Background

Linseed (Linum usitatissimum L.; 2n=30) is one of the most important oil crops in the highlands of Ethiopia. It is cultivated for seed production, which is used for extracting edible oil. Ethiopia is the seventh world linseed producing countries after Canada, Russia, China, Kazkhstan, United States and India (FAO, 2012). According to CSA (2014) data, it covered an area of 82, 325.78 ha of cultivated land, and produced 83, 130.5 tones with productivity of 1.0 t ha-1. It is commonly grown on marginal lands with less or no input (Teklewolde et al., 1992). Increasing seed yield of linseed is the main breeding objective along with others such as breeding for quality, disease resistance, frost resistance etc.

Genetic variation is a prerequisite for any crop breeding. Studies on genetic variation have been carried out by several researchers (Fu et al., 2002; Adugna and Labuschangne, 2004; Tadesse et al., 2009; Kandil et al., 2012). As seed yield is an intricate character, studying the association of characters is of paramount importance in order to improve the seed yield through indirect selection of yield related characters. Several researches have been executed on characters association (Kapoor and Chawla, 1983; Mirza et al., 1996; Adugna and Labuschangne, 2003; Saeid et al., 2003; Copur et al., 2006). This study was undertaken to determine the genetic variation among 202 linseed accessions and determine the association of characters.

1 Results and Discussion

1.1 Days to flowering

Days to flowering had variation ranged from 77.00 to 135.00 with a mean value of 99.65 and coefficient of variation 13% (Table 1). The frequency distribution ranged from 77 to 137 days (Figure 1). Early flowering accessions (77-80 days) were acce # 10 143, 125 162, 125 163, 235 165, 235 166, 241 828, 242 588, and 242 596, while accessions such as acce # 10 084, 10 086, 10 087, 13 712, 13 755, and 13 756 were late to flower, which had recorded 124-135 days to mature.

1.2 Days to maturity

This character varied between 170 and 180 days with a mean value of 175.5 and coefficient of variation 1% (Table 1). The frequency distribution showed that the accessions ranged between 170 and 182 days in maturity (Figure 2). Acce # 10 077, 10 078, 10 079, 10 084, 10 086, 10 087, 13 578, 13 598, 13 620, and 13 649 were the earliest to mature, which took 170-172 days. On the other hand, acce # 212 854, 212 857, 215 716, 10 103, 10 104, and 10 105 were late to mature with 180-182 days to maturity.

1.3 Plant height

Significant variability was observed for plant height, which is ranged from 17.7 cm to 97 cm with a mean value of 53.27 cm and coefficient of variation 22% (Table 1). Majority of the accessions were between 41.7 cm and 57.7 cm (Figure 3). Accessions such as acce # 234 009 (17.7 cm), 234 008 (19.4 cm), 234 010 (21.1 cm), and 234 005 (24.5) were the shortest in height, where as acce # 13 507 (84 cm), 207 786 (88.5) and 13 682 (97 cm) were the tallest accessions.

1.4 Number of primary branches

The magnitude of variation for this character was between 3 and 6.7 with a mean value of 4.25 and coefficient of variation 16% (Table 1). 86.1% of the accessions had 3-5 primary branches per plant (Figure 4).

1.5 Number of secondary branches

The variability of this character ranged from 3.4 to 9.5 with a mean value of 6.61 and coefficient of variation 18% (Table 1). 78.7% of the accessions had 5.4 -8.4 secondary branches per plant (Figure 5).

1.6 Number of capsules

A range of 14.0 to 38.8 was recorded for this character with a mean value of 23.12 and coefficient of variation 19% (Figure 6). 49% of the accessions recorded above the average (Figure 6). Eight accessions such as ace # 230 822, 10 070, 10 105, 10 086, 10 069, 10 079, 10 052, and 219 969 had number of capsule ranged from 32-41. Variation in number of capsules was also reported by Wakjira (2011).

1.7 Seed yield

Significant variation was observed for seed yield, which ranged from 104 kg/ha to 1 982 kg/ha with a mean value of 984.37 kg/ha and coefficient of variation 39% (Table 1). 48.5% of the accessions showed above the mean value (Figure 7). Accessions such as acce # 10 067, 10 061, 10 084, 10 069, 13 755, 10 070, 10 054, 10 068, 241 826, 10 064, and 10 150 were high yielding, which ranged between 1 592 kg/ha to 1 990 kg/ha. Similarly, Wakjira (2011) reported high variation in yield per plant. Significant variation in seed yield was also reported by Ottai et al. (2012).

1.8 Correlation analysis

Correlation coefficient analysis is presented (Table 2). This analysis depicted that seed yield had highly significant postive association with plant height (0.215 9), number of secondary branches (0.204 8), and number of capsule per plant (0.384 6), while it recorded significant negative relationship with days to maturity (-0.142 6) and number of primary branches (-0.298 2). Similar results were recorded on days to maturity, number of secondary branches, and number of capsule, but the opposite for number of primary branches (Savita, 2006). Number of capsule had also significant positive association with seed yield (Popescu et al., 1999; Belete and Wolde Yohanes, 2013). Days to maturity showed significant postive correlation with number of primary branches (0.142 9). Number of secondary branches recorded significant postive association with plant height (0.147 8), number of primary branches (0.203 7), and number of capsule per plant (0.515 3).

2 Materials and Methods

The present study was carried out at Holetta Agricultural Research Center in 2012 cropping season. The experiment was laid out using augmented design with no replication, and 202 accessions including checks were grown in two rows of 3 m length and 30 cm between rows. All the management practices were followed as per the recommendation. Ten plants from each accession were selected to record data on days to flowering, days to maturity, plant height (cm), number of primary branches, number of secondary branches and number of capsule per plant. Seed yield (kg/ha) was recorded on plot basis. Descriptive statistics and Pearson correlation coefficients were determined using AGROBASETM software (Agronomix Software Inc., Canada).

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