Evaluation of White fleshed sweet potato (Ipomoea batatas (L) Lam) Genotypes in different agro-ecologies of Southwest Ethiopia
Tewodros Mulualem*, Getachew Etanna, Neim Semman
Jimma Agricultural Research Center, Department of Horticultural Crops, P.O. Box 192, Jimma, Ethiopia.
*Corresponding Author E-mail: tewodrosmulualem@gmail.com
ABSTRACT:
A multi-locational evaluation trial of six white fleshed sweet potato genotypes was conducted in four locations during 2019 and 2020 cropping seasons. The objective of this study was to determine the magnitude of genotype by environment interaction (GEI) for storage root yield and yield-related traits of sweet potato genotypes and to assess the adaptability and stability of sweet potato genotypes in different production environments in southwest Ethiopia. Six genotypes were evaluated across eight diverse environments using a randomized complete block design (RCBD) with three replications. Data were collected on yield and yield components in all tested locations. The results showed highly significant difference (p<0.01) for genotype effect, environmental affect and environment by genotype interaction (GEI) for all the traits studied. The analysis also revealed that the magnitude of the mean square of environment was higher than those of the genotype and GEI for all the traits studied indicating the uniqueness of the tested environments. The genotypes Hawassa-83, and Tula were identified both high mean root yield and high stability, closest to the ideal genotype for root performance and consistency of performance across environments. This study provides valuable information that could be utilized in a breeding program to ameliorate genotypes of sweet potato in Ethiopia.
KEYWORDS: Environment, genotype, GEI, storage root, sweet potato, yield.
INTRODUCTION:
Sweet potato (Ipomoea batatas [L.] Lam), is one of the most important food security and economical crops globally (Emmanuel et al., 2021). In many developing countries, sweet potato is reported to be the fifth most important food crop after rice, wheat, maize, and cassava (Aina et al., 2007). Globally, over 110 million metric tons of sweet potato was produced in 2018 (FAOSTAT, 2018). Of which 20.7 million tons which represents about 25.4% of the world production was produced from Africa.
In Ethiopia, sweet potato is the second most important root crop after enset [Ensete ventricosum (Welw.) Cheesman] (CSA, 2015). During the 2014/15 main season, sweet potato, potato and taro covered about 81% of the total area cultivated with root and tuber crops in country. Nearly 20 million Ethiopians are dependent on sweet potato as a staple food, reflecting the importance of the crop for food security and the livelihoods of rural communities. It is mainly grown in the eastern, southern and south western parts of the country (Tofu et al., 2007; Fekadu et al., 2019). Currently, sweet potato is being distributed to drought affected areas of the country where maize and other crops regularly fail due to recurrent droughts for food, feed and economic uses (Fekadu et al. 2015; Getachew et al., 2020).
In the last decades, production of sweet potato in Ethiopia has increased many folds. However, the increased production can be attributed to the expansion of land under sweet potato cultivation rather than increased yield per unit area, as yield remains abysmally low at an average of <8.0 tons/ha. This low yield is mainly due to the widespread use of poor agronomic practices, chief of which are the use old, unimproved cultivars. Furthermore, sweet potato genotypes are evaluated for yield in multi-location trials, wide differences are frequently observed in yield performances of the genotypes over the growing environments. This wide agro-ecological variability is the major challenge for sweet potato production in the country. Therefore, one important way of mitigating against poor root yield in farmers’ fields is to develop and release new sweet potato varieties with stable and high root yield potential into the farming system is crucial (Fekadu et al., 2015). Understanding the response of crop genotypes to changing environmental conditions is of key importance in plant breeding (Emmanuel et al., 2021). Assessing the nature of interaction that exist between genotypes and the growing environment for a particular traits are valuable (Sabri et al., 2020). When genotypes are evaluated across a wide range of locations and/or years, their yield performances could vary significantly. In this regards, different reports revealed that sweet potato is sensitive to environmental changes. According to Madawal et al. (2015), Fekadu et al. (2017), and Ngailo et al. (2019), changes in environmental conditions have been reported to affect sweet potato storage root yield and yield components.
This makes the analysis for G × E interaction crucial for genotype selection, cultivar release, and identification of suitable production environments for optimum yield. Therefore, having a basic understanding on G × E interactions, stability parameters, and genetic correlations for root yield and yield components are considered necessary for sweet potato breeders in making an informed choice concerning which locations and input systems should be used in their breeding efforts (Gruneberg et al., 2005).
Different statistical tools mainly Additive Main effect and Multiplicative Interaction (AMMI) (Gauch, 2013) and Genotype plus Genotype by Environment Interaction (GGE bi-plot) (Yan and Kang, 2003; Yan and Tinker, 2006) are the most commonly used statistical methods for analyzing multi-environment data. Therefore this study was planned to determine the magnitude of GEI for storage root yield, yield-related traits of sweet potato genotypes and to assess the adaptability and stability of sweet potato genotypes in different production environments in southwest Ethiopia.
MATERIALS AND METHOD:
Description of study sites:
The field evaluation was conducted in four testing locations namely; Jimma, Agaro, Metu and Haru Agricultural research center and sub- sub-centers which are considered as the representative sweet potato growing areas of southwest Ethiopia. The experiment was conducted for two cropping seasons (2019-2020) in all four locations. This made a total of eight environments considering one location and one cropping season as one environment. The description of agro-ecological and climate conditions of the study sites is summarized in Table 1.
Plant materials, experimental design and management:
A total of six white fleshed sweet potato released varieties were used for this study. The varieties were selected based on their high yield and root dry matter (RDMC) contents. The experiment was laid out as a randomized complete block design (RCBD) with three replications in each environment. Each sweet potato varieties was assigned to one plot in each replication. The gross plot size for each treatment was 1.5 x 2.4 (3.6m2), using inter-row spacing of 0.6cm and intra-rows spacing of 0.3cm. Vines of the same size and age were used as planting material. Planting was done at mid of April during the main growing season after the rain commenced and the soil was moist enough. One month after planting, seedlings were earthed up followed by frequent weeding. All other agronomic practices were followed according to the recommendations.
Data collection:
Data were collected from nine plants from each plot and the average values were used for data analysis. The characters that are used for data collection were: vine length (cm), marketable storage root number, storage root length (cm) storage root girth (cm), weight of above ground biomass (t/ha), total storage root weight (t/ha) and harvest index (%).
Table 1. The summary of agro-ecological and climatic description of the study areas
|
Location |
Altitude (m.a.s.l.) |
Latitude
|
Longitude
|
Rainfall (mm) |
Temperature (0C) |
|
|
Maximum |
Minimum |
|||||
|
Jimma |
1753 |
7o 40.00' N |
36o 47’.00’ E |
1521.1 |
26.2 |
12.1 |
|
Agaro |
1560 |
7°51′ .00' N |
36°51′ 35’ E |
1520 |
23.35 |
12.6 |
|
Metu |
1550 |
8°18′ .00' N |
35°35′ .00’ E |
1520 |
28.0 |
12.2 |
|
Haru |
1750 |
8o58’00' N |
38048’00’ E |
1727 |
21.5 |
12.2 |
Source: JARC, 2010
Data analysis:
Homogeneity of residual variance was tested prior to combined analysis over locations in each year as well as over locations and years (for the combined data) using Bartlet’s test (Steel and Torrie, 1980). Accordingly, the data collected indicated homogenous variance. Normality test was also conducted and all data showed normal distribution. A combined analysis of variance was performed using GenStat 14th edition (Payne et al. 2011) and SAS version 9.0 (SAS, 2000) statistical soft wares. Treatment means was separated by using the Fisher’s protected least significant difference (LSD) test at 1% and 5% probability. The model employed in the analysis was;
Yijk=µ+Gi+Ej+Bk+GEij+ɛijk
Where: Yijk is the observed mean of the ith genotype (Gi) in the jth environment (Ej), in the Kth block (Bk); µ is the overall mean; Gi is effect of the ith genotype; Ej is effect of the jth environment; Bk is block effect of the ith genotype in the jth environment; GEij is the interaction effects of the ith genotype and the jth environment; and ɛijk is the error term.
AMMI and AMMI bi-plot analysis, showing the genotype and environment means against Interaction Principal Component Analysis one (IPCA1), and Interaction Principal Component Analysis one (IPCA1) against Interaction Principal Component Analysis two (IPCA2) were also performed using Meta- analysis procedure-I using the same statistical software. The correlation analysis was also executed to measure of the extent and direction of the relationship between tested traits by using SAS software (SAS, 2000).
RESULT:
The results obtained from the combined analysis of variance of all the appraised traits and genotype is showed in Table 2. The genotypes, environment and genotype x environment interaction (GEI) variance were analyzed to convey the overall performance of the genotypes and evaluated traits. Accordingly, the genotypes, the environments and their interaction showed highly significant variation (p<0.001) for all evaluated traits of white fleshed sweet potato.
The results also further revealed that the environments (both locations and growing years) on which the experiments were conducted were different from one another in treating the tested sweet potato genotypes. Likewise, it also indicates that the response of the genotypes were varied and vacillated in their trait expression with change in the environments. These all this phenomenon evidently established the presence of GEI in this study.
Table 2. Mean squares for yield and related traits of white fleshed sweet potato varieties across tested locations.
|
Sources of variation |
DF |
Mean square |
|||||||
|
TSRW |
VL |
SRL |
SRG |
TSRNPP |
AGB |
HI |
CHI |
||
|
Block |
16 |
115 |
278 |
21.41*** |
1.24 |
0.97 |
376*** |
62.2* |
0.004 |
|
Genotype (G) |
5 |
5820*** |
4244*** |
19.39** |
53.89*** |
4.16*** |
1352*** |
2707*** |
0.08*** |
|
Environment (E) |
7 |
4335*** |
23038*** |
127.4*** |
29.37*** |
25.4*** |
2745*** |
2596*** |
0.06*** |
|
G*E |
35 |
679*** |
418*** |
13.31** |
2.04*** |
3.86*** |
1006*** |
483.9*** |
0.009*** |
|
Residual |
15 |
294 |
190 |
5.20 |
0.66 |
1.32 |
233 |
130.6 |
0.002 |
*, **, *** significant at 0.05, 0.01 and 0.001 % of probability level. DF= Degree of freedom, TSRW= TSRW= Total storage root weight (t/ha), VL= Vine length (cm), SRL= Storage root length (cm), SRG= Storage root girth (mm), TSRNPP= Total storage root number per plant, AGB= above ground biomass (t/ha)HI= Harvest index (%) and, CHI= Commercial harvest index (%)
Table 3. Combined mean storage root yield and yield related traits of white fleshed sweet potato varieties across all tested environments
|
Variety |
TSRW |
VL |
SRL |
SRG |
TSRNPP |
AGB |
HI |
CHI |
|
Hawasa-09 |
63.88a |
134.1b |
18.54abc |
7.48a |
4.17a |
38.13bc |
60.42b |
0.925ab |
|
Tula |
59.47ab |
133.6b |
17.71bc |
7.35a |
3.82ab |
29.55d |
65.02a |
0.945ab |
|
Awassa-83 |
51.39c |
117.1c |
19.07ab |
6.74b |
2.98c |
45.22ab |
55.17c |
0.965a |
|
Adu |
20.44d |
114.4c |
17.03c |
3.57c |
3.52bc |
39.76cd |
37.37e |
0.792c |
|
Berku |
57.00b |
149.4a |
18.70ab |
7.41a |
3.96ab |
33.11cd |
65.25a |
0.917b |
|
Temes |
47.71c |
120.6c |
19.46a |
6.44b |
3.59ab |
49.66a |
50.49d |
0.927b |
|
Mean |
49.98 |
128.20 |
18.42 |
6.50 |
3.67 |
39.24 |
55.62 |
0.91 |
|
LSD |
5.16 |
8.17 |
1.58 |
5.31 |
0.59 |
7.28 |
0.67 |
0.003 |
|
CV(%) |
18.0 |
11.12 |
15.01 |
14.26 |
18.21 |
32.7 |
17.80 |
7.16 |
Means followed by the same letter are not statistically different from each other DF= Degree of freedom, TSRW= Total storage root weight (t/ha), VL= Vine length (cm), SRL= Storage root length (cm), SRG= Storage root girth (mm), TSRNPP= Total storage root number per plant, AGB= above ground biomass (t/ha)HI= Harvest index (%) and, CHI= Commercial harvest index (%)
Table 4. Combined analysis of variance and significant tests for white fleshed sweet potato yield and related traits of ten genotypes tested in two years and four locations.
|
Sources of variation |
DF |
Mean square |
|||||||
|
TSRW |
VL |
SRL |
SRG |
TSRNPP |
AGB |
HI |
CHI |
||
|
Environment (E) |
3 |
6819.8*** |
43103*** |
216.7*** |
3196.8*** |
32.89*** |
5229*** |
5152*** |
0.12*** |
|
Genotype (G) |
5 |
5702.2*** |
4243.67*** |
19.4** |
5508.8*** |
4.16*** |
1351*** |
80.4*** |
0.08*** |
|
Year (Y) |
1 |
285.7 |
7178.8*** |
0.19 |
6.83 |
7.99*** |
925.5*** |
5206*** |
0.008 |
|
Y*E |
3 |
3218.7*** |
7622.7*** |
80.7*** |
3683.8*** |
23.73*** |
867.1*** |
5278*** |
0.026*** |
|
G*E |
15 |
667.1*** |
206.4 |
17.1** |
303*** |
5.82*** |
1717*** |
86.0*** |
0.008** |
|
G*Y |
5 |
514.4*** |
727.0*** |
21.35** |
9.5 |
3.55** |
498.2** |
86.5*** |
0.017*** |
|
G*Y*E |
15 |
725.9*** |
527.2*** |
6.83 |
172.2* |
1.99* |
464.4** |
84.1*** |
0.007* |
|
Error |
94 |
|
203.2 |
7.62 |
85.92 |
1.06 |
161.4 |
1.37 |
0.40 |
*, **, *** significant at 0.05, 0.01 and 0.001 % of probability level. TSRW= Total storage root weight (t/ha), VL= Vine length (cm), SRL= Storage root length (cm), SRG= Storage root girth (mm), TSRNPP= Total storage root number per plant, AGB= above ground biomass (t/ha), HI= Harvest index (%) and, CHI= Commercial harvest index (%)
The agronomic performance of sweet potato varieties:
The mean performance of total storage root weight of six white fleshed sweet potato varieties across eight environments was 49.98 t/ha. Genotype Hawasa-09 had the highest average total storage root yield (63.88 t/ha), followed by Tula (59.47 t/ha) and Awassa-83 (51.39 t/ha), respectively, while, genotype Adu was the lowest yielding genotype (20.44 t/ha) (Table 3).
Similarly, genotype Tula had the highest average storage root yield, storage root girth and harvest index (59.47 t/ha, 7.35 mm, 65.02%), and total storage root number per plant (3.82). While, genotype Adu produced the lowest storage length and girth (17.03 cm and 3.57 cm). Similarly, Genotype Adu had the lowest above ground biomass, harvest index and commercial harvest index (39.76 t/ha, 37.37 % and 0.792), respectively (Table 3).
Variance estimate for yield and related traits of white fleshed sweet potato genotypes:
The combined analyses of variance (ANOVA) of the yield and yield related traits of eight environments revealed that there were highly significant variation (p<0.01) among the genotypes, environments (year, location, year x location) and genotype by environment interaction (genotype x year, genotype x location and genotype x year x location) (Table 4). These significant variations of the genotypes, environments and the genotype by environment interaction indicated that the response of the genotypes were variable and varied in their yield and yield related traits with change in environment and these phenomenon clearly declared the presence of GEI in this study.
Total storage root yield:
The storage root weight of six white fleshed sweet potato genotypes was highly variable over the eight environments, showing highest storage root yield cross-over interaction from environment to environment. Amongst the environments the highest total storage root yield (63.86 t/ha) was observed from genotype Hawassa-09 and Haru (2019/2020) is the best environment. While, the lowest root yield (24.19 t/ha) was recorded from genotype Adu and Agaro is the least suitable environment for white fleshed sweet potato production (Table 5).
Storage root girth:
The mean root girth (mm) of the genotypes is shown in Table 6. Jimma (2019/2020) was the best environment with root girth of 85.98mm. The widest root was recorded from variety Hawassa-09 at Jimma, Agaro and Metu, which was 104.2 and 88.1mm of each, respectively, and for Tula and Berku at Jimma which was 99.33 and 104.0mm, respectively. The lowest mean root girth was recorded for variety Adu at Metu and Jimma, and for Awassa-83 at Haru and Jimma. Most of the genotypes studied had mean root girth values that were above the average (64.95 mm) across the test environments (Table 6).
Table 5. Mean total storage root yield (t/ha) performance of six white fleshed sweet potato varieties tested across eight environments
|
Variety |
Environments |
Over all mean |
|||||||
|
2018/2019 |
2019/2020 |
||||||||
|
Jimma |
Agaro |
Metu |
Haru |
Jimma |
Agaro |
Metu |
Haru |
||
|
Hawasa-09 |
63.33b |
55.74a |
73.5ab |
29.63c |
44.44a |
69.6a |
78.0a |
96.67a |
63.86 |
|
Tula |
65.18b |
41.70b |
63.2b |
49.25a |
29.63bc |
77.4a |
88.9a |
60.37b |
59.45 |
|
Awassa-83 |
56.29b |
35.74b |
63.1b |
48.89a |
24.07c |
48.3b |
86.1a |
48.52b |
51.38 |
|
Adu |
10.13d |
33.5bc |
29.9c |
26.29d |
13.70d |
20.5d |
15.2b |
17.22d |
20.81 |
|
Berku |
79.62a |
22.2cd |
76.7ab |
41.48b |
34.44b |
55.9b |
92.2a |
53.33b |
56.98 |
|
Temes |
43.33c |
15.55d |
97.4a |
44.44b |
25.00c |
41.5c |
80.8a |
32.59c |
47.58 |
|
Mean |
53.15 |
34.08 |
67.33 |
40.0 |
28.54 |
52.21 |
73.62 |
51.45 |
50.01 |
|
LSD |
11.23 |
11.41 |
32.5 |
3.34 |
6.81 |
11.87 |
16.86 |
12.21 |
|
|
CV (%) |
11.62 |
18.4 |
26.54 |
4.58 |
13.12 |
12.50 |
12.59 |
13.05 |
|
Table 6. Mean storage root girth (mm) performance of six white fleshed sweet potato varieties tested across eight environments
|
Variety |
Environments |
Over all mean |
|||||||
|
2018/2019 |
2019/2020 |
||||||||
|
Jimma |
Agaro |
Metu |
Haru |
Jimma |
Agaro |
Metu |
Haru |
||
|
Hawasa-09 |
104.2a |
81.8a |
61.9a |
50.3ab |
74.06a |
69.4a |
81.8a |
75.08a |
74.82 |
|
Tula |
99.33ab |
82.93a |
60.2a |
57.6a |
71.12a |
67.0a |
92.7a |
58.98bc |
79.79 |
|
Awassa-83 |
90.67ab |
61.4bc |
63.8a |
55.4a |
59.36c |
63ab |
94.2a |
51.33cd |
67.40 |
|
Adu |
41.76c |
29.26d |
27.9b |
41.7b |
25.33d |
37.1c |
46.9b |
32.11e |
35.26 |
|
Berku |
104.0a |
70.2ab |
73.3a |
50.8ab |
69.19ab |
70.1a |
95.0a |
60.66b |
74.16 |
|
Temes |
75.93b |
55.93c |
73.6a |
50.2ab |
61.53bc |
58.7b |
89.4a |
49.55d |
64.36 |
|
Mean |
85.98 |
63.60 |
60.16 |
51.02 |
60.10 |
60.92 |
83.38 |
54.62 |
64.95 |
|
LSD |
27.2 |
14.14 |
20.19 |
10.08 |
7.69 |
7.68 |
22.26 |
8.23 |
|
|
CV(%) |
17.40 |
12.22 |
18.45 |
10.86 |
7.0 |
6.93 |
14.67 |
8.28 |
|
Table 7. Mean storage root number per plant performance of six white fleshed sweet potato varieties tested across eight environments
|
Variety |
Environments |
Over all mean |
|
|||||||
|
2018/2019 |
2019/2020 |
|
||||||||
|
Jimma |
Agaro |
Metu |
Haru |
Jimma |
Agaro |
Metu |
Haru |
|
||
|
Hawasa-09 |
3.93b |
6.06a |
8.2b |
4.93d |
4.6a |
10.4a |
6.26a |
4.93a |
6.16 |
|
|
Tula |
4.33b |
4.6a |
10.6a |
6.0ab |
3.9a |
5.7b |
5.56a |
6.0a |
5.84 |
|
|
Awassa-83 |
3.8b |
3.93a |
5.67c |
6.67a |
3.6a |
5.2b |
4.33a |
6.67a |
4.98 |
|
|
Adu |
5.59a |
5.93a |
6.93bc |
5.67c |
4.6a |
5.01b |
4.46a |
6.0a |
5.52 |
|
|
Berku |
4.53b |
6.2a |
8.93ab |
6.53ab |
4.8a |
5.3b |
4.93a |
6.53a |
5.97 |
|
|
Temes |
4.20b |
4.53a |
7.46bc |
6.67a |
4.2a |
5.3b |
5.56a |
6.67a |
5.57 |
|
|
Mean |
4.39 |
5.21 |
7.96 |
6.07 |
4.3 |
6.16 |
5.16 |
6.13 |
5.68 |
|
|
LSD |
0.91 |
1.11 |
2.38 |
0.59 |
1.77 |
2.14 |
3.0 |
1.99 |
|
|
|
CV(%) |
11.49 |
11.80 |
16.48 |
5.38 |
22.73 |
19.11 |
32.54 |
17.87 |
|
|
Storage root number per plant:
The mean of storage root number per plant for the genotypes is presented in Table 7. Metu was the best environment for storage root number with a mean yield of 7.96, followed by Haru with 6.13. Jimma was the poorest environment in both growing season, providing a mean root number of 4.3, followed by Agaro with 5.21. Hawasa-09 had the highest root number per plant across environments with a mean of 6.16, followed by Berku and Tula, which produced 5.97and 5.84 numbers of roots per plant, respectively. The lowest mean root number per plant of 4.98 was recorded for Awassa-83.
Additive main effect and multiplicative interaction (AMMI-2) bi-plot analysis:
The AMMI 2 bi-plot analyses of total storage root weight, storage root girth and number of roots per plant of the six white fleshed sweet potato genotypes evaluated in eight environments are shown in Figures 1–3, respectively. For total storage root weight, the percentage of variation accounted by the IPCA-1 and IPCA-2 axes was 50.27% and 31.16%, respectively (Figure 1). Genotypes Hawassa-83 and Tula had broad adaptability as they were located closer to the center of the bi-plot. Genotypes Adu, Temes, Berku, and Hawassa-09 are placed furthest from the point of origin, showing specific adaptation to the environments within their proximity on the bi-plot.
For the storage root girth, the results of biplot showed that Hawassa-09 performed very well in the overall performance and was closely followed by Tula. However, Hawassa-09 and Berku were the most stable genotypes across the tested locations. Also, the biplot result identified Haru-2 and Agaro-1 as the ideal environments across the eight locations.
Similarly, the results of biplot analysis for the number of roots per plant showed that Hawassa-09 was the most stable genotypes across the tested locations. Also, the biplot result identified Agaro-2, Metu-2 and Agaro-1 as the ideal environments across the eight locations. Therefore, the use of GGE in this study has not only identified the most stable genotypes across locations but is also able to identify the locations that optimize the genotypes performance.
Figure 1. AMMI 2 bi-plot for IPCA 1 against IPCA 2 scores for six white fleshed sweet potato genotypes and eight environments on total storage root weight
Figure 2. AMMI 2 bi-plot for IPCA 1 against IPCA 2 scores for six white fleshed sweet potato genotypes and eight environments on storage root girth
Figure 3. AMMI 2 bi-plot for IPCA 1 against IPCA 2 scores for six white fleshed sweet potato genotypes and eight environments on number of storage root per plant
Correlation study:
Correlation coefficient is a measure of the extent and direction of the relationship between any two traits (variables). The positive and strong relationships between total storage root yield and storage root girth (r = 0.989), harvest index (r =0.935), and commercial harvest index (r= 0.861) suggests that these traits are important root yield components of sweet potato, and that their simultaneous selection will be a good approach to increasing root yield. This same relationship had been observed by Afuape et al. (2011) and Yahaya et al. (2015).
Table 8. Combined Pearson correlation coefficients (r) of agronomic traits evaluated in four locations and 2 years.
|
Traits |
TSRW |
SRG |
TSRNPP |
HI |
CHI |
|
TSRW |
1.00 |
0.989** |
0.434 ns |
0.935** |
0.861** |
|
SRG |
|
1.00 |
0.374 ns |
0.947** |
0.890** |
|
TSRNPP |
|
|
1.00 |
0.456 ns |
-0.062 ns |
|
AGB |
|
|
|
-0.559 ns |
0.017 ns |
|
HI |
|
|
|
1.00 |
0.769** |
|
CHI |
|
|
|
|
1.00 |
**means Very highly significant at P = 0.1%. ns, not significant.
TSRW= Total storage root weight (t/ha), SRG= Storage root girth (mm), TSRNPP= Total storage root number per plant, HI= Harvest index (%) and, CHI= Commercial harvest index (%)
CONCLUSION AND RECOMMENDATION:
Crop yield is a complex trait that is influenced by a number of component characters along with the environment either directly or indirectly. The G × Y and G × L effects were significant for most of the traits indicating specific adaptation of the genotypes across the locations. However, the study is able to identify the most stable genotypes and the ideal environments across the locations that optimize the genotypes performance.
The authors would like to acknowledge Ethiopian Institute of Agricultural Research/Jimma Agricultural Research Center (JARC) for the financial support of this study.
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Received on 03.06.2022 Modified on 24.06.2022
Accepted on 17.07.2022 ©A&V Publications All right reserved
Res. J. Pharmacognosy and Phytochem. 2022; 14(4):240-246.