Selection for High Yielding and Stemphylium Blight Resistant Lentil (Lens culinaris Medikus) Genotype based on Principle Component and Selection Indices

Lentil (Lens culinaris Medik.), an important pulses crop of the world, stands as the second-most significant pulse crop in terms of acreage, production and consumer preference (Bahl et al., 1993). In India the states of Uttar Pradesh, Madhya Pradesh, West Bengal and Bihar account for nearly 95% of the land dedicated to the crop and it is one of the most nutrient-rich rabi pulse crops consumed primarily as dal. However, the production of the crop seems to be declining each year owing to a number of constraints viz., limited genetic resources, vulnerability to diseases, smaller areas and due to delay in sowing.
       
Susceptibility to disease is one of the major constraints, Stemphylium blight, caused by fungal pathogen Stemphylium botryosum being one of them which during epidemic can cause a total failure of the crop (Taylor et al., 2007). It is therefore crucial to estimate genetic and phenotypic parameters of the available resources to ascertain such parameters as heritability and expected response to selection that will direct the future breeding programme.
       
The selection index can effectively aid in the identification of potential genotype/s when several parameters are considered at once. In addition to conventional selection techniques employed for population development, breeders may use selection indices to aid in the simultaneous selection of several characteristics too (Smith, 1936). Besides selection indices offer useful guidance on how to combine characters (Banziger and Lafitfe, 1997). Several other researchers have successfully employed selection indices as a selection criterion in their breeding work for other crops (Vikram and Roy, 2003; Xie et al., 1998 and Dolan et al., 1996).
       
Considering the above-mentioned factors, the present investigation was carried out to assess the genetic diversity among the lentil genotypes using principal component analysis and to identify the lentil genotype/s for yield and attributing traits using selection index (Hazel, 1943).

The present study was conducted in the Agricultural Instruction Farm of Uttar Banga Krishi Viswavidyalaya, Pundibari, Cooch Behar under terai agro-climatic region of West Bengal located at 26024¢09.1²N latitude and 89023'08.3"E longitude and 43 m above mean sea level. A total of forty lentil germplasm including two checks, viz., WBL77 and IPL220 were used in the experiment carried out during rabi season of 2019-20 and 2020-21 (Table 1).


       
The statistical design namely alpha lattice implied with three replications for field experiment. The crop was raised by following the standard cultural methods with a plant-to-plant and row-to-row distance of 10 cm and 30 cm respectively in each plot measuring 2 m x 1.5 m. The morphological observations were made on five randomly selected plants in each each replication. Characters viz., days to 50% flowering (DF), plant height (PH), days to 90% maturity (DOM), primary branches plant^-1 (PBPP), secondary branches plant^-1 (SBPP), pods plant-1 (PODPL), seed pod-1 (SPP), pod weight plant^-1 (PODWT), 100 seed weight (HSW), yield plant^-1 (YPP) and harvest index (HI) were measured as per DUS descriptors. For biochemical analysis random sampling was performed for each replication for characters change in phenol (CP), change in OD phenol (CODP), polyphenol oxidase (PPO), seed iron (Fe), seed zinc (Zn) and seed protein (SP) content the former three being important biochemical parameters in disease reaction. The percent disease index (PDI) based on disease scoring against Stemphylium blight was done after (Hashemi et al., 2005) assessing the disease severity from initiation till the maturity of the crop. Area Under Disease Progress Curve (AUDPC) was also calculated to ascertain the spread of the disease.
       
The direction of selection and their weightage has been presented in Table 2 where, the scale 0.00 to 1.00 has been used. Under the present experiment, 20% of selection intensity was considered. Principal component analysis (PCA) was carried out using R v 4.1.1 software using the mean data for the traits and characters contributing the most towards variability were identified through PCA (Jolliffe 2002). Hence based on the PCA loadings the weightage was applied and rescaling of derived values for the traits was done following mini-max normalization formula:
 
 
 
 
Where,
x¢  = original value.
x¢ = normalized value.


       
Subsequently the selection index for each genotype was computed and they were ranked based on all the characters including the morphological and biochemical with derived index values. To calculate the selection index PB Tools v. 1.3 software was used.

The analysis revealed significant differences among the studied genotypes for all the characters in conformity with similar findings by (Vanave et al., 2019; Akter  et al., 2020; Ahmad et al., 2021 and Satpathy and Debnath 2020).
       
The principle component analysis identified seven PCs having eigen value more than 1 accounting for a cumulative proportion more than 77.08% of the total variation. The PC with eigen value more than 1 are designed to be used in selection indices (Mattjik  et al.,  2011) due to the variance being readily explained. The characters viz., primary branches plant^-1 (0.405), pods plant^-1 (0.393), pod weight plant^-1 (0.443), showed high eigen vector values in PC1 while in PC2 100 seed weight (0.366), change in phenol content (0.402), change in OD phenol (0.441) and seed protein revealed highest loading value. In PC3 the characters viz; seeds pod^-1 (0.458), polyphenol oxidase (0.347) and seed zinc content (0.368) showed maximum loading value with days to 90% maturity (0.467) and 100 seed weight (0.346) showing maximum weightage in PC4.  Whereas plant height (0.281) showed maximum weightage in PC6 harvest index (0.779) showed the same in PC7 (Table 3). Similar such reports on principal component analysis in lentil have been made by Pratap et al., 2014, Siahsar  et al., 2010, Crippa et al., 2009, Cristobal et al., 2014, Kumar and Solanki, 2014 and Mahmood et al., 2019 among the others.


       
The smith index values calculated based on PCA loadings varied from -1.15 (IC241072) to 0.77 (IC241061 and IC614827) for the studied germplasm and eight lentil genotypes were identified with highest selection index value with a selection intensity of 20% as presented in Table 4. The selection differential for the studied characters was estimated by subtracting the value of mean of each character for all individual from the mean of selected individuals which revealed that the characters pods plant^-1 (13.41) had the highest selection differential followed by seed iron content (11.09) and harvest index (4.11). The characters AUDPC (-14.49) and days to 50% flowering (-0.47) were found to have negative selection differential values and thus were also likely to fit in the improvement goals in the sense that disease and flowering traits are favourable in negative direction. The study further revealed that the expected genetic gain for the selected genotypes were maximum for seed iron content (15.91) followed by pods plant^-1 (11.67) and seed zinc content (5.62) and the minimum genetic gain was noted for AUDPC (-62.85) followed by seeds pod^-1 (0.01) and primary branches plant^-1 (0.02). (Pavithra and Vengadessan, 2020) in an experiment with 48 genotypes of rice similarly studied on principal component analysis (PCA) and selection index with the objective to select discriminating rice genotypes for drought tolerance and likewise reported a PCA based reduction of the 12 traits into six traits viz., days to 50 per cent flowering, plant height, panicle length, grains per panicle, grain yield and stress percentage across the environments. They further computed selection index with the economic weights derived from PCA found the genotype IET-27693 with high index score for drought tolerance. The use of stress tolerance index and PCA was similarly considered effective in the formation of the selection index on salinity screening in rice by Farid et al., (2021).

Selection index for the studied genotypes was computed based on the relative weight of the 18 characters towards the corresponding principal component axis and the rescaled PCA loadings for all the characters towards corresponding principal component axes were used to calculate the selected index score (smith index) for the lentil genotypes which revealed that the genotype IC241061 and IC614827 had the highest smith index value (0.77) while the genotype IC241072 had the lowest smith index value (-1.15). Eight lentil genotypes were identified with highest smith index value based on all the characters with 20% selection intensity and seed iron content and AUDPC had the highest (15.91) and the lowest (-62.85) expected genetic gain respectively. Selection based on the above can thus be fruitful to develop superior genotype with high yielding potential and resistance to the disease Stemphylium blight as well along with such qualitative traits as high seed protein, zinc and iron content.
 

All authors declared that there is no conflict of interest.