carter and Knapp, 2001). Canopy spectral reflectance provides an important method for plant canopy study under different environmental conditions. The visible region of the vegetation reflectance spectrum is characterized by low reflectance and transmittance due to strong absorptions by foliar pigments. For example, chlorophyll pigments absorb violet-blue and red light for photosynthesis. Green light is not absorbed for photosynthesis, hence most plants appear green. Recently, digital imagery has become a new trend in plant color analysis. Digital cameras or scanners in combination with computers and appropriate software can be used to photograph, scan and evaluate leaves for color with relative ease and at an affordable cost. In agriculture, digital technology has been used to characterize color in apples (Schrevens and Raeymaeckers, 1992), evaluate senescence rates in spring wheat (Adamsen et al., 1999) and durum wheat (Hafsi et al., 2000; Guendouz and Maamari, 2011; Guendouz et al., 2012). The aim of this study is to evaluate the efficiency of using the numerical image analysis indices to select tolerant and adapted genotypes under semi arid conditions.

MATERIALS AND METHODS

Plant material and experiment designs
 
Set of 26 genotypes of barley (Hordeum vulgare L.) (Table 1) were planted on 17 December 2017, in the experimental fields of ITGC, Setif, Algeria (5°20’E, 36°8”N, 958m above sea level) genotypes were grown in randomized block design with three replicates. Plots were 5 m × 6 rows with 0.20 m row spacing and sowing density was adjusted to 250 g m-2.
 

Table 1: Origin and spike type of the genotypes used in study.


 
Flag leaf reflectance (FLR) and leaf senescence (S)
 
Flag leaf reflectance (FLR) and leaf senescence (S) was evaluated by numerical image analysis (NIA) according to Guendouz et al., (2012) and Hafsi et al (2000). To estimate the reflectance at Red, Green and Blue (RGB); leaves were photographed on black surface, between 11:00 and 12:00 solar time with a color digital camera (Canon, Power Shot A460, AiAF, CHINA). Images were stored in a JPEG (Joint Photographic Expert Group) prior to downloading onto a PC computer and analyzed using Mesurim Pro (Version 3.3) software (Fig 1). Senescence was expressed as the ratio of senesced area to total leaf area (in per cent). Measurements were carried out eight times between flowering and the end of senescence for each genotype. In addition to S, two parameters calculated to characterize the dynamics of senescence; average senescence (S %) was calculated as the mean of the S1 to S8 values. The velocity of senescence (Sv) was calculated for each date of senescence measurement as (Si+1 - Si) / (Σti+1 - Σti).
 

Fig 1: Description of measuring the reflectance at RGB (Red, Green, Blue) using Mesurim Pro software.


 
Chlorophyll content (Chl)
 
The SPAD-502 measures the amount of chlorophyll (Chl) in the leaf, which is related to leaf greenness, by transmitting light from light emitting diodes (LED) through a leaf at wavelengths of 650 and 940 nm. SPAD meters have been used to estimate chlorophyll concentrations and infer nitrogen status of single leaves of wheat, corn (Zea mays L.) and other plants (Wood et al., 1993; Blackmer and Schepers, 1995).
 
Agronomical measurements
 
Furthermore, grain yield (GY) and thousand-kernel weight (TKW) was determined from sub-samples taken from harvested grains of each plot.
 
Statistical analysis
 
Data were analyzed using Costat, version 6.4. The analysis of variance was performed for all agronomical and physiological traits and Fisher’s LSD multiple range test was employed for the mean comparisons. Linear correlation analysis was used to determine the relationships between the traits studied.

RESULTS AND DISCUSSION

Flag leaf reflectance (FLR) and senescence (S)
 
In this study flag leaf reflectance was measured at Red (654 nm), Blue (450 nm) and Green (500 nm); as shown in Table 2, analysis of variance revealed that the reflectance at different wavelengths (Red, Blue and Green) was highly significant genotypes effects (P < 0.001). Reflectance at Red ranged from 29.98% for G19 to 45% for G1 with an average of 35.8% over all genotypes; for this parameter the good genotypes are those we have the lowest values such as G19. The local genotypes have the highest values with a global mean of 37.84%. In addition, the mean of the reflectance at Blue in the introduce genotypes was 30.19% and in the local genotypes was 30.02% with a total mean of 30.22%. The lowest value of the reflectance at Blue registered in the genotype G22 this one was in the same homogenize group of G11, G15 and the local genotype Saida 183 (G 24), there was no significant difference for the reflectance at Blue between the local and introduce genotypes. As shown in Table 2, the parameters of senescence there was significant difference between genotypes. The values of the mean senescence varied from 34.96% for the introduce genotype G12 to 66.05% for G 17 (introduce genotype) with total mean of 56.19%. The analysis of variance based on the origin effects showed the absence of difference between the local and introduce genotypes for all senescence parameters. The preferable genotypes were those which have low values for the mean senescence and senescence velocity; based on this raison the best genotype was the introduced genotype G12. The data of the Table 3 demonstrate that the best genotypes were the genotype which in the top of the ranking list. Based on the indices of FLR and S the best genotypes were G22, G15, G11, G5, G19, G17 and G18, respectively. The total score of the genotypes based on the all parameters demonstrated that the best genotypes were G11, G12, G5, G15 and G18, respectively. Spectral reflectance measurements have been successfully used to estimate biomass, leaf area index, photosynthesis and/or yield in several species of trees (Richardson et al., 2001), bread wheat (Filella et al., 1995) and durum wheat (Aparicio et al., 2004). Ferrio et al., (2005) showed that the higher grain yield was correlated with lower reflectance are visible. The results of Guendouz et al., (2013) indicated the potential of using flag leaf reflectance wheat yield prediction.    
 

Table 2: Response of Reflectance, Senescence, chlorophyll content, GY and TKW of barley genotypes tested under semi-arid conditions.


 

Table 3: Genotypes ranking based on the Reflectance and Senescence indices.


  
GY, TKW and chlorophyll content (Chl)
 
The genotypes effect was significant for the grain yield and thousand-kernel weight (Table 2). The values of grain yield varied between 43.09 q/ha for the introduce genotype G20 to 74.4 q/ha for the local landrace Fouarra. Based on the test of means comparison the genotypes with highest GY were G1, G2, G3, G7, G12 and local landrace Fouarra; in addition, the highest TKW was registered by the introduced genotype G12. The homogenize group of genotypes which have the highest TKW were G12 and the local landrace Saida 183. For the chlorophyll content and as shown in Table 2 the analysis of variance showed a significant effect of genotypes, the highest values registered by the introduced genotype G5 this one was in the same homogenize group of G2, G4, G8 and G18. Based on the origin of genotypes the analysis of variance demonstrated that the reflectance at Red, Green and the chlorophyll content there were significant difference between the two groups (local and introduce), for this raison the highest values of Chl were registered by the introduced genotypes (Table 2). As shown in Table 4 the ranking of the genotypes was based on the rank of each genotype for Senescence and Chl, GY and TKW parameters; the best genotypes were G5, G12, G11, G7, G3 and G2, respectively. The study of correlation showed a negative correlation between the mean of senescence, GY and TKW (r = - 0.63***, r = - 0.59 **, respectively); this correlation in good agreement with Rawson et al., (1983) and Ellen (1987). Contrary to these findings many studies have demonstrated that delayed senescence delays remobilization and leads to reduced grain weight (Yang et al., 1997). 
 

Table 4: Genotypes ranking based on Agronomic traits.

CONCLUSION

The genetic diversity among genotypes explains the significant differences for all traits. Based on the origin effects the reflectance at Red, Green and Chlorophyll content are significantly difference between the local and introduce genotypes. The highest GY and TKW are registered in the local landrace Fouarra and Saida 183, respectively. The highest values of chlorophyll content registered by the introduce genotype G5; the values of the reflectance at Red, Green and Blue are varied from the local to the introduce genotypes. The ranking of genotypes based on all parameters suggested that the genotypes G11, G12, G5, G15 and G18, respectively (introduce genotypes) were the ideal genotypes under these conditions.    

REFERENCES


  1. Adamsen, F.J., Pinter Jr, P.J., Barnes, E.M., LaMorte, R.L., Wall , G.W., Leavitt, S.W. and Kimball, B.A. (1999). Measuring wheat senescence with a digital camera. Crop Science. 39 (3): 719-724.

  2. Aparicio, N., Villegas, D., Royo, C., Casadesus, J., Araus, J.L. (2004). Effect of sensor view angle on the assessment of agro-    nomic traits by ground level hyper-spectral reflectance measure-ments in durum wheat under contrasting Mediterranean conditions. Int. J. Remote Sens. 25: 1131-1152.

  3. Blackmer, T.M. and Schepers, J.S. (1995). Use of a chlorophyll meter to monitor nitrogen status and schedule fertigation for corn. Journal of Production Agriculture. 8: 56-60.

  4. Carter, G.A. and Knapp, A.K. (2001). Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. Am. J Bot. 84: 677–684.

  5. Ceccarelli, S. and Grando, S. (1996). Drought as a challenge for the plant breeder. Plant Growth Regulation. 20: 149–155.

  6. Ellen, J. (1987). Effects of plant density and nitrogen fertilization in winter wheat (Triticum aestivum L.): I. Production pattern and grain yield. Neth. J. Agric. Sci. 35: 137-153.

  7. Ferrio, J.P., Villegas, D., Zarco, J., Aparicio, N., Araus, J.L. and Royo, C. (2005). Assessment of durum wheat yield using visible and near-infrared reflectance spectra of canopies. Field Crops Res. 94: 126-148.

  8. Filella, I., Serrano, L., Serra, J., Penuelas, J. (1995). Evaluating wheat nitrogen status with canopy reflectance indices and discriminate analysis. Crop Sci. 35: 1400–1405.

  9. Guendouz, A. and Maamari, K. (2011). Evaluating durum wheat performance and efficiency of senescence parameter usage in screening under Mediterranean conditions. Electronic Journal of Plant Breeding. 2(3): 400-404.

  10. Guendouz, A., Guessoum, S., Maamari, K. and Hafsi, M. (2012). Predicting the efficiency of using the RGB (Red, Green and Blue) reflectance for estimating leaf chlorophyll content of Durum wheat (Triticum durum Desf.) genotypes under semi arid conditions. American-Eurasian Journal of Sustainable Agriculture. 6:102-106.

  11. Guendouz, A., Guessoum, S., Maamri, K., Benidir, M., Hafsi, M. (2013). Flag leaf reflectance efficiency as indicator for drought tolerance in durum wheat (Triticum durum Desf.) under semi arid conditions. International Journal of Agronomy and Plant Production. 4: 1204-1215.

  12. Grant, L. (1987). Diffuse and Specular Characteristics of Leaf Reflectance. Rem. Sens. Environ. 22: 309-322.

  13. Hafsi, M., Mechmeche, W., Bouamama, L., Djekoune, A., Zaharieva, M. and Monneveux, P. (2000). Flag leaf senescence, as evaluated by numerical image analysis and its relationship with yield under drought in durum wheat. J. Agronomy and Crop Sci. 185: 275–280.

  14. Hafsi, M. and Guendouz, A. (2012). Some Aspects of Leaf Senescence. In: Tetsuji Nagata, Senescence, IntechOpen, pp. 107-    116. DOI: 10.5772/32787.

  15. Rawson, H.M., Hindmarsh, J.H., Fisher, R.A. and Stockman, Y.M. (1983). Changes in leaf photosynthesis with plant ontogeny and relationships with yield per ear in wheat cultivars and 120 progeny. Aust. J. Plant Physiol. 10: 503-514.

  16. Richardson, A.D., Berlyn, G.P. and Gregoire, T.G. (2001). Spectral reflectance of Picea rubens (Pinaceae) and Abies balsamea (Pinaceae) needles along an elevational gradient, Mt.Moosilauke, New Hampshire, USA. Am. J. Bot. 88: 667-676.

  17. Schrevens, E. and Raeymaeckers, L. (1992). Colour characterization of golden delicious apples using digital image processing. Acta Horticulturae. 304: 159-166.

  18. Wood, C.W., Reeves, D.W. and Himelrick, D.G. (1993). Relationships between chlorophyll meter readings and leaf chlorophyll concentration, N status and crop yield: A review. Proceedings - Agronomy Society New Zealand. 23: 1-9.

  19. Yang, J., Wang, Z., Zhu, Q. (1997). Photosynthetic characteristics, dry matter accumulation and its translocation in inter-    specific hybrid rice. Acta Agron. Sincia. 23: 82-88.
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