Multivariate Analysis Study of Limmu Coffee (Coffea arabica L.) Accessions in South Western Ethiopia

Agricultural Science Research Journal Vol. 6(7): 166-174, July 2016 Available online at http://resjournals.com/journals/agricultural-science-research-journal.html ISSN: 2026 6073 2016 International Research Journals Full Length Research Paper Multivariate Analysis Study of Limmu Coffee (Coffea arabica L.) Accessions in South Western Ethiopia * Lemi Beksisa 1 and Ashenafi Ayano 2 1,2 Jimma Agricultural Research Center, P.O. Box, 192, Jimma, Ethiopia 1 College of Agriculture and Veterinary Medicine, Jimma University, P.O. Box, 370, Jimma Ethiopia *Corresponding author E-mail: lbeksisa@gmail.com Abstract The experiment was conducted in south western Ethiopia; at Agaro Agricultural Research Sub Center from 2001 to 2012 cropping seasons with the objective of estimating the genetic diversity of Limmu Coffee accessions using multivariate analysis. Sixty two Arabica coffee accessions representing Limmu Coffee with two coffee berry disease (CBD) resistance verities known as F-59 and 744 as checks were planted in 8x8 simple lattice design with two replications. Mean yield data from the last six years of cropping seasons were used, while the other agronomic traits were taken once throughout the experimental period. Accessions showed highly significance difference at P<0.01 and P<0.05 for all traits. Sixty four Arabica coffee accessions were grouped into five distinct groups by cluster analysis indicating a wide genetic diversity of Limmu Coffee. Cluster I, II, III, IV and V contained 15 (23.44%), 24 (37.5%), 10 (15.63%), 12 (18.75%) and 3 (4.68%) of the accessions, respectively. The X 2 test showed that all inter-cluster squared distances were highly significant at p<0.01. Maximum inter cluster distance (D 2 ) was observed between cluster I and II (D 2 =1259.67), while the minimum inter cluster distance (D 2 ) was obtained between cluster III and V (329.464). Highest mean value of accessions was observed in cluster V for almost all traits. The first principal component was accounted for 43.50% of the total accessions variation. Meanwhile, the first three principal components with Eigen values of more than one cumulatively have explained 76.40% of the variation. In this study, the number of first primary branch, numbers of main stem nodes, total plant height, stem diameter, canopy diameter and internodes length were chiefly contributed towards the variability of accessions. Therefore, accessions can be selected based on these traits for future genetic improvement of the Arabica coffee through either hybridization or selection. Key words: Coffea arabica L., Genetic divergence, Cluster, Principal components. Introduction Arabica coffee (Coffea arabica L.) of the family Rubiacae originates from the South Western montane rainforests of Ethiopia, the place where it has its center of genetic diversity (Seyoum, 2003). Among 124 species in the genus Coffea (Davis et al., 2012), Arabica coffee which is the only tetraploid species (2n = 4x = 44), and Robusta coffee (Coffea canephora P.) diploid (2n = 2x = 22) chromosomes are the two most important commercial species (Gichuru et al., 2008) covering the area of about 10 million hectares worldwide (Bunn, 2015). Economically, 166

Arabica coffee is the second most exported commodity after oil worldwide (Gray et al., 2013). It is also one of the most important commodities in the international agricultural trade, representing a significant source of income to several Latin American, African and Asian countries (Anthony et al., 2001). In addition, Ethiopia is the largest producer of coffee in Sub-Saharan Africa and is the fifth largest coffee producer in the world next to Brazil, Vietnam, Colombia and Indonesia, contributing about 7 to 10% of total world coffee production (Gray et al., 2013). Coffee cultivation plays a vital role in both the cultural and socio-economic life of the Ethiopian. According to International Coffee organization (2014), economically Arabica coffee is the major agricultural export crop, which provides 30% of the foreign exchange earnings. It is also important to the Ethiopian economy as there are about 15 million people whose livelihoods are directly or indirectly derived from coffee (Gray et al., 2013). In terms of cultural values, the culture of drinking brewed coffee is deep-rooted and widespread, which is known throughout almost all ethnic groups in Ethiopia (Tadesse et al., 2001). In fact, the past history of coffee in Ethiopia showed that, coffee was earlier used of coffee was as a food rather than a beverage (Tadesse, 2015). As the county of origin for crop, people simply call coffee as; Ethiopia s gift to the world. Not only as the primary center of origin, it is also a diversification for Arabica coffee, which is expected to exist for yield and components of yield, diseases and pest resistance as well as other traits (Mesfin, 1982). Thus, detecting and quantifying genetic variability in crop species is important for a successful conservation of genetic resources and plant breeding. There are a few researchers who have conducted a diversity study on coffee. For instance, Olika et al. (2011) and Getachew et al. (2013) each on 49 Limmu coffee accessions, Ermias (2005) on 81 West Wellega coffee accessions, Yigzaw (2005) on 16 North West and South West of Ethiopia coffee accessions, while Mesfin and Bayetta (2005) on Harar coffee accessions at pre-bearing stage have reported genetic variability among the accessions for most of the traits studied. However, despite the vast area of cultivation, wealth of tremendous genetic diversity and importance to the national economy, the average productivity per unit hectare as a whole in Africa had surprisingly remained at 1960 s levels, while the other two major coffee growing regions, namely America and Asia have seen nearly a 100% increase (Bunn, 2015). Various researchers in Ethiopia have repeatedly reported major contributing factors for such low yields such as conventional husbandry and processing practices, limited availability and adoption of improved coffee cultivars and lack of well characterized and distinctly variable breeding materials readily available for use (Seyoum, 2003). Interestingly, the increase in coffee production can be possibly more than the world standard since Ethiopia has indigenous genetic diversity materials and favorable agro climatic condition in coffee growing areas of the country. However, intensive study was seen to be lacking of information regarding the genetic diversity of Limmu coffee type in Ethiopia. Therefore, the present study is conducted to estimate the genetic diversity of Limmu Coffee accessions using multivariate analysis. Materials and Methods Site Description The experiment was conducted at Agaro Agricultural Research Sub Center of the Jimma Agricultural Research Center located at the south-western part of Ethiopia. It is 45 km far from Jimma and 397 km from the capital city of the country, Addis Ababa. Agaro is located at a latitudinal gradient of 7 50 35 7 51 00 N and a longitudinal gradient of 36 35 30 E with an altitude of 1650 m above sea level. The mean annual rainfall of the area is about 1616 mm with an average maximum and minimum air temperatures of 28.4 C and 12.4 C, respectively (Elias, 2005). The major soil type is Mollic Nitisols with ph 6.2, 7.07% organic matter, 0.42% nitrogen, 11.9 ppm phosphorus and 39.40 cmol(+)/kg CEC (Zebene et al.,2008). Plant Materials Sixty two accessions with two released coffee berry disease (CBD) resistant varieties, F-59 and 744 as checks were considered in this study (Table 1). 167

Table: 1. Passport data of coffee accessions collected from Limu coffee growing areas in 2001 Collection Farmers Local name of Altitude range of Collections number/s districts Association accessions in the collected areas area (m.a.s.l) Limu- Kossa Weleke -sombo Gajo 1550-1550 L01/2001, L03/2001, L04/2001 >> Debello >> 1720-1720 L06/2001,L07/2001 >> Suntu Dalecho 1530-1850 L12/2001, L13/2001, L14/2001, L15/2001, L16/2001, L17/2001, L18/2001, L19/2001, L20/2001, L23/2001 >> Dambi -gabena - 1725 L28/2001 >> Chakawo - 1720-1740 L29/2001, L30/2001 >> Mecha -dire - 1500 L32/2001, L33/2001, L34/2001 >> Charake Mi aa 1650 L35/2001, L36/2001, L37/2001, L38/2001, L39/2001, L40/2001 >> Tenabo >> 1620 L41/2001 >> Chime Kerenso 1660 L43/2001, L44/2001, L45/2001 >> Meto -Gundib - >> Tenabo - 1620 L51/2001 >> Chime - 1660 L52/2001 >> Mecha- Dire Mi aa 1500 L53/2001 1725-1760 L46/2001, L47/2001, L48/2001, L49/2001, L50/2001 >> Cheraki >> L54/2001, L55/2001 >> Yedo Gota L56/2001 >> Limu- Kossa Dalecho 1540-1600 L65/2001, L66/2001, L67/2001, L68/2001, L69/2001, L70/2001 Limu-Seka Gujil - 1600-1620 L24/2001, L25/2001, L26/2001, L27/2001 >> Dego Jiru >> 1550 L57/2001, L58/2001, L59/2001, L60/2001, L61/2001 >> Gejib Kerenso L62/2001, L63/2001, L64/2001 - - - - 744(Check) - - - - F-59(Check) Source: Extracted from passport data existing in Jimma Agricultural Research Center (JARC) coffee breeding department Implementation and Experimental Design The trial was carried out from 2001 to 2012 cropping seasons in 8x8 simple lattice design with two replications and each replicate had border rows. The plot is consisted of two rows with four trees per row, while the planting space was 2mx2m between rows and plants. All field management practices were done properly and timely as per the recommendation of the area (Endale et al., 2008). Mean yield data of the last six years of cropping seasons were used, while the other agronomic traits were taken once throughout the experimental period. Four plants were taken at random from each plot for data collection on different agronomic traits, whereas all plants per plot were considered for evaluation of the accessions for yield. Data and data management Data were collected for the following quantitative traits like: Height up to first primary (cm): The height from ground level up to first primary branch was measured using cm. Total Plant height (cm): Measured in centimeter from the ground level to the tip of apical shoot using meter tape. Number of first primary branches: Total number of primary branches was counted for each tree. Main stem diameter (mm): The diameter of the main stem was measured at 5 cm above the ground level using digital caliper. Canopy diameter (cm): The diameter of the trees was measured in East-West and added to the South-North diameter and divided by two. Internodes length (cm): Computed as where, TH=total height, HFPB=height up to first primary branch, NN=number of nodes on main stem. Numbers of main stem nodes: Number of nodes on main stem counted. 168

Length of the 1 st primary branch (cm): Length of first longest primary branch measured from main stem to the tip of the branch. Yield (kg/ha): Fresh cherries were harvested from all plants of the plot and converted to clean coffee bean yield in kg per hectare. Statistical Analysis Analysis of Variance (ANOVA) Statistical Analyses Software version 9.2 (SAS, 2008) statistical software was used for statistical computations and estimation of differences among accessions. Cluster Analysis Clustering of accessions was done using the PROC clustering strategy of SAS 9.2 (SAS,2008) and appropriate numbers of clusters were determined from the values of Pseudo F and Pseudo T 2 statistics (SAS, 2008). Genetic Divergence Mahalanobis (1936) statistics was used to estimate the genotypic divergence between clusters. All the accessions used were clustered into different groups based on D 2 statistics. The D 2 values of all the combinations were arranged in descending order. D 2 statistics is defined by the following formula: 2 D ij 1 Xi Xj S Xi Xj Testing the significance of the squared distance values obtained for a pair of clusters was taken as the calculated value of 2 (chi-square) and tested against the tabulated 2 values at p-1 degree of freedom at 1% and 5% probability level, where p = number of traits used for clustering accessions. Principal Component Analysis Principal component analysis was conducted using PRINCOMP procedure in SAS 9.2 (SAS 1988) to evaluate the contribution of each quantitative character in the total variation of accessions. PCs with Eigen values >1 were selected for further analysis. Agglomerative hierarchical cluster analysis was used to determine the differences and similarities among accessions, while the Euclidean distance was used to reflect the differences between accessions clusters (Kendall, 1980). Result and Discussion Simple lattice design was more efficient than Randomized Complete Block Design (RCBD) almost for all traits (Table 2). Therefore, the use of simple lattice design was important. Accessions showed significant difference at P<0.01 and P<0.05 for all traits (Table 2). This indicates the presence of diversity among accessions which creates immense opportunity for an effective selection and hybridization programme to obtain wide spectrum of variation among the segregants. Furthermore, Mesfin and Bayetta (2005) and Getachew et al. (2013) also reported the presence of significant difference between Arabica Coffee accessions for different traits. Where, D 2 ij = the square distance between any two accessions i and j; X i and X j = the vectors for the values for accession i th and j th genotypes; and S -1 = the inverse of pooled variance covariance matrix within groups. Table 2: Analysis of variances (mean square) and relative efficiency for different morphological traits of sixty four Arabica Coffee Accessions Traits Degree of freedom Number of 1 st primary branch Canopy diameter Internodes length Number of main stem nodes Length of 1 st primary branch Height up to 1 st primary branch Treatment Error Replication Blocks within Adjusted Unadjusted rep(adj) Intra block RCBD CV% 1 63 63 14 49 63 190.37* 63.63** 80.09 59.71 21.21 29.76 8.84 17555.87** 282.98** 309.85 262.99 126.03 156.47 6.73 0.97** 0.46** 0.55 0.22 0.13 0.15 5.71 88.44** 16.86** 21.27 15.34 4.19 6.67 7.55 104.53 ns 148.14* 161.26 70.18 78.31 76.5 10.29 21.70 ns 32.72** 39.27 24.68 12.94 15.55 10.48 RE 122.7 5 111.2 7 105.5 5 136.9 7 97.70 108.6 8 169

0.06 ns 123.7 Stem diameter 0.26** 0.35 0.23 0.08 0.11 7.23 1 7658.76** 119.5 Total plant height 762.48* 1168.90 1050.21 400.99 545.26 9.62 5 Yield 619634.95 n s 265669.46* * 293666 195330 118739 135760 18.99 105.1 7 *, **, ns indicates significance at 0.05, 0.01 probability levels and none significance respectively, CV=Coefficient of variations, RCBD=Randomized complete block design and RE= Relative efficiency Cluster Analysis Sixty two coffee accessions with two check varieties were grouped into five distinct groups by cluster analysis (Fig.1 and Table 3). Among them, cluster II was the largest of all containing 24 (37.5%) accessions followed by cluster I with 15 (23.44%) and IV by 12 (18.75%) accessions. The remaining clusters, namely cluster III and V both consisted 10 (15.63%) and 3 (4.68%) accessions respectively. In this study, most of the accessions were not distributed according to their geographical site of origin (Table 1 and 3). On the other hand, accessions collected from different districts were clustered together. This includes the accessions collected from Limmu Kossa and Limmu Seka district that were clustered together in cluster II. Therefore, there is no need to go for regional or altitudinal sources to collect genetically diverse plants except when the objective is breeding for disease resistance. Moreover, this study is in agreement with the previous work stating that morphological diversity is more important factor rather than variation in geographical origin as an indicator of genetic diversity in coffee (Bayetta, 2001). The formation and characterization of genotype groups are essential to guide the selection of parents in breeding programs, to establish combinations based on the magnitude of the dissimilarity between genotypes, and to explore the potential of parents by selecting dissimilar parents that also present high agronomic performance (Rodrigues et al., 2015). Moreover, Cruz et al. (2004) suggested employing individuals with different patterns of dissimilarity in the breeding programs, evading the restriction of the genetic variability and providing greater gains through the selection process. Therefore, it can concluded that, breeding by hybridization and selection among accessions from the groups established in this study may contribute to the introduction of new genetic diversity and may contribute to an improvement in the yield of Arabica coffee. Mean performance of accessions in each cluster The highest mean performance of the accessions for yield was showed in cluster V (2580.89 ± 42.27 kgha -1 ), followed by cluster III (2334.53 ± 90.06 kgha -1 ) and cluster II (1908.23 ± 96.88 kgha -1) (Table 4). Meanwhile, the highest mean value of accessions for all traits was observed in cluster V except for length of the first primary branch. The reason behind this is because the best performed accession in all aspects L60/2001 and 744 (Check) was grouped in this cluster among the three accessions under this cluster. This may make the selection difficult for all traits from this cluster depending on the mean recorded due to minimum number of accessions. Therefore, breeders can use the second option where the largest numbers of accessions available and highest mean performances were recorded. For instance, selection can be done from cluster II for length of first primary branch and canopy diameter, cluster III for number of main stem nodes, and cluster IV for total plant height, internodes length and height up to the first primary branch. Table 3: Cluster membership of sixty four Arabica Coffee accessions Clusters Accessions No Name of accessions I 15 L48/2001, L54/2001, L40/2001, L64/2001, L14/2001, L50/2001, L36/2001, L06/2001, L62/2001, L34/2001, L59/2001, L25/2001, L44/2001, L32/2001, L46/2001, II 24 L69/2001, L52/2001, L07/2001, L01/2001, L21/2001, L26/2001, L23/2001, L20/2001, L61/2001, L56/2001, L66/2001, L47/2001, L58/2001, L51/2001, L37/2001, L33/2001, L29/2001, L13/2001, L38/2001, L15/2001, L35/2001, L17/2001, L24/2001, L03/2001 III 10 L65/2001, L27/2001,F-59, L55/2001, L43/2001,L39/2001,L16/2001, L04/2001,L67/2001, L30/2001 IV 12 L41/2001, L18/2001, L19/2001, L45/2001, L53/2001, L22/2001, 68/2001, L12/2001, L57/2001, L28/2001, L70/2001, L49/2001 V 3 L63/2001, 744, L60/2001 170

Fig.1: Tree diagram of 64 Arabica Coffee Table 4: Mean values of five clusters for the nine traits of the sixty four Arabica coffee accessions Traits I II III IV V TPH 201.88±15.32 208.59±22.78 215.42±21.76 193.57±23.32 240.05±15.33 HuFPB 34.25±3.54 34.12±4.7 32.97±4.72 35.44±5.15 36.35±2.93 SD 3.72±0.37 4.02±0.34 4.09±0.29 3.66±0.41 4.31±0.6 LFPB 84.1±7.07 88.43±8.73 87.55±9.4 83.19±8.62 79.5±15.83 NFPB 50.5±5.78 53.74±4.71 54.44±4.65 47.51±8.54 57.43±5.14 NMSN 26.49±2.87 27.85±2.47 28.07±2.64 24.95±4.57 29.83±2.79 IL 6.37±0.46 6.34±0.45 6.53±0.66 6.41±0.64 6.85±0.72 CD 165.41±12.17 169.34±12.69 167.12±9.67 158.57±10.21 182.78±10.69 Yld 1588.5±60.02 1908.23±96.88 2334.53±90.06 1287.11±107.3 2580.89±42.27 TPH= total plant height, HuFPB= height up to first primary branch, SD =Stem diameter, LFPB= average length of primary branches, NFPB= number of primary branches, NMSN= number of main stem nodes, IL= Inter node length, CD= canopy diameter, Yld= Yield. Inter Cluster Distances The 2 (chi-square) test showed that all inter-cluster squared distances were highly significant at p<0.01 (Table 5). Maximum distance observed between cluster I and II (D 2 =1259.67) was found to be highly divergent from each other, followed by cluster II and IV (D 2 = 1029.82) and between cluster I and cluster V (D 2 = 954.18), which suggest a wide diversity between these groups. Moderate inter cluster distance showed between cluster II and III (D 2 = 634.93) and between cluster I and III (D 2 = 624.84). Intermating between highly divergent accessions may give high heterotic response and thereby, better segregants. Rahman et al. (2009) also reported that the high heterotic result is largely depends of the degree of genetic diversity in the parental lines. However, in present study, though highest inter cluster distance could be registered between cluster I and II, the superior derivatives could not be expected from the intermating between accessions of the two clusters due to low mean performance from both clusters. Instead, accessions of cluster III can be utilized as the donor parent for improving yield due to its high mean performance for yield and most of the yield contributing 171

traits with good amount of genetic divergence. Furthermore, moderately high inter cluster distance between II and III and I and III with high mean performance of one or more traits contributing towards yield of coffee suggests that some sampled crosses between these clusters may be attempted to select the recombinants of coffee yield. Table 5: Inter cluster distance (D 2 ) for sixty four Arabica Coffee evaluated for 9 traits Cluster II III IV V I 1259.67** 624.84** 230.44** 954.18** II 634.93** 1029.82** 305.52** III 394.99** 329.46** IV 724.40** 2 = 15.51 at 5%; 2 = 20.09 at 1% Principal Component Analysis The first three principal components (PCs) with Eigen values greater than one are accounted for 76.40% of the total accessions variation, leaving the remaining 23.60% in the last six principal components (Table 6). The first two PCs are accounted for 64.8% of the total variation, while the first principal component axis (PCA1) accounts for 43.50% of the total accessions variation with the large loadings on the number of first primary branch, stem diameter, number of main stems node, total plant height, canopy diameter and yield, which indicates the lion share of these traits towards the divergence (Table 7). Similar findings were also obtained from different Arabica coffee experiments in different years by different researchers (Mesfin, 1982; Mesfin and Bayetta, 2005 and Getachew et al., 2013). Interestingly, all the traits in these clusters are going to the same direction, which indicates a positive correlation among them. The second PC has large loading effects on the internodes length and height up to first primary branch; whereas the third PC was highly depended on the length of the first primary branch (Table 7). Based on the biplot of first two principal components (PC I and PC II) in Fig.2, the variability of accessions was largely depend on the number of first primary branch, number of main stem nodes, total plant height, stem diameter, canopy diameter and internodes length. This technique could be used as an efficient tool for selecting accessions based on desired traits in the early stages of the breeding process. Therefore, it may be possible to conclude that these traits should be taken into consideration by breeders while selecting the genetically diverse parents for further utilization. Table 6: Eigen Values of the Correlation Matrix Principal components 1 2 3 4 5 6 7 8 9 Eigen value Proportion Cumulative 3.9116 0.435 0.435 1.9229 0.214 0.648 1.0378 0.115 0.764 0.8438 0.094 0.857 0.5707 0.063 0.921 0.3756 0.042 0.962 0.2602 0.029 0.991 0.0699 0.008 0.999 0.0074 0.001 1 172

Table 7: Eigen vectors and Eigen values of the first three principal components (PCs) of accessions Character PC I PC II PC III TPH 0.428 0.164 0.164 HuFPB 0.037 0.454 0.337 SD 0.444 0.082-0.053 LFPB 0.085 0.36-0.708 NFPB 0.442-0.308 0.071 NMSN 0.434-0.329 0.096 IL 0.031 0.596 0.215 CD 0.364 0.266-0.025 Yld 0.307 0.033-0.459 Eigen value 3.9116 1.9229 1.0378 Percent Proportion 43.5 21.4 11.5 Cumulative variance % 43.5 64.8 76.4 TPH= total plant height, HuFPB= height up to first primary branch, SD =Stem diameter, LFPB= average length of primary branches, NFPB= number of primary branches, NMSN= number of main stem nodes, IL=Inter node length, CD= canopy diameter, Yld= Yield. Fig. 2: Biplot of the first two principal components (PC1 and PC2) for 64 Arabica Coffee Conclusion The present study illustrated the existence of a wide range of variations for all traits studied, which provides huge opportunities for genetic gain through selection and/or hybridization. Accessions have showed a high significance difference at P<0.01 and P<0.05 for all traits. Number of first primary branch, stem diameter, number main stem nodes, total plant height, canopy diameter and yield are the factors chiefly contributing towards the divergence. The highest and lowest mean performance of the accessions for yield showed in cluster V (2580.89 ± 42.27 kgha -1 ) and IV (1287.11 ± 107.3 kgha -1 ) respectively. Meanwhile, maximum inter-cluster distance (D 2 ) was 173

observed between cluster I and II. Hence crossing coffee accession between these clusters will result in good heterosis and recombinant in segregating generations; however, mean performance of the accessions could be under consideration. Maximum contribution towards the variability of accessions was due to number of first primary branch, number of main stem nodes, total plant height, stem diameter, canopy diameter and internodes length. Therefore, information obtained from cluster and principal component analyses in this study will be helpful for coffee breeders to design breeding programmes for the future use. Acknowledgments Authors are thankful to their colleagues specially Jimma Coffee project staff members for their collaborative work during the experiment execution and data collection. They also thankful to Agaro Agricultural Research Sub Center staffs for maintaining well experimental fields and help in data recording. References Anthony, F., B. Bertrand, O. Quiros, P. Lashermes J. Berthaud and A. Charrier (2001). Genetic diversity of wild coffee (Coffea arabica L.) using molecular markers. Euphytica, 118: 53-65. Bayetta, B., (2001). Arabica coffee breeding for yield and resistance to coffee berry disease (Colletotrichum kahawae Sp.nov.). A PhD degree thesis submitted to the University of London. Bunn, Ch., (2015). Modeling the climate change impacts on global coffee production. Dissertation for the completion of the academic degree Doctor rerum agriculturarum submitted to the faculty of Life Sciences at Humboldt-Universität zu Berlin. Cruz, C. D., A. J. Regazzi and P.C.S. Carneiro, (2004). Modelos Biométricos Aplicados Ao Melhoramento Genético. Viçosa: Imprensa Universitária. 480p. Davis, A.P., T.W. Gole, S. Baena and J. Moat (2012). The impact of climate change on natural populations of Arabica coffee: Predicting future trends and identifying priorities. PLoS ONE, 7(11): e47981. Elias, A., (2005). Economics of Coffee bean marketing: A case study of Gomma woreda in Jimma zone of Ethiopia. M.Sc. Thesis, Graduate studies of Haramaya University, Haramaya, Ethiopia. Endale, T., K. Taye, N. Antenhe, Sh. Tesfaye, Y. Alemseged and A. Tesfaye (2008). Research on coffee field management. pp.187-195. In: Girma, A., Bayetta, B., Tesfaye, S., Endale, T., Taye, K. (eds.). Coffee Diversity and Knowledge. Proceedings of a National Workshop Four Decades of Coffee Research and Development in Ethiopia, 14-17 August 2007, Addis Ababa, Ethiopia Ermias, H., (2005). Evaluation of Wellega Coffee Germplasm for yield, yield Components and Resistance to Coffee Berry Disease at early bearing stage. A Thesis Submitted to the faculty of the department of Plant Sciences, School of Graduate Studies Alemaya University in Partial Fulfillment of The Requirements for the Degree of Master of Science in Agriculture (Plant Breeding). Gray, Q., A. Tefera, and T. Tefera, (2013). Ethiopia: Coffee Annual Report. GAIN Report No. ET 1302. Getachew, W., A. Sentayehu, K. Taye and B. Tadesse, (2013). Genetic Diversity Analysis of Some Ethiopian Specialty Coffee (Coffea arabica L.) Germplasm Accessions Based on Morphological Traits. Time Journals of Agriculture and Veterinary Sciences. Vol. 1 (4): 47-54. Gichuru, E.K., C.O. Agwanda, M.C. Combes, E.W. Mutitu, E.C.K. Ngugi, B. Bertrand and P. Lashermes, (2008). Identification of molecular markers linked to a gene conferring resistance to Coffee berry disease (Colletotrichum kahawae) in Coffea arabica. Plant Pathol. 57:1117-1124. International Coffee Organization (ICO), (2014). Fourth International World coffee Conference. 112th session from 7-14 march 14. London, United Kingdom. Available on: http://dev.ico.org/documents/cy2013-14/wcc-ethiopiapresentation.pdf Kendall, M., 1980. Multivariate analysis (2nd ed.). London: Charles Griffin and Co. Mahalanobis, P.C., (1936). On the generalized distance in statistics. In. Proceedings of National Institute of Science. India, B, 2: 49-55. Mesfin, A., 1982. Heterosis in Crosses of indigenous Coffee (Coffea Arabica L) Selected for Yield and Resistance to Coffee Berry Disease at first bearing stage. Eth.J. Agric.Sci.4: 33-43 Mesfin, K. and B. Bayetta, (2005). Genetic divergence of Hararge Coffee (Coffea arabica L.) germplasm accessioncs at pre-bearing stage. Proceedings of the 20 th International conference on Coffee Science, Oct.11-15, Bangalore, India. pp. 1107-1112. Olika, K., A. Sentayehu, K. Taye, and G. Weyessa, (2011). Variability of quantitative Traits in Limu Coffee (Coffea arabica L.) in Ethiopia. Int. J. Agric. Res. 6: 482-493. Rahman, M. M., and M. A. Z. Al Munsur, (2009). Genetic divergence analysis of lime. Journal of the Bangladesh Agricultural University, 7: 33 37. Rodrigues, W. N., M. A. Tomaz and M. A. G. Ferrão, (2015). Diversity Among Genotypes Of Conilon Coffee Selected in Espírito Santo State. Biosci. J. V.31 (6):1643-1650. SAS, (2008). Statistical analysis system (version 9.2), SAS Institute, Cary, NC.USA. Seyoum, S., (2003). Genetic divergence for seedling parameters and associations among agronomic traits in the Ethiopian coffee (Coffea arabica L.) germplasm. An M.Sc. thesis submitted to the School of graduate studies of Alemaya University. Tadesse, W.G., M. Denich, T. Demel, P.L.G.Vlek, (2001). Human impacts on coffea arabica genetic pools in Ethiopia and the need for its in situ conservation. In: Managing plant genetic diversity. R.Rao, A.Brown, M. Jackson (eds), CABI International and IPGRI, 237 247. Tadesse, W.G., (2015). Environment and Coffee Forest Forum (ECFF). Addis Ababa, Ethiopia. Yigzaw, D., (2005). Assessment of genetic diversity of Ethiopian Arabica coffee accessions using morphological, biochemical and molecular markers. A PhD Dissertation, University of the free state, South Africa. p197. Zebene, M. and T. Wondwosen (2008). Potential and constraints of Nitosol and Acrisols. Coffee Diversity and Knowledge. Proceedings of a National Workshop Four Decades of Coffee Research and Development in Ethiopia, 14-17 August 2007, Addis Ababa, Ethiopia. pp. 203-216.. 174