Cite this asBelay F, Mekbib F, Tadesse T (2021) Univariate stability analysis and relationship among parameters for grain yield of striga resistant sorghum [Sorghum bicolor (L.) Moench] hybrids in Ethiopia. Open J Plant Sci 6(1): 069-081. DOI: 10.17352/ojps.000036
Sorghum (Sorghum bicolor) known as a Camel crop of cereals, is among the dominant staple food grains for the majority of Ethiopians. Forty nine sorghum genotypes (hybrids + open pollinated varieties) were tested at five locations in a simple lattice design with two replications during the 2016 main cropping season. The objectives of this study were to determine yield stability using univariate methods and to assess the association among stability parameters of striga resistant sorghum genotypes in the dry lowland areas of Ethiopia. The result of the combined analysis of variance for grain yield revealed highly significant (P≤0.001) difference among Environment (E), Genotype (G) and Genotype × Environment Interaction (GEI). Based on the combined ANOVA over locations, the mean grain yield of environments ranged from 588 kg ha-1 in Humera to 4508 kg ha-1 in Sheraro. The highest yield was obtained from ESH-1 (3278 kg ha-1), while the lowest was from K5136 (735 kg ha-1) and the average grain yield of genotypes was 2184 kg ha-1. Different stability models were used in measuring of genotype stability such as AMMI Stability Value (ASV), Yield Stability Index (YSI), coefficient of regression (bi) and deviation from regression (S2di). Yield was significantly correlated with bi (0.91), r2 (0.55) and ASV (-0.56), while it was not correlated with S2di (-0.26). The non-significant correlation among yield and stability statistics indicated that, stability statistics provide information that can not be collected from average yield. The high positive correlation among mean grain yield and stability parameters is expected as the values of these parameters were higher for high yielding genotypes and the vice versa. Highly correlated stability parameters indicate that they can measure stability similarly. However, there were inconsistencies with the univariate stability parameters used, which created uncertainty to select or recommend the stable genotypes. Therefore, as the data is from one year, it is necessary to repeat the experiment at least for one more year across diverse dry lowland areas of Ethiopia.
Sorghum [Sorghum bicolor (L.) Moench] is naturally self-pollinated monocotyledon crop plant with the degree of spontaneous cross pollination, in some cases, reaching up to 30%, depending on panicle type . It is a staple crop for more than 500 million people in 30 sub-Saharan Africa and Asian countries . In Ethiopia, sorghum is produced by five million small holder farmers and its production is estimated to be four million metric tons from nearly two million hectares of land, giving the potential average grain yield of around two tons per hectare. It is ranked third in area coverage and fourth in total production . However, low yields of sorghum have been recorded due to a number of biotic and abiotic constraints. Sorghum production constraints vary from region to region within Ethiopia; but, drought and striga are reported to be important sorghum production constraints in the north and northeastern parts of the country .
Stiriga hermonthica, the dominant striga species, is the most severe in the highly degraded north, northwestern and eastern parts of the country, viz. Tigray, Wollo, Gonder, Gojam, North Shewa and Hararghe . Where soil fertility (nutrient deficiency) and moisture stress are limiting factors, i.e. striga is rapidly expanding in areas where the soil has low fertility and drought is frequent. Nationally, striga causes annual yield loss as high as 65-70% and, at times, leaves plot uncultivated .
Many researchers [7,8] have reported variability in sorghum responses to striga infestation. The presence of a wide range of variability in striga resistant and/or drought tolerance traits among sorghum genotypes suggests an opportunity to develop high yielding and resistant/tolerant genotypes through hybridization . In order to address the constraints affecting sorghum, and increase its production, the National Agricultural Research Systems (NARS) in collaboration with international research centers like, ICRISAT and Purdue University are developing hybrid sorghums.
The numerous importances attached to sorghum hybrids stems from the fact that there has been a yield advantage of sorghum hybrids whenever they are compared to the improved and landrace cultivars, commonly in order of 20 to 60% . Sorghum hybrids have been shown to yield 15 to 41% higher than open pollinated varieties under small holder conditions in India and West Africa [11,12]. Reports from research has shown that sorghum hybrids holds a lot of importance and appear to be more reliable than inbred varieties in erratic environments, typically of sorghum growing regions in the semi-arid tropics .
One of the importance attached to sorghum hybrids whenever they are compared to the open pollinated and landrace cultivars, increase the yield in order of 20 to 60% . Beside yield superiority over open-pollinated varieties, hybrids are more stable across different environments  and more tolerant to moisture stress. In Ethiopia, hybrids give 27-30% more grain yield advantage as compared to check varieties and proved to be early maturing than their parental lines [6,15,16].
The yield advantage in sorghum hybrid is due to the complementarity effect of the two inbred lines on the F1 hybrid . It is thus presumed that inbred lines that have striga resistant genes complement each other and the F1 hybrids express superiority in reaction to striga and could give better yield. Abebe, et al.  also reported that most resistant sorghum hybrids produced consistently higher grain yields under S.hermonthica infestation, supported fewer emerged parasites, and less sustained minimal parasite damage symptoms across locations. However, there is no information on yield stability of striga resistant sorghum hybrids in Ethiopia. Therefore, the specific objectives of the study were to determine yield stability using univariate methods and to assess the association among commonly used stability parameters for striga resistant sorghum hybrids in dry lowland areas of Ethiopia.
The field experiment was conducted during the 2016 main cropping season at five locations (Sheraro, Kobo, Mehoni, Fedis and Humera), representing the dry lowland areas of Ethiopia located in the altitude range of 609 - 1600 meter above sea level (m.a.s.l), where sorghum is widely grown. The detailed agro-ecological features of the locations are presented in Table 1, Figure 1.
Breeding materials comprised of 49 sorghum genotypes that include three striga resistant check varieties, Gobye (P9401), Abshir (P9403) and Birhan; two striga susceptible hybrids, ESH-1 and ESH-4 released by the national program and 44 striga resistant sorghum hybrids introduced from Purdue University. The majority of the introduced hybrids were derived from the locally adapted striga resistant sorghum inbred lines with best performing seed parent developed at Purdue. The detailed information of the tested genotypes is presented on Table 2.
The trial was laid out using a 7x7 lattice design with two replications in each location. Each plot consisted of two rows of 5 m length with 0.75 m and 0.20 m, between rows and plants, respectively. All plots were fertilized uniformly with 100 kg ha-1 Di-ammonium Phosphate (DAP) and 50kg ha-1 Urea. Full dose of DAP and half of urea were applied at the time of planting and the remaining half was side dressed at knee height stage of the crop. All of the other agronomic management practices were applied as required at all locations as per the recommendations for sorghum in dry lowland areas of Ethiopia.
Data were collected both on plot and plant basis, based on the descriptors list for sorghum (IBPGR/ICRISAT, 1993). Phenological data (days to emergence, flowering, grain filling period and maturity date), morphological data (plant height and panicle length), and yield and yield related traits (grain yield and thousand grain weight) were collected.
From the two rows five plants were selected randomly and tagged to collect the morphological data such as, plant height and panicle length. The detail of the data collection for each trait was carried out as follows:
Plant height (PH): was determined from the average height of five plants in cm from ground level to the tip of the panicle (at physiological maturity).
Panicle length: was measured (cm) from the base of the panicle to the tip from five randomly selected plants per plot at maturity.
Days to 50% seedling emergence: The number of days from the date of sowing to the date at which 50% of the seedlings in a plot were emerged.
Days to 50% flowering: The number of days from 50% seedling emergence to the date at which 50 % of the plants in a plot started flowering.
Days to 90% maturity: The number of days from emergence to the stage when 90% of the plants in a plot have reached physiological maturity.
Grain filling period: The numbers of days from flowering to maturity, i.e. the number of days to maturity minus the number of days to flowering and it includes watery ripe stage, milk stage, soft dough stage, hard dough stage and ripening stage.
Grain yield (kg ha-1): The panicles from the two rows of each plot were threshed, cleaned and adjusted to standard moisture level at 12.5% and weighted to get the grain yield per plot in grams and converted to kg ha-1 for analysis.
Thousand grain weight: The weight of 1000 randomly sampled grains from each plot was measured in grams using sensitive balance and adjusted at 12.5% moisture content.
Homogeneity of residual variances was tested prior to analysis over locations using Bartlett’s tests . Analysis of variance for each environment, combined analysis of variance over environments, correlation coefficient among stability parameters and agronomic traits were computed using GenStat 18th edition (2016. Coefficient of regression (bi) and deviation from regression (S2di) stability parameters were also analyzed using SPAR 2.0 software.
As the error variance was homogenous for all traits continued to combined analysis of variance from the mean data of all environments to detect the presence of GEI. Genotypes were assumed to be fixed and environment effects were treated as random. Genotype by environment interaction was quantified using pooled analysis of variance, which partitions the total variance into its component parts (genotype, environment, genotype x environment interaction and pooled error). Mean separations for the treatment means having significant differences at 5% probability levels was done using Duncan’s Multiple Range Test (DMRT) comparison procedure. GenStat 16th edition (2016) statistical software was used for statistical analyses. The relative efficiency of the simple lattice design over Randomized Complete Block Design (RCBD) was checked. For most of the yield and yield related traits RCBD was found to be more efficient than that of the lattice design. The analysis of variance for each location and combined analysis of variance over locations was used as suggested by Gomez and Gomez (1984). The model employed in the analysis was;
Yijk = μ + Gi + Ej + Bk + GEij + εijk 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 εijk is the error term.
Eberhart and Russell  procedure involves the use of joint linear regression where the yield of each genotype is regressed on the environmental mean yield. Then, the behavior of the genotype was assessed by the model: Yij = µi +βiIj +δij using Spar 2.0 statistical software.
Where: Yij = the mean performance of the ith genotype in the jth environment, µi = the grand mean of the ith genotype over all the environments, βi= the regression coefficient which measures the response of the ith genotype on environmental index, Ij = the environmental index obtained by the difference between the mean of each environment and the grand mean and δij = the deviation from regression of ith variety in the jth environment
The pooled deviations mean square was tested against the pooled error mean square by the F-test to evaluate the significance of the differences among the deviations of genotypes being evaluated from their expected performances. As a result, in order to test the validity of the hypothesis that whether there is significant difference among the 49 genotypes with respect to their mean grain yields or not and whether there is significant difference among the regression coefficient or not, genotypes mean square and regression mean square were tested against the pooled deviation using the F-test.
Spearman’s correlation coefficient between different stability parameters and among agronomic traits and coefficient of determination (r2) for grain yield of each genotype was estimated by using GenStat 18th edition (2016) statistical software and Microsoft excel, respectively.
In order to compute and rank genotypes according to their yield stability, the additive main effect and multiplicative interaction effect stability value (ASV) was proposed by Purchase . It was calculated using Microsoft excel (2007) by employing the following formula:
Where: ASV = AMMI’sstability value, IPCA1= interaction principal component analysis one, and IPCA 2= interaction principal component analysis II.
Similarly yield stability index (YSI) was also computed by summing up the ranks from ASV and mean grain yield :
Where: RASV is rank of AMMI stability value and RGY is rank of mean grain yield to statistically compare the stability analysis procedures used in the study.
The overall performance of 49 sorghum genotypes tested based on mean grain yield and other agronomic traits across locations is presented in Tables 3. In this study days to flowering, maturity, plant height, panicle length, grain yield and thousand grain weight were highly significantly (P≤ 0.001) affected by the combined effect of both genotype and growing conditions of locations, whereas days to emergence and grain filling period were non-significant (Table 4). The mean day to emergence at Humera was faster than the four locations.