Printer Friendly

Heritability Analysis and Phenotypic Characterization of Spider Plant (Cleome gynandra L.) for Yield.

1. Introduction

Cleome gynandra, also known as "African spider plant", is among the most important traditional leafy vegetables widely used in Africa [1]. It belongs to the family of Capparaceae. It is also an erect herbaceous annual herb that is mainly self-pollinated [2]. The plant is highly nutritive and contains health promoting bioactive compounds important in combating malnutrition and reducing human degenerative diseases. Spider plant is native to the Southern Africa, Western Africa, Central Africa, Eastern Africa, and South East Asia [3]. In South Africa, spider plant has been found to grow in the wild in KwaZulu-Natal, Free State, Northern Cape, Limpopo, and North West provinces [3]. In Kenya, the plants are mainly found in Western, Rift Valley, Eastern, and Coastal regions. The key counties producing the crop include Kisii, Nyamira, Kericho, Migori, and Siaya [4]. Despite this wide adaptation and continued increase in production and consumption, there have been limited efforts towards its improvement. There is lack of critical information on the extent and structure of phenotypic variation crucial for the breeding and conservation of spider plant [2, 5]. Genetic diversity is particularly useful in characterizing individual accessions and cultivars, in detecting genetic materials with novel genes and thereby rescuing them from genetic erosion, and as a general guide, in selecting appropriate parents in breeding programs. Most of the genetic diversity observed in spider plant in Kenya and South Africa has traditionally been maintained by farmers in situ. This poses the risk of species extinction due to loss of natural habitat as humans continue to exploit and develop land, divert water flow, and change the environment. Secondly, as human population continues to increase, there is pressure on natural land being cleared by human activity. The need for cultivation, conservation, and characterization of spider plant remains imperative in maintaining the integrity of the genetic information and diversity.

Mendelian analysis of discrete morphological traits can be used to estimate genetic diversity in plants [6] and has been successfully used in spider plant [5]. Some of the key traits that have been used as a guide in selection for good genotypes in previous studies included high heritability traits such as days to flowering, plant height, and number of leaves per plant [7]. Omondi [7] observed that higher leaf yield, plant uniformity, longer vegetative phase, late flowering, and drought tolerance could form the best criterion in selection of good performing spider plant accessions. However, for an efficient crop improvement program, information on estimates of heritability for these desirable traits must be established [8]. Due to limited knowledge on the genetic variability, more research remains of the essence to elucidate the genetic and phenotypic diversity of existing spider plant accessions. Thus, the main thrust of this study was to understand the extent of phenotypic diversity and heritability of qualitative traits among 49 spider plant accessions assembled from Kenya and South Africa. Promising spider plant accessions can be utilized in various breeding programs and have the potential of enhancing its utilization while aiding to fight hidden hunger in Kenya.

2. Materials and Methods

2.1. Plant Materials. The study used 49 spider plant accessions, mainly local landraces assembled from 3 sources: Gene bank of Kenya (25), Gene bank of South Africa (9), and Kenyan farmers' landraces (15) (Table 1).

2.2. Experimental Design and Study Site. The experiments were carried out at the University of Nairobi's Kabete Field station (Nairobi, Kenya) for two seasons from March 2014 to May 2014 and October 2014 to January 2015. The experiments were laid out in a randomized complete block design with three replications. Kabete Field station lies at 36[degrees]41'E and 01[degrees]15'S with an altitude of 1737 m above sea level. It receives an average temperature of 23[degrees]C with a bimodal rainfall pattern and an annual precipitation of 600 mm to 1800 mm. The soil type is well drained very dark reddish, brown to dark red friable clay locally known as Kikuyu red clay loam with an average pH of 6.2 [9].

2.3. Crop Husbandry. Pregermination for each accession was done for 72 hours under treatment with 0.2% gibberellic acid to break seed dormancy and enhance germination [10]. Each individual accession was planted by hand in two rows comprising ten seeding holes per row (20 plants in a plot). Row plots were 3 m in length with inter-row spacing of 30 cm and intra-row spacing of 30 cm. Farmyard manure was applied to rows at the rate of 10.5g/accession and mixed with soil at planting. Hand weeding was done throughout the experimental period. The experiment was conducted under rain-fed conditions with supplemental overhead irrigation when required.

2.4. Data Collection and Analysis. There were two sets of data collected in the study, namely, qualitative (morphological) and quantitative (agronomic).

2.5. Qualitative Traits. Spider plant traits that were considered qualitative included growth habit, flower colour, stem colour, stem hairiness, petiole colour, petiole hairiness, leaf colour, and leaf pubescence based on the list of modified spider plant descriptors [11] (Table 2). Three randomly selected plants were tagged per accession per replicate during crop growth, before flowering. The data was subjected to DARwin 5.0 software as described by Perrier and Jacquemoud-Collet [12]. Euclidean distance matrix and hierarchical clustering analyses of Unweighted Pair Group Method of Arithmetic averaging were used to estimate dissimilarities among the accessions and results displayed in a dendrogram. This was followed by the identification of the most significant descriptors contributing to most phenotypic variation among the spider plant accessions through a stepwise regression analysis.

2.6. Quantitative Traits. All the yield and yield related traits were considered quantitative, including days to 50% flowering, SPAD values, plant height, number of primary branches, leaf length, leaf width, single leaf area, and number of leaves per plant. Using Genstat version 14 software as described by [13], the data was subjected to Analysis of Variance to establish any significance differences among the traits and to obtain genotype means which were then separated using the Fishers protected least significant differences (LSD) at P < 0.05.

To establish the relationship among the traits collected, a two-tail correlation analysis was performed to estimate quantitative relationships among the traits and also to identify those traits that could be of great significance in a spider plant breeding program.

Heritability in the broad sense was estimated as a ratio of genotypic variance to the phenotypic variance and expressed in percentage [14] as per the following equation.

Heritability ([H.sup.2]) = (Vg/Vp x 100) (1)

where [V.sub.g] is the genotypic variance and [V.sub.p] is the phenotypic variance.

Genotypic variance ([[sigma].sup.2]g) was derived by subtracting error mean sum of squares (EMS) from the genotypic mean sum of squares (GMS) and divided by the number of replications as given by the following equation.

[[sigma].sup.2]g = GMS - EMS/r (2)

where

GMS is the genotype mean sum of squares, EMS is the error mean sum of squares, and r is the number of replications.

Phenotypic variance ([[sigma].sup.2]p) was derived by adding genotypic variance with error variance as per the following equation.

[[sigma].sup.2]p = [[sigma].sup.2]e + [[sigma].sup.2]g (3)

3. Results and Discussions

3.1. Qualitative Traits. There were three distinct flower colours displayed by the spider plant accessions: purple, pink, and white. Among these, the purple flower colour was the most dominant among the Kenyan accessions at 49% (Table 3) while the most dominant flower colour among the South African accessions was white. Most of the South African accessions displayed a green stem with a green petiole as opposed to the Kenyans accessions, which displayed purple stems and pubescence. Previous study by Masuka and Mazarura [15] reported that purple-stemmed plants tended to be more hairy (trichomes) than the green-stemmed plants. Anthocyanins have been implicated as responsible for the stem pigmentation in most herbaceous plants [16] and have also been widely studied for their potential medicinal value [17]. Although spider plants have been traditionally used for medicinal purposes [18], the obvious contrast between South African and Kenyan accessions in their anthocyanin content calls for more studies in order to elucidate the benefits of the variations observed. Presence of trichomes, on the other hand, has been associated with insect resistance in several studies including soybean [19], pigeonpea [20], and Brassicaceae [21]. Trichomes are considered a domestication trait and are often more abundant in unadapted landraces than in improved germplasm. The absence of trichomes in South African accessions may suggest that they have undergone much more intense selection cycles than the Kenyan accessions, although more studies would need to be done to confirm this fact.

Erect growth habit was more dominant and was observed in 90% of the accessions as opposed to the semi-erect growth habit observed among 10% of the accessions. This observation agrees with earlier findings [22], which reported over 80% erect growth in the studied spider plant accessions. Other past research has also shown that majority of the spider plant morphotypes present an erect type of growth [5]. Growth habit is crucial in vegetable breeding as reported in other indigenous vegetables [23]. Bushy growth habit results in many small leaves arising from numerous shoots that are not a preference trait to producers while the erect growth habit with only primary and secondary branches maximizes the leaf area. This implies that yield improvement in spider plant could be exploited through selection of genotypes exhibiting erect growth habit and therefore large leaf size. Further reports have suggested that, in mixed cropping, farmers could adopt the semi-erect type whereas the erect types are ideal for intercrop adaptability [14].

3.2. Cluster Analysis. Sufficient phenotypic variation was observed among the accessions as revealed by the cluster analysis (Figure 1). Two major clusters, namely, clusters 1 and 2, were distinguished using the eight morphological descriptors. Cluster 1 included 9 accessions of South African origin with an exception of one Kenyan accession 1959 while cluster 2 is comprised of 40 Kenyan accessions inclusive of one South African origin. Stem and petiole colour were the major traits that contributed to the Kenyan accession 31990 grouping together with the South African accessions that were mainly green stemmed with green petiole. The exceptional South African accession grouped together with the Kenyan accessions due to the purple stem colour and profuse pubescence that were predominant among the Kenyan accessions. This clearly revealed the differences in the genetic makeup of the accessions from the two regions. However, the South African accessions 2279 and 2000, which were collected from the Northern Province, showed similarity in their phenotypic traits. Most of the Kenyan gene bank accessions, namely, 50339, 50330, 50328, 50326, 50299, and 50273, from Nyamira region clustered closely together with accession 30316 from Western region despite being collected from different regions. Additionally, Kenyan accession 45451 from central region and Kenyan accession 14 collected from Kisii region grouped together suggesting some degree of similarity in their morphological traits. Most of the Kenyan farmers' landraces clustered together based on their origin. There were major overlaps with the accessions assembled from the gene bank implying that they could have same genetic makeup. A study by K'opondo [5] has demonstrated a close relationship among spider plant genotypes following the evaluation of the variability in seed proteins among them. In addition, this uniformity could also arise from the self-pollination status of spider plant. However, more characterization needs to be done to validate such findings. The cluster analysis, which clearly grouped the accessions according to their geographical origin, suggests that crop improvement of spider plant could be achieved through exploiting the variation revealed. However, the current cluster analysis was done using morphological traits, which can be influenced by several environmental factors. There will be need to undertake a more detailed genetic analysis using molecular markers to confirm the existence of genetic variation across the different geographical regions. There is need for specific regional breeding efforts to target preferred traits [24, 25].

3.3. Quantitative Traits

3.3.1. Analysis of Variance. Spider plant accessions showed significant differences (P<0.05) with no seasonal effect for all the traits, namely, days to 50% flowering, single leaf area, leaf length and width, chlorophyll content, number of leaves per plant, number of primary branches, and plant height (Table 4), implying the existence of variability for the respective traits among spider plant accessions. Accessions that exhibited longer days to 50% flowering also yielded more leaf count. Late flowering enables a plant to have a longer vegetative phase during growth period [7]. Past research has associated late flowering with increased leaf yield and consequently early flowering as a limit to leaf yield in other indigenous vegetables [23]. This suggests that late flowering would be a good selection criterion for yield improvement in spider plant.

Other traits that contributed to increased leaf count were plant height and number of primary branches. Kenyan accessions were taller than the South African accessions with plant height varying from 21 cm for accession 2249 to 113 cm for accession 50296. The Kenyan accessions performed better than South African genotypes for the number of leaves per plant, number of primary branches, leaf length, leaf width, plant height, single leaf area, and chlorophyll content conforming to past research by Wasonga [22]. The best 5 outstanding accessions with regard to yield related traits like 50% flowering, single leaf area, and number of leaves per plant included Kenyan accessions: 3, 7, 45451, 50296, and South African accession 2241 (Table 5).

3.3.2. Correlation among the Traits. Knowledge of the correlations among yield and the yield related traits is of considerable importance in crop improvement because it aids in indirect selection [26]. There was positive and significant correlation between leaf length, leaf width, and leaf area with number of days to 50% flowering Table 6. This agrees with the findings of Kiebre et al. [27] who also reported a positive significant correlation between number of days to 50% flowering with leaf length and width. This further suggests that the late flowering genotypes could be selected for their big size, which is a crucial trait to the producers who regard leaf biomass as key in leafy vegetables production.

There was a significant positive correlation between plant height and number of primary branches conforming to the results of Kiebre et al. [27] indicating the taller the plant, the more the number of primary branches. This is further supported by the observed correlations, where yield in terms of number of leaves per plant had a positive and significant correlation with plant height (r = 0.69) and number of primary branches (r = 0.63) implying that the higher the number of the branches and the taller the plant, the higher the number of leaves. Single leaf area correlated positively with leaf length, width, and days to 50% flowering at r = 0.92, r = 0.88, and r = 0.21, respectively. As expected, the leaf area would be determined by its length and width suggesting the longer and wider the leaf, the bigger the leaf area. This suggests leaf length and width as important traits in selecting for vegetative yield in spider plant.

However, there was a nonsignificant negative correlation between leaf size and leaf yield indicating the more the number of leaves in the plant, the smaller the leaves. Yield is influenced by complex soil plant interactions in many crops. In this study, the chlorophyll content measured in SPAD value had a positive significant correlation with number of leaves per plant (r = 0.45), number of primary branches (r = 0.54), and plant height (r = 0.59). This contradicts the findings of [28] who reported negative correlations between chlorophyll readings with yield related traits except for plant height in beans. This positive significance correlation between SPAD values and yield related traits calls for more studies to elucidate this phenomenon. Leaf yields may be improved through selection of accessions that showed high leaf count as well as large single leaf area.

3.4. Stepwise Regression. The most important traits that have a considerable effect on the dependant variable are verified through a stepwise regression analysis. The traits selected through the regression model can then be used as a selection criterion for indirect selection in a breeding program

[29]. A multiple linear regression analysis was calculated by considering the number of leaves as the dependent variable and other characters as the independent variables. Results of regression analysis showed that plant height had a significant influence on yield ([R.sup.2] = 46.7, P value [less than or equal to] 0.05) (Table 7). This implies that selection based on plant height will influence and increase vegetative yield in C. gynandra. This further agrees with other findings by Nwangburuka et al. [30] in vegetable C. olitorius where plant height was found to significantly increase leaf yield.

3.5. Heritability Estimates for Yield and Yield Related Traits. The estimates of heritability in broad sense for all the traits ranged from 78% to 99% (Table 8). High percentages of broad sense heritability were estimated for number of leaves per plant, plant height at 99%, and SPAD value at 96%. Leaf width exhibited a moderately lower percentage at 78% followed by single leaf area and leaf length at 86% and 89%, respectively (Table 8). High heritability plays a great role in selection for crop improvement as the traits to be improved depend immensely on their heritability and variability [27]. In this study, the genotypic variance of all traits was higher than the environmental variance implying that much of the phenotypic variation among the accessions was attributed to variation in genotype as opposed to the environment. The high estimates of heritability displayed in the study suggest that selection for yield improvement in spider plant could be based on traits like number of leaves per plant and plant height.

4. Conclusions and Recommendations

This study reported the existence of significant phenotypic variation in Cleome gynandra as evidenced by the morphological characterization which clearly distinguished the accessions from the two regions. The new knowledge generated on the spider plant morphological structure could offer a great potential in developing relevant genetic and genomic resources for spider plant breeding programs. It is also clear that indirect selection for improved spider plant accessions could be based on the yield related traits like number of leaves per plant, plant height, number of primary branches, and days to flowering which exhibited high heritability. This study recommends the complementation of morphological characterization with the use of molecular markers for germplasm characterization and genetic diversity since they are under little influence from the environment.

https://doi.org/10.1155/2018/8568424

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.

Acknowledgments

The authors would like to acknowledge the University of Nairobi for the opportunity to undertake the research. This paper is part of the M.S. thesis entitled "Genetic Characterization and Nutrition Analysis of Eastern and South African Cleomegynandra (Spider Plant) Accessions" submitted to the University of Nairobi towards achievement of an M.S. degree in plant breeding and biotechnology.

References

[1] R. R. Schippers, "African indigenous vegetables: an overview of the cultivated species," 2000.

[2] J. A. Chweya and N. A. Mnzava, Cat's whiskers. gynandra L.: Promoting the conservation and use of underutilized and neglected crops, vol. 11,1997.

[3] Department of Agriculture Forestry and Fisheries (DAFF), Cleome, Resource Centre, Pretoria, South Africa.

[4] HCDA, Horticulture Data 2011-2013 Validation Report, Horticultural Crops Development authority.

[5] F. B. K'opondo, "Morphological characterization of selected spider plant (Cleome gynandraL.) types from western Kenya," Annals of Biological Research, vol. 2, no. 2, pp. 54-64, 2011.

[6] S. A. Mohammadi and B. M. Prasanna, "Analysis of genetic diversity in crop plants--salient statistical tools and considerations," Crop Science, vol. 43, no. 4, pp. 1235-1248, 2003.

[7] C. O. Omondi, Variation and Yield Prediction Analyses of Some Morphological Traits in Six Kenyan Landraces Population of Spider flower (Gynandropsis gynandra (L.) Briq) [PhD. thesis], Univ. Nairobi, Nairobi, Kenya.

[8] S. Shukla, A. Bhargava, A. Chatterjee, A. Srivastava, and S. P. Singh, "Genotypic variability in vegetable amaranth (Amaranthus tricolor L.) for foliage yield and its contributing traits over successive cuttings and years," Euphytica, vol. 151, no. 1, pp. 103-110, 2006.

[9] R. Jaetzold, "Natural conditions and farm management information of Central Kenya. Farm management handbook of Kenya," 2004.

[10] F. Koyuncu, "Breaking seed dormancy in black mulberry (Morus nigra L.) by cold stratification and exogenous application of gibberellic acid," Acta Biologica Cracoviensia Series Botanica, vol. 47, no. 2, pp. 23-26, 2005.

[11] Food and Agricultural Organisation (FAO), Production year book, vol. 49, Food and Agricultural Organisation (FAO), Rome, Italy, 1995.

[12] X. Perrier and J. P. Jacquemoud-Collet, "DARwin software".

[13] R.W Payne, D. A. Murray, S. A. Harding, D. B. Baird, and D. Soutar, "An introduction to GenStat for Windows," vol. 2011, VSN International, Hemel Hempstead, UK.

[14] C. H. Hanson, H. F. Robinson, and R. E. Comstock, "Biometrical Studies of Yield in Segregating Populations of Korean Lespedeza 1," Agronomy journal, vol. 48, no. 6, pp. 268-272,1956.

[15] A. Masuka, M. Goss, and U. Mazarura, "Morphological characterization of four selected spider plant (Cleome gynandra L.) morphs from Zimbabwe and Kenya," Asian Journal of Agriculture and Rural Development, vol. 2, no. 4, p. 646, 2012.

[16] K. S. Gould, D. A. Dudle, and H. S. Neufeld, "Why some stems are red: Cauline anthocyanins shield photosystem II against high light stress," Journal of Experimental Botany, vol. 61, no. 10, pp. 2707-2717,2010.

[17] H. E. Khoo, A. Azlan, S. T. Tang, and S. M. Lim, "Anthocyanidins and anthocyanins: colored pigments as food, pharmaceutical ingredients, and the potential health benefits," in Food & Nutrition Research, vol. 61, 2017.

[18] S. Venter, W. S. Jansen van Rensburg, H. J. Vorster, E. Van den Heever, and J. J. B. Zijl, "Promotion of African leafy vegetables within the Agricultural Research Council-Vegetable and Ornamental Plant Institute: The impact of the project".

[19] H.-X. Chang, A. E. Lipka, L. L. Domier, and G. L. Hartman, "Characterization of disease resistance loci in the USDA soybean germplasm collection using genome-wide association studies," Journal of Phytopathology, vol. 106, no. 10, pp. 1139-1151, 2016.

[20] R. Aruna, D. M. Rao, L. J. Reddy, H. D. Upadhyaya, and H. C. Sharma, "Inheritance of trichomes and resistance to pod borer (Helicoverpa armigera) and their association in interspecific crosses between cultivated pigeonpea (Cajanus cajan) and its wild relative C. scarabaeoides," Euphytica, vol. 145, no. 3, pp. 247-257, 2005.

[21] U. Alahakoon, J. Adamson, L. Grenkow, J. Soroka, P. Bonham-Smith, and M. Gruber, "Field growth traits and insect-host plant interactions of two transgenic canola (Brassicaceae) lines with elevated trichome numbers," The Canadian Entomologist, vol. 148, no. 5, pp. 603-615, 2016.

[22] D. O. Wasonga, J. L. Ambuko, G. N. Cheminingwa, D. A. Odeny, and B. G. Crampton, "Morphological Characterization and Selection of Spider Plant (Cleome Gynandra) Accessions from Kenya and South Africa," Asian Journal of Agricultural Sciences, vol. 7, no. 4, pp. 36-44, 2015.

[23] C. O. Ojiewo, O. Mbwambo, I. Swai et al., "Selection, evaluation and release of varieties from genetically diverse African nightshade germplasm," International Journal of Plant Breeding, vol. 7, no. 2, pp. 76-89, 2013.

[24] Z. Kiebre, P. Bationo-Kando, N. Sawadogo, M. Sawadogo, and J. D. Zongo, "Selection of phenotypic interests for the cultivation of the plant Cleome gynandra L. in the vegetable gardens in Burkina Faso," Journal Of Experimental Biology and Agriculture Sciences, vol. 3, pp. 288-297, 2015.

[25] E. D. Sogbohossou, E. G. Achigan-Dako, P. Maundu et al., "A roadmap for breeding orphan leafy vegetable species: a case study of Gynandropsis gynandra (Cleomaceae)," Horticulture Research, vol. 5, no. 1, p. 2, 2018.

[26] N. Ali, F. Javidfar, J. Y. Elmira, and M. Y. Mirza, "Relationship among yield components and selection criteria for yield improvement in winter rapeseed (Brassica napus L.)," Pakistan Journal of Botany, vol. 35, no. 2, pp. 167-174,2003.

[27] Z. Kiebre, P. Bationo-Kando, A. Barro et al., "Estimates of genetic parameters of spider plant (Cleome gynandra L.) of Burkina Faso," International Journal of Agricultural Policy and Research, vol. 5, no. 9, pp. 138-144, 2017.

[28] S. Guler and H. Ozcelik, "Relationships between leaf chlorophyll and yield related characters of dry bean (Phaseolus vulgaris L)," Journal of Plant Sciences, vol. 6, no. 4, pp. 700-703, 2007.

[29] N. Sabaghnia, H. Dehghani, B. Alizadeh, and M. Mohghaddam, "Interrelationships between seed yield and 20 related traits of 49 canola (Brassica napus L.) genotypes in non-stressed and water-stressed environments," Spanish Journal of Agricultural Research, vol. 8, no. 2, pp. 356-370, 2010.

[30] C. C. Nwangburuka, O. J. Olawuyi, K. Oyekale, K. O. Ogunwenmo, O. A. Denton, and E. Nwankwo, "Growth and yield response of Corchorus olitorius in the treatment of Arbuscular mycorrhizae (AM, Poultry manure (PM)," Combination of AMPM and Inorganic Fertilizer (NPK). in Applied Science Research, vol. 3, no. 3, pp. 1466-1471,2012.

Ann Kangai Munene (0,1) Felister Nzuve, (1) Jane AmbukoQ, (1) and Damaris Odeny (2)

(1) Department of Crop Science and Crop Protection, University of Nairobi, Kenya

(2) The International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Nairobi, Kenya

Correspondence should be addressed to Ann Kangai Munene; wakaireann@gmail.com

Received 30 November 2017; Revised 30 March 2018; Accepted 12 July 2018; Published 31 July 2018

Academic Editor: Mumtaz Cheema

Caption: Figure 1: Phenogram showing relationship among accessions characterized using morphological traits.
Table 1: List of Kenyan and South African spider plant accessions
evaluated in the study.

Entry     Accession no.    Country of origin          Region

1            j (ke)              Kenya                Siaya
2            2 (ke)              Kenya               Bungoma
3            3 (ke)              Kenya               Kakamega
4            4 (ke)              Kenya                Kitale
5            5 (ke)              Kenya                Mbale
6            6 (ke)              Kenya                Bomet
7            7 (ke)              Kenya                Busia
8            9 (ke)              Kenya               Marakwet
9            10 (ke)             Kenya                Kisumu
10           11 (ke)             Kenya               Homabay
11           12 (ke)             Kenya                Nandi
12           13 (ke)             Kenya               Kakamega
13           14 (ke)             Kenya                Kisii
14           15 (ke)             Kenya                Mbale
15           16 (ke)             Kenya                 Meru
16          1959 (sa)         South Africa          Mpumalanga
17          1988 (sa)         South Africa          Mpumalanga
18          2000 (sa)         South Africa          Mpumalanga
19          2232 (sa)         South Africa      Northern province
20          2241 (sa)         South Africa      Northern province
21          2249 (sa)         South Africa      Northern province
22          2279 (sa)         South Africa      Northern province
23          2289 (sa)         South Africa          Mpumalanga
24          2299 (sa)         South Africa          Mpumalanga
25         30316 (ke)            Kenya               Western
26         31990 (ke)            Kenya               Western
27         31992 (ke)            Kenya               Western
28         45426 (ke)            Kenya               Western
29         45446 (ke)            Kenya               Central
30         45451 (ke)            Kenya               Central
31         50259 (ke)            Kenya                Kisii
32         50264 (ke)            Kenya               Nyamira
33         50265 (ke)            Kenya               Nyamira
34         50273 (ke)            Kenya               Nyamira
35         50290 (ke)            Kenya               Nyamira
36         50296 (ke)            Kenya               Nyamira
37         50298 (ke)            Kenya               Nyamira
38         50299 (ke)            Kenya               Nyamira
39         50307 (ke)            Kenya                Kisii
40         50319 (ke)            Kenya               Nyamira
41         50325 (ke)            Kenya                Kisii
42         50326 (ke)            Kenya               Nyamira
43         50328 (ke)            Kenya               Nyamira
44         50330 (ke)            Kenya               Nyamira
45         50332 (ke)            Kenya                Kisii
46         50339 (ke)            Kenya               Nyamira
47         50353 (ke)            Kenya               Nyamira
48         50584 (ke)            Kenya               Nyamira
49         50600 (ke)            Kenya                Kisii

(ke)=originated from Kenya; (sa)=originated from South Africa.

Table 2: Character, descriptor, and codes used for
characterization of qualitative traits in spider plant
accessions.

S/No.        Character                 Descriptor and code

1           Growth habit          Erect (2), semi-erect (4) and
                                          prostrate (6)

2          Flower colour        White (1), purple (2) and pink (3)
3           Stem colour        Green (1), pink (2), violet (3) and
                                            purple (4)

4          Stem hairiness     Glabrous (1), weak/sparse (3), medium
                                       (5) and profuse (7)

5          Petiole colour      Green (1), pink (2), violet (3) and
                                           purple (4),

6        Petiole hairiness    Glabrous (1), weak/sparse (3), medium
                                       (5) and profuse (7)

7           Leaf colour        Dark green (1) and light green (2),
8          Leafhairiness      Glabrous (1), weak/sparse (3), medium
                                       (5) and profuse (7)

Source: Food and Agriculture Organization of the United Nations
(FAO, 1995); numbers in brackets on the right-hand side are the
corresponding descriptor codes listed in the FAO publication with
modifications during the development of the list.

Table 3: Morphological descriptors recorded for the 49 field
grown spider plant accessions for the combined season.

Entry    Accession      Origin     Flower      Stem      Petiole
            no.                     colour     colour     colour

1            1          Kenya       Purple     Purple     Green
2            2          Kenya       White      Purple      Pink
3            3          Kenya        Pink      Purple      Pink
4            4          Kenya        Pink      Purple      Pink
5            5          Kenya       White      Purple     Purple
6            6          Kenya        Pink      Purple      Pink
7            7          Kenya        Pink      Purple     Purple
8            9          Kenya       White      Purple     Purple
9            10         Kenya       Purple     Purple     Purple
10           11         Kenya        Pink      Purple     Purple
11           12         Kenya        Pink      Purple     Purple
12           13         Kenya       Purple     Purple     Purple
13           14         Kenya       Purple     Green      Purple
14           15         Kenya        Pink      Purple     Purple
15           16         Kenya        Pink      Purple      Pink
16          1959      S. Africa      Pink      Purple      Pink
17          1988      S. Africa     White      Green      Green
18          2000      S. Africa     White      Green      Green
19          2232      S. Africa     White      Green      Green
20          2241      S. Africa     White      Green       Pink
21          2249      S. Africa     White      Green       Pink
22          2279      S. Africa     White      Green      Green
23          2289      S. Africa     White      Green       Pink
24          2299       SAfrica      White      Green      Green
25         30316        Kenya       Purple     Purple     Purple
26         31990        Kenya       Purple     green      Green
27         31992        Kenya        Pink      purple     Green
28         45426        Kenya       Purple     Purple     Green
29         45446        Kenya       White      Purple     Purple
30         45451        Kenya        Pink      Purple     Purple
31         50259        Kenya        Pink      Purple     Purple
32         50264        Kenya       Purple     Purple     Purple
33         50265        Kenya       Purple     Purple     Purple
34         50273        Kenya       Purple     Purple     Purple
35         50290        Kenya       Purple     Purple     Purple
36         50296        Kenya       Purple     Purple     Purple
37         50298        Kenya       Purple     Purple     Purple
38         50299        Kenya       Purple     Purple     Purple
39         50307        Kenya       Purple     Purple     Purple
40         50319        Kenya       Purple     Purple     Purple
41         50325        Kenya        Pink      Purple     Purple
42         50326        Kenya       Purple     Purple     Purple
43         50328        Kenya       Purple     Purple     Purple
44         50330        Kenya       Purple     Purple     Purple
45         50332        Kenya       Purple     Purple     Purple
46         50339        Kenya       Purple     Purple     Purple
47         50353        Kenya       Purple     Purple     Purple
48         50584        Kenya       Purple     Purple     Purple
49         50600        Kenya       Purple     Purple     Purple

Entry      Stem        Petiole        Leaf          Leaf
         hairiness    hairiness       colour      hairiness

1         Profuse       Medium      dark green      Medium
2         Profuse       Medium     light green      Sparse
3         Profuse       Medium      dark green      Sparse
4         Profuse       Medium     light green      Medium
5         Profuse      Profuse     light green     Profuse
6         Profuse      Profuse      dark green      Medium
7         Profuse       Medium      dark green      Medium
8         Profuse      Profuse      dark green      Sparse
9          Medium       Medium     light green      Sparse
10         Medium       Medium      dark green      Medium
11        Profuse       Medium     light green      Sparse
12         Medium       Medium     light green      Sparse
13        Profuse      Profuse      dark green      Medium
14         Medium       Sparse      dark green      Sparse
15         Medium       Sparse     light green      Sparse
16        Profuse       Medium     light green      Medium
17         Sparse       Sparse     light green      Sparse
18        Glabrous     Glabrous    light green     Glabrous
19        Glabrous     Glabrous     dark green     Glabrous
20         Sparse       Sparse     light green      Sparse
21        Glabrous     Glabrous    light green     Glabrous
22        Glabrous     Glabrous    light green     Glabrous
23         Medium       Sparse      dark green      Sparse
24        Glabrous      Sparse     light green     Glabrous
25        Profuse       Medium      dark green      Sparse
26         Medium       Sparse     light green      Sparse
27        Profuse       Medium      dark green      Medium
28        Profuse       Sparse      dark green      Sparse
29        Profuse       Medium      dark green      Sparse
30        Profuse      Profuse      dark green      Medium
31        Profuse       Medium      dark green      Sparse
32        Profuse       Medium      dark green      Sparse
33        Profuse       Medium      dark green      Medium
34        Profuse       Medium      dark green      Sparse
35        Profuse       Medium      dark green      Medium
36        Profuse       Medium      dark green      Medium
37         Medium       Sparse     light green      Sparse
38        Profuse       Medium      dark green      Sparse
39         Medium       Medium      dark green      Sparse
40        Profuse       Medium      dark green      Medium
41         Medium       Medium      dark green      Sparse
42        Profuse       Medium      dark green      Sparse
43        Profuse       Medium      dark green      Sparse
44        Profuse       Medium      dark green      Sparse
45        Profuse       Medium      dark green      Medium
46        Profuse       Medium      dark green      Sparse
47        Profuse       Medium      dark green      Medium
48         Medium       Medium      dark green      Sparse
49        Profuse       Medium      dark green      Sparse

Entry      Growth
            habit

1           Erect
2           Erect
3           Erect
4           Erect
5           Erect
6           Erect
7           Erect
8           Erect
9           Erect
10          Erect
11          Erect
12          Erect
13          Erect
14          Erect
15          Erect
16          Erect
17        Semi erect
18        semi erect
19          Erect
20          Erect
21          Erect
22        Semi erect
23          Erect
24          Erect
25          Erect
26          Erect
27          Erect
28          Erect
29          Erect
30          Erect
31          Erect
32          Erect
33          Erect
34          Erect
35        Semi erect
36          Erect
37        Semi erect
38          Erect
39          Erect
40          erect
41          erect
42          erect
43          erect
44          erect
45          erect
46          erect
47          erect
48          erect
49          erect

Table 4

(a) Analyses of variance showing the mean squares for the
agronomic traits in Cleome gynandra season one (April-July 2014).

Source of         d.f.      DTF       LL        LW        NPB
variation

Rep                 2       4.3       0.6       1.7       1.6
Genotype           48     34.7 *     5.2 *    16.3 *    14.7 *
Residual           96        1        0.2       1.4       0.3
Total              146

(b) Analyses of variance showing the mean squares for the
agronomic traits in Cleome gynandra season one (October-July
2014).

Source of         d.f.      DTF       LL        LW        NPB
variation

Rep                 2        5        0.5       0.5       0.4
Genotype           48     31.1 *     5.1 *    16.5 *    11.9 *
Residual           96       0.8       0.1       0.5       0.5
Total              146

(c) Analyses ofvariance showing the mean squares for the
agronomic traits in Cleome gynandra for the combined seasons.

Source of         d.f.      DTF       LL        LW        NPB
variation

Rep                 2       9.1        1        1.2       1.6
Genotype           48     64.6 *    10.3 *    32.6 *    26.1 *
Season              1     161.6 *    1.0 *     4.1 *     2.1 *
Genotype Season    48       1.2       0.1       0.2       0.4
Residual           194      0.9       0.2       0.9       0.4
Total              293

Source of            NLPP         PH         SLA      SPAD
variation

Rep                  27.9         6.5        0.4        1
Genotype           6895.6 *    2734.0 *     8.1 *    165.4 *
Residual             13.4         8.2        0.4       2.1
Total

(b) Analyses of variance showing the mean squares for the
agronomic traits in Cleome gynandra season one (October-July
2014).

Source of            NLPP         PH         SLA      SPAD
variation

Rep                  1.5          3.2        0.6       3.6
Genotype           6961.3 *    2723.8 *     7.9 *    139.5 *
Residual             17.6         6.4        0.2       4.3
Total

(c) Analyses ofvariance showing the mean squares for the
agronomic traits in Cleome gynandra for the combined seasons.

Source of            NLPP         PH         SLA      SPAD
variation

Rep                   16          4.7        0.9       3.2
Genotype          13840.2 *    5451.1 *    15.9 *    300.8 *
Season                54        33.4 *      2.1 *     4.8 *
Genotype Season      16.7         6.7        0.1       4.1
Residual             15.5         7.3        0.3       3.2
Total

* Significant at P <0.05, DTF: days to 50% flowering, SLA: single
leaf area ([cm.sup.2]), LL: leaf length (cm), LW: leaf width
(cm), NLPP: number of leaves per plant, NPB: number of primary
branches, PH: plant height (cm), and SPAD: soil plant analysis
development.

Table 5: Mean comparison of the quantitative traits of 49 spider
plant accessions from Kenya and South Africa grown in the
University of Nairobi Field at Kabete, for the two combined
seasons.

Entry     Accession       Origin       DTF       LL       LW      NPB
              No.

1              1           Kenya       39.7     5.1      14.2     6.3
2              2           Kenya       45.7     6.4      11.5     5.7
3              3           Kenya       45.3     6.7       17       7
4              4           Kenya       43.8     5.2      13.8     7.5
5              5           Kenya       45.3      5       13.2     7.5
6              6           Kenya       39.7      5       11.7     7.5
7              7           Kenya        45      5.9       16      7.7
8              9           Kenya       45.5     6.2      14.3     6.5
9             10           Kenya       41.5     5.6      11.7     5.7
10            11           Kenya        45      3.6      10.6     5.5
11            12           Kenya        46      4.6      11.9     7.5
12            13           Kenya       37.8      5       13.1     6.2
13            14           Kenya       39.8     4.7      11.3     7.8
14            15           Kenya       38.7     3.7      9.3      5.7
15            16           Kenya       45.2     7.8      15.6      9
16           1959        S. Africa     43.8     5.8       12      6.7
17           1988        S. Africa     34.2     6.8      12.6     5.7
18           2000        S. Africa     32.8     6.5      13.6     4.2
19           2232        S. Africa     39.8     5.3       10      4.7
20           2241        S. Africa     45.3     7.8      15.4     7.8
21           2249        S. Africa     39.3     6.2      13.2     4.2
22           2279        S. Africa     53.2     6.3      12.3     6.7
23           2289        S. Africa      44      7.2      13.9     6.3
24           2299        S. Africa      40      5.1      9.8      7.3
25           30316         Kenya       37.7     5.9      10.3      10
26           31990         Kenya        41      5.7      13.2     8.5
27           31992         Kenya        42       8        11      9.2
28           45426         Kenya        43      7.4      11.5      10
29           45446         Kenya       42.3     8.8       13      8.3
30           45451         Kenya       47.7     7.2      16.1     9.2
31           50259         Kenya        44      5.7      10.6     9.2
32           50264         Kenya       40.2     4.4      10.5     11.7
33           50265         Kenya       44.3     5.5      11.6     10.3
34           50273         Kenya       40.3     5.1      9.5      8.7
35           50290         Kenya       40.3     4.8      10.4     9.3
36           50296         Kenya        40      10.5     18.3     10.7
37           50298         Kenya        40      4.4      7.9      11.5
38           50299         Kenya       41.7     4.8      9.4      10.5
39           50307         Kenya        40       6        12      8.3
40           50319         Kenya       40.3     6.2      12.5     9.5
41           50325         Kenya        40      6.8      12.1      10
42           50326         Kenya       44.3     5.6      11.1     12.5
43           50328         Kenya       40.2     5.4      8.6      8.7
44           50330         Kenya       39.8     5.7      10.4     9.8
45           50332         Kenya       40.5     4.2      8.8      9.2
46           50339         Kenya       42.7     5.2      12.8     12.5
47           50353         Kenya       42.3     4.5      8.4       11
48           50584         Kenya       39.7     4.8      9.8      9.7
49           50600         Kenya       38.8     5.9      8.8      9.5
Mean                                   41.8     5.8       12       8
LSD (p<0.05)                           1.5      0.7      1.6       1
CV%                                    2.2      7.4      8.1      7.8

Entry      NLPP       PH       SLA      SPAD

1           105      38.3      8.7      56.4
2          58.8      31.5      8.8      57.6
3          112.2      41       10.9     50.3
4          75.8      36.7      8.7      56.9
5          53.8      46.8      8.3      57.6
6          57.2      47.8      7.8      56.9
7          91.3      51.2      9.9       53
8          94.3       39       9.6      56.6
9           68       40.7      8.3      58.3
10         100.5     40.1      6.3      56.8
11          61       42.2      7.6      58.8
12         64.8      38.3      8.3      55.9
13         83.3      63.2      7.4      53.3
14         55.7      37.8       6       46.7
15         75.7      75.5      11.2     61.6
16         89.2      34.3      8.5      54.3
17         50.7      45.2      9.5      49.9
18         19.7      30.8      9.6      53.1
19          56       26.3      7.4      24.3
20          41       42.3      11.2      44
21         20.3      21.2      9.2      42.9
22         23.2      22.1       9       39.6
23         31.3      41.7      10.2      43
24          78       30.3      7.2      43.7
25          146       83        8       57.1
26         109.3     91.3      8.8      55.4
27          201      91.7      9.9      59.5
28          97        97       9.5      58.4
29          68       111.7     11.1     57.3
30         247.5     109.3     10.9     53.9
31          182      101.3     7.9      58.1
32         172.2     108.8     6.9      60.8
33         78.7      92.2      8.2      60.1
34         92.8      89.7      7.1      57.5
35         105.2     93.2      7.1      62.2
36         109.5      113      14.2     57.9
37          140      82.7      6.1      59.5
38         101.3     99.8      6.9      58.9
39         140.7     93.8      8.7      58.4
40          101      77.8       9       62.5
41         94.8       95       9.3      57.9
42         165.8     101.2     8.1      57.5
43         96.5      75.7      7.1      58.6
44          172      104.3     7.8      60.8
45         115.8     110.5     6.2      62.5
46         149.8     101.7     8.3      61.6
47         146.8      86       6.3      57.8
48         88.8      78.7       7       57.9
49         78.8      98.8      7.5      56.2
Mean        97       68.4      8.5      55.1
LSD (p<0.0  6.3       4.4      0.9      2.9
CV%          4         4       6.5      3.3

DTF: days to 50% flowering, SLA: single leaf area ([cm.sup.2]),
LL: leaf length (cm), LW: leaf width (cm), NLPP: number of leaves
per plant, NPB: number of primary branches, PH: plant height
(cm), and SPAD: soil plant analysis development.

Table 6: Correlation in combined seasons.

           DTF         LL         LW        NPB        NLPP

DTF         --
LL        0.12 *       --
LW       0.28 **    0.62 **       --
NPB        0.09       0.02     -0.20 **      --
NLPP       0.1       -0.01      -0.11     0.63 **       --
PH        -0.07      0.16 *    -0.19 *    0.82 **    0.69 **
SLA      0.21 **    0.92 **    0.88 **     -0.08      -0.06
SPAD      -0.04      -0.07      -0.11     0.54 **    0.45 **

            PH       SLA    SPAD

DTF
LL
LW
NPB
NLPP
PH          --
SLA        0.01      --
SPAD     0.59 **    -0.1     --

* implies significance difference at P<0.05; ** implies
significance difference at p<0.001 (2-tailed), DTF-days to 50%
flowering, SLA-single leaf area ([cm.sup.2]), LL-leaf length
(cm), LW-leaf width (cm), NLPP-number of leaves per plant, NPB-
number of primary branches, PH-plant height (cm), SPAD-soil plant
analysis development.

Table 7: Stepwise regression analysis of the 7 evaluated traits.

Step       Variable        Partial     Adjusted      F-test
                           R square     R square

1        Plant height        0.48         0.47      43.00 *

* Significant at p [less than or equal to] 0.05 y = 1.102x + 21.947,
y = number of leaves per plant, and x = plant height.

Table 8: Estimates of yield and yield related components of 49
spider plant accessions.

Traits                   YE        VG         VP      HBS (%)

DTF        Season 1     1.0       11.2       12.2       91.8
           Season 2     1.0       10.1       11.1       91.0
LL         Season 1     0.2       1.7        1.9        89.3
           Season 2     0.2       1.7        1.9        89.3
LW         Season 1     1.4       5.0        6.4        78.0
           Season 2     1.4       5.3        6.7        79.2
NPB        Season 1     0.3       4.8        5.1        94.1
           Season 2     0.3       3.8        4.1        92.7
NLPP       Season 1     13.4     2294.1     2307.5      99.4
           Season 2     13.4     2314.6     2328.0      99.4
PH         Season 1     8.2      908.6      916.8       99.1
           Season 2     8.2      905.8      914.0       99.1
SLA        Season 1     0.4       2.6        3.0        86.5
           Season 2     0.4       2.6        3.0        86.5
SPAD       Season 1     2.1       54.4       56.5       96.3
           Season 2     2.1       45.1       47.2       95.5

VE = environmental variance, VG = genotypic variance, VP =
phenotypic variance, HBS = broad sense heritability, NLLP =
number of leaves per plant, NPB = number of primary branches, LL
= leaf length, LW = leaf width, PH = plant height, SLA = single
leaf area, and SPAD = soil plant analysis development.
COPYRIGHT 2018 Hindawi Limited
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2018 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Title Annotation:Research Article
Author:Munene, Ann Kangai; Nzuve, Felister; Ambuko, Jane; Odeny, Damaris
Publication:Advances in Agriculture
Date:Jan 1, 2018
Words:7309
Previous Article:Sugarcane Landraces of Ethiopia: Germplasm Collection and Analysis of Regional Diversity and Distribution.
Next Article:Pathogenic Variability of Wheat Stem Rust Pathogen (Puccinia graminis f. sp. tritici) in Hararghe Highlands, Ethiopia.
Topics:

Terms of use | Privacy policy | Copyright © 2021 Farlex, Inc. | Feedback | For webmasters |