doi: 10.14720/aas.2017.109.2.02 Original research article / izvirni znanstveni članek Multivariate analysis to assess abscisic acid content association with different physiological and plant growth related traits of Petunia Akhtar MAHMOOD1, Muhammad Saleem HAIDER1, *Qurban ALI2, Idrees Ahmad NASIR2 Received February 13, 2016; accepted June 01, 2017. Delo je prispelo 13. februarja 2016, sprejeto 01. junija 2017. ABSTRACT Petunia is an important and beautiful ornamental flowering plant, grown throughout the world for its beauty and attraction. Different Petunia hybrids have been developed by petunia growing countries of the world. The prescribed study was conducted to investigate the association of abscisic acid with seed yield and its contributing traits of petunia line. Data for different physiological, morphological and petunia seed yield traits was recorded, analyzed and interpreted for final inferences. From results it was showed that the petunia lines IAGS-P8, IAGS-P9 and IAGS-P11 performed well for most of the studied traits. It was shown from multivariate analysis techniques that stomata conductance, chlorophyll b contents, seed area, chlorophyll a contents, flower fresh mass, flowers per plant, seed mass and abscisic acid contributed higher to seed yield per plant in petunia. The abscisic acid contents showed positive and significant association and contribution towards seed yield of petunia genotypes. It was suggested that selection on the basis of abscisic acid may be useful to develop good seed yield per plant and large number of flowers per plant in petunia under stressful environmental conditions. Key words: petunia; multivariate analysis; heritability; genetic advantage; abscisic acid content; seed yield IZVLEČEK UPORABA MULTIVARIATNE ANALIZE ZA OCENITEV POVEZAVE MED VSEBNOSTJO ABSCIZINSKE KISLINE IN RAZLIČNIMI Z RASTJO POVEZANIMI FIZIOLOŠKIMI ZNAKI PRI PETUNIJI Petunija je pomembna in lepa okrasna rastlina, ki se goji širom po svetu zaradi lepote in privlačnosti. V številnih državah, kjer jo gojijo, so bili vzgojeni različni križanci. Pričujoča raziskava je bila opravljena z namenom preučiti povezavo med vsebnostjo abscizinske kisline in lastnostmi, povezanimi s pridelkom semena preučevanih linij petunij. Izmerjene so bile različne fiziološke in morfološke lastnosti, ki vplivajo na pridelek semen, analizirana in pojasnjena je bila njihova povezava. Izsledki so pokazali, da so se linije petunij IAGS-P8, IAGS-P9 in IAGS-P11 izkazale kot primerne za večino analiziranih lastnosti. Multivariatna analiza je pokazala, da so parametri kot so stomatarna prevodnost, vsebnost klorofila b in a, površina semen, sveža masa cvetov, število cvetov na rastlino, masa semen in vsebnost abscizinske kisline prispevali največ k večjemu pridelku semena na rastlino. Vsebnost abscizinske kisline je imela značilen pozitiven vpliv na pridelek semena vseh genotipov petunij. Zaradi tega se priporoča, da je izbor genotipov petunije na osnovi večje vsebnosti abscizinske kisline primeren za vzgojo rastlin z velikim pridelkom semena na rastlino in velikim številom cvetov v stresnih okoljskih razmerah. Ključne besede: petunija; multivariatna anliza; dednost; genetska prednost; vsebnost abscizinske kisline; pridelek semena 1 INTRODUCTION The genus Petunia is an important ornamental plant of high economic imperativeness on the whole agriculture. It gives dominant qualities to serve as model plant for contemplating plant improvement. The history of Petunia development as a crop is accent with implications for an advanced array of added ornamental crops (Cantor et al., 2015; Gerats and Vandenbussche, 2005). This generally developed genus of flowering plants fits in with Solanaceae gang. It is an important ornamental plant in landscape because of colour diversity. A large number of hybrids and varieties have been developed with diverse color and patterns (Ganga, 1 Institute of Agricultural Sciences, University of the Punjab Lahore, Pakistan 2 Centre of Excellence in Molecular Biology, University of the Punjab Lahore, Pakistan; *Corresponding author: saim1692@gmail.com Acta agriculturae Slovenica, 109 - 2, september 2017 str. 175 - 186 Akhtar MAHMOOD et al. 2011). This plant is native to Brazil, Argentina or Uruguay and included over 35 species (Dole and Wilkins, 1999). Petunias are considered an annual ornamental plant but may be perennial in warmer climates (Berenschot et al., 2008). Petunia genus includes about 20 species of South American origin, mostly perennials but developed as annuals (Mallona et al., 2010), 14 annual species (Toma, 2009), but Petunia hybrida Hort. (P. axillaris Lam. x P. violacea Lindl.) is a species which presents the biggest decorative value (Berenschot et al., 2008; Vandenbussche et al., 2016). Presently the researches have also confirmed that the genus Petunia is consisted of 14 closely related species (Stuurman et al., 2004). The modern petunias have been developed through hybrid breeding like the violet petunia (P. violacea, and P. integrifolia (Hook.) Schinz & Thell.) and ambrosial agrarian or white petunia (Petunia axillaris). A large number of researchers are also working on finding the real ancestors because their ancestors are rarely cultivated today. Petunia is a small sprawling plant with large number of flowers, grown throughout the world for its beauty and interactive colors (Anderson, 2006; Bala, 2007). Petunias blossom abundantly even in hot summers and new varieties even in seasons with top clamminess (Bala, 2007; Florin et al., 2012). The petunia is long day plant due to which it is used for landscape proposes (Anderson, 2006; Currey and Lopez, 2013). The present study was conducted to develop inbred lines of petunia through selfing for growing seasons. The data of various morphological, physiological and seed yield traits was recorded to access the performance of inbred lines under development. The identification of promising inbred lines for the development of petunia hybrids. 2 MATERIAL AND METHODS Prescribed research work was conducted in the research area of Institute of Agricultural Sciences, University of the Punjab Lahore, Pakistan. Twelve petunia lines, IAGS-P1, IAGS-P2, IAGS-P3, IAGS-P4, IAGS-P5, IAGS-P6, IAGS-P7, IAGS-P8, IAGS-P9, IAGS-P10, IAGS-P11 and IAGS-P12 were selected and grown in the field during 2015. Selfing of all the lines was carried out for 4 successive growing seasons (2011-14) to develop inbred lines. The selfed seed was collected to develop next generation, grown in 2015 and for obtaining data for various traits, such as leaf temperature (LT), photosynthetic rate (A), stomata conductance (gs), water use efficiency (WUE), sub-stomata CO2 concentration (Ci) and transpiration rate (E) (by using IRGA-LI-6262 (Infrared Gas Analyzer, LI-COR Biosciences designs, USA), chlorophyll a content (Chl. a), chlorophyll b content (Chl. b) in fresh matter (measured through the dimethyl sulfoxide extraction method (Hiscox and Israelstam, 1979), plant height (PH), leaves per plant (LPP), flowers per plant (FPP), leaf area (LA), stem diameter (SD), leaf length (LL), fresh leaf mass (FLM), seeds per fruit (SPF), leaf width (LW), flower mass (FM), seed mass (SM), fresh stem mass (FSM), seed area (SA, measusered by using Digital Micrometer Screw Guage, Model: 1658DGT/25), 100-seed mass (HSM), abscisic acid (ABA) contnets (using HPLC method (Seo and Koshiba, 2002)), and seed yield per plant (SYP). The data were statistically analyzed by using analysis of variance technique (Steel et al., 1997). 3 RESULTS AND DISCUSSION The results from Table 1 persuaded that significant differences among all the studied traits were found. The highest heritability (h2bs) was found for photosynthetic rate, sub-stomata CO2 concentration, stomata conductance, water use efficiency, plant height, leaves per plant, flowers per plant, seeds per fruit, leaf area, seed yield per plant and abscisic acid contents. The genetic advantage was found higher for all studied traits except sub-stomata CO2 concentration, flowers per plant, seeds per fruit, seed mass while moderate for abscisic acid content and leaves per plant. The higher broad sense heritability referred the dominance type of gene action and suggested that the selection for such traits may be helpful to develop petunia hybrids with much of vigor and ability to tolerate harsh environmental conditions as higher value of h2bs was recorded for abscisic acid content. Higher concentration of abscisic acid contnets in plant body gives an extra advantage to grow in drought conditions with higher and well performance. Various researchers while working on different crop plants have described about higher heritability for these traits as reported in our study (Aaliya et al., 2016; Ali et al., 2015; Ali et al., 2013; Ali et al., 2014a; Mahmood and Haider, 2016). Genetic advance indicated the presence of additive type of gene action hence the traits with higher genetic advance suggested that the selection of lines may also be helpful to develop synthetic varieties. The similar findings for different crops have been reported by various researchers (Ali and Ahsan, 2015; Ali et al., 2014b; Ali et al., 2014c; Khorasani et al., 2011; Mahmood and Haider, 2016). The results 176 Acta agriculturae Slovenica, 109 - 2, september 2017 Multivariate analysis to assess ... with different physiological and plant growth related traits of Petunia (Supplementary Material Table S1) indicated that the lines IAGS-P8, IAGS-P9 and IAGS-P11 were better performing than all of the other lines also seen form figure 1a (principal component analysis) that the lines IAGS-P2, IAGS-P8, IAGS-P9 and IAGS-P11 fall in quadrant I which indicates the highest and best performance for respective traits. The lines which showed the best performance may be used for the development of good quality cultivars, with large number of flowers, stress tolerant and multicolor petunia hybrids and varieties (Ali et al., 2013; Florin et al., 2012; Mahmood and Haider, 2016). Table 1: Genetic components for morpho-physiology and yield traits of petunia Traits M.S G.M GV GCV % PV PCV % EV ECV % h2bs% GA% Photosynthetic rate (^g CO2 s ^ 123.533* 14.104 39.803 167.991 43.927 176.480 4.124 54.074 90.612 74.728 Leaf tTemperature (oC) 137.453* 21.194 38.440 134.674 60.574 169.058 22.134 102.194 63.459 40.898 Chlorphyll a (mg g"1 fr. mass.) 14.245* 3.199 4.238 115.095 5.770 134.298 1.532 69.203 73.447 96.786 Chlorphyll b (mg g"1 fr. mass) 17.345* 1.578 3.037 138.730 11.271 267.256 8.234 228.429 26.945 100.608 Stomata conductance (mmol m"2 s"1) 1.323* 0.031 0.417 366.764 0.489 397.167 0.072 152.400 85.276 337.958 Transpiration rate (mm day"1) 1.025* 0.884 0.258 54.013 0.509 75.873 0.251 53.286 50.678 71.773 sub-stomata CO2 concentration (^mol mol 1 CO2) 234.534* 148.889 77.000 71.914 80.533 73.546 3.533 15.404 95.613 10.114 Water use efficiency (%) 36.345* 6.792 11.700 131.248 12.945 138.055 1.245 42.814 90.382 84.026 Leaves per plant 233.342* 86.750 77.452 94.489 78.438 95.089 0.986 10.661 98.743 17.692 Plant height (cm) 219.245* 55.818 70.571 112.441 78.104 118.290 7.533 36.736 90.355 25.107 Stem diameter (cm) 1.026* 0.511 0.308 77.594 0.411 89.647 0.103 44.896 74.919 164.889 Flowers per plant 287.345* 141.000 92.791 81.123 101.764 84.955 8.973 25.227 91.183 11.449 Leaf length (cm) 36.124* 6.324 11.333 133.868 13.458 145.880 2.125 57.967 84.210 85.731 Leaf width (cm) 4.897* 1.358 1.017 86.539 2.863 145.198 1.846 116.591 35.522 77.676 Leaf area (cm2) 41.255* 6.353 12.934 142.685 15.387 155.628 2.453 62.138 84.058 91.087 Fresh leaf mass (g) 3.522* 0.654 0.629 98.096 2.263 186.031 1.634 158.066 27.806 112.255 Fresh stem mass (g) 214.255* 49.224 67.574 117.166 79.107 126.771 11.533 48.404 85.421 27.088 Flower mass (g) 2.148* 0.601 0.391 0.806 1.366 1.945 0.975 127.396 28.608 97.648 Seeds per fruit 996.357* 866.167 326.331 61.380 343.694 62.992 17.363 14.158 94.948 3.567 100"seed mass (mg) 64.235* 12.049 16.357 116.515 31.520 161.741 15.163 112.180 51.895 42.437 Seed area (mm) 2.087* 0.357 0.434 110.317 1.218 184.745 0.784 148.192 35.657 193.490 Seed yield per plant 1.024* 0.117 0.313 163.648 0.397 184.283 0.084 84.732 78.859 745.625 Seed mass (mg) 97.573* 50.140 24.671 70.146 48.231 98.078 23.560 68.548 51.152 12.434 Abscisic Acid contents (mg/100g fresh leaf mass) 524.156* 115.124 162.969 118.979 198.219 131.217 35.250 55.335 82.217 17.646 * = significant at 5 % probability level, mean sum of squares (M.S), grand mean (G.M), genotypic variance (GV), genotypic coefficient of variance (GCV %), phenotypic variance (PV), phenotypic coefficient of variance (PCV %), environmental variance (EV), environmental coefficient of variance (ECV %), broad sense heritability (h2bs %), genetic advance (GA) The correlation analysis provides best opportunity to the researchers for selecting genotypes of crop plant to improve crop plant growth and production (Ali et al., 2016; Ali et al., 2014c). The results from table 2 indicated that significant correlation was found for photosynthetic rate with chlorophyll a contents, plant height, sub-stomata CO2 concentration, leaf width, abscisic acid and seeds per fruit. Abscisic acid contents was found to be significantly correlated with most of the studied traits including photosynthetic rate, chlorophyll a contents, chlorophyll b contents, plant height, sub-stomata CO2 concentration, transpiration rate, water use efficiency, leaf temperature, leaf area, leaf width, fresh shoot mass, seeds per fruit, 100-seed mass, seed mass and seed yield per plant. Seed yield per plant was significantly correlated with photosynthetic rate, transpiration rate, leaf temperature, leaves per plant, fresh leaf mass, stem diameter, seed area, seed mass, seeds per fruit, flowers per plant and abscisic acid contents. The positive and significant correlation revealed that the selection of lines to develop hybrids and synthetic varieties may be helpful to improve the growth and development of petunia. The significant correlation of abscisic acid content with morphological traits, seed yield and physiological traits indicated that the selection of petunia lines on the basis of good abscisic acid production may be fruitful to improve drought tolerance in petunia (Aaliya et al., 2016; Abbas et al., 2016; Filipovic et al., 2014). The growth of petunia was adversely affected by changing the environmental optimum temperature of 25 °C with minimum circadian light intensity to be 13 Wm-2 (Kaczperski et al., 1991). It has been observed that temperature and light caused major effects on growth and development of petunia. Therefore, new petunia varities and hybrids should be developed which can tolerate varying environmental conditions to Acta agriculturae Slovenica, 109 - 2, September 2017 Akhtar MAHMOOD et al. continue optimal plant growth and development. However, the holdup of plant growth, development, the access of CO2 by stomata in an optimized environmental condition has shown not any extensive adverse effect on petunia plants (Blanchard and Runkle, 2009). Stepwise regression analysis was performed to predict the trait(s) that were highly contributing towards the petunia seed yield per plant. Stepwise regression analysis provides an opportunity to select crop plant genotypes with higher contribution traits to improve crop yield and production (Aaliya et al., 2016; Abbas et al., 2016). The results from Table 3 showed that stomata conductance, chlorophyll a contents, flowers per plant, leaf area, flower fresh mass, seed area, seeds per fruit, seed mass and abscisic acid contenta contributed more to seed yield per plant but it could be biased as preceding literature has also been reported the error effects of stepwise regression (El-Badawy and Mehasen, 2011) while handling a large number of independent variables. The Intercept = 145.754, R2 = 0.863, Adjust R2 = 0.336 and Standard Error = 0.812 was found with expected regression equation as fellow: Y = 145.754 + (7.144X0 + (-1.254 X2) + (3.898 X3) + (-6.651X4) + (121.14X5) + (-4.582X6) + (0.018X0 + (-0.042X8) + (1.145X9) + (0.163X10) + (-0.063X„) + (2.463X12) + (-0.125X13) + (-17.215X14) + (41.006X15) + (11.267X16) + (-11.175X17) + (2.825X18) + (8.982X19) + (0.256X20) + (6.902X21) + (21.267X22) + (25.926X23) The use of PCA (principal component analysis) to overcome the error effect of large number of independent variables in breeding experiments and find overall attributed variation in dependent structure (Ali et al., 2015; Filipovic et al., 2014; Goodarzi et al., 2015; Marjanovic-Jeromela et al., 2011). It has also been reported that the eigenvalues (in PCA) showed primary significance for numerical diagnostics to evaluate variation endorsed by a large number variables on the dependent structure and their data matrix in a graphical display (Greenacre, 2010). Therefore, we have also performed principle component analysis (PCA) to inspect the traits which were contributing higher towards petunia seed yield per plant. Our data generated four PCA as shown in Table 4 with diverse variation among all of the studied traits. It was found that the PC1, PC2, PC3 and PC4 contributed variation of 35.60 %, 24.60 %, 17.90 % and 11.3 % while their cumulative proportion was 25.2 %, 43.20 %, 57.20 % and 73.10 % respectively. PC1 and PC2 contributed higher variation for respective studied traits (Fig. 1a) the eigenvalues of these four PCs was higher than 1 (Fig. 1b). The Figure 1a also showed that the petunia lines IAGS-P2, IAGS-P8, IAGS-P9 and IAGS-P11 showed better performance for most of the studied traits. 17ft 176 Acta agriculturae Slovenica, 109 - 2, september 2017 Multivariate analysis to assess ... with different physiological and plant growth related traits of Petunia Table 2: Correlation among various morpho-physiological and yield traits of petunia Traits A LT Chl. a Chl. b gs E Ci WUE LPP PH SD FPP LL LW LA FLM FSM FM SPF HSW SA SM ABA LT 0.2024 Chl. a 0.8228* 0.0852 Chl. b 0.0197 -0.2476 0.0282 gs -0.2867 -0.2754 -0.2040 0.4668* E 0.0079 0.4140* -0.0727 0.8246* 0.4179* Ci 0.4950* -0.2424 0.4296* 0.0152 0.4287* 0.0697 WUE 0.1222 -0.2718 0.4619* 0.7206* 0.4996* 0.8442* 0.2587 LPP -0.2284 0.2949 -0.2064 -0.2864 -0.2048 0.6049* -0.2224 0.6517* PH 0.4889* 0.0884 0.1840 0.5719* 0.2407 0.4948* 0.1474 -0.0464 -0.2226 SD -0.0772 0.2017 0.2204 -0.2140 -0.2642 0.4087* 0.2221 -0.2726 0.0482 0.1820 FPP 0.1809 -0.4272* 0.4499* 0.4462* 0.6668* -0.2286 -0.2616 0.4426* -0.2204 0.2420 0.2846 LL -0.0172 0.1487 -0.0152 0.4146* 0.2267 0.5144* -0.0225 0.4184* 0.2298 0.6272* 0.7846* -0.4044* LW 0.4454* -0.2097 0.2184 0.2649 0.4011* 0.2166 0.1872 0.4426* 0.4649* 0.4250* 0.2819 0.4487* -0.0998 LA -0.0988 0.1709 0.0484 0.4282* 0.4186* 0.6729* 0.2268 0.4761* -0.0258 0.4480* 0.6828* -0.4292* 0.9210* 0.2848 FLM -0.2756 -0.0711 0.2048 0.4242* 0.2044 0.0602 0.2902 0.2690 0.4792* -0.0848 -0.2441 0.4787* 0.2478 -0.2162 0.2826 FSM 0.2586 0.0874 -0.0951 0.8227* 0.7284* 0.7286* -0.2976 0.6617* 0.2229 0.4228* 0.4016* 0.7602* 0.1291 0.4848* 0.6144* 0.4962* FM 0.4268* 0.4892* 0.2216 0.5122* -0.2800 0.8444* 0.0062 -0.0209 0.2824 -0.0691 -0.2471 -0.0049 0.4442* 0.2229 -0.4496* -0.2210 0.4468* SPF 0.4417* -0.2624 -0.2290 0.0262 0.6072* 0.2427 -0.4290* 0.0702 -0.1222 -0.0821 -0.2815 0.6472* -0.0104 -0.2489 -0.2488 -0.2559 0.4417* 0.0109 HSM 0.2220 0.0852 0.0289 0.2801 0.2698 0.2241 -0.2069 0.2428 0.2402 0.4799* -0.2092 -0.4772* 0.4224* 0.4472* 0.4400* -0.0049 0.1089 0.2227 -0.2910 SA 0.2224 -0.0276 0.4487* -0.4649* -0.4144* -0.4196* -0.2204 -0.2422 0.0090 0.4724* 0.1642 0.2487 0.4104* -0.2822 -0.4246* 0.4184* 0.1262 0.4474* -0.0584 -0.4251 SM -0.2780 0.4446* 0.2201 0.2084 0.2294 -0.0742 -0.0224 0.2144 -0.2874 0.2422 0.4928* -0.2649 0.4114* -0.4291 -0.1608 0.0275 -0.0047 -0.2452 -0.2056 -0.4778* -0.2148 ABA 0.4874* 0.5678* 0.4291* 0.6019* 0.6201* 0.5211* 0.2949 0.5404* 0.7011* 0.0150 -0.2548 0.5052* 0.2578 0.4029* 0.4122* -0.2109 0.4672* -0.0241 0.4402* 0.4122* -0.0252 0.5046* SYP 0.4686* 0.4744* -0.2714 -0.2221 0.4062* 0.4179* -0.2422 0.4978* 0.4494* -0.0664 0.4180* 0.4444* -0.2852 -0.2274 -0.2542 0.4222* 0.2217 0.0212 0.4284* 0.0642 0.4851* 0.4295* 0.5642* *= Significant at 5 % probability level, A = photosynthetic rate, LT = leaf temperature, Chl. a = chlorophyll a content, Chl. b = chlorophyll b content, E = transpiration rate, gs = stomata conductance, Ci = sub-stomata CO2 concentration, WUE = water use efficiency, LPP = leaves per plant, PH = plant height, SD = stem diameter, FPP = flowers per plant, LL = leaf length, LW = leaf width, LA = leaf area, FLM = fresh leaf mass, FSM = fresh stem mass, FM = flower mass, SPF = seeds per fruit, HSM = 100-seed mass, SA = seed area, SM = seed mass, ABA = abscisic acid content, SYP = seed yield per plant Acta agriculturae Slovenica, 109 - 2, september 2017 Akhtar MAHMOOD et al. Table 3: Stepwise regression analysis for various traits of petunia for seed yield Traits Coefficients B Standard Error t Stat Cumulative R Partial R2 % Xi Photosynthetic rate 7.144 0.553 2.013 0.1673 16.73 X2 Leaf temperature -1.254 0.127 -2.320 0.2151 21.51 X3 Chlorphyll a 3.898 2.531 1.114 0.2573 25.73 X4 Chlorphyll b -6.651 1.632 1.052 0.2661 26.61 X5 Stomata conductance 121.14 45.125 2.525 -0.235 23.50 X6 Transpiration rate -4.582 3.153 -1.172 0.266 26.60 X7 Sub-stomata CO2 concentration 0.018 0.053 -1.512 -0.263 26.30 X8 Water use efficiency -0.042 0.351 -0.153 0.5631 56.31 X9 Leaves per plant 1.145 0.121 1.522 0.2634 26/34 X10 Plant height 0.163 0.086 -1.315 0.2534 25.34 X11 Stem diameter -0.063 0.015 0.063 0.4743 47.43 X12 Flowers per plant 2.463 8.535 0.279 0.5386 53.86 X13 Leaf length 0.125 0.015 2.233 -0.2157 21.57 X14 Leaf width -17.215 16.815 -1.037 0.2353 23.53 X15 Leaf area 41.006 24.150 -0.137 -0.2327 23.27 X16 Fresh leaf mass 11.267 8.759 0.521 0.0333 3.33 X17 Fresh stem mass -11.175 5.115 -1.255 -0.0847 8.47 X18 Flower mass 2.825 0.131 0.522 0.3562 35.62 X19 Seeds per fruit 8.982 10.525 -1.248 -0.2237 22.37 X20 100-seed mass 0.256 0.052 -0.113 0.3644 36.44 X21 Seed area 6.902 4.315 1.522 -0.1245 12.45 X22 Seed mass 21.267 15.517 1.535 0.3252 32.52 X23 Abscisic acid contents 25.926 11.258 0.463 0.5437 54.37 Intercept = 145.754, R2 = 0.863, Adjust R2 ' = 0.336, Standard Error = 0.812 1 80 176 Acta agriculturae Slovenica, 109 - 2, september 2017 Multivariate analysis to assess ... with different physiological and plant growth related traits of Petunia Table 4: Principal component analysis Eigen value 6.147 3.9536 4.-0717 3.0313 Proportion 0.356 0.246 0.179 0.113 Cumulative 0.252 0.432 0.572 0.731 Traits PC2 PC2 PC3 PC4 Photosynthetic rate 0.689 0.425 0.026 0.055 Leaf temperature -0.25 0.083 -0.336 -0.093 Chlorphyll a 0.035 0.376 0.07 0.036 Chlorphyll b 0.286 -0.005 -0.025 -0.227 Stomata conductance 0.222 0.228 -0.297 -0.047 Transpiration rate 0.323 -0.037 0.252 -0.262 Sub-stomata CO2 concentration 0.087 0.163 0.029 0.002 Water use efficiency 0.291 0.183 0.223 -0.202 leaves per plant -0.204 -0.125 -0.34 0.257 Plant height -0.166 0.372 0.237 -0.033 Stem diameter -0.113 0.143 -0.044 -0.224 flowers per plant -0.413 -0.216 0.325 -0.208 Leaf length 0.114 -0.202 -0.02 0.258 Leaf width 0.214 0.323 0.076 0.248 Leaf area 0.301 -0.017 0.028 0.242 Fresh leaf mass 0.231 -0.007 -0.245 -0.042 Fresh stem mass -0.314 -0.064 0.216 0.002 Flower mass -0.291 0.216 0.018 0.337 Seeds per fruit -0.031 -0.216 0.414 -0.234 200-seed mass 0.262 0.109 -0.074 0.372 Seed area -0.291 0.212 0.012 -0.206 Seed yield per plant -0.231 -0.012 -0.126 -0.056 Seed mass -0.061 0.135 -0.105 -0.499 Abscisic acid contents 0.284 0.218 0.136 0.075 «-£3 2 «»o 0 -1 - -2 -3 - 1AGS-P2 Plant height Chl / stem diameter SS C / IAGS-P1 Flower weight f O seed araa Temp^a^^ seed yield per plant 1£GS-P11 orphyll a ^«v Leaf width °IAGS-P6 \ O2 Concentration N. Abscisic Acid Wateruseefficiency TA,-c- Da \ t Stomata Conductance 0AGS^pr-»rA 100-seed weight 0IAGS-p9\ Fresh leaf weightChlorPhy|1 b | ^ IAOS p3 leaves per plant \ flowers per plaffieds per Photosynthetic R - ^^^^ IAC Transpiration Rate Leaf Area 1 Genotypes fruit Leaf length J JAGS-P10 / ate3AGS-P5 ^^ ÎS-P7 ^^ 0IAGS-P4 OIAG^-- -5.0 -2.5 0.0 First Principle Component (29.6%) 2.5 5.0 a: Principle components 0 3 0 Acta agriculturae Slovenica, 109 - 2, september 2017 str. 175 - 186 Akhtar MAHMOOD et al. 8 7 6 5 > 4 c eg W 3 2 1 0 8 10 12 14 16 18 Principle Component Number b: Scree plot Figure 1: a. Principle component analysis of yield and its attributing traits, b. Scree plot and respective eigen values Principal factor analysis was performed by using principle component analysis values, to check that traits which were directly contributing and highly associated with petunia seed yield per plant. The factor 1 was found to be highly contributing factor trait which contributes 48.20 % in total variation were chlorophyll a, chlorophyll b, transpiration rate, stomata conductance, leaves per plant, water use efficiency, leaf area, leaf length, seed yield per plant stem diameter and abscisic acid content (Table 5). Abscisic acid content and seed yield per plant were found the most contributing traits of petunia. Various researchers have suggested that the selection of crop plant genotypes on the basis of the factor analysis (traits from factor 1) may be supportive to develop higher yield hybrids and synthetic varieties of crop plants. While the traits which fall in factor 2 (from factor loading table 5) indicated that the selection of crop plant genotypes on the basis of such traits will not be helpful as the segregation will take place in the next growing generations (Ali et al., 2016; Filipovic et al., 2014; Mahmood and Haider, 2016). The better performance of petunia lines for chlorophyll a, transpiration rate, chlorophyll b, leaves per plant, stomata conductance, leaf length, water use efficiency, leaf area, stem diameter, seed yield per plant, seed mass and abscisic acid content revealed that the accumulation or assimilation of organic matter/compounds will be higher in the plant body. It has been also found the accumulation or assimilation of organic biomass in plant body is very essential for the proper enhanced growth and development of petunia plant (Hladni et al., 2011; Huang, 2007; Huang and Yeh, 2009; Mahmood and Haider, 2016). The accumulation of organic compounds generally takes place in the leaves, stem and flowering parts of a plant body. The results from our study were well supported by results which demonstrated the role of factor analysis for effective selection criteria in maize breeding program (Filipovic et al., 2014). In order to understand about the genetic association among petunia lines, cluster analysis was performed (Khorasani et al., 2011; Mostafavi et al., 2011). The results from clustering showed that the petunia lines IAGS-P2 and IAGS-P12 followed by IAGS-P8 and IAGS-P9 were highly associated with each other as compared with other petunia lines (Fig. 2a) the association was verified through the development of minimum spanning tree (Fig. 2b) that showed smaller distance between petunia lines IAGS-P8 and IAGS-P9 while IAGS-P2 and IAGS-P12 were having IAGS-P1 in between them through the use of eigen values. So, from results it may be revealed that the petunia lines IAGS-P8 and IAGS-P9 were highly associated with each other and may be used as two separate male or female lines to develop petunia hybrids as also verified by mean performance and principal component analysis Figure 1a results of these lines. Also the petunia line IAGS-P11 showed better performance for almost all under studied traits, so it may also be used as male to develop good quality petunia hybrids (Mahmood and Haider, 2016). It was also suggested that in future breeding program of IAGS-P8, IAGS-P9 and IAGS-P11, these traits may be important for primary selection of synthetic petunia varieties and hybrids to increase seed yield per plant of petunia under various environmental regimes of Pakistan and other growing countries. Moreover, the hybrid seed production technology proved to be more efficient as it reduced the cost, time and increase 0 1 ft? 176 Acta agriculturae Slovenica, 109 - 2, september 2017 Multivariate analysis to assess ... with different physiological and plant growth related traits of Petunia efficacy for better selection in petunia improvement should cover different years and locations. programs. Still, further studies are required which Table 5: Factor loadings for different traits of petunia Factor1 Factor loadings % Communality Chlorophyll a 0.674 Chlorophyll b 0.735 Stomata conductance 0.643 Transpiration rate 0.743 Water use efficiency 0.879 Leaves per plant 0.568 Stem diameter 0.789 Leaf length 0.678 Leaf area 0.568 Seed yield per plant 0.567 Abscisic acid content 0.876 48.2 Factor2 Sub-stomata CO2 concentration -0.563 Plant height -0.577 Fresh leaf mass -0.636 22.1 Factor3 Leaves per plant 0.323 Leaf length 0.325 Leaf area 0.327 Fresh leaf mass 0.241 Seed yield per plant 0.263 11.1 Factor4 Photosynthetic rate 0.221 Leaves per plant 0.135 Plant height 0.119 Stem diameter 0.219 Leaf area 0.287 Flower mass 0.153 100-seed mass 0.206 8.62 Cumulative variance 90.02 Acta agriculturae Slovenica, 109 - 2, September 2017 Akhtar MAHMOOD et al. IAGS-P1 IAGS-P2 IAGS-P12 IAGS-P11 IAGS-P7 IAGS-P3 IAGS-P4 IAGS-P5 IAGS-P6 IAGS-P9 IAGS-P8 IAGS 10 a: Dendrogram J_l_I_I_I_I_I_I_I_L 1.00 0.98 0.96 0.94 0.92 0.90 0.88 0.86 0.84 Minimum Spanning Tree 0.4 ^IAG „IAGS-P3 ■T / \ N. i \ -i \ i \ i 0.2 \ i / V i * i x IAGS-P2 ^iAGS-P1 _0.0 .S-P4 X X IAGS-P5 x IAGS-P8 x IAGS-P9 x IAGS- -0.3 -0.2 -0.1 -0.0 0 \ \ x IAGS-P1 2 -0.2 \ \ \ ___ x IAGS-P 1 1 -0 4 1 0.2 0.3 0.4 0.5 x IAGS-P7 i i {IAGS-P10 P6 second dimension b: Minimum spanning tree Figure 2: a. Dendrogram analysis based on hierarchal clustering. Association of petunia lines on genetic basis of all studied traits, b. Minimum spanning tree using eigene values for petunia lines on the basis of all studied traits 184 176 Acta agriculturae Slovenica, 109 - 2, september 2017 Multivariate analysis to assess ... with different physiological and plant growth related traits of Petunia 4 CONCLUSION The present study concluded that abscisic acid contents showed positive and significant association and contribution towards seed yield of petunia genotypes. It was suggested that selection on the basis of abscisic acid content may be useful to develop good seed yield per plant and large number of flowers per plant in petunia under stressful environmental conditions. 5 CONFLICT OF INTEREST The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. 6 REFERENCES Aaliya, K., Qamar, Z., Nasir, I. A., Ali, Q., and Munim, A. F. (2016). 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