Heritability and genotype x environment interactions ...

2003 ‫ ابريل‬12-17 ‫المؤتمر الدولى الثامن والعشرين لالحصاء وعموم الحاسب وتطبيقاتها‬ The 28th International Conference For Statistics, Computer Science and Its Application 12-17 April 2003

Heritability and genotype x environment interactions for durum wheat under various environments Bayoumi, T. Y. and M. A. El-Ashry Agronomy Dept. Fac. of Agric., Suez Canal University ABSTRACT There is a worldwide interest among plant breeders, geneticists, and production agronomists in genotype x environment (GE) interaction. A stability analysis is often conducted to estimate and interpret GE interaction. The objective of this study was to compare various moisture regimes, locations and years as evaluation environments for durum wheat (Triticum durum L.) genotypes. The experiment included 20 durum wheat genotypes introduced from ICARDA and four local check varieties which were evaluated for grain yield and some yield attributes under 12 environmental conditions. The components of genotype x environment interaction, stability, heritability and genetic advance were computed for number of days to 50% heading, spike length, 1000 kernel weight, grain yield and relative water content. The results revealed highly significant differences among wheat genotypes, environments and their interactions. Wheat genotypes differed in their response to the changes in environments. The most stable desired wheat genotypes for the most environments were No. 10 and 19 whereas, genotypes No. 1, 4 and 13 appeared promising in better environment. While genotypes No. 2, 3, 6, 9, 12, 22 (Sohag 1) and 23 (Sohag 3) were suitable for drought stress environments. Relative water content was the most adaptive trait, where it showed a larger heritability and lower genotype x environment interaction under various environments. NTRODUCTION Breeding genotypes with wide adaptability has long been a universal goal among plant breeders. To achieve this goal, replication of breeding genotypes over time and space has become an integral part of any plant breeding program. Despite such rigorous tests and subsequent selections, genotype x environment (G x E) interaction, i.e., failure of genotypes to perform consistently relative to each other under varying environments, remains a major problem. Standard analysis of variance procedure is useful for estimating the magnitude of G x E interactions, but it fails to provide information on the contribution of individual genotypes to G x E interaction. Regression of phenotypic performance over an environmental index has been suggested by Finlay & Wilkinson (1963),


Eberhart and Russell (1966). Such an approach would provide information as to the stability of genotypes to the changing environments. The same procedure, also, used by Ragab et al (1995), Bruns & Peterson (1998) and Salem et al. (2000). Some literature is also devoted to the relationships between the instability of grain yield and the instability of yield components (Nachit et al., 1992; Jackson et al., 1994; Brancourt et al., 2000 and Cooper et al., 2001). Explaining genotype x environment interaction observed on grain yield is a real challenge as grain yield results from complex compensations between yield components. Ceccarelli (1989) suggests analytical approaches to breeding for yield stability under stress conditions can be made more effective by employing constitutive characters in a selection program. A character is said to be constitutive when its expression is environment-independent, i.e. differences between genotypes are relatively constant in range of environments. In this context, the aims of this paper are : (1) to evaluate twenty four different wheat genotypes for grain yield and some of yield attributes under 12 environments; (2) to determine the nature of GE interaction; (3) to study the yield stability and adaptability of different genotypes using the regression analysis; (4) to assess the inheritance of some characters under the contrasting environments. MATERIALS AND METHODS To assess the genotype x environment interactions, stability and heritability of wheat grain yield, 20 durum wheat (Triticum durum L.) genotypes introduced from ICARDA and four local check varieties were evaluated for grain yield and some attributes under 12 environments (2 locations x 2 years x 3 moisture regimes). The pedigree and origin of the evaluated durum wheat are given in Table (1). These genotypes were evaluated in two locations at the experimental farm Fac., of Agric., Suez Canal University in Ismailia (Location 1) and Sinia (East lakes, location 2) Governorate during 1999/2000 and 2000/2001 seasons. All genotypes were grown under three separate moisture regimes; regime (1) received two irrigations (one irrigation after emergence and one about 40 days after emergence) and water was withheld until harvesting; regime (2) received four irrigations (one after emergence and the others after 40, 60 and 80 days from emergence); regime (3) received a total of six optimally spaced irrigations. The combination among the three moisture regimes, two locations and two years resulted in 12 diverse environments. The experimental design used in each environment was randomized complete


Table (1): Pedigree and origin for the introduced and local durum wheat genotypes Name/Cross Entry/Pedigree Origin No. 1

Gezira 17 / Scaup

2 3 4

BIT / Creso A 63040/sty/lds/3/win/u/Erp/ruso Can 2101/Magh/Stk/3/Wills 65/50 Bit / creso / Imuris D 2/Bit Bit/21563/Jo /D dwarfsis /Cr F// 68.44/NZt /3 /Cuc’s Ruff /Ru Sabil 1 Marrocos /46/3617 Gran Rabi / 3/G 11 // LDS / RL 3601 /4/ F9 /S / Cn D-2 /Waha Zincirli /4/KKZ/3/Nai 60 // Om Rabi 6 Norin 61 Wa 476 /3/ 391 // 56D-81-14-53 / 1015 Gallareta PIC /Cr// Stk /3/Dom/Dack /Kiwi Bani suef 1 Sohag 1 Sohag 2 Sohag 3

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

ICD-HA81-1917-3AP1AP-IAPOAP CD 34346-2TR-2AP-IAP-OAP CD 35072-C-SY-IM-OY CD 15111-35-2AP-2 AP-3AP-OAP

SYRIA

CD 20796-4AP-6AP-2AP-OAP CD 2079-2TR6-AP3-AP-OAP LO414-3L-LAP-1AP-4AP-OAP SWM6637-2AP-OAP-IK-OAP ICD 80-1419-1AP-3AP-OAP-OJB ICD 79-143725-AP-1AP –OAP PI 191621 CD 40150-14B-IY-2M-OY-MY ICD 80-0692-9AP-IAP-3AP-3AP

Syria Syria Turkey Turkey Syria Syria Morocco Syria Syria

ICD 79-0282-10AP-3APL-2AP-1AP ICW-HA81-1545-1AP-1AP-3AP LO 589-3L-IAP-2AP-1AP-OSH SWW 765784*OR8300226 SWM 6525-1AP-OAP-IK-OAP

Syria Syria Turkey Japan Turkey

CD 22344-A-8M-1Y-IN-1Y-2Y-0Y CD 27748-B-2N-2Y-1Y-0Y NATIONAL CHECK VARIETY NATIONAL CHECK VARIETY NATIONAL CHECK VARIETY NATIONAL CHECK VARIETY

Cyprus/Syria Iran Egypt Egypt Egypt Egypt

SYRIA SYPRUS Leb./Syr.

block design with three replicates. The experimental plot consisted of 6 rows, 3m long and 20 cm apart in which grains were drilled by hand. The normal recommended agricultural practices of wheat production were applied at the proper time. The differences between locations in relation to soil types as well as the metereological data during the growing seasons are presented in Tables (2 and 3). Samples of ten guarded plants were taken to determine the following characters: 1- number of days to 50% heading 2- Spike length (cm) 3- 1000-kernel weight (g) 4- Grain yield (ton/Fad.) 1Relative water content (RWC) was determined according to Schonfeld et al. (1998) as a better indicator of plant water status under drought from the following equation:


RWC% 

Fresh weight - Dry weight 100 Turgid weight - Dry weight

Table (2): Mechanical and chemical analysis of the experimental soil in Ismailia and Sinai locations Location Constitutions Ismailia Sinai Particle size analysis Sand (%) 92.59 94.88 Silt (%) 4.88 4.08 Clay (%) 2.63 1.04 Soil texture Sand Sand Chemical analysis 0.398 0.670 EC (m mhos/cm/25) pH 7.58 7.75 Soluble Nitrogen (ppm) 0.095 0.029 Available phosphorus (ppm) 74 70 Table (3): Average monthly relative humidity (%) and rainfall at Ismailia and Sinai during the two growing seasons. 1999/2000 2000/2001 Months

Relative

Rainfall

Relative

Rainfall

humidity (%)

(mm)

humidity (%)

(mm)

Ismailia Sinai Ismailia Sinai Ismailia

Sinai Ismailia Sinai

November

72.0

74.0

0.00

13.5

70.0

72.6

0.00

3.3

December

68.7

71.3

1.30

10.0

69.3

73.9

0.70

13.8

January

68.7

75.8

0.80

9.1

67.7

74.8

0.60

14.1

February

71.3

76.1

1.70

8.5

72.3

77.4

0.30

11.6

March

64.7

65.6

0.03

0.4

60.7

62.3

0.01

7.5

April

61.7

62.0

0.00

0.2

60.0

61.2

0.00

0.00


1-

2-

1234-

3-

Statistical Procedures Genotype x environment interaction The obtained data were subjected to the conventional analysis of variance according to Steel and Torrie (1980). To provide information about genotype x environments interaction effects and examining relative magnitude of the different sources of variation, combined analysis of variance were computed over years and locations to estimate their effects on the studied genotypes. Combined analysis was again carried out over all environments (i.e. years and locations combination) with genotypes as a fixed variable and environments as random using a microcomputer software M STAT-C (1986). Stability statistics Lin et al. (1986) introduced a brief description for stability statistics as follows: The variance of a genotype across environments, can be a measure of stability. The conventional coefficient of variability (C.V%) of each genotype used as a stability measure. The regression coefficient (bi) for each genotype is taken as a stability parameter. The residual mean square (MS) of deviation from the regression defined as stability measure. Moreover, Bilbro and Ray (1976) considered that genotype with b=1.0 was adapted for all environments; genotypes with b < 1.0 was considered adapted for low yielding environments and genotypes with b > 1.0 was considered better adapted for high yielding environments, depending upon the genotype mean yield. Broad sense heritability Heritability estimate was calculated by the following formula according to Kenneth et al. (2000) 2 2 2 2  2  Gy  GR  GL  E  2 2    ) h   G /( G  y R L rYRL   where, σ2G, σ2GY, σ2GR, σ2GL, σ2GE and σ2E refer to the G, G x y, G x R, G x L, and error variance, respectively; Y, R, L and r refer to the number of years, regimes, locations and replications per location per year, respectively. Genetic advance (GA) was estimated from the following formula: GA = ih2 σp where, σp = phenotypic standard deviation, h2 heritability, i = 1.76 (A 10% selection intensity).


RESULTS AND DISCUSSION Genotype x environment interaction When durum wheat genotypes are exposed to 12 diverse environments, genotype x environment (GE) interactions were highly significant for heading date, spike length, 1000-kernel weight and grain yield but not significant for relative water content (Table 4). Large GE interaction variances may occur as a consequence of differential response of genotypes from year to year variation in rainfall or rainfall distribution. Also, deficient moisture can exacerbate other sources of experimental error such as soil heterogeneity. Ceccarelli, (1989) reported that environments which used to test breeding material often differ widely in their effect on crop yield. The extremes are generally referred to as “stress” and non-stress” environments. In this study, the partitioning of GE into genotypes x moisture regimes (G x R), genotypes x years (G x Y), genotypes x locations (G x L), G x R x L, G x R x Y and G x L x Y may be interpret how to overcome on the higher of GE. The significant first order interaction of G x R, G x Y and G x L indicated that there were changes in the relative rankings or magnitudes of differences among genotypes. The significant second order interaction of G x R x L, G x R x Y and G x L x Y implies that there were differences in the relative ranking of genotypes over moisture regimes, years and locations combinations (Voltas et al., 2000). Moreover, the non significant GE interactions for the relative water content (RWC) may be indicate that this trait can be described as a constitutive trait. A character is said to be constitutive when its expression in environment-independent, i.e. differences between genotypes are relatively constant in a range of environments. Constitutive character is not expected to show high GE interaction, therefore constitutive characters can be of advantage in some environments as a tool for selection to drought under optimal conditions Ceccarelli (1989) and Kenneth et al., (2000). Stability parameters When moisture regimes, years and locations combinations were considered as environments, joint regression analysis of variance (Table 5) showed that mean differences among genotypes were significant, indicating genetic variation among these genotypes. Highly significant mean squares due to environment + genotype x environment interaction revealed that genotypes interacted considerably with different moisture regimes, locations and years. A major portion of these interactions may be


Table (4): Partitioning of environmental variation and genotype x environment (GE) interaction based on moisture regimes, years and locations for the studied characters in durum wheat genotypes Source of variance Environment (E) Regime (R) Year (Y) Location (L) RxY RxL LxY LxR Rep. (E) Genotype (G) GxE GxR GxY GxL GxRxL GxRxY GxLxY Pooled error

df 11 2 1 1 2 2 1 2 24 23 253 46 23 23 46 46 23 552

Spike Heading date length 9785.65** 86.402** 2351.43** 19.34** 1660.86** 12.85** ** 7091.71 18.27** 218.21ns 0.076 ns ** 895.36 5.632** 1156.30** 0.113 ns 1143.01** 2.695** 495.83 0.306 6519.26** 45.244** ** 615.28 30.350** 187.57** 6.589** ** 223.39 3.473** 241.13** 6.711** 211.17** 8.95** 271.84** 9.17** 435.26** 14.65** 122.35 0.060

Mean square 1000-kernel Relative water weight content 859.75** 941.14** 203.64** 189.5** 189.81** 116.11** ** 196.62 120.58** 150.79** 47.81 ns ** 164.53 19.84 ns 148.61** 31.40 ns 159.68** 25.79 ns 86.75 66.78 552.39** 335.56** ** 657.42 18.58 ns 69.77** 11.48 ns ** 47.99 10.80 ns 55.86** 12.09 ns 89.19** 12.19 ns 91.48** 12.45 ns 160.70** 21.95 ns 33.56 23.58

Grain yield 15.374** 3.037** 2.285** 3.271** 1.133** 1.120** 1.149** 2.031** 0.379 8.746** 8.053** 2.044** 2.50** 2.034** 4.069** 3.020** 6.678** 0.087

Ns, * and **: not significant, significant at p< 0.05 and 0.01, respectively Table (5): Joint regression analysis of heading date, spike length, 1000kernel weight, relative content and grain yield for durum genotypes used in stability analysis. Source of variance Genotypes (G) E+GxE E (Linear) G x E (linear) Pooled deviation Pooled error

df

Heading date

23 364 1 23 96

1027.2** 188.1* 4920.3** 152.3* 148.1* 139.3

Spike length 5.78** 4.46** 48.46** 4.25** 2.16** 1.01

Mean square 1000-kernel Relative water Grain yield weight content 124.3** 216.66** 1.48** 18.10* 12.01 5.49** 525.6** 36.39** 7.18** 27.0* 11.69 2.22** 10.2* 10.09 3.21** 3.6 10.58 0.04

E, environment ; G, genotypes * and **: not significant, significant at p< 0.05 and 0.01, respectively attributable to linear component for yield and yield components, whereas significant pooled deviation contributed more to total interaction for grain yield. Pooled deviation was significant for all the components except relative water content. For heading date, spike length and 1000-kernel


weight, the linear component was significant against pooled deviation, revealing that prediction of performance in different environments was possible for these yield components. While, relative water content was a better indicator for stability specially under moisture stress conditions. Stability analyses were conducted for each trait as described by Lin et al. (1986) to identify on durum genotype that confered an overall productive advantage or disadvantage to various environments (Tables 6 and 7). Misra et al. (1991) emphasized that a genotype which had a small variance, coefficient of variability (C.V%) and non linear standard deviation from regression (S2d) can be considered as a stable genotype. Accordingly, it was possible to judge on the stability of genotypes considering to their mean performance, linear regression coefficient (bi) and the other stability parameters. Six genotype (No. 9, 10, 12, 16, 17 and 18) significantly flowered earlier than the mean (128.4 – 1.72=126.68 days). Genotypes No. 9, 10 and 12 had non significant deviation from regression (S2d) for heading date, indicating their stability but the other three genotypes were non stable (Table 6). In view of the phenomena that grain yield (final yield) is an integral of the growth over the whole season, a trait that influences the ability of the plant to grow during or to survive a period of moisture stress may be relatively important in drought tolerance. Six genotypes (No. 1, 4, 10, 13, 19 and 22 (Sohage 1)) yielded significantly higher than population mean (1.41 ± 0.35 = 1.76 to/fad.) Table (7). These genotypes with the highest grain yield were that of the best combined of higher relative water content, spike length and 1000-kernel weight. All these genotypes had small variance, coefficient of variability and non significant deviation from regression, indicating their stability. These predictable genotypes were classified for their response to regression coefficient (bi) as follow; genotypes No. 1, 4 and 13 had regression coefficient more than 1 (bi > 1), higher mean than population mean and least S2d , appeared promising in better environment; genotypes No. 2, 3, 6, 9, 12, 22 (Sohage 1) and 23 (Sohage 2) were with regression slop less than unity (bi < 1) and least S 2d, indicating its responsiveness towards unfavourable growing conditions (drought stress). A joint consideration of mean and stability parameters revealed that genotypes No. 10 and 19 possess higher grain yield and its components, bi = 1 and S2d = 0, approximately. These genotypes appeared promising for adaptation and could be utilized in further breeding programme.

2003 ‫ ابريل‬12-17 ‫المؤتمر الدولى الثامن والعشرين لالحصاء وعموم الحاسب وتطبيقاتها‬ The 28th International Conference For Statistics, Computer Science and Its Application 12-17 April 2003 

Table (6): Mean values ( x ) and stability parameters for heading date, spike length and 1000-kernel weight for 24 durum wheat genotypes. Genotypes



x

Heading date (days) S2 C.V bi (%) 11.963 2.7 1.7 27.541 4.8 0.3 48.907 5.4 0.5 39.605 4.9 1.2 23.323 3.7 1.3 28.251 4.1 0.8 38.841 4.8 1.4 35.799 4.6 1.2 28.301 4.1 0.3 9.776 2.4 1.03 34.921 4.6 2.1 29.325 5.5 0.5 18.846 3.3 1.1 33.632 4.5 1.2 79.571 6.9 0.7 16.516 3.1 0.4 12.973 2.8 1.6 39.813 4.9 2.3 8.732 2.3 1.02 40.532 4.9 0.3 23.174 3.7 2.5 10.548 2.5 0.3 17.389 3.2 0.91 22.741 3.7 1.5

S2d

1 130 22.0 2 128 56.5 3 130 4.7 4 131 48.0 5 128 23.8 6 135 37.2 7 128 131.2** 8 134 145.9** 9 125 -9.5 10 126 12.0 11 129 -17.5 12 121 44.4 13 130 14.5 14 127 210.8** 15 128 165.7** 16 126 185.4** 17 125 134.0* 18 123 914.8** 19 128 10.6 20 130 914.8** 21 129 314.6** 22 131 5.0 23 130 -3.6 24 130 376.4** G. 128.4 *, ** significant at p = 0.05 and p=0.01, respectively



x 7.05 5.66 2.10 6.57 6.92 7.43 5.40 6.88 5.59 8.93 5.61 5.03 7.88 5.94 5.2 7.71 7.60 5.45 8.56 5.67 4.97 7.30 5.59 5.40 6.39

Spike length (cm) S2 C.V bi (%) 0.812 14.4 1.17 0.965 15.7 1.2 0.419 10.3 0.83 0.639 12.7 1.12 0.905 15.2 1.6 0.734 13.6 0.98 0.929 14.8 1.95 0.568 12.0 1.1 0.994 15.9 0.84 0.323 9.0 0.01 0.719 13.5 1.07 0.953 15.5 0.83 0.384 9.9 1.03 0.678 13.1 0.71 1.106 16.8 1.42 0.376 9.8 1.19 0.271 8.3 0.93 0.675 13.1 1.05 0.243 7.8 1.01 0.704 13.4 1.08 0.575 12.1 0.78 0.609 12.4 0.65 0.384 9.9 0.75 0.661 12.9 0.84 G: grand mean

S2d 0.067 -0.02 -0.14 0.08 -0.07 0.39* 0.75** 0.58** 0.09 0.03 -0.25 0.27 0.001 -0.18 -0.42** 0.02 -0.07 0.52** 0.01 0.71** 0.69** 0.02 0.06 0.17



x 34.7 32.5 31.6 34.9 31.7 30.5 28.0 32.0 32.0 35.8 30.1 30.3 35.9 30.3 33.0 34.5 34.7 30.6 37.1 28.8 32.4 34.8 32.0 30.9 32.64

1000-kernel weight S2 C.V bi (%) 11.769 10.6 1.5 14.228 11.7 0.3 17.387 12.9 0.8 16.627 12.6 1.3 11.814 10.6 2.06 14.621 11.8 0.7 16.541 12.6 1.1 19.424 13.7 1.81 20.192 13.9 0.5 10.479 10.0 1.0 18.943 13.5 0.4 20.543 14.0 0.5 13.754 11.5 0.7 15.547 12.2 0.1 22.635 14.7 1.1 8.974 9.3 1.46 5.864 7.5 1.2 21.674 14.4 0.1 2.926 5.3 1.00 26.864 16.1 3.8 16.479 12.6 0.3 5.431 7.2 0.83 6.743 8.1 0.75 12.143 10.8 0.92

S2d 2.5 -3.0 -2.7 0.6 0.16 -1.9 3.6** 4.03** 0.14 0.001 8.5** -2.9 .11 1.01 6.31** 0.12 0.08 8.62** 0.003 7.9** 7.4** 0.051 0.032 0.45


Further examination of the GE interaction is necessary to determine if the interaction is primarily due to genotype rank changes or magnitude changes. So, partitioning the GE variance component into separate parts σ2G, σ2Gy, σ2GL, σ2GR, σ2GRL, σ2GRY and σ2GLY as well as hertability in broad sense and genetic advance are presented in Table (8). The largest source of GE interaction for heading date, spike length, 1000kernel weight, relative water content and grain yield were G x L x Y variance component followed by G x R x Y and G x R x L. to reduce the GE interaction bias for the studied traits, screening in more locations and years would be helpful. The same conclusion was noticed by Araus (2000). Table (7): Mean values (x) and stability parameters for relative water content and grain yield for 24 durum wheat genotypes Genotypes

Relative water content % x

S2

C.V (%) 16.9 14.6 17.5 19.5 12.9 17.5 19.9 14.6 18.1 9.2 12.8 15.8 13.6 18.0 17.7 13.5 12.0 11.6 10.1 22.8 21.0 20.2 22.6 19.8

bi

77.89 131.74 1.334 1 65.38 98.55 0.982 2 63.43 142.07 0.875 3 76.68 175.87 1.147 4 72.31 77.45 1.156 5 65.60 141.83 0.704 6 64.71 183.94 1.198 7 63.53 98.91 1.223 8 64.59 151.47 0.795 9 78.77 39.37 1.040 10 70.46 76.15 1.336 11 69.50 115.18 0.958 12 76.64 84.86 1.296 13 64.40 149. 7 4 1.182 14 62.49 145.40 0.725 15 67.05 84.68 0.834 16 67.82 67.44 1.207 17 60.42 27.57 1.360 18 78.74 47.62 1.050 19 60.35 241.07 0.663 20 63.12 204.69 1.234 21 78.36 188.36 0.891 22 63.92 235.78 0.923 23 63.74 181.90 1.166 24 G 68.32 *, ** significant p=0.05 and p=0.01 respectively

Grain yield (ton/fad) S2d 0.709 0.196 0.734 0.166 0.291 0.136 1.908** 1.617** 0.309 0.031 1.239** 0.606 0.411 1.216** 1.528** 1.094* 1.671** 1.817** 0.086 1.952** 1.428** 0.216 0.528 1.867**

x

S2

1.99* 0.099 1.07 0.172 1.11 0.156 1.79* 0.135 1.43 0.098 1.28 0.118 0.97 0.489 1.02 0.314 1.24 0.108 2.14* 0.059 1.25 0.116 1.17 0.345 1.83* 0.079 0.95 0.276 0.99 0.334 1.33 0.211 1.35 0.388 1.00 0.462 2.11* 0.052 0.88 0.513 1.56 0.158 1.80* 0.102 1.74 0.109 1.62 0.129 1.41 G: grand mean

C.V (%) 22.3 29.4 28.0 26.0 22.2 24.3 49.5 39.7 23.3 17.2 24.1 41.6 19.9 37.2 40.9 32.5 44.1 48.1 16.1 50.7 28.1 22.6 23.4 25.4

bi

S2d

1.230 0.0035 0.931 -0.0046 0.792 0.0091 1.170 0.0075 1.312 0.0018 0.848 0.0029 1.211 0.0897** 1.151 -0.0954** 0.908 0.0089 1.001 0.0005 1.412 0.0379** 0.891 0.0735** 1.324 0.0013 1.185 0.0645** 0.919 0.0813** 0.986 0.0241** 1.179 0.0253** 1.23 8 0.0879** 1.008 0.0001 0.946 0.0951** 1.332 0.0596** 0.812 0.0012 0.902 0.0031 1.095 0.0029


Heritability in broad sense for grain yield was very low (15.5%) as well as 1000-kernel weight (32.6%) and spike length (32.8%), while, heritability values were higher for heading date (85.8%) and relative water content (84.4%). The expected genetic advance as a percentage of the mean were 17.0, 21.5, 14.8, 8.9 and 29.6% for heading date, spike length, 1000-kernel weight, relative water content and grain yield, respectively. Table (8): Variance component estimates of GE interaction, heritability and genetic advance for heading date, spike length, 1000kernel weight, relative water content and grain yield for durum genotype tested under 12 environments Estimate

Heading date

Spike length

2G 2GR 2GL 2GY 2GRL 2GRY 2GLY 2E Heritability (h2) p Genetic advance (GA%)

181.09 15.63 13.39 12.41 35.19 45.30 48.36 40.78 85.80 14.52 17.0

1.89 0.549 0.372 0.192 1.491 1.52 1.62 0.02 32.8 2.40 21.5

1000kernel weight 23.01 5.81 3.10 2.66 14.86 15.24 17.85 11.19 32.6 8.40 14.82

Relative water content 13.98 0.95 0.67 0.60 2.03 2.07 2.43 7.86 84.4 4.07 8.90

Grain yield 0.364 0.170 0.113 0.113 0.678 0.503 0.742 0.029 15.5 1.53 29.6

p= phenotypic standard deviation GA = i h2 p/experimental mean; I = 1.76 (A 10% selection intensity) In summary, the low heritability and large GE interactions indicate that grain yield and yield components, (spike length and 1000kernel weight) point to the necessity of evaluating breeding materials under a wide range of environmental conditions (multi-years and multi locations) to achieve broad adaptation. In weighing selection criteria when breeding was under drought conditions, relative water content was the most adaptive trait. It showed a larger heritability and lower GE interaction under various environments. Genotypes No. 10 and 19 may be considered as best adapted to the target area and the highest yielding above all genotypes.


REFERENCES Araus, J. L. (2000). Integrated approach to breeding cereals for drought resistance, Workshop Proceeding in Rabat-Morocco, Organized by CIHEAM and INRA. Bibro, J. D. and L. L. Ray (1976). Environmental stability and adaptation of several cotton cultivars. Crop Sci., 16:821-824. Brancourt, H. M.; J. B. Denis and C. Lecomte (2000). Determining environmental covariates which explain genotype environment interaction in winter wheat through probe genotypes and biadditive factorial regression. Theo. Appl. Genet, 100:285-298. Bruns, R. and C. J. Peterson (1998). Yield and stability factors associated with hybrid wheat. Wheat prospects for global improvement. Proceeding of the 5th International Wheat Conference, Ankara, Turkey. Ceccarelli, S. (1989). Wide adaptation: How wide ? Euphytica 40: 197-205. Cooper, M.; D. R. Woodruff; I. G. Phillips; K. E. Basford and A. R. Gilmour (2001). Genotype-by-management interactions for grain yield and grain protein concentration of wheat. Field Crop Research. 69:47-67. Eberhart, S. A. and W. A. Russell (1966). Stability parameters for comparing varieties. Crop Sci. 6: 36-40. Finlay, K. W. and G. N. Wilkinson (1963). The analysis of adaptation in a plant breeding programme. Aust. J. Agric. Res., 14: 742-754. Jackson, P. A., D. E. Byth; K.S. Fischer; R. P. Johnston (1994). Genotype x environment interactions in progeny from a barley cross. II variation in grain yield, yield components and dry matter production among lines with similar times to anthesis. Field Crop Res. 37: 11-23. Kenneth, A.; M. Gravois and J. L. Bernhardt (2000). Heritability and Genotype x Environment Interactions for Discolored Rice Kernels. Crop Sci. 40: 314-318. Lin, C. S.; M. R. Binns, ; L. P. Lefkovitch (1986). Stability Analysis: Where do we stand? Crop Sci. 26: 894-900. Misra, S. C.; M. D. Bhagwat; R. N. Dixit and V. P. Patil (1991). Stability of component characters and yield in rainfed wheat. Indian J. of Agric. Sci. 61 (1): 7-10. M STAT-C (1986). Amicrocomputer for the design, management and analysis of Agronomic Research Experiments. Michigan State Univ., USA. Nachit, M. M.; M. E. Sorrells; R. W. Zobel; H. G. Gauch and R. A. Fischer (1992). Association of morpho-physiological traits with grain yield and components of genotype-environment interaction in durum wheat. I J. Genet Breed. 46:363-368. Ragab, A. I.; A. A. Hoballah and Kassem (1995). Genotype x environment interaction effect for seed yield and oil content of sesame. Zagazig J. Agric. Res. Vol. 22 No. (4) 963-974. Salem, A. H.; S. A. Nigem, M. M. Eissa and H. F. Oraby (2000). Yield stability parameter for some bread wheat genotypes . Zagazig J. Agric. Res. Vol. 27 No. (4) 789-803.

‫المؤتمر الدولى الثامن والعشرين لالحصاء وعموم الحاسب وتطبيقاتها ‪ 12-17‬ابريل ‪2003‬‬ ‫‪The 28th International Conference For Statistics, Computer Science and Its Application‬‬ ‫‪12-17 April 2003‬‬

‫‪Schonfeld, M. A.; R. C. Johnson, ; B. F. Carver and D, W. Mornhinweg‬‬ ‫‪(1988). Water relations in winter wheat as drought resistance indicators.‬‬ ‫‪Crop Sci. 28, 536-541.‬‬ ‫‪Steel, R. G. D. and J. H. Torrie (1980). Principles and Procedures of statistics. A‬‬ ‫‪Biometrical Approach. Second ed. McGraw-Hill pp. 167-173.‬‬ ‫‪Voltas, J., van Eeuwijk; F. A. ; A. Sombrero, A. Lafarga and I. Romagosa‬‬ ‫‪(1999). Integrating Statistical and Ecophysiological Analysis of genotype‬‬ ‫‪by environment interaction for grain filling of barley. I. Individual grain‬‬ ‫‪weight. Field Crops Research. 62:63-74.‬‬

‫كفاءة التوريث والتفاعالت بين البيئة والتركيب الوراثى لقمح الديورم فى بيئات متباينة‬ ‫محمد محمد عبد اهلل العشرى‬

‫طارق يوسف بيومى –‬ ‫قسم المحاصيل – كمية الزراعة – جامعة قناة السويس‬

‫ىناك اىتمام عالمى كبير بين عمماء تربية النبات والوراثة ومنتجوا المحاصيل بالتفاعل‬ ‫ما بين البيئة والتركيب الوراثى ‪ ،‬حيث انو غالبا ما يستخدم تحميل الثبات لتقدير وشرح التفاعل ما‬ ‫بين البيئة والتركيب الوراثى‪ .‬تيدف ىذه الدراسة الى مقارنة بعض التراكيب الوراثية لقمح الديورم‬ ‫والمستوردة من االيكاردا مع اربع اصناف محمية لمحصول الحبوب وبعض صفات المحصول‬ ‫االخرى تحت ‪ 12‬بيئة مختمفة تتضمن ( ‪ 3‬معامالت اجياد رطوبى وموقعين وسنتين) وقد تم‬ ‫تقدير مكونات التفاعل بين التركيب الوراثى والبيئة ومعامل الثبات وكفاءة التوريث ومقدار التحسين‬ ‫المتوقع لصفات عدد االيام حتى ‪ %50‬تزىير وطول السنبمة ووزن ‪ 100‬حبة ومحصول الحبوب‬ ‫والمحتوى المائى الوراق النبات‪ .‬اوضحت النتائج ان ىناك اختالفات معنوية بين التراكيب الوراثية‬ ‫والبيئات وتفاعالتيا ‪ ،‬كما اختمفت التراكيب الوراثية لمقمح فى استجابتيا بتغيير الظروف البيئية‬ ‫وكانت اكثر التراكيب الوراثية ثباتا واقممة لمظروف البيئية المختمفة ىما التركيبان رقم ‪.19 ، 10‬‬ ‫أظيرت التراكيب الوراثية رقم‬

‫‪ 13 ، 4 ، 1‬مالئمة لمظروف البيئية الجيدة (اإلنتاج‬

‫المرتفع) ‪ .‬بينما كانت التراكيب الوراثية رقم ‪ 12 ، 9 ، 6 ، 3 ، 2‬وسوىاج ‪ 1‬وسوىاج ‪ 2‬اكثر‬ ‫مالئمة لظروف الجفاف ‪ .‬كانت صفة المحتوى المائى لألوراق اكثر الصفات أقممة لظروف‬ ‫الجفاف ويمكن استخداميا كمعيار لالنتخاب تحت ظروف الجفاف حيث أظيرت ارتفاع فى قيم‬ ‫معامل التوريث وانخفاض فى التفاعل ما بين التراكيب الوراثية والبيئات المختمفة‪.‬‬