Research Article

Estimation of Different Genetic Parameters in Various Safflower (Carthamus tinctorius L.) Genotypes under Field Condition

Rao Wali Muhammad1, Hafiz Muhammad Wasif Ali2*, Amir Hamza1, Muhammad Qadir Ahmad2, Abdul Qayyum2, Waqas Malik2 and Etrat Noor2

1PARC Research and Training Station, Pakistan Agriculture Research Council, Multan, Pakistan; 2Department of Plant Breeding and Genetics, FAST, Bahauddin Zakariya University, Multan, Pakistan; 3Arid Zone Research Institute, Pakistan Agriculture Research Council, Bahawalpur, Pakistan.

Received | February 17, 2020; Accepted | September 18, 2020; Published | October 17, 2020

*Correspondence | Hafiz Muhammad Wasif Ali, Department of Plant Breeding and Genetics, FAST, Bahauddin Zakariya University, Multan, Pakistan; Email: wasifdogar@gmail.com

Citation | Muhammad, R.W., H.M.W. Ali, A. Hamza, M.Q. Ahmad, A. Qayyum, W. Malik and E. Noor. 2020. Estimation of different genetic parameters in various safflower (Carthamus tinctorius L.) genotypes under field condition. Pakistan Journal of Agricultural Research, 33(4): 849-857.

DOI | http://dx.doi.org/10.17582/journal.pjar/2020/33.4.849.857

Keywords | Safflower, Genetic correlation, Heritability, Phenotypic correlation, PCA

Introduction

Safflower is famous oil seed crop of ancient world and belongs to compositae or Asteraceae family. It is a good source of high-quality oil mostly used for industrial, ornamental, biofuel and food purposes (Sehgal et al., 2009; Canavar et al., 2014). It is a multipurpose crop, mostly cultivated as cut flower, medicinal plant, vegetable crop, fodder crop, dye and oil extracting source for paint industry (Emongor, 2010; Emongor et al., 2015).

Safflower oil is rich source of vitamin ‘E’, polyunsaturated (linoleic acid) and monounsaturated (oleic acid) fatty acids, which are helpful in lowering blood cholesterol level (Baydar and Turgot, 1999; Arslan et al., 2003). Percent concentration of linoleum acid (70-87%) and oleic acid (11-87%) is much high as compared to olive oil, peanut, soybean, cotton seed and corn oil (Reza et al., 2013). Oil is also used in preparation of soft margarines and salad oil (Conge et al., 2007). Safflower seeds are rich in vitamins, minerals and tocopherols (Velasco et al., 2005). Petals of flower are used in manufacturing of dyes, food color and medicines (Istanbulluoglu, 2009; Emongor, 2010).

The crop was mainly cultivated in arid and semi-arid regions of the world with low irrigation, low fertilizer input and on marginal lands (Hojati et al., 2011). In past, safflower was cultivated on limited area, as minor crop (Canavar et al., 2014). Now, the scenario has changed. Efforts have been made to raise the cultivated area and productivity of the crop all over the world. Naturally, safflower is a temperate zone crop, but has capability of bearing temperature ranges from -7 to 40 oC with zero frost injury during vegetative and flowering growth periods. Crop is widely grown in more than 60 countries of the world, being resistance to many abiotic stresses. India, China, USA, Ethiopia, Kenya, Mexico, Argentina, Australia, Canada, Italy, Spain, Turkey, Iraq, Syria, Kazakhstan, Iran, Uzbekistan, Morocco, Israel, Russia and Pakistan are the commercial growers of the Safflower in the world (Emongor and Oagile, 2017).

Cropping system of Pakistan is deficit in space for cultivation of both conventional and non-conventional oil seed crops and these are considered as minor crops. However, to meet the requirement of vegetable oil for humans, animals and industry, rearing of conventional oil seed crops over limited area is not fruitful. So, it is a dire need of the time to motivate the growers for sowing of non-conventional oil seed crops in Pakistan. Northern areas of Sindh and Baluchistan are suitable for cultivation of safflower as an oil seed crop. While, in Punjab and KPK provinces, arid and semi-arid regions have favorable environmental conditions for safflower production (Amjad, 2014). Pakistan expends a huge amount around US$ 1.5 billion to buy in the edible oil during 2018-19. The loss of foreign exchange reserves is much less than FY 2017-18, in which about US$ 3.0 billion were spent on import of edible oil. It is need of the time to enhance the cultivated area of the non-conventional oilseed crops like safflower and sunflower in Pakistan to meet the demand of annual vegetable oil of the country (Anonymous, 2018-19).

Present study was designed to investigate the importance of yield and its contributing parameters of a plant and to determine the high potential yielding germplasm based on different agronomic parameters by collecting and screening the national and international diverse genetic material. The selected genotypes may be included in further breeding programs enabling to help the safflower plant breeders to maintain and improve the genetic constitutions of the germplasm.

 

Materials and Methods

The genetic material was comprised of two hundred accessions (Table 1). Germplasm was collected from Institute of Agricultural Biotechnology and Genetic Resources (IABGR), NARC, Islamabad. Genetic material was evaluated for various yield contributing parameters during 2016-17 at experimental area of PARC Research and Training Station, Faculty of Agriculture, Bahauddin Zakariya University, Multan. Randomized Complete Block Design was implemented along with three replications by maintaining 15-20 cm interplant distance and row to row distance was 40-45cm. Balode et al. (2012) and Shinwari et al. (2014) screened 155 and 122 accessions of safflower for various screening purposes. All cultural practices were done as per requirement. Ten randomly chosen plants from each genotype were used to record data of the following parameters; days to flowering (DF), plant height (PH), pods per plant (PPP), no. of primary branches per plant (PBP), no. of secondary branches per plant (SBP), thousand grain weight (TGW), yield per plant (YPP) and days to maturity (DM).

Recorded data was put to estimate the analysis of variance (Steel et al., 1997) to check the existence of significant genetic variability. Heritability (h²) in the broad sense and genetic advance for all parameters were estimated according to the formulae as described by Allard (1960) and Falconer (1981), respectively. Principal component analysis was performed by using XLSTAT 2014.

 

Results and Discussion

Analysis of variance with genetic advance and heritability for 200 lines revealed valuable differences for all the traits under study (Table 2). Magnitude of genetic advance among studied parameters ranged between 11.99-27.21% for PBP and YPP, respectively. Whereas, coefficient of variance (CV) ranged from 2.67 to 19.71% for DM and PBP, respectively. Estimation of heritability ranged between 77.4% to 99.0% for DF and PPP in observed traits (Table 2).

 

Table 1: List of safflower germplasm.

Sr. No.	Accessions	Genus	Species	Origin	Sr. No.	Accessions	Genus	Species	Origin
1	016173	Carthamus	tinctorius	India	101	016327	Carthamus	tinctorius	Afghanistan
2	016186	Carthamus	tinctorius	India	102	016329	Carthamus	tinctorius	Afghanistan
3	016188	Carthamus	tinctorius	India	103	016331	Carthamus	tinctorius	Afghanistan
4	016189	Carthamus	tinctorius	India	104	016333	Carthamus	tinctorius	Afghanistan
5	016190	Carthamus	tinctorius	India	105	016334	Carthamus	tinctorius	Afghanistan
6	016191	Carthamus	tinctorius	India	106	016335	Carthamus	tinctorius	Afghanistan
7	016192	Carthamus	tinctorius	India	107	016337	Carthamus	tinctorius	Afghanistan
8	016193	Carthamus	tinctorius	India	108	016338	Carthamus	tinctorius	Afghanistan
9	016194	Carthamus	tinctorius	India	109	016341	Carthamus	tinctorius	Iran
10	016195	Carthamus	tinctorius	Turkey	110	016342	Carthamus	tinctorius	Iran
11	016199	Carthamus	tinctorius	Kenya	111	016343	Carthamus	tinctorius	Iran
12	016200	Carthamus	tinctorius	Turkey	112	016345	Carthamus	tinctorius	Iran
13	016201	Carthamus	tinctorius	India	113	016346	Carthamus	tinctorius	Iran
14	016202	Carthamus	tinctorius	Afghanistan	114	016347	Carthamus	tinctorius	Ethiopia
15	016203	Carthamus	tinctorius	Afghanistan	115	016349	Carthamus	tinctorius	Portugal
16	016204	Carthamus	tinctorius	Iran	116	016351	Carthamus	tinctorius	Portugal
17	016205	Carthamus	tinctorius	Ethiopia	117	016353	Carthamus	tinctorius	Portugal
18	016206	Carthamus	tinctorius	Iran	118	016354	Carthamus	tinctorius	Portugal
19	016207	Carthamus	tinctorius	Australia	119	016356	Carthamus	tinctorius	Pakistan
20	016209	Carthamus	tinctorius	Morocco	120	016357	Carthamus	tinctorius	Pakistan
21	016210	Carthamus	tinctorius	Morocco	121	016358	Carthamus	tinctorius	Pakistan
22	016211	Carthamus	tinctorius	Spain	122	016359	Carthamus	tinctorius	Pakistan
23	016216	Carthamus	tinctorius	India	123	016360	Carthamus	tinctorius	India
24	016217	Carthamus	tinctorius	India	124	016361	Carthamus	tinctorius	India
25	016218	Carthamus	tinctorius	India	125	016362	Carthamus	tinctorius	India
26	016220	Carthamus	tinctorius	Pakistan	126	016364	Carthamus	tinctorius	India
27	016225	Carthamus	tinctorius	India	127	016365	Carthamus	tinctorius	India
28	016229	Carthamus	tinctorius	India	128	016366	Carthamus	tinctorius	India
29	016230	Carthamus	tinctorius	India	129	016367	Carthamus	tinctorius	India
30	016231	Carthamus	tinctorius	India	130	016368	Carthamus	tinctorius	India
31	016233	Carthamus	tinctorius	India	131	016369	Carthamus	tinctorius	India
32	016234	Carthamus	tinctorius	India	132	016373	Carthamus	tinctorius	India
33	016235	Carthamus	tinctorius	India	133	016374	Carthamus	tinctorius	Australia
34	016236	Carthamus	tinctorius	India	134	016375	Carthamus	tinctorius	Australia
35	016237	Carthamus	tinctorius	India	135	016377	Carthamus	tinctorius	Australia
36	016238	Carthamus	tinctorius	India	136	016379	Carthamus	tinctorius	Australia
37	016239	Carthamus	tinctorius	India	137	016381	Carthamus	tinctorius	Afghanistan
38	016240	Carthamus	tinctorius	India	138	016383	Carthamus	tinctorius	Ethiopia
39	016241	Carthamus	tinctorius	India	139	016386	Carthamus	tinctorius	Egypt
40	016242	Carthamus	tinctorius	India	140	016387	Carthamus	tinctorius	India
41	016243	Carthamus	tinctorius	India	141	016390	Carthamus	tinctorius	India
42	016245	Carthamus	tinctorius	India	142	016391	Carthamus	tinctorius	India
43	016246	Carthamus	tinctorius	India	143	016392	Carthamus	tinctorius	India
44	016247	Carthamus	tinctorius	India	144	016393	Carthamus	tinctorius	India
45	016249	Carthamus	tinctorius	India	145	016396	Carthamus	tinctorius	India
46	016250	Carthamus	tinctorius	India	146	016397	Carthamus	tinctorius	India
47	016252	Carthamus	tinctorius	India	147	016398	Carthamus	tinctorius	India
48	016253	Carthamus	tinctorius	India	148	016402	Carthamus	tinctorius	Israel
49	016254	Carthamus	tinctorius	India	149	016407	Carthamus	tinctorius	Iran
50	016259	Carthamus	tinctorius	Iran	150	016408	Carthamus	tinctorius	Iran
51	016260	Carthamus	tinctorius	Egypt	151	016409	Carthamus	tinctorius	Iran
52	016261	Carthamus	tinctorius	Egypt	152	016410	Carthamus	tinctorius	Iran
53	016262	Carthamus	tinctorius	Egypt	153	016411	Carthamus	tinctorius	Iran
54	016264	Carthamus	tinctorius	Egypt	154	016412	Carthamus	tinctorius	Iran
55	016265	Carthamus	tinctorius	Pakistan	155	016413	Carthamus	tinctorius	Iran
56	016266	Carthamus	tinctorius	Pakistan	156	016414	Carthamus	tinctorius	Iran
57	016267	Carthamus	tinctorius	Pakistan	157	016415	Carthamus	tinctorius	Iran
58	016268	Carthamus	tinctorius	Pakistan	158	016416	Carthamus	tinctorius	Iran
59	016269	Carthamus	tinctorius	Pakistan	159	016419	Carthamus	tinctorius	Iran
60	016270	Carthamus	tinctorius	Pakistan	160	016420	Carthamus	tinctorius	Iran
61	016271	Carthamus	tinctorius	Egypt	161	016421	Carthamus	tinctorius	Iran
62	016272	Carthamus	tinctorius	Egypt	162	016423	Carthamus	tinctorius	Iran
63	016273	Carthamus	tinctorius	Egypt	163	016425	Carthamus	tinctorius	Iran
64	016274	Carthamus	tinctorius	Egypt	164	016426	Carthamus	tinctorius	Turkey
65	016276	Carthamus	tinctorius	Egypt	165	016428	Carthamus	tinctorius	Afghanistan
66	016278	Carthamus	tinctorius	India	166	016430	Carthamus	tinctorius	Afghanistan
67	016279	Carthamus	tinctorius	Egypt	167	016431	Carthamus	tinctorius	Afghanistan
68	016280	Carthamus	tinctorius	Egypt	168	016432	Carthamus	tinctorius	India
69	016281	Carthamus	tinctorius	Iran	169	016434	Carthamus	tinctorius	India
70	016283	Carthamus	tinctorius	Iran	170	016435	Carthamus	tinctorius	India
71	016284	Carthamus	tinctorius	Iran	171	016436	Carthamus	tinctorius	India
72	016285	Carthamus	tinctorius	Iran	172	016438	Carthamus	tinctorius	India
73	016287	Carthamus	tinctorius	Iran	173	016439	Carthamus	tinctorius	India
74	016288	Carthamus	tinctorius	Iran	174	016441	Carthamus	tinctorius	Sudan
75	016289	Carthamus	tinctorius	Iran	175	016442	Carthamus	tinctorius	Sudan
76	016290	Carthamus	tinctorius	Iran	176	016443	Carthamus	tinctorius	Sudan
77	016291	Carthamus	tinctorius	Iran	177	016446	Carthamus	tinctorius	Russia
78	016292	Carthamus	tinctorius	Iran	178	016447	Carthamus	tinctorius	Egypt
79	016293	Carthamus	tinctorius	Iran	179	016451	Carthamus	tinctorius	Egypt
80	016295	Carthamus	tinctorius	Iran	180	016453	Carthamus	tinctorius	Egypt
81	016296	Carthamus	tinctorius	Iran	181	016458	Carthamus	tinctorius	India
82	016297	Carthamus	tinctorius	Iran	182	016459	Carthamus	tinctorius	India
83	016298	Carthamus	tinctorius	Iran	183	016460	Carthamus	tinctorius	India
84	016299	Carthamus	tinctorius	Iran	184	016464	Carthamus	tinctorius	India
85	016301	Carthamus	tinctorius	Iran	185	016465	Carthamus	tinctorius	India
86	016303	Carthamus	tinctorius	Iran	186	016466	Carthamus	tinctorius	India
87	016304	Carthamus	tinctorius	Iran	187	016467	Carthamus	tinctorius	India
88	016306	Carthamus	tinctorius	Iran	188	016469	Carthamus	tinctorius	India
89	016308	Carthamus	tinctorius	Iran	189	016470	Carthamus	tinctorius	Turkey
90	016310	Carthamus	tinctorius	Iran	190	016471	Carthamus	tinctorius	Iran
91	016312	Carthamus	tinctorius	Turkey	191	016474	Carthamus	tinctorius	Iran
92	016313	Carthamus	tinctorius	Turkey	192	016478	Carthamus	tinctorius	Iran
93	016314	Carthamus	tinctorius	Turkey	193	016479	Carthamus	tinctorius	Iran
94	016316	Carthamus	tinctorius	Turkey	194	016482	Carthamus	tinctorius	Iran
95	016317	Carthamus	tinctorius	Turkey	195	016483	Carthamus	tinctorius	USA
96	016318	Carthamus	tinctorius	Spain	196	016484	Carthamus	tinctorius	USA
97	016320	Carthamus	tinctorius	Germany	197	016489	Carthamus	tinctorius	China
98	016324	Carthamus	tinctorius	Iraq	198	016492	Carthamus	tinctorius	China
99	016325	Carthamus	tinctorius	Iraq	199	016495	Carthamus	tinctorius	China
100	016326	Carthamus	tinctorius	Iraq	200	016501	Carthamus	tinctorius	USA

 

Table 2: Means and analysis of variance (ANOVA) for eight traits among 200 safflower genotypes.

Parameters	MS (R)	MS (V)	MS(E)	Means ± SE	h2 (%)	GA (%)	CV (%)
DF	9.512	50.028	11.294	121.97	77.4	22.22	2.755
PH	8.202	567.962	13.287	104.77	97.7	19.6	3.479
DM	9.052	87.593	16.453	151.70	81.2	17.33	2.673
PPP	1.872	224.181	2.223	38.31	99.0	16.51	3.892
PBP	0.140	4.902	0.246	2.57	95.0	11.99	19.31
SBP	1.415	74.672	1.1369	8.54	98.5	23.51	12.48
TGW	5.612	157.537	4.012	37.78	97.5	14.73	5.301
YPP	15.247	1248.901	20.240	83.82	98.4	27.21	5.367

DF: days to flowering; PH: plant height; DM: days to maturity; PPP: pods per plant; PBP: number of primary branches per plant; SBP: number of secondary branches per plant; TGW: thousand grain weight; YPP: yield per plant; MS(R): mean square of replications; MS(V): mean square of varieties; MS(E): mean square of errors; h2: heritability; GA: genetic advance; CV: coefficient of variability.

 

Table 3: Genotypic (rg) and phenotypic (rp) correlation between various morpho-physiological traits of safflower.

Traits	DF	PH	DM	PPP	PBP	SBP	TGW	YPP
DF
G	1.000	-0.0279*	0.9275*	0.0325*	-0.0992*	-0.0371*	0.0679*	-0.0168*
P	1.000	-0.0246	0.7489**	0.0271	-0.0770	-0.0300	0.0561	-0.0619
PH
G		1.0000	-0.1590*	0.6102*	-0.783*	-0.0690*	0.1032*	0.6551*
P		1.0000	-0.1399	0.5999*	-0.0784*	-0.0677*	0.0993	0.6423**
DM
G			1.0000	-0.0989*	-0.0055	-0.0085	-0.0670*	-0.1165*
P			1.0000	-0.0847	-0.0019	-0.0047	-0.0582	-0.1051
PPP
G				1.0000	-0.0005	-0.0694*	0.1099*	0.7520*
P				1.0000	0.0014	-0.0670	0.1082	0.7437**
PBP
G					1.0000	0.7807*	-0.1782*	-0.0826*
P					1.0000	0.7541**	-0.1666	-0.0791
SBP
G						1.0000	-0.1445*	-0.1388
P						1.0000	-0.1432	-0.1367
TGW
G							1.0000	0.1231*
P							1.0000	0.1222
YPP
G								1.0000
P								1.0000

For abbreviations, See Table 2; *Significant; ** Highly significant.

 

Genotypic and phenotypic correlations were observed among eight (8) morpho-physiological parameters in safflower. Days to flowering (DF) showed negatively significant interrelationship with PH, PBP, SBP and YPP, while positive significant interrelationship was observed among DF, DM, PPP and TGW. Plant height (PH) showed negatively significant genotypic and phenotypic interrelations with DM, PBP and SBP. However, there was positive and significant genotypic interrelation among PH, PPP, TGW and YPP. Negatively significant genotypic and phenotypic interrelations were present among days to maturity and other yield related parameters. Pods per plant (PPP) had negatively significant interrelation with PBP and SBP, while positively significant correlation was observed among PPP, TGW and YPP. Results revealed the existence of positive correlation among PBP and SBP, while both traits showed negative interrelation with TGW and YPP. However, TGW showed positive interrelation with YPP.

Principal component analysis

Results of PCA showed that on basis of eigen value, data is considered up to three principal components. It was noted that 74 % variability of the total variation lies in three PC’s. First PC has 30.6 % variability, while PC2 and PC3 has 22.9 % and 20.5 % variability of the total existing variation of the data. These PC’s are orthogonal with each other. In first PC, four parameters i.e. PH, PPP, TGW and YPP were correlated with each other in negative direction, while remaining traits showed positive correlation with each other. In PC2, five yield related parameters viz; PH, PPP, PBP, SBP and YPP were negatively correlated with each other, while remaining traits were positively interrelated with each other. Under PC3, only one parameter (TGW) showed negative correlation with other parameters, which is a valuable yield index. The component with eigenvalues > 1 contribut­ed 74% (Table 4) of the total variability among accessions of safflower for various morph-physiological traits. Two hundred accessions of safflower have been divided into eight cluster groups on the basis of their performance for studied parameters. Cluster 7 contain maximal (41) number of accessions, while cluster number 5 and 6 consist of lowest (15) number of accessions each. Cluster number 1 and 3 consist of 34 and 28 accessions respectively. While cluster number 4, 2 and 8 comprised of 26, 21 and 20 accessions respectively (Table 5).

Results of ANOVA revealed the presence of high genetic variability among all accessions of safflower for studied parameters and proved that data was fit for further statistical analysis. Existence of variation is useful for various genetic analysis and ultimately helpful in selection and improvement of crop (Kose et al., 2018). The basic purpose of correlation studies was to observe a common re­lationship between different characters and their level of the involvement to the yield (Panhwar et al., 2003). Plant parameters viz., DF, DM, PBP and SBP showed negative impact on yield and grain yield reduces with increase in number of DF, DM, PBP and SBP. However, plant attributes like PH, PPP and TGW had positive effect on yield of the crop as yield increases with increase in magnitude of these traits. It was observed that grain yield had significant interaction with PPP, PH and TGW (Ahmadzadeh, 2013; Kose et al., 2018). To intensify the crop yield, magnitude of plant attributes like PPP and TGW must be increased because these parameters had direct influence on YPP (Elfadl et al., 2010; Eslam et al., 2010; Safavi, 2011). Based upon the results of correlation, it is suggested that genotypes having higher magnitude of PH, branches per plant and grain weight will be selected for future breeding program to enhance yield (Kose et al., 2018). If value of ‘r’ (correlation) is near to 1, interrelation among two variables is positive and traits are highly dependent on each other. If ‘r’ is nearly zero among different variables no interdependency is observed, while ‘r’ with negative sign among variables proved negative relation among variables (Katar, 2013).

 

Table 4: Principal Component Analysis (PCA) of germplasm.

	PC 1	PC 2	PC 3
Eigen values	2.448	1.832	1.640
Proportion of variance	30.600	22.903	20.497
Cumulative variance	30.600	53.503	74.000
Eigen vectors
Variables	PC 1	PC 2	PC 3
DF	0.108012	0.674046	0.644409
PH	-0.808049	-0.111115	0.199538
DM	0.255235	0.634851	0.640596
PPP	-0.831564	-0.104359	0.308155
PBP	0.287557	-0.685164	0.554232
SBP	0.331518	-0.656911	0.549469
TGW	-0.263355	0.214418	-0.143479
YPP	-0.874539	-0.068448	0.222903

 

Estimation of heritability is a promising indication about the transmittance of various parameters from parents to progeny. Appraisal of heritability is very helpful in selection of suitable genotypes/ accessions among various environmental and field conditions from a heterogeneous breeding population (Tahernezhad et al., 2018). Based upon percent (%) magnitude, heritability could be classified into low (0-30%), medium (30-60%) and high (>60%) (Reddy et al., 2013). Results revealed that magnitude of heritability was greater than 70% for all studied parameters (Table 2), indicating high transmittance percentage. It is also predicted that these parameters

 

Table 5: Cluster-wise accession membership.

Cluster No.	Accessions
I	A5, A9, A23, A24, A29, A35, A50, A52, A53, A54, A55, A78, A79, A86, A90, A96, A99, A100, A102, A105, A108, A109, A114, A118, A121, A122, A131, A137, A141, A150, A162, A163, A185, A189
II	A33, A41, A48, A58, A59, A73, A85, A97, A101, A106, A110, A119, A142, A143, A144, A164, A172, A175, A177, A182, A190
III	A14, A15, A16, A17, A28, A32, A37, A43, A47, A56, A57, A69, A70, A74, A84, A91, A92, A94, A98, A104, A111, A112, A115, A117, A126, A128, A132, A138
IV	A2, A3, A10, A13, A27, A36, A38, A40, A60, A63, A64, A65, A83, A87, A93, A95, A103, A107, A113, A124, A129, A133, A134, A135, A136, A139
V	A7, A8, A11, A18, A20, A21, A26, A34, A42, A49, A62, A68, A71, A72, A82
VI	A1, A6, A12, A19, A22, A25, A30, A31, A39, A61, A66, A67, A123, A127, A140
VII	A4, A44, A45, A46, A51, A75, A76, A77, A80, A81, A88, A89, A116, A120, A125, A130, A145, A148, A149, A152, A153, A154, A155, A156, A158, A159, A160, A161, A165, A166, A169, A173, A174, A178, A179, A186, A191, A193, A196, A199, A200
VIII	A146, A147, A151, A157, A167, A168, A170, A171, A176, A180, A181, A183, A184, A187, A188, A192, A194, A195, A197, A198

 

are less influenced by environment and highly suitable for early selection due to presence of additive nature of genetic inheritance. These results are in accordance with the findings of Arslan (2007), Sirisha (2009) and Elfadl et al. (2010).

PCA is a multivariate analysis technique, which is usually used to develop coordinated axis of an orthogonal and to estimate the relative importance of classified variables. This technique is characterized by conversion of complex plant data analysis into simple form (Slavkovic et al., 2004). Maximum variation was observed among first three PC’s and it contained 74% of the total variability. Ahmadzadeh (2013) reported that 72.92 percent of the total variation was found in first three PC’s, while Kose et al. (2018) found 65.4 percent of total variation in first two PC’s. If the eigen values are greater than one, then diversity is not found in the traits and values are less than one, then diversity is found in all the characters. Negative eigen values were ignored because these values have no importance while positive values considered diversity is found in the characters. Cluster analysis classified the germplasm into eight groups on basis of similarity in their mean performance for observed parameters. Cluster analysis is a helpful technique to categorize the germplasm into well-defined subgroups and groups depending upon resemblance and deviation among mean performance of observed parameters (Biljana and Onjia, 2007).

 

Conclusions and Recommendations

Significant genetic variability was observed among germplasm regarding yield and its related attributes. The highest estimates of genetic advance and heritability for all traits showed their significance in selection of particular parents to be used in future breeding program. Higher heritability magnitude showed the predominance of additive genetic effects for studies traits, due to which direct and early selection is useful. From the results of correlation, it was concluded that three parameters viz., plant height, pods per plant and thousand grain weight had positively significant genotypic interrelationship with yield per plant. So, genotypes having higher value of PH, PPP and TGW can be selected to develop high yielding safflower varieties for Pakistan.

 

Acknowledgments

This study supported by the PARC, Government of Pakistan. We pay our gratitude to Dr. Yusuf Zafar (T.I), worthy chairman PARC and IABGR (Institute), NARC, Islamabad for supplying seeds of safflower accessions. We are thankful to Dr. Sohail (Department of Statistics), who provided computer programs for statistical analysis.

 

Novelty Statement

Safflower is a good source of high-quality oil and mostly used for food, industry, ornamental purpose and as a biofuel. Pakistan expands a huge amount of foreign exchange on import of oil. By evaluation of available germplasm in the country, we will be able to develop high yielding safflower varieties with less input resources. This will not only helpful to meet the oil requirement of the country but also to improve health of the people and save the foreign exchange.

 

Author’s Contributions

Rao Wali Muhammad and Abdul Qayyum planed the proposal and conducted the whole study. Methodology and analysis were carried out by Muhammad Qadir Ahmad and Waqas Malik. Hafiz Muhammad Wasif Ali completed the manuscript write up. Overall assessment of write up was done by Amir Hamza. Statistical analysis was accomplished by Etrat Noor.

Conflict of interest

The authors have declared no conflict of interest.

 

References

Ahmadzadeh, A., 2013. Genetic diversity and classification of spring safflower (Carthamus tinctorius L) cultivars using morphological characters. Adv. Biores., 4(4): 125-131.

Allard, R.W., 1960. Principles of Plant Breeding. John Wiley and Sons Inc., New York, USA. pp. 485.

Amjad, M., 2014. Oilseed crops of Pakistan: Status paper. Pak. Agric. Res. Council Islamabad, (PARC). pp. 1-40. https://doi.org/10.18356/a55dee27-en

Anonymous, 2018-19. Pakistan bureau of statistics, ministry of finance. Govt. of Pakistan, Islamabad.

Arslan, B., 2007. Assessing of heritability and variance components of yield and some agronomic traits of different safflower (Carthamus tinctorius L.) cultivars. Asian Plant Sci., 6(3): 554- 557. https://doi.org/10.3923/ajps.2007.554.557

Arslan, B., F. Altuner and M. Tuncturk. 2003. An investigation on yield and yield components of some safflower varieties which grown in Van. 5th Field Crops Cong. Turkey, 1: 468-472.

Balode, K.L., P.N. Mane, P.K. Rathod and S.N. Deshmukh. 2012. Evaluation of safflower germplasm for resistant to Alternaria leaf spot. J. Oilseeds Res., 29: 97-99.

Baydar, H. and I. Turgut. 1999. Some morphological composition of fatty acids in oilseed plants and change according to physiological properties and ecological regions. Turk. J. Agric. For., 23(1): 81-86.

Biljana, S. and A. Onjia. 2007. Multivariate analyses of microelement contents in wheat cultivated in Serbia. Food Contr., 18(4): 338–345. https://doi.org/10.1016/j.foodcont.2005.10.017

Canavar, O., K.P. Gotz, Y.O. Koca and F. Ellmer. 2014. Relationship between water use efficiency and δ13c isotope discrimination of safflower (Carthamus tinctorius L.) under drought stress. Turk. J. Field Crops. 19(2): 212-220. https://doi.org/10.17557/tjfc.28375

Conge, B., B. Gürbüz and M. ve Kıralan. 2007. Oil content and fatty acid composition of some safflower (Carthamus tinctorius L.) varieties sown in spring and winter. Int. J. Natur. Eng. Sci., 1(3): 11-15.

Elfadl, E., C. Reinbreeht and W. Claupein. 2010. Evaluation of phenotypic variation in a worldwide germplasm collection of safflower (Carthamus tinctorius L.) grown under organic farming conditions in Germany. Genet. Resour. Crop Evol., 57(2): 155-170. https://doi.org/10.1007/s10722-009-9458-7

Emongor, V.E., 2010. Safflower (Carthamus tinctorius L.) the underutilized and neglected crop: A review. Asian J. Plant Sci., 9(6): 299-306. https://doi.org/10.3923/ajps.2010.299.306

Emongor, V.E. and O. Oagile. 2017. Safflower production. The Botswana University of Agriculture and Natural Resources, Gaborone Botswana. pp. 1-67.

Emongor, V.E., O. Oagile and B. Kedikanetswe. 2015. Effects of plant population and season on growth and development of safflower (Carthamus tinctorius L.) as an ornamental plant. Acta Hortic., 1077: 35-45. https://doi.org/10.17660/ActaHortic.2015.1077.3

Eslam, B.P., H. Monirifar and M.T. Ghassemi. 2010. Evaluation of late season drought effects on seed and oil yields in spring safflower genotypes. Turk. J. Agric., 34(5): 373-380.

Falconer, D.S., 1981. Introduction to quantitative genetics. 2nd Edition, Longman Group Ltd., London. pp. 1-133.

Hojati, M., S.A.M. Modarress-Sanavy, M. Karimi and F. Ghanati. 2011. Responses of growth and antioxidant systems in Carthamus tinctorius L. under water deficit stress. Acta Physiol. Plant, 33(1): 105-112. https://doi.org/10.1007/s11738-010-0521-y

Istanbulluoglu, A., 2009. Effects of irrigation regimes on yield and water productivity of safflower (Carthamus tinctorius L.) under Mediterranean climatic condition. Agric. Water Manage., 96(12): 1792-1798. https://doi.org/10.1016/j.agwat.2009.07.017

Katar, D., 2013. Determination of efficiency of yield components on oil yield per plant in safflower breeding by different statistical methods. Glob. J. Sci. Front. Res. Agric. Vet. Sci., 13(8): 11-20.

Kose, A., A. Onder, O. Bilir and F. Kosar. 2018. Application of multivariate statistical analysis for breeding strategies of spring safflower (Carthamus tinctorius L.). Turk. J. Field Crops. 23(1): 12-19. https://doi.org/10.17557/tjfc.413818

Panhwar, R.N., H.K. Keerio, Y.M. Memon, S. Junejo, M.Y. Arain, M. Chohan, A.R. Keerio and B.A. Abro. 2003. Response of Thatta–10 sugarcane variety to soil and foliar application of zinc sulphate (ZnSO4. 7H2O) under half and full doses of NPK fertilizer. J. Appl. Sci., 3(4): 266-269. https://doi.org/10.3923/jas.2003.266.269

Reddy, M.P., B.N. Reddy, B.T. Arsul and J.J. Maheshwari. 2013. Genetic variability, heritability and genetic advance of growth and yield components of linseed (Linum usitatissimum L.). Int. J. Curr. Microbiol. App. Sci., 2(9): 231-237.

Reza, A.M., M.J. Mirhadi, B. Delkhosh and A. Omidi. 2013. Evaluation of native and exotic safflower (Carthamus tinctorius L.) genotypes for some important agronomic traits and fatty acid composition. Ann. Biol. Res., 4(6): 200-204.

Safavi, S.M., 2011. Heritability and genetic gain of some morphological traits in safflower (Carthamus tinctorius L.). Am. J. Sci. Res., 13: 14-18.

Sehgal, D., S.N. Raina, R.M. Devarumatha, T. Sasanuma and T. Sasakuma. 2009. Nuclear DNA assay in solving issues related to ancestry of the domesticated diploid safflower (Carthamus tinctorius L.) and the polyploid (Carthamus) taxa, and phylogenetic and genomic relationships in the genus Carthamus L. (Asteraceae). Mol. Phylogenet., 53(3): 631-644. https://doi.org/10.1016/j.ympev.2009.07.012

Shinwari, Z.K., H. Rehman and M.A. Rabbani. 2014. Morphological traits based genetic diversity in safflower (Cathamus tinctorius L.). Pak. J. Bot., 46(4): 1389-1395.

Sirisha, M., 2009. Studies on genetic divergence and character association in safflower (Carthamus tinctorius L.). M.Sc. thesis, Acharya N.G. Ranga Agric. Univ. India.

Slavkovic, L., B. Skrbic, N. Miljevic and A. Onjia. 2004. Principal component analysis of trace elements in industrial soils. Environ. Chem. Lett., 2(2):105–108. https://doi.org/10.1007/s10311-004-0073-8

Steel, R.G.D., J.H. Torrie and D.A. Dickey. 1997. Principles and procedures of statistics: A biometrical approach. 3rd ed. McGraw Hill Book Co. Inc. New York. pp. 400-428.

Tahernezhad, Z., J. Saba, M. Zein-al-abedini, S.S. Pourdad, and M.R. Ghaffari. 2018. Estimation of broad-sense heritability and variance components for seed yield and agronomic traits in native and exotic safflower (Carthamus tinctorius L.) genotypes. Bangladesh J. Bot., 47(3): 501-508. https://doi.org/10.3329/bjb.v47i3.38718

Velasco, L., B. Perez-Vich and J.M. Fernandez-Martinez. 2005. Identification and genetic characterization of a safflower mutant with modified tocopherol profile. Plant Breed, 124(5): 459-473. https://doi.org/10.1111/j.1439-0523.2005.01150.x