On farm Diversity, Farmers’ Practices and Status of Genetic Erosion of Harar Coffee (Coffea arabica L.)

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Ibsa Aliyi, Zekeria Yusuf, Yohannes Petros This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4775854/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Jul, 2025 Read the published version in Discover Life → Version 1 posted 15 You are reading this latest preprint version Abstract Farmers’ varieties are often well adapted to specific environments, and tend to have a advantage than in marginal areas. The present study was undertaken to assess farmers’ practices, on farm phenotypic diversity and status of genetic erosion of Harar coffee ( Coffea arabica) Eastern Ethiopia. The principal component analysis was employed to identify morphological traits contributing to the variations in genotypes and associated traits. The result indicated that the highest preference index (25.60) with preference rank first was recorded for Fandisha genotype. The first principal component had high positive scores from number of secondary branches/ tree (0.94) and number of trunks/tree (0.30) as the most discriminating. The second component had high positive component loads for number of primary branches (0.76), number of nodes / trunk (0.33), and stem diameter (0.25) which were mainly responsible for the variations. Likewise, the third component had high positive scores for hundred bean weight (0.77), number of trunks/ tree (0.49) and stem internode length/tree (0.22). The result of the study indicated that secondary branches/ tree, trunks/ tree, nodes/ trunk, stem diameter, hundred bean weight and stem internode length/tree are the most discriminating traits among the set of coffee genotypes assessed. Genetic resources Genotypes Morphological descriptors Preference index Principal component analysis Traits Figures Figure 1 Figure 2 Figure 3 1. INTRODUCTION Coffee belongs to the family Rubiaceae and the genus Coffea that comprises of nearly 124 species among which the two economically important species including Arabica coffee ( Coffea arabica L.) accounts for approximately 80% of commercial production while Robusta coffee ( Coffea canephora P.) account for nearly 20% commercial production) (Davis et al., 2006; Gray et al., 2013 ). Higher beverage quality is associated with C. arabica (Lashermes et al., 1999) which is a true allotetraploid (2n = 44) with an allogamy index of about 10%, and originated from the interspecific hybridization of C. canephora and C. eugenioides (Freitas et al., 2007). The remaining Coffea species are diploid (2n = 22), and are usually self-sterile (Pearl et al., 2004 ). However, the coffee species share genome, making possible interspecific hybridizations and hybrid production either within Coffea species (Le Pierrès, 1995), or between Coffea and Psilanthus species (Couturon et al., 1998 ). Ethiopia remains the biggest coffee maker in Africa and the world's fifth biggest coffee maker after Brazil, Vietnam, Colombia and Indonesia, creating around 4.2% of world generation of entire coffee (ICO, 2015). Ethiopia's coffee root potential is nearing termination. There has too been small consideration paid to the evaluation of the differing qualities and preservation of innate coffee seeds (Tesfa, 2019 ). Ethiopia produces Arabica coffee, which is accepted to have begun basically from the woodlands of Western Ethiopia. The development and utilization of coffee has spread from the Horn of Africa to the rest of the world, particularly in the southwest of Ethiopia, where Arabica coffee begun and begun. Most of the coffee delivered in Ethiopia is conventional coffee, counting timberland coffee, semi-forest coffee, cultivate coffee, and ranch coffee. As a result, roughly 90% of the coffee in Ethiopia is delivered by smallholder coffee ranchers. In expansion, roughly 25% of the Ethiopian populace is straightforwardly or in a roundabout way subordinate on coffee generation, handling and promoting (Esayas, 2005 ). It developed in the Hararghe locale of eastern Ethiopia. Coffee development has been found in the locale as early as 850 Advertisement (Meyer, 1968 ). Harar coffee is characterized by medium, yellow-green coffee beans with direct causticity and a interesting mocha flavor. Most ranchers develop Harar coffee on intensely overseen farmlands, and the normal family is assessed to claim less than 0.5 hectares of arrive (Daba et al., 2023 ). In spite of the colossal money related potential of Harar coffee, its generation is constrained. The normal abdicate of the trim is moo basically due to moo precipitation, unsteady precipitation designs, need of progressed differences, need of agrarian advancement and predominance of bugs and maladies (Tesfahunegn, 2014 ). Capacity of qualities in quality banks is a issue due to destitute behavior of coffee seeds. Hence, collection of present day assortments and characterization of coffee genotypes can lead to feasible coffee advancement (Krishnan, 2013 ). Agriculturists, plant breeders, quality bank supervisors, and edit researchers draw on differing edit hereditary assets to enhance, back, and advantage society at huge (Smale, 2006). Biodiversity is an imperative component of environmental frameworks (Recuperate, 2000), and misfortune of it can have antagonistic impacts on the working of these frameworks, counting impedance of their capability to deliver (Loreau and Hector, 2001 ). Genetic resources are characteristic resources that are renewable but moreover helpless to misfortunes due to characteristic or man-made mediations (Di Falco and Chavas, 2006). With the help of their long experience farmers used to make crop variety preferences based disposition paid for desirable traits. Varietal yield, income, environmental adaptability and stability, household resource endowments (particularly land holdings and livestock assets), years of farming experience, and contact with extension services are the major factors causing household heterogeneity of crop variety preferences (Asrat et al., 2009 ). Therefore, the present study was undertaken to assess on farm phenotypic diversity, farmers’ preference, production constraints and status of genetic erosion of Harar coffee ( Coffea arabica L.) in Lagahama and Awale localities in Kombolcha and Dire Dewa districts respectively, East Hararghe, Ethiopia. 2. MATERIALS AND METHODS 2.1. Description of the Study Area The experiment was conducted at Awale locality in Dire Dewa Adminstrative Region, and Legahama locality in Kombolcha district, East Haraghe, Ethiopia (Fig. 1 ). 2.2. Sampling method and sample size determination The coffee producing households were purposively selected to provide information about on farm diversity, status of genetic erosion and management of Harar coffee in the study area. The sample size was determined following the formula as described by Yamane ( 1967 ): \(\:\text{n}=\frac{\text{N}}{1+\text{N}{\text{e}}^{2}}\) (Eq. 1) where, n: sample size; N: the population size coffee growing farmers in the study area which as estimated to be 240 households (N = 240) with 130 from Awale, and 110 from Lagahama; e: is the desired level of precision or marginal error which was set as 0.05 at 95% confidence level. During this study, the target population was the number of households in each study site. The household sample (HS) was determined according to Rao ( 1968 ) method as per the use of a head of household to be used as a respondent. Thus, the respondent household sample (HS) was determined as HS= \(\:\frac{\left(\text{n}\right)\left({\text{H}\text{H}}_{\text{i}}\right)}{{\text{H}\text{H}}_{\text{t}}}\) (Eq. 2) Where: HS: respondent household sample; n: the sample size of the household population; HHi: the number of coffee growing households in the study sites; HHt: the total number households across study sites. 2.3. Data Collection and Analysis Data on morphological traits were collected and recorded from ten randomly selected coffee genotypes representing each study population. Fifteen morphological descriptors (Table 1 ) with seven qualitative, and eight quantitative descriptors including PH: Plant height (m); IPB: insertion of primary branch; IF: Inflorescence position; SS: seed shape; PL: Petiole length (mm); NPF: number of petals per flower; LW: leaf width (mm); NT: number of trunks/tree; SB; number of secondary branches per trunk; NPB: number of nodes per primary branch; NPT: number of nodes per trunk; IL: internode length (mm); NBB: number of berries per primary branch; SD: stem internode length (mm); hundred bean weight were assessed. Morphological traits collected from September to October 2021, were based on descriptors for coffee in accordance with International Plant Genetic Resources Institute (IPGRI, 1996). Table 1 Coffee morphological descriptors used in the study S.N Traits Code Unit of measurement and scale used 1 Plant habit PHB Measuring the height of coffee trees using 10 metre ruler and scored as follows: 1 = bush (< 5 mwithout distinct trunk), 2 = shrub or small tree ( 5 m- single trunk) 2 Plant height (m) PH Visual estimation: 1 = very short, 3 = short, 7 = tall and 9 = very tall 3 Stem diameter (mm) SD Diameter of stem at five cm above ground measured using caliper 4 Seed shape SS Round, Obovate, Ovate, Elliptic, & Oblong 5 Number of berries per node along primary branches BN The mean number of berries per node on the selected four primaries from five trees per plot. 6 Inflorescence position IFP 1 = axillary and 2 = terminal 7 Number of petals / flowers NPF Average of 10 flowers, randomly selected from different nodes 8 Number of trunks/tree NT Total number of trunk per tree 9 Number of secondary branches/trunk SB Total number of secondary branches per tree 10 Number of nodes/ primary branches NB Total numbers of nodes count per branch 11 Number of nodes/trunk NPT Total numbers of nodes count per tree 12 Internode length/trunk (cm) IL Computed per tree as (TH–HFPB)/TNN-1, where TH = total plant height, HFPB = height up to the first primary branch, TNN = total number of main stem nodes 13 Insertion of primary branches IPB Observation is done on the main stem (1 = drooping, 2 = horizontal and 3 = semi erect) 14 Leaf width (mm) LW Average of five mature (> node 3 from the terminal bud) leaves, measured at the widest part 15 100 bean weight (g) HBW Determined at (11% moisture) content as follows: (“Bean weight at 0% moisture content” x 100)/bean number x0.89) 2.3.1. Survey Data Collection The survey was carried out based on the potential diversity indicators by Brush (1999) and structured interview checklists on the socio-economic situation of the farmers, their farming practices, cultural background and current agricultural trends. Additionally, farmers were asked about the number of distinct coffee landraces grown at the time of the survey and about their observation of losses of varieties in the recent past. All survey results were summarized per community; the mean value were calculated for the quantitative data, and in the case of qualitative multi-state questions, the percentage for each class was presented. 2.3.2. Biodiversity Indices Sorensen–Similarity index (SI) The SI, independently developed by Sorensen ( 1948 ) and Dice ( 1945 ), is a statistic used for analyzing the similarity between two data samples. For any two finite sets, A & B, it is defined as follows \(\:\text{S}\text{I}=\frac{2{\text{N}}_{11}}{{\text{N}}_{10}+{\text{N}}_{01}+{2\text{N}}_{11}}\) (Eq. 3) Where N 10 : total number attributes where A is 1 & B is 0; N 01 : total number of attributes where A is 0 & B is 1; N11: total number of attributes where both elements A & B are 1. Farmers’ Varietal Preference the preference criteria with regard to coffee cultivars as perceived by the growers and the relative ranking of preference criteria were based on their importance perceived by the farmers in selection or adoption of a variety. Before starting the study, respondents (coffee growers) were asked about local names of the coffee varieties they grow and also state the important traits of their preference which they usually take into account in judging the variety whether to continue its cultivation or not. The information was collected by personal interview of 60 purposively selected coffee growers across locations. The respondent farmers were also asked to assign a score of 1 to 5 to each criterion according to its importance as they perceived. Based on these scores, weighted scores for each selected criteria were calculated out to represent their significance as per the following formula (Bhuyan et al., 2023) W \(\:=\frac{\text{m}}{\text{a}\text{b}}\) (Eq. 4) Where, W = weighted score of the parameter; a = highest possible total score obtainable by the parameter; b = number of parameter; m = total of preference scores assigned to the parameter. The preferences of the farmers for varieties were measured using a preference index (PI). To calculate PI, the respondents were asked to score from 1–5 for various quality parameters for each of coffee varieties. These scores of individual farmers were added together to arrive at the total score for a particular parameter of a variety (Bhuyan et al., 2023). The varieties were then ranked according to the preference index score as follows. PI \(\:\:=\frac{\text{P}1\text{W}1+\text{P}2\text{W}2+\dots\:.\text{P}\text{i}\text{W}\text{i}}{\text{N}\text{R}}\) (Eq. 5) Where, PI = varietal preference index; P1, P2…..Pi = total of preference scores of the variety in respect of each individual parameter; 1, 2….i = Quality parameters i = 8; W1, W2…. Wi = weight of each individual parameter; NR = number of respondents. The preference criteria of coffee descriptors used in the present study were: 1) bean yield; 2) bean size; 3) disease resistance; 4) duration of maturity; 5) size or height of the coffee tree; 6) inflorescence duration per year; 7) suitability for intercropping system; and 8) cup quality. 2.3.3. Multivariate Analysis Principal component analysis (PCA) was performed based on the mean using the standardized variables (Ringner, 2008). The biplots were plotted using GenStat 18th Edition (VSN International, 2012 ). Only the principal components with eigen values greater than one were considered in determining variability among the genotypes (Iezzoni and Pritts, 1991 ). 2.4. Genetic erosion Genetic erosion (GE) for coffee landraces was calculated per surveyed village using the formula GE = [(c-k)/N]*100 (Eq. 6) where, c = number of cultivars cultivated by few households on small areas, k = number of newly introduced cultivars and N = total number of cultivars recorded in the village. Accordingly in the present study, the genetic erosion was determined by identifying the number of cultivars grown by few households on small areas (n = 2 i.e. Danga and Muyira genotypes were rare), since no new cultivars (k) were introduced in the past 10 years a k = 0 was used. 2.5. Data Analysis Morphological data were subjected to one-way analysis of variance (ANOVA) and multivariate analysis using the SAS software version 9.2. The least significant difference (LSD) was used to compare means among and within treatments. 3. RESULTS 3.1. Grouping of Farmers’ Variety Using Morphological Descriptors of coffee ( Coffea arabica L.) Totally seven cultivars of C. arabica were recorded in the study area with four cultivars from Awale district, whereas three cultivars were identified in Legahama locality (Table 2). The seven cultivars belonged to five different farmers’ varieties with two varieties common to the studied localities. With the exception of Shekhana in Awale which was found to be of medium height and Fandisha in Lagahama which was found to be short, all the genotypes were found to be tall (Table 2). Terminal floral position was observed for Fandisha, Khoriso, and Muyira genotypes. Horizontal insertion of primary branches was observed for Fandisha and Khoriso genotypes. Drooping insertion was observed for Muyira, and Danga genotypes. Semierect insertion of primary branch was observed only for Shekhana genotype. Elliptical seed shape was observed for Muyira, and Danga genotypes, whereas Round seed shape was observed for Khoriso genotype, and oblong seed shape was recorded for Shekhana, and Fandisha genotypes. Table 2. Descriptors of coffee genotypes in the study area Location Genotype PH IF IPB SS PHB NPF LW(mm) Awale Fandisha Tall T H Elliptical tree 6 6 Muyira Tall A D Elliptical tree 5 5 Shekhana Medium A S Oblong shrub 5 4 Khoriso Tall T H Round tree 5 6 Lagahama Danga Tall A D Elliptical tree 5 6 Fandisha Short T H Oblong bush 5 3 Muyira Tall T D Elliptical shrub 5 6 PH: Plant height; IPB: insertion of primary branch; IF: Inflorescence position; SS: seed shape; PHB: Plant habit; NPF: number of petals per flower; LW: leaf width; T: terminal; A: axial; H: horizontal; D: drooping; S: semierect. The coffee genotypes were assessed based on eight quantitative morphological descriptors (Table 3). The highest number of trunks per tree, NT (80.40) was recorded for Danga genotype, followed by Muyira genotype in Lagahama with NT of 77.20. However, the least PB (33.4) was recorded for Fandisha genotype in Lagahama. Number of secondary branches per tree (SB) was highest (192.60) for Fandisha in Awale, and the least SB ( 95.20) was recorded for Khoriso genotype. Number of nodes per branch (NPB) was found to be highest (20.80) for Danga, and the least (12.60) for Muyira genotype in Lagahama, and Fandisha genotype in Awale. Number nodes per trunk (NPT) was the highest (43.20) for Danga genotype and least (17.60) for Shekhana genotype. Internode length (IL) was the highest (10.00) for Danga, Muyira in Awale and Shekhana genotypes and least (5.00) for Fandisha in Awale. Number of berries per branch (NBB) was the highest (16.40) for Muyira in Awale, 16. 00 for Fandisha in Awale, and 15.60 for Shekhana genotypes, least (10.00) NBB was recorded for Danga, and 10.40 NBB for Muyira in Lagahama genotype. Muyira genotype in Awale hads the highest (43.10) stem diameter (SD) while Fandisha genotype in Lagahama hads the least SD of 28.58. The highest hundred bean weight, HBW (96.40g) was recorded for Shekhana, In contrast, HBW was found to be the least (66.00g) for Danga and 66.60g for Khoriso genotype. Table 3. Mean values for morphological traits of coffee genotypes Location Genotype NT SB NPB NPT IL NBB SD HBW Lagahama Danga 80.40a 120.20b 20.80a 43.20a 10.00a 10.00c 42.78a 66.00d Lagahama Muyira 77.20a 117.00bc 12.60c 40.40b 10.00a 10.40c 42.66a 73.80c Awale Muyira 45.20c 122.00b 17.80b 25.00e 10.00a 16.40a 43.10a 75.60c Awale Fandisha 64.20b 192.60a 12.60c 28.20d 5.00b 16.00a 30.66cd 76.20c Lagahama Fandisha 33.40d 119.40b 17.40b 34.40c 5.80b 13.40b 28.58d 86.60b Awale Shekhana 32.20d 98.60cd 18.00b 17.60f 10.00a 15.60a 32.36c 96.40a Awale Khoriso 42.40c 95.20d 12.80c 33.20c 5.80b 13.20b 38.32b 66.60d Means followed by the same letter within a column were not significantly different at 0.05 probability level. Small letters (a, b, c,…..): significance within a column. NT: number of trunk/tree; SB; number of secondary branches per tree; NPB: number of nodes per branch; NPT: number of nodes per trunk; IL: stem internode length/trunk; NBB: number of berries per branch; SD: stem diameter (cm); HBW: 100 bean weight (g). 3.2. Similarity and farmers’ preference indices of coffee genotypes in the study areas The Sorensen’s similarity index was found to be 57% between the two localities (Table 4). Even though the localities are geographically close, they exhibited variations in cultivar distribution. Table 4. Sorensen’s similarity index for genotype distribution across localities Location Fandisha Muyira Shekhana Khoriso Danga Sorenson’s similarity index between the two locations (SI) Awale 1 1 1 1 0 57% Lagahama 1 1 0 0 1 (1): presence of genotype; (0): absence of genotype. Per the preference of each respondent for important coffee traits the highest weight score (0.55) was recorded for inflorescence duration per year, followed by bean size (0.53), and duration of maturity (0.50) while the least weight score of 0.42 was recorded for disease resistance (Table 5). Table 5. Preference scores and weight of coffee traits as perceived by the respondents S.N Preference traits Highest score Total score Weight 1 Bean yield 300 1080 0.45 2 Bean weight 300 1260 0.53 3 Disease resistance 180 600 0.42 4 Duration of maturity 300 1200 0.50 5 Height of the coffee tree 300 1080 0.45 6 Inflorescence duration per year 300 1320 0.55 7 Suitability for intercropping system 240 900 0.47 8 Cup quality 180 660 0.46 The preference index (PI) with rank was determined based on respondents’ preferences among eight traits including bean yield, hundred bean weight, disease resistance, duration of maturity, height of the coffee tree, inflorescence duration per year, suitability for intercropping system, and cup quality (Table 6). Accordingly, the highest PI (25.60) was recorded for Fandisha genotype, followed by Muyira genotype with PI of 22.50, Khoriso with PI of 21.03, Shekhana with PI of 19.53, and Danga with the least PI of 11.28. Table 6. Total preference scores, preference index with rank of preference of respondents Genotype Preference index Rank Fandisha 25.60 1 Muyira 22.50 2 Shekhana 19.53 4 Khoriso 21.03 3 Danga 11.28 5 3.3. Multivariate analysis of coffee genotypes based on morphological descriptors The Eigen values greater than one account for most of the variances in PC analysis (Ringner, 2008; Abdi and Williams, 2010; Sileshi et al., 2023). Accordingly, Eigen values for the first three principal components (PC1-3) accounting for 99. 6% of the variances among genotypes and 98% of the variances for morphological traits (Table 7; Fig 2 & 3) were considered for the interpretation of the association of genotypes with their respective traits. High positive or negative scores are deemed to be the most discriminating while scores close to the origin (zero) are not discriminating. Genotypes with high scores in the same quadrant (vector angle <90 0 ) have correlated effect meaning that they exhibit similar performance in the studied traits. In the present study, PC1 hads high positive component scores from number of secondary branch per tree, SB (0.94) and number of trunks per tree, NT (0.30); the corresponding genotypes with high positive scores were Fandisha from Awale (0.58), followed by Muyira (0.36) from Lagahama, Fandisha (0.37) and Danga (0.35) from Lagamhama locality indicating that such genotypes can be discriminated by their high primary and secondary branches. Table 7. Principal component analysis (PCA) of coffee genotypes based on eight quantitative morphological descriptors PCA for coffee genotypes PCA for coffee descriptors Parameters PC1 PC2 PC3 Parameters PC1 PC2 PC3 Eigen value 9969.02 350.21 188.38 Eigen value 1063.12 416.29 55.92 Difference 9618.81 161.82 160.55 Difference 646.83 360.37 41.59 Proportion 0.95 0.03 0.02 Proportion 0.68 0.27 0.04 Cumulative 0.94 0.98 0.996 Cumulative 0.68 0.95 0.98 Fd 0.58 -0.15 -0.75 NT 0.30 0.76 0.49 Md 0.31 0.12 0.22 SB 0.94 -0.24 -0.17 Sd 0.32 0.65 0.26 NPB 0.02 0.03 0.14 Kd 0.28 0.06 0.25 NPT 0.11 0.33 -0.25 Dk 0.35 -0.52 0.28 IL -0.02 0.05 0.22 Fk 0.37 0.37 -0.09 NBB -0.02 -0.10 -0.04 Mk 0.36 -0.35 0.41 SD -0.08 0.25 -0.10 HBW 0.04 -0.42 0.77 PCA: Principal component analysis; PC: Principal component; Fd: Fandisha from Awale; Md: Mura from Awale; Sd: Shekhana from Awale; Kd: Khoriso from Awale; Dk: Danga from Laga Hama; Fk: Fandisha from Laga Hama; MK: Muyra from Laga Hama; NT: number of trunks/tree; SB: number of secondary branches per tree; NPB: number of nodes per branch; NPT: number of nodes per tree; IL: stem internode length/tree; NBB: number of berries per branch; SD: stem diameter (cm); HBW: 100 bean weight. PC2 had high positive component loads for number of trunks NT (0.76), number of nodes per tree NPT (0.33), and stem diameter SD (0.25) while the corresponding genotypes with high positive scores were Shekhana (0.65) followed by Fandisha (0.37) from Lagahama. On the contrary, PC2 had high negative loading for hundred bean weight HBW (-0.42), and number of secondary branches per tree SB (-0.24); and the corresponding genotypes were Muyira from Lagahama (-0.35), and Danga (-0.52) indicating that such genotypes were among the least performing with respect to HBW and SB. PC3 had high positive scores for HBW (0.77), NT (0.49) and IL (0.22); the corresponding genotypes were Muyira (0.41), Danga (0.28), Khoriso (0.25), Shekhana (0.26) from Lagahama indicating that such genotypes produce high bean weight. By contrast, PC3 had high negative loading for NPT (-0.25); and the corresponding genotypes were Fandisha (-0.75) from Awale indicating that Fandisha from Awale was among those genotypes with the least performance in number of nodes per tree (NPT). 3.4. The status of genetic erosion of Harar Coffee in the study area There were five total cultivars found in the study areas. Thus, genetic erosion of Harar coffee cultivar in the study area was found to be 40% which calls for implementation of conservation strategies. 3.5. Trends in coffee cultivation and production constraints Most of the farmers (63.33%) suggested that they used to cultivate one coffee variety (Table 8). However, 36.67% of the farmers cultivate more than one variety. The cultivation of a variety could be due to the reduction of coffee cultivation as farmers prefer to plant alternative cash crops like Khat. The majority of the farmers (73.33%) indicated familiarity with <3 varieties of coffee. About 85% of the respondents suggested observation of variety losses mainly due to replacement with other commercial crops like khat and vegetables, change in climatic conditions, Land scarecity, and shortage of supply of improved varieties. Almost all farmers (96.67%) used seedling raised from seeds harvested from their own farms. The coffee production constraints as suggested by the respondents were lack of improved variety (46.67%), replacement by other commercial crops like khat (40%), climatic conditions (10%), and land scarcity (3.33%) but, the majority of the farmers (90%) had intention to cultivate coffee. However, 86.67% of the farmers suggested decreasing trend in coffee production; and decreasing number of planted coffee varieties (86.67%). Even though the market price is increasing as suggested by all (100%) farmers, there were no support (92%) given to farmers by government and research institutions / nongovernmental organizations (NGOs). Table 8. Trends in coffee cultivation, and production constraints Items Category Respondent score (%) Chi Sq (x2) P value 1.Amount of varieties planted by a farmer One 95 (63.33) More 55 (36.67) 10.67 P<0.05 2. Number of varieties known by farmer 3 40 (26.67) 32.67 P<0.05 3. Observation of variety losses Yes 128 (85.33) No 22 (14.67) 74.91 P<0.05 4. Source of planting material farmers own 145 (96.67) Research institutions 0 Nongovernmental organizations (NGOs) 5 (3.33) 273.75 P<0.05 5. Constraints in production of coffee Land scarcity 5 (3.33) climate change (erratic rain) 15 (10) replacement by commercial crop (khat) 60 (40) lack of improved 70 (46.67) 83.33 P<0.05 6. Farmer's intention to continue coffee growing Yes 135 (90) No 15 (10) 96 P<0.05 7. Trend in coffee cultivation Increase 8 (5.33) Decrease 130 (86.67) Stable 12 (8.00) 194.12 P<0.05 8.Trend in number of planted coffee varieties Increasing 10 (6.67) Decreasing 130 (86.67) no change 10 (6.67) 193.95 P<0.05 9. Trend in market price Increasing 150 (100) Decreasing 0 150 P<0.05 10. Support given for coffee growers by government, research institutions/NGOs Yes 12 (8) No 138 (92) 105.84 P<0.05 4. Discussion The present study identified five different Harar coffee farmers’ varieties with some varieties occurring in more than one localities. It was found that Fandisha genotype had tall plant height architechture in Awale but Fandisha short architechture in Lagahama. Such variation in morphological traits of same cultivar is inevitably due to environmental effect. There is no doubt about the identity of the cultivar since farmers had long experience of the cultivar. That notwithstanding, molecular fingerprinting of such genotypes may be the sure bet to confirm the identity so as to justify the observations better. The present study had revealed that the farmers consider a number of preference criteria in selecting a variety. The fact that high weight score was recorded for inflorescence duration per year, bean size, and duration of maturity while the least weight score for disease resistance indicates that farmers experience in crop cultivation and the indigenous knowledge can have significant contribution for crop breeding, and conservation of crop genetic resources. Productivity of a variety alone is not all in all for the growers. Farmers’ adoption of a particular variety does not only depend on yield but also depends on other attributes of a variety such as bean size, duration of maturity, inflorescence duration per year are preferred by the farmers. However, a single variety cannot hold all the desired attributes like bean yield, duration of maturity, cup quality, and stress resistance as need varies depending on factors relating to various situations of the farmers, and end users. Hence, variety development program should ensure participation of the end users to make the program user accountable. Participatory varietal improvement and development programs will be an effective approach to address the grower’s specific problems and develop a variety desired by the users. The results of the study can guide breeders to develop more resource efficient and farmer-oriented coffee breeding programs. The principal component (PC) analysis revealed differential contribution of morphological traits for the variations among the coffee genotypes. A yield trait namely hundred bean weight (HBW) was found to have direct relationship with only few traits such as number of primary branches per tree (PB) and stem internode length (IL) per tree indicating the need for further investigations to explore traits having high additive effect with coffee bean yield traits. The contribution of morphological traits to the variation in Harar coffee accessions was also observed in other similar studies (Kebede and Belachew, 2008; Adem et al., 2020 ) and also among C. arabica accessions from South West Ethiopia (Gessese et al., 2015 ; Beksisa et al., 2021 ). In the present study, the low genetic erosion of Harar coffee cultivars in the study area might be due to little supply of improved varieties to replace the landraces, and farmers cultivating coffee on small plots of land that is not usually adequate for cultivation of other crops. Additionally the significant genetic erosion may be attributed to replacement of coffee cultivation with a commercial crop like khat, recurrent shortage of rainfall, and land degradation and scarcity. There is therefore, the need to determine the status of cultivation of coffee, and farmers’ mediated in situ conservation of Harar coffee supported by policy directives and farmers’ initiatives. Similar finding was reported by Mercer & Perales (2010) who reported that significant losses in diversity can be difficult to distinguish from ‘normal’ levels of change in response to farmer, market or environmental drivers. Mathur ( 2011 ) suggested that genetic erosion can also be caused by environmental degradation, urbanization and land clearing through deforestation and bush fires, improper management and inadequate regeneration procedures in germplasm collections and gene banks. On farm variety assessment not only indicates the diversity that has been lost, but also what has replaced it (Khoury et al., 2022 ). The present study has identified the major coffee production constraints in the study area as replacement with commercial crop like khat, limited supply of improved coffee varieties, climatic conditions including biotic and abiotic factors, land scarcity, and little support to coffee growers. Similar study was also conducted by Tadesse et al ( 2020 ) who suggested biotic coffee production constraints including diseases, insect pests, weed species and vertebrate animals; abiotic factors like recurrent drought, frost, fluctuating rainfall pattern, high humidity, high temperature, low moisture, hail, storm, wind and reduced soil fertility are affecting coffee production that could cause as much as 70% yield loss in Ethiopia. Implications for coffee breeding and conservation of genetic resources Information on on-farm genetic diversity helps in designing breeding objectives based on preference traits, designing conservation strategies, and identifying production constraints. Multivariate analysis of morphological descriptors helps in identifying traits contributing for the variation and discrimination of genotypes. Farmers’ indigenous knowledge and practical experience with the crop genotypes helps in identifying and naming of varieties, contributing to crop genetic diversification, utilization of crop genetic resources, and remonstrating intervention strategies for conservation. The use of farmers’ indigenous knowledge as participatory variety selection has been used in plant breeding for decades. However, such technique is mainly limited to food crops. The present study has generated the role of participatory varietal preference approach, loss of genetic diversity and threats for coffee cultivation in the study area. Conclusion The preference index analysis revealed Fandisha, Muyira and Khoriso genotypes as the most preferred coffee cultivars in the study area. The principal component analysis (PCA) has identified traits contributing for the variation of genotypes and traits having direct association with bean yield traits. No much support and opportunities are being given for coffee cultivation in the study area despite the fact that farmers have good intention to grow coffee. The extent of genetic erosion or loss of genetic diversity was 40% that suggests the need for intervention actions for coffee breeding, policy strategies for coffee production and conservation of its genetic resources. Declarations Ethics approval and consent to Participate The authors confirm that all methods were carried out by relevant guidelines and regulations of Haramaya University Ethical Committee. The experimental protocols were approved by the Haramaya University Research Office. The collection of the plants used in the study complies with local or national guidelines with no need for further affirmation. All the guidelines were followed as per the University research ethics for collection, characterization and documentation of coffee landraces or germplasm accessions. Informed consent Informed Consent was obtained from all the participants involved in the study. Consent for Publication Not applicable Availability of Data and Materials The data supporting the findings of this study will be available on request from the corresponding author. Human and Animal Rights No humans and animals were used in this study Research Involving Plants The plant species used in this study are not en-dangered. Disclosure Statement The authors declare no conflict of interest, financial or otherwise. Funding This project was funded by Haramaya University Research grant, under project code: HURG_2021_06_01_75 Acknowledgements The authors are grateful to Haramaya University Research Office for their financial support and Laboratory facility. Authors’ contribution Zekeria Yusuf: initiation and design of the study, Lab experiment, data analysis; Ibsa Aliyi: field data collection, and write up of the document; Yohannes Petros: Analysis and interpretation of data. 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Coffee Opportunities in Ethiopia. Federal Democratic Republic of Ethiopian Ministry of Trade (FDREMT), Addis Ababa, Ethiopia. Freitas, Z.M.T.S. de, F. J. de oliveira, S. P. de Carvalho, V. F. dos Santos, J. P. Santos. 2007. de O. Avaliação de caracteres quantitativos relacionados com o crescimento vegetativo entre cultivares de café arábica de porte baixo. Bragantia, v.66, p.267-275. Gessese, M. K., B. Bellachew , M. Jarso. 2015. Multivariate Analysis of Phenotypic Diversity in the South Ethiopian Coffee ( Coffea arabica L.) for Quantitative Traits. Adv Crop Sci Tech S1: 003. doi:10.4172/2329-8863.S1-003. Gole, T.W., M. Denich, D. Teketay, and P. L. G. Vlek. 2002. Human impacts on the Coffea arabica genepool in Ethiopia and the need for its in situ conservation. In: Engels JMM, Rao VR, Brown AHD, Jackson MT (eds) Managing plant genetic diversity. CABI Publishing, Oxon, pp 237–247. Gray, Q., A.Tefera, T. Tefera. 2013. Ethiopia: Coffee annual report. GAlN Report No. ET- 1302, GAIN Report Assessment of Commodity and Trade by USDA, USA. Hammer, K., H. Knupffer, L. Xhuveli, P. Perrino. 1996. Estimating genetic erosion in landraces two case studies. Genetic Resources and Crop Evolution, 43: 329–36. Heal, G. (2000). Nature and the Marketplace: Capturing the Value of Ecosystem Services. New York: Island Press. ICO (International Coffee Orgaization), 2015. Coffee Production Data. www.ico.org. Accessed on August 8, 2015: International Coffee Organization. Iezzoni, A. F., M. P. Pritts. 1991. Applications of Principal Component Analysis to Horticultural Research. Hortscience, vol. 26(4), APRIL 1991. International Plant Genetic Resource Institute (IPGRI). 1996. Description for Coffea sp. and Psilanthus sp. International Plant Genetic Resource Institute , Rome, 1-36. Kebede, M., B. Bellachew. 2008. Phenotypic Diversity in the Hararghe Coffee ( Coffea arabica L.) Germplasm for Quantitative Traits. East African Journal of Sciences, Volume 2 (1) 13-18. Khoury, C. K., S. Brush, D. E. Costich, H. A. Curry et al. 2022. Crop genetic erosion: understanding and responding to loss of crop diversity. Tansley review. New Phytologist, 233: 84–118 doi: 10.1111/nph.17733. Krishnan, S. 2013. Current status of coffee genetic resources and implications for conservation. Article in CAB Reviews Perspectives in Agriculture Veterinary Science Nutrition and Natural Resources. Lashermes, P., M. C. Combes, J. Robert, P. Trouslot, A. D'Hont, F. Anthony, A. Charrier.1999. Molecular characterization and origin of the Coffea arabica L. genome. Mol Gen Genet. Mar; 261(2):259-66. doi: 10.1007/s004380050965. PMID: 10102360. Le Pierres, D. 1995. Etude des hybrides interspecifiques tetraplo€ıdes de premere generation entre Coffea arabica L. etles cafeiers diplo€ıdes. These Doctorale, Universite Paris XI, Orsay (France). Legesse, A. 2019. Assessment of Coffee ( Coffea Arabica L.) Genetic Erosion and Genetic Resources Management in Ethiopia. Int. J. Agr. Ext. 07 (03): 223-229. Loreau, M. and A. Hector. 2001. Partitioning Selection and Complementarity in Biodiversity Experiments. Nature 412: 72-76. Mathur P. N. 2011. Chapter 4: Assessing The Threat of Genetic Erosion. Collecting plant genetic diversity: technical guidelines—2011 update. Bioversity International Sub- Regional Office for South Asia. Mercer, K. L, H. R. Perales. 2010. Evolutionary response of landraces to climate change in centers of crop diversity. Evolutionary Applications 3: 480–493. Meyer, F. G. 1968. Further observation on the history and botany of the Arabica coffee plant, Coffea arabica L., in Ethiopia. FAO Mission to Ethiopia 1964-65, Rome. Pearl, H. M., C. Nagai, P. H. Moore, D. L Steiger, R. V. Osgood and R. Ming. 2004. Construction of a genetic map for Arabica coffee. Theor. Appl. Genet. 108: 829-835. Pp 131–142. Rao, T. J. 1968. On the allocation of sample size in stratified sampling. Pp 158-166. Ringnér, M. 2008. What is principal component analysis? Nat. Biotechnol. 26: 303–304. Sileshi, T., Z. Yusuf, M. Desta. 2023. Enzymatic Properties of red beet ( Beta vulgaris L.) Leaf, Root pulp and peel. Recent Patents on Biotechnology. 17(4): 395-404. Smale, M., J. Hartell, P.W. Heisey and B. Senauer. 1998. The Contribution of Genetic Resources and Diversity to Wheat Production in the Punjab of Pakistan. American Journal of Agricultural Economics 80: 482-493. Sorensen, T. 1948. A method of establishing groups of equal amplitude in plant sociology based on similarity of species content. Det Kong Danske Vidensk Selesk Biol Skr, 5(1):1– 34. Tadesse T., B. Tesfaye and G. Abera. 2020. Coffee production constraints and opportunities at major growing districts of southern Ethiopia. Cogent Food & Agriculture, 6: 1741982. Tesfa, M. 2019. Review on Post-Harvest Processing Operations Affecting Coffee ( Coffea Arabica L.) Quality in Ethiopia. Journal of Environment and Earth Science, Vol.9, No.12: 30-39. DOI: 10.7176/JEES/9-12-04. Tesfahunegn, G. B. 2014. “Response of yield and yield components of Teff ( Eragrostis tef (Zucc.) Trotter.) to tillage, nutrient, and weed management practices in Dura area, Northern Ethiopia.” International Scholarly Research Notices, vol. 2014. Article ID 439718, 9pages. VSN International. 2012. GenStat for Windows. Hemel Hempstead, UK. Yamane, T. 1967. Statistics, An Introductory Analysis, 2nd Ed., New York: Harper and Row. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4775854","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":338457567,"identity":"f8e9ca50-9a92-488a-9c40-472a747c1c8e","order_by":0,"name":"Ibsa Aliyi","email":"","orcid":"","institution":"Haramaya University","correspondingAuthor":false,"prefix":"","firstName":"Ibsa","middleName":"","lastName":"Aliyi","suffix":""},{"id":338457569,"identity":"ee8fc452-2bec-4cb9-ab9e-0e3709d9fa22","order_by":1,"name":"Zekeria Yusuf","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYFACNgjF3t7AwEyaFp4zB0jWciOBSC3y7ccSH/O21SX2SL4x/FxQYcPA396dgFcLY0/aYWPetsOJPdI5xtIzzqQxSJw5uwGvFmaG9DZp3rYDifulcwyAjMMMBhK5+LWw8T8HaQE57Izxb6K08EikHQNqYU7skeAxI84WCYlnyYZzzh027uFJK7PmOZPGQ9Av8v1phg/elNXJ9rAf3nybp8JGjr+9F78WEGDiAVMcBmCXElQOAow/wBT7A6JUj4JRMApGwcgDAHg3QY0I/TtzAAAAAElFTkSuQmCC","orcid":"","institution":"Haramaya University","correspondingAuthor":true,"prefix":"","firstName":"Zekeria","middleName":"","lastName":"Yusuf","suffix":""},{"id":338457570,"identity":"020f359b-f5e8-4619-8345-95ca14a882f6","order_by":2,"name":"Yohannes Petros","email":"","orcid":"","institution":"Haramaya University","correspondingAuthor":false,"prefix":"","firstName":"Yohannes","middleName":"","lastName":"Petros","suffix":""}],"badges":[],"createdAt":"2024-07-21 08:08:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4775854/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4775854/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11084-024-09672-3","type":"published","date":"2025-07-28T16:13:22+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":63457471,"identity":"d10970eb-8127-492d-86c1-0c4160e5f694","added_by":"auto","created_at":"2024-08-28 10:45:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":274886,"visible":true,"origin":"","legend":"\u003cp\u003eMap of Ethiopia showing the specific study sites: Lega Hama and Biyo Awale localities\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4775854/v1/b84d36271488705b49032e58.png"},{"id":63456976,"identity":"299ab02f-1238-43bb-83bc-f53d874a0ef8","added_by":"auto","created_at":"2024-08-28 10:37:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":264052,"visible":true,"origin":"","legend":"\u003cp\u003ePlot of the first two principal component axes for coffee genotypes across locations. Fd: Fandisha from Awale; Md: Mura from Awale; Sd: Shekhana from Awale; Kd: Khoriso from Awale; Dk: Danga from Laga Hama; Fk: Fandisha from Laga Hama; MK: Muyra from Laga Hama.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4775854/v1/0c116c2360a2811525e7fa54.png"},{"id":63456974,"identity":"8a67b894-402a-442d-b758-060f84301e77","added_by":"auto","created_at":"2024-08-28 10:37:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":79383,"visible":true,"origin":"","legend":"\u003cp\u003ePlot of the first two principal component axis for morphological descriptors. NT: number of trunks per tree; SB: number of secondary branches per trunk; NPB: number of nodes per branch; NPT: number of nodes per trunk; IL: total internode length/tree; NBB: number of berries per branch; SD: stem diameter (mm); HBW: 100 bean weight.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4775854/v1/21a2fe57d4571a5f48d61931.png"},{"id":88268385,"identity":"8f8cd497-4093-42e0-9d40-d71c3cb83d4d","added_by":"auto","created_at":"2025-08-04 16:51:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1682134,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4775854/v1/42fe8662-a655-4cb0-b489-905f82511877.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"On farm Diversity, Farmers’ Practices and Status of Genetic Erosion of Harar Coffee (Coffea arabica L.)","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003e Coffee belongs to the family Rubiaceae and the genus \u003cem\u003eCoffea\u003c/em\u003e that comprises of nearly 124 species among which the two economically important species including Arabica coffee (\u003cem\u003eCoffea arabica\u003c/em\u003e L.) accounts for approximately 80% of commercial production while Robusta coffee (\u003cem\u003eCoffea canephora\u003c/em\u003e P.) account for nearly 20% commercial production) (Davis et al., 2006; Gray et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Higher beverage quality is associated with \u003cem\u003eC. arabica\u003c/em\u003e (Lashermes et al., 1999) which is a true allotetraploid (2n\u0026thinsp;=\u0026thinsp;44) with an allogamy index of about 10%, and originated from the interspecific hybridization of \u003cem\u003eC. canephora\u003c/em\u003e and \u003cem\u003eC. eugenioides\u003c/em\u003e (Freitas et al., 2007). The remaining \u003cem\u003eCoffea\u003c/em\u003e species are diploid (2n\u0026thinsp;=\u0026thinsp;22), and are usually self-sterile (Pearl et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). However, the coffee species share genome, making possible interspecific hybridizations and hybrid production either within \u003cem\u003eCoffea\u003c/em\u003e species (Le Pierr\u0026egrave;s, 1995), or between \u003cem\u003eCoffea\u003c/em\u003e and \u003cem\u003ePsilanthus\u003c/em\u003e species (Couturon et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEthiopia remains the biggest coffee maker in Africa and the world's fifth biggest coffee maker after Brazil, Vietnam, Colombia and Indonesia, creating around 4.2% of world generation of entire coffee (ICO, 2015). Ethiopia's coffee root potential is nearing termination. There has too been small consideration paid to the evaluation of the differing qualities and preservation of innate coffee seeds (Tesfa, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Ethiopia produces Arabica coffee, which is accepted to have begun basically from the woodlands of Western Ethiopia. The development and utilization of coffee has spread from the Horn of Africa to the rest of the world, particularly in the southwest of Ethiopia, where Arabica coffee begun and begun. Most of the coffee delivered in Ethiopia is conventional coffee, counting timberland coffee, semi-forest coffee, cultivate coffee, and ranch coffee. As a result, roughly 90% of the coffee in Ethiopia is delivered by smallholder coffee ranchers. In expansion, roughly 25% of the Ethiopian populace is straightforwardly or in a roundabout way subordinate on coffee generation, handling and promoting (Esayas, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). It developed in the Hararghe locale of eastern Ethiopia. Coffee development has been found in the locale as early as 850 Advertisement (Meyer, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1968\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHarar coffee is characterized by medium, yellow-green coffee beans with direct causticity and a interesting mocha flavor. Most ranchers develop Harar coffee on intensely overseen farmlands, and the normal family is assessed to claim less than 0.5 hectares of arrive (Daba et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In spite of the colossal money related potential of Harar coffee, its generation is constrained. The normal abdicate of the trim is moo basically due to moo precipitation, unsteady precipitation designs, need of progressed differences, need of agrarian advancement and predominance of bugs and maladies (Tesfahunegn, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Capacity of qualities in quality banks is a issue due to destitute behavior of coffee seeds. Hence, collection of present day assortments and characterization of coffee genotypes can lead to feasible coffee advancement (Krishnan, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAgriculturists, plant breeders, quality bank supervisors, and edit researchers draw on differing edit hereditary assets to enhance, back, and advantage society at huge (Smale, 2006). Biodiversity is an imperative component of environmental frameworks (Recuperate, 2000), and misfortune of it can have antagonistic impacts on the working of these frameworks, counting impedance of their capability to deliver (Loreau and Hector, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Genetic resources are characteristic resources that are renewable but moreover helpless to misfortunes due to characteristic or man-made mediations (Di Falco and Chavas, 2006). With the help of their long experience farmers used to make crop variety preferences based disposition paid for desirable traits. Varietal yield, income, environmental adaptability and stability, household resource endowments (particularly land holdings and livestock assets), years of farming experience, and contact with extension services are the major factors causing household heterogeneity of crop variety preferences (Asrat et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Therefore, the present study was undertaken to assess on farm phenotypic diversity, farmers\u0026rsquo; preference, production constraints and status of genetic erosion of Harar coffee (\u003cem\u003eCoffea arabica\u003c/em\u003e L.) in Lagahama and Awale localities in Kombolcha and Dire Dewa districts respectively, East Hararghe, Ethiopia.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Description of the Study Area\u003c/h2\u003e\n \u003cp\u003eThe experiment was conducted at Awale locality in Dire Dewa Adminstrative Region, and Legahama locality in Kombolcha district, East Haraghe, Ethiopia (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Sampling method and sample size determination\u003c/h2\u003e\n \u003cp\u003eThe coffee producing households were purposively selected to provide information about on farm diversity, status of genetic erosion and management of Harar coffee in the study area. The sample size was determined following the formula as described by Yamane (\u003cspan class=\"CitationRef\"\u003e1967\u003c/span\u003e):\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{n}=\\frac{\\text{N}}{1+\\text{N}{\\text{e}}^{2}}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e (Eq. 1)\u003c/p\u003e\n \u003cp\u003ewhere, n: sample size; N: the population size coffee growing farmers in the study area which as estimated to be 240 households (N\u0026thinsp;=\u0026thinsp;240) with 130 from Awale, and 110 from Lagahama; e: is the desired level of precision or marginal error which was set as 0.05 at 95% confidence level.\u003c/p\u003e\n \u003cp\u003eDuring this study, the target population was the number of households in each study site. The household sample (HS) was determined according to Rao (\u003cspan class=\"CitationRef\"\u003e1968\u003c/span\u003e) method as per the use of a head of household to be used as a respondent. Thus, the respondent household sample (HS) was determined as\u003c/p\u003e\n \u003cp\u003eHS= \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\left(\\text{n}\\right)\\left({\\text{H}\\text{H}}_{\\text{i}}\\right)}{{\\text{H}\\text{H}}_{\\text{t}}}\\)\u003c/span\u003e\u003c/span\u003e (Eq. 2)\u003c/p\u003e\n \u003cp\u003eWhere: HS: respondent household sample; n: the sample size of the household population; HHi: the number of coffee growing households in the study sites; HHt: the total number households across study sites.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Data Collection and Analysis\u003c/h2\u003e\n \u003cp\u003eData on morphological traits were collected and recorded from ten randomly selected coffee genotypes representing each study population. Fifteen morphological descriptors (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) with seven qualitative, and eight quantitative descriptors including PH: Plant height (m); IPB: insertion of primary branch; IF: Inflorescence position; SS: seed shape; PL: Petiole length (mm); NPF: number of petals per flower; LW: leaf width (mm); NT: number of trunks/tree; SB; number of secondary branches per trunk; NPB: number of nodes per primary branch; NPT: number of nodes per trunk; IL: internode length (mm); NBB: number of berries per primary branch; SD: stem internode length (mm); hundred bean weight were assessed. Morphological traits collected from September to October 2021, were based on descriptors for coffee in accordance with International Plant Genetic Resources Institute (IPGRI, 1996). \u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCoffee morphological descriptors used in the study\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS.N\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTraits\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCode\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnit of measurement and scale used\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlant habit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMeasuring the height of coffee trees using 10 metre\u003c/p\u003e\n \u003cp\u003eruler and scored as follows: 1\u0026thinsp;=\u0026thinsp;bush (\u0026lt;\u0026thinsp;5 mwithout\u003c/p\u003e\n \u003cp\u003edistinct trunk), 2\u0026thinsp;=\u0026thinsp;shrub or small tree (\u0026lt;\u0026thinsp;5\u003c/p\u003e\n \u003cp\u003em \u0026ndash; one or more trunks) and 3\u0026thinsp;=\u0026thinsp;tree (\u0026gt;\u0026thinsp;5 m- single\u003c/p\u003e\n \u003cp\u003etrunk)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlant height (m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVisual estimation: 1\u0026thinsp;=\u0026thinsp;very short, 3\u0026thinsp;=\u0026thinsp;short, 7\u0026thinsp;=\u0026thinsp;tall and 9\u0026thinsp;=\u0026thinsp;very tall\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStem diameter (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiameter of stem at five cm above ground measured using caliper\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeed shape\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRound, Obovate, Ovate, Elliptic, \u0026amp; Oblong\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of berries per node along primary branches\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe mean number of berries per node on the selected four primaries from five trees per plot.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInflorescence position\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIFP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026thinsp;=\u0026thinsp;axillary and 2\u0026thinsp;=\u0026thinsp;terminal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of petals / flowers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNPF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage of 10 flowers, randomly selected from different nodes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of trunks/tree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal number of trunk per tree\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of secondary branches/trunk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal number of secondary branches per tree\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of nodes/ primary branches\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal numbers of nodes count per branch\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of nodes/trunk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal numbers of nodes count per tree\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInternode length/trunk (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComputed per tree as (TH\u0026ndash;HFPB)/TNN-1, where TH\u0026thinsp;=\u0026thinsp;total plant height, HFPB\u0026thinsp;=\u0026thinsp;height up to the first primary branch, TNN\u0026thinsp;=\u0026thinsp;total number of main stem nodes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInsertion of primary branches\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservation is done on the main stem (1\u0026thinsp;=\u0026thinsp;drooping, 2\u0026thinsp;=\u0026thinsp;horizontal and 3\u0026thinsp;=\u0026thinsp;semi erect)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeaf width (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage of five mature (\u0026gt;\u0026thinsp;node 3 from the terminal bud) leaves, measured at the widest part\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100 bean weight (g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHBW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDetermined at (11% moisture) content as follows: (\u0026ldquo;Bean weight at 0% moisture content\u0026rdquo; x 100)/bean number x0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.1. Survey Data Collection\u003c/h2\u003e\n \u003cp\u003eThe survey was carried out based on the potential diversity indicators by Brush (1999) and structured interview checklists on the socio-economic situation of the farmers, their farming practices, cultural background and current agricultural trends. Additionally, farmers were asked about the number of distinct coffee landraces grown at the time of the survey and about their observation of losses of varieties in the recent past. All survey results were summarized per community; the mean value were calculated for the quantitative data, and in the case of qualitative multi-state questions, the percentage for each class was presented.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.2. Biodiversity Indices\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eSorensen\u0026ndash;Similarity index (SI)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe SI, independently developed by Sorensen (\u003cspan class=\"CitationRef\"\u003e1948\u003c/span\u003e) and Dice (\u003cspan class=\"CitationRef\"\u003e1945\u003c/span\u003e), is a statistic used for analyzing the similarity between two data samples. For any two finite sets, A \u0026amp; B, it is defined as follows\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{S}\\text{I}=\\frac{2{\\text{N}}_{11}}{{\\text{N}}_{10}+{\\text{N}}_{01}+{2\\text{N}}_{11}}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e (Eq. 3)\u003c/p\u003e\n \u003cp\u003eWhere N\u003csub\u003e10\u003c/sub\u003e : total number attributes where A is 1 \u0026amp; B is 0; N\u003csub\u003e01\u003c/sub\u003e: total number of attributes where A is 0 \u0026amp; B is 1; N11: total number of attributes where both elements A \u0026amp; B are 1.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFarmers\u0026rsquo; Varietal Preference\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ethe preference criteria with regard to coffee cultivars as perceived by the growers and the relative ranking of preference criteria were based on their importance perceived by the farmers in selection or adoption of a variety. Before starting the study, respondents (coffee growers) were asked about local names of the coffee varieties they grow and also state the important traits of their preference which they usually take into account in judging the variety whether to continue its cultivation or not. The information was collected by personal interview of 60 purposively selected coffee growers across locations. The respondent farmers were also asked to assign a score of 1 to 5 to each criterion according to its importance as they perceived. Based on these scores, weighted scores for each selected criteria were calculated out to represent their significance as per the following formula (Bhuyan et al., 2023)\u003c/p\u003e\n \u003cp\u003eW \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:=\\frac{\\text{m}}{\\text{a}\\text{b}}\\)\u003c/span\u003e\u003c/span\u003e (Eq. 4)\u003c/p\u003e\n \u003cp\u003eWhere, W\u0026thinsp;=\u0026thinsp;weighted score of the parameter; a\u0026thinsp;=\u0026thinsp;highest possible total score obtainable by the parameter; b\u0026thinsp;=\u0026thinsp;number of parameter; m\u0026thinsp;=\u0026thinsp;total of preference scores assigned to the parameter.\u003c/p\u003e\n \u003cp\u003eThe preferences of the farmers for varieties were measured using a preference index (PI). To calculate PI, the respondents were asked to score from 1\u0026ndash;5 for various quality parameters for each of coffee varieties. These scores of individual farmers were added together to arrive at the total score for a particular parameter of a variety (Bhuyan et al., 2023). The varieties were then ranked according to the preference index score as follows.\u003c/p\u003e\n \u003cp\u003ePI\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:=\\frac{\\text{P}1\\text{W}1+\\text{P}2\\text{W}2+\\dots\\:.\\text{P}\\text{i}\\text{W}\\text{i}}{\\text{N}\\text{R}}\\)\u003c/span\u003e\u003c/span\u003e (Eq. 5)\u003c/p\u003e\n \u003cp\u003eWhere, PI\u0026thinsp;=\u0026thinsp;varietal preference index; P1, P2\u0026hellip;..Pi\u0026thinsp;=\u0026thinsp;total of preference scores of the variety in respect of each individual parameter; 1, 2\u0026hellip;.i\u0026thinsp;=\u0026thinsp;Quality parameters i\u0026thinsp;=\u0026thinsp;8; W1, W2\u0026hellip;. Wi\u0026thinsp;=\u0026thinsp;weight of each individual parameter; NR\u0026thinsp;=\u0026thinsp;number of respondents.\u003c/p\u003e\n \u003cp\u003eThe preference criteria of coffee descriptors used in the present study were: 1) bean yield; 2) bean size; 3) disease resistance; 4) duration of maturity; 5) size or height of the coffee tree; 6) inflorescence duration per year; 7) suitability for intercropping system; and 8) cup quality.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.3. Multivariate Analysis\u003c/h2\u003e\n \u003cp\u003ePrincipal component analysis (PCA) was performed based on the mean using the standardized variables (Ringner, 2008). The biplots were plotted using GenStat 18th Edition (VSN International, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). Only the principal components with eigen values greater than one were considered in determining variability among the genotypes (Iezzoni and Pritts, \u003cspan class=\"CitationRef\"\u003e1991\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4. Genetic erosion\u003c/h2\u003e\n \u003cp\u003eGenetic erosion (GE) for coffee landraces was calculated per surveyed village using the formula GE = [(c-k)/N]*100 (Eq.\u0026nbsp;6)\u003c/p\u003e\n \u003cp\u003ewhere, c\u0026thinsp;=\u0026thinsp;number of cultivars cultivated by few households on small areas, k\u0026thinsp;=\u0026thinsp;number of newly introduced cultivars and N\u0026thinsp;=\u0026thinsp;total number of cultivars recorded in the village. Accordingly in the present study, the genetic erosion was determined by identifying the number of cultivars grown by few households on small areas (n\u0026thinsp;=\u0026thinsp;2 i.e. Danga and Muyira genotypes were rare), since no new cultivars (k) were introduced in the past 10 years a k\u0026thinsp;=\u0026thinsp;0 was used.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5. Data Analysis\u003c/h2\u003e\n \u003cp\u003eMorphological data were subjected to one-way analysis of variance (ANOVA) and multivariate analysis using the SAS software version 9.2. The least significant difference (LSD) was used to compare means among and within treatments.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003e\u003cstrong\u003e3.1. Grouping of Farmers\u0026rsquo; Variety Using Morphological Descriptors of coffee (\u003cem\u003eCoffea arabica\u003c/em\u003e L.)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotally seven cultivars of \u003cem\u003eC. arabica\u003c/em\u003e were recorded in the study area with four cultivars from Awale district, whereas three cultivars were identified in Legahama locality (Table 2). The seven cultivars belonged to five different farmers\u0026rsquo; varieties with two varieties common to the studied localities. With the exception of Shekhana in Awale which was found to be of medium height and Fandisha in Lagahama which was found to be short, all the genotypes were found to be tall (Table 2). Terminal floral position was observed for Fandisha, Khoriso, and Muyira genotypes. Horizontal insertion of primary branches was observed for Fandisha and Khoriso genotypes. Drooping insertion was observed for Muyira, and Danga genotypes. Semierect insertion of primary branch was observed only for Shekhana genotype. Elliptical seed shape was observed for Muyira, and Danga genotypes, whereas Round seed shape was observed for Khoriso genotype, and oblong seed shape was recorded for Shekhana, and Fandisha genotypes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. \u0026nbsp;Descriptors of coffee genotypes in the study area\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.842105263157894%\" valign=\"top\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003ePH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eIPB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003ePHB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eNPF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eLW(mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.842105263157894%\" valign=\"top\"\u003e\n \u003cp\u003eAwale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003eFandisha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eTall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eElliptical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003etree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.842105263157894%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003eMuyira\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eTall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eElliptical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003etree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.842105263157894%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003eShekhana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eOblong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eshrub\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.842105263157894%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003eKhoriso\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eTall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eRound\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003etree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.842105263157894%\" valign=\"top\"\u003e\n \u003cp\u003eLagahama\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003eDanga\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eTall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eElliptical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003etree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.842105263157894%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003eFandisha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eShort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eOblong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003ebush\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.842105263157894%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003eMuyira\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eTall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eElliptical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eshrub\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePH: Plant height; IPB: insertion of primary branch; IF: Inflorescence position; SS: seed shape; PHB: Plant habit; NPF: number of petals per flower; LW: leaf width; T: terminal; A: axial; H: horizontal; D: drooping; S: semierect.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe coffee genotypes were assessed based on eight quantitative morphological descriptors (Table 3). The highest number of trunks per tree, NT (80.40) was recorded \u0026nbsp; for Danga genotype, followed by Muyira genotype in Lagahama with NT of 77.20. However, the least \u0026nbsp; PB (33.4) was recorded for Fandisha genotype in Lagahama. Number of secondary branches per tree (SB) \u0026nbsp; was highest (192.60) for Fandisha in Awale, and the least SB ( 95.20) was recorded for Khoriso genotype. Number of nodes per branch (NPB) was found to be highest (20.80) for Danga, and the least (12.60) for Muyira genotype in Lagahama, and Fandisha \u0026nbsp; genotype in Awale. \u0026nbsp;Number nodes per trunk (NPT) was the highest (43.20) for Danga genotype and least (17.60) for Shekhana genotype. Internode length (IL) was the highest (10.00) for Danga, Muyira in Awale and Shekhana genotypes and least (5.00) for Fandisha in Awale. Number of berries per branch (NBB) was the highest (16.40) for Muyira in Awale, 16. 00 for Fandisha in Awale, and 15.60 for Shekhana genotypes, least (10.00) NBB was recorded for Danga, and \u0026nbsp;10.40 NBB for Muyira in Lagahama genotype. Muyira genotype in Awale hads the highest (43.10) stem diameter (SD) while Fandisha genotype in Lagahama hads the least SD of 28.58. The \u0026nbsp;highest hundred bean weight, HBW (96.40g) was recorded for Shekhana, In contrast, HBW was found to be the least (66.00g) for Danga and 66.60g for Khoriso genotype.\u003c/p\u003e\n\u003cp\u003eTable 3. Mean values for morphological traits of coffee genotypes\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"684\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.157894736842104%\" valign=\"top\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.257309941520468%\" valign=\"top\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003eNT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003eSB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003eNPB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.064327485380117%\" valign=\"top\"\u003e\n \u003cp\u003eNPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.771929824561404%\" valign=\"top\"\u003e\n \u003cp\u003eIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003eNBB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.23391812865497%\" valign=\"top\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.47953216374269%\" valign=\"top\"\u003e\n \u003cp\u003eHBW\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.157894736842104%\" valign=\"top\"\u003e\n \u003cp\u003eLagahama\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.257309941520468%\" valign=\"top\"\u003e\n \u003cp\u003eDanga\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e80.40a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e120.20b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e20.80a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.064327485380117%\" valign=\"top\"\u003e\n \u003cp\u003e43.20a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.771929824561404%\" valign=\"top\"\u003e\n \u003cp\u003e10.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e10.00c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.23391812865497%\" valign=\"top\"\u003e\n \u003cp\u003e42.78a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.47953216374269%\" valign=\"top\"\u003e\n \u003cp\u003e66.00d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.157894736842104%\" valign=\"top\"\u003e\n \u003cp\u003eLagahama\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.257309941520468%\" valign=\"top\"\u003e\n \u003cp\u003eMuyira\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e77.20a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e117.00bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e12.60c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.064327485380117%\" valign=\"top\"\u003e\n \u003cp\u003e40.40b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.771929824561404%\" valign=\"top\"\u003e\n \u003cp\u003e10.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e10.40c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.23391812865497%\" valign=\"top\"\u003e\n \u003cp\u003e42.66a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.47953216374269%\" valign=\"top\"\u003e\n \u003cp\u003e73.80c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.157894736842104%\" valign=\"top\"\u003e\n \u003cp\u003eAwale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.257309941520468%\" valign=\"top\"\u003e\n \u003cp\u003eMuyira\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e45.20c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e122.00b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e17.80b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.064327485380117%\" valign=\"top\"\u003e\n \u003cp\u003e25.00e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.771929824561404%\" valign=\"top\"\u003e\n \u003cp\u003e10.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e16.40a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.23391812865497%\" valign=\"top\"\u003e\n \u003cp\u003e43.10a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.47953216374269%\" valign=\"top\"\u003e\n \u003cp\u003e75.60c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.157894736842104%\" valign=\"top\"\u003e\n \u003cp\u003eAwale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.257309941520468%\" valign=\"top\"\u003e\n \u003cp\u003eFandisha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e64.20b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e192.60a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e12.60c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.064327485380117%\" valign=\"top\"\u003e\n \u003cp\u003e28.20d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.771929824561404%\" valign=\"top\"\u003e\n \u003cp\u003e5.00b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e16.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.23391812865497%\" valign=\"top\"\u003e\n \u003cp\u003e30.66cd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.47953216374269%\" valign=\"top\"\u003e\n \u003cp\u003e76.20c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.157894736842104%\" valign=\"top\"\u003e\n \u003cp\u003eLagahama\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.257309941520468%\" valign=\"top\"\u003e\n \u003cp\u003eFandisha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e33.40d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e119.40b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e17.40b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.064327485380117%\" valign=\"top\"\u003e\n \u003cp\u003e34.40c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.771929824561404%\" valign=\"top\"\u003e\n \u003cp\u003e5.80b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e13.40b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.23391812865497%\" valign=\"top\"\u003e\n \u003cp\u003e28.58d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.47953216374269%\" valign=\"top\"\u003e\n \u003cp\u003e86.60b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.157894736842104%\" valign=\"top\"\u003e\n \u003cp\u003eAwale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.257309941520468%\" valign=\"top\"\u003e\n \u003cp\u003eShekhana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e32.20d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e98.60cd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e18.00b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.064327485380117%\" valign=\"top\"\u003e\n \u003cp\u003e17.60f\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.771929824561404%\" valign=\"top\"\u003e\n \u003cp\u003e10.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e15.60a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.23391812865497%\" valign=\"top\"\u003e\n \u003cp\u003e32.36c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.47953216374269%\" valign=\"top\"\u003e\n \u003cp\u003e96.40a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.157894736842104%\" valign=\"top\"\u003e\n \u003cp\u003eAwale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.257309941520468%\" valign=\"top\"\u003e\n \u003cp\u003eKhoriso\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e42.40c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.403508771929825%\" valign=\"top\"\u003e\n \u003cp\u003e95.20d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e12.80c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.064327485380117%\" valign=\"top\"\u003e\n \u003cp\u003e33.20c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.771929824561404%\" valign=\"top\"\u003e\n \u003cp\u003e5.80b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e13.20b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.23391812865497%\" valign=\"top\"\u003e\n \u003cp\u003e38.32b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.47953216374269%\" valign=\"top\"\u003e\n \u003cp\u003e66.60d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMeans followed by the same letter within a column were not significantly different at 0.05 probability level. Small letters (a, b, c,\u0026hellip;..): significance within a column. NT: number of trunk/tree; SB; number of secondary branches per tree; NPB: number of nodes per branch; NPT: number of nodes per trunk; IL: stem internode length/trunk; NBB: number of berries per branch; SD: stem diameter (cm); HBW: 100 bean weight (g).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. Similarity and farmers\u0026rsquo; preference indices of coffee genotypes in the study areas\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Sorensen\u0026rsquo;s similarity index was found to be 57% between the two localities (Table 4). Even though the localities are geographically close, they exhibited variations in cultivar distribution.\u003c/p\u003e\n\u003cp\u003eTable 4. Sorensen\u0026rsquo;s similarity index for genotype distribution across localities\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003eFandisha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eMuyira\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003eShekhana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eKhoriso\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003eDanga\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003eSorenson\u0026rsquo;s similarity index between the two\u003c/p\u003e\n \u003cp\u003elocations (SI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003eAwale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e57%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003eLagahama\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(1): presence of genotype; (0): absence of genotype.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePer the preference of each respondent for important coffee traits the highest weight score (0.55) was recorded for inflorescence duration per year, followed by bean size (0.53), and duration of maturity (0.50) while the least weight score of 0.42 was recorded for disease resistance (Table 5).\u003c/p\u003e\n\u003cp\u003eTable 5. \u0026nbsp;Preference scores and weight of coffee traits as perceived by the respondents\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.185567010309279%\" valign=\"top\"\u003e\n \u003cp\u003eS.N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003ePreference traits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003eHighest score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" valign=\"top\"\u003e\n \u003cp\u003eTotal score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003eWeight\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.185567010309279%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003eBean yield\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e1080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.185567010309279%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003eBean weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e1260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.185567010309279%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003eDisease resistance\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.185567010309279%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003eDuration of maturity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e1200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.185567010309279%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003eHeight of the coffee tree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e1080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.185567010309279%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003eInflorescence duration per year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e1320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.185567010309279%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003eSuitability for intercropping system \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.185567010309279%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003eCup quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" valign=\"top\"\u003e\n \u003cp\u003e660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe preference index (PI) with rank was determined based on \u0026nbsp;respondents\u0026rsquo; \u0026nbsp;preferences \u0026nbsp; among eight traits including bean yield, hundred bean weight, disease resistance, duration of maturity, height of the coffee tree, inflorescence duration per year, suitability for intercropping system, and cup quality (Table 6). Accordingly, the highest PI (25.60) was recorded for Fandisha genotype, followed by Muyira genotype with PI of 22.50, Khoriso with PI of 21.03, Shekhana with PI of 19.53, and Danga with the least PI of 11.28.\u003c/p\u003e\n\u003cp\u003eTable 6. Total preference scores, preference index with rank of preference of respondents\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.714285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.673469387755105%\" valign=\"top\"\u003e\n \u003cp\u003ePreference index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.612244897959183%\" valign=\"top\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.714285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eFandisha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.673469387755105%\" valign=\"top\"\u003e\n \u003cp\u003e25.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.612244897959183%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.714285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eMuyira\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.673469387755105%\" valign=\"top\"\u003e\n \u003cp\u003e22.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.612244897959183%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.714285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eShekhana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.673469387755105%\" valign=\"top\"\u003e\n \u003cp\u003e19.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.612244897959183%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.714285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eKhoriso\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.673469387755105%\" valign=\"top\"\u003e\n \u003cp\u003e21.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.612244897959183%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.714285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eDanga\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.673469387755105%\" valign=\"top\"\u003e\n \u003cp\u003e11.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.612244897959183%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. Multivariate analysis of coffee genotypes based on morphological descriptors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Eigen values greater than one account for most of the variances in PC analysis (Ringner, 2008; Abdi and Williams, 2010; Sileshi et al., 2023). Accordingly, Eigen values for the first three principal components (PC1-3) accounting for 99. 6% of the variances among genotypes and 98% of the variances for morphological traits (Table 7; Fig 2 \u0026amp; 3) were considered for the interpretation of the association of genotypes with their respective traits. High positive or negative scores are deemed to be the most discriminating \u0026nbsp;while scores close to the origin (zero) are not discriminating. Genotypes with high scores in the same quadrant (vector angle \u0026lt;90\u003csup\u003e0\u003c/sup\u003e) have correlated effect meaning that they exhibit similar performance in the studied traits. In the present study, PC1 hads high positive component scores from number of secondary branch per tree, SB (0.94) and number of trunks per tree, NT (0.30); the corresponding genotypes with high positive scores were Fandisha from Awale (0.58), followed by Muyira (0.36) from Lagahama, Fandisha (0.37) and Danga (0.35) from Lagamhama locality indicating that such genotypes can be discriminated by their high primary and secondary branches.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 7. Principal component analysis (PCA) of coffee genotypes based on eight quantitative morphological descriptors\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.484848484848484%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003ePCA \u0026nbsp;for coffee genotypes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.515151515151516%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003ePCA for coffee descriptors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003ePC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003ePC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003ePC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003ePC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003ePC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003ePC3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eEigen value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e9969.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e350.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e188.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eEigen value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e1063.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e416.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e55.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eDifference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e9618.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e161.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e160.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eDifference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e646.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e360.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e41.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eProportion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eProportion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eCumulative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eCumulative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eFd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e-0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eNT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eMd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eSB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e-0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e-0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eSd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eNPB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eKd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eNPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e-0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eDk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e-0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eFk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eNBB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eMk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e-0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;HBW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e-0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePCA: Principal component analysis; PC: Principal component; Fd: Fandisha from Awale; Md: Mura from Awale; Sd: Shekhana from Awale; Kd: Khoriso from Awale; Dk: Danga from Laga Hama; Fk: Fandisha from Laga Hama; MK: Muyra from Laga Hama; NT: number of trunks/tree; SB: number of secondary branches per tree; NPB: number of nodes per branch; NPT: number of nodes per tree; IL: stem internode length/tree; NBB: number of berries per branch; SD: stem diameter (cm); HBW: 100 bean weight.\u003c/p\u003e\n\u003cp\u003ePC2 had high positive component loads for number of trunks NT (0.76), \u0026nbsp; number of nodes per tree NPT (0.33), and stem diameter SD (0.25) while the corresponding genotypes with high positive scores were Shekhana (0.65) followed by Fandisha \u0026nbsp;(0.37) from Lagahama. On the contrary, PC2 had high negative loading for hundred bean weight HBW (-0.42), and number of secondary branches per tree SB (-0.24); \u0026nbsp;and the corresponding genotypes were Muyira from Lagahama (-0.35), and Danga (-0.52) indicating that such genotypes were among the least performing with respect to HBW and SB. \u0026nbsp;PC3 had high positive scores for HBW (0.77), NT (0.49) and IL (0.22); the corresponding genotypes were Muyira (0.41), Danga (0.28), Khoriso (0.25), Shekhana (0.26) from Lagahama indicating that such genotypes produce high bean weight. By contrast, PC3 had high negative loading for NPT (-0.25); and the corresponding genotypes were \u0026nbsp;Fandisha (-0.75) from Awale indicating that Fandisha from Awale was among those genotypes with the least performance in number of nodes per tree (NPT).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4. The status of genetic erosion of Harar Coffee in the study area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere were five total cultivars found in the study areas. Thus, genetic erosion of Harar coffee cultivar in the study area was found to be 40% which calls for implementation of conservation strategies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5. Trends in coffee cultivation and production constraints\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMost of the farmers (63.33%) suggested that they used to cultivate one coffee variety (Table 8). However, 36.67% of the farmers cultivate more than one variety. The \u0026nbsp;cultivation of a variety could be due to the reduction of coffee cultivation as farmers prefer to plant alternative cash crops like Khat. The majority of the farmers (73.33%) indicated familiarity with \u0026lt;3 varieties of coffee. \u0026nbsp;About 85% of the respondents suggested observation of variety losses mainly due to replacement with other commercial crops like khat and vegetables, change in climatic conditions, Land scarecity, and shortage of supply of improved varieties. Almost all farmers (96.67%) used seedling raised from seeds harvested from their own farms. \u0026nbsp;The coffee production constraints as suggested by the respondents were lack of improved variety (46.67%), replacement by other commercial crops like khat (40%), climatic conditions (10%), and land scarcity (3.33%) but, the majority of the farmers (90%) had intention to cultivate coffee. However, 86.67% of the farmers suggested decreasing trend in coffee production; and decreasing number of planted coffee varieties (86.67%). Even though the market price is increasing as suggested by all (100%) farmers, there were no support (92%) given to farmers by government and research institutions / nongovernmental organizations (NGOs).\u003c/p\u003e\n\u003cp\u003eTable 8. Trends in coffee cultivation, and production constraints\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\" valign=\"top\"\u003e\n \u003cp\u003eItems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.612244897959183%\" valign=\"top\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\" valign=\"top\"\u003e\n \u003cp\u003eRespondent score (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003eChi Sq (x2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"25\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.Amount of varieties planted by a \u0026nbsp;farmer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.612244897959183%\" valign=\"top\"\u003e\n \u003cp\u003eOne\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\" valign=\"top\"\u003e\n \u003cp\u003e95 (63.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"26\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.25352112676056%\" valign=\"top\"\u003e\n \u003cp\u003eMore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.169014084507044%\" valign=\"top\"\u003e\n \u003cp\u003e55 (36.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.084507042253522%\" valign=\"top\"\u003e\n \u003cp\u003e10.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.492957746478874%\" valign=\"top\"\u003e\n \u003cp\u003eP\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"22\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2. Number of varieties known by farmer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.612244897959183%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\" valign=\"top\"\u003e\n \u003cp\u003e110 (73.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"25\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.25352112676056%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.169014084507044%\" valign=\"top\"\u003e\n \u003cp\u003e40 (26.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.084507042253522%\" valign=\"top\"\u003e\n \u003cp\u003e32.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.492957746478874%\" valign=\"top\"\u003e\n \u003cp\u003eP\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"22\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e3. Observation of variety losses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.612244897959183%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\" valign=\"top\"\u003e\n \u003cp\u003e128 (85.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"24\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.25352112676056%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.169014084507044%\" valign=\"top\"\u003e\n \u003cp\u003e22 (14.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.084507042253522%\" valign=\"top\"\u003e\n \u003cp\u003e74.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.492957746478874%\" valign=\"top\"\u003e\n \u003cp\u003eP\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"22\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e4. Source of planting material\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.612244897959183%\" valign=\"top\"\u003e\n \u003cp\u003efarmers own\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\" valign=\"top\"\u003e\n \u003cp\u003e145 (96.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"22\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.25352112676056%\" valign=\"top\"\u003e\n \u003cp\u003eResearch institutions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.169014084507044%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.084507042253522%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.492957746478874%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"26\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.25352112676056%\" valign=\"top\"\u003e\n \u003cp\u003eNongovernmental organizations (NGOs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.169014084507044%\" valign=\"top\"\u003e\n \u003cp\u003e5 (3.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.084507042253522%\" valign=\"top\"\u003e\n \u003cp\u003e273.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.492957746478874%\" valign=\"top\"\u003e\n \u003cp\u003eP\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"22\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e5. Constraints in production of coffee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.612244897959183%\" valign=\"top\"\u003e\n \u003cp\u003eLand scarcity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\" valign=\"top\"\u003e\n \u003cp\u003e5 (3.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"24\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.25352112676056%\" valign=\"top\"\u003e\n \u003cp\u003eclimate change (erratic rain)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.169014084507044%\" valign=\"top\"\u003e\n \u003cp\u003e15 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.084507042253522%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.492957746478874%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.25352112676056%\" valign=\"top\"\u003e\n \u003cp\u003ereplacement by commercial crop (khat)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.169014084507044%\" valign=\"top\"\u003e\n \u003cp\u003e60 (40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.084507042253522%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.492957746478874%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"44\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.25352112676056%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003elack of improved\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.169014084507044%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e70 (46.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.084507042253522%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e83.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.492957746478874%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eP\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"34\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"34\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e6. Farmer\u0026apos;s intention to continue coffee growing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.612244897959183%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\" valign=\"top\"\u003e\n \u003cp\u003e135 (90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"28\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.25352112676056%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.169014084507044%\" valign=\"top\"\u003e\n \u003cp\u003e15 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.084507042253522%\" valign=\"top\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.492957746478874%\" valign=\"top\"\u003e\n \u003cp\u003eP\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"22\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e7. Trend in coffee cultivation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.612244897959183%\" valign=\"top\"\u003e\n \u003cp\u003eIncrease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\" valign=\"top\"\u003e\n \u003cp\u003e8 (5.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"22\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.25352112676056%\" valign=\"top\"\u003e\n \u003cp\u003eDecrease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.169014084507044%\" valign=\"top\"\u003e\n \u003cp\u003e130 (86.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.084507042253522%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.492957746478874%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"22\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.25352112676056%\" valign=\"top\"\u003e\n \u003cp\u003eStable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.169014084507044%\" valign=\"top\"\u003e\n \u003cp\u003e12 (8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.084507042253522%\" valign=\"top\"\u003e\n \u003cp\u003e194.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.492957746478874%\" valign=\"top\"\u003e\n \u003cp\u003eP\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"22\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e8.Trend in number of planted coffee varieties\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.612244897959183%\" valign=\"top\"\u003e\n \u003cp\u003eIncreasing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\" valign=\"top\"\u003e\n \u003cp\u003e10 (6.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"22\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.25352112676056%\" valign=\"top\"\u003e\n \u003cp\u003eDecreasing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.169014084507044%\" valign=\"top\"\u003e\n \u003cp\u003e130 (86.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.084507042253522%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.492957746478874%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"22\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.25352112676056%\" valign=\"top\"\u003e\n \u003cp\u003eno change\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.169014084507044%\" valign=\"top\"\u003e\n \u003cp\u003e10 (6.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.084507042253522%\" valign=\"top\"\u003e\n \u003cp\u003e193.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.492957746478874%\" valign=\"top\"\u003e\n \u003cp\u003eP\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"22\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e9. Trend in market price\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.612244897959183%\" valign=\"top\"\u003e\n \u003cp\u003eIncreasing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\" valign=\"top\"\u003e\n \u003cp\u003e150 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"22\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.25352112676056%\" valign=\"top\"\u003e\n \u003cp\u003eDecreasing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.169014084507044%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.084507042253522%\" valign=\"top\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.492957746478874%\" valign=\"top\"\u003e\n \u003cp\u003eP\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"22\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e10. Support given for coffee growers by government, research institutions/NGOs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.612244897959183%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\" valign=\"top\"\u003e\n \u003cp\u003e12 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"25\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.25352112676056%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.169014084507044%\" valign=\"top\"\u003e\n \u003cp\u003e138 (92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.084507042253522%\" valign=\"top\"\u003e\n \u003cp\u003e105.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.492957746478874%\" valign=\"top\"\u003e\n \u003cp\u003eP\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"22\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe present study identified five different Harar coffee farmers’ varieties with some varieties occurring in more than one localities. It was found that Fandisha genotype had tall plant height architechture in Awale but Fandisha short architechture in Lagahama. Such variation in morphological traits of same cultivar is inevitably due to environmental effect. There is no doubt about the identity of the cultivar since farmers had long experience of the cultivar. That notwithstanding, molecular fingerprinting of such genotypes may be the sure bet to confirm the identity so as to justify the observations better.\u003c/p\u003e \u003cp\u003e The present study had revealed that the farmers consider a number of preference criteria in selecting a variety. The fact that high weight score was recorded for inflorescence duration per year, bean size, and duration of maturity while the least weight score for disease resistance indicates that farmers experience in crop cultivation and the indigenous knowledge can have significant contribution for crop breeding, and conservation of crop genetic resources. Productivity of a variety alone is not all in all for the growers. Farmers’ adoption of a particular variety does not only depend on yield but also depends on other attributes of a variety such as bean size, duration of maturity, inflorescence duration per year are preferred by the farmers. However, a single variety cannot hold all the desired attributes like bean yield, duration of maturity, cup quality, and stress resistance as need varies depending on factors relating to various situations of the farmers, and end users. Hence, variety development program should ensure participation of the end users to make the program user accountable. Participatory varietal improvement and development programs will be an effective approach to address the grower’s specific problems and develop a variety desired by the users. The results of the study can guide breeders to develop more resource efficient and farmer-oriented coffee breeding programs.\u003c/p\u003e \u003cp\u003eThe principal component (PC) analysis revealed differential contribution of morphological traits for the variations among the coffee genotypes. A yield trait namely hundred bean weight (HBW) was found to have direct relationship with only few traits such as number of primary branches per tree (PB) and stem internode length (IL) per tree indicating the need for further investigations to explore traits having high additive effect with coffee bean yield traits. The contribution of morphological traits to the variation in Harar coffee accessions was also observed in other similar studies (Kebede and Belachew, 2008; Adem et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and also among \u003cem\u003eC. arabica\u003c/em\u003e accessions from South West Ethiopia (Gessese et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Beksisa et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the present study, the low genetic erosion of Harar coffee cultivars in the study area might be due to little supply of improved varieties to replace the landraces, and farmers cultivating coffee on small plots of land that is not usually adequate for cultivation of other crops. Additionally the significant genetic erosion may be attributed to replacement of coffee cultivation with a commercial crop like khat, recurrent shortage of rainfall, and land degradation and scarcity. There is therefore, the need to determine the status of cultivation of coffee, and farmers’ mediated \u003cem\u003ein situ\u003c/em\u003e conservation of Harar coffee supported by policy directives and farmers’ initiatives. Similar finding was reported by Mercer \u0026amp; Perales (2010) who reported that significant losses in diversity can be difficult to distinguish from ‘normal’ levels of change in response to farmer, market or environmental drivers. Mathur (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) suggested that genetic erosion can also be caused by environmental degradation, urbanization and land clearing through deforestation and bush fires, improper management and inadequate regeneration procedures in germplasm collections and gene banks. On farm variety assessment not only indicates the diversity that has been lost, but also what has replaced it (Khoury et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe present study has identified the major coffee production constraints in the study area as replacement with commercial crop like khat, limited supply of improved coffee varieties, climatic conditions including biotic and abiotic factors, land scarcity, and little support to coffee growers. Similar study was also conducted by Tadesse et al (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) who suggested biotic coffee production constraints including diseases, insect pests, weed species and vertebrate animals; abiotic factors like recurrent drought, frost, fluctuating rainfall pattern, high humidity, high temperature, low moisture, hail, storm, wind and reduced soil fertility are affecting coffee production that could cause as much as 70% yield loss in Ethiopia.\u003c/p\u003e \u003cp\u003e \u003cb\u003eImplications for coffee breeding and conservation of genetic resources\u003c/b\u003e \u003c/p\u003e \u003cp\u003eInformation on on-farm genetic diversity helps in designing breeding objectives based on preference traits, designing conservation strategies, and identifying production constraints. Multivariate analysis of morphological descriptors helps in identifying traits contributing for the variation and discrimination of genotypes. Farmers’ indigenous knowledge and practical experience with the crop genotypes helps in identifying and naming of varieties, contributing to crop genetic diversification, utilization of crop genetic resources, and remonstrating intervention strategies for conservation.\u003c/p\u003e \u003cp\u003eThe use of farmers’ indigenous knowledge as participatory variety selection has been used in plant breeding for decades. However, such technique is mainly limited to food crops. The present study has generated the role of participatory varietal preference approach, loss of genetic diversity and threats for coffee cultivation in the study area.\u003c/p\u003e "},{"header":"Conclusion","content":"\u003cp\u003e The preference index analysis revealed Fandisha, Muyira and Khoriso genotypes as the most preferred coffee cultivars in the study area. The principal component analysis (PCA) has identified traits contributing for the variation of genotypes and traits having direct association with bean yield traits. No much support and opportunities are being given for coffee cultivation in the study area despite the fact that farmers have good intention to grow coffee. The extent of genetic erosion or loss of genetic diversity was 40% that suggests the need for intervention actions for coffee breeding, policy strategies for coffee production and conservation of its genetic resources.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that all methods were carried out by relevant guidelines and regulations of Haramaya University Ethical Committee. The experimental protocols were approved by the Haramaya University Research Office. The collection of the plants used in the study complies with local or national guidelines with no need for further affirmation.\u0026nbsp;All the guidelines were followed as per the University research ethics for collection, characterization and documentation of coffee landraces or germplasm accessions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed Consent was obtained from all the participants involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study will be available on request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman and Animal Rights\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo humans and animals were used in this study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Involving Plants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe plant species used in this study are not en-dangered.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest, financial or otherwise.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was funded by Haramaya University Research grant, under project code: HURG_2021_06_01_75\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to Haramaya University Research Office for their financial support \u0026nbsp;and Laboratory facility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZekeria Yusuf: initiation and design of the study, Lab experiment, data analysis; Ibsa Aliyi: field data collection, and write up of the document; Yohannes Petros: Analysis \u0026nbsp; and interpretation of data. All authors contributed to drafting the article and revising it \u0026nbsp;critically for important intellectual content.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdi, H., and L. J. Williams. 2010. Principal component analysis. WIREs Comp. Stat.\u003cem\u003e \u003c/em\u003e2, 433\u0026ndash; 459.\u003c/li\u003e\n\u003cli\u003eAdem, A., H. Mohammed, A. Ayana. 2020. Phenotypic Diversity for Quantitative Characters in \u003cem\u003eArabica \u003c/em\u003eCoffee Landraces from Eastern Ethiopia. Asian Journal of Plant Science and Research, 10(5):51-57.\u003c/li\u003e\n\u003cli\u003eAsrat, S., M. Yesuf , F. Carlssonc, E. Wale. 2009. Farmers\u0026apos; Preferences for Crop Variety Traits: Lessons for On-Farm Conservation and Technology Adoption. Working Papers in Economics No 357. School of Business, Economics and Law, University of Gothenburg, Sweden. \u003c/li\u003e\n\u003cli\u003eBeksisa, L., T. Benti, G. Weldemichael. 2021. 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Avalia\u0026ccedil;\u0026atilde;o de caracteres quantitativos relacionados com o crescimento vegetativo entre cultivares de caf\u0026eacute; ar\u0026aacute;bica de porte baixo. Bragantia, v.66, p.267-275.\u003c/li\u003e\n\u003cli\u003eGessese, M. K., B. Bellachew , M. Jarso. 2015. Multivariate Analysis of Phenotypic Diversity in the South Ethiopian Coffee (\u003cem\u003eCoffea arabica\u003c/em\u003e L.) for Quantitative Traits. Adv Crop Sci Tech S1: 003. doi:10.4172/2329-8863.S1-003.\u003c/li\u003e\n\u003cli\u003eGole, T.W., M. Denich, D. Teketay, and P. L. G. Vlek. 2002. Human impacts on the \u003cem\u003eCoffea arabica\u003c/em\u003e genepool in Ethiopia and the need for its in situ conservation. In: Engels JMM, Rao VR, Brown AHD, Jackson MT (eds) Managing plant genetic diversity. CABI Publishing, Oxon, pp 237\u0026ndash;247.\u003c/li\u003e\n\u003cli\u003eGray, Q., A.Tefera, T. Tefera. 2013. Ethiopia: Coffee annual report. GAlN Report No. ET- 1302, GAIN Report Assessment of Commodity and Trade by USDA, USA.\u003c/li\u003e\n\u003cli\u003eHammer, K., H. Knupffer, L. Xhuveli, P. Perrino. 1996. Estimating genetic erosion in landraces two case studies. Genetic Resources and Crop Evolution, 43: 329\u0026ndash;36.\u003c/li\u003e\n\u003cli\u003eHeal, G. (2000). Nature and the Marketplace: Capturing the Value of Ecosystem Services. New York: Island Press.\u003c/li\u003e\n\u003cli\u003eICO (International Coffee Orgaization), 2015. Coffee Production Data. www.ico.org. Accessed on August 8, 2015: International Coffee Organization.\u003c/li\u003e\n\u003cli\u003eIezzoni, A. F., M. P. Pritts. 1991. Applications of Principal Component Analysis to Horticultural Research. Hortscience, vol. 26(4), APRIL 1991.\u003c/li\u003e\n\u003cli\u003eInternational Plant Genetic Resource Institute (IPGRI). 1996. 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Collecting plant genetic diversity: technical guidelines\u0026mdash;2011 update. Bioversity International Sub- Regional Office for South Asia.\u003c/li\u003e\n\u003cli\u003eMercer, K. L, H. R. Perales. 2010. Evolutionary response of landraces to climate change in centers of crop diversity. Evolutionary Applications 3: 480\u0026ndash;493.\u003c/li\u003e\n\u003cli\u003eMeyer, F. G. 1968. Further observation on the history and botany of the Arabica coffee plant, \u003cem\u003eCoffea arabica\u003c/em\u003e L., in Ethiopia. FAO Mission to Ethiopia 1964-65, Rome.\u003c/li\u003e\n\u003cli\u003ePearl, H. M., C. Nagai, P. H. Moore, D. L Steiger, R. V. Osgood and R. Ming. 2004. Construction of a genetic map for Arabica coffee. Theor. Appl. Genet. 108: 829-835. Pp 131\u0026ndash;142.\u003c/li\u003e\n\u003cli\u003eRao, T. J. 1968. On the allocation of sample size in stratified sampling. Pp 158-166.\u003c/li\u003e\n\u003cli\u003eRingn\u0026eacute;r, M. 2008. What is principal component analysis? 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Review on Post-Harvest Processing Operations Affecting Coffee (\u003cem\u003eCoffea Arabica\u003c/em\u003e L.) Quality in Ethiopia. Journal of Environment and Earth Science, Vol.9, No.12: 30-39. DOI: 10.7176/JEES/9-12-04.\u003c/li\u003e\n\u003cli\u003eTesfahunegn, G. B. 2014. \u0026ldquo;Response of yield and yield components of Teff (\u003cem\u003eEragrostis tef\u003c/em\u003e (Zucc.) Trotter.) to tillage, nutrient, and weed management practices in Dura area, Northern Ethiopia.\u0026rdquo; International Scholarly Research Notices, vol. 2014. Article ID 439718, 9pages. \u003c/li\u003e\n\u003cli\u003eVSN International. 2012. GenStat for Windows. Hemel Hempstead, UK.\u003c/li\u003e\n\u003cli\u003eYamane, T. 1967. Statistics, An Introductory Analysis, 2nd Ed., New York: Harper and Row. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-life","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Life](https://link.springer.com/journal/11084)","snPcode":"11084","submissionUrl":"https://submission.springernature.com/new-submission/11084/3","title":"Discover Life","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Genetic resources, Genotypes, Morphological descriptors, Preference index, Principal component analysis, Traits","lastPublishedDoi":"10.21203/rs.3.rs-4775854/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4775854/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFarmers\u0026rsquo; varieties are often well adapted to specific environments, and tend to have a advantage than in marginal areas. The present study was undertaken to assess farmers\u0026rsquo; practices, on farm phenotypic diversity and status of genetic erosion of Harar coffee (\u003cem\u003eCoffea arabica)\u003c/em\u003e Eastern Ethiopia. The principal component analysis was employed to identify morphological traits contributing to the variations in genotypes and associated traits. The result indicated that the highest preference index (25.60) with preference rank first was recorded for Fandisha genotype. The first principal component had high positive scores from number of secondary branches/ tree (0.94) and number of trunks/tree (0.30) as the most discriminating. The second component had high positive component loads for number of primary branches (0.76), number of nodes / trunk (0.33), and stem diameter (0.25) which were mainly responsible for the variations. Likewise, the third component had high positive scores for hundred bean weight (0.77), number of trunks/ tree (0.49) and stem internode length/tree (0.22). The result of the study indicated that secondary branches/ tree, trunks/ tree, nodes/ trunk, stem diameter, hundred bean weight and stem internode length/tree are the most discriminating traits among the set of coffee genotypes assessed.\u003c/p\u003e","manuscriptTitle":"On farm Diversity, Farmers’ Practices and Status of Genetic Erosion of Harar Coffee (Coffea arabica L.)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-28 10:37:13","doi":"10.21203/rs.3.rs-4775854/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-21T06:50:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-21T06:47:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-19T17:27:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-18T15:37:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"322691995537712179380317187342801062472","date":"2024-10-18T14:23:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"214920354305329999857462994204224103181","date":"2024-10-15T05:58:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"330115177946397217011901320829380308323","date":"2024-10-14T18:44:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-04T02:11:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"295717117644604596917287640674909570482","date":"2024-09-29T10:48:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"150495990487675967406327252803320743111","date":"2024-09-27T14:50:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"326115287364365059333472856596071445011","date":"2024-09-27T09:25:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-13T13:47:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-02T10:07:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-01T04:13:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Life","date":"2024-07-21T08:03:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-life","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Life](https://link.springer.com/journal/11084)","snPcode":"11084","submissionUrl":"https://submission.springernature.com/new-submission/11084/3","title":"Discover Life","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"667e621e-ba4e-4e59-9810-d4c5598d779a","owner":[],"postedDate":"August 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-04T16:43:39+00:00","versionOfRecord":{"articleIdentity":"rs-4775854","link":"https://doi.org/10.1007/s11084-024-09672-3","journal":{"identity":"discover-life","isVorOnly":false,"title":"Discover Life"},"publishedOn":"2025-07-28 16:13:22","publishedOnDateReadable":"July 28th, 2025"},"versionCreatedAt":"2024-08-28 10:37:13","video":"","vorDoi":"10.1007/s11084-024-09672-3","vorDoiUrl":"https://doi.org/10.1007/s11084-024-09672-3","workflowStages":[]},"version":"v1","identity":"rs-4775854","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4775854","identity":"rs-4775854","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}