Phenotypic Analysis of Pigeon Pea Reveal Genotypic Variability under Different Environmental Interaction

*Maletsema Alina Mofokeng, Zaid Bello and Kingstone Mashingaidze*

## **Abstract**

Pigeon pea is one of the most important leguminous crop globally. However it is a neglected pulse crops in South Africa in terms of research and production. Most farmers grow local landraces with low yields and there is lack of diverse material. The objective of the study was to determine the presence of genetic diversity among the pigeon pea genotypes using quantitative and qualitative phenotypic traits. The trials were conducted in Mafikeng and Nelspruit in South Africa. The trials were laid out in randomised complete block designs replicated three times. The quantitative and qualitative phenotypic data were recorded according to pigeon pea descriptor list. The phenotypic data were analysed using analysis of variance, Pearson's correlations, principal component analysis, and biplots constructed using principal coordinate analysis, Shannon weaver diversity indices and frequencies. The results showed highly significant differences among the genotypes based on plant height, pod bearing and seed number per pod meaning there was vast genetic diversity among the genotypes. Seed yield was positively correlated with seed number per pod, seed number per plant and pod weight whereas pod bearing was negatively associated with hundred seed weight meaning improving seed yield will automatically improve other positively correlated traits. Principal component analysis showed five most important PCs contributing to a total variation of 84.7%. The traits that contributed to the most variation to the total variation observed were plant height, pod length, seed yield, pod bearing and days to flowering. The Shannon weaver indices ranged between 0.98 and 1.00 showing the presence of variation among the qualitative traits measured. The clustering grouped genotypes into three clusters with Tumia and ICEAP 00540 being the most diverse. The diverse genotypes can be used as parents for hybridization and development of transgressive segregants in breeding programmes. There was vast presence of genetic diversity among the pigeon pea genotypes evaluated.

**Keywords:** agro-morphology, characterisation, genetic diversity, PCA, pigeon pea

## **1. Introduction**

Pigeon pea (*Cajunus cajan*), a diploid legume crop species (2n = 2x = 22) [1]. This crop is considered as underutilised plant species despite its importance. The crop is a

perennial legume crop, that can be considered as multipurpose crop due to its use for livestock feed, and food for humans. It also improves the fertility of the soil through atmospheric nitrogen fixation [2, 3]. The crop can be intercropped with other crop species. The crop plays an important role in food and nutritional security [4]. Pigeon pea is a good source of mineral elements and vitamins [5]. This crop has high potential to cope with climate change and providing nutritional and food security. It has the ability to survive and give good economic benefits when planted under dryland farming conditions, when there is limitation of rainfall and sustain the livelihood of poor rural populations in tropical and sub-tropical regions of the African continent. Furthermore, the crop helps in protecting the environment from soil erosion, towards enhancing productivity of marginal agricultural lands. The seed of the crop can be eaten as a green vegetable and dry pulse and is an important source of nutritional components [3]. The green pods and foliage of the plant are mainly used as livestock feed [6]. It is climate change crop including heat and tolerate drought [7]. The crop is cultivated by the resources poor small scale farmers with the low input agriculture. Despite the important of pigeon pea for food security and income generation, the cultivation of this crop is neglected in Southern Africa due to unavailability of improved cultivars. Hence, genetic improvement of this crop is important to increase production and productivity of the crop in Southern Africa.

For an efficient evaluation and utilisation of the genetic materials, detailed knowledge about genetic diversity, and information on collection and classification are important and the basis for crop improvement programs [8, 9], which is elucidated through different marker systems such as agro-morphological, biochemical and molecular markers. Among these, agro-morphological characterisation is considered as the initial step for designing breeding programs [10, 11] although influenced by environment unlike with DNA-based markers. The assessment of genetic diversity using agro-morphological traits is still of paramount importance to plant breeders and curators because they will be able to select potential parents based on yield and its components, and farmer preferred agronomic traits. Yohane et al. [12] assessed eighty one pigeon pea accessions for presence of genetic diversity using agro-morphological traits. Assessing genetic diversity helps to study heterosis [13], selection of transgressive segregants and genes of novelty, and has a role in collection and conservation of germplasm for crop improvement [14]. In order to have all these done, sound statistical tools are required for data analysis for assessment of genetic divergence [15]. In order to reduce the volume of data and identify a few key or minimum descriptors that effectively account for the majority of the diversity observed, saving time and effort for future characterisation efforts the data must be subjected to multivariate analysis [16].

Multivariate analytic tools have proved to be vital in crop improvement [17]. The tools include principal component analysis and cluster analysis among others. These tools are currently effective for studying the variability and relationships between accessions [18, 19]. The principal component analysis (PCA) includes the total variance of variables, explains maximum of variance within a data set, and is a function of primary variables. PCA shows which of the traits are decisive in genotype differentiation [20]. It enables easier understanding of impacts and connections among different traits by finding and explaining them [16]. Cluster analysis identifies and classifies objects individuals or variables on the basis of the similarity of the characteristics they possess, so the degree of association will be strong between members of the same cluster and weak between members of different clusters. It aims to allocate a set of individuals to a set of mutually exclusive, exhaustive groups such that the individuals within a particular group are similar to one another while the individuals in the different groups are dissimilar. It is also helpful for parental selection in the breeding program and crop modelling [16]. PCA and cluster analysis are preferred tools for morphological

*Phenotypic Analysis of Pigeon Pea Reveal Genotypic Variability under Different… DOI: http://dx.doi.org/10.5772/intechopen.99285*

characterisation of genotypes and their grouping on similarity basis [21, 22]. Combination of these two approaches gives comprehensive information of characters which are critically contributing for genetic variability in crops [23]. The knowledge of different landraces and their evaluation are necessary for improvement strategy development in any crop [24], as these traditional landraces are the potential donor parents for improved varieties [25]. Hence, the aim of the study was to determine the presence of agro-morphological diversity using quantitative and qualitative traits.

## **2. Materials and methods**

### **2.1 Plant material and experimental sites**

Nineteen pigeon pea genotypes were obtained from ICRISAT in Kenya and Tanzania (**Table 1**). The trials consisting of 19 pigeon pea genotypes were planted in Mafikeng (t 25° 48<sup>0</sup> S, 45° 38<sup>0</sup> E; 1012 m.a.s.l.) and Nelspruit (�25.451496 S, 30.969084 E; 670 m.a.s.l.) in 2019/20 growing season in North West and Mpumalanga Provinces of South Africa. Mafikeng is located eight kilometres from the city of Mafikeng towards the border between Botswana and South Africa. It falls within a semi-arid tropical savannah region and receives a summer rainfall, with an annual mean of 571 mm [26]. The rainfall on site is erratic which makes the prospects for crop cultivation highly vulnerable. Approximately, 68% of the annual precipitation in this area falls between November and January in a few relatively heavy downpours, with a pronounced dry season from April to September. The mean maximum temperature is 37°C, while the mean minimum temperature ranges from 7–11°C. The field in Nelspruit was characterised by sandy loam soil with mean temperature of 19.8°C and an annual precipitation of about 796 mm. Nelspruit is the capital city of Mpumalanga province which neighbours Mozambique.

#### **2.2 Trial design and management**

The trials were laid out in a randomised complete block design replicated three times with a plot consisting of two rows of 4 m length in each site. The spacing between the rows was 90 cm and the spacing between the plants was 60 cm. The insect pests that were prevalent were aphids and pod borers and were controlled by insecticides used on legumes. Plants were irrigated thrice a week. Weeding was done manually using hand hoes.

#### **2.3 Data collection**

Data were recorded according to standard descriptor list of pigeon pea [27]. The quantitative data recorded included plant height (PHT), days to 50% flowering (DFF), pod bearing (PDB), leaf length (LFL), leaf width (LFW), pod length (PDL), pod width (PDW), pod weight (PWT), stem diameter (STD), number of branches (BRN), seed number per pod (SNT), number of seeds per plant (SNP), hundred seed weight (HSW) and seed yield (SYD). The qualitative data included base flower colour, second flower colour, vigour at 50% flowering, pod form, seed colour pattern, seed shape, and pattern of streaks.

#### **2.4 Statistical data analysis**

The recorded quantitative data were analysed using analysis of variance, principal component analysis, and Pearson correlations. The qualitative data were analysed


#### **Table 1.** *A list of pigeon pea germplasm used in the study.*

using frequencies, spearman correlations, and Shannon weaver diversity index. The biplots were constructed using principal coordinate analysis in SAS version 9.6. A dendrogram was constructed using R-Studio in R software version 3.4.

## **3. Results**

## **3.1 Genotype by environment interaction**

Significant differences were observed on site, genotype and genotype x site interaction on **Table 2**. There were highly significant differences for sites based on days to flowering, plant height, branch number, stem diameter, pod bearing, pod length, pod weight and significant differences for seed number per pod. There were highly significant differences on genotype based on pod length and pod weight. There was a site x genotype interaction based on plant height, pod bearing and seed number per pod.

## **3.2 Pearson's correlations**

Correlations of 14 quantitative traits measured in the study are shown in **Table 3**. Days to flowering was highly significantly and positively correlated with plant height, branch number, stem diameter, and hundred seed weight. Also significantly and positively correlated with pod weight and negatively correlated with pod bearing. Plant height was highly significant and positively correlated with


### *Phenotypic Analysis of Pigeon Pea Reveal Genotypic Variability under Different… DOI: http://dx.doi.org/10.5772/intechopen.99285*

**Table 3.**

*Pearson correlations of the quantitative traits measured on*

 *MD pigeon pea.* branch number per plant, stem diameter, and hundred seed weight, and negatively associated with pod bearing. Branch number had a negative association with stem diameter and pod bearing, and a positive correlations with hundred seed weight. Stem diameter had a positive correlation with leaf length, pod bearing and a negative association with hundred seed weight. Leaf length showed a positive correlation with leaf width and pod bearing. Leaf width had a negative association with seed number per pod. Pod bearing had a highly significant negative correlation with hundred seed weight. Pod length showed a positive association with seed number per pod, pod weight, seed number per plant, seed yield. Pod width showed a positive and highly significant correlations with seed number per plant and seed yield. Seed number per pod was positively correlated with pod weight, seed number per plant, and seed yield. Pod weight had positive correlations with seed number per plant and seed yield. Seed number per plant was highly significant and positively correlated with seed yield.

## **3.3 Principal component analysis**

Five most important PCs were identified contributing 32.9%, 24.9%, 12.7%, 8.3% and 5.9%, to the total variation of 84.7%, respectively (**Table 4**). The first PC had pod length, pod weight, seed number per plant and seed yield contributing the most variation. In the second Pc, days to flowering, plant height, branch number, stem diameter contributed the most variation. Leaf length, and leaf width contributed the most variation in third PC. In the fourth PC, pod width was the most


*DFF = Days to 50% flowering, PHT = plant height, BRN = Branch number, LLT = Leaf length, LWT = Leaf width, PDB = Pod bearing, 100SW = hundred seed weight, PDL = Pod length, PDW = Pod width, SNP Seed number per pod, PWT = Pod weight, SEP = Seed number per plant, STD = Stem diameter, SYD = seed weight per plant.*

#### **Table 4.**

*Factor loadings of the most import PCs of the MD short duration pigeon pea.*

contributor to variation whereas in the fifth PC, pod width and seed number per pod were the traits that contributed the most variation.

## **3.4 Principal coordinate analysis**

The principal component biplot of the quantitative traits, F1 had 32.97% and F2 had 24.96% (**Figure 1**). Stem diameter and pod bearing were negatively correlated with plant height, branch number, seed yield, and 100 seed weight. Seed number per pod, pod length, pod width, pod weight, seed yield, and seed number per plant were positively correlated with hundred seed weight, branch number and plant height. The same traits were also correlated with stem diameter, pod bearing, leaf width and leaf length.

The biplot for the qualitative traits, the F1 showed 37.97% and F2 had 20.5% (**Figure 2**). The first quadrant showed base flower colour, flowering pattern, vigour at 50% flowering, second flower colour. The second quadrant had pod form and seed colour pattern. These traits were positively correlated with one another in both quadrants. The third quadrant consisted of pattern of streaks which was positively correlated to vigour at flowering, seed colour pattern, pod form, and seed shape. The fourth quadrant consists of seed shape which is also correlated with pattern of streaks, flowering pattern, base flower colour, and second flower colour and negatively correlated with seed colour pattern.

## **3.5 Frequencies of qualitative traits**

The frequencies of eleven qualitative traits measured are shown in **Table 5**. Vigorousness at flowering was high with 71.4% of plants being vigorous and intermediate was 23.2%. The base flower colour was dominated by yellow flowers followed by orange-yellow. The second flower colour was predominantly composed of red flowers (71.4%). The pattern of streaks was dominated by sparse streaks

**Figure 1.** *PCA biplot for quantitative traits of medium duration (MD) pigeon pea.*

**Figure 2.** *PCA biplot of qualitative traits for MD pigeon pea.*

(35.1%), followed by uniform coverage of second colour and dense streaks. Flowering patter was hundred percent determinate for all genotypes. All plants of various genotypes had 100% stems thicker than 13 mm with green stems dominating (63.2%). The growth habit was predominantly composed of spreading types (75.4%) followed by erect and compact at 22.8%. The genotypes were dominated by cylindrical pods 96.40 with speckled seed colour pattern at 71.4% followed by mottled and speckled at 17.9%. The shape of the seed was predominantly globular (64.3%) with oval shape being 21.4%.

#### **3.6 Shannon weaver diversity**

Shannon weaver diversity indices are shown in **Table 5**. The diversity indices ranges from 0.96 (second flower colour) to 1.00 (flowering pattern and stem thickness). All traits showed significant variation except for flowering pattern and stem thickness (**Figure 3**).

#### **3.7 Hierarchical clustering**

A dendrogram was constructed using hierarchical clustering in GenStat version 20. The dendrogram grouped genotypes into three clusters. The first cluster was composed of one genotype, Tumia. The second cluster was composed of two sub clusters that were divided into sub-sub clusters. The cluster consisted of seventeen genotypes as shown in the dendrogram. The third cluster consisted only ICEAP00540. The genotype Tumia and ICEAP00540 were far distantly related with the rest of the genotypes, and the other seventeen genotypes were significantly related as were grouped together. Tumia and ICEAP00540 has tallest plants and matures later than other genotypes, but the latter has small seed size and highest pod bearing whereas the former has big seed size. The rest of the plants are intermediate.


*Phenotypic Analysis of Pigeon Pea Reveal Genotypic Variability under Different… DOI: http://dx.doi.org/10.5772/intechopen.99285*

#### **Table 5.**

*Frequency percentages of qualitative traits for MD pigeon peas.*

## **4. Discussion**

The knowledge of genetic variation for a trait and trait correlations are important components of any breeding objective. There are highly significant differences for sites based on days to flowering, plant height, branch number, stem diameter, pod bearing, pod length, pod weight and significant differences for seed number per pod. This indicates that the expression of the significant traits varied with the environments were tested on. Their performance were not stable across sites. There were highly significant differences on genotype based on pod length and pod

weight. This highlights the presence of genotypic variation among the genotypes evaluated based on the two traits which can be exploited for cultivar improvement in future breeding programmes. Additionally, there was also a site x genotype interaction based on plant height, pod bearing and seed number per pod. The significant differences on genotype x site interaction could be attributed to the different reactions of the accessions to sites or due to differences between the sites. In each environment, phenotypic manifestation is the result of the action of the genotype under the influence of the environment. However, when considering a series of environments, in addition to the genetic and environmental effects, an additional effect can be detected from their interaction [28, 29]. Ssignificant genotype environment interaction on yield and yield components of in this study concur with the results by Kimaro [30] as well as in other legume crops such as dry bean and cowpea [31, 32].

The positive correlations exhibited by most secondary traits show that multiple trait selection would be possible and the weak correlations among the traits would result in an inefficient selection or low genetic gains [12]. In this study seed yield was positively correlated with seed number per pod, seed number per plant and pod weight whereas pod bearing was negatively associated with hundred seed weight. The positive correlations of various traits in this study shows the usefulness of the traits for selection in crop improvement and they can further be used for improvement of seed yield [33, 34]. Similar trends were reported by Sodavadiya et al. [35] and Linge et al. [36] and Prasad et al. [37] in pigeon pea studies. Furthermore, Yohane et al. [12] reported a significant positive correlation between grain yield and a hundred seed weight, Kinhoégbè et al. [38] reported positive correlation with pod length, pods per plant, branches per plant and number of seeds per pod which concurs with the results in this study. This findings suggests the usefulness of this trait for selection. The results are in accordance with the correlations in this study.

The Principal component reveal five most important PCs with pod length, pod weight, seed number per plant, seed yield, leaf length, leaf width, days to flowering,

#### *Phenotypic Analysis of Pigeon Pea Reveal Genotypic Variability under Different… DOI: http://dx.doi.org/10.5772/intechopen.99285*

plant height, and stem diameter being the most contributing traits to the total variation observed. This suggests that these traits are useful for selection. Other reports indicated that trait contribution to different PCs varies with genetic diversity within the tested germplasm and the number of traits evaluated [25]. The biplot also showed the different grouping of pigeon pea genotypes based on specific traits. These findings suggested that both qualitative variables and quantitative variables data can reveal diversity providing different but complementary information.

Majority of pigeon pea landraces showed a strong tendency to spreading growth habit, yellow based flower colour, with red second flower colour, sparse pattern of streaks, green stems, with globular and speckled seed colour pattern. The results are in contrast with the results of Kinhoégbè et al. [38] where the authors reported genotypes with semi-spreading growth habit, lanceolate leaflet shape, light yellow base flower colour, and plain seed colour pattern. Similar results have already been reported in the morphological variability of Tanzanian pigeon pea germplasm [39] and world-wide collection [40]. Shannon weaver indices also confirmed the presence of genetic diversity based on qualitative traits. Thus, in spite of the influence of environmental factors, qualitative variables can be used to characterise pigeon pea genetic resources.

The pigeon pea genotypes were clustered into three major groups, indicating that there genotypes in the three groups are distantly related. The ones in the same cluster they are closely related and they maybe of the same source or origin. Selection of genotypes from these cluster may not be desirable to get higher yield benefits and transgressive segregants [40, 41]. Therefore, for any hybridization programs, the choice of suitable diverse parents based on genetic divergence analysis would be more fruitful than the choice based on the geographical distances. ICEAP 00540 and Tumia would be the ideal genotypes for use as a parents in any pigeon pea breeding programme for agronomic improvement. The identified genotypes in different clusters show that their interrelationship may be due to free exchange of materials that may have overlapped in the previous diversity distribution pattern of the domesticated species [42, 43]. Niranjana et al. [44] also reported three clusters in their findings on pegion pea. Reddy and Jayamani [45] reported seven major groups of the sixteen pigeon pea genotypes studied for genetic diversity using multivariate analysis. Qutadah et al. [46] also reported seven clusters in their pigeon pea genetic diversity study. Other cluster groups were revealed by various researchers [38, 47].

In conclusion, the study revealed the presence of genetic diversity among the pigeon pea genotypes studied based on the analysis of variance and multivariate tools used for analyses. The results indicated that the higher level of genetic diversity observed within the acquired genotypes from ICRISAT and Malawi will enable efficient utilisation and pigeon pea improvement in breeding programs in South Africa and other countries. The variability among the genotypes will also help to select the parents for hybridization. The selection combined yield related traits will reduce the more breeding work therefore suggested that yield correlated traits selection with respective genotypes. Further characterisation using molecular techniques as well as conservation attention for these germplasms should be conducted.

#### **Acknowledgements**

The first author would like to thank the Department of Agriculture, Land Redistribution and Rural Development for funding. Additionally, the authors would like to thank the technical assistance and trial management of Paul Rantso, Dinah Scott, Deon Du Toit, and Theodora Mathobisa.

## **Funding**

This work was supported by the Department of Agriculture, Land Redistribution and Rural Development.

## **Notes on contributors**

Dr. Maletsema Alina Mofokeng is a Researcher in Plant Breeding at the Agricultural Research Council-Grain Crops, Potchefstroom, South Africa.

Dr. Zaid Bello is a Researcher in Agronomy department of Agricultural Research Council-Grain Crops, Potchefstroom, South Africa.

Dr. Kingstone Mashingaidze is a Senior Research Manager in the Agricultural Research Council-Grain Crops, Potchefstroom, South Africa.

Dr. Gerrano Abe is a Senior Researcher in the Agricultural Research Council-Vegetable, Industrial and Medicinal Plants, Pretoria, South Africa.

## **Disclosure statement**

The authors have not declared any conflict of interests.

## **Author details**

Maletsema Alina Mofokeng\*, Zaid Bello and Kingstone Mashingaidze Agricultural Research Council-Grain Crops, Potchefstroom, South Africa

\*Address all correspondence to: mofokenga@arc.agric.za

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Phenotypic Analysis of Pigeon Pea Reveal Genotypic Variability under Different… DOI: http://dx.doi.org/10.5772/intechopen.99285*

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## **Chapter 6**
