Performance of Selected Tef Genotype for High Potential Areas of Ethiopia

Genetic improvement of native crops is a promising strategy to combat hunger in the developing world. Tef is the major staple food crop for approximately 73 million people in Ethiopia. As an indigenous cereal, it is well adapted to diverse climatic and soil conditions; however, its productivity is extremely low mainly due to lack of high yielder genotypes and susceptibility to lodging, biotic and abiotic stresses. To circumvent this problem, an experiment comprising 20 tef genotypes including the standard and local checks were evaluated in a randomized complete block design with four replications at nine environment to develop high yielding, stable and farmers preferred variety (ies) for high potential areas. Combined analysis of variance revealed highly significant (P ≤ 0.01) variations due to genotype, environment for most of traits and significant (p ≤0.05) genotype by environment interaction effects (GEI) for grain yield. AMMI analysis revealed 7.62%, 67.27%, 25.11% variation in grain yield due to genotypes, environments and GEI effects, respectively. The mean grain yield value of genotypes averaged over environments indicated that G12 (DZ-Cr-387 X Rosea (RIL-133) had the highest grain yield (2761 kgha -1 ) compared to the standard check variety Negus (2526kgha -1 ). In addition this candidate variety proved stable across environments for grain yield during the variety evaluation experiment. Therefore, this genotype was evaluated by the national variety released committee for release as a new variety for the year of 2019/20 and the technical committee approved it for fully released as new variety in 2020. Thus, this variety should be used as a commercial variety for potential tef growing areas to increase tef productivity and production in the country.


Introduction
Background Tef (Eragrostis tef) is being labeled as one of the latest super foods of the 21 st century, like the ancient Andean grain quinoa, tef's international popularity is rapidly growing [3] mainly because the grains are free from gluten to which many people are allergic [24] a causal agent for celiac disease; hence, tef is becoming globally popular as a life-style crop [22]. Tef is a resilient crop that performs better than other cereals under local conditions including drought, waterlogging, and poor soil. Since it produces a reasonable yield when grown in areas that experience moisture scarcity, it is considered as a low risk crop [15,16]. Tef is nutritious due to its high protein and mineral content [1,9]. Tef is one of the most significant crops for farm income, food and nutrition security in Ethiopia. It serves both as a staple and cash crop in the country.
Tef is versatility crop in adapting to adverse environmental conditions and staple food for ~73 million people in Ethiopia where it is annually cultivated by 7 million small-scale farmers on more than 30% of the total area allocated to cereal crops [4] In a country of more than 100 million people, tef accounts for about 15% of all calories consumed and, contribute well over 66% of the protein intake of the population consuming it as their staple food. The crop is preferred both by farmers and consumers because of its excellent nutritional quality (well balanced protein and minerals) and it makes good quality "injera", pancake-like soft bread. The straw serves as an indispensable feed for cattle and has almost equal value as the grain.
Grain yield is a complex character which is dependent on a number of other characters and is highly influenced by many genetic factors as well as environmental fluctuations. On the other hand, the genotype x environment interaction (GEI) is an important aspect of both plant breeding program and the introduction of new crop cultivars [8,12,20] Despite its importance, the productivity of tef is much lower than other cereals. The national average yield of tef is about 1.75 tha -1 , compared to maize (3.2tha -1 ) and wheat (2.4tha -1 ), respectively [4]. The major constraints limiting productivity and production of tef are; 1) limited availability of varieties suitable for different agro-ecologies; 2) limited use of improved varieties; 3) presence of biotic and abiotic stresses; and 4) inadequate seed and extension systems.
Tef research and development efforts in Ethiopia began in the late 1950s with the objectives of addressing the afore-mentioned constraints. Over the past 24 years, tef productivity increased by about 100%, from just 0.7 tha -1 in 1994 to 1.75 tha -1 in 2018. In tef improvement effort grain yield constituted the highest priority [13] Therefore, tackling some of the high priority problems mentioned above is vital to increase tef productivity in the Country. Consequently, the objective of the study was to evaluate the performances of selected tef genotypes across multi-locations and identify candidate variety (ies) having broad and /or specific adaptation to different environments.

Plant Materials
Eighteen promising recombinant inbred lines(RILs) selected from preliminary yield trial plus two checks (local and standard check) were used. The 18 promising recombinant inbred lines were obtained through single seed descent (SSD) method from two different crosses. In both crosses Quncho (DZ-Cr-387/RIL 355) was used as the ovule parent. The cultivars Rosea and Alba described by [17] were used as pollen parent. The former cultivar is characterized by high number of florets per spikelet and hence used to pyramid yield traits into the popular variety Quncho released in 2006 [13]. Likewise, the cultivar Alba was the paternal parent for six of the 18 RILs, and the cross of Quncho with cultivar Alba aimed at introgressing higher panicle length for yield as well as. thick and strong culm for increased lodging tolerance into the popular variety Quncho. The standard check variety was the variety Nigus released in 2017 [21] for agro-ecologies similar to the particular set of test locations and classified as high potential tef growing areas. On the other hand, the local check is a farmers' variety commonly grown around each of the respective test locations.

Experimental Locations and Seasons
Although the experiment was done for two seasons at each of the six locations and also additional other locations, the data for some of the years and locations were excluded because of the heterogeneity of variance in the combined analyses of grain yield data over all environments (locations and seasons). The test locations represent high potential tef growing areas with optimum rainfall and other climatic and edaphic conditions suitable for tef production ( Table 2).

Experimental Design and Management
The field experiment was conducted using a randomized complete block design with four replications of 2 m x 2 m (4m 2 ) plot size during the two main seasons of 2017 and 2018. The field experiment was managed as per the research recommendation of agronomic practices of the respective test locations.

Data Collected
Data on agronomic yield and yield related traits were collected both on plot and individual plant base. Data on days to heading or panicle emergence using the sowing date as a reference, lodging index, grain and biomass yield were taken on plot bases. Days of heading and maturity were taken when each plot attained 50% heading (panicle emergency) and 90% physiological maturity respectively, and days were calculated beginning from the date of sowing. Lodging index was assessed using the method of [2] by considering assessments of both the lodging degree or the angle of leaning on 0 (completely upright) to 5 (completely flat on the ground) and the severity as the percentage of the plot stand manifesting each of the 0-5 degrees of lodging. Then, lodging index for each plot was taken as the product sum of the degree of leaning and the respective per cent severity divided by five. Grain yield (g) of each plot was measured on clean, sun dried seed and the measured grain yield value (g) has converted to kilogram per hectare for data analysis.
Plant height (cm), and panicle length (cm) were taken on the five individual samples of plants which were randomly taken from the central rows of each plot, and the averages of five sample plants were as used for analysis.

Data Analyses
For each trait analysis of variance was made first for individual location, and ultimately upon getting positive results from tests of homogeneity of variances using the method F-max of [11], a combined analysis of variance was made across the environments (locations and years) in order to determine the differences between genotypes across environments, among environments and their interaction. For the analysis of variance, Proc GLM (general linear model) suitable for the experimental design were employed [10] using SAS software version 9.00 [23] and the average performance for different traits presented below (Table 3). AMMI (additive main effects, multiplicative interactions analysis was used to adjust the main or additive genotype and environmental effects by analysis of variance, in addition to the adjustment of the multiplicative effects for the G×E interaction by principal component analysis. The sum of squares of the G×E interaction was divided into an singular axis or Interaction Principal Component Axis (IPCA), which reflects the standard portion in which each axis corresponded to a particular AMMI model. Mean comparison for traits showing significant differences in the analyses of variance were made using Least Significant Difference (LSD). GEA-R (2015) software version 2.0 was used for the stability analysis and GGE biplot analysis to visualize which genotypes performed bets in which environment.

Components of Variation
ANOVA from additive main effect and multiplicative interaction (AMMI) for most of traits showed significant (p ≤0.01) for genotypes and environments and significant (P ≤0.05) effect for genotype by environment interaction (GEI). The effect of environment, genotypes and genotype by environment interaction accounted for 67.27%, 7.62% and 25.11% of the total sum squares (Table 3), respectively. A large sum of squares for environments indicated that the test environments were diverse with large differences among environmental means which causing most of the variation in grain yield. Therefore, this result designated the reliability of the multi environment experiments. The variation in temperature, rainfall, soil type, soil fertility, and moisture availability might be the main reasons for the presence of variation. The AMMI analysis also showed that the first interaction principal component (PC1) and second interaction principal component (PC2) explained 39.32% and 19.61% of the interaction sum squares, respectively. The mean squares for PC1 was highly significant (p<0.01). Likewise, analysis of variance revealed highly significant (p< 0.01) effect GEI for aboveground biomass, days to heading, days to maturity, panicle length, plant height and lodging index. The significant interaction indicated that the genotypes respond differently across the different environments. The significant interaction indicated that the genotypes respond differently across different environments. The significant variability of genotypes traits showed in the present study for different traits of tef genotypes are in agreement with the previous report by different authors for genotype variability [12,18].
On the other hand, mean grain yield value of genotypes averaged over environments ranged from 2340 kgha -1 (G10) to 2761kgha -1 (G12) ( Table 4). The significant GEI in the present study indicates unstable performance of the tef genotypes across the testing environments ( Figure 1). Thus, it implied that the genotypes respond differently across the different environments. The test genotype G12 (DZ-Cr-387 X Rosea RIL133) was the top yielder at E5 (Holetta-2018), and the second highest yielder at E1 (Akaki-2017), E4 (Adadimariam-2017), E7 (Adadimariam-2018) and E9 (Adet-2017) ( Table 4). Overall, the genotype code G12 (candidate variety), although not at all of the environments, performed better than others at least at two low yielding environments (Adadimariam-2018, and Adet-2017) and three high yielding environment (Akaki -2017), Adadimariam-2017 and Minjar-2018). The huge variability in the grain yield among the 20 tef genotypes at the nine environments might be due to wide variability in climatic and soil conditions. This finding is in accordance with previous studies [7,12,19] that similarly reported which thereby complicates the selection and recommendations stable genotype across environment.
In genotype x environment interaction (GEI) the result exhibited the genotypes gave statistically higher grain yield and above ground biomass than the standard check variety. In addition to this considering the current tef and straw price, 36 Birrkg -1 and 5 birr kg ha-1 (1 USD=27 birr), respectively, there was an economically meaningful difference among tested genotypes. Therefore, one promising candidate variety, DZ-Cr-387 X Rosea (RIL-133) gave grain yield (2761kgha -1 ) and aboveground biomass 13802 kgha -1 compared to the standard check variety Negus depicting grain yield (2526.4kg/ha) and aboveground biomass 11402 kgha -1 , respectively. Therefore, DZ-Cr-387 X Rosea (RIL-133) has been evaluated in by the National Variety Release Technical Committee in the variety verification trial during 2019/2020 and released as a new variety in 2020. From the variety verification trial, the candidate variety showed promising performance than the newly released standard check variety Ebba.

Stability Analysis
Mean grain yield performance and its stability 20 tef genotypes over nine environments are shown in table 6 and Figure 2. From GGE biplot graph for stability analysis Average environmental axis (AEA) is a line passing through the origin and pointing to the positive direction with its distance equal to the longest vector. Besides, an ideal environment is a point on the AEA in the positive direction of the biplot origin and is equal to the longest vector of all environments [25]. This line was reported to be useful to evaluate mean grain yield and stability of genotypes [25]. According to such reports, genotypes considered to be stable are those appeared closer to the origin with the shortest vector from the AEC. Thus, Figure   2 in the present study shows the mean performance and stability of the genotypes. Based on this, G12 with the shortest vector from the AEC axis was identified as the most stable genotypes while G10 with the longest vector from AEC was the most unstable genotypes.
The mean grain yield value of genotypes averaged over environments indicated that G12 had the highest (2761kg ha -1 ) and G10 the lowest grain yield (2340 kg ha -1 ), respectively. Genotype superiority with the small measured value indicates the more stable genotypes (Table 6). Therefore, from the present study, G12 was the most stable and G10 most unstable genotypes, respectively. N. B: Numbers in brackets give the position of each genotype, ranked according to the stability coefficient (running downwards from 1 = best). The New variety DZ-Cr.497/DZ-Cr-387 X Rosea (RIL-133)/ has got the following major advantages.
1) It showed advantage of 235kgha -1 (9.3%) in grain yield and 2399 kgha-1 (21%) in aboveground biomass yield over the standard check variety Negus as well as 16.1% in grain yield and 36.8% in aboveground biomass over the local check cultivar. 2) Moreover, the selected genotype showed highly stability (1st rank) among evaluated genotypes, indicating its suitability for multi environment in the high potential tef growing areas. 3) This genotype has also got immense farmers' preference and attention due to its overall performance and white caryopsis colour during the participatory variety evaluation. A summary of the description of the candidate variety including its pedigree, adaptation agro-ecological conditions, required cultural practices, and pheno-morphologic and agronomic traits is presented on Table 7. Grain yield on farm (kgha-1) 2140 26 Aboveground biomass (kgha-1) 13802

Conclusions and Recommendations
Crop yield is a complex trait that is influenced by a number of component characters along with the environment directly or indirectly. If high yielding stable recombinant inbred lines tef could be developed for diverse environments, it would be possible to provide diverse and stable varieties for the tef growing farmers. Stability analysis is a powerful approach to select the most stable high yielding recombinant inbreeds lines for specific as well as for diverse environments. In the present study, 20 tef genotypes including 18 promising RILs originating from two crosses and selected on the basis of previous preliminary variety trials as well as as standard check variety Negus and a local check (farmers' variety) from each location were field evaluated at nine environments (six location and two main seasons of 2017 and 2018). Combined analysis of variance revealed highly significant (P ≤ 0.01) variations due to genotypes, environments for most of traits and significant (p ≤0.05) genotype by environment interaction effects (GEI) for grain yield. AMMI analysis revealed 7.62%, 67.27%, 25.11% variation in grain yield due to genotypes, environments and GEI effects, respectively. Thus, the GEI mean squares showed tef genotypes exhibited differential performances across the different environments. Consequently, most of the genotypes showed environment specificity. The mean grain yield value of genotypes averaged over environments indicated that G12 had the highest (2761 kgha-1) and G10 the lowest yield (2340kgha -1 ), respectively. It is noted that the variety G12 showed higher grain yield than all other varieties when averaged over all the environments.
One promising late set candidate variety, DZ-Cr-387 X Rosea (RIL-133) gave higher grain yield of (2761kgha -1 ) compared to the standard check variety Negus (2526.4kgha -1 ). Therefore, DZ-Cr-387 X Rosea (RIL-133) has been selected and evaluated by the National Variety Release Committee in 2019/2020 and released in 2020. Thus, it is recommended for high potential tef growing regions in the country. Multi environmental trial should be conducted continuously to get high yielding tef varieties for different tef growing areas to increase production and productivity of tef.
Overall, the tef varieties released have shown steady and incremental genetic gain through tef breeding in Ethiopia of 0.90% year under lodging controlled (wire-mesh support) conditions from the earliest release in 1970 until 1995 [26,27], and 0.58% per annum under lodging uncontrolled natural conditions from 1970 until 2013 [5,6]. These figures are relatively good by the standards of most breeding programs for similar crops, except for the most important world crops like maize, wheat and rice. However, to bring breakthrough, instead of the steady increment, in in tef improvement further intensified crossing/hybridization in order to stack productivity traits/genes, break the apparent linkage between culm thickness and culm length for reduced lodging vulnerability, and use advanced breeding techniques including genomics are vital so as to get substantially high yielder and stable genotypes with the required qualities. Moreover, future research strategies on tef genetic engineering, high throughput mutant line development, and mining of the tef genetic resources including the wild relative species must be given due emphasis in the national tef breeding program.