GENOTYPIC VARIANCE AND SELECTION CRITERIA IN GROUNDNUT (ARACHISHYPOGAEAL.) BASED ON OIL QUALITY AND AGRONOMIC TRAITS

Variability gives room for recombination which is important for any crop improvement program. Based on this contextual, this work was conducted to evaluate genetic variability among groundnut germplasm and establish relationships between oil quality and agronomic traits using multivariate analysis. To achieve this objective, fifteen groundnut genotypes were evaluated in a randomized complete block design with three replications. Data were collected on oil and yield quality traits. The estimates of genotypic coefficient of variation (GCV) and phenotypic coefficient of variation (PCV) were high for number of pods and number of seeds per plant, carbohydrate and protein. Broad sense heritability estimates for agronomic and oil content traits ranged from 49.57% 99.06% while the genetic advance expressed as percentage of mean estimates ranged from 17.73% -114.38%. The evaluated genotypes were clustered into four main groups based on oil and yield quality traits using UPGMA dendrogram. Hence, hybridization of group II with either group I, III or IV could be used to achieve higher vigor or heterosis among the genotypes. The number of pods per plant showed a significant correlation with pod weight per plant (r =0.79) and the number of seeds (0.99). However, most of the oil content traits recorded a non-significant negative correlation. It was concluded that number of pods, seeds per plant, and fat content might be the major agronomic and oil quality traits as selection criteria for improving groundnut genotypes. Also, this assessment could be used in development of reliable selection criteria for important agronomic traits in groundnut.


INTRODUCTION
Groundnut (Arachis hypogaea L.) is a valuable leguminous cash crop grown for both food and oil (FAOSTAT 2010). Although the crop originated in South America, it is now produced in over 100 nations. It is grown on a total of 23 million hectares, with a production estimate of 36.45 million tonnes and an average productivity of 1520 kg/ha. Around 90% of overall production is concentrated in semi-arid tropics emerging countries, with India and China accounting for nearly half of world output. Nigeria, Senegal, and Sudan are important producers in Africa. After common beans (Phaseolus vulgaris L.), the crop is Uganda's second most significant legume (FAOSTAT 2019). It is primarily produced in Uganda's semiarid, dry eastern and northern areas (Ronner and Giller, 2012). It is a non-animal protein source that's also a cash and food crop. Furthermore, smallholder farmers grow groundnut with little or no inputs (Mugisha et al., 2011;Mugisha et al., 2014).
Research on improvement of groundnut has mainly focused on improving agronomic traits. Very few efforts have been made to improve groundnut's nutritional quality, as biochemical estimation of quality is laborious through traditional breeding.Only a few attempts have been made to evaluate various germplasm for nutritional traits in combination with agronomic performance (Sarvamangala et al., 2011). Kernels with a high oil content and a low Oleic acid/ Linoleic acid ratio (O/L ratio) are preferred for edible oil, however the quality requirements for confectionery groundnut are more severe because it is not an oilseed crop. They will necessitate further work to develop confectionery-grade types with large pod and seed sizes, high protein and sugar content, high oil content, low aflatoxin risk, and a high O/L ratio. However, the limited genetic variability in groundnut oil content limits the potential for significant increases or decreases in oil content through conventional breeding Pasupuleti et al., (2013). However, by attempting crosses that are properly planned, especially in light of the parents' diversity, a lot of variation can be created. The goals of this study were to determine the degree of association between agronomic and oil quality traits, as well as to estimate genetic variability, heritability, and expected genetic advance; identify genotype(s) with high levels of oil content and better performance; and estimate genetic variability, heritability, and expected genetic advance.

Planting Materials
Genetic material comprised of 15 groundnut genotypes details in Table 1.

Data Collection
Plant height (cm), number of pods per plant, pod weight per plant (g), seed weight per plant (g), 100-seed weight (g), and shelling percentage (%) were among the agronomic traits measured. Proximate analysis, such as moisture content, ash content, fibre, lipids (fat), protein, and carbohydrate, were also taken, and these were gathered using established procedures (AOAC 2012).

Statistical Analysis
Results were analyzed using SASsoftware (version 9.1) for all traits to carry out the analysis where the variance component could be obtained.and means were compared/ separated using Fischer's LeastSignificance Difference (LSD) at 5% level.
The model for analysis of RCBD is Observed effect fori th replicationj th genotype andk th block,   grand mean of the experiment,  i r effect due to i th replication,  j g effect due to j th genotype,  ijk  effects due to the residual or random error of the experiment.

Estimate of Genetic Parameters
The extent of genetic progress expected by character selection was computed as follows (Johnson et al., 1955): In this work, genetic characteristics such as genotypic and environmental variance, broad-sense heritability, and genetic advance (GA) of Arachishypogaea variants were calculated. Variation information is only available through genotypic and phenotypic variations, but heritability assessment determines the heritable part of this variation. A sufficient genetic variety of these qualities, as well as their heritability values, are required for efficient selection of traits under improvement. In a broad sense, heritability was calculated as the ratio of homozygous parents' genetic variance to their phenotypic variance. The heritability was assessed on a mean basis by averaging the total genetic variation and on a varietal basis by calculating the genetic variance for each of the 15 genotypes. According to Singh and Chaudhary (1985), the mean values were utilized for genetic analysis to determine phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV) using: Pearson's formula was used to calculate the correlation coefficient between pairs of characters at phenotypic levels.

Cluster Analysis
Cluster analysis (CA) was utilized to evaluate the genetic diversity of oil quality and agronomic parameters in this study.
To differentiate the varieties, the agromorphological data were computed using multivariate analysis. Individuals were grouped together using cluster analysis based on their features. As a result, individuals with comparable traits were correctly clustered together based on relatedness, similarity, and distance of variations. To begin, the data were standardized to eliminate the effects of parameter variances using the STAND option, then the distance coefficient was calculated using the DICE similarity index. In the numerical taxonomy system NTSYS pc 2.02 software, the standardized data and information were represented in a dendrogram using the Shan clustering program and the unweighted pair group method with arithmetic average (UPGMA). To support the cluster analysis, the DECENTRE, EIGEN, and GRAPHICS tools of NTSYS pc were used to calculate principal component analysis (PCA).

Mean square for agronomic and oil content traits
The results of analysis of variance showed highly significant difference (P≤0.01) among the genotypes for both agronomic oil content traits. The mean squares for plant height, days to 50% flowering, number of pods, pod weight, number of seeds, seed weight, 100 seed weight, shelling percentage, moisture content, fat, ash, carbohydrate and protein is presented in Table  2.

Mean Performance for agronomic traits
The mean for the agronomic traits for the 15 groundnut genotypes is presented in (Table 3). Wide range of variation was recorded for all the traits. The overall mean performance for plant height is 50.58 cm and it ranged from 39.87 cm for SAMNUT 21 to 63.07 cm for GH119-20. Three genotypes SAMNUT 21 (39.87cm), ICGV 94222 (43.43cm) and SAMNUT 14 (44.10 cm)

Variance components estimated of agronomic and oil content traits
The extent of variability in respect of phenotypic and genotypic variances, phenotypic and genotypic coefficients of variance (PCV) and (GCV) respectively, broad-sense heritability   2 H and genetic advance expressed as percentage of mean ΔG(%) for agronomic traits for the 15 groundnut genotypes are presented in (Table 4)  (%) G  = Genetic advance expressed as a % of mean.
Broad sense heritability for agronomic traits indicated high heritability for all the traits (>50%).For each character, the predicted genetic advance values are reported as a percentage of the genotype mean (Table 4). High heritability and genetic progress are essential elements in anticipating the subsequent effect and picking the finest people. Number of pods per plant exhibited a high heritability (84.87%) and a high genetic advance (59.63%), whereas other variables had a high heritability but a low genetic advance. For all oil quality traits (Table 4), the genetic component accounted for a considerable amount of the phenotypic variance, with genetic variance contributing >90% to phenotypic variance. GCV estimates were high for ash (26.92 percent), carbohydrate (33.07 %), and protein (35.64 %), but moderate to low for moisture and fat. PCV estimations for protein (35.84 percent), carbohydrate (33.86 percent), and ash were also high (28.97%). PCV estimations for moisture and fat were moderate to low. For all of the oil quality traits, PCV values were higher than GCV values, indicating that the environment had an impact on trait expression. All of the oil quality traits reported had high heritability values, according to the results of heritability calculations. For glucose and protein, the genetic advance expressed as a percentage of mean was significant, but for all other variables, the GA was moderate to low.

Cluster Analysis based on oil quality and yield traits
The homogeneous agro-morphological traits were employed to calculate the Euclidean distances among the 15 groundnut accessions. TheUPGMAdendrogram was constructed using these values presented in Table 3 for cluster analysis and PCA. In thedendrogram shown in figure 1 and 2, the 15 groundnut accessions were grouped into 4main clusters. Among the four clusters, group II had the largest number of genotypes (7), groupI and IV had 3 genotypes each while group III had only 2 genotypes. Correlation between agronomic and oil content traits A highly significant positive correlation was recorded between number of pods per plant and all agronomic traits except 100 seed weight (P≤0.01) while it was negatively correlated with ash and carbohydrate with non-significant different (P>0.05). Significant (P≤0.05) positive correlation was recorded between moisture and carbohydrate. Protein had a strong negative and highly significance with carbohydrate (Table 5).

DISCUSSION
The wide range of variation, highly significant difference (P≤0.01) observed in the mean square of all the agronomic traits suggest that a reasonable amount of variability was present in the experimental materials for all the traits. Similar trends of variability for different agronomic traits were reported by Wolf et al. (2000) and Vasicet al. (2001). This variation could be attributed to genetic and environmental effects as well as their interactions. The measurement, evaluation and existence of variability provide the opportunity for improvement through selection (Marwedeet al., 2004, Subramanian et al., 2010. The mean performances of carbohydrate obtained suggest that groundnut could be used to manage protein malnutrition since a great amount of protein and fat is also found present. Mean values of carbohydrate obtained for the 15 groundnut genotypes are not significantly different from each other. ICGV 93194 had the highest crude protein of 24.91% and SAMNUT 23 had the lowest crude protein content of 12.39%. All 15 groundnut genotypes were non-significant in their crude fat contents. The crude fat values obtained were higher than Asibouet al., (2008) crude fat values of 33.60-54.96 percent. Dietary fat is significant because it aids in the absorption of fat-soluble vitamins. It is a high-energy nutrient that does not make up a significant portion of the diet (Atasieet al., 2009). The high crude fat values found could indicate that groundnut genotypes could be employed to improve the palatability of meals that contain them. These groundnut cultivars have high crude fat contents, indicating that they are good sources of oil and may be appropriate for commercial oil production. SAMNUT 10 has a high protein level, however the largest fat content was found in SAMNUT 23 and SAMNUT 24. SAMNUT 23 and SAMNUT 24 had the highest fat content, high to moderate protein, and ash content mean performances, which was consistent with the findings of Aurandet al. (1987) for the same genotypes.

Coefficient of Variation
The calculation of genotypic coefficient of variation (GCV) in connection to their respective phenotypic coefficient of variation could better explain the comparison of traits in terms of the level of genetic variation (PCV). In both crosses and generations, relatively minimal differences between GCV and PCV were identified for agronomic and oil quality parameters such as seed number per pod (Table 6). It implies that the observed characteristic changes were primarily attributable to genetic factors. The environment, on the other hand, had only a minor impact on the expression of thesetraits. The genotypic coefficient of variation (GCV) compares the variability present in different traits and evaluates the degree of diversity in crops. The phenotypic coefficient of variation (PCV) estimate was larger than the genotypic coefficient of variation (GCV) estimate for all traits evaluated among the 15 groundnut genotypes in this study. This revealed that the environment has a significant impact on the manifestation of these features. Similar observations in pearl millet were discovered by (Abuali Al, 2006, Subramanian et al., 2010, Bezaweletawetet al., 2006, Sumathiet al., 2010, Ghazyet al., 2012. The GCV and heritability estimates give accurate estimates of how much genetic progress can be predicted through phenotypic selection (Burton, 1952). For the parameters pod number per plant, 100-seed weight, and seed yield per plant, however, there was a significant difference between GCV and PCV. This revealed the importance of the environment in shaping this character's personality (Table 6). High GCV was seen in this experiment in features such as seed yield per plant. The significant GCV for this variable suggested that genotype improvement may be achieved by further selection.
The magnitude of inheritance of traits is determined by heritability. The information on heredity alone may not be sufficient to make an informed decision. As a result, heritability estimations combined with expected genetic advancements will be more accurate. According to Johnson et al. (2011), the effectiveness of selection is determined not just by heredity but also by genetic progress. In the number of pods, number of seeds per plant, and oil quality parameters, strong heritability was associated by high genetic progress as a percentage of the mean. This suggests that certain characteristics are inherited. The heritability is most likely due to additive gene effects, and selection for these traits may be successful in early generations. Similar findings have been observed by other researchers (Ali et al., 2008). However, the substantial heritability of pod weight, seed weight, plant height, and shelling %, along with the poor genetic advance, suggests non-additive gene action.
The unweighted pair group method with arithmetic mean (UPGMA) dendrogram approximately clustered the groundnut accessions into four main groups at dissimilarity coefficients 1.40. This indicates a high level of agro-morphological diversity among the evaluated genotypes. This study showed the efficacy of morphological or quantitative traits in grouping groundnut accessions. It was reported that analysis of genetic variance among groundnut accessions based on morphological traits can be used to differentiate and classify genotypes in a population (Swamyet al., 1988). This genetic diversity plays a significant role in selection of diverse cultivar for improvement of groundnut through selective breeding (Asibouet al., 2008). The evaluated genotypes were clustered into four main groups based on oil and yield quality traits using UPGMAdendrogram. Hence, to achieve higher vigour orheterosis among the genotypes, hybridization of group II with either group I, III or IV.
Regarding the associations among the agronomic traits, it was observed that number of pods per plants had a highly significant positive association with seed weight, number of seeds and shelling percentage. Another important yield viz. The number of seeds positively associated shelling percentage and seed weight (SwamyRaoet al., 1988). Therefore, any direct selection for increased number of pods is likely to be associated with improvement of these traits. A weak association of shelling percentage 100 seed weight noticed indicate that large seeded types have poor shelling and negative relationship needs to be broken. However, seed weight and protein content observed to be negatively correlated which was also reported by (Mishra et al., 1992) as they do not go hand in hand but efforts to enhance protein content in the large seed kernels would be of prime interest in future breeding programs but must observe a high significant positive correlation with shelling percentage. The present study revealed a higher magnitude of variability for most agronomic and oil quality traits, accompanied by high heritability and genetic advance. Therefore, direct selection may yield desired combination of traits wherever favourable Study of association among the oil quality traits is important. As the association of pod yield with oil content is positive, it is difficult to combine high yield with low oil in a single genotype, which is essential for confectionery genotype. However, notice of negative and significant association of pod yield with protein content is again undesirable. Further, seed weight was observed to be negatively correlated with protein content, carbohydrate, moisture and ash (Mishra et al., 1992) as they do not go hand in hand but efforts to enhance protein content in the large seed kernels would be of prime interest in future breeding programs.

CONCLUSION
The present study revealed considerable amount of genetic variability for most of the agronomic and oil quality traits, which was also accompanied by high heritability and genetic advance. Therefore, direct selection for yield and oil quality may be desired in combination with other traits wherever they are favourably associated. The genetic parameters discussed here are functions of environmental variability, so estimates may differ in other environment. Based on the high heritability and genetic progress of several variables, such as the number of pods per plant, the number of seeds per plant, protein, and fat, it may be argued that the genetic influences of phenotypic expression of these traits are primarily additive. As a result, after numerous selection cycles, a high response should be possible. It is concluded that SAMNUT 10, SAMNUT 11, SAMNUT 23 and SAMNUT 24 have been identified as the best performing genotypes in terms of yield (number of pods per plant) and high oil (fat) content.