IDENTIFICATION OF STABLE GENOTYPES OF RAPESEED USING SOME PARAMETRIC AND NON-PARAMETRIC METHODS UNDER DRYLAND CONDITIONS

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1 Internatonal Research Journal of Appled and Basc Scences 2011 Avalable onlne at ISSN X / Vol, 2 (1):73-84 Scence Explorer Publcatons IDENTIFICATION OF STABLE GENOTYPES OF RAPESEED USING SOME PARAMETRIC AND NON-PARAMETRIC METHODS UNDER DRYLAND CONDITIONS HOSSEIN HATAMZADEH 1, AZIM KHAZAI 2, KHOSHNOOD ALIZADEH 3* 1 Dryland agrcultural research staton-shrvan, Iran; 2 Dry land Agrcultural Research staton -Khorram Abad, Iran; 3 Dryland Agrcultural Research Insttute, Iran *Correspondng author: E-mal: khoshnod2000@yahoo.com Abstract: Rapeseed breeders amed to develop varetes whch perform relatvely well over the target regon. However the presence of genotype envronment (GE) hnders selecton progress. Ths study was conducted on 20 mproved rapeseed genotypes usng some stablty ndces at three locatons n Iran durng The combned analyss of varance ndcated sgnfcant envronment and genotype man effects and a sgnfcant GE nteracton. The nteracton of the studed rapeseed genotypes was predcted by the frst two prncpal components of genotypes and envronments. The nteracton prncple components axes scores presented a dsproportonate genotype response whch was the major source of varaton for any crossover genotype by envronment nteracton. Addtve man effects and multplcatve nteracton (AMMI) b-plot showed that VDH , Avso and Elvs had the lowest nteracton n across envronments. Avso produced the hghest yeld across envronments. The GE pattern revealed by AMMI b-plots ndcated that the tested rapeseed genotypes are narrowly adapted, and Avso was dentfed as wdely adapted genotype accordng to AMMI stablty value and yeld relablty ndex. Key words: Brassca napus, AMMI analyss, GE nteracton, Ranfed. Abbrevatons: AMMI-Addtve man effects and multplcatve nteracton; ASV-AMMI stablty value; GE-genotype envronment; IPCA-Interacton prncple components axes; MET-Mult envronment trals. INTRODUCTION Rapeseed s grown as a ran fed crop on the West and North- east of Iran. Rapeseed breeders amed to develop varetes whch perform relatvely well over the target regon. However the presence of GE hnders selecton progress. Dfferental yeld responses are attrbuted to dfferences n phenology, growth habt, vernalzaton and photoperodc requrements (Van Oosterom et al., 1993). To mtgate ths problem, stratfcaton of envronments and mult envronment trals are necessary to dentfy adapted and hgh yeldng genotypes (Basford and Cooper, 1998). Statstcal methods for determnng stablty and adaptaton of crop cultvars tested across dvert envronments are used to assst breeders n selectng hgher yeldng and adapted genotypes (Ln and et al., 1986). GE result n genotype rank changes from one envronment to another, a dfference n scale among envronments, or a combnaton of these two stuatons. If relatve performances of genotypes grown n dfferent envronments are dfferent, then GE nteracton becomes a major challengng factor to crop breedng programs (Zobel et al., 1988). In such a case, the breeder s faced ether wth developng specfc breedng populatons for each envronment and/or wth selectng genotypes that generally perform well across a range of envronments (McKeand et al., 1990). Understandng of the causes of GE nteracton can be used to establsh breedng objectves, dentfy deal test condtons, and formulate and recommendatons for areas of optmal genotype adaptaton (Yan and Hunt, 2001). Number methods have been used for an understandng of the causes of GE nteracton (Van Eeuwjk et al., 1996). These methods can be categorzed nto two major strateges. The frst strategy nvolves factoral regresson analyss of the GE matrx (Barl et al., 1995). The second strategy s assocated wth the use of the addtve man effects and multplcatve

2 nteracton (AMMI) model n MET data analyss. The AMMI model s a hybrd analyss that ncorporates both the addtve and multplcatve components of the two way data structure. AMII s the only model that dstngushes clearly between the man and nteracton effects and ths s usually desrable n order to make relable yeld estmatons (Gauch, 1992). AMMI b-plot analyss s consdered to be an effectve tool to dagnose GE nteracton patterns graphcally. The AMMI model descrbes the GE nteracton n more than one dmenson, and t offers better opportuntes for studyng and nterpretng GE nteracton than analyss of varance and regresson of the means (Vargas et al., 2001). The AMMI b-plots allow vsual assessment of the genotype and the envronment (year ste combnaton) man effects (Ma et al., 2004). The nteracton prncpal component axes (IPCA) scores of a genotype n the AMMI b-plot analyss ndcate the stablty of a genotype across envronments. The closer the IPCA scores to zero the more stable the genotypes are across ther testng envronments (Carbonell et al., 2004). The bplot dsplay of IPCA scores plotted aganst each other provdes vsual nspecton and nterpretaton of the GE nteractons. Integratng b-plot dsplay and genotypc stablty statstcs enables genotypes to be grouped based on smlarty of performance across dverse envronments (Thllanathan and Fernandez, 2001). Ths method has been shown to be effectve because t captures a large porton of the GE nteracton sun of squares, t clearly separates man and nteracton effects that present agrcultural researches whch dfferent knds of opportuntes, and the model often provdes ergonomcally meanngful nterpretaton of the data (Ebdon and Gauch, 2002). The results of AMMI analyss are useful n supportng breedng program decsons such as specfc adaptaton and selecton of envronment (Gauch and Zobel, 1996). Usually, the results of AMMI analyss s ndcated n common graphs as b-plot. The b-plot shows both the genotypes and the envronments value and relatonshp usng sngulars vectors technque (Tarakanovas and Ruzgas, 2006). More recently, Purchase et al. (2000) developed the AMMI model s IPCA1 and IPCA2 scores for each genotype. ASV s the dstance from the coordnate pont to the orgn n a two dmensonal scatter gram of IPCA1 scores aganst IPCA2 scores. The practcal nterest of combnng hgh levels of mean yeld and yeld stablty has led to the development of the yeld relablty concept (Eskrdge 1990; Kang, 1997) where a relable genotype s characterzed by havng consstently hgh yeld across envronments (Annccharco, 2002). The use of a yeld relablty ndex facltes the selecton and recommendaton of the best adapted varetes, as the mean yeld and the yeld stablty are combned nto a unque measure of genotype mert (Annccharco, 2002). The objectves of ths study were to () nterpret GE nteracton obtaned by AMMI analyss of yeld performances of 20 rapeseed genotypes over 6 envronments () Assessment of adaptaton and stablty of advanced rapeseed genotypes. () Study the relatonshps, smlartes and dssmlartes among parametrc and non parametrc stablty methods. MATERIALS AND METHODS 20 advanced rapeseed genotypes,.e., Parade, Orent, Okap, VDH , Alce, Pastell, Elte, Herald, Beln, Avso, Turner, Modena, Hopper, Ryder, Opera, Kvntel, Pauc- 61, Zarfam, Ebonte, Elvs were studed at Dryland Agrcultural Research Statons- Sararood, Ilam and Lorestandurng ) under ran-fed condtons. Expermental layout was a randomzed complete blocks desgn wth three replcatons. Each expermental plot contaned 5 plantng rows of 6 meters length wth 30 centmeters row spacng. In all experments, ntrogen (Urea) and trple super phosphate fertlzer were appled as 90 and 45 kgha -1, respectvely. Gran yeld of each plot was detected usng central plants n each plot after droppng the margnal effects. Gran yeld was analyzed for each envronment separately to test for the genotype effect. In the combned analyss of varance, each combnaton of locaton year was treated as an envronment (E) and the date were analyzed accordng to the fallowng model: Y jk = m + G + E j + R(E) jk + (G E) j + e jk where Y jk s the observaton of the th genotype (G) n the jth envronment (E) n the kth replcaton (R) nested wthn envronment; m s the general mean, e jk s the varaton due to random error, assocated wth th genotype, n the kth block of the lth envronment and (G E) j s the genotype by envronment nteracton. The G E nteracton was parttoned accordng to the jont regresson (Fnlay and Wlknson 1963; Eberhart and Russell, 1966) and the addtve man effect and multplcatve nteracton (AMMI) models (Gauch and Zobel, 1997). 74

3 The ASV was the dstance from the coordnate pont to the orgn n a two dmensonal scatter gram of IPCA1 scores aganst IPCA2 scores n the AMMI model (Purchase et al., 2000). Because the IPCA1 score contrbutes more to the GE nteracton sum of squares, a weghted value s needed. Ths weght s calculated for each genotype and each envronment accordng to the relatve contrbuton of IPCA1 to IPCA2 to the nteracton SS as follows: ASV = SS SS Where IPCA1 IPCA2 SS SS 2 ( IPCAscore 1 ) + ( IPCA2 score) 2 IPCA1 IPCA2 s the weght gven to the IPCA1-value by dvdng the IPCA1 sum of squares by the IPCA2 sum of squares. Yeld relablty ndex (I) proposed by Kataoka (1963) for economc analyss was calculated as followng: I = S Where m Ζ ( p ) m = mean yeld, S = square root of the Ζ = percentle envronmental varance, and ( p) from the standard normal dstrbuton for whch the cumulatve dstrbuton functon reaches the value P Ζ ( p can assume the followng values ) dependng on the chosen Ζ =0.675 for ( p ) P=0.75; for P=0.80; for P=0.85; for P=0.90; and for P=0.95. We consdered Z =1.645 for P=0.95 to calculate ( p ) yeld I. Non parametrc measures were calculated accordng to Thennarasu (1995) and Nassar and Huehn (1987). Combned analyss of varance and stablty analyss ncludng AMMI analyss on the values of gran yeld was processed usng SAS (2001) and IRRISTAT program (2002), respectvely. genotype and GE nteracton effects, respectvely (Table 1). Ths s suggestve of hgh varaton n the growth condtons leadng to dfferental genotypc response to the envronmental changes. Kad et al. (2010) reported that envronment, genotype and GE nteracton sum of square explaned 55.4%, 17.8% and 26.7% of the total sum of square, respectvely. A large amount of SS for envronments ndcated that the envronments were dverse, wth large dfferences among envronmental means causng most of the varaton n gran yeld. The magntude of the GE nteracton SS was 2.5 tmes larger than that for genotypes, ndcatng that there were sustanable dfferences n genotypc response across envronment. Mohammad et al. (2007), Kaya et al. (2002) and Tarakanovas and Puzgas (2006) reported that the magntude of the GE nteractons were 2, 3.5 and 2.2 tmes larger that for genotypes, respectvely. Ths shows the dffcultes n selectng superor genotypes; these dffcultes arse manly from the maskng effects of varable envronments (Goncalves et al., 2003). Envronment (lnear) mean square aganst GE (lnear) mean square was sgnfcant at 1% probablty level (Table 2). Test of the GE (lnear) mean square aganst pooled devatons showed non- sgnfcant dfferences; meanng, dfferent genotypes have smlar slopes (Table 2). In contrast, Kad et al. (2010) and Hossan et al. (2003) reported that there was sgnfcant dfferences for GE (lnear). Test of the mean square due to pooled devatons aganst the average error ndcated that all genotypes had S d 2 sgnfcantly dfferent from zero and thus were unstable accordng to Eberhart and Russell (1966). Regresson coeffcents (b) were not sgnfcantly dfferent from unt, ndcatng average stablty. Parade, Elte, Avso, Elvs and Alce had above average yeld and thus showed general adaptablty over the tested envronments. RESULTS AND DISCUSSION The analyss of varance of the ndvdual trals (data not shown) ndcated a sgnfcant genotype effect, suggestng the exstence of potental useful genetc varablty. The combned analyss of varance ndcated sgnfcant envronment and genotype man effects and a sgnfcant GE nteracton (Table 1). The results showed that 67.67% of the total sum of square (SS) was attrbutable to envronment effects, 9.08% and 23.25% to 75

4 Table 1. Combned analyss of varance and AMMI analyss of GE nteracton for Gran yeld of twenty rapeseed genotypes n 6 envronments. Source df MS % G E Explaned Total 359 Treatments Envronment (E) ** Rep (E) Genotype (G) ** 9.08 G E ** IPCA ** IPCA ** IPCA ns IPCA ns Resdual ns Pooled error ** and ns sgnfcant at 1% probablty level and non sgnfcant, respectvely Results from analyss of varance (Table 1) also showed that the frst nteracton prncple component axs (IPCA1) captured 56.13% of the nteracton SS and the mean squares for IPCA1 and IPCA2 were sgnfcant at 1% probablty level and cumulatvely contrbuted to 75.15% of total GE nteracton. In the other words, the nteracton of the 20 rapeseed genotypes wth 6 envronments was the best predcted by the frst two prncpal components of genotypes and envronments. These results ndcated that tha AMMI model fts the data well. Table 2. Stablty analyss of twenty rapeseed genotypes n 6 envronments based on Eberhart and Russell's method Source df MS Total 119 Genotype (G) ** Envronment (E) * G E ** E+ G E E (lnear) ** G E(lnear) ns Pooled devatons ** Average error *, ** and ns sgnfcant at 5%, 1% probablty level and non sgnfcant, respectvely + Average error df= Pooled error df n combned analyss of varance; and, Average error SS = Pooled error SS /r. r=3 (number of replcatons). Parametrc measures were summarzed n tables 3-5. Accordng to the envronmental varance, Turner, Pastell, Alce, Avso, Elvs showed the lowest varaton across envronments and Parade, Ebonte and Pauc-61 had the largest varaton (Table 3,5). The superor genotype should be the one wth the lowest P value, thus Alce wth the low P value ranked thrd and ffth for P value and yeld performance (Table 3,5). In such case, genotypes of greatest nterest would be those wth the lowest P values, most of whch would be attrbuted to genetc devaton (Ln and Bnns, 1988). Accordngly, Alce just had hgh yeld and the low P value and Elte ncluded hgh yeld and the medum P value (Table 3,5). ASV ranked Orent, Zarpham, VDH and Elvs as the four most stable genotypes (Table 3-5). Alce, Turner, Pauc-61, Pastell and Elte had the hghest yeld relablty ndex, where as Zarfam, Okap, Elvs and Beln had the lowest relablty ndex. Geometrc adaptablty ndex (GAI) showed sgnfcant and postve correlaton wth gran yeld, N p 4 ) ( 6 ), S (, RS and TOP parameters at 1% 76

5 probablty level and ( 3) S at 5% probablty level (Table 6). Superorty ndex ntroduced Parade, Elte and Avso as adapted genotypes whch ranked as top three genotypes n a hgh percentage of envronments (Table 4). Thennarasu, s (1995) non parametrc stablty statstcs were calculated by ranks of adjusted yeld means (Table 4). Frst parameter ntroduced Orent, Beln and Okap as stable genotypes. The correlaton between parametrc and non parametrc ndces was nvolved n table 6. The analyss of genotypes and envronment parameters resultng from AMMI, descrbed the behavor of genotypes. Genotypes near to orgn ndcated lttle nteracton, whle genotypes far from t assgn the most nteractve genotypes (Fg 1). Parade, Hopper, Alce, Turner, Herald, Ryder, Ebonte, Elte and Modena were the most nteractve, whle Orent, Elvs, Beln, Pastell, Zarfam and VDH were the least nteractve. Out of nne the most nteractve genotypes, Alce, Hopper, Turner and Herald had the most smlar nteracton pattern. They Showed postve nteracton effect at the envronment A (Fg 2). Elte, Avso and Pauc-61 ndcated postve nteracton effects at E, A and F envronments, respectvely (Fg 2). VDH , Avso and Elvs had postve nteracton n envronments wth hgh yeld and also, showed the lowest nteracton n across envronments. Accordng to ASV, genotypes Orent, Zarpham, VDH , Elvs and Avso were stable genotypes. The largest yeld was recorded at envronments E and F and the smallest yeld was recorded at envronments B and C (Table 7). The envronments A and C were close to each other n the AMMI2 b-plot ndcatng smlar dscrmnaton of genotypes ( Fg 2). The envronments B, F and E dffered from all other envronments n nteracton wth genotypes. The man result from the AMMI bplot was the eght hghest nteractve genotypes (Parade, Ebonte, Ryder, Elte, Hopper, Alce, Herald, Turner) were separated from the sx lowest nteractve genotypes (Orent, Elvs, Beln, Pastell, Zarfam, VDH ). Accordng to Fox et al.,. (1990), a genotype usually found n the top thrd of entres across envronments can be consdered relatvely well adapted. Then, Avso was adapted because t ranked n the top thrd of genotypes n a hgh percentage of envronments (hgh TOP value 67%). The undesrable genotypes n ths method were Orent, Beln, Hopperand Opera (Table 4 and 5). In ths study, Avso gave the hghest thrd yeld and was stable over the envronments. In general, The GE pattern revealed by AMMI b-plots ndcated that the tested rapeseed genotypes are narrowly adapted, and Avso just was found to have hgh performances n all envronments. 77

6 78

7 Genotypes Gran yeld 2 b S d Table 3 Mean gran yeld and 10 estmates of parametrc stablty measures for rapegran yelds across 6 envronments 2 S x CV P W 2 σ 2 I AMMI Model GAI Mean Rank IPCA1 IPCA2 ASV Parade ns ** Orent ns ** Okap ns ** VDH ns ** Alce ns ** Pastell ns ** Elte ns ** Herald ns ** Beln ns ** Avso ns ** Turner ns ** Modena ns ** Hopper ns ** Ryder ns ** Opera ns ** Kvntel ns ** Pauc ns ** Regent Cobra ns ** Ebonte ns ** Elvs ns ** Grand Mean 785 *, ** and ns sgnfcant at 5%, 1% probablty level and non sgnfcant, respectvely Contrb uton to GE (%) 79

8 Genotypes Gran yeld Table 4 Mean gran yeld and nonparametrc stablty measures (Z1 and Z2) for 20 rapeseed genotypes ( 1) S ( 1)a 1 Ζ ( 2 ) S ( 2)a Ζ 2 ( ( 6 ) S 3) S p 1) p 2 ) p 3 ) ( N p 4 ) TOP% MID% LOW% RS Parade Orent Okap VDH Alce Pastell Elte Herald Beln Avso Turner Modena Hopper Ryder Opera Kvntel Pauc Regent Cobra Ebonte Elvs

9 Yeld 2 b S d Table 5 Ranks of 20 rapeseed genotypes usng stablty parametrc and nonparametrc measures Parametrc measures Non parametrc measures 2 S x P W 2 σ 2 I ASV GAI CV ( 1) S ( 2 ) S ( 3) S ( 6 ) S p 1) p 2 ) p 3 ) ( N p 4 ) TOP RS Parade Orent Okap VDH Alce Pastell Elte Herald Beln Avso Turner Modena Hopper Ryder Opera Kvntel Pauc Regent Cobra Ebonte Elvs

10 b Table 6 Spearman correlaton between mean yelds and stablty parametrc and nonparametrc measures for 20 rapeseed genotypes. Yeld 2 b S d ns ns - S d 0.18 ns ns 0.83 ** 0.21 ns S x P ns 0.72 ** 0.36 ns 0.87 ** W ns ns 0.88 ** 0.09 ns 0.22 ns 2 S x P W 2 σ 2 I σ ns ns 0.88 ** 0.09 ns 0.22 ns 1 ** I ns 0.50 * ns 0.29 ns 0.25 ns ns ns ASV ns ns ns ns * ns ns -0.25ns GAI 0.92 ** 0.06 ns ns ns 0.09 ns 0.09 ns 0.03ns ns CV ASV ns 0.83 ** 0.21 ns 1 ** 0.87 * * 0.09 ns 0.09 ns 0.29ns ns ns GAI CV ( 1) ( 2 ) S S ( 3) ( 6 ) S S p 1) p 2 ) p 3 ) p 4 ) TOP ( 1) 0.10 ns - S 0.04 ns 0.58 ** 0.31 ns 0.28 ns 0.60 ** 0.60 ** -0.33ns 0.17 ns 0.22 ns 0.31 ns ( 2 ) 0.08 ns - S 0.07 ns 0.63 ** 0.29 ns 0.27 ns 0.63 ** 0.63 ** -0.37ns 0.13 ns 0.19 ns 0.29 ns 0.98 ** ( 3 ) 0.39 ns - S 0.08 ns 0.48 * 0.16 ns 0.19 ns 0.51 * 0.51 * -0.34ns 0.06 ns 0.48 * 0.16 ns 0.91 ** 0.90 ** ( 6 ) 0.60 ** - S 0.06 ns 0.36 ns 0.07 ns 0.12 ns 0.41 ns 0.41 ns -0.19ns ns 0.70 ** 0.07 ns 0.73 ** 0.73 ** 0.92** N p ) 0.05 ns ns 0.69 ** 0.18 ns 0.15 ns 0.69 ** 0.69 ** -0.25ns 0.05 ns 0.13 ns 0.18 ns 0.78 ** 0.76 ** 0.71** 0.61 ** p 2 ) ns 0.26 ns 0.51 * 0.53 * 0.43 ns 0.38 ns 0.38 ns -0.09ns 0.08 ns 0.14 ns 0.53 * 0.82 ** 0.84 ** 0.78** 0.66 ** 0.72 ** p 3 ) 0.01ns 0.27 ns 0.62 ** 0.58 * 0.55 * 0.53 * 0.53 * -0.08ns ns 0.21 ns 0.58 ** 0.84 ** 0.84 ** 0.81** 0.72 ** 0.76 ** 0.94 ** p 4 ) 0.71 ** ns 0.48 * 0.06 ns 0.06 ns 0.54 * 0.54 * -0.12ns ns 0.79 ** 0.06 ns 0.56 ** 0.57 ** 0.74** 0.86 ** 0.54 * 0.54 * 0.62 ** TOP 0.68 ** 0.03 ns ns -.22 ns ns - RS 0.68** * 0.62** ns 0.40 n s n s 0.13ns ns 0.60 ** ns * ns ns 0.04 ns * ns ns 0.24 ns ** 0.75** -0.43ns 0.67** 0.26ns 0.12ns 0.20ns *, ** and ns sgnfcant at 5%, 1% probablty level and non sgnfcant, respectvely. 0.55* 0.55* 0.66** 0.71** 0.55* 0.29ns 0.41ns 0.84** 0.11ns 82

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