Identifying Optimal Testing Environments of Barley Yield in the Northern Highlands of Ethiopia by Biplot Analysis

Transcription

1 JOURNAL OF THE DRYLANDS 2(1): 40-47, 2009 Identifying Optimal Testing Environments of Barley Yield in the Northern Highlands of Ethiopia by Biplot Analysis Fetien Abay* 1 and Asmund Bjornstad 2 Fetien Abay and Asmund Bjornstad Identifying optimal testing environments of barley yield in the Northern Highlands of Ethiopia by Biplot Analysis. Journal of the Drylands 2(1): Genotype x Environment Interaction (GEI) is commonly observed by breeders as differential ranking of variety yields among locations or years. Officially recommended released barley varieties have not been adopted by farmers in Tigray. A study was conducted across environments in order to identify optimum testing sites of the low input conditions of the northern highland of Ethiopia. Ten barley varieties, including a local check, were tested in replicated RCBD design over two years in 14 locations. Using AMMI and GGE statistical models, two mega environments with their winning genotypes were observed. Himblil is a winner genotype for Tigray; Shege and Misrach for Holeta and Debrebrhan locations. All Tigray locations were more or less positively correlated and appear to form three groups. The first two groups were most discriminating (longer vectors) but less representatives because of the largest angles with the Average Environmental Axis. The third group Bolenta (2004), Mugulat (2005), Buket ( ) and Neksege (2005) were less discriminating but more representatives. Repeatable and less discriminatory reactions were observed at Mekhan ( ) and Menkere ( ). High average July and September temperatures were negatively related to grain yield and TN (%). The association of Himblil in low N and moisture regimes and Shege with high yielding and high N environments indicates the importance of specific adaptation. The study contributes to the importance of direct selection on representative sites. Key words: GxE interaction, biplot analysis, specific adaptation Abbreviations: AEA, Average Environment Axis; GGE, Genotype + Genotype Environment; AMMI, Additive Main effect and Multiplicative Interactive; TN (%), Total Nitrogen (%) *1 Mekelle University, Department of Dry land Crop and Horticultural Sciences, P.O. BOX 231, fetien.abay@yahoo.com 2 Norwegian University of Life Sciences, Department of Plant and Environment, POBOX 5003, 1432 AAS, Norway Received July 23, 2009, Accepted October 18, Introduction Genotype x Environment Interaction (GEI) is commonly observed by breeders as differential ranking of variety yields among locations or years. A multilocational trial was conducted in order to identify superior cultivars for a target region. The analysis and understanding of causes of GEI in a particular region has received considerable attention by breeders. Rainfall and temperature may be unique to each year x site combinations and varieties respond differently. Soil environment also plays a role in determining the extent of GE interaction. Sinebo et al. (2002) and Ceccarelli et al. (1998) suggested the differential cultivar response observed on barley in Ethiopia and Syria were mainly due to the amount of soil nitrogen. Drought and low N stresses (Mitku et al. 2003) accompanied by water logging are factors most frequently limit crop production in the region. This has led Ceccarelli (1996) to argue that a significant GE can be positively exploited to identify stable genotypes for specific target areas. Experimental evidences of the genetic variation of low nitrogen adaptation and nitrogen use efficiency was reported for barley (Go rny, 2001). Muruli and Paulsen (1981) reported a pronounced differential adaptation to low and high N environments in a selection study with the wide range germplasm of temperate and tropical maize populations. Barley in Ethiopia is grown under a wide range of environmental conditions, and in marginal areas where the production of other cereals is limited. It is an important crop for poor farmers in marginal areas like in Tigray, which is one of the major barley-growing regions of the country. However, these areas have benefited far less from the yield recent increases achieved by the formal modern breeding. Officially recommended released barley varieties have not been adopted by farmers in Tigray. The frequent failure of varieties developed through conventional system has been ascribed to the fact that the varietals development and testing is done in conditions not representative of those of resource-poor farmers, and that breeding materials evaluated were only partly relevant to such conditions. Further identification of the variety that yields best at specific growing environment would be useful to breeders and producers. Recently, Abay and Bjornstad (2009) investigated the importance of specific adaptations in Ethiopia, but the use of multivariate efforts in characterizing varietals and environmental responses of GE have not been investigated. The objective of this study was to identify optimum 40

2 testing sites of barley through genotype x tester environment interaction analysis since yield estimates based only on genotypes and environments effects are insufficient. Another objective was to demonstrate by AMMI (Additive Main effect and Multiplicative Interactive) and GGE (Genotype + Genotype Environment) analysis, the causes of this poor adoption and the genotype x tester relationship. The AMMI model analysis is useful in visualizing the main effects of genotype x environment interactions. It can estimate the genotype responses and separate noise from real source of variation through partitioning of the GEI. IPCA scores of genotypes in AMMI analysis are the key to interpret the pattern of genotype response across environments (Gauch and Zobel, 1997). The model is important for cultivar evaluation, recommendations and selection of test sites (Gauch and Zobel, 1997). It provides a graphical representation or biplot to summarize information on the main effects and the first principal component scores of the interactions (IPCA1) of both genotypes and environments simultaneously (Crossa, 1990). Although the AMMI biplot can be very effective in summarizing the variation and in visualizing main effects, it does not show which variety is high yielding in which environment (Yan et al. 2000) nor identify which environment is most representative is not visualized in this model (Yan, 2001). This is, however, possible through the GGE approach. GGE biplot is composed of genotype and genotype x environment factors, which are important for varietals response trials. It is more important in visualizing the cross over of G x E interaction (Ma et al. 2004). Yan and Rajcan (2002) demonstrated the effectiveness of GGE biplot for factor x environment and trait x environment relationships. High yielding and stable varieties as well as representative and discriminating environments can be identified using the GGE biplot. Materials and Methods Experimental Design Replicated experiment of 10 barley varieties that represent the major varieties grown in the highland region of Tigray and those that were nationally recommended for the highlands were tested in this study. They were Himblil, Demhay, Misrach, Dimtu, HB-42, Shege, Sihumay, Atona and Rie. The former two varieties are Farmer Developed Varieties (FDV), the next four are Improved Varieties (IV) and last three are local varieties. The experiments were conducted across 14 locations for two cropping seasons in 2004 (7 locations) and 2005 (14 locations). The trial design was unbalanced in terms of locations x years but balanced for varieties x year and varieties x locations. Two of the test sites of the second year were Ethiopian Institute of Agricultural Research (EIAR) station sites at Holleta and Debrebrhan where the improved varieties were released. The total and distribution of the rain was highly variable between years and locations. In Tigray, rainfall is erratic and insufficient, and soils are generally poor. The soil at Holetta was found to be better in terms of organic matter and total N than other sites. A detailed background information of test materials and sites have been presented by Abay et al. (2008) and Abay and Bjørnstad (2009). In each location a Randomized Complete Block Design (RCBD) with two replications were used. Data were recorded on five randomly selected plants from each plot. Grain yields (GY) of the central four rows (2m 2 ) were hand harvested and threshed. Monthly rainfall, minimum and maximum temperature data were recorded from the meteorological laboratories located in the village, but for some of the sites, the climatic data were procured from metrological stations located in the district. Composite soil samples were taken from experimental sites and subjected for laboratory analysis of organic matter, ph level and Total Nitrogen (TN %). Statistical Analysis Additive Main Effects and Multiplicative Interaction Model Analysis The AMMI model analysis is useful in displaying the main effects of genotypes and environments and their interactions. It can estimate the genotype responses and separate noise from real sources of variation through partitioning of the GEI (Zobel et al. 1988, Crossa, 1990). In AMMI, standard ANOVA procedures are used to separate the additive variance from the multiplicative variance (genotype by environment interaction). The ANOVA for AMMI was performed using IRRISTAT version 5 (IRRI 2002). In the analysis, each combination between the 14 locations and two years was considered as environments making a total of 21 environments. The AMMI model is: Y ger -u - α g -β e = n٨ n τ gn δ en +p ge +٤ ger where Y ger is the grain yield of genotype (g) in environment (e) for replicate (r), u is the grand mean, α g are genotype mean, β e are the environment mean deviations, ٨ n is the singular value for IPCA axis n, τ gn are genotype eigenvector values for IPCA axis n, δ en are the environment eigenvector values for (PCA) axis n, p ge are the residuals and ٤ ger is the error term. AMMI Biplot Analysis The results of the AMMI model analysis was interpreted on basis of AMMI biplot that showed the main and first interaction principal components analysis (IPCA1) axis effects of both G and E (Gauch and Zobel, 1997). GGE Biplot analysis Y ger - β e = n٨ n τ gn δ en +p ge +٤ ger 41

3 The GGE biplot methodology was used to visually analyze the results of multi-location data. This methodology uses a biplot to show the two factors (G plus GE) that are important in cultivar evaluation and that are also the source of variation (Yan et al. 2001). The analysis was performed using the GGE software (Yan et al. 2000) to compare the performance of different genotypes at an environment, identify the highest yielding genotypes at the different mega environments, identify ideal cultivars and test locations and compare factor x environment relationships. In most cases environment signifies a combination of location x year. This might be a problem in variety recommendation for specific locations. A highly significant GEI was previously observed (Abay and Bjørnstad, 2009). The underlying reasons of this variation can now be explored further by visualizing the which own where pattern in GGE. Yan et al. (2001) and Yan and Rajcan (2003) used environments for analyzing wheat trials in Ontario and barley (Dehghami et al., 2006) in Iran. As discussed by these authors, we believe that the Single Year Multi location Trial (SYMLT) data are useful to identify the similarity and representativeness of the sites. Biplot analysis based on single year was hence performed because of its relevance to cultivar evaluation and mega environment identification (Yan and Rajcan, 2003). In this case, GGE 1 (model 1) was applied in order to measure the relationships and discriminating ability of the environments. This model is based on GxE table with no scaling (scaling = 0) and its metric centering and environment (SVP = 2). The vector length of the environment is directly related to the within environment standard deviation, and they are not equal. The similarity among the environments is shown by both the length and cosine of the angle between them, and the distance between them measure their dissimilarity in discriminating genotypes (Yan and Tinker, 2006). The Average Environment Axis (AEA) is the straight line that passes through the average environment and the biplot origin. A test environment that has small angle with AEA is more representative of the other environments (Yan and Tinker, 2006). GGE2 (model 2) was used for the purpose of variety evaluation. The environment centered data were scaled or standardized in order for comparing the results with all environments having same length of the vector. (Scaling =1, centering=2, SVP=2). RESULTS Cross over GE Interaction An indication of the presence of GE interaction is the differential yield ranking of cultivars across environments. As expected, in this study, different varieties produced highest grain yields at different environments. Himblil was the highest yielding variety in Tigray locations. Shege, Dimtu and Misrach were the highest yielder at Holeta and Debrebrhan and HB-42 not associated with any one of the environments. All improved varieties were highest yielding at the highest yielding environments while Himblil was highest yielding in Tigray. AMMI model analysis The AMMI analysis of variance for grain yield showed that barley grain yields were significantly affected by E and G. Environments (E), Genotypes (G) and Genotypes x Environments were highly significant and accounted for 60, 37 and 3% of the total sum squares (SS), respectively (Table 1). The partitioning of GE through AMMI model analysis showed all factors were significant. Table 1 Additive Main Effect and Multiplicative Interaction (AMMI) Analysis of Variance for Grain Yield (Kg/Ha) Including Four Interaction Principal Component (IPC) Axes AMMI DF MS (x 10 4 ) Varieties ** Environments (Location x Year) ** Varieties x Environment ** AMMI ** AMMI ** AMMI ** AMMI ** GXE residual 80 AMMI Biplot Analysis The main and IPCA 1 effects of both G and E on grain yield were shown in Fig. 1. The AMMI biplot illustrates 75.1 % main effect and IPC1 interaction effects. Since IPCA1 SS is 53.1% that of G SS, this emphasizes the importance of taking GE interaction into consideration when estimating cultivar yield at different locations and when targeting barley cultivars in a specific locations. Barley varieties that had IPCA 1 scores > 0 responded positively (adaptable) to environments that had IPCA1 scores >0 (i.e. their interaction was positive) but negatively to environments that had IPCA1 scores <0. The reverse applies for barley varieties that had IPCA 1 scores <0. Hence environments showed variability in terms of main and interaction effects. As shown in Figure 1 of AMMI1 biplot groups in the main effect, the environments are divided into two, below and above average means. The above mean group includes Holleta (2005), Debrebrhan (2005) Mugulat (2005), Habes (2005), Melfa (2005) and Fala (2005), all from year two implying the marked effect of locations x year on grain yield of varieties. Himblil and local interacted positively with most Tigray locations, but negatively with Holleta and Debrebrhan. All improved varieties provided higher yield in Holleta and Debrebrhan, where they were selected. 42

4 Figure 1. AMMI1 Biplot showing the main and IPCA Effects of Genotypes and Environments on Grain Yield, 1=Bolenta,2=Buket,3=Habes,4= Mugulat,5=Mekhan, 6=Menkere,7=Neksege,8=Fala, 9= HabesFTC,10= Melfa, 11=Mekelle, 12= Holleta,13= Debrebrhan and 14= Adinefas, 15= Bolenta,16= Buket,17= Habes,18= Mugulat,19= Mekhan, 20=Menkere, 21= Neksege The differences among cultivars in terms of direction and magnitude along the x axis (yield) and axis (IPCA 1 scores) were also important. The best cultivars should be high yielding and stable across environment. Four groups of genotypes are evident in the experiment. The first (upper left quadrant) includes HB-42, Shege and Sihumay with grain yield below grand mean and positive interaction term. The second (upper right) is Dimtu, and Misrach with above average yield and positive interaction. The third (lower right) is Himblil and Local above grand mean and negative interaction with Holleta and Debrebrhan. The fourth (lower left) is Demhay and Atona were below the grand mean and negative interaction term. Except in-group II, variation between varieties of same group was observed. For example in-group I Shege contributed the highest negative interaction, Himblil ingroup III and Demhay in group IV. GGE biplot Analysis The Which Won Where Polygon View The importance of genotype x environment interaction was expressed in both years. The magnitude of variance and pattern of location grouping is different across years. The biplots explained 86.1% and 77.4 for the years 2004 and 2005, respectively (Figure 2a-b). The biplot based analysis of Tigray locations differentiated between those above and below the origin of right side of the PC1. In 2004, all sites fell in same sector and shared same winner genotype, Himblil (Figure 2a). In 2005 experiment, two mega environments with their winning genotypes observed. Himblil is a winner genotype for Tigray; Shege and Misrach for Holeta and Debrebrhan locations (Figure 2b). In relation to their responses to locations, genotypes are grouped into five: Shege in one sector Dimtu, Rie and Misrach in second, HB-42 in third, Sihumay Demhay and Atona in fourth, and Himblil and local in fifth sector. The high input station site, i.e. Holleta, is associated with variety Shege and the moderate site, i.e. Debrebrhan, is associated with Misrach. 43

5 Figure 2. Yearly Polygon View for Yield Data from in Different Locations of Ethiopia. The codes of the test sites are given in number 1=Bolenta,2=Buket,3= Habes,4= Mugulat,5=Mekhan, 6=Menkere,7=Neksege,8=Fala, 9= Habes FTC,10= Melfa, 11=Mekelle, 12= Adinefas,13= Holleta and 14= Debrebrhan. Number in paranthesis is the year of the experiment 04= 2004 Relation and representativeness of the environment The relationship among environments is presented in Figure 3. The Figure 3a is the GGE biplot based on a balanced subset of the data with the Tigray locations. The biplot explained 83.8 % of the total variation of the GE (Figure 3a). Therefore, all the environments were more or less positively correlated and they appear to form three groups. The Bolenta and Habes (both in 2005) form one group, Neksege and Mugulat (both in 2004) another group and the remaining, the third group. The first two groups were most discriminating (longer vectors) but less representatives because of the largest angles with the AEA. The third group Bolenta (2004), Mugulat (2005) Buket ( ) and Neksege (2005) but less discriminating but more representatives. Repeatable (consistency between years) and less discriminatory (short vector) reactions were observed at Mekhan ( ) and Menkere ( ) (Figure 3a). This implies that these two test locations were less useful in genotype evaluation. This further suggests that fewer (rather than more) environments are needed to identify the genotypes. Fig 3b indicates the best variety to be recommended to this mega environment is Himblil. Relationship of GEI to environmental factors Holleta and Debrebrhan are associated with high rainfall, high yield and high TN (%). A significant positive correlation (r=0.64, p<0.05) was revealed with grain yield by total nitrogen (TN %), but negative by soil ph (r=-0.72, p <5%) (data not shown). In contrast, Tigray locations are associated with lower yields, low TN and lower rainfall. We found that high minimum-maximum temperature and high ph value is associated with most locations of Tigray. Because of the large influence by the high input (e.g. increased fertility) sites of Debrebrhan and Holleta, clear pattern of other sites cannot be visualized. Further analysis on the subset of the data was performed based on Tigray locations. As can be seen from Figure 4b, a high average maximum temperature in July and September negatively affected grain yield and TN (%). Rainfall distribution in September is important and associated with TN % and grain yield. The low yielding sites are known for season end drought and no rain fell in September. Within Tigray locations, high altitude, rainfall in May and August and high %TN are associated with high yield. The mid altitude sites of Menkere (2005) and Adinefas (2005) are mainly affected by low rainfall in June, high maximum temperature in June, July and August. The close association of these variables with soil nitrogen and grain yield demonstrates the confounding effect of variables. This implies it is difficult to recommend without testing conducting an experiment in representative target sites. 44

6 Figure 3. Relations between Environments across Genotypes and Years In and Genotype Ranks Based On Mean and Stability, code of environments given in numbers. 1= Bolenta 05, 2= Buket 05, 3=Habes 05, 4=Mugulat 05 5=Mekhan 05 6= Menkere 05 7= Neksege 05, 8=Fala 05, 9= Habes FTC 05, 10= Melfa 05, 11= Menkere 04, 12= Adinefas 05, 13= Bolenta 04, 14= Buket 04, 15= Habes 04, 16= Mugulat 04, 17= Mekhan 04, 18= Menkere 04, 19= Neksege 04. (04 and 05 indicates year 2004 and 2005). Discussion The large influence of environments in causing most of the variation in grain yield is in agreement with the findings by Ceccarelli et al. (2004). The stability and high yielding ability of the varieties has been graphically depicted by the AMMI and GGE biplot (Figs 1 & 2). Almost similar results were obtained from both models. However, representative sites along with superior varieties were better visualized by the GGE biplot model. The two years provided valuable information because of their contrasting rainfall distribution. The presence of crossover interaction led Ceccarelli et al.. (1998) to argue that selection for stress environment is important in the target environment. By doing this, the GEI positively exploited through selection of specifically adapted genotypes. The result of the best performance of Himblil was in accordance with the argument made by Abay and Bjørnstad (2009). These authors found the superior performance of Farmers Developed Varieties (FDV) in low input conditions. Our findings on the contribution of soil and rainfall factors in determining the grain yield is in agreement to the findings of Ceccarelli and Grando (2007) in Mediterranean environments. A significant correlation of variables implies not only importance of direct selection in target environment but also the benefit of using right genetic material for effective selection and heritability under stress (Abay and Bjørnstad, 2009; Al-Yasin et al. 2005). On the other hand, Atlin and Frey (1989) and Sinebo et al. (2002) explained that the higher genetic correlation of yield under low and high soil nitrogen and suggested the possibilities and effectiveness of selection under high N for both low and high input environments. The low fertility of Tigray soils as also explained by Mitku et al. (2003) can be associated with the low rainfall and long cultivation history of the region. Similar studies done in southern Queensland soils subjected to long periods of cultivation have shown that N uptake by winter cereals (mainly wheat), generally decreased with increasing periods of cultivation (Dalal and Mayer 1986). Increased rainfall may reduce soil N via leaching but high rainfall obviously leads to greater biomass production. Our finding on the association of high yielding with high nitrogen is in accordance to Sinebo et al. (2002). These authors also found a differential response of barley varieties in Holleta for high and low fertilizer inputs. The high response of Himblil to low input and low yielding environments and Shege to high nitrogen and high rainfall soils can thus be associated with their respective selection environments. Still, the environment of Bolenta had high N levels in Tigray. Misrach was selected for water logging tolerance in Debrebrhan. Still Himblil is better, which indicate a better discrimination of the shorter water logging periods in Tigray, due to shortened rainfall. This paper demonstrated the usefulness of AMMI and GGE biplot analyses in the interpretation of barley grain yield data from a multi-environment experiment. The AMMI model analysis provided estimates of the magnitude and significance of the GE interaction and 45

7 its interaction principal component. The GGE biplot analysis results were used to determine the relationship of factors with environments, identify the highest yielding genotypes at the different mega environments, and identify ideal cultivars and test environments. Figure 4. Factors x Environment Biplots of Each Environment, 04-05MG= Mugulat in 2004 & 2005, 05FA=Fala in 2005, 04-05HB=Habes in 2004 & 2005, 05HBT= HabesFTC 2005, 04-05NK= Neksege in , 04-05BO= Bolenta in 2004 &2005, 04-05MH= Mekhan in2004 &2005, 05MF=Melfa in 2005, 05AD= Adinefas 2005, 04-05BU= Buket in 2004 & 2005, 05MK= Mekele in 2005, 04-05MR= Menkere in 2004 & 2005, 05DB= Debrebrhan in 2005, 05HO=Holleta in 2005, 05MF= Melfa in TN (%)=Total Nitrogen, RM-RSEP= Rainfall from May-September, MINTM-MINTSEP=Minimum Temperature May-September, MAXTM-MAXTSEP=Maximum Temperature May- September, respectively, and OM, = Organic Matter. Acknowledgements - The authors appreciate the financial support provided by NUFU and acknowledge Professor W. Yan for GGE biplot software & his valuable comments on the draft output. References Abay F and Å Bjørnstad (2009): Specific Adaptation of Barley Varieties in Different Locations in Ethiopia. Euphytica, 167 (2): Abay F, A Waters-Bayer and Å Bjørnstad (2008): Farmers Seed Management and Innovation in Varietal Selection: Implications for Barley Breeding in Tigray, Northern Ethiopia. Ambio, 37: Al-Yasin, A, S Grando, O Kafawin, A Tell, and S Ceccarelli (2005): Heritability Estimates in Contrasting Environments as Influenced by the Adaptation Level of Barley Germplasm. Ann. Appl. Biol., 147: Atlin, G. N., and K.J. Frey (1989): Predicting the Relative Effectiveness of Direct versus Indirect Selection for Oat Yield in Three Types of Stress Environments. Euphytica, 44: Ceccarelli S (1996): Positive interpretation of genotype by environment interactions in relation to sustainability and biodiversity. In: M. Cooper and G.L. Hammer (Eds.), Plant Adaptation and Crop Improvement, pp CAB International, Wallingford, England. Ceccarelli S, S. Grando, SM Baum and M Udupa (2004): Breeding for Drought Resistance in a Changing Climate: Challenges and Strategies for Dry land Agriculture. CSSA Special Publication No. 32. Madison: Crop Science Society of America and American Society of Agronomy. Ceccarelli S and S Grando (2007): Decentralized 46

8 Participatory Plant Breeding: an Example of Demand Driven Research. Euphytica, 155: Crossa J (1990): Statistical Analysis of Multiplication Trials. Advances in Agronomy, 44: Dalal C R and R J Mayer (1986): Long-term Trends in Fertility of Soils under Continuous Cultivation and Cereal Cropping in Southern Queensland. Overall Changes in Soil Properties and Trends in Winter Cereal Yields. Aust. J. Soil Research, 24: Dehghami H, A Ebadi and A Yousefi (2006): Biplot Analysis of Genotype by Environment Interaction for Barley Yield in Iran. Agron. J. 98: FAO (1990): Guidelines for Soil Profile Description, 3 rd Edition. Food and Agriculture Organization of the United Nations, International Soil Reference Information centre, Land and Water Development Division. Rome, p.70 Gauch H G and Zobel R W (1997): Identifying Megaenvironments and Targeting Genotypes. Crop Sci., 37: Go rny A G (2001): Variation in Utilisation Efficiency and tolerance to Reduced Water and Nitrogen Supply Among Wild and Cultivated Barleys. Euphytica., 117:59-66 IRRI International Rice Research Institute. IRRISTAT 4.3 for Windows. Ma B L, W Yan, L M Dwyer, J Fregeau-Reid and H D Voldeng (2004): Graphic Analysis of Genotype Environment, Nitrogen Fertilizer and Their Interactions on Spring Wheat Yield. Agron. J., 96: Mitku H, G Berhanu and B Amare (2003): The status of soil fertility in Tigray In: Gebremedhin B, Pender J, Ehui S K and Mitku H (eds) Policies for sustainable land management in the highlands of Tigray, northern Ethiopia. Proceedings of a workshop held at, Mekele, Ethiopia,. Socioeconomics and Policy Research Working Paper 54. ILRI, Nairobi, Kenya. 75 pp. Muruli, B J and G M Paulsen (1981): Improvement of Nigrogen Use Efficiency and its Relationship to other Traits in Maize. Maydica, 26: Sinebo W, R Gretzmacher and A Edelbauer (2002): Environment of selection for grain yield in low fertilizer input barley. Field Crops Research, 74: Van Oosterom E J, S Ceccarelli, J M Peacock (1993): Yield response of barley to rainfall and temperature in Mediterranean environments. J. Agric. Sci. (Cambridge), 121: Yan W, P L Corelius, J Crossa and L A Hunt (2001): Two types of GGE Biplots for Analyzing Multienvironment Trial Data. Crop Sci., 41: Yan W (2001): GGEbiplot a Windows Application for Graphical Analysis of Multi-environment Trial Data and other Types of Two-Way Data. Agronomy, 93: Yan W and N A Tinker (2006): Biplot Analysis of Multi-environment Trial Data Principles and Applications. Can. J. Plant Sci., 86: Yan W and I Rajcan (2003): Prediction of Cultivar Performance Based on Single versus Multiple Year Tests in Soybean. Corp Sci., 43: Yan W and I Rajcan (2002): Biplot Analysis of test Sites and Trait relations of Soybean in Ontario. Corp Sci., 42: Zobel R W, Wright M.J and Gauch G (1988): Statistical Analysis of a Yield Trial. Agron. J., 80: