Review Article

Evaluating Agronomic Performance and Yield Stability of Improved Bread Wheat Varieties Across Low Moisture Stress Areas of Guji Zone, Southern Oromia

Obsa Chimdesa*

¹Bore Agricultural Research Center, Ethiopia

Submission:April 26, 2019; Published: June 25, 2019

*Corresponding author: Obsa Chimdesa, Bore Agricultural Research Center, PO Box 21, Bore, Ethiopia

How to cite this article: Obsa C. Evaluating Agronomic Performance and Yield Stability of Improved Bread Wheat Varieties Across Low Moisture Stress Areas of Guji Zone, Southern Oromia. Agri Res& Tech: Open Access J. 2019; 22(1): 556183. DOI: 10.19080/ARTOAJ.2019.21.556183

Abstract

In Ethiopia, many low moisture stress tolerant bread wheat varieties that consistently have high yield in a variety of environments had been developed by different research centers. However, none of these cultivars are tested and selected for low moisture stressed areas of Guji Zone. As a result, the study was conducted at two mid land districts of Guji Zone (Adola and Wadera) at six testing sites (Dufa, Gobicha, Kiltu Sorsa, Chalo Sigida, Tulam and Andoya Haro) to evaluate the performance and yield stability of eight released low moisture stress tolerant bread wheat varieties in different low land areas of Guji Zone prior to varietal recommendation. Seven low moisture stress tolerant improved bread wheat varieties (Adel Werer, Amibara, Fantale, Gambo, Lucy, Mekele 01 and Mekele 02) were used as testing materials with local check (farmers variety. The analysis of variance (ANOVA) indicated presence of highly significant differences at (P≤0.01) among the evaluated bread wheat varieties, the testing sites and GEI for most of the characters studied. Different parametric and non-parametric stability models were used to assess stability and yield performance of the evaluated varities across the testing environment. The highest grain yield (25.66qt/ha) was recorded for Mekele 02 followed by Mekele 01 (24.13qt/ha). But, low yield of 10.0qt/ha was obtained from local (control) variety. Therefore, based on the agronomic performance and yield stability analysis, variety Mekele 02 followed Mekele 01 were high yielding and comparably stable across the tested environments and then recommended for production to the study areas.

Keywords: Bread wheat; Low moisture stress; Yield stability

Abbrevations: DTH: Days to Heading; DTM: Days to Maturity; GFP: Grain Filling Period; PH: Plant Height; SL: Spike Length; TPP: Total Number of Tillers/Plant; NPT: Total Number of Fertile Tillers/Plant; SPP: Spikelets Per Spike; KPS: Kernels Per Spike

Background & Justification

Ethiopia is inimitably characterized by diverse agro-ecology and climatic conditions which is suit for the production of various cereal crops. So, having different range of altitudes, soils and climatic conditions provide ecological settings suitable for the cultivation of diverse species of wheat [1]. Wheat is the second most important crop next to tef in terms of area coverage, but most of the production is concentrated in the highland plateaus of the country [2]. About one third of the developing world’s wheat area is located in environments that are regarded as marginal for wheat production because of drought, heat and soil problems [3].

In Ethiopia, half percent of the arable land is classified as semi-arid and arid agro-ecology where moisture stress is the major problem in such areas. Development of drought tolerant wheat genotypes for such drought prone areas of the country would enhance utilization of the marginal areas of the country. Many low moisture stress tolerant bread wheat varieties that consistently have high yield in a variety of environments had been developed by different research centers of the country. However, none of these cultivars are tested and selected for low moisture stressed areas of Guji Zone. As a result, it’s important to collect and test the adaptability and yield stability of these varieties under such marginal areas. The adaptability of a variety over a diverse environment is usually tested by the degree of its interaction with different environments under which it is planted [4]. This could be performed by exposing the varieties to different soil types, soil fertility, moisture levels, environments and cultural practices at farmers’ fields in order to evaluate the performance and stability of the varieties across the various testing locations. Assessing grain yield of a set of cultivars in a multi-environmental trial, changes are commonly observed in the relative performance of genotypes with respect to each other across locations. This difference of genotypes from one environment to another is called genotype × environment (GE) interaction. This could enable the breeders to select superior genotypes for the target environment. In order to identify superior genotypes across multiple environments, plant breeders conduct trials across locations and years, especially during the final stages of cultivar development. When a genotype performs consistently over a wide range of environments, then the genotype is considered as widely adaptable. On the other hand, a genotype showing considerable genotype by environment interaction effects is not suited for diverse environments [5] and is said to have specifically adapted variety. A variety is more adaptive if it has a high mean yield with low degree of fluctuation in yield components grown over diverse climatic conditions [6].

Multi environment yield trial can be analyzed to extract more information on stability, adaptability and yield performance using various statistical methods. Stability analysis is performed using two ways models i.e. uni-variate and multivariate models. Uni-variate model is the most commonly used method which is based on regression and variance estimates. Among multi variate stability analysis, Additive main effect and multiplicative interaction is the most commonly used model to investigate GEI. AMMI is a better model for analysis of G×E interaction in multi-location varietal trials [7]. It not only gives estimate of total G×E interaction effect of each genotype but also partitions it into interaction effects due to individual environments. In addition to AMMI, GGBP is also another multivariate model recently developed to estimate GEI and varietal adaptability [8]. So, it’s important to evaluate the adaptability and stability of yield performance of the variety using all the mentioned models to have a coincide result on the varietal recommendation there by to reduce the risk of varietal failure. Several studies of genotype by environment interactions (GxE) and yield stability have been reported on wheat grown under different locations and conditions of Ethiopia [9-11]. However, none of these studies have been performed under low moisture stressed conditions of Guji Zone. Therefore, the objectives of the present study were to evaluate the performance and yield stability of eight released low moisture stress tolerant bread wheat varieties in different low land areas of Guji Zone prior to varietal recommendation.

Materials and Methods

Description of the study area

The experiment was conducted at two mid land districts of Guji Zone (Adola and Wadera) at six testing sites (Dufa, Gobicha, Kiltu Sorsa, Chalo Sigida, Tulam and Andoya Haro). Adola district is located at 475km to the South from Addis Ababa and 120 km to the North from the Zone town (Nagele Borena) with a geographic coordinate of 5044’10”-6o12’38”N latitudes and 38o45’10”- 39o12’37”E longitudes. The district is situated at an altitude of 1500-2000masl. The average annual rainfall of the district is about 900mm and the mean annual temperature is 25 °C.

The first rainy season is from early March up to August and the second season starts in early September and ends to late November. The major soil types are Nitosols (red basaltic soils) and Orthic Aerosols [12]. The soil is clayey in texture and slightly acidic with pH value of around 6.4 [13]. Wadera district is located at 530 km to the South from Addis Ababa and 60km to the North from the Zone town (Nagele Borena) with a geographic coordinate of 5039’5”- 6o2’28”N latitudes and 39o5’30”-39o27’52”E longitudes. The district is situated at an altitude of 1500-1900masl. The district is characterized by two type of typical climatic zone. Namely, an arid and semiarid climate with mean annual temperature of 12-34°C and it has a bimodal rainfall pattern. The average annual rainfall of the district is about 1500mm.The long rainy season start from mid-March to May while the short rainy season starts from Mid-September to October. The major soil types are oxsois and alfisols [12].

Both districts are characterized by three agro-climatic zones, namely Dega (High land), Weina dega (Mid land) and Kola (Low land) with different coverage. Based on these conditions, two times cropping seasons were commonly practiced. For instance, Arfasa is the main cropping season and starts from March to April, especially for maize, haricot bean, wheat and barley. The second cropping season is called Gana (short cropping season) which was practiced as double cropping using small seeds cereal crops like tef, wheat and barley after harvesting previous main cropping season crops. This study was conducted during short cropping season at selected low moisture stressed areas.

Description of plant material and experimental design employed

Seven low moisture stress tolerant improved bread wheat varieties (Adel Werer, Amibara, Fantale, Gambo, Lucy, Mekele 01 and Mekele 02) were used as testing materials with local check (farmers variety) The treatments were arranged in randomized complete block design with three replications at each location. The selected materials were planted on a plot size 2.5m x 1.2m, 6 rows, 20cm between rows. In puts (seeds 150kg/ha, fertilizers P205 60kg/ha, N; 60kg/ha) and management practices were applied as recommended for wheat production to the area.

Data collection: Data were collected from each plot and selected plants of the plot for agronomic traits and diseases resistance scores. Collected agronomic data includes: Days to heading (DTH), Days to 90% maturity (DTM), Grain filling period (GFP), Plant height (PH), Spike length (SL), Total number of tillers/ plant(TPP), Total number of fertile tillers/plant(NPT), Number of spikelets per spike(SPP), Number of kernels per spike (KPS), 1000-seed weight (TSW) and Grain yield/ha (qt/ha).

Data analysis: Collected data was subjected to ‘’SAS’ ’software (version 9.0) to evaluate the variability of the tested varieties across the locations. This was done through computing analysis of variance for all characters studied according to the method given by Gomez and Gomez [14].

Stability analysis was performed following different procedures: Regression coefficient (bi) was done following proce dure developed by [15] later revised (b and S2d) by Eberhart and Russell [16]. Ecovalence (Wi) which is the contribution of each genotype to the GEI sum of squares was estimated with the method Wricke’s [17].

AMMI stability analysis was carried out using IRRISTAT computer software IRRI STAT. ASV was also done following the technique of Purchase et al. [18].

Where, 1 2 SSIPCA SSIPCA is the weight given to the IPCA1 value by dividing the IPCA1 sum squares by the IPCA2 sum of squares. The larger the IPCA score, either negative or positive, the more specifically adapted a genotype is to certain environments. Smaller IPCA score indicate a more stable genotype across environment

Genotype Selection Index (GSI): Based on the rank of mean grain yield of genotypes (RYi) across environments and rank of AMMI Stability Value (RASVi) a selection index GSI was calculated for each genotype which incorporate both mean grain yield and stability index in a single criteria (GSIi)[18] as:

GGBiplot analysis was also illustrated following the principle suggested by Yan and Rajcan [19].

Result & Discussion

The combined analysis of variance (ANOVA) over locations for grain yield and other agronomic characters of 8 bread wheat varieties is presented in Table 1. The analysis of variance (ANOVA) indicated presence of highly significant differences at (P≤0.01) among the evaluated bread wheat varieties and the testing sites for most of the characters studied except grain filling period. This indicates presence of variability among the evaluated bread wheat varieties as well as the testing sites. In other cases, highly significant effect of GEI was exhibited on days to heading, thousand seed weight and grain yield. In addition, non- significant effect of GEI was depicted on most of the characters considered. Several authors were also reported presence of highly significant difference among bread wheat genotypes for grain yield due to genetic variability of the genotypes and environments [10,11,20,21].

Mean performance of improved bread wheat varieties

** = highly significant at P ≤ 0.001; *= significant at P ≤ 0.05; ns = not significant at P= 0.05; a Numbers in parentheses are degrees of freedom associated with the corresponding source of variation; DH: Days to heading, DTM: Days to maturity, GFP: Grain filling period, PH: Plant height in centimetre, SL: Spike length in centimetre, TPP: Tillers per plant, NPT: Number of productive tillers, TSW: Thousand seed weight in gram, Gy: Grain yield/ha in quintals.

The mean performance of the evaluated varieties across the testing sites showed highly significant variability for days to heading and days to maturity for the genotypes, locations as well as GEI. In other case, non-significant effect of genotypes was observed on grain filling period (Table 1). Thus, the study result clearly reflects that presence of variability among the evaluated varieties as well as the testing environments. Among the testing sites, varieties were early headed, grain filled and matured at Kiltu Sorsa (55.75 days,33.25 days and 89 days) respectively. Whereas late headed at Tulam (64.88 days), late grain filled and matured at Dufa (56.92 days and 114.08 days) respectively (Table 2).

From the combined analysis, the variation with respect to days to heading and days to maturity was ranged from 53 to 62 and 103 to 126.67 days respectively, showing a wide range of variation among the varieties for maturity. Based on the study result, the longest days to heading was revealed by control (62 days) followed by Gambo (58.33 days). However, early heading was recorded for varieties Fantale and Amibara (53 days) followed by Mekele 02 (56 days), Mekele 01 (56.33 days), Lucy (57 days) and Adel Werer (57 days). In other cases, variety Mekele 02 was early maturing variety (103 days) followed by Mekele 01 (111.33days). Among the tested varieties, control was late maturing variety with 126.67 days followed by Fantale (124 days).

With regards to growth parameters, the highest plant height was recorded at Chalo Sigida and Andoya Haro 80.89cm and 80.46cm respectively and the shortest was scored at Kiltu Sorsa (63.26cm) (Table 2). Plant height was sensitive to environmental fluctuations and it indicated that the relative performance of genotypes was markedly inconsistent over the locations [22]. Significant variability was also depicted among the evaluated bread wheat varieties and locations on spike length. The longest spike length was recorded at Andoya Haro and Chalo Sigida (8.61cm and 8.54cm) respectively and the shortest was scored at the rest of the testing sites (Table 2).

The current study also illustrated significant variability of the evaluated bread wheat varieties for yield and related traits (Total number of tillers, number of productive tillers, number of spiklet per spike, number of kernels per spike, thousand seed weight and grain yield /hectare). As it is depicted from the study result, varieties were showed best performance in yield and related traits at Chalo Sigida and inconsistent performance for the traits in other locations (Table 2). These indicate the presence of variability among the varieties, environments and GEI for yield and related traits.

DHT: Days to heading, DTM: Days to maturity, GFP: Grain filling period, PHT: Plant height, SL: Spike length, SPPS: Spiklet per spike, KPS: Kernels per spike, TPP: Tillers per plant, NPT: Number of productive tillers, TSW: Thousand seed weight, Gy: Grain yield/ha.

The overall mean yield of the location varied from 13.82qt/ha to 26.45qt/ha (Table 3) and thus, the five environments showed wide variation in yield potential. The highest mean grain yield was obtained at Chalo Sigida (26.45qt/ha) and the lowest was from Kiltu Sorsa (13.82qt/ha). Among the evaluated varieties, Lucy (29.61qt/ha), Gambo (29.34qt/ha), Mekelle 02(33.11qt/ ha), Amibara (31.93qt/ha) and Mekelle 01(31.18qt/ha) were best performed at Chalo Sigida. In other case two varieties; Fantele (23.52qt/ha) and Adel Werer (22.86qt/ha) were best yielded at Gobicha where as the local (control) variety was performed at Dufa site (Table 3).

DHT: Days to heading, DTM: Days to maturity, GFP: Grain filling period, PHT: Plant height, SL: Spike length, SPPS: Spiklet per spike, KPS: Kernels per spike, TPP: Tillers per plant, NPT: Number of productive tillers, TSW: Thousand seed weight, Gy: Grain yield/ha

DHT: Days to heading, DTM: Days to maturity, GFP: Grain filling period, PHT: Plant height, SL: Spike length, SPPS: Spiklet per spike, KPS: Kernels per spike, TPP: Tillers per plant, NPT: Number of productive tillers, TSW: Thousand seed weight, Gy: Grain yield/ha.

The combined analysis also showed significant effect of genotypes, environments and GEI on mean grain yield of the evaluated bread wheat varieties which was ranged from 10.56 to 25.66qt/ ha with the mean value of 20.16qt/ha and coefficient of variation 14.35%. The highest grain yield (25.66qt/ha) was recorded for Mekele 02 followed by Mekele 01 (24.13qt/ha). But, low yield of 10. qt/ha was obtained from local (control) variety (Table 4).

Stability Analysis

The results of different parametric and non-parametric stability statistics are discussed term by term following the models suggested.

Eberhart and Russell’s joint regression stability analysis

Based on the analysis variance of E and R stability model, the current study showed the mean square for G × E (linear) interaction was highly significant which indicates presence of genetic differences among the varieties over the testing environments (Table 5). In such case, it’s important to partition GEI into environment linear, G x E (linear) interaction effects (sum squares due to regression (bi) and unexplained deviation from linear regression (pooled deviation mean squares (S2di). Further partitioning of GEI into linear and nonlinear components revealed highly significant mean squares (MS) for these components indicating the presence of both predictable and unpredictable components of GEI

The joint regression of the mean genotypic performance on the environment showed that results from the two stability parameters bi and S2di were not consistent in assessing the reaction of genotypes to varying environmental conditions. All genotypes showed regression coefficient (bi) values that were significantly different from unity (Table 6) but, in contrast, some genotypes showed significant deviation from regression (S2di) values of greater than zero (Table 5). Thus, based on the regression coefficients, all genotypes had an average response in all test environments. According to Becker and Leon, genotypes with bi values of unity showed an average response to changing environmental conditions [24]. Eberhart and Russell found that genotypes with high mean performance, a regression coefficient of unity (bi = 1), and deviation from regression of zero (S2di = 0) showed better general adaptability across environments. Based on this principle, none of the evaluated varieties showed better general adaptability even if three varieties, namely Mekele 02, Mekele 01 and Amibara were performing above-average grain yield but their regression coefficient (bi) values was significantly different from unity, and deviation from regression (S2di) values also significantly different from zero. In other cases, three varieties namely Fantele, Adel Werer and Local were found to be among the lowest yielders with bi <1 indicating that the varieties were poorly adapted to the test environments.

Wricke’s (Wi) ecovalence

Wricke’s (Wi) ecovalence is the contribution of a genotype to the interaction sum of squares. The lower the value of Wi the smaller will be the fluctuations from the predictable response in different environments so much that the genotype with the least ecovalence is considered to be the ideal from the point of view of yield stability. From the current study, variety Mekele 02, Adel Werer and Fantele were showed lower Wi value indicating that the varities are stable. In other case, variety Gambo showed high Wi value which could reflects its instability (Table 7).

Additive main effects and multiplicative interaction (AMMI) analysis for grain Yield of bread wheat varieties

The AMMI analysis of variance of 8 bread wheat varieties tested over six Environments revealed that 40.51% of the total sum of squares (SS) was attributable to the genotypes (G), 35.79 % to the environment (E) and 23.70% to GE interaction effects (Table 6). A large SS due to G indicated that the genotypes were genetically different for mean yields as a result of their selection domains. The small proportion of SS due to E indicated that the differences among the environmental means were not very high. The magnitude of GE SS was 49.33 times smaller than that of SS due to G, thus, indicating that the differences in the response of the genotypes across environments were not that substantial and the genotypes need multi-location testing. Bose et al. [23] also reported larger SS variability attributed on rice genotypes due to genotype variability. In contrast, larger SS revealed due to environments on bread wheat was reported by many authors [10,11,20,21].

Among the four principal components, AMMI Component 1 and AMMI component 2 were highly significant at P = 0.01%. The AMMI Component 1 (IPCA-1) accounted for 56.65% of the interaction. Similarly, AMMI Component 2 (IPCA-2) explained further 20.09% of the interaction SS and cumulatively contributed to 76.74% of the total interaction and used for biplot. This indicates that the two AMMI components were adequately explained the variations existed on the yield of the tested bread wheat varieties due to GEI. Many authors also suggested that first two principal components of AMMI model are the most accurate in predicting total variation explained due to GEI [11,20,25-27].

AMMI stability value (ASV)

ASV measures the distance from the genotype coordinate point to the origin in a two-dimensional scatter diagram of IPCA2 against IPCA1 scores. Genotypes with the lowest ASV values are identified by their shortest projection from the biplot origin and considered the most stable. Accordingly, Mekelle 02 and Adel Were the most stable varieties. However, Gambo and Amibara were the most unstable varieties (Table 8).

Genotype Selection Index (GSI)

It is a non-parametric index used to identify best performing and stable genotypes/variety based on the rank of ASV and mean grain yield. Mohammadi stated that, Stability per see alone not be the only parameter for selection, because the most stable genotypes would not necessarily give the best yield performance unless supported by other approaches like GSI. In GSI, variety that holds the least value is considered as the most stable with high grain yield. Accordingly, Variety Mekelle 02, Adel were and Mekelle 01 with relatively lower values were found high seed yielding and broadly adapted varieties (Table 9).

GG Biplot analysis

By this analysis, which- won- where, mean performance and stability of genotypes, discriminating ability and representativeness of environments and others can be addressed graphically. So, the environments and genotypes obtained in the concentric are considered as ideal environments and stable genotypes respectively [19]. A variety is more desirable if it is located closer to the ideal variety. Using the ideal genotypes/variety as the center, concentric circles were drawn to help visualize the distance between each variety and the ideal variety. Therefore, ranking based on the genotypes-focused scaling, assumes that stability and mean yield are equally important [28]. Accordingly, Variety Mekelle 02 and Mekelle 01 which fells closest to the center of concentric circles were ideal varieties in terms of yielding ability and stability as compared to the rest of the tested bread wheat varieties.

With regards to the testing environments, Koba Sorsa, Dufa and Tulam which fells near to the ideal environment were identified as the best desirable testing environment in terms of being the most representative of the overall environments and powerful to discriminate the tested bread wheat varieties (Figure 1) [29,30].

Conclusion

With the current climate change, conducting varietal adaption across various environments is very pertinent to have good varietal recommendation. In such cases, the varieties are allowed to be tested whether they have specific or general adaptability to the testing environments. To concur such result, assessing the stability as well as the performance of the varieties across diverse environment is mandatory. So, the current study was conducted to evaluate the performance and yield stability of eight released low moisture stress tolerant bread wheat varieties in different low land areas of Guji Zone. The combined study result indicated that, highly significant difference (P≤0.01) was observed among the evaluated varieties across the testing environments as well as within their interaction for grain yield and other agronomic parameters. The highest mean grain yield was obtained at Chalo Sigida (26.45qt/ha) and the lowest was from Kiltu Sorsa (13.82qt/ ha). The highest grain yield (25.66 qt/ha) was recorded for Mekelle 02 followed by Mekelle 01 (24.13qt/ha). But, low yield of 10. qt/ha was obtained from local(control) variety. In this study, different models were employed to test yield stability of the evaluated varieties. According to Joint regression model, none of the evaluated varieties showed better general adaptability. In other cases, three varieties namely Fantele, Adel Werer and Local were poorly adapted to the tested environments. As of Wi variety Mekele 02, Adel Werer and Fantele were showed lower Wi value indicating that the varieties are stable. In other case, variety Gambo showed high Wi value which could reflects its unstable. AMMI stability value indicated, Mekele 02 and Adel Werer the most stable varieties. However, Gambo and Amibara were the most unstable varieties. Genotypic selection found, Mekele 02, Adel werer and Mekelle 01 as high seed yielding as broadly adapted varieties.

Generally, the study results clearly indicated the possibility of exploiting the yield potential of bread wheat varieties namely; Mekele 02 and Mekele 01 under specific locations of low moisture stressed areas of Guji Zone. To give coincide conclusion, considering both yield performance and yield stability of the evaluated varieties are must. Accordingly, variety Mekele 02 followed Mekele 01 were identified as high yielder and comparably stable across the tested environments and then recommended for production to the study areas.

Acknowledgement

The Author would like to thank the financial support provided by the Oromiya Agricultural Research Institute (IQQO) and Bore Agricultural research Center for its facilitation. of Agricultural Research (OARI) is also dully acknowledged. All Researchers and technical assistant of cereal team at Bore Agricultural Research Center are dully acknowledged for handling the experiment and facilitation.

References

1. Harlan JR (1971) Agricultural origins: Centers and Non-centers. Science 174(4008): 468-473.
2. (2013) AU-SAFGRAD, Africa.
3. Heisey PW, Lantican MA, Dubin HJ (2002) Impacts of International Wheat Breeding Research in Developing Countries, 1966-97. CIMMYT, p. 81.
4. Ashraf I, Muhammad S, Siddiqui BN (2002) Effectiveness of Different Communication Methods/Media Used By Novartis Pesticide Company in Tehsil Arifwala. Int J Agri Bio 4(3): 335-337.
5. Thillainathan M, Fernandez GC (2001) SAS applications for Tai’s stability analysis and AMMI model in genotype x environmental interaction (GEI) effects. J Hered 92(4): 367-371.
6. Amin M, Mohammad T, Khan AJ, Irfaq M, Ali A, et al. (2005) Yield stability of spring wheat (Triticum aestivum ) in the Khyber Pukhtunkhwa, Pakistan. Songklanakarin J Sci Technol 27(6): 1147-1150.
7. Zobel RW, Wright MJ, Gauch HG (1988) Statistical Analysis of a Yield Trial. Agronomy Journal 80(3): 388-393.
8. Ebdon JS, Gauch HG (2002) Additive Main Effect and Multiplicative Interaction Analysis of National Turfgrass Performance Trials. Crop Science 42(2): 497-506.
9. Ayalneh T, Letta T, Abinasa M (2013) Assessment of Stability, Adaptability and Yield Performance of Bread Wheat (Triticum aestivum) Cultivars in South Eastern Ethiopia. American-Eurasian J Agric & Environ Sci 13(7): 885-890.
10. Gebru H, Abay F (2013) Evaluation of Bread Wheat Genotypes for their Adaptability in Wheat Growing Areas of Tigray Region, Northern Ethiopia. J Biodivers Endanger Species 1: 104.
11. Zerihun Tadesse, Habtemariam Zegeye, Dawit Asnake, Tafesse Solomon, Yewubdar Shewaye, et al. (2018) Identification of Stable Bread Wheat (Triticum Aestivum L) Genotypes using AMMI Analysis in Ethiopia. International Journal of Research in Agriculture and Forestry 5(6): 6-14.
12. Yazachew GE, Tongoona PB, Elohor Ifie B (2011) Performances of selected tropical white maize single-cross hybrids for yield and yield attributing traits. J Plant Breed Crop Sci 9(9): 160-164.
13. Alemayehu M, Kebede G, Feyissa F, Assefa G (2017) Review on Major Feed Resources in Ethiopia: Conditions, Challenges and Opportunities. Academic Research Journal of Agricultural Science and Research 5(3): 176-185.
14. Gomez KA, Gomez AA (1984) Statistical procedures for agricultural research (2^nd edn), John wiley and sons, New York, USA, p. 680.
15. Finlay KW, Wilkinson GN (1963) The analysis of adaptation in a plant-breeding program. Aust J Agric Res 14: 742-754.
16. Mohan SC, Mishra SB, Anil P, Madhuri A (1966) Eberhart - Russell’ and AMMI Approaches of Genotype by Environment Interaction (GEI) for Yield and Yield Component Traits in Vigna radiata Wilczek. International Journal of Agriculture, Environment and Biotechnology 7(2): 277-292.
17. Wricke G (1962) On a method of understanding the biological diversity in field research, Z Pfl Zucht. pp. 92-146.
18. Purchase JL, Hatting H, Vandenventer CS (2000) Genotype by environments interaction of wheat in South Africa: stability analysis of yield performance. South Africa J of plant sci 17: 101-107.
19. Yan W, Rajcan I (2002) Biplots analysis of the test sites and trait relations of soybean in Ontario. Crop Sci 42(1): 11-20.
20. Bavandpori F, Ahmadi J, Hossaini SM (2015) Yield Stability Analysis of Bread Wheat Lines using AMMI model. Agricultural Communications 3(1): 8-15.
21. Ferney GBH, Alexei M, Aigul A (2006) Evaluation of grain yield stability, reliability and cultivar recommendations in Spring Wheat (Triticum aestivum) from Kazakhstan and Siberia. Journal of Central European Agriculture 7(4): 649-659.
22. Tilahun A, Teklu B, Hoag D (2017) Challenges and contributions of crop production in agro-pastoral systems of Borana Plateau, Ethiopia. Pastoralism 7: 2.
23. Bose LK, Jambhulkar NN, Singh ON (2014) Additive Main Effects and Multiplicative Interaction (Ammi) Analysis of Grain Yield Stability in Early Duration Rice. The Journal of Animal & Plant Sciences 24(6): 1885-1897.
24. Becker HC, Leon J (1988) Stability analysis in plant breeding. Plant Breeding 101: 1-23.
25. Gauch HG, Zobel RW (1996) AMMI analyses of yield trials. In: Kang MS, Gauch (Eds.), Genotype by Environment Interaction. CRC. Boca Raton, Florida, USA. pp. 85-122.
26. Yan W, Hunt LA, Sheng O, Szlavnics Z (2000) Cultivar evaluation and mega- environment investigation based on the GGE biplot. Crop Sci 40(3): 597-605.
27. Nayak D, Bose LK, Singh S, Nayak P (2008) Additive main effects and multiplicative interaction analysis of host-pathogen relationship in rice-bacterial blight pathosystems. Plant Path J 24(3): 337-351.
28. Negahbana M, Moharramipour S, Sefidkon F (2007) Fumigant toxicity of essential oil from Artemisia sieberi Besser against three stored-product insects. Journal of Stored Products Research 43(2): 123-128.
29. Hayati D, Ranjbar Z, Ezatollah K (2010) Measuring Agricultural Sustainability. Biodiversity, Biofuels, Agroforestry and Conservation Agriculture 5: 73-100.
30. Abay F, Bjornstad A (2009) Specific adaptation of barley varieties in different locations in Ethiopia. Euphytica 167(2): 181-195.