Phenotypic Diversity of Jatropha curcas L. from ...

February 2011, Volume 5, No.2 (Serial No. 33) Journal of Agricultural Science and Technology, ISSN 1939-1250, USA

Phenotypic Diversity of Jatropha curcas L. from Diverse Origins F. L. Zapico1, S. K. Nival1, C. H. Aguilar1 and M. N. Eroy2 1. Science Department, College of Natural Sciences and Mathematics, Mindanao State University, Fatima, General Santos City 9500, Philippines 2. Philippine Coconut Authority Region 11, Bago Oshiro, Tugbok District, Davao City 8000, Philippines

Received: March 17, 2010 / Accepted: June 9, 2010 / Published: February 15, 2011. Abstract: The study was conducted to assess intraspecific variation/interrelationships and to determine the association of geographical distribution and phenotypic diversity in the Jatropha curcas accessions studied. Ex situ morphological characterization of 13 Jatropha curcas genotypes from various sources was undertaken using 21 quantitative traits. The generated data was subjected to two phenetic analysis methods (principal components analysis and cluster analysis). Principal Components Analysis (PCA) reduced the collected data to 5 principal components that cumulatively explained 88.81% of total variance. PCA also subdivided the Jatropha provenances into distinct groups on the basis of height. The two clustering mechanisms, Average Linkage-Unweighted Pair Group Method with Arithmetic Mean (UPGMA) and Centroid Method (UPGMC), divided the provenances into two major groups and revealed the divergence of Tubao-Philbio from the rest of the provenances. However, the study failed to adequately establish a relationship between spatial dispersal and phenotypic divergence in the Jatropha genotypes implying possible genetic homogeneity for the accessions studied. Screening for more quantitative traits and the use of more advanced molecular marker technologies are therefore recommended for a more accurate estimation of genetic diversity in Jatropha. Key words: Jatropha curcas, Principal Components Analysis (PCA), Average Linkage-Unweighted Pair Group Method with Arithmetic Mean (UPGMA), Average Linkage-Unweighted Pair Group Method with Centroid Method (UPGMC), morphological characterization.

1. Introduction Jatropha curcas L. (2n = 18) is known to be a drought-resistant plant which shows great potential as a biodiesel source. Its seeds contain 30%-60% oil which exhibits great lubricity and reduces engine wear. This perennial plant also has an average lifespan of 50 years with 45 years of continually producing fruits [1]. Several studies on Jatropha curcas utilizing a wide array of marker technologies [2, 3] have revealed low genetic diversity in the Jatropha curcas genepool. It is also widely believed that geographic distribution usually correlates with genetic variability [4, 5]. In this

Corresponding author: F. L. Zapico, M.Sc., research fields: plant genetic resource conservation and management. E-mail: [email protected].

study, 13 Jatropha provenances from 5 countries and 2 continents were subjected to morphological characterization to detect possible phenotypic similarities in the provenances despite spatial distribution.

2. Materials and Methods 2.1 Planting Materials Mature seeds of thirteen Jatropha curcas provenances from the Philippine Coconut Authority (PCA)-Davao City collection were characterized using 21 quantitative traits. Table 1 is the list of the Jatropha provenances. 2.2 Field Planting and Management The one-month old Jatropha seedlings were transplanted in the field in the RCBD layout with the

216

Table 1 Code No. 1 2 3 4 5 6 7 8 9 10 11 12 13

Phenotypic Diversity of Jatropha curcas L. from Diverse Origins

List of the Jatropha provenances. Code Name Tubao-Philbio Jolo-Uy Indian-PhilBio Phil-PhilBio Phil-Nestle Indian-D1 Oil Mexican-PhilBio Chinese-PhilBio JAS-Phil Foreign-Quejada Mexican Nontoxic Indonesian-Uy Phil-Quejada

Abbreviation TuP JU InP PhP PhN InD1 MexP chP JASP FQ

Country of Origin Philippines Philippines India Philippines Philippines India Mexico Mainland China Philippines unknown

MNT

Mexico

IU PhQ

Indonesia Philippines

provenances as blocks. The different provenances were planted sequentially with one row and 10 replicates per entry. This is the normal field layout used by gene banks for field characterization. Normal cultural practices were followed throughout the duration of the experiment. 2.3 Data Collection and Analysis Morphological characterization of the different was done using 21 quantitative traits. Data on percentage of germination, percentage of survival, number of branches and leaves were collected during various stages of growth. Stem collar diameter, seedling and field height increments were also noted. Yield variations were determined by weighing the seed capsules produced per plant and per provenance after harvesting. The characterization data were subjected to phenetic analysis techniques such as principal components analysis (PCA) and cluster analysis. Genetic distances between provenances were determined using Squared Euclidean Distance. Dendrograms were then constructed using Average Linkage-Unweighted Pair Group Method with Arithmetic Mean (UPGMA) and Centroid Method (UPGMC). All these analyses were done using the SPSS/PC + 16.0 statistical package.

3. Results and Discussions

3.1 Principal Components Analysis Principal Component Analysis (PCA) was used as one of the various techniques for the detection of possible relationships among Jatropha curcas provenances despite spatial distribution. PCA allows reduction of the variations exhibited to a manageable level and gives an easier interpretation and analysis of the data. Table 2 shows the eigenvalues of the five principal components and their sum simulation percentages. In this study, PCA reduced the original 21 morphological characters into 5 principal components which accounted for approximately 88.81% of the total cumulative variance. Accurate interpretations of the 5 principal components are made possible by the examination of the different eigenvector values which are the relative weights accounted for by the evaluated quantitative characters. Prin 1, which is the most important component, with the latent root/eigenvalue of 7.82 explained 37.25% of the total variation. The variables with the highest loadings on Prin 1(> 0.7) were mostly associated with the number of leaves and height during the seedling stage and after field transplantation. Other noteworthy contributions (< 0.7) were from the number of branches, percentage of germination and survival and weight of the seed capsule. Prin 2 with eigenvalue of 5.39 accounts for 25.685% of variance. Height at 113 days after planting (DAP), 190 DAP and 267 DAP, stem collar measurements and the number of branches made substantial contributions to Prin 2. As for Prin 3, its latent root values mostly were associated with stem collar at 190 DAP and height at 267 DAP. Prin 3 with 2.74 totals accounts for Table 2 Computed eigenvalues for the five principal components. Component 1 2 3 4 5

Total 7.823 5.394 2.744 1.390 1.300

Initial Eigenvalues % of Variance Cumulative 37.250 37.250 25.685 62.936 13.066 76.002 6.618 82.620 6.193 88.813


the 13.06% of variation. Results showed that Prin 3 correlated moderately well with stem collar at 190 DAP and field height value taken at 267 DAP. For Prin 4 and 5 with very low eigenvalues at1.3, these explained about 6.19%-6.61% of the total variance. Only the number of branches and weight of capsule at 190 DAP had moderate loadings on Prin 4 and Prin 5 respectively. The PCA scatterplot (Fig. 1) shows the formation of 6 main groups and highlighted the divergence of TuP (1) from the rest of the provenances. The position of TuP at the extreme lower right portion of the biplot is due to the extremely high Prin 2 and low Prin 1 values it obtained for the quantitative traits evaluated. TuP also had the lowest measurements/values for percentage of germination and survival and seedling height. It appears that TuP which performed poorly in the field in terms of height caught up with the rest of the provenances and exhibited the greatest height values at 190 DAP and 267 DAP. Foreign Quejada (10) occupied the upper central portion of the biplot by virtue of the very high Prin 1 and intermediate values it obtained for Prin 2. The high Prin 1 value is primarily due to high percentage of germination, height values and seed capsule weight at 190 DAP. As for MexP (7), its high Prin 1 and Prin 2 values placed it on the upper right hand portion of the PCA quadrant. This provenance obtained the highest values for leaf-associated

217

characters. Meanwhile, the grouping of JU (2), IU (12), and at the middle left potion of the biplot is due to low or negative values for Prin 1 and Prin 2. These provenances had near to similar values for percentage of germination and stem collar measurements. As for the group composed of provenances PhP (4), PhN (5), InD1 (6), and ChP (8) occupying the exact middle of the quadrant, these obtained intermediate values for Prin 1 and Prin 2. 3.2 Cluster Analysis The dissimilarity matrix from the 21 quantitative characters was subjected to two clustering algorithms, UPGMA and UPGMC. The UPGMA dendrogram (Fig. 2) divided the provenances into two major groups and revealed the divergence of the Tubao-Philbio from the rest of the provenances. At the 5-cluster level, provenances JASP (9), MNT (11) and PhQ (13) fused to form 1 cluster. These are provenances with medium to high height values. The UPGMA tree also showed the close relationship of provenances 2, 12 and 13 through their merger into a single cluster. Provenances 7, 8, 5 and 4 also fused to form a single cluster, implying morphological similarities. As for the Centroid dendrogram (Fig. 3), it showed a high degree of similarity to the UPGMA tree except for a single inconsistency involving provenance Foreign Quejada. The results of the study have demonstrated the utility of the two phenetic tools (PCA and Cluster Analysis) in elucidating morphological variability in the Jatropha curcas provenances examined. Apart from a few inconstancies involving within group composition, the over all results reflect low morphological variation in Jatropha curcas provenances from diverse origins. These results are in accordance with the findings of Basha et al. [3] using RAPD and ISSR markers. In another research involving RAPD, AFLP and cTBP (combinatorial tubulin based polymorphism) narrow

Fig. 1 Scatterplot of PC scores of PRIN 1 and PRIN 2 based on 21 morpho-agronomic characters. Legend: 1. TuP; 2. JU; 3. InP; 4. PhP; 5. PhN; 6. Ind1; 7. MexP; 8. ChP; 9. JASP; 10. FQ; 11. MNT; 12. IU; 13. PhQ

genetic diversity was revealed in 38 Jatropha curcas accessions from 13 countries and 3 continents [4]. Moreover, most plan breeders assume that geographic

218


Fig. 2 Dendrogram using Average Linkage (UPGMA- Between Groups).

Fig. 3 Dendrogram using Centroid Method (UPGMC).

diversity reflects genetic diversity [5, 6]. In this study, no such association was detected for the Jatropha curcas accessions from various geographical origins. In another study on the genetic diversity of wild and cultivated Indian J. curcas provenances using RAPD markers, Jatropha accessions from different geographical regions shared 80% similarity [7]. Using oil character analysis, Gohil and Pandya [8] also failed to find a direct relationship between geographical distribution and genetic divergence. Neeraj Jain et al. [9] raised the possibility that Jatropha curcas accessions from different geographical regions can be genetically similar. This genetic homogeneity can attributed to similarities in agro-climatic conditions and/or seed movement [7].

4. Conclusions Both PCA and cluster analysis divided the 13 Jatropha provenances into two major groups. Height was the most discriminant character in the clustering of accessions. Moreover, while the results of the study can not conclusively resolve the spatial variability of the 13 Jaropha curcas provenances tested, it unequivocally showed the divergence of Tubao-Philbio from the rest of the provenances. The study also revealed morphological homogeneity among the Jatropha provenances studied. Possible causes to this are similarities in agro-climatic conditions, seed movement and the paucity of polymorphic and informative morphometric markers. Further studies


then involving more Jatropha provenances and using more morphological characters are warranted. Lastly, morphological characterization of Jatropha curcas accessions should be done in tandem with molecular characterization to ascertain if the trends from the former will be consistent at the DNA level.

References [1] [2]

[3]

[4]

M.H. Jones, S. Csurhes, Pest plant risk assessment, Physic Nut Jatropha Curcas (2008) 5-19. T. Jha, P. Mukherjee, M.M. Datta, Somatic Embryogenesis in Jatropha curcas Linn., an important biofuel plant, Plant Biotechnology Reports, Korean Society of Plant Biotechnology, 2007. S.K. Basha, J. Arati, M.K. Ghose, Biotechnological approach to increase the yield of bio-diesel in the Indian context, eNREE 4 (2007) 2. S. Popluechai, D. Brevario, H. Makkar, M. Raorane, A. Reddy, E. Palchetti, A. Gatehouse, A. O’Donell, A. Kohli, Narrow genetic and apparent phenetic diversity in Jatropha curcas: initial success with generating low

[5]

[6] [7]

[8]

[9]

219

phorbol ester interspecific hybrids, Natures Precedings, 2009. S. Shweta Gupta, M. Srivastava, G. Mishra, P.K. Naik, R.S. Chauhan, S.K. Tiwari, M. Kumar, Analogy of ISSR and RAPD markers for comparative analysis of genetic diversity among different Jatropha curcas genotypes, African Journal of Biotechnology 7 (2008) 4230-4243. Z. Jianchao, Cluster analysis of soybean varieties, ARC China Training Report, 1993. K. Subramanyam, D. Muralidhararao, N. Devanna, Genetic diversity assessment of wild and cultivated varieties of Jatropha curcas (L.) in India by RAPD analysis, African Journal of Biotechnology 8 (2009) 1900-1910. R.H. Gohil, J.B. Pandya, Genetic diversity assessment in physic nut (Jatropha curcas L.), International Journal of Plant Production (2008) 2-4. A.K. Neeraj Jain, V. Shasany, S. Sundaresan, M.P. Rajkumar, G.D. Darokar, A.K. Bagchi, S.P.S. Khanuja, Molecular diversity in Phyllanthus amarus assessed through RAPD analysis, Plant Molecular Biology and Genomics 85 (2003) 1454-1458.