By Belay, F (2024). Greener Journal of Plant Breeding and Crop Science 12(1): 13-20.
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Table of Contents
ISSN: 2354-2292
Vol. 12(1), pp. 13-20, 2024
Copyright ©2024, Creative Commons Attribution 4.0 International
https://gjournals.org/GJPBCS
Alamata Agricultural Research Center, P.O.Box 56, Alamata, Ethiopia.
Type: Research
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Sorghum known as a Camel crop of cereals, is a versatile and resilient cereal grain that has been cultivated for millennia. Six drought tolerant sorghum genotypes were evaluated with the objectives to identify stable and high yielding ones across six locations in the dry lowland environments of Ethiopia during the 2016 main cropping season. The field experiment was conducted using a randomized complete block design with three replications at each location. Agronomic and striga counts data were collected but only grain yield was used for stability analysis. The combined analysis of variance revealed highly significant (P≤0.01) difference among genotypes (G) environments (E) and genotype × environment interaction (GEI). Genotypic mean grain yield ranged from 1730 to 3650 kg ha-1 with average mean grain yield of 2421 kg ha-1, while environment ranged from 1670 to 3422 kg ha-1. GGE bi-plot model was used to identify stable genotype for partitioning the GEI into the causes of variation and the best multivariate model in this study. The first two principal components were used to create a 2- dimensional GGE bi-plot analysis explained 97.83% of the total variation caused by G+GE of PC1 and PC2 accounted for 93.26% and 4.57% sum of squares, respectively, while 2.17% was attributed to noise. Thus, model diagnosis (fitting) showed that the first two PCs were significant and can be taken to interpret this data. The which-won-where bi-plot further identified one winning genotype in one mega environment. The winning genotype across locations was Melkam. Therefore, Melkam can be recommended for wider cultivation due to better grain yield and stability performance across testing locations in the dry lowland areas of Ethiopia.
Published: 17/09/2024
Fantaye Belay
E-mail: fantaye933@gmail.com
Sorghum [Sorghum bicolor (L.) Moench] known as a Camel crop of cereals, is a versatile and resilient cereal grain that has been cultivated for millennia. Originating from Africa, it has spread across the globe due to its adaptability to diverse climates and its numerous uses ranging from food and feed to industrial applications. Sorghum is a staple crop for more than 500 million people in 30 sub-Saharan African and Asian countries and is essential to the food security of over 300 million people in Africa (Mace et al., 2013). Ethiopia is considered as a center of origin and diversity for the four (bicolor, guinea, caudatum and durra except kafir) of the five major races and the second largest sorghum producing country in eastern Africa next to Sudan. Of the cereals, sorghum covers 15% of the total area and contributed 16% of the total grain production in the country. Sorghum ranks 4th in Ethiopia in terms of total production (45.2 million quintal), area cultivated (1.7 million hectare), and number of farmers (4.3 million) producing the commodity (CSA, 2020).
There are numerous varieties of sorghum cultivated globally, each adapted to different environmental conditions and intended uses. These varieties can be broadly categorized into grain sorghum, sweet sorghum and forage sorghum. It is utilized in various ways. Sorghum flour (fermented or unfermented) is used for human food such as breads, porridges, couscous, and snacks and beverages. The grain and fresh or dry biomass has diverse use and good source for sugar, syrup, and molasses industry (McGuire, 2007). It is also the second most important crop for “injera” quality next to tef in Ethiopia. In addition, sorghum stalks and leaves are an important source of dry season feed for livestock, source of energy for cooking their daily foods, for construction of houses and fences, and as fuel wood (MoANR, 2016). However, a number of constraints have been standing on the way towards sorghum production.
Drought and striga are reported to be the most important abiotic and biotic constraints limiting the production and productivity of sorghum in the north and northeastern parts of Ethiopia (Wortmann et al., 2006). Over 80% of sorghum in Ethiopia is produced under severe to moderate drought stress conditions. Most farmers grow long maturing local landraces, some of which take 7-8 months to mature further complicating the drought problem. Striga, a parasitic weed, 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 (AATF, 2011). In spite of biotic and abiotic stress tolerant, yield stability is also one of the setbacks to select and recommend genotypes for different environments. Therefore, the objectives of this study were to identify stable sorghum genotypes and/or assess their performance across locations in dry lowland areas of Ethiopia.
Field experiments were conducted in the 2016 main cropping season at six locations representing the major sorghum growing dry lowland agro-ecologies in Ethiopia, namely Fedis, Kobo, Mehoni, Abergelle, Sheraro and Humera. The agro-ecology of the locations are described as semi-arid belt of the eastern lowlands of Hararghe (Fedis), sub-moist hot warm lowlands (Kobo, Mehoni, Abergelle and Sheraro) and hot to warm semiarid plain (Humera) sub agro-ecology (SA1-1) (EIAR, 2011) with a variation in elevation.
Table 1. Description of the study sites
Fall
type
code
Source: respective research centers, 2016
Six stress tolerant sorghum genotypes; two early maturing and drought tolerant varieties (Meko-1 and Melkam); three striga resistant and drought tolerant varieties, Gobye (P9401), Abshir (P9403) and Birhan and one local check, obtained from Melkassa Agricultural Research Center in Ethiopia were used and /or evaluated as presented in Table 2.
Table 2: Description of genotypes used in the study
Adaptation
WSV387
MARC = Melkassa Agricultural Research Center in Ethiopia.
The field experiment was carried out in a randomized complete block design (RCBD) with three replications across locations. Each plot was consist of 5 rows of 5m length with inter and intra row spacing of 0.75m and 0.20 m, respectively. The three middle rows were harvested and two border rows were left to exclude border effect. The gross area of the experimental plot and the harvestable area had a size of 18.75 m2 (3.75 m x 5 m) and 11.25m2 (2.25 m x 5 m), respectively. All plots were fertilized uniformly with 100 kg ha-1 Di-ammonium Phosphate (DAP) and 50kg ha-1 Urea. Full dose of P (18 % N and 46 % P2O5) and half of N (46 % N) were applied at the time of planting and the remaining half was side dressed at knee height stage of the crop. Weeding and other agronomic practices were done uniformly and properly as per the recommendations for sorghum in dry lowland areas of Ethiopia.
Grain yield was determined as the total weight of clean grains from the central three rows (harvestable net plot size of 11.25m2), leaving a border rows from both sides of the plot and measured with sensitive balance, adjusted at 12.5% seed moisture content and the obtained grain yield per plot in grams was converted to kg ha-1 for analysis.
Homogeneity of residual variances was tested prior to a combined analysis using Bartlett’s test (Steel and Torrie, 1998). Analysis of variance for each location, combined analysis of variance over locations and GGE biplot analysis was computed using Genstat 16th edition (Payne, 2014) software following a procedure appropriate to RCBD (Gomez and Gomez., 1984). Mean separation was done using Fisher’s least significant difference (LSD) test at 5% probability level.
The combined analysis of variance of six drought tolerant sorghum genotypes over six locations for grain yield (kg ha-1) is presented in Table 4. The result revealed that genotypes, locations, and their interaction (GEI) had a highly significant difference (p<0.01), indicating that the performances of genotypes varied across different locations. The environments (locations) in the study were assumed as random effects and the genotype effects were treated as fixed. The genotype (G) explained 53.93% to the treatment (total variation) sum squares for grain yield, while location (E) and genotype by environment interaction contributed 38.13% and 7.42% respectively, suggesting that the environmental conditions were relatively consistent across locations while the contribution of GEI to the total variation showed minimal role.
Based on mean performance of genotypes over locations result the highest yield was obtained from Melkam (3650 kg ha-1), while the lowest was from Local (1730 kg ha-1) and the average grain yield was 2421 kg ha-1. The variation in grain yield was attributed to factors such as genotypic variation, soil fertility, rainfall patterns, temperature, and moisture availability across different environments. In agreement with this finding, several studies (Gebeyehu et al., 2019; Yitayeh et al., 2019; Alemu et al., 2020; Amare et al., 2020; Belay et al., 2020; Belete et al., 2020; Worede et al., 2020; Birhanu et al., 2021; Enyew et al., 2021; Habte et al., 2021; Teressa et al., 2021; Nesrya et al., 2024) have been conducted to scrutinize genotype by environment interaction and reported significant variation for grain yield on sorghum in Ethiopia.
Table 3. Mean grain yield (kg ha-1) of six sorghum genotypes tested across six locations
Where: GM= genotypic means, EM= environment means; LSD = least significance difference, CV (%) = coefficient of variation in percent and values with the same letters in a column are not significantly different at P≤ 0.05.
Table 4. Combined analysis of variance for six sorghum genotypes (G) across six locations (E)
465680
**= significant at P≤ 0.01, DF = degree of freedom, SS = sum of squares, MS = mean squares
GGE Biplot Analysis
In the GGE model, genotype (G) main effect plus genotype by environment interaction (GEI), are the two sources of variation of GGE biplot whereas in the AMMI model only the GEI term is absorbed. In GGE biplot, the best genotype is the one with large PC1 scores (high mean yield) and near zero PC2 scores (stable). The partitioning of GEI through GGE biplot analysis in this study displayed that PC1 and PC2 accounted for 93.26% and 4.57% of sum of squares, respectively, with a total of 97.83% of GGE variation for grain yield.
‘Which-Won-Where’ Pattern and Mega-environment Identification
The ‘which-won-where’ pattern as graphically described in Figure 1 revealed that the testing environments (Fedis, Kobo, Mehoni, Abergelle, Sheraro and Humera) fall into the same mega environment with winning genotype Melkam. This pattern suggested that Melkam was vertex genotype for sector that gave the highest yield for the environments i.e. broadly adapted. As a result the genotype would be selected for proper exploitation of resources across the tested dry lowland environments of Ethiopia. On the contrary, the genotypes Meko-1, Gobye, Abshir, Birhan and local fall in sectors where there were no locations at all; these genotypes are poorly adapted to the testing environments. In agreement with this finding Yitayeh et al. (2019); Alemu et al. (2020); Belay et al. (2020); Belete et al. (2020); Worede et al. (2020); Birhanu et al. (2021); Enyew et al. (2021), and Nesrya et al. (2024) are among the many authors who used GGE bi-plot to identify mega environments, to evaluate the genotypes and to test the environments in Ethiopian sorghum cultivars.
Figure 1. Polygon view of GGE biplot graph for which-won-where pattern of six sorghum genotypes across six environments.
Ranking of Genotypes Based on Mean Grain Yield and Stability Performance
Figure 2 shows ranking of genotypes based on their mean yield and stability performance by AEC (average environment coordination) line which passes through the average environment (represented by small circle) and bi-plot origin. Genotypes on the right side most of this line have high yield performance (above average mean yield). Hence, Melkam and Meko-1 gave above average mean yield across locations while Gobye, Abshir, Birhan and local scored below average mean yield (left side to AEC). The stability of the genotype is determined by their projection on to the middle horizontal line. The greater the absolute length of the projection of a genotype, the less stable it is. According to the bi-plot (Fig. 2) Melkam has the shortest vector from the ATC abscissa with high yield and most stable genotype, suggesting it’s adaptation to a wide range of environment.
Meko-1 was longer projection from average line and indicating highest yielding but less stable genotype while, Gobye, Abshir, Birhan and local were the lowest yielding and least stable genotypes across locations in the present study having large contribution to the genotype by environment interaction in agreement with the findings of Yitayeh et al. (2019); Alemu et al. (2020); Belay et al. (2020); Belete et al. (2020); Worede et al. (2020); Birhanu et al. (2021); Enyew et al. (2021), and Nesrya et al. (2024) reported high yielder and stable genotypes as well as low yielding and poorly stable one’s in sorghum.
Figure 2. The mean performance and stability view of the GGE biplot with scaling focused on genotypes across environments.
Evaluation of Varieties Based on the Ideal Genotype
The ideal genotype is located in the first concentric circle in the biplot as indicated in Figure 3. An ideal genotype should have both high mean yield performance and stable across locations. From this study, Melkam was the “ideal” genotype with the highest mean grain yield and stable across locations. Starting from the middle concentric circle pointed with arrow was drawn to help visualize the distance between genotypes and the ideal genotype (Yan and Tinker 2006). Genotypes closer to the ideal genotype were the stable ones, while genotypes far from the ideal genotypes were the unstable. Meko-1 was plotted close to the ideal genotype considered as desirable genotype, while Gobye, Abshir, Birhan and local were low yielding genotypes associated with genotypic instability (Figure 3). Therefore, desirable genotypes are those nearest to the ideal genotype (the center of concentric circle). Similar result was reported by many researchers (Gebeyehu et al., 2019; Yitayeh et al., 2019; Alemu et al., 2020; Amare et al., 2020; Belay et al., 2020; Belete et al., 2020; Worede et al., 2020; Birhanu et al., 2021; Enyew et al., 2021; Habte et al., 2021; Teressa et al., 2021; Nesrya et al., 2024) on sorghum in Ethiopia.
Figure 3. GGE-biplot showing the “ideal” genotype.
The combined analysis of variance result revealed that sorghum genotypes evaluated in the study were highly significantly (p<0.01) influenced by genotype, environment and genotype x environment interaction (GEI). The total sum of squares explained by the genotype was 53.93% followed by environment 38.13%, while the genotype x environment interaction explained least 7.42%. Based on combined analysis of variance over locations, the mean grain yield of environments ranged from 1670 to 3422 kg ha-1. The highest yield was obtained from Melkam (3650 kg ha-1), while the least was from Local (1730 kg ha-1) and the average grain yield of genotypes were 2421 kg ha-1. GGE model was employed in determining the most stable and high yielding sorghum genotypes in this study. The first two principal components for grain yield stability of the GGE biplot analysis explained 97.83% of the total variation caused by G+GE of PC1 and PC2 accounted for 93.26% and 4.57% sum of squares, respectively, while 2.17% was attributed to noise. The which-won-where biplot identified one winning genotype in one mega environment. Melkam was the winning genotype and considered as the most desirable and stable ones, therefore can be recommended for wider cultivation due to better grain yield and stability performance across the testing environments in the dry lowland areas of Ethiopia.
The author has not declared any conflict of interest.
The author thank to the national sorghum improvement program of Ethiopia for financing and providing working facility.
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