Plant height (Table 2) was recorded maximum in clone SI-10 (5.97 m) which was statistically at par with clones SO-1(5.80 m), HA-4 (5.62 m), BI-3 (5.53 m), SI-7 (5.52 m), SO-3 (5.50 m) and SI-15 (4.83 m). The maximum basal diameter was recorded in clone BI-3 (16.75 cm), which was statistically at par with clones SO-1 (16.20 cm), SI-7 (15.93 cm), SO-3 (15.13 cm), SI-15 (14.40 cm), SI-10 (14.23 cm), SI-16 (12.57 cm) and SO-7 (12.47 cm). The maximum value of internodal length was observed in clone SH-3 (4.72 cm), was statistically at par with clones CH-1 (4.39 cm), SH-7 (4.39 cm), BI-4 (4.31 cm), CH-6 (4.09 cm), MA-2 (3.61 cm), CH-3 (3.44 cm), HA-4 (3.44 cm) and SO-10 (3.28 cm). The maximum number of primary branches was recorded in clone SI-10 (5.97 m) which was statistically at par with clones HA-4 (7.67), SO-1 (7.67), SI-7 (7.33), SI-10 (7), SI-15 (7), SI-16 (7), SO-10 (7) and HA-2 (6.67), SO-7 (6.67). The above findings on variation of morphological parameters are in accordance with findings of Sankhyan et al. (2020) who concluded that various populations of Grewia optiva had the plant height in the range (8.57–3.73 mm), diameter (29.63-12.01cm) and the maximum number of primary branches in the range (6.67–2.67). Bhagta (2015) reported that in the seedling seed orchard maximum plant height, collar diameter and internodal length recorded were 54.49 cm, 0.52 cm and 4.15 respectively.
Clonal Identity
|
Plant height
(m)
|
Basal diameter
(cm)
|
Internodal distance (cm)
|
No. of primary branches
|
Table 2
Variation in Plant growth parameters among Grewia optiva clones
BI-3
|
5.53
|
16.75
|
4.13
|
8.33
|
BI-4
|
1.85
|
5.28
|
4.31
|
5.00
|
CH-1
|
3.13
|
7.02
|
4.39
|
4.67
|
CH-3
|
2.60
|
8.28
|
3.44
|
5.00
|
CH-6
|
3.92
|
11.62
|
4.09
|
5.33
|
HA-2
|
3.82
|
11.80
|
2.85
|
6.67
|
HA-3
|
2.92
|
10.28
|
3.11
|
6.00
|
HA-4
|
5.62
|
15.85
|
3.44
|
7.67
|
MA-2
|
2.78
|
8.80
|
3.61
|
4.33
|
SH-3
|
2.82
|
8.10
|
4.72
|
4.67
|
SH-7
|
2.88
|
7.43
|
4.39
|
6.00
|
SI-7
|
5.52
|
15.93
|
3.06
|
7.33
|
SI-10
|
5.97
|
14.23
|
2.83
|
7.00
|
SI-14
|
3.43
|
8.90
|
2.78
|
4.33
|
SI-15
|
4.83
|
14.40
|
2.61
|
7.00
|
SI-16
|
4.52
|
12.57
|
2.55
|
7.00
|
SO-1
|
5.80
|
16.20
|
2.67
|
7.67
|
SO-3
|
5.50
|
15.13
|
2.61
|
6.00
|
SO-4
|
4.35
|
10.83
|
2.56
|
5.67
|
SO-7
|
4.60
|
12.47
|
2.78
|
6.67
|
SO-10
|
4.10
|
10.77
|
3.28
|
7.00
|
Mean
|
4.12
|
11.55
|
3.34
|
6.16
|
C.D.(0.05)
|
1.25
|
4.83
|
1.44
|
1.97
|
C.V.
|
18.28
|
25.21
|
26.05
|
19.29
|
The maximum number of leaves per 30 cm on secondary branches were recorded for clones SI-10 (7.67 per 30 cm) which was statistically at par with clones SI-15 (7.22), HA-3 (6.78), CH-3 (6.67) and SI-14 (6.56) (Table 3). The maximum fresh weight of 100 leaves was recorded for the clone SI-7 (128.24g) which was statistically at par with clones HA-4 (122.15 g) and SH-7 (110.13 g) and the maximum value for dry weight of 100 leaves was recorded in the clone SI-7 (62.35g) which was statistically at par with clones SO-10 (61.94 g), HA-4 (60.03 g), SI-15 (55.23 g) and SH-7 (53.69 g). The leaf dry matter content was maximum (60.29%) in the clone SO-10 which was statistically at par with clones SO-4 (59.33%), SI-14 (57.61%), MA-2 (56.62%), HA-3 (55.27%), SI-15 (55.87%), SH-7 (53.96%) and SO-3 (52.67%). The maximum leaf length was recorded in clone HA-4 (17.38 cm) which was significantly at par with clones SO-1 (16.13 cm), SI-7 (16.10 cm) and SI-15 (15.70 cm). The maximum leaf width was recorded in the clone HA-4 (11.6 cm) which was significantly at par with clone SI-7 (10.81) and the maximum leaf area was recorded in the clone HA-4 (129.89 cm2), which was significantly at par with the clone SI-7 (128.93 cm2). Sankhyan et al. (2020) found that fresh weight as well as the maximum dry weight of 100 leaves of Grewia optiva leaves ranged between (23.20-20.35g) and (15.70-12.14g), respectively. In the seedling seed orchard of Grewia optiva the maximum leaf area was recorded in HA-2 (51.04 cm2) (Bhagta, 2016).
Clonal Identity
|
Number of leaves (per 30 cm)
|
Fresh weight of 100 Leaves
(g)
|
Dry weight of 100 leaves
(g)
|
Leaf dry matter content
(%)
|
Leaf length
(cm)
|
Leaf width
(cm)
|
Leaf area
(cm2)
|
Table 3
Leaf parameters of Grewia optiva Drummond clones in clonal seed orchard
BI-3
|
5.89
|
80.13
|
36.43
|
46.85
|
12.38
|
8.51
|
79.94
|
BI-4
|
5.67
|
69.82
|
33.75
|
42.02
|
13.13
|
8.20
|
80.37
|
CH-1
|
5.44
|
70.42
|
33.18
|
46.70
|
11.81
|
7.02
|
61.72
|
CH-3
|
6.67
|
94.01
|
43.37
|
46.82
|
12.94
|
7.98
|
79.53
|
CH-6
|
6.00
|
92.83
|
47.05
|
50.30
|
13.20
|
8.87
|
78.52
|
HA-2
|
7.11
|
97.99
|
44.60
|
46.42
|
13.41
|
8.73
|
80.18
|
HA-3
|
6.78
|
82.61
|
45.50
|
55.27
|
12.39
|
7.22
|
70.37
|
HA-4
|
5.78
|
122.15
|
60.03
|
49.81
|
17.38
|
11.60
|
129.89
|
MA-2
|
4.00
|
83.42
|
44.53
|
56.62
|
15.11
|
8.89
|
98.69
|
SH-3
|
5.67
|
99.93
|
44.38
|
44.19
|
13.83
|
8.64
|
85.78
|
SH-7
|
4.56
|
110.13
|
53.69
|
53.96
|
15.36
|
9.69
|
110.96
|
SI-7
|
5.67
|
128.24
|
62.35
|
49.04
|
16.10
|
10.81
|
128.93
|
SI-10
|
7.67
|
80.07
|
40.13
|
52.19
|
12.92
|
7.72
|
69.87
|
SI-14
|
6.56
|
95.28
|
50.90
|
57.61
|
12.19
|
8.06
|
71.50
|
SI-15
|
7.22
|
99.86
|
55.23
|
55.87
|
15.70
|
9.08
|
96.07
|
SI-16
|
5.33
|
84.95
|
42.21
|
52.21
|
13.87
|
8.67
|
90.03
|
SO-1
|
5.56
|
99.66
|
47.16
|
48.28
|
16.13
|
9.69
|
103.67
|
SO-3
|
6.11
|
91.57
|
47.00
|
52.67
|
13.54
|
8.42
|
83.90
|
SO-4
|
6.11
|
81.87
|
45.37
|
59.33
|
12.97
|
9.81
|
93.98
|
SO-7
|
6.11
|
108.52
|
50.33
|
47.51
|
15.23
|
9.69
|
103.72
|
SO-10
|
5.11
|
104.02
|
61.94
|
60.29
|
12.27
|
8.00
|
72.70
|
Mean
|
5.95
|
94.17
|
47.10
|
51.14
|
13.90
|
8.82
|
89.06
|
C.D.(0.05)
|
1.44
|
19.22
|
10.76
|
7.66
|
1.88
|
1.22
|
19.98
|
C.V.
|
14.58
|
12.32
|
13.79
|
9.04
|
8.18
|
8.31
|
13.54
|
The maximum fresh fruit weight for 100 fruits (Table 4) was recorded in the clone SI-7 (22.17g) which was significantly at par with clones HA-4 (21.75 g), SO-1 (21.44 g), HA-2 (21.40 g), CH-6 (21.36 g), SI-10 (21.31 g), SH-3 (21.05 g), SI-15 (20.82 g), CH-3 (20.52 g), SO-7 (20.48 g), SI-16 (20.41 g) and BI-3 (20.25 g). The maximum dry fruit weight was recorded in the clone CH-3 (15.56 g) which was statistically at par with clones CH-1 (15.42 g), CH-6 (14.49 g), BI-3 (14.48 g) and HA-3 (14.71 g). The maximum weight of 100 seeds was recorded in clone CH-1 (9.54 g), which was statistically at par with clone CH-3 (9.42 g). The maximum seed length was recorded for the clone BI-3 (6.53 mm) which was statistically at par with clones SH-7 (6.50 mm), SO-7 (6.44 mm), SH-3 (6.30 mm), SI-10 (6.29 mm), MA-2 (6.28 mm), HA-4 (6.26 mm), CH-1 (6.20 mm) and HA-3 (6.19 mm). Clone SH-3 recorded the maximum value for seed width (5.38 mm) which was statistically at par with clones SH-3 (5.38 mm), BI-3 (5.21 mm), SI-10 (5.12 mm), SI-15 (5.10 mm), HA-4 (5.09 mm) and CH-3 (5.08 mm). Maximum seed thickness was recorded in the clone MA-2 (5.04 mm) which was statistically at par with clones CH-3 (5.01 mm), SO-4 (4.91 mm), SO-7 (4.85 mm), HA-4 (4.79 mm), SH-7 (4.77 mm), BI-4 (4.73 mm), SI-14 (4.71 mm), SH-3 (4.70 mm), HA-3 (4.69 mm), CH-1 (4.69 mm), CH-6 (4.66 mm), SO-10 (4.59 mm) and HA-2 (4.48 mm).
Bhagta (2015) reported that in the seedling seed orchard of Grewia optiva, the variations among genotypes in the seedling seed orchard were in the range: 100 fresh fruit weight (17.96–28.48 g), 100 dry fruit weight (10.82–17.48 g), seed length (4.78–6.99 mm), seed width (3.69–5.82 mm), seed thickness (3.18–5.92 mm) and 100 seed weight (7.25–13.41 g). Similarly, Sankhyan et al. (2020) while studying populations of Grewia optiva observed that maximum seed length (6.26 mm), width (5.12 mm) and thickness (5.20 mm) were found in population site HM3, MN2, HM4, respectively.
Clonal
Identity
|
Fresh weight of 100 fruits
(g)
|
Dry weight of 100 fruits
(g)
|
100 seed weight
(g)
|
Seed length
(mm)
|
Seed width
(mm)
|
Seed thickness
(mm)
|
Table 4
Fruit and seed parameters among different clones of Grewia optiva Drummond
BI-3
|
20.25
|
14.48
|
8.90
|
6.53
|
5.21
|
5.01
|
BI-4
|
18.28
|
14.27
|
8.80
|
6.01
|
4.84
|
4.73
|
CH-1
|
18.47
|
15.42
|
9.54
|
6.20
|
4.99
|
4.69
|
CH-3
|
20.52
|
15.56
|
9.42
|
5.98
|
5.08
|
5.01
|
CH-6
|
21.36
|
14.49
|
8.87
|
6.18
|
4.98
|
4.66
|
HA-2
|
21.40
|
13.83
|
9.01
|
6.12
|
4.58
|
4.48
|
HA-3
|
19.94
|
14.71
|
9.06
|
6.19
|
4.98
|
4.69
|
HA-4
|
21.75
|
14.29
|
9.12
|
6.26
|
5.09
|
4.79
|
MA-2
|
19.15
|
13.95
|
9.07
|
6.28
|
4.95
|
5.04
|
SH-3
|
21.05
|
14.17
|
8.91
|
6.30
|
5.38
|
4.70
|
SH-7
|
19.93
|
13.68
|
8.62
|
6.50
|
4.93
|
4.77
|
SI-7
|
22.17
|
13.69
|
8.59
|
5.70
|
4.89
|
4.34
|
SI-10
|
21.31
|
13.71
|
9.01
|
6.29
|
5.12
|
4.40
|
SI-14
|
19.37
|
12.95
|
8.65
|
5.80
|
4.88
|
4.71
|
SI-15
|
20.82
|
13.26
|
8.81
|
6.09
|
5.10
|
4.24
|
SI-16
|
20.41
|
13.11
|
8.26
|
5.77
|
4.70
|
4.44
|
SO-1
|
21.44
|
13.98
|
8.89
|
5.86
|
4.72
|
4.05
|
SO-3
|
20.52
|
13.79
|
8.68
|
6.05
|
4.90
|
4.12
|
SO-4
|
19.86
|
12.99
|
8.76
|
6.16
|
4.97
|
4.91
|
SO-7
|
20.48
|
13.71
|
8.94
|
6.44
|
4.89
|
4.85
|
SO-10
|
19.81
|
13.29
|
8.36
|
5.84
|
4.93
|
4.59
|
Mean
|
20.39
|
13.97
|
8.87
|
6.12
|
4.96
|
4.63
|
C.D.(0.05)
|
2.13
|
1.13
|
0.39
|
0.34
|
0.37
|
0.56
|
C.V.
|
6.31
|
4.88
|
2.67
|
3.39
|
4.53
|
7.27
|
Ether Extract (Table 5) was recorded maximum for the clone SI-16 (5.8%) which was statistically at par with the clones SO-7 (5.76%), HA-3 (5.73%), SO-3 (5.72%), CH-6 (5.71%), CH-3 (5.69%), SI-15 (5.69%) and SO-1 (5.69%). For crude fibre it was found that the maximum value was recorded in the clone CH-1 (21.78%) which was statistically at par with clones HA-2 (21.77%), SO-4 (21.65%), HA-4 (21.64%), SI-16 (21.63%), SI-15 (21.61%), CH-3 (21.60%), SH-7 (21.49%), SI-7 (21.42%), SO-7 (21.40%), SO-10 (21.37%) and SI-14 (21.33%). The maximum value of crude protein was manifested in the clone HA-3 (23.51%) which was statistically at par with the clones SO-10 (23.01%) and CH-3 (22.30%). The maximum value of total ash content was recorded for the clone HA-2 (13.83%) which was statistically at par with the clones SO-3 (13.79%), SO-7 (13.71%), SO-10 (13.29%), SH-3 (13.05%), SI-14 (12.95%), BI-3 (12.84%) and HA-4 (12.62%). The maximum value of Nitrogen Free Extract was observed in clone MA-2 (42.04%) which was statistically at par with the clones SH-3 (41.84%), SI-14 (41.37%), BI-4 (41.23%), CH-6 (41.09%), SH-7 (40.96%) and SI-10 (40.92%).
Ether extract or the crude fat content of leaves is composed of waxes, fats, oils and pigments, lipids and fat soluble vitamins as A, D, E and K and fatty acid esters (Sharma et al. 2021). The main role of crude fat in leaves is determination of palatability plays role in providing sustenance energy to the animals, helps in milk production in lactating cows and is a source of energy for metabolism. Bhagta et al (2016) reported that ether extract among families in the seedling seed orchard ranged from 5.49–4.62 per cent. Ether extract increases as the leaves mature (Kant et al. 2017), (Singh and Todaria 2012). Crude fiber comprises of comparatively insoluble fractions of carbohydrates, cellulose and hemicellulose but they act as energy source to the ruminants. The main role of crude fiber is to help in fodder digestion and sustain ruminal function. Lactating cattles have more demand for fiber. As per the study conducted by the minimum percentage of crude fiber required in dairy cows is 17 per cent (Moran, 2005). Crude protein is one of preferred criteria for assessment of fodder quality (Saddul et al. 2005). It was found that values of crude protein ranged between (19.35 to 23.51%). The crude protein content is maximum in young leaves and decreases with maturity (Kamalak et al. 2005). Reason for this could be associated with translocation of nitrogen in leaves back into branches before leaf fall as well as dilution components are linked with development and maturity of leaves (Khosla et al. 1992). During dry season as a result of water stress, lignocellulosic substances form which dilute nitrogen in leaves (Anele et al. 2009). Total ash is basically the inorganic residue remaining after removal of organic matter from the leaves. It is the measure of minerals in the feed that provide energy for growth and biological processes in animals. Mature leaves have more ash content since they accumulate more minerals (Jadhav et al. 2020). Nitrogen free extract constitutes the soluble carbohydrates, sugars, starches which are used by animals to generate heat, energy and fats. Nitrogen free extract was found comparatively less in mature leaves (Hashmi and Waqar, 2014).
Clonal
Identity
|
Ether extract
(%)
|
Crude fibre (%)
|
Crude protein (%)
|
Total ash
(%)
|
Nitrogen free extract (%)
|
Table 5
Variation in fodder quality parameters among different clones
BI-3
|
5.49
(2.55)
|
20.48
(4.64)
|
21.81
(4.78)
|
12.84
(3.72)
|
39.38
(38.85)
|
BI-4
|
5.15
(2.48)
|
21.05
(4.70)
|
20.16
(4.60)
|
12.41
(3.66)
|
41.23
(39.93)
|
CH-1
|
5.60
(2.57)
|
21.78
(4.77)
|
21.22
(4.71)
|
11.96
(3.60)
|
39.44
(38.89)
|
CH-3
|
5.69
(2.59)
|
21.60
(4.75)
|
22.30
(4.83)
|
11.88
(3.59)
|
38.53
(38.35)
|
CH-6
|
5.71
(2.59)
|
18.73
(4.44)
|
21.98
(4.79)
|
12.49
(3.67)
|
41.09
(39.85)
|
HA-2
|
4.88
(2.43)
|
21.77
(4.77)
|
21.40
(4.73)
|
13.83
(3.85)
|
38.12
(38.10)
|
HA-3
|
5.73
(2.59)
|
19.43
(4.52)
|
23.51
(4.95)
|
11.78
(3.58)
|
39.55
(38.95)
|
HA-4
|
5.58
(2.56)
|
21.64
(4.76)
|
21.50
(4.74)
|
12.62
(3.69)
|
38.66
(38.43)
|
MA-2
|
5.59
(2.57)
|
20.75
(4.66)
|
19.78
(4.56)
|
11.84
(3.58)
|
42.04
(40.40)
|
SH-3
|
4.71
(2.39)
|
19.35
(4.51)
|
21.05
(4.70)
|
13.05
(3.75)
|
41.84
(40.29)
|
SH-7
|
5.25
(2.50)
|
21.49
(4.74)
|
19.93
(4.57)
|
12.38
(3.66)
|
40.96
(39.78)
|
SI-7
|
5.19
(2.49)
|
21.42
(4.74)
|
22.17
(4.81)
|
12.36
(3.66)
|
38.87
(38.55)
|
SI-10
|
5.42
(2.53)
|
20.43
(4.63)
|
21.31
(4.72)
|
11.92
(3.60)
|
40.92
(39.75)
|
SI-14
|
4.97
(2.44)
|
21.33
(4.73)
|
19.37
(4.51)
|
12.95
(3.74)
|
41.37
(40.02)
|
SI-15
|
5.69
(2.59)
|
21.61
(4.76)
|
20.82
(4.67)
|
12.37
(3.66)
|
39.52
(38.93)
|
SI-16
|
5.80
(2.61)
|
21.63
(4.76)
|
20.41
(4.63)
|
12.32
(3.65)
|
39.85
(39.13)
|
SO-1
|
5.69
(2.59)
|
21.24
(4.72)
|
21.44
(4.74)
|
11.76
(3.57)
|
39.86
(39.14)
|
SO-3
|
5.72
(2.59)
|
19.77
(4.56)
|
20.52
(4.64)
|
13.79
(3.85)
|
40.21
(39.34)
|
SO-4
|
5.24
(2.50)
|
21.65
(4.76)
|
20.37
(4.62)
|
12.99
(3.74)
|
39.75
(39.07)
|
SO-7
|
5.76
(2.60)
|
21.40
(4.73)
|
21.87
(4.78)
|
13.71
(3.84)
|
37.27
(37.61)
|
SO-10
|
4.84
(2.42)
|
21.37
(4.73)
|
23.01
(4.90)
|
13.29
(3.78)
|
37.50
(37.74)
|
Mean
|
5.41
(2.53)
|
20.95
(4.68)
|
21.23
(4.71)
|
12.60
(3.72)
|
39.81
(39.10)
|
C.D.(0.05)
|
0.02
|
0.05
|
0.14
|
0.11
|
0.96
|
C.V.
|
0.50
|
0.67
|
1.77
|
1.73
|
2.48
|
(*note: Figures In Parenthesis Are Values After Transformation Of Per Cent Data)
Genetic Parameters
The Genetic estimates (Table 6) shows that the parameter which recorded the highest heritability among morphological parameters was plant height (70.46%) followed by leaf area (66.90). Among fodder quality parameters highest heritability was shown by ether extract (96.85%) followed by crude fiber (90.32%). The maximum genetic advance among morphological characters was observed in leaf area (28.89%) and maximum genetic advance among fodder quality parameters was recorded in nitrogen free extract (1.99%). The maximum value of genetic gain was recorded in plant height (48.79%) among morphological parameters and ether extract (5.44%) among fodder parameters. The characters having high heritability as well as genetic gain were plant height, basal diameter and leaf area. Therefore, selection can be carried out directly on basis for these two characters viz., high heritability and genetic gain.
Sr.No.
|
Parameter
|
Heritability
(%)
|
Genetic advance
(%)
|
Genetic gain
(%)
|
Table 6
Genetic estimates of Grewia optiva Drummond clones in clonal seed orchard
1.
|
Plant height (m)
|
70.46
|
2.01
|
48.79
|
2.
|
Basal diameter (cm)
|
50.98
|
4.37
|
37.82
|
3.
|
Internodal Length (cm)
|
25.81
|
0.54
|
16.08
|
4.
|
No. of primary branches
|
40.74
|
1.29
|
21.03
|
5.
|
Leaf density (per 30 cm)
|
40.06
|
0.93
|
15.54
|
6.
|
Fresh weight of 100 leaves (g)
|
58.29
|
21.57
|
22.91
|
7.
|
Dry weight of 100 leaves (g)
|
55.92
|
11.27
|
23.93
|
8.
|
Leaf Dry matter content (%)
|
46.48
|
6.05
|
11.83
|
9.
|
Leaf length (cm)
|
61.13
|
2.30
|
16.52
|
10.
|
Leaf width (cm)
|
66.21
|
1.72
|
19.51
|
11.
|
Leaf area (cm)
|
66.90
|
28.89
|
32.44
|
12.
|
Fresh weight of 100 Fruits (g)
|
23.73
|
0.72
|
3.53
|
13.
|
Dry weight of 100 Fruits (g)
|
42.62
|
0.79
|
5.65
|
14.
|
100 seed weight (g)
|
56.02
|
0.41
|
4.64
|
15.
|
Seed Length (mm)
|
49.41
|
0.30
|
4.85
|
16.
|
Seed Width (mm)
|
22.28
|
0.12
|
2.35
|
17.
|
Seed Thickness (mm)
|
27.25
|
0.22
|
4.78
|
18.
|
Ether Extract
|
96.85
|
0.14
|
5.44
|
19.
|
Crude Fibre
|
90.32
|
0.19
|
4.04
|
20.
|
Crude Protein
|
59.62
|
0.16
|
3.43
|
21.
|
Total ash content
|
62.50
|
0.13
|
3.60
|
22.
|
NFE
|
61.05
|
1.99
|
4.99
|
Bhagta (2015) reported that in the seedling seed orchard of Grewia optiva, high heritability with moderate genetic gain was recorded for seed length, seed thickness, seed weight which reveals that traits were under genetic control and wide range (1.54–21.9) was observed for genetic gain for different characters. Kaushal (1978) also showed genetic variation in the proximate principles and mineral nutrients of Grewia optiva. Johnson et al. (1955) suggested that a high heritability estimated along with high genetic gain is usually more useful than the heritability values alone in predicting the resultant effect form selecting the best individual and therefore, a high heritability estimate does not necessarily mean an increased genetic advance. Verma (2014) conducted a study on Grewia optiva and reported that maximum heritability was observed in collar diameter and number of leaves, plant height had the maximum genetic advance (5.27) and genetic gain (32.67%). High heritability values for height (0.67–0.71) and diameter (0.50–0.59) with higher genetic gains were also obtained by Luna and Singh (2009) in Eucalyptus hybrids. Singh et al. 2012 while studying genetic estimates in Salix clones concluded that relatively high heritability and genetic gain for plant height and leaf area indicated that selection can be carried out directly on the phenotypic basis for these two characters.
Principal Component Analysis
Principal component analysis for morphometric traits revealed five components with Eigen value greater than one (Table 7). Principal component 1 had eigen value (λ1 = 6.424) and comprised 37.787 per cent variation. Basal diameter had the maximum loading value (0.786), followed by leaf width (0.775), fresh weight of 100 leaves (0.774), plant height (0.763), fresh weight of 100 fruits (0.753), dry weight of 100 leaves (0.752), leaf area (0.746), leaf length (0.733) and number of primary branches (0.716). Principal component 2 (λ2 = 2.963) recorded 17.4 per cent variations. Internodal length had the maximum loading value (0.635), followed by seed length (0.577) and dry weight of 100 fruits (0.501). Principal component 3 (λ3 = 2.438) comprised of 14.3 per cent variation and leaf density had the maximum loading value (0.693) followed by plant height (0.481) and weight of 100 seeds (0.480). In Principal component 4 (λ4 = 1.357) 8 per cent variation was recorded. Seed width had the maximum loading value (0.605), followed by leaf dry matter content (0.593), followed by seed length (0.497). Principal component 5 (λ5 = 1.042) comprised of 6.128 per cent variation and leaf density had the maximum loading value (0.445)
Table 7. Principal component analysis for morphometric traits among different clones
Parameters
|
Principal components
|
PC1
|
PC2
|
PC3
|
PC4
|
PC5
|
Plant height (m)
|
0.763
|
-0.124
|
0.481
|
0.186
|
-0.235
|
Basal diameter (cm)
|
0.786
|
-0.055
|
0.504
|
0.14
|
-0.199
|
Internodal length (cm)
|
-0.569
|
0.635
|
-0.12
|
-0.02
|
-0.112
|
No of primary branches
|
0.716
|
-0.002
|
0.408
|
0.102
|
-0.356
|
Leaf density (per 30 cm)
|
0.058
|
-0.355
|
0.693
|
0.1
|
0.445
|
Fresh weight of 100 leaves (g)
|
0.774
|
0.363
|
-0.212
|
-0.048
|
0.363
|
Dry weight of 100 leaves (g)
|
0.752
|
0.08
|
-0.381
|
0.171
|
0.41
|
Leaf dry matter content (%)
|
0.213
|
-0.48
|
-0.442
|
0.593
|
0.144
|
Leaf length (cm)
|
0.733
|
0.499
|
-0.158
|
-0.192
|
-0.031
|
Leaf width (cm)
|
0.775
|
0.488
|
-0.215
|
-0.045
|
-0.073
|
Leaf area (cm2)
|
0.746
|
0.53
|
-0.279
|
-0.116
|
-0.072
|
Fresh weight of 100 fruits (g)
|
0.753
|
0.186
|
0.414
|
-0.055
|
0.25
|
Dry weight of 100 fruits (g)
|
-0.539
|
0.501
|
0.424
|
-0.283
|
0.148
|
Weight of 100 seeds (g)
|
-0.473
|
0.46
|
0.480
|
-0.112
|
0.267
|
Seed length (mm)
|
-0.269
|
0.577
|
0.219
|
0.497
|
-0.272
|
Seed width (mm)
|
-0.222
|
0.476
|
0.252
|
0.605
|
0.217
|
Seed thickness (mm)
|
-0.525
|
0.471
|
-0.252
|
0.372
|
-0.013
|
Eigen values (λ)
|
6.424
|
2.963
|
2.438
|
1.357
|
1.042
|
Percentage of variance
|
37.787
|
17.43
|
14.339
|
7.981
|
6.128
|
Cumulative percentage of variance
|
37.787
|
55.216
|
69.556
|
77.536
|
83.665
|
The principal component analysis for fodder quality parameters (Table 8) showed that three components had Eigen value greater than one. Principal component 1 (λ1 = 1.754) exhibited 35.1 per cent variation and crude protein had the maximum loading value (0.671). Principal component 2 (λ2 = 1.488) comprised of 29.8 per cent variation in which ether extract had the maximum loading value (0.783) followed by crude protein (0.519). Principal component 3 (λ3 = 1.156) recorded 23.1 per cent variation and crude fibre had the maximum loading value (0.835). Therefore, among morphometric parameters, the basal diameter had the maximum loading value (0.786) and among fodder parameters crude protein, recorded the maximum loading value (0.671). These parameters could be considered for selection including other parameters in PCA 1.
Mehta et al. (2022) conducted a study to evaluate Grewia optiva clones for fodder yield and other related traits among nine different clones in lower Shivalik North Western Himalayas and Principal component analysis revealed total variability of 77.8% among genotypes contributed by PC-1 (57.3%) and PC-2 (20.5%). Similarly, Isik Tophu (2004) considered growth trait in black poplar as most important on the basis of principal analysis. Factor analysis was applied for 13 components and 71.46 per cent of total variance was explained in first five components while studying morphological characters of Populus deltoides hybrid clones in nursery by Ozel et al. (2010).
Table 8. Principal component analysis for fodder quality parameters among different clones
Parameters (%)
|
Principal components
|
PC1
|
PC2
|
PC3
|
Ether extract
|
0.034
|
0.783
|
0.318
|
Crude fiber
|
0.409
|
-0.338
|
0.835
|
Crude protein
|
0.671
|
0.519
|
-0.453
|
Total ash
|
0.387
|
-0.701
|
-0.378
|
Nitrogen free extract
|
-0.993
|
-0.035
|
-0.099
|
Eigen values (λ)
|
1.754
|
1.488
|
1.156
|
Percentage of variance
|
35.1
|
29.8
|
23.1
|
Cumulative percentage of variance
|
35.1
|
64.8
|
88.0
|
Cluster Analysis
Based on the evaluation for morphological parameters, the 21 clones of Grewia optiva Drummond were grouped into 3 clusters (Table 9). Cluster 1 recorded the highest mean for most of the morphological parameters (Table 10). Mean was maximum in cluster 1 for plant height, basal diameter, number of primary branches, weight of 100 fresh leaves, weight of 100 dry leaves, leaf length, leaf width, leaf area, weight of 100 fresh fruits and weight of 100 dry fruits. In cluster 2 the maximum mean was recorded for leaf dry matter content. Mean was maximum in cluster 3 for internodal length followed by leaf density, 100 seed weight, seed length, seed width and seed thickness.
PARAMETERS
|
Cluster No.
|
No. of clones
|
Clones
|
Table 9
Clustering for morphological and fodder parameters among different clones
MORPHOLOGICAL
|
1
|
4
|
HA-4, SH-7, SI-7, SO-7
|
|
2
|
12
|
CH-3, CH-6, HA-2, MA-2, SH-3, SI-14, SI-15, SI-16, SO-1, SO-3, SO-4, SO-10
|
3
|
5
|
BI-3, BI-4, CH-1, HA-3, SI-10
|
FODDER QUALITY
|
1
|
6
|
CH-3, HA-2, HA-4, SI-7, SO-7, SO-10
|
|
2
|
7
|
BI-4, CH-6, MA-2, SH-3, SH-7, SI-10, SI-14
|
|
3
|
8
|
BI-3, CH-1, HA-3, SI-15, SI-16, SO-1, SO-3, SO-4
|
Morphometric Parameters
|
Cluster 1
|
Cluster 2
|
Cluster 3
|
Table 10
Means of morphological and fodder parameters for different clones of Grewia optiva included in cluster
Plant Height (m)
|
4.66
|
4.04
|
3.88
|
Basal Diameter (cm)
|
12.92
|
11.45
|
10.71
|
Internodal Length (cm)
|
3.42
|
3.15
|
3.75
|
No of primary branches
|
6.92
|
5.89
|
6.20
|
Leaf Density (per 30 cm)
|
5.53
|
5.95
|
6.29
|
Fresh Weight of 100 leaves (g)
|
117.26
|
93.78
|
76.61
|
Dry Weight of 100 leaves (g)
|
56.60
|
47.81
|
37.80
|
Leaf Dry Matter Content (%)
|
50.08
|
52.55
|
48.61
|
Leaf Length (cm)
|
16.02
|
13.76
|
12.53
|
Leaf Width (cm)
|
10.45
|
8.74
|
7.73
|
Leaf Area (cm2)
|
118.38
|
86.21
|
72.45
|
Fresh weight of 100 Fruits (g)
|
21.08
|
20.48
|
19.65
|
Dry weight of 100 Fruits (g)
|
13.84
|
13.78
|
14.52
|
Weight of 100 seeds (g)
|
8.82
|
8.81
|
9.06
|
Seed length (mm)
|
6.23
|
6.04
|
6.24
|
Seed Width (mm)
|
4.95
|
4.93
|
5.03
|
Seed Thickness (mm)
|
4.69
|
4.58
|
4.70
|
Fodder Parameters
|
|
Ether extract (%)
|
2.52
|
2.50
|
2.57
|
Crude Fiber (%)
|
4.75
|
4.63
|
4.69
|
Crude protein (%)
|
4.80
|
4.64
|
4.72
|
Total ash (%)
|
3.74
|
3.67
|
3.67
|
Nitrogen free extract (%)
|
38.16
|
41.35
|
39.70
|
Based on cluster analysis for fodder parameters, 21 clones of Grewia optiva Drummond were grouped into 3 clusters (Table 9). Clones in cluster 1 recorded the maximum mean values for the two most important fodder quality parameters viz. crude fiber and crude protein as well as for total ash (Table 10).
Therefore, based on cluster analysis it can be concluded that clones classified for morphological parameters were HA-4, SH-7, SI-7 and SO-7 and the clones for fodder quality parameters were CH-3, HA-2, HA-4, SI-7, SO-7, SO-10. Mehta et al. (2022) based on their study concluded that the Cluster I contained the highest number of genotypes followed by Cluster II, III, IV and Cluster V. The highest inter cluster values were recorded between cluster II and IV exhibiting wide genetic diversity and crossing between genotypes of these clusters and Grewia optiva clones with (C8, C1, and C4) higher fresh fodder and fuel wood productivity were identified.
Dendrogram
Based on the data, 21 clones were clustered into major clusters using SPSS 16 SOFTWARE.
Correlation Studies
Plant height showed a positive and highly significant correlation with basal diameter (0.951), number of primary branches (0.799), fresh fruit weight (0.657). Basal diameter recorded highly significant correlation with number of primary branches (0.839) and fresh fruit weight (0.723). Internodal distance was negatively correlated with leaf density (-0.440) and leaf dry matter content (-0.465). Number of primary branches showed highly significant correlation with fresh fruit weight (0.586) and significant correlation with leaf width (0.435). Fresh weight of 100 leaves recorded highly significant correlation with leaf dry weight (0.885), leaf area (0.757) and fresh fruit weight (0.668). Leaf dry weight exhibited highly significant correlation with leaf area (0.61) and significant correlation with leaf dry matter content (0.462). Leaf dry matter content showed highly significant negative correlation with Dry fruit weight (-0.58). Fresh fruit weight had highly significant correlation with number of primary branches (0.586), leaf fresh weight (0.668), and leaf width (0.560) and significant relation with leaf dry weight (0.488) and leaf area (0.493). Dry fruit weight had highly significant correlation with seed weight (0.803). Among fodder quality parameters, correlation was highly significant and negative among nitrogen free extract and crude fiber (-0.469) and between nitrogen free extract and crude protein (-0.63). Significant negative correlation was found between nitrogen free extract and number of primary branches (-0.549) (Table 11).
Table 11. Correlation for morphometric and fodder quality parameters among different clones of Grewia optiva Drummond (Beul)
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)
The main objective of Tree Improvement is to produce best genotypes that bring economic returns. In this regard, practical and efficient means of screening is important. It is important to recognize how various characters relate to one another (Luna and Singh, 2009). With regard to early selection, in any tree improvement programme, the criterias taken are highly correlated genetically (Singh et al. 2012). These correlations indicate that improvement of one character will result in the improvement of another. Bhagta (2016) reported that 100 dry fruit weight was highly correlated with 100 seed weight at genotypic and phenotypic level, (0.22 and 0.17 respectively) in the seedling seed orchard. Khurana et al. (1992) also reported high correlation between plant height and collar diameter in poplar clones. Mehta et al (2022). conducted a study to evaluate Grewia optiva clones for fodder yield and other related traits among nine different clones in lower Shivalik North Western Himalayas and the results demonstrated positive correlations between collar diameter and fresh fodder weight. Similarly, diameter at breast height was also strongly correlated with collar diameter, fresh fodder weight and fuel wood weight.