Introduction
Protein energy malnutrition (PEM), primarily the result of intake of diets poor in bio-available protein, can lead to reduced intellectual and physical abilities in pre-school children and irregular menstrual cycles among women of child-bearing age (Monica et al., Reference Monika, Natasha, Saloni, Payal, Aman, Venkatesh and Priya2018). PEM is prevalent in south-east Asian countries including India. Grain legumes are the primary source of dietary protein and energy to human beings, especially to those depending on vegetarian diet (Welch and Graham, Reference Welch and Graham2004). Dolichos bean is one of the most important grain legume crops cultivated and consumed by millions of people in large quantities on a daily basis in India (Ramesh and Byregowda, Reference Ramesh and Byregowda2016). It can withstand drought better than cowpea (Ewansiha and Singh, Reference Ewansiha and Singh2006), adapt to acidic soils (Mugwira and Haque, Reference Mugwira and Haque1993) and saline soils (Murphy and Colucci, Reference Murphy and Colucci1999) and produce higher green foliage and biomass (Magoon et al., Reference Magoon, Amar Singh and Mehra1974) better than other legumes. The introduction of dolichos bean pure-line varieties selected and/or bred for increased grain protein content through plant breeding approach is considered as most effective approach to combat PEM (Welch and Graham, Reference Welch and Graham2004).
Exploitation of genetic variability within the natural populations such as germplasm accessions (GAs)/breeding populations is the short-term strategy for identification and delivery of protein-rich dolichos bean cultivars to cater to the immediate needs of the farmers for production and target population for consumption. It is expected to reduce PEM in target population at least by 50% through regular and large-scale intake of protein-rich diets based on the products of crops (Welch and Graham, Reference Welch and Graham2004) such as dolichos bean. The objectives of the present investigation were to assess genetic variability for grain protein contents among GAs and advanced breeding lines (ABLs).
Experimental
A set of 118 genotypes which included 96 GAs and 20 ABLs and two released varieties (RVs) (HA 3 and HA 4, which are photoperiod insensitive and high yielding with determinate growth habit) constituted the genetic material for the study. These were evaluated in augmented design (Federer, Reference Federer1956) at the experimental plots of Department of Genetics and Plant Breeding (GPB), College of Agriculture, Bengaluru, India during 2016 rainy season. The entries were sown in single rows of 3.0 m length with a row-to-row spacing of 0.45 and 0.3 m between plants within a row in five blocks, each containing 27 entries. Data were recorded on five randomly selected plants from each entry on grain yield per plant using descriptors (Byre gowda et al., Reference Byre Gowda, Girish, Ramesh, Mahadevu and Keerthi2015). The mean grain yield per plant of each genotype was adjusted for block effects.
Dry pods from five randomly selected selfed plants from each genotype were harvested manually and hand-threshed. The grains were sun-dried and washed with double-distilled water to remove any surface contaminants and dried in hot-air oven at 70°C for 72 h. Washed and fine-powdered 1.0 g of grain samples divided into two replicates were used for estimating grain nitrogen content following Kjeldahl method (AOAC, 2005) and expressed in per cent. Per cent protein was calculated by multiplying per cent nitrogen with the factor 6.25 (AOAC, 2005). The data on grain protein contents averaged across two replicates were used for the statistical analysis. Absolute range (AR) = (highest–lowest) and standardized range (SR) = (AR/mean) × 100, and phenotypic coefficient of variability (PCV) (Fisher, Reference Fisher1950) of grain protein content were estimated. The significance of differences in grain protein content between GAs and (ABLs + RVs) was examined using two-sample t-test (Fisher, Reference Fisher1950). The genotypes were grouped into different clusters using model-based ‘K-means’ clustering algorithm (MacQueen, Reference MacQueen1967) implemented using SPSS software. The statistical significance of mean and variances of grain protein contents of the genotypes grouped under different clusters were examined using ‘F’ (Fisher, Reference Fisher1950) and Levene's (Levene, Reference Levene and Olkin1960) tests, respectively. Correlation coefficients were estimated between grain protein content and adjusted mean grain yield per plant.
Discussion
A fairly high estimates of AR (24.5–18.82%) and SR (4.82) which indicate the occurrence of contrasting genotypes, and PCV (6.32%) which is a measure of average inter-genotypic differences in relation to the mean (21.73%) suggested adequate variability for grain protein content among 118 genotypes (Table 1). Ravelombola et al. (Reference Ravelombola, Shi, Weng, Motes, Chen, Srivastava and Wingfield2016) and Weng et al. (Reference Weng, Shi, Revelomba, Yang, Qin, Motes, Moseley and Chen2017) reported comparable range (23.7–27.4% and 21.03–29.69%) and average (25.4 and 25.53%) grain protein content, respectively, in cowpea. The magnitude of estimates of AR, SR and PCV for grain protein content was higher among GAs than those among ABLs + RVs. However, mean grain protein content of GAs was comparable to those of ABLs + RVs (Table 1). Nearly 50% of the genotypes had significantly higher protein content than those of RVs, HA 3 and HA 4 (Fig. 1). Five genotypes, GL, 434, GL 444, GL 331 and GL 68 and FBP 15 had protein content more than twice of critical difference (online Supplementary Table S1). Grouping the genotypes into five different clusters with significant differences in the mean and comparable variances of the genotypes classified under different clusters further confirmed adequate variability for grain protein content (online Supplementary Table S2). Substantial variability among the genotypes could be attributed to the existence of differences in the expression of genes controlling protein synthesis and accumulation (Bliss, Reference Bliss1990). The genotypes grouped under clusters 4 and 5 were contrasting with highest and lowest estimates of grain protein contents. The genotypes from these two clusters could be selected for use in investigating genetic and physiological basis of accumulation of protein and as potential donors for breeding varieties rich in grain protein content.
Fig. 1. Graph depicting frequency distribution for protein content in germplasm accessions and advanced breeding lines of dolichos bean.
Table 1. Estimates of mean, absolute range, standardized range and phenotypic coefficient of variability among germplasm accessions advanced breeding lines for grain protein content in dolichos bean
The results suggested ample scope for selecting and/or breeding protein-rich dolichos bean varieties. However, it is necessary to develop protein-rich varieties in high yielding genetic background for acceptance and adoption by farmers. A rather poor correlation (−0.052) between grain protein content and grain yield per plant (online Supplementary Fig. S1) suggested their independent genetic control and hence possibility of developing protein-rich dolichos bean varieties without compromise in grain yield. Our results and inferences draw strong support from the studies of Singh et al. (Reference Singh, Sharma, Sekhon, Sharma, Verma and Vishalakshi2017) in faba bean and Hussan et al. (Reference Hussan, Khuroo, Lone, Dar, Dar and Dar2018) in lentil, who reported non-significant correlation between grain protein content and yield among GAs.
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/S1479262118000424
Acknowledgement
Financial support from Directorate of Research, University of Agricultural Sciences (UAS), Bengaluru, India is gratefully acknowledged (No. C/B&R/C-5-6676/113/2016-17). The present study is the part of the Ph.D. thesis research.
