Diyala Journal of Agricultural Sciences (DJAS), 10(2): 29-42, 2018 Al-Assafi 29 http://www.agriculmag.uodiyala.edu.iq/ STABILITY OF WHEAT ENTRIES ACROSS YEARS AND NITROGEN RATES BY AMMI ANALYSIS Radhi Dheyab Al-Assafi dr.raid@gmail.com Field Crops Dept., College of Agriculture, Univ. of Baghdad, Iraq. ABSTRACT To study the stability of spring bread wheat entries across years and nitrogen rates, yield trials were conducted from 2010 to 2013 preceding by screening trail in 2009-2010 at farm of field crops, college of agriculture in Abu-Graib located in the middle of Iraq. Randomized complete block design (RCBD) with split plots arrangement in three replicates was followed. Across years, nitrogen rates occupied main plots whereas, genotypes were in subplots. Five promising genotypes of CIMMYT entries viz: 106s, 107s, 108s, 109s, 110s and local variety (abugraib-3) that were symbolized by letters G1, G2 to G6, respectively. Nitrogen rates were 25, 100, 175kg.N.ha-1. Each nitrogen rate within year was considered as an environment, so that, nine environments were generated. Statistical analysis results revealed that the percentage of genotypes variation from total was 65.6%, also, the percentage of environments and interaction sum of square from total variation was 26.1% and 8.3%, respectively. Sum of square of investigated variation of PCA1, PCA2 and PCA3 was 60.54%, 25.1% and 10.6%, respectively. The total of interaction variation investigated was 96.3%. Grain yield of environments ranged from 3.739 t.ha-1 that ranked the first to 2.801t.ha-1 that ranked the lowest. In addition, the grain yield of genotypes ranged from 3.783 t.ha-1 for G5 that ranked the first to 2.267 t.ha-1 for G1 that ranked the lowest. G4 was more stable than other genotypes; consequently, it was wide adapted and high yield over years. However, this statistical technique was a powerful tool for diagnosing the stable genotypes in grain yield across years of research. We can recommend cultivating G4 for its wide adapted and high stability. Key words: stability, years, wide adaptation, nitrogen rates, environment. INTRODUCTION Wheat is grown on 200mha worldwide annually with productivity reached 2.7 t ha-1(Rajaram and Braun, 2009). This productivity has varied largely across countries and regions. Western Europe such as France was the highest grain yield per hectare (8 t ha-1) compared to one t.ha-1 in middle and West of Asia and North Africa (Rajaram and Braun, 2009). It is necessary to achieve maximum potential yield and increasing genetic gain to face a raising demand of http://www.agriculmag.uodiyala.edu.iq/ mailto:dr.raid@gmail.com Diyala Journal of Agricultural Sciences (DJAS), 10(2): 29-42, 2018 Al-Assafi 30 http://www.agriculmag.uodiyala.edu.iq/ wheat grain supply. Generally, Mediterranean region was the large importer of wheat grain during the last decades. To achieve a sufficient, big efforts are required for creating or improving superior genotypes with high yield ability, adapted for specific environment to reduce the gap between production and consumption. Genetic improvement for high yield has been a major goal of breeding program. The cooperation with CIMMYT led to get sets of spring wheat entries. Some genotypes have high response under specific environment but the performance of other genotypes are indifferent across wide range of environments. Genetic environment interaction (GEI) refers to the differential response between genotypes for various environments. The aim was to evaluate some of these sets in Iraqi environment and then selecting the best entries depending on their yield and stability. The potential yield for any genotype is an outcome of interaction with different environments factors such as years, soil fertility, moisture, planting dates, temperature and day length that vary across locations. These factors have big effects on different plant stages (Crossa, 1990; Johansson et al., 2003). The climate change may cause fluctuation in precipitation, temperature and drought cycles that requires adapted genotypes for wide variations in environment. GEI plays essential role in proportional expression on maximum yield of various genotypes (Reza et al., 2007). Stability refers to stable performance of genotypes across sets of environments (Romagosa et al., 1993). Optimum genotype must achieve high yield and at the same time has low degree of fluctuation in productivity across years and locations (Tarakanovas and Ruzgas, 2006) and low GEI, high response of maximum yield and low aberrations of expected response in target environment (Mohammadi et al., 2011). Generally, the variation in yield is large because the yield is quantitative trait with low heritability. Therefore, grain yield may be affected not by genotypes but also with environment and GEI. Depending on the magnitude of the interactions or the differential genotypic responses to environments, the varietal ranking can differ greatly across environments (Kaya et al., 2002). Many approaches are used to investigate GEI. Additive main effects and multiplicative interaction (AMMI) is the most active way because it investigates the large portion of mean square variation of GEI in addition to isolating of main effects and interaction (Ebdon and Gauch, 2002). The results of AMMI analysis are very useful in determining specific adaptation and choice the best environment (Gauch and Zobel, 1997). Developing high yield cultivars with wide adaptability is the final target of plant breeders in spite of the difficulty of this goal because of GEI. AMMI model proven as an effective tool http://www.agriculmag.uodiyala.edu.iq/ Diyala Journal of Agricultural Sciences (DJAS), 10(2): 29-42, 2018 Al-Assafi 31 http://www.agriculmag.uodiyala.edu.iq/ in diagnosing GEI fashion (Crossa, 1990). According to Line et al.(1986); Becker and Leon (1988) there are two controversy perceptions about stability. The first type is the static and the second is the dynamic. The first type includes the inclination of best genotypes to persist on stable yield across environments, while, the second type includes the stable and responsive genotype for yield in each environment (Annicchiarico, 2002). The determining and analysis of GEI lead to reduce errors in breeding process in addition to the selection at one environmental condition will not give the same advantage in another condition. This will make the diagnosing of superior genotypes across environments and selecting the best genotypes is more complicated. The undesirable effects of GEI are the result of poor correlation between phenotypic value and genotypic value alongside reducing the response of selection leading to bias in estimates of heritability and prediction of selection progress (Farshadfar et al., 2000; Alghamdi, 2004). The objective of this research was to estimate the yield stability of some spring bread wheat introduced from CIMMYT across range of years and nitrogen rates and choice the genotypes that have high stable performance yield. MATERIALS AND METHODS Trials were conducted at farm of Agric.-College in Baghdad located in the middle of Iraq for four winter seasons 2009-2010, 2010-2011, 2011-2012 and 2012-2013. The aim was to screen advanced entries of spring wheat introduced from CIMMYT. Fourteen entries of spring wheat besides the check variety (Abugraib-3) were planted. Entries with poor performance were discarded at the end of 2009-2010 season. Five entries were selected depending on their superiority on local variety in grain yield. Five entries and local variety were planted for three successive winter seasons to determine grain yield stability under three levels of nitrogen were 25, 100, 175 kg N ha-1. Letters from G1 to G6 as shown in table1 symbolized the entries. Randomized complete block design (RCBD) according to split plots arrangement with three replicates was used. Nitrogen levels were occupied the main plots whereas the sub plots were assigned for genotypes. The plot dimension was 3m x 2m. Each entry was in a small package that contained about 300 grains planted with two rows. The length of row was 2.5 m and the distance between rows was 0.4m to allow maximum gene expression of genotypes and reducing the competition among plants to minimum. Seeding rate was 100kg.ha-1. Phosphorous fertilizer as a rate of 100 kg P2O5 per hectare was added to the soil at tillage. Nitrogen fertilizer was added as urea form (46%N) according to required level with two http://www.agriculmag.uodiyala.edu.iq/ Diyala Journal of Agricultural Sciences (DJAS), 10(2): 29-42, 2018 Al-Assafi 32 http://www.agriculmag.uodiyala.edu.iq/ applications; the first application was at planting and the second was at anthesis. Soil and crop managements were performed as recommended. At maturity, samples represented 1m2 were taken to estimate grain yield and then converted to total grain yield t ha-1 after adjusting the moisture content of grain to 14%. Statistical analysis Data primarily were analyzed according to RCBD of combined analysis of treatments planted in one area across years. If the interaction between genotypes and years is a significant, the next step will include estimating of interaction components by AMMI on the basis that each nitrogen within the year is considered as environment to form nine environments as shown in table1. AMMI analysis includes the additive components of single main effects of genotypes and environments in addition to multiplicative components of interaction effects (Yan and Kang, 2003). Therefore, the mean of genotype response i in environment j will be as following formula: Yij = μ + Gi + Ej + GEij +ij Where: μ is the general mean, Gi is the genotype effects, Ej is the environment effects, GEij is the interaction effects that adjusted to Σ k=1λk yik αjk + pij and the final model will be as following: Yij = μ + Gi + Ej + Σ k=1λk γik αjk + pij +ij where λk is the eigenvalue value associated with kth of main components, γik is eigenvector of λk associated with genotypes, αjk is the elements of jth eigenvector of λk that associated with environments, pij is the additive residual and ij is the error ij th that associated with mathematical model. AMMI was used to analysis of variance of main effects (additive portion) and analysis of main components (PCA) and analysis the residue non-additive across ANOVA. In analysis, each combination of nitrogen level and year is considered as an environment (table 1). The AMMI stability value (ASV) described by Purchase et al., (2000) was calculated as follows: The higher the IPCA score, either negative or positive, the more specifically adapted a genotype is to certain environments. Lower ASV scores indicate a more stable genotype across environments. http://www.agriculmag.uodiyala.edu.iq/ Diyala Journal of Agricultural Sciences (DJAS), 10(2): 29-42, 2018 Al-Assafi 33 http://www.agriculmag.uodiyala.edu.iq/ Table 1. Environments and genotypes Nitrogen rates × year Environments N25 kg.ha -1 x year(2010-2011) E1 N100 kg.ha -1 x year(2010-2011) E2 N175 kg.ha -1 x year(2010-2011) E3 N25 kg.ha -1 x year (2011-2012) E4 N100 kg.ha -1 x year(2011-2012) E5 N175 kg.ha -1 x year(2011-2012) E6 N25 kg.ha -1 x year(2012-2013) E7 N100 kg.ha -1 x year(2012-2013) E8 N175 kg.ha -1 x year(2012-2013) E9 Genotypes used, their symbols and origin Origin Symbol Genotypes CIMMYT G1 106S CIMMYT G2 107S CIMMYT G3 108S CIMMYT G4 109S CIMMYT G5 110S Iraq G6 Abugraib-3 RESULTS AND DISCUSSION Results of combined analysis of variance for treatments planted in one location across years revealed significant differences of all sources of variation (table 2). The interaction between genotypes and years was significant; this indicator to a different behavior of genotypes across years and GEI had a role in performance of genotypes yield across years. Significant environmental effects stated the differential performance of genotypes across environments as results of fluctuation of weather conditions, soil fertility and other environmental variations from year to year. Yan and Kang (2003) stated the genotypic makeup of any individual remains constant from environment to another if the mutation will not occur. Therefore, the phenotypic variation for any genotype is a reflection to genotypic factors under environmental conditions in spite of there are wide ranges to produce number of phenotypes depending on the kinds of genotypic composition and their interaction with growth factors. Mostly, the highest grain yield of genotypes is correlated with low stability (Padi, 2007). Results in table 3 (AMMI analysis) revealed the percent of genotypes variance out of treatments variance was 65.6% that refers to ability of improving grain yield efficiently. The percent of environmental variance from treatments variance was 26.1% whereas the percent of interaction between genotypes and environments from treatments variance was 8.3%. All these effects were http://www.agriculmag.uodiyala.edu.iq/ Diyala Journal of Agricultural Sciences (DJAS), 10(2): 29-42, 2018 Al-Assafi 34 http://www.agriculmag.uodiyala.edu.iq/ significant that refers to the importance of these sources in analysis. Genotypes effect had the major source of variance because of its high contribution in treatments variance indicating different response of genotypes across environments. PCA1 explained 60.54% from interaction variance out of degree of freedom 30.5% whereas PCA2 and PCA3 explained 25.1% and 10.6%, respectively, that account for 96.3% of interaction explained. Sivaplan et al. (2000) recommended a predictive AMMI model with the first four PCAs while Yan and Rajcan (2002) reported that the most accurate for AMMI could be predicted by using the first two PCAs. Table2. Combined analysis of variance with RCBD for nitrogen rates, genotypes and years F. pr V.R M.S S.S D.f S.O.V 6.91 0.08193 0.16386 2 Replicates <.001 80.87 0.95937 1.91874 2 years 1.46 0.01186 0.04745 4 Error(1) <.001 791.20 6.45046 12.90092 2 nitrogen 0.028 3.96 0.03232 0.12928 4 Nitrogen x Years 0.64 0.00815 0.09783 22 Error(2) <.001 591.17 7.51038 37.55189 5 genotypes <.001 28.39 0.36071 3.60707 21 Genotypes x Years 0.036 2.06 0.02612 0.26118 10 Genotypes x Nitrogen <.001 3.43 0.04353 0.87060 20 Genotypes x Years x Nitrogen 0.01270 1.14339 90 Error(3) 58.69222 161 Total Table 3. AMMI analysis of grain yield of six genotypes of spring wheat planted at nine environments Sources D.f S.S M.S V.R F_Pr. Treats 53 57.24 1.080 85.01 0.00000 Genotypes 5 37.55 7.510 591.17* 0.00000 Environ. 8 14.95 1.869 108.80* 0.00000 Interaction 40 4.74 0.118 9.33* 0.00000 IPCA1 12 2.87 0.239 18.84* 0.00000 IPCA2 10 1.19 0.119 9.36* 0.00000 IPCA3 8 0.50 0.0628 4.94?* 0.00004 IPCA4 6 0.12 0.0205 1.62 0.15163 Residual 4 0.05 0.0131 1.03 0.39754 Block 18 0.31 0.0172 1.35 0.17634 Error 90 1.14 0.013 Total 161 58.69 0.365 http://www.agriculmag.uodiyala.edu.iq/ Diyala Journal of Agricultural Sciences (DJAS), 10(2): 29-42, 2018 Al-Assafi 35 http://www.agriculmag.uodiyala.edu.iq/ AMMI has a valuable and effective tool to diagnose genotypes according to their adaptation if it is wide or specific. Genotype is defined as ideal depending on its performance and stability across environments (Aina et al. 2009). Genotypes that located near to horizontal axes have wide adaptation and stable whereas genotypes that located apart from the horizontal axes have specific adaptation for some environments so they have high GEI (Ebdon and Gauch; 2002). Grain yield of environments was the lowest in first environment reached 2.801t.ha-1 to 3.739 t.ha-1 in the ninth environment that had the first rank and it was the highest grain yield. This indicating that the environments had high variability (table5). Grain yield of genotypes ranged from 3.783t.ha-1 in G5 that occupied the first rank to 2.276 t.ha-1 in G1 that occupied the latest rank (table 4). G1 and G2 had the highest scores of PCA1, therefore, they were more adapted to specific environments such as environment 3 for G2 and environment 4 for G1. Specific adaptation can be described as synchronizing of growth stages developments of plant with environmental conditions that reduce risks to extreme factors such as drought, coldness and nutrients deficiency. Therefore, in specific area that well characterized, the specific adaptation is considered the key to improve yield (Najafian et al; 2010). The genotype can be considered more favorable if it has high yield and stable performance across a wide range of environments. Depending on that, G4 was more adapted, stable and high grain yield because it has low scores of PCA1 and high grain yield whereas G6 was low stability because it has low yield and high scores of PCA1 that is, adapted to specific environment. Kang (2002) reported the importance of GEI depending on the target by plant breeder. If the plant breeder aims to produce cultivars with high yield across many environments, he must look for cultivars selected based on low GEI. Otherwise, if the plant breeder is interested to get a cultivar with specific adaptation, the contribution of genotype in GEI will be important. AMMI can be used through biplot diagram for main effects and scores of 1PCA1 between genotypes and environments. The differences among genotypes are related to their direction and magnitude along the X-axes (yield) and Y-axes (1PCA1 scores) (Kadhem, 2014). Genotypes that locate on vertical line have the same grain yield while those locate on the horizontal line have the same GEI (Crossa, 1990). Genotypes or environments that locate on the right side from the zero point of vertical line (yield mean) have high grain yield compared to that locate on the left side. PCA scores of genotype in AMMI are considered as an indicator to genotype stability or adapted across environments. There are two http://www.agriculmag.uodiyala.edu.iq/ Diyala Journal of Agricultural Sciences (DJAS), 10(2): 29-42, 2018 Al-Assafi 36 http://www.agriculmag.uodiyala.edu.iq/ types of drawing; the first is used to investigate AMMI-1 biplot that showed if any genotypes or environments scores are close to zero that is, contributing little to the interaction (stable). The greater the PCA scores, either negative or positive the more specific adapted (Gauch and Zobel, 1996). In AMMI-2, the scores of PCA1 and PCA2 are plotted to diagnose the best genotypes in which environment is. Variation produced by genotypes was greater than variation of environmental differences. G5 gave the highest yield while G1 gave the lowest yield (figure 1). The environments 9, 6 and 3 were the favorable but environment 9 was the best whereas the environment 1 was the lowest. Genotypes or environments with high scores negative or positive of IPCA1 had high interaction, whereas those had IPCA1 scores close to zero (near to horizontal line) possess low interaction across environments therefore, they were more stable than those located far from horizontal line. Stone and Savin (2000) stated that grain yield and quality of wheat are considered a complex trait as a result of interaction between biochemical processes and large number of genes that control it. Figure 1 showed that G5 was the best in grain yield followed by G4 and G2 while the lowest was G1 and G6. E9 gave the highest mean in grain yield followed by E6 and E3 while E1 gave the lowest grain yield. G4 was more stable because it had low scores of PCA1 and was the closet to horizontal line. That is, G4 is more favorable for wide adaptation. Piepho (1996) reported that the deep knowledge of GEI and exploiting it in plant breeding can be contributed in improving genotypes yield. If the genotype is selected across many locations, the stability and yield mean across environments will be the most important than grain yield in specific environments. Figure 2 of AMMI-2 biplot model includes IPCA1 and IPCA2 that captured 85.64% from GEI of grain yield. G4 was the closet to the center of origin, that is, it had low variation in GEI, and therefore, it was more stable than other genotypes. G5 was more stable in PCA2 because it located on horizontal line that means it had low PCA2 scores. G2, G6, G1 were far from center of origin that made them less stable and they were adapted for specific environments. In respect to total environment, G1 was more adapted to E6, E4 and E5 while G3 was more adapted to E7, E8 and E9. G2 was more adapted to E1, E2 and E3. The environments E1, E2 and E3 were closest from zero in respect to PCA2; this indicates less contribution of these environments in IPCA2 variation. Data in table 5 showed the rank of three first superior genotypes in each environment. G5 captured the first rank in six environments (E4, E5, E6, E7, E8 http://www.agriculmag.uodiyala.edu.iq/ Diyala Journal of Agricultural Sciences (DJAS), 10(2): 29-42, 2018 Al-Assafi 37 http://www.agriculmag.uodiyala.edu.iq/ and E9). Further, G5 recorded the second rank in E1, E2 and E3. G5 was the best in grain yield followed by G2 that captured the first rank in three environments (E1, E2 and E3) and the second rank in E4. High yield criteria must not be taken the only ones when doing selection because genotypes with high yield may be unstable. (Kadhem, 2014). Therefore, stability and high yield must be considered together at selection. Base on that, G4 was better than other genotypes because it had high stability as shown from the AVS value that was the lowest reached 0.3560 that is, its yield is more stable across environments studied. Figure 1. Biplot of grain yield of six genotypes planted at nine environments E4 E5 E7 G3 G1 G4 G5 G6 G2 E8 E6 E9 E3 E2 E1 PC1=60.6% Yield.Mean AMMI1 Biplot http://www.agriculmag.uodiyala.edu.iq/ Diyala Journal of Agricultural Sciences (DJAS), 10(2): 29-42, 2018 Al-Assafi 38 http://www.agriculmag.uodiyala.edu.iq/ Figure 2. Biplot of AMMI-2 shown PCA1 against PCA2 of six genotypes planted at nine environments Table 4. Grain yield mean and IPCA1, IPCA2 scores of six genotypes planted at nine environments Envi. E1 E2 E3 E4 E5 E6 E7 E8 E9 Geno. Mean IPCA 1 score IPCA 2 score Geno. G1 1.656 1.931 2.231 2.167 2.326 2.864 2.117 2.504 2.687 2.276 0.431 -0.279 G2 3.319 3.558 4.019 3.011 3.240 3.744 3.230 3.307 4.078 3.501 -0.609 0.270 G3 2.763 3.000 3.326 2.994 3.153 3.735 3.195 3.558 3.930 3.295 0.302 0.208 G4 3.107 3.336 3.716 3.075 3.258 3.824 3.347 3.602 4.165 3.492 -0.055 0.357 G5 3.209 3.463 3.776 3.567 3.726 4.289 3.660 4.036 4.322 3.783 0.368 -0.003 G6 2.749 3.052 3.476 2.886 3.120 3.544 2.681 2.783 3.255 3.061 -0.438 -0.554 Envi. mean 2.801 3.057 3.424 2.950 3.137 3.667 3.038 3.298 3.739 IPCA 1 -0.379 -0.386 -0.534 0.183 0.105 0.199 0.199 0.515 0.096 IPCA 2 0.078 0.001 0.013 -0.343 -0.366 -0.249 0.173 0.208 0.484 AVS values of genotypes G1 G2 G3 G4 G5 G6 0.6702 0.9032 0.4757 0.3650 0.5210 0.8319 G6 G2 E3 E1 E2 E5 PC1 G3 G1 G5 G4 E4 E6 E8 E7 E9 PC1=60.6% ; PC2=25.1% AMMI Biplot for Yield.Mean G6 G2 E3 E1 E2 E5 PC1 http://www.agriculmag.uodiyala.edu.iq/ Diyala Journal of Agricultural Sciences (DJAS), 10(2): 29-42, 2018 Al-Assafi 39 http://www.agriculmag.uodiyala.edu.iq/ Table 5. Shown AMMI-2 for the first three genotypes for each environment Environment Yield mean IPCA1 Score IPCA2 Score First Second Third E1 2.801 -0.37959 0.07865 G2 G5 G4 E2 3.057 -0.38662 0.00086 G2 G5 G4 E3 3.424 -0.53418 0.01358 G2 G5 G4 E4 2.950 0.18369 -0.34375 G5 G4 G2 E5 3.137 0.10556 -0.36639 G5 G4 G2 E6 3.667 0.19920 -0.24958 G5 G4 G2 E7 3.038 0.19969 0.17340 G5 G4 G2 E8 3.298 0.51549 0.20841 G5 G4 G3 E9 3.739 0.09676 0.48483 G5 G4 G2 REFERENCES Aina, O., A. Dixon, I. Paul and E. Akinrinde. 2009. G×E Interaction effects on yield and yield components of cassava (landraces and improved) genotypes in the Savanna regions of Nigeria. Afr. J. Biotechnol., 8(19): 4933-4945. Alghamdi, S. S. 2004. Yield stability of some soybean genotypes across diverse environment. Pak. J. Bio. Sci. 7(12): 2109-2114. Annicchiarico, P. 2002. Defining adaptation strategies and yield stability targets in breeding programmes. In: Kang, M. S.(eds) Quantitative Genetics, Genomics, and Plant Breeding. 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AMMIتحليل حنطة عبر السنوات ومستويات النتروجين باستخدامالاستقرارية مدخالت من راضي ذياب العسافي dr.raid@gmail.com ، العراق.جامعة بغداد، كلية الزراعةقسم المحاصيل الحقلية، المستخلص السنوات ومستويات النتروجين لمدخالت من حنطة الخبز بهدف دراسة االستقرارية الوراثية عبر في 2121-2112سبقتها تجربة غربلة في عام 2122الى 2121اجريت تجارب حقلية من الربيعية، غريب. صممت التجارب بالقطاعات الكاملة يبأمحطة ابحاث المحاصيل الحقلية في كلية الزراعة في ثالثة مكررات اذ وضعت في كل سنة مستويات النتروجين في وبترتيب الواح منشقة ب RCBDالمعشاة االلواح الرئيسة والتراكيب الوراثية في االلواح الثانوية. استخدمت خمسة تراكيب وراثية واعدة من حنطة أبو والصنف المحلي للمقارنة 110Sو 109Sو 108Sو 107Sو 106Sهي CIMMYT))السمت 25بالتتابع. كانت مستويات النتروجين المدروسة هي G2 ....G6و G1واعطيت الرموز 2-غريب تسع بيئات وعلى نتجبيئة لت على انه السنة x. اعتبر كل مستوى نتروجين 2-هـ نتروجين كغم 275و 211و ضوئها تم تحليل االستقرارية. بينت اهم نتائج التحليل االحصائي ان نسبة تباين التراكيب الوراثية من ونسبة %26.2في حين كانت نسبة تباين البيئات من التباين الكلي %65.6للمعامالت بلغت يالتباين الكل %61.54نسبة PCA3و PCA2و PCA1. فسرت تباينات %3.2تباين التداخل الوراثي البيئي http://www.agriculmag.uodiyala.edu.iq/ mailto:dr.raid@gmail.com mailto:dr.raid@gmail.com Diyala Journal of Agricultural Sciences (DJAS), 10(2): 29-42, 2018 Al-Assafi 42 http://www.agriculmag.uodiyala.edu.iq/ . تراوح حاصل حبوب البيئات %26.2بالتتابع ليكون مجموع ما مفسر من التداخل %21.6و %25.2و في البيئة االولى 2-ـه طن 2.312في البيئة التاسعة التي احتلت المرتبة االولى الى 2-ـه طن 2.722من في التركيب 2-ـه طن 2.732التي احتلت المرتبة االخيرة. تباين حاصل حبوب التراكيب الوراثية من احتل المرتبة الذي G1في التركيب الوراثي 2-ـه طن 2.276الذي احتل المرتبة االولى الى G5الوراثي اذ انه كان ذو تكيف واسع واعطى حاصل عال G4االخيرة. كان أكثر التراكيب الوراثية المستقرة هو عبر البيئات المدروسة. كانت هذه التقانة االحصائية فعالة جدا في تشخيص التركيب الوراثي المستقر ذو بر سنوات الدراسة ومستويات النتروجين لذا يمكن التوصية بزراعة التكيف الواسع في حاصل الحبوب ع .G4التركيب الوراثي ، بيئة.مستويات النتروجين، التكيف الواسع، سنوات: الالكلمات المفتاحية http://www.agriculmag.uodiyala.edu.iq/