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African Crop Science Journal
African Crop Science Society
ISSN: 1021-9730 EISSN: 2072-6589
Vol. 8, Num. 2, 2000, pp. 153-157
African Crop Science Journal, Vol. 8. No. 2, pp. 153-157

African Crop Science Journal, Vol. 8. No. 2, pp. 153-157

ADAPTATION OF COTTON CULTIVARS

ABDISHEKUR WONDIMU
Ethiopian Agricultural Research Organisation (EARO), Awassa Research Centre,
P. O. Box 6 Awassa, Ethiopia

(Received 6 October, 1997; accepted 14 June, 2000)

Code Number: CS00016

INTRODUCTION

The irrigable cotton (Gossypium hirsutum L.) growing regions of Ethiopia range from 300 to 1200 metres in altitude. The rainfed cotton belt roughly follows the 1600 metre contour (Bedada Girma,1982). However, in the western sub-humid low elevation location of the country, cotton is cultivated under rainfed condition by the State farms. In such areas, like other lowlands of the country, poor harvests and crop failures resulting from drought are common. This is due to unreliable and erratic rainfall as well as short growing season. According to Miller et al. (1959), cotton is grown in an array of edaphic, climatic, disease, and insect conditions. They also indicated that these factors vary from one location to another, and from year to year in the same location, and their effects are reflected in yields of cotton cultivars. Adaptation of improved cultivars is an important factor for increasing the productivity of the crop. But inconsistent yield performance of the cultivars under diverse environmental conditions due to high genotype-environment interaction is a major contributing factor to reduction in productivity (Misra and Panda, 1990). Lack of stability of performance is a serious problem in the lowlands of the country where seasonal fluctuations are large (Abebe et al.,1984a, b). Therefore, identifying the most stable and broadly adapted cultivars with high yield potential is an important consideration.

Regression techniques have often been used to estimate the stability of individual cotton genotypes (Abebe et al.,1984a, b ). The mean of genotypes in each environment has been used as an index of that environment. The mean yield of each genotype in each environment has also been linearly regressed on the mean yield of all genotypes in each environment as suggested by Finlay and Wilkinson (1963). Stable genotypes are considered to have slopes b < 1.0 and non stable genotypes to have slopes b > 1.0. The latter could be termed genotypes responsive to changing environment (Heinrich et al.,1983). Cultivar x year interactions are greater than locality and locality x year and, for good average comparison of cultivars, it is necessary to replicate trials over years than over locality within years ( Patterson et al.,1983).

Considering the above, six breeding cotton line crosses of AMS1 (74) with Acala 1517Br and Albar 637 and five other standby and promising breeding lines were tested along with the check cultivar for six consecutive years (1985 - 1990) at Abobo Research Centre. The objectives of the study were to determine yield stability and pattern of response of the entries across environments so as to identify and select the most stable entries for the western sub-humid lowlands of Ethiopia.

MATERIALS AND METHODS

Cotton National Variety Trials were conducted from 1985 to 1990 at Abobo Research Centre. The years largely differed in the amount of rainfall such that each year was considered as an individual environment ( Table 1 ). Abobo Research Centre is characterised by low elevation (530 metres above sea level), a sub-humid climate and loamy clay soil.

TABLE 1. Rainfall (mm) and temperature (°C) at Abobo Research Centre, 1985-1990
Year Total rainfall (mm)

May - September
Temperature (°C)

Maximum average
1985 889.0 33.0
1986 627.0 33.0
1987 711.0 34.0
1988 990.0 33.0
1989 681.0 32.5
1990 438.0 33.0

In this study twelve genotypes were used. These consisted of breeding line crosses of Albar 637 with two breeding lines of AMS1 (74), 4 crosses of Acala 1517Br by different lines of AMS1 (74), five other standby and promising breeding lines, and a check cultivar L-299-10. Cultivar L-299-10, which is known as Arba was produced by the state farms during the time of this experiment.

The treatments were arranged in a randomised complete block design with four replicates each year. Plot size was five rows of 10 metre length, with 80 cm spacing between rows and 25 cm between plants, giving planting density of 50,000 plants per hectare. The three centre rows were harvested for seed cotton yield.

Yield stability parameters were calculated as suggested by Finlay and Wilkinson (1963). The yield of each genotype (Y) was linearly regressed on the yield of all genotypes in each environment (X) to estimate the regression coefficient (b), the coefficient of determination (R2 ), and the standard deviation of b (sd). To determine if b was significantly different from zero for each cultivar, the regression mean square was compared with the deviation mean square for that cultivar (Patterson et al., 1983).

RESULTS AND DISCUSSION

The entry mean seed cotton yield values in Table 2 are rounded to the nearest kilogramme and ranged from 1300 - 1900 kg ha-1. Thus, there existed wide variation in yield. Ceccarelli et al. (1991) reported barley average yields of 1562 kg ha-1 and 32 kg ha-1 from the same locality in two successive years with minor differences in total rainfall. They also noted that under such variable conditions GE interactions were large suggesting the need for stability analysis.

TABLE 2. Seed cotton yield and environmental response (b) of different cotton genotypes grown at Abobo Research Centre, 1985 to 1990
Entry n Mean yield (kg ha-1) b R2
Reba B-50 6 1300 1.09 + 0.58+ 0.88
A-333-57 6 1800 1.07 + 0.35 0.95
Albar 637 6 1300 0.99 + 0.32 0.93
AMS1 -34 6 1600 1.02 + 0.53 0.99
L-299-10 6 1400 1.20 + 0.28 0.97
Lafrego bract2 6 1400 0.80 + 0.81 0.73
Albar 637xAMS1 (74)#11 6 1800 0.93 + 0.45 0.91
Albar 637xAMS1 (74)#24 6 1500 1.02 + 0.58 0.86
Acala 1517BrxAMS1(74)#6 6 1600 0.90 + 0.08 0.99
Acala 1517BrxAMS1(74)#40 6 1800 0.90 + 1.00 0.85
Acala 1517BrxAMS1(74)#41 6 1900 1.03 + 0.35 0.94
n = number of environments; ± = Standard deviation of b

All crosses and two breeding lines showed yield increases over the high yielding check cultivar (L-299-10) or Arba. Percent yield increases over the check cultivar for the most stable breeding lines are shown in Table 3. Combined analysis of variance for seed cotton yield (Table 4) over years revealed significant differences among cultivars and environment.

Table 3. Seed cotton yield kg ha-1 for the most stable genotypes (1985 - 1990)
Year Breeding line/cultivar
A-333-57 Albar 637 x Acala 1517Br x L- 299-10
1985 1940 2213 2317 1850
1986 1250 1300 1338 1029
1987 1514 1206 1804 681
1988 2046 1763 1946 1467
1989 3054 2808 2886 2642
Mean 1829 1794 1899 1383
% increase over check (1985-1990) 30.2 29.7 30.7  


Table 4. Combined analysis of variance for seed cotton yield over six crop season, 1985 to 1990
Source of variation d.f. SS MS Computed Fa
Years 5 577.5473 111.5095 366.80**
Replications within years 3 22.0206 7.3402  
Breeding line 11 67.0168 6.0924 20.04**
Year x breeding line 55 63.42321 1.1531 3.7931**
a* = significant at 1% level

Estimates of stability parameters are shown in Table 2. The regression analysis showed that 73 to 99% of the variation in entry mean yield was explained by variation in the year mean yield.Of all entries four breeding lines and four crosses had R2 values falling within the range of 91 to 99%. Langer et al. (cited by Abebe et al., 1984b) proposed the use of R2 for measuring stability. The cultivars with the highest yield exhibited, within narrow limits (regression coefficient close to 1.0), a similar degree of adaptation to all environments (Table 2).

Figure 1 depicts the various adaptation responses of four cotton entries. Cultivars characterised by regression coefficient of 1.0 have average stability in all environments. For example, the breeding line A-333-57 and three crosses, Acala 1517Br x AMS1 (74)#11 (Fig. 1), Albar 637 x AMS1 (74)#40 showed average stability with linear regression coefficients (b) of 1.07, 1.03, 0.93 and 0.90, respectively. All cultivars produced above average yield in all years, except Acala 1517Br x AMS1 (74) #40 which in 1986 cropping season produced slightly below average yield. This indicated that they had general adaptability. On the other hand, breeding lines Albar 637 and Reba B-50 also had regression coefficients ( b) of 0.99 and 1.09, respectively. However, these lines consistently produced below average yields in almost all environments (Fig. 1 for Albar 637), which indicated that they were poorly adapted to different environments, specifically seasonal variations typical of the western lowlands of Ethiopia. Thus, the report by Bedada Girma (1982) that these two cultivars are promising for the rainfed conditions is not in agreement with our results. The check cultivar (L-299-10) is typical of cultivars which are sensitive to changes in the environment (below average stability): small changes in the environment produced large changes in yield. Under favourable conditions it was one of the top yielding cultivars. L-299-10 could, therefore, be described as being specifically adapted to high yielding environments and is characterised by the highest regression coefficient of b=1.20 (Fig. 1).

The cotton breeding line Lafrego bract 2 exhibited the opposite type of adaptation, with little change in yield despite large changes in environment (above average stability). This line produced above average yield in low yielding environment of 1990 (Fig. 1), but, being insensitive to environmental change, yielded relatively little in a high yielding environment. With the lowest regression coefficient ( b=0.85 ) it typified cultivars specifically adapted to low yielding environments.

Benti et al. (1996) selected the most stable maize cultivars for use across locations by utilising regression analysis. Yang and Baker (1991) considered significant family x year and line x year interaction to be due primarily to heterogeinity of variances, and that such type of non-crossover interactions have little impact on crop improvement.

Combination of various parameters for identification of suitable hybrids for use across diverse environments was suggested by Abebe et al. (1984a, b). Based on this criteria the most stable crosses Acala 1517Br x AMS1 (74) #41, Albar 637 x AMS1 (74) #11 breeding line A-333-57 with high yields and, regression coefficient approximating to 1.0, high R2 values (> 90%) and yield advantage of over 29 to 30% over the check are recommended for use in diverse environments of the western lowlands of the country.

REFERENCES

  1. Abebe Menkir, Yilma Kebede, and Brhane Gebrekidan. 1984a.Genotype x environment interaction and yield stability in sorghums of intermediate maturity. Ethiopian Journal of Agricultural Sciences 6:1-10.
  2. Abebe Menkir, Yilma Kebede, and Brhane Gebrekidan. 1984b.Yield potential and stability of early maturing sorghum hybrids. Ethiopian Journal of Agricultural Sciences 6:58- 66.
  3. Bedada Girma. 1982. Cotton yield trials. In: Proceedings of the Symposium: Cotton Production Under Irrigation in Ethiopia. Institute of Agricultural Research, Addis Ababa, Ethiopia. pp. 35-49.
  4. Benti Tolessa, Gezahegne Bogale and Asefa Afeta. 1996. Genotype x environment interaction and yield stability of maize cultivars. Ethiopian Journal of Agricultural Sciences 15:1-7.
  5. Ceccarelli, S. and Grando, S. 1991. Environment of selection and type of germplasm in barley for low yielding conditions. Euphytica 57: 207-219.
  6. Finlay, K.W. and Wilkinson, G.N. 1963. The analysis of adaptation in plant breeding program. Australian Journal of Agricultural Research 14:742-754.
  7. Heinrich, G.M., Francis, C.A. and Eastin, J.D. 1983. Stability of grain sorghum yield components across diverse environments. Crop Science 23: 209-212.
  8. Miller, P.A., Williams, J.C. and Roninson, H.F. 1959. Variety x environment interactions in cotton variety tests and their implications on testing methods. Agronomy Journal 51:132 - 134.
  9. Misra, R. C. and Panda, B.S. 1990. Adaptability and phenotypic stability of improved soybean (Glycine max L.) varieties. Oil crops Newsletter 8:36-37.
  10. Patterson, R.M., Weaver, D.B. and Thurlow, D.L. 1983. Stability parameters of soybean cultivars in maturity groups VI, VII, and VIII. Crop Science 23:569-571.
  11. Yang, R.C. and Baker, R.J. 1991. Genotype - environment interactions in two wheat crosses. Crop Science 31:83-87.

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