Introduction
Millets are widely cultivated in arid and semi-arid regions of the world as a source of food and fodder (Dwivedi et al., Reference Dwivedi, Upadhyaya, Senthilvel, Hash, Fukunaga, Diao, Santra, Baltensperger and Prasad2012). Finger millet is a C4 species (Ueno et al., Reference Ueno, Kawano, Wakayama and Takeda2006) that has both drought resilience features as well as nutritional importance. The grain has a high concentration of mineral nutrients and fibre with minimum anti-nutritional factors like phytic acid and tannins. Moreover, finger millet has better health benefits compared to major cereals (Wondimu, Reference Wondimu, Tefera, Belay and Sorrells2001; Devi et al., Reference Devi, Vijayabharathi, Sathyabama, Malleshi and Priyadarisini2014; Chandra et al., Reference Chandra, Chandra, Pallavi and Sharma2016; Kumar et al., Reference Kumar, Metwal, Kaur, Gupta, Puranik, Singh, Singh, Gupta, Babu, Sood and Yadav2016; Netravati et al., Reference Netravati, Geetha, Vikram, Nanja Reddy, Joshi and Shivaleela2018). About 3.5 billion people in the world are at risk of calcium deficiency (Kumssa et al., Reference Kumssa, Joy, Ander, Watts, Young, Walker and Broadley2015) and finger millet could be a better option to provide nutritional security in developing countries in Asia and Africa (Puranik et al., Reference Puranik, Kam, Sahu, Yadav, Srivastava, Ojulong and Yadav2017). Finger millet also serves as the best cattle feed (Baath et al., Reference Baath, Northup, Gowda, Rocateli and Turner2018) because of its superior quality with 61% digestible nutrients (NRC, 1996).
Globally, the finger millet occupies the fourth place after sorghum, pearl millet and foxtail millet in terms of importance (Dida et al., Reference Dida, Srinivasachary, Ramakrishnan, Bennetzen, Gale and Devos2007). The total millet area and production in the world is 33.49 million hectares with 31.74 million tonnes, respectively. In India, it is 9.22 million hectares with 11.63 million tonnes during 2018 (http://fao.org). Finger millet accounts for 8 to 12% of the area and 11% production of the food grains in the world (Bennetzen et al., Reference Bennetzen, Dida, Wanyera and Devos2003; http://exploreit.icrisat.org/profile/smallmillets/187). In India, finger millet is grown in 1.19 million hectares with a production of 2.0 million tonnes (Sakamma et al., Reference Sakamma, Umesh, Girish, Ravi, Satishkumar and Bellundagi2018), and in East Africa (Uganda, Tanzania, Kenya) it is ~0.7 million hectares (Mgonja et al., Reference Mgonja, Lenne, Manyasa and Sreenivasaprasad2007).
Finger millet productivity has been increased over the years, by breeding and selection based on yield per se. In finger millet yields are reported higher (1661 kg/ha) than major millets like sorghum (998 kg/ha) and pearl millet (1237 kg/ha) (Sakamma et al., Reference Sakamma, Umesh, Girish, Ravi, Satishkumar and Bellundagi2018; http://www.indiaagristat.com). In India, more than 140 varieties were released and cv. GPU-28 is considered one of the popular varieties (Krishne Gowda et al., Reference Krishne Gowda, Nagaraja, Gowda, Krishnappa and Bharathi2009; Swetha, Reference Swetha2011; Gowda et al., Reference Gowda, Nanja Reddy, Pushpalatha, Deepika, Pramila and Jadhav2014). However, worldwide, the average farm yield is 400–800 kg/ha (Uganda, Tenywa et al., Reference Tenywa, Nyende, Kidoido, Kasenge, Oryokot and Mbowa1999), 1661 kg/ha (India, Sakamma et al., Reference Sakamma, Umesh, Girish, Ravi, Satishkumar and Bellundagi2018) and 2260 kg/ha (Ethiopia, Degu et al., Reference Degu, Adugna, Tadesse and Tesso2009). The yield improvement has reached a plateau (Swetha, Reference Swetha2011). Recent reports show that the farm yield potential of finger millet is >3000 kg/ha (Mgonja et al., Reference Mgonja, Lenne, Manyasa and Sreenivasaprasad2007; Sakamma et al., Reference Sakamma, Umesh, Girish, Ravi, Satishkumar and Bellundagi2018). This could be possible by the selection of germplasm lines for specific traits contributing to grain yield (Ojo et al., Reference Ojo, Omikunle, Oduwaye, Ajala and Ogunbayo2006; Upadhyaya et al., Reference Upadhyaya, Gowda and Reddy2007, Bharathi et al., Reference Bharathi, Veerabadhira, Gowda and Upadhyaya2013; Patil, Reference Patil2016). The yield improvement in cereals can be achieved through enhanced partitioning of biomass to grain (Wilson et al., Reference Wilson, Sanogo, Nutsugah, Angarawai, Fofana, Traore, Ahmadou and Muuka2008; Fischer and Edmeades, Reference Fischer and Edmeades2010; Swetha, Reference Swetha2011). Therefore, the genotypes were evaluated for morpho-physiological traits and yield attributes and proposed possible approaches to enhance the grain yield of finger millet.
Material and methods
Experimental site
The field experiment was conducted at the field unit, AICRP on Small Millets, Zonal Agricultural Research Station, University of Agricultural Sciences, GKVK, Bengaluru, India situated at 12°58´N latitude and 77°35´E longitude at an altitude of 930 m above the mean sea level (MSL).
Crop management
Thirty-one genotypes including popular varieties were evaluated in completely randomized block design with three replications. Each replication had an area of 3.1 m row length × 0.9 m width (three rows). The crop was sown directly on 17 August 2009, with a spacing of 30 cm between rows and 10 cm between plants. Thinning was performed twice within 15 days after sowing (DAS) to maintain one seedling per hill. The recommended dose of 50:40:25 nitrogen, phosphorus and potassium (NPK) (kg/ha) fertilizer was applied in a split dose. Half of the recommended dose of nitrogen (N) and a full dose of P and K were given as basal dose at the time of sowing. The remaining 50% N was provided as top dressing at 40 DAS. Manual weeding was undertaken twice within 30 DAS. The crop was raised as a rainfed crop in the monsoon season. Besides, three protective irrigations (10 mm each) were provided during rain-free period. (from 03 October 2009 to 28 October 2009, coincided with ear emergence).
Collection of data
At the time of flowering, five plants (0.5 m row length) were harvested to measure the leaf area and biomass. From these plants, sample leaf area was measured by length × width × 0.75 (factor for leaf shape) in randomly selected 10 leaves and their dry weight was measured to arrive at a specific leaf area (SLA). SLA was computed by dividing the leaf area with its leaf dry weight (cm2/mg). Total leaf area was calculated by multiplying the total leaf dry weight of five plants with SLA. The leaf area index (LAI) was computed by dividing the leaf area of five plants by spacing for five plants. Leaf anatomical characters were recorded in the leaf next to the flag leaf. Thin transverse sections were stained with potassium – iodide solution and observations on leaf thickness, leaf width and leaf vein frequency (LVF) were made under 10 × (1 ocular unit = 9.01 um) (Nanja Reddy et al., Reference Nanja Reddy, Prasad and Udaya Kumar1996). During the same period, gas exchange parameters were measured using IRGA (LI 6400) at 11.00 AM. At the time of harvest, yield and yield attributes were recorded.
Data analysis
The data were statistically analysed for analysis of variance (ANOVA) in randomised completely block design (RCBD) using OPSTAT (Sheoran et al., Reference Sheoran, Tonk, Kaushik, Hasija and Pannu1998). Furthermore, correlation and path analysis were followed to measure the relationship between traits and their contribution respectively to grain yield. Furthermore, using Microsoft Excel Toolpak, stepwise multiple linear regressions (MLRs) (stepwise backward regression) were followed to identify significant traits associated with grain yield. In stepwise regression, non-significant parameters were eliminated one by one to obtain the significant traits contributing to grain yield. For such contributing traits, the data were transformed to unity, MLR was followed and the contribution of each trait was computed by dividing the regression co-efficient of each trait by the sum of the regression coefficients.
Results
Selection of genotypes for grain yield
The genotypes showed significant variations for physiological, anatomical and yield parameters (Table 1). None of the genotypes was significantly superior to the cv. GPU-28 for grain yield (351 g/m2). The grain yield was in the range of 359 to 392 g/m2 in GPU-66, GPU-67, GE-1013, GE-1034 and GE-1293 (Table 1). The mean grain yield of nine cultivated varieties (321.2 g/m2) was significantly superior over the mean yield of 22 germplasm accessions (275.1 g/m2; Table 2).
Genotypic variation in physiological, anatomical and yield attributes in finger millet
DFF: Days to 50% flowering, LAI: leaf area index, A: Photosynthetic rate (μMol/m2/s1), LT: Leaf thickness (um), DMF: Biomass at flowering (g/m2), PDM: Post-anthesis biomass (g/m2), Ped. Length: Peduncle length (cm), LVF: Leaf vein frequency (No. cm−1 leaf width), FL: Finger length (cm), PT: Productive tillers/hill, MEW: Mean earhead weight (g/ear), Th: Threshing ratio, TEW: Total ear weight/unit land area (g/m2), DMH: Total dry matter at harvest (g/m2); HI: Harvest index, GY: Grain yield (g/m2).
Differences in grain yield in response to varieties/selected germplasm, duration, total ear weight and threshing ratio in finger millet
Note: Different superscript letters indicates the significant difference
Selection of specific trait: possible approaches to enhance grain yield
Primary traits
Primarily, the grain yield can be calculated as the product of the crop duration × ear weight/unit land area × threshing ratio (Fig. 1).
Approaches to enhance grain yield in finger millet (DFF: Days to 50% flowering, GFP: Grain filling period, LAI: Leaf area index, NAR: Net assimilation rate, SLW: Specific leaf weight)
Crop duration: Medium duration genotypes (74 days to 50% flowering, DFF) gave higher grain yield (319.2 g/m2) as compared to short duration (66 DFF; 276.5 g/m2) and long duration (80 DFF; 268.9 g/m2) genotypes (Table 2). This was confirmed through a negative correlation between DFF and grain yield (r = −0.223ns; online Supplementary Table S1) with a negligible direct effect of DFF on grain yield (0.02; online Supplementary Table S2). Six out of 9 released varieties were of medium duration (Table 1).
Ear weight per unit land area: Grain yield is the product of total ear weight (TEW) per unit land area and its threshing ratio (Fig. 1). The mean TEW of nine cultivated varieties was significantly high (431.0 g/m2) as compared to the mean of 22 accessions (357.4 g/m2 (Table 1). With an increase in TEW significant increase in yield was observed (Table 2). The TEW had a significant positive correlation to grain yield (r = 0.903**; online Supplementary Table S1) and influenced the grain yield with a high direct positive effect (0.70) and lower indirect positive effect through biomass at harvest (0.10) and harvest index (0.14) (online Supplementary Table S2).
Threshing ratio: Genotypes differed significantly in threshing ratio with a large genotypic variation from 0.68 to 0.88. Genotypes, GPU-67, GE-1034, GE-5192 and GE-5252 had a significantly higher threshing ratio of 0.85 as compared to GPU-28 (0.78) but the grain yields were similar (Table 1). Furthermore, the threshing ratio of nine cultivated varieties (0.75) and 22 germplasm accessions (0.77) were similar, but cultivated varieties had higher grain yield as compared to germplasm accessions (Table 1). A higher threshing ratio above 0.78 did not show higher grain yield (Table 2) although the threshing ratio was positively correlated to grain yield (r = 0.325ns; online Supplementary Table S1) and the threshing ratio influenced the grain yield with a high direct positive effect (0.31) (online Supplementary Table S2).
Contribution of primary traits towards grain yield: Considering the crop duration, threshing ratio and TEW towards grain yield, the MLR, showed an equal contribution by TEW (49.1%) and threshing ratio (48.3%) with the least contribution (2.6%) by days to flowering (Table 3). Applying MLR equation to GPU-28, the contribution towards yield was 2.2% by DFF, 44.8% by threshing ratio and 53.0% by TEW (Table 3). An increase in threshing ratio from 0.78 to 0.82 in GPU-28 estimated to increase the grain yield by 4.1%, while an increase in total ear weight from 450 to 500 g/m2 would increase the grain yield by 11.1% with an unaltered contribution by DFF and an increase in both these parameters would increase the yield by 15.4% (Table 3).
Possible contribution (%) of primary yield contributing parameters on grain yield in finger millet
Secondary independent traits
Mean ear weight: Total ear weight (TEW) is the product of independent traits namely, mean ear weight (MEW) and the number of productive tillers (PTs) (Fig. 1). None of the genotypes was significantly superior to GPU-28 (7.79 g/ear) in ear weight. However, accessions GE-1034, GE-2672 and GE-5252 had 1.0 g higher mean ear weight than cv. GPU-28 (Table 1). The mean ear weight had a positive correlation to total ear weight (r = 0.328ns) but negatively with PT number (r = −0.559**; online Supplementary Table S1). The mean ear weight was significantly and positively correlated with the finger length (r = 0.564**). The mean ear weight <6.52 g decreased grain yield significantly (Table 4).
Differences in grain yield in response to varieties/selected germplasm, duration, total ear weight and threshing ratio in finger millet
Note: Different superscript letters indicates the significant difference
PT number: Five genotypes were significantly superior to GPU-28 with more than 2.6 tillers per plant. But all these genotypes except GPU-67 had lower grain yield as compared to GPU-28 (Table 1). PTs had no significant correlation with total ear weight (r = −0.213ns) and grain yield (r = −0.312ns; online Supplementary Table S1). An increase in PT per plant to 3.53 decreased the grain yield significantly (Table 4).
Contribution of secondary traits towards grain yield: The mean ear weight had a high direct positive effect (0.385) on grain yield. Furthermore, MLR between MEW and PT towards grain yield (y = 216.5 + (−10.42 PT) + 14.56 MEW, MLR = 0.446*), the mean ear weight showed a positive effect on total ear weight (78.8%), but PTs showed a negative effect (22.2%) (data not shown).
Physiological traits
LAI, photosynthetic rate and leaf anatomical traits: The mean ear weight and PTs are dependent on basic physiological components namely, the LAI, photosynthetic rate and translocation of photosynthates to sinks. Fifteen genotypes had significantly higher LAI over the GPU-28 (2.39, LAI), but not the corresponding grain yield (Table 1). An increase in LAI > 1.75 did not increase the grain yield significantly. The genotypes with LAI of < 1.75 were relatively monoculm and had a higher grain yield of 318.0 g/m2 (Table 4). LAI had a positive correlation with pre-anthesis biomass production (r = 0.732***; online Supplementary Table S1) but not with the grain yield. The photosynthetic rate was significantly higher in 15 genotypes over the GPU-28 (17.4 μMol/m2/s) and has a positive correlation towards grain yield (r = 0.330ns) and harvest index (r = 0.533**; online Supplementary Table S1). The photosynthetic rate showed an indirect lower positive effect on grain yield through TEW (0.14) and harvest index (0.13) (online Supplementary Table S2). The leaf thickness had a negative correlation with the dry matter at harvest (r = −0.496**) but positive with HI (r = 0.454*). An increase in leaf thickness did not influence grain yield (Table 4). The anatomical parameter, LVF had a significant negative correlation with MEW (r = −0.368*) and HI (r = −0.356*).
Among the traits associated with the grain yield, independent traits contributing significantly towards grain yield were identified based on backward stepwise regression (y = 52.1 + 1.65 photosynthetic rate + 5.81 mean ear weight, MLR = 0.60**) (data not shown).
Selection of genotypes for specific traits
Genotypes superior over the cv. GPU-28 for selected traits contributing to grain yield are presented in Table 5. Genotype, GPU-67 was superior for photosynthetic rate (26.4 μM/m2/s) and GE-2672 for MEW (8.99 g).
Trait specific superior genotypes compared to Cv. GPU-28 in finger millet
Note: MEW: Mean ear weight (g/ear), DMF: Dry matter at flowering (g/m2), PDM: post-anthesis biomass ((g/m2), A: Photosynthetic rate (μM m/2/s1), LAI: Leaf area index, PT: Productive tillers per hill, GY: Grain yield (g/m2), SD: Std. Deviation.
Discussion
Finger millet yield improvement was achieved by targeting the grain yield directly. Of the 140 varieties that have been released so far (Gowda et al., Reference Gowda, Nanja Reddy, Pushpalatha, Deepika, Pramila and Jadhav2014), cv. GPU-28 is considered as a stable, blast-resistant popular variety cultivated in >60% of finger millet area in India (Krishne Gowda et al., Reference Krishne Gowda, Nagaraja, Gowda, Krishnappa and Bharathi2009). In this study, none of the genotypes was significantly superior to cv. GPU-28 for yield attribute. It appears that direct selection of the genotype, based on the higher grain yield would be difficult. Hence, it could be apt to select genotypes for specific traits contributing to grain yield. In this direction, yield improvement of cereals through enhanced harvest index has been reported (Wilson et al., Reference Wilson, Sanogo, Nutsugah, Angarawai, Fofana, Traore, Ahmadou and Muuka2008; Fischer and Edmeades, Reference Fischer and Edmeades2010; Swetha, Reference Swetha2011). Selection/improvement of varieties better than cv.GPU-28 would be highly useful at the farm level, for which existing genetic diversity for yield attributing traits can be exploited (Upadhyaya et al., Reference Upadhyaya, Gowda and Reddy2007; Bharathi et al., Reference Bharathi, Veerabadhira, Gowda and Upadhyaya2013; Patil, Reference Patil2016).
The primary traits associated with the grain yield of finger millet are, the crop duration × ear weight/ unit land area × threshing ratio (Fig. 1). One of the primary characteristics of a genotype is the crop duration. Crop duration in finger millet is mainly determined by the days to flowering (DFF) with relatively unaltered post-anthesis period (Udaya Kumar et al., Reference Udaya Kumar, Sashidhar, Prasad, Seetharam, Riley and Harinarayana1986). It has a positive correlation to grain yield (Owere et al., Reference Owere, Tongoona, Derera and Wanyera2015; Kandel et al., Reference Kandel, Dhami and Shrestha2019). However, in the present study and way back in 1969 a non-significant negative correlation between duration and grain yield are reported (Chaudhari and Acharya, Reference Chaudhari and Acharya1969; Ashok et al., Reference Ashok, Patro, Jyothsna and Divya2016). Furthermore, long-duration varieties will be caught up with end season stress and prone to lodging. Therefore, finger millet genotypes selection could be aimed at medium-duration with 70–75 DFF and 110–115 days to maturity.
The threshing ratio is the ratio of grain to the ear weight. The threshing ratio is generally very low in the case of wild races (18.1%) as against 79.6% in cultivated genotypes (Anon, 2015; online Supplementary Fig. S1). Among cultivated varieties, genetic variability for the threshing ratio is narrow (0.74 to 0.79) but reports elicit a positive relationship with grain yield (Prasanna Kumar and Naveen Kumar, Reference Prasanna Kumar and Naveen Kumar2012; Kumari et al., Reference Kumari, Pushpakumara, Weerakoon, Senanayake and Upadhyaya2018; online Supplementary Table S1). In the present study, threshing ratio did not differ between cultivated varieties and germplasm accessions and; genotypes with a higher threshing ratio > 0.78 (GPU-28) did not produce higher grain yield. Furthermore, genotypes with a higher threshing ratio would be vulnerable to shattering and bird damage (naked types; online Supplementary Fig. S1). Therefore, the optimum threshing ratio could be nearly 0.80 to 0.85 in the selection process. The MLR and path analysis for these three parameters towards grain yield also showed that the total ear weight (TEW) is a better trait than the threshing ratio and DFF (Table 3).
The total ear weight in turn is the product of ear number and mean ear weight (Fig. 1). Of these, mean ear weight (MEW) was prominent with a positive correlation (r = 0.439*; online Supplementary Table S1) and a moderate indirect effect on grain yield through total ear weight (online Supplementary Table S2; Lenka and Mishra, Reference Lenka and Mishra1973). Mean ear weight was the major selection criterion for higher finger millet yield during the 1970s, when tiller number per plant was not a limitation (Krishnamurthy, Reference Krishnamurthy1971). Farmer participatory trials also showed that farmers choose large ear size for higher grain yield (Ojulong et al., Reference Ojulong, Letayo, Sakwera, Ziwa, Mgonja, Sheunda, Kibuka, Otwani, Audi, Mgonja and Manyasa2017). In the present study, none of the genotypes possess significantly higher mean ear weight as compared to cv. GPU-28 (7.79 g/ear), implying that GPU-28 has a better MEW (Table 1). However, MLR (using MEW and PT) showed the possibilities for enhancing the grain yield of cv. GPU-28 by 4.74% through an increase in MEW by 1.0 g/ear1 using the specific genotype like GE-2672 or GE-5252 possessing relatively higher MEW. The MEW can be improved through its component traits, the finger number, finger length, finger width (Owere et al., Reference Owere, Tongoona, Derera and Wanyera2015; Kumari et al., Reference Kumari, Pushpakumara, Weerakoon, Senanayake and Upadhyaya2018), test weight and the number of seeds/ear head (Fig. 1). Such ear components would be in turn determined by the photosynthesis-associated characters like partitioning of assimilates, LVF and peduncle length (Fig. 1). Short peduncle length is expected to have rapid translocation of assimilates. Accordingly, a negative (r = −0.189ns) correlation between peduncle length and MEW was observed in the present study (online Supplementary Table S1). Furthermore, thicker leaves (higher SLW) are expected to have higher mesophyll volume with less leaf area and high water use efficiency suitable to rainfed areas (Sastry et al., Reference Sastry, Udayakumar and Vishwanath1982) and; leaf thickness showed a positive correlation with MEW (r = 0.240ns). Higher LVF is expected to result in higher translocation rates but, in the present study, it was negatively related (r = −0.368*) suggesting that higher LVF might reduce the photosynthetic lamina area as genotypic variability for leaf width could be narrow. The mean ear weight was dependent upon finger length (r = 0.564**).
The PTs per unit area which is responsible for ear number was reported to increase with higher planting and showed a positive correlation with grain yield (Mujahid et al., Reference Mujahid Anjum, Nanja Reddy and Sheshshayee2020). However, a poor relationship between PT per plant and total ear weight in the present study (online Supplementary Table S1) was due to compensation between these two parameters. A similar, negative relationship between PTs per plant and grain yield has been reported earlier (Goswami et al., Reference Goswami, Prasad and Joshi2015; Ashok et al., Reference Ashok, Patro, Jyothsna and Divya2016; Nanja Reddy, Reference Nanja Reddy2020). In the present study, photosynthates might have been utilized for maintenance of stems instead of translocation to ear, hence, under rainfed conditions, it could be apt to have an optimum number of PTs (3 to 4), while under adequate input conditions PT number has a positive influence and could go up to eight per hill depending on the variety (Mujahid et al., Reference Mujahid Anjum, Nanja Reddy and Sheshshayee2020). Therefore, the optimum PTs could be between 2.1 and 3.2 per hill, for rainfed conditions, above which the grain yield would be dependent more on MEW.
To achieve the required yield components like mean ear weight, two principal physiological parameters, the leaf area index (LAI) and photosynthetic rate are important. LAI was reported to have a positive relationship with grain yield (Subramanyam, Reference Subramanyam2000; Nanja Reddy et al., Reference Nanja Reddy, Gowda, Ashok, Krishne Gowda and Gowda2019). In the present study, although LAI was high in many genotypes, but the yield did not increase correspondingly, probably the reserved photosynthates might not have remobilized to ear, in contrast, the low LAI types showed higher grain yield. Hence, for rainfed situations, the low LAI with higher WUE and stay green types could be appropriate (Sastry et al., Reference Sastry, Udayakumar and Vishwanath1982). However, an increase in LAI of shy tillering genotypes like GE-5252 through increased plant density can be explored to increase the grain yield of finger millet. Photosynthetic rate showed an indirect lower positive effect on grain yield through total ear weight and harvest index (online Supplementary Table S2). In addition to leaf photosynthesis, ear photosynthesis is also important in finger millet which contributes up to 41% (Tieszen and Imbamba, Reference Tieszen and Imbamba1978). Therefore, the selection of genotypes with a longer grain filling period and stay green leaf could be a better option for higher current photosynthesis and grain yield.
Among several yield associated parameters, independent traits which had a direct positive effect on grain yield were the mean ear weight followed by photosynthetic rate and leaf area index (online Supplementary Table S3). Among these, the photosynthetic rate is an instantaneous measurement and already high in cv. GPU-28, therefore, selection for higher mean ear weight could be a suitable trait.
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/S1479262121000265.
Data
Transparent.
Acknowledgements
The authors thank the AICRP (small millets) for facilitating the conduct of the experiment and Mr. K. Seenappa, Dr P.S. Jagadish, Dr E.G. Ashok, Dr A. Nagaraja, Dr M. Krishnappa, for their support and suggestions. The authors are thankful to Dr PSSV Khan, Yogi Vemana University, Kadapa for technical suggestions and Dr P Shaila Sastry, Professor of English, Rajamundry for grammatical corrections of the publication.
Author contributions
YANR formulated and conducted the experiment and wrote the manuscript. JG provided the germplasm and guided in conducting the experiment and; KTKG advised in formulating, initiation and advised in conduct of the experiment.
Financial support
Nil.
Conflict of interest
No conflict of interests regarding this publication.
