ORIGINAL_ARTICLE
Genetic analysis of resistance to stripe rust in some Iranian bread wheat cultivars and elite lines
Stripe rustis the most important disease of wheat in many wheat growing areas in Iran. Good knowledge of thegenetic basis of resistance to stripe rust in commercial bread wheat cultivars and selected elite lines is an important objective in wheat breeding programs. This study aimed to identify resistance genes and modes of inheritance of stripe rust resistance in recently released Iranian commercial wheat cultivars (Aflak, Parsi, Sivand, Uroum, and Pishgam) and elite bread wheat lines (M-84-14 and M-83-6). Crosses were made between these cultivars and elite lines with Avocet S and the F1, F2, and F3 generations were developed. Two F3-derived families (one adult plant stage and one seedling stage), as well as parents and controls, were grown under field and greenhouse conditions and inoculated with stripe rust pathotypes 134E158 A+, 166E150 A+, and 6E150 A+, Yr27. The adult plant responses of parental cultivars Aflak, Uroum, Parsi, Pishgam, Sivand, and elite lines M-84-14 and M-83-6 to stripe rust in the field were 40MR, 10R, 50M, 10R, 50MS, respectively, in 2011, and 60MR, 5R, 40MR, 30MR, and 40M, respectively, in 2013. Cultivars and elite lines were resistant to stripe rust at the seedling stage test. Avocet S was susceptible at both adult plant and seedling stages. In addition to the seedling resistance responses of the parents, frequencies of F3 lines for each of the crosses in both adult plant and seedling stages conformed well with those expected for segregating for a trait at two loci, indicating that all five cultivars and two elite lines carry two dominant seedling resistance genes that have so far been effective for controlling stripe rust in Iran.
https://cbjournal.areeo.ac.ir/article_107102_8784dd7f4f117b1af850bc3057888a63.pdf
2016-11-01
1
8
10.22092/cbj.2016.107102
Bread wheat
genetic analysis
pathotype
resistance gene
stripe rust
A.
Zakeri
zakeriabd@yahoo.com
1
Fars Agricultural and Natural Resources Research and Education Center of Fars Province, Agricultural Research, Education and Extension Organization (AREEO), Zarghan, Iran
LEAD_AUTHOR
F.
Afshari
2
Seed and Plant Improvement Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran.
AUTHOR
M.
Yasaie
3
Fars Agricultural and Natural Resources Research and Education Center of Fars Province, Agricultural Research, Education and Extension Organization (AREEO), Zarghan, Iran.
AUTHOR
A. R.
Nikzad
4
Fars Agricultural and Natural Resources Research and Education Center of Fars Province, Agricultural Research, Education and Extension Organization (AREEO), Zarghan, Iran.
AUTHOR
S.
Rajaei
5
Fars Agricultural and Natural Resources Research and Education Center of Fars Province, Agricultural Research, Education and Extension Organization (AREEO), Zarghan, Iran.
AUTHOR
Afshari, F. 2006. Inheritance of resistance to stripe rust (Puccinia striiformis f. sp. tritici) in some cultivars and promising lines of wheat. Seed and Plant 22: 489-501.
1
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2
Afshari, F., A. Zakeri, M. A. Dehghan, M. Chaichi, R. Hoshiar, S. A. Saffavi, M. Hassanpour Hosni, M. Dalvand, S. Ebrahimnejad, M. Atta Hossaini, K. Shabazi, G. Ahmadi, and A. Nabati. 2012. Virulence survey of Puccinia striiformis, the causal agent of wheat yellow rust by trap nursery during 2010-2012. Final report of research project.Seed and Plant Improvement Institute (In Persian).
3
Afshari, F., K. Nazari, M. Aghaei, G. Najafian, M. Esmaeilzadeh, A. Yazdansepas, M. Khodarahmei, A. Amini, R. Roohparvar, M. Hashamei, and A. Malihipour. 2014. The wheat stripe rust pathogen in Iran and achievement of resistant wheat cultivars over the last 10 years.p. 5. In Proceedings of the Second International Wheat Stripe Rust Symposium, Izmir, Turkey.
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31
ORIGINAL_ARTICLE
Temporal variation in phonological and agronomic traits of some irrigated facultative/winter bread wheat (Triticum aestivum L.) cultivars released between 1943 and 2011 in Iran
A field experiment was conducted at the Seed and Plant Improvement Institute Research Field Station in Karaj, Iran, during the 2009-10 and 2010-11 growing seasons to estimate genetic progress and the variation in penological and agronomic characteristics in 13 irrigated facultative/winter bread wheat (Triticum aestivum L.) cultivars released in Iran between 1943 and 2011. Trends of temporal variation of the traits measured revealed that grain yield and some related phonological and agronomic traits have increased in the more recently released cultivars. Thousand grain weight decreased slightly compared to older cultivars. Number of days to heading and anthesis decreased in new cultivars, butgrain-filling period and days to physiological maturity did not change. Spike length also increased but plant height decreased in more recently released cultivars. These changes may explain the increase in grain yield of newly released facultative/winter bread wheat cultivars.
https://cbjournal.areeo.ac.ir/article_107103_c373fd38e3f3cb942c4ff35b43b316c2.pdf
2016-11-01
9
16
10.22092/cbj.2016.107103
agronomic characteristics
Bread wheat
days to flowering
grain filling duration
thousand grain weight
M.
Esmaeilzadeh Moghaddam
esmaeilzadehmohsen@ymail.com
1
Seed and Plant Improvement Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran.
LEAD_AUTHOR
M. R.
Jalal Kamali
2
Internatioanl Maize and Wheat Improvement Cenetr (CIMMYT), Karaj, Iran.
AUTHOR
N.
Soda
3
Islamic Azad University, Karaj, Iran.
AUTHOR
S.
Sadre Jahani
4
Agriculture faculty of Varamin, Islamic Azad University, Varamin, Iran.
AUTHOR
M.
Ghodsi
5
Khorasan-e-Razavi Agriculture and Natural Research Center, Agricultural Research, Education and Extension Organization (AREEO), Mashhad, Iran.
AUTHOR
Alvaro, F., J. Isidro, D. Villegas, L. F. Garcia-Delmoral, and C. Royo. 2008. Old and modern durum wheat varieties from Italy and Spain differ in main spike components. Field Crops Res.106: 86–93.
1
Araus, J., G. A. Slafer, M. P. Reynolds, and C. Royo. 2004. Physiology of yield and adaptation in wheat and barley. Pp. 1-49. In Nguyen H. T., and A. Blum (eds.). Physiology and Biotechnology Integration for Plant Breeding. CRC Press. Taylor and Francis Group.
2
Aisawi, K., M. J. Foulkes, M. P. Reynolds, and S. Mayes. 2010. The physiological basis of genetic progress in yield potential of CIMMYT wheat varieties from 1966 to 2009. Pp. 349. In Dzyubenko, N. I. (ed.). Proceedings of 8th International. Wheat Conference. St. Petersburg, Russia.
3
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Dixon, J., H. J. Braun, P. Kosina, J. Crouch. 2009. Wheat facts and figures 2009. Mexico, D. F.: CIMMYT. 95 pp.
9
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27
ORIGINAL_ARTICLE
Multi-environment evaluation of winter bread wheat genotypes under rainfed conditions of Iran-using AMMI model
Genotype × environment interaction is an important and challenging issue for plant breeders in developing new improved varieties. This study aimedto estimate the impact of genotype × environment interactions for grain yield in winter wheat under rainfed conditions using the additive main effects and multiplicative interaction (AMMI) model, and to select genotypes with high grain yield, yield stability, and adaptation for cold rainfed environments in Iran. Twenty-two breeding lines and two commercial winter wheat cultivars, representing winter wheat-growing cold rainfed areas of Iran, were tested in eight locations over three crop cycles (2011-14). Environment was the pre dominant source of variation, accounting for 84.8% of the total sum of squares, with the remainder due to the genotype × environment interaction effect (which was almost four times that of the genotype effect). Average grain yield varied from 1125 to 1608 kg ha-1 across the 24 environments, with an average of 1385 kg ha-1. The AMMI biplots identified genotypes with wide and specific adaptation as well as environments with high and low genotype discrimination and characterization. Relative humidity, freezing days, and plant height were among the environmental factors and genotypic co-variables that contributed highly to genotype × environment interactions for grain yield. These findings could identify breeding lines as potential genetic resources for improving and stabilizing grain yield in winter bread wheat breeding programs for cold rainfed areas of Iran, through exploitingand minimizing thegenotype × environment interaction.
https://cbjournal.areeo.ac.ir/article_107104_1a25f2eae9d14e6b2d9d9e339c83ef53.pdf
2016-11-01
17
31
10.22092/cbj.2016.107104
genotypic and environmental co-variables
grain yield improvement
Specific Adaptation
wide adaptation
winter wheat
S.
Golkari
sgolkari@yahoo.com
1
Dryland Agricultural Research Institute (DARI), Agricultural Research, Education and Extension Organization (AREEO), Maragheh, Iran
LEAD_AUTHOR
R.
Hagparast
2
Dryland Agricultural Research Institute (DARI), Agricultural Research, Education and Extension Organization (AREEO), Kermanshah, Iran
AUTHOR
E.
Roohi
3
Kurdistan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Sanandaj, Iran
AUTHOR
S.
Mobasser
4
Seed and Plant Certification and Registration Institute (SPCRI), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran
AUTHOR
M. M.
Ahmadi
5
North Khorasan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Bojnord, Iran
AUTHOR
K.
Soleimani
6
Zanjan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Zanjan, Iran
AUTHOR
G.
Khalilzadeh
7
West Azarbaijan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Uromieh, Iran
AUTHOR
G.
Abedi-Asl
8
Ardabil Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Ardabil, Iran
AUTHOR
T.
Babaei
9
Markazi Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Arak, Iran
AUTHOR
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Rodriguez, M., D. Rao, R. Papa, and G. Attene. 2008. Genotype by environment interactions in barley (Hordeum vulgare L.): different responses of landraces, recombinant inbred lines and varieties to Mediterranean environment. Euphytica 163: 231-247.
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Yan, W., and L. A. Hunt. 2001. Interpretation of genotype × environment interaction for winter wheat in Ontario. Crop Sci. 41: 19–25.
51
Yan, W., and M. S. Kang. 2002. GGE biplot analysis: a graphical tool for breeders, geneticists, and agronomists. CRC Press, Boca Raton, Florida, USA.
52
Yan, W., and I. Rajcan. 2002. Biplot analysis of test sites and trait relations of soybean in Ontario. Crop Sci. 42:11–20.
53
Yan, W., L. A. Hunt, Q. Sheng, and Z. Szlavnics. 2000. Cultivar evaluation and mega-environment investigation based on GGE biplot. Crop Sci. 40:596-605.
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56
ORIGINAL_ARTICLE
Effect of terminal drought stress on seed yield and its components of some new winter rapeseed lines
Drought causes significant reductions in crop productivity in many parts of the world, including Iran. Identifying genotypes tolerant to drought stress is therefore one of the foremost goals of crop breeding programs. This study investigated the effect of terminal drought stress on yield and yield components of new winter rapeseed lines, and identified new winter rapeseed lines tolerant to terminal drought stress. A field experiment was conducted to evaluate 17 new winter rapeseed lines and two commercial cultivars (Ahmadi and Opera) under three moisture conditions (optimum irrigation, elimination of irrigation from flowering stage, and elimination of irrigation from silique stage) during two cropping seasons (2012-13 and 2013-14) at the Agricultural Research Station of Islamabad-e-Gharb, Kermanshah, Iran. Drought stress significantly affected all measured traits except days to flowering and 1000-seed weight. Based on the average of three conditions, KS7, KR4, L183, Opera, and HW118 had higher seed yields. These lines (except Opera) also produced higher seed yields when irrigation was eliminated from the flowering and silique development stages, and were identified as winter rapeseed lines tolerant to terminal drought stress with high seed yield potential underoptimum irrigation conditions.
https://cbjournal.areeo.ac.ir/article_107105_443aae2a1548666ac465761c261b2910.pdf
2016-11-01
33
39
10.22092/cbj.2016.107105
flowering
Rapeseed
seed yield
silique
terminal drought tolerance
A.
Rezaeizad
arezaizad@yahoo.com
1
Kermanshah Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Kermanshah, Iran.
LEAD_AUTHOR
A. H.
Shirani Rad
2
Seed and Plant Improvement Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran.
AUTHOR
Boyer, J. S. 1982. Plant productivity and the environment. Sci. 218: 443-448.
1
Champolivier, L., and A. Merrien. 1996. Effects of water stress applied at different growth stages of Brassica napus var. Olieifera I. on yield, yield components and seed quality. Europ. J. Agron. 5: 153-160.
2
Diepenbrock, W. 2000. Yield analysis of winter oilseed rape (Brassica napus L.): a review. F. Crop. Res. 67(1): 35-49.
3
Din, J., L. Khan, and R. Gumani. 2011. Physiological and agronomic response of canola varieties to drought stress. J. Animal and Plant Sci. 21(1): 78-82.
4
Edwards, J., and K. Hertel. 2011. Canola Growth and Development. Department of Primary Industries, State of New South Wales, Australia.
5
Ghasemyan Ardestani, H., A. H. Shirani Rad, and P. Zandi, 2011. Effect of drought stress on some agronomic traits of two rapeseed varieties grown under different potassium rates. Aus. J. Basic and Appl. Sci. 5(12): 2875-2882.
6
Ghobadi, M., M. Bakhshandeh, G. Fathi, M. H. Gharineh, K. Alami-Said, A. Naderi, and M. E. Ghobadi. 2006. Short and long periods of water stress during different growth stages of canola (Brassica napus L): Effect on yield components, seed oil and protein contents. J. Agronomy 5(2): 336-341.
7
Ghodrati, G. R. 2012. Response of grain yield and yield components of promising genotypes of spring rapseed (Brassica napus L.) under non-stress and moisture-stress conditions. Crop Breed. J. 2(1):49-56.
8
Jenks M. A., and P. M. Hasegawa. 2005. Plant abiotic stress. Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK. 290 pp.
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Monajem, S., V. Mohammadi, and A. Ahmadi. 2011. Evaluation of drought tolerance in some rapeseed cultivars based on stress evaluation indices. Elec. J. Crop. Prod. 4(1): 151-169.
10
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11
Nielsen, D. C. 1997. Water use and yield of canola under dryland conditions in the central great plains. J. Prod. Agric. 10 (2): 307-313.
12
Pasban Eslam, B. 2009. Evaluation of physiological indices, yield and its components as screening techniques for water deficit tolerance in oilseed rape cultivars. J. Agric. Sci. Tech. 11: 413-422.
13
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14
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16
SAS System for Windows, 1996. Release 8.02. SAS Institute Inc. Cary, USA.
17
Shirani Rad, A. H., A. Abbasian, and H. Aminpanah. 2013. Evaluation of rapeseed (Brassica Napua L.) cultivars for resistance against water deficit stress. Bulg. J. Agric. Sci. 19(2): 266-273.
18
Shirani Rad, A. H., A. Abbasian, and H. Aminpanah. 2014. Seed and oil yields of rapeseed (Brassica napus L.) cultivars under irrigated and non-irrigated conditions. J. Animal and Plant Sci. 24(1): 204-210.
19
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20
ORIGINAL_ARTICLE
Application of GGE biplot analysis to evaluate grain yield stability of rainfed spring durum wheat genotypes and test locations by climatic factors in Iran
Grain yield stability is an important feature of crop breeding programs due mainly to the high annual variation in mean yield, particularly in arid and semi-arid areas. Conventional statistical models of stability analysis provide little or no insight into patterns of genotype × environment (GE) interaction, though the genotype plus GE (GGE) biplot method can more effectively account for the under GE interaction patterns. This study evaluated the yield stability of 20 spring durum wheat genotypes grown in five different warm locations in Iran across four cropping cycles (2009-2013) and used GGE biplot analysis to evaluate the yield stability of the genotypes and test locations by climatic factors. The combined analysis of variance revealed that the main effects of genotypes, locations, and years were significant, as well as the corresponding interaction effects. A polygon view of GGE biplot indicated that there were three winning genotypes (G10, G8, and G20) in three mega-environments for durum wheat in rainfed conditions. An ideal test location view of the GGL biplot showed that Gachsaran is the most desirable test location; genotype evaluation at this location maximized the observed genotypic variation among genotypes for durum wheat grain yield. Useof GGE biplots facilitated visual comparisons and identification of superior durum wheat genotypes for each target location. Genotype G10 was better than the other genotypes and is recommended for warm rainfed spring durum wheat growing areas of Iran.
https://cbjournal.areeo.ac.ir/article_107106_3d48b29f2bf97b8a463b735b929f89c6.pdf
2016-11-01
41
49
10.22092/cbj.2016.107106
GE interaction
Grain yield
multi-environment trials
Stability
R.
Karimizadeh
karimizadeh_ra@yahoo.com
1
Faculty of Agriculture, University of Mohaghegh Ardabili, Ardabil, Iran. Dryland Agricultural Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Ghachsaran, Iran
LEAD_AUTHOR
A.
Asghari
2
Faculty of Agriculture, University of Mohaghegh Ardabili, Ardabil, Iran.
AUTHOR
R.
Chinipardaz
3
Faculty of Agriculture, University of Mohaghegh Ardabili, Ardabil, Iran.
AUTHOR
O.
Sofalian
4
Faculty of Agriculture, University of Mohaghegh Ardabili, Ardabil, Iran.
AUTHOR
A.
Ghaffari
5
West Azarbaijan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Urmia, Iran
AUTHOR
Becker, H. C., and J. Leon. 1988. Stability analysis in plant breeding. Plant Breed. 101: 1–23.
1
Burgueno, J., J. Crossa, and M. Vargas. 2001. SAS programs for graphing GE and GGE biplots. Biometrics and Statistics Unit, CIMMYT, Int. Mexico, DF.
2
Cooper, M., R. E. Stucker, I. H. DeLacy, and B. D. Harch. 1997. Wheat breeding nurseries, target environments, and indirect selection for grain yield. Crop Sci. 37: 1168–1176.
3
Crossa, J., and P. L. Cornelius.1997. Site regression and shifted multiplicative model clustering of cultivar trials sites under heterogeneity of error variances. Crop Sci. 37: 406–415.
4
Dehghani, H., A. Ebadi, and A. Yousefi. 2006. Biplot analysis of genotype by environment interaction for barley yield in Iran. Agron. J. 98: 388–393.
5
Dehghani, H., N. Sabaghnia, and M. Moghaddam. 2009. Interpretation of Genotype-by-Environment Interaction for late maize hybrids grain yield using a biplot method. Turk.J. Agric. Forest. 33: 139–148.
6
Ebadi-Segherloo, A., S. H. Sabaghpour, H. Dehghani, and M. Kamrani. 2010. Screening of superior chickpea genotypes for various environments of Iran using genotype plus genotype × environment (GGE) biplot analysis. J. Plant Breed. Crop Sci. 2: 286–292.
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Flores F., M. T. Moreno, and J. I. Cubero.1998. A comparison of univariate and multivariate methods to analyze environments. Field Crops Res. 56: 271–286.
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Gauch, H. G., and R. W. Zobel. 1996. AMMI analysis of yield trials. Pp. 85–122. In Kang, M.S. and H.G. Gauch (eds.) Genotype-by-environment interaction. CRC Press, Boca Raton, Florida, USA.
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14
Karimizadeh, R. A., M. Mohammadi, N. Sabaghnia, A. A. Mahmoodi, B. Roustami, F. Seyyedi, and F. Akbari. 2013. GGE biplot analysis of yield stability in multi-environment trials of lentil genotypes under rainfed condition. Not. Sci. Biol. 5(2): 256-262.
15
Ma, J., C. Y. Zhang, G. J. Yan, and C. J. Liu. 2013. Improving yield and quality traits of durum wheat by introgressing chromosome segments from hexaploid wheat. Genet. Mol. Res. 12: 6120–6129.
16
Mohammadi, M., and R. Karimizadeh. 2013. Challenges and research opportunities for wheat production in warm dryland regions of Iran. Agric. Forest. 59(3): 163-173.
17
Mohammadi, R., and A. Amri. 2012. Analysis of genotype-environment interaction in rainfed durum wheat of Iran using GGE-biplot and non-parametric methods. Can. J. Plant Sci. 92: 757–770.
18
Mohammadi, R., R. Haghparast, A. Amri, and A. Ceccarelli. 2010. Yield stability of rainfed durum wheat and GGE biplot analysis of multi-environment trials. Crop and Pasture Sci. 61: 92–101.
19
Sabaghnia, N. 2014. Study of grain yield and several morphological traits diversity in some durum wheat genotypes. Annal. Univ. Mar. Pol. 3: 11-19.
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Sabaghnia, N., H. Dehghani, and S. H. Sabaghpour. 2008. Graphic analysis of genotype × environment interaction of lentil yield in Iran. Agron. J. 100: 760–764.
21
Sabaghnia, N., H. Dehghani, and S. H. Sbaghpour. 2006. Nonparametric methods for interpreting genotype by environment interaction of lentil genotypes. Crop Sci. 46: 1100-1106.
22
Sabaghnia, N., R. Karimizadeh, and M. Mohammadi. 2013. GGL biplot analysis of durum wheat (Triticum turgidum spp. durum) yield in multi-environment trials. Bulg. J. Agric. Sci. 19: 756-765.
23
Yan, W., and M. S. Kang. 2003. GGE biplot analysis: A graphical tool for breeders, geneticists, and agronomists. CRC Press, Boca Raton, Florida, USA.
24
Yan, W. 2001. GGEBiplot- a windows application for graphical analysis of multi-environment trial data and other types of two-way data. Agron. J. 93: 1111–1118.
25
Yan, W., and I. Rajcan. 2002. Biplot evaluation of test sites and trait relations of soybean in Ontario. Crop Sci. 42: 11–20.
26
Yan, W., and L. A. Hunt. 2002. Biplot analysis of diallel data. Crop Sci. 42: 21–30.
27
Yan, W. 2002. Singular value partitioning in biplot analysis of multi-environment trial data. Agron. J. 94: 990–996.
28
Yan, W., M. S. Kang, B. Ma, S. Woods, and P. L. Cornelius. 2007. GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci. 47: 643–655.
29
Yan, W., L. A. Hunt, Q. Sheng, and Z. Szlavnics. 2000. Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Sci. 40: 597–605.
30
Yang, R. C., J. Crossa, P. L. Cornelius, and J. Burgueño. 2009. Biplot analysis of genotype × environment interaction: proceed with caution. Crop Sci. 49: 1564–1576.
31
ORIGINAL_ARTICLE
Evaluation of soybean genotypes under moisture stress conditions
Seed yield in soybean (Glycine max L.) is a function of moisture availability and is highly related to environmental conditions such as rainfall and irrigation. This study aimed to investigate differences among soybean lines and varieties in terms of phenology, above ground dry matter, seed yield, and yield components under different moisture regimes conditions, and to analyze the relationship between phenological characteristics and seed yield. Forty soybean genotypes were grown in two field experiments exposed to different irrigation regimes for three growing seasons (2010–2012). Results showed that variation in the duration of phenological stages was a determinant factor in increasing seed yield. There was a positive linear relationship between days to flowering and number of pods per plant, explaining 79 and 74% of the variation in seed yield under control and stress conditions, respectively. For both control and stress conditions, number of seeds m-2 was correlated with days to flowering and pod set. There were both linear positive and polynomial relationships between days to maturity and above ground dry matter at maturity, explaining 84 and 74% of the variation under control and stress conditions, respectively. These results suggest that’ soybean breeding programs in Iran should focus more on the duration of phenological stages in developing superior soybean genotypes for both stress and optimal conditions.
https://cbjournal.areeo.ac.ir/article_107107_9d7669bbe02980f91346ca554f332f0f.pdf
2016-11-01
51
57
10.22092/cbj.2016.107107
above ground dry matter
developmental stage
drought
flowering
seed yield
A.
Faraji
abolfazlfaraji@yahoo.com
1
Golestan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Gorgan, Iran
LEAD_AUTHOR
Adamsen, F. J., and T. A. Coffelt. 2005. Planting date effects on flowering, seed yield, and oil content of rape and crambe cultivars. Ind. Crops Prod. 21: 293-307.
1
Ashraf, M., N. A. Akram, F. Al-Qurainy, and M. R. Foolad. 2011. Drought Tolerance: Roles of organic osmolytes, growth regulators, and mineral nutrients. Adv. Agron. 111: 249-296.
2
Blum, A. 2005. Drought resistance, water use efficiency and yield potential – are they compatible, dissonant or mutually exclusive? Aust. J. Agric. Res. 56: 1159-1168.
3
Calvino, P. A, V. O. Sadras, and F. H. Andrade. 2003. Quantification of environmental and management effects on the yield of late-sown soybean. Field Crops Res. 83: 67-77.
4
Daneshian, J., H. Hadi, and P. Jonoubi. 2009. Study of quantitative and quality characteristics of soybean genotypes in deficit irrigation conditions. Iran. J. Crop Sci. 11: 393-409.
5
Egli, D. B. 2004. Seed-fill duration and yield of grain crops. Adv. Agron. 83: 243-279.
6
Faraji, A. 2010. Flower formation and pod/flower ratio in canola (Brassica napus L.) affected by assimilates supply around flowering. Int. J. Plant Prod. 4: 271-280.
7
Faraji, A. 2011. Quantifying factors determining seed weight in open pollinate and hybrid genotypes of oilseed rape (B. napus L.). Crop Breed. J. 1: 41-55.
8
Faraji, A., N. Latifi, A. Soltani, and A.H. Shirani Rad. 2009. Seed yield and water use efficiency of canola (B. napus L.) as affected by high temperature stress and supplemental irrigation. Agric. Water Manag. 96: 132-140.
9
Fehr, W. R., and C. E. Caviness.1977. Stages of soybean development. Cooperative Ext., Serv., Special Report 80, Iowa State Univ., USA.
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Kargar, S. M., M. R. Ghannadha, R. Bozorgi-Pour, A. A. Khajeh Attari, and H. R. Babaei. 2004. An investigation of drought tolerance indices in some soybean genotypes under restricted irrigation conditions. Iran. J. Agric. Sci. 35: 129-142 (In Persian).
11
Nielsen, D. C. 2011. Forage soybean yield and quality response to water use. Field Crops Res. 124: 400-407.
12
Pahlavani, M. H., G. Saeidi, and A. F. Mirlohi. 2007. Genetic analysis of seed yield and oil content in safflower using F1 and F2 progenies of diallel crosses. Int. J. Plant Prod. 1: 129-140.
13
Sadok, W., and T. R. Sinclair. 2009. Genetic variability of transpiration response to vapor pressure deficit among soybean (Glycine max L. Merr.) genotypes selected from a recombinant inbred line population. Field Crops Res. 113: 156-160.
14
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15
SAS Institute. 1996. SAS/STAT software: Changes and enhancements through release 6.12. SAS Inst., Cary, NC.
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Sinha, S., G. Sinam, R. K. Mishra, and S. Mallick. 2010. Metal accumulation, growth, antioxidants and oil yield of Brassica juncea L. exposed to different metals. Ecotox. Env. Saf. 73: 1352-1361.
17
Turner, N. C. 2004. Agronomic option for improving rainfall use efficiency of crops in dryland farming systems. J. Exp. Bot. 55: 2413-2425.
18
Xiaobing, L., J. Jin, S. J. Herbert, Q. Zhang, and Q. Wang. 2005. Yield components, dry matter, LAI and LAD of soybeans in Northeast China. Field Crops Res. 93: 85-93.
19
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20
Zare, M., H. Zinali Khaneghah, and J. Daneshian. 2004. An evaluation of tolerance of some soybean genotypes to drought stress. Iran. J. Agric. Sci. 35: 859-867.
21
ORIGINAL_ARTICLE
Assessment of yield stability of spring bread wheat genotypes in multi-environment trials under rainfed conditions of Iran using the AMMI model
Selecting bread wheat (Triticum aestivum L.) genotypes with wide adaptation across various test environments is important for enhancing the adoption rate of newly released wheat cultivars for rainfed spring wheat growing areas of Iran. This study analyzed the grain yield of 18 bread wheat genotypes at four dryland locations in Iran during the 2010-11, 2011-12, and 2012-2013 cropping cycles using the AMMI (additive main effects and multiplicative interaction) model. The biplot of AMMI-1 and AMMI-2 models facilitated the visual evaluation and identification of suitable genotypes, which is useful for genotype recommendation and mega-environment determination. Combined analysis of variance (ANOVA) revealed significant genotype × environment interaction for bread wheat yield. According to the AMMI-2 biplot, there were six best genotypes and five best mega-environments. The AMMI-1 model indicated that genotypes G2, G5, G9, G13, G14, G16, and G17 were superior, with moderate yield and yield stability, based on the lowest genotype × environment interactions. Genotypes G1 and G15 performed successfully in Khorramabad and Gonbad (two distinct mega-environments), respectively. The AMMI model was a useful tool for identifying yield stability of spring bread wheat genotypes for rainfed spring wheat growing areas of Iran. The significant genotype × environment interaction suggested that breeding strategies for specific adaption genotypes in homogeneously grouped environments should be considered in the national rainfed spring bread wheat breeding program in Iran.
https://cbjournal.areeo.ac.ir/article_107108_f6247f2fb5b1fd8d8cf279f67951188f.pdf
2016-11-01
59
66
10.22092/cbj.2016.107108
adaptability
biplot
drought
Dryland
Grain yield
M.
Mohammadi
mohtashammohammadi@yahoo.com
1
Dryland Agriculture Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Gachsaran, Iran
LEAD_AUTHOR
H.
Ghojigh
2
Golestan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Gonbad, Iran.
AUTHOR
H.
Khanzadeh
3
Ardabil Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Moghan, Iran.
AUTHOR
T.
Hosseinpour
4
Lorestan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Khorramabad, Iran.
AUTHOR
M.
Armion
5
Ilam Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Ilam, Iran.
AUTHOR
Annicchiarico, P. 1997. Additive main effects and multiplicative interaction (AMMI) analysis of genotype-location interaction in variety trials repeated over years. Theor. Appl. Genet. 94: 1072-1077.
1
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2
Burgueno, J., J. Crossa, and M. Vargas. 2001. SAS programs for graphing GE and GGE biplots. Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), México.
3
Ebdon, J. S., and H. G. Gauch. 2002. Additive main effect and multiplicative interaction analysis of national turf grass performance trials: I. Interpretation of genotype × environment interaction. Crop Sci. 42: 489–496.
4
Finlay, K. W., and G. N. Wilkinson. 1963. The analysis of adaptation in a plant-breeding programme. Aust. J. Agric. Res. 14: 742–754.
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Gauch, H. G. 2006. Statistical analysis of yield trials by AMMI and GGE. Crop Sci. 46: 1488–1500.
7
Gauch, H. G., and R. W. Zobel. 1996. Optimal replication in selection experiments. Crop Sci. 36: 838–843.
8
Gauch, H. G., and R. W. Zobel. 1997. Identifying mega-environments and targeting genotypes. Crop Sci. 37: 311–326.
9
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29
ORIGINAL_ARTICLE
Evaluation of Iranian pomegranate collection using simple sequence repeat and morphological traits
Pomegranate, Punica granatum L., is one of the oldest cultivated fruit species. This study used morphological data and a set of simple sequence repeat markers to investigategenetic diversity among 202 Iranian pomegranate accessions during the 2010 and 2011 growing seasons at Saveh Research Station, Saveh, Iran. Principal component analysis showed that leaf traits were predominant in the first and second component during both years, indicating that these traits are not only useful in assessing genetic diversity, but also for characterizing pomegranate germplasm. There was high correlation between the length of style and flower shape, implying that these traits are directly associated with tree performance. There was also close correlation between leaf length with leaf width, and total leaf length as well as and flower traits such as flower diameter and width. Twenty-three alleles (ranging from two to nine per locus) were detected using seven SSR markers with ABRII-MO26 showing the highest level of polymorphism. The average expected heterozygosity and mean PIC values were 0.36 and 0.34, respectively.Cluster analysis showed a simple matching coefficient ranging from 0.24 to 1 indicating high genetic diversity. Punica microsatellite markers and morphological characters revealed a relatively high genetic diversity among 202 pomegranate accessions. This great variation in the pomegranate collection of Saveh Research Station ensures the future of pomegranate breeding programs in Iran. Strategic research on the base collection and characterization of accessions provides useful information to breeding programs and will enhance the development of core collections.
https://cbjournal.areeo.ac.ir/article_107109_4db933d34f475602c7d0c574d997620e.pdf
2016-11-01
67
78
10.22092/cbj.2016.107109
Genetic diversity
Heterozygosity
molecular markers
Morphological characters
Pomegranate
T.
Basaki
t.basaki@pnu.ac.ir
1
Department of Agricultural Science, Payame Noor University, Tehran, Iran.
LEAD_AUTHOR
M.
Khayam Nekouei
2
Biotechnology Research and Development Center, Tarbiat Modares University,Tehran, Iran.
AUTHOR
R.
Choukan
r_choukan@yahoo.com
3
Seed and Plant Improvement Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran.
AUTHOR
M.
Mardi
4
Agricultural Biotechnology Research Institute of Iran, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran.
AUTHOR
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46
ORIGINAL_ARTICLE
Genetic diversity in Jujube germplasm (Ziziphus jujuba Mill.) based on morphological and pomological traits in Isfahan province, Iran
Identifying and selecting superior genotypes in native germplasm is one method for breeding fruit trees. Five different ecotypes of Jujube (Ziziphus jujuba Mill.) were collected from different regions of Isfahan province, Iran, for evaluation of their morphological and pomological traits during 2011-13. Results showed that quantitative traits were more significant within ecotypes. ‘‘Najafabad’ ecotype had the highest dimensions of leaves (48×28 mm), fruit weight (2.1 g), and stone weight (0.35g). The largest fruit width (17mm) and peduncle length (13mm) was observed in ‘Ardestan’ ecotype, whereas the largest fruit length (22mm) was observed in ‘Dehaghan’ ecotype. According to the results, the smallest size and weight of fruit, stone weight, and the longest and highest number of annual thorns in shoots were measured in ‘Kouhpayeh’ ecotype. Results showed significant negative and positive correlations between some traits. According to the cluster analysis, ecotypes with desirable traits of fruit were placed in separate clusters from other ecotypes. ‘Najafabad’ ecotype, followed by ‘Ardestan’ and ‘Dehaghan’ ecotypes, can be recommended as promising ecotypes for establishing Jujube orchards and use in Jujube breeding programs in Iran.
https://cbjournal.areeo.ac.ir/article_107110_c32b6949e917024966e14ddbfed0a0f1.pdf
2016-11-01
79
85
10.22092/cbj.2016.107110
cluster analysis
ecotype
Genetic variation
jujube
morphological traits
M.
Tatari
mtatari1@gmail.com
1
Isfahan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Isfahan, Iran.
LEAD_AUTHOR
A.
Ghasemi
2
Isfahan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Isfahan, Iran.
AUTHOR
A.
Mousavi
3
Chaharmahal Bakhtyari Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Shahrkord, Iran.
AUTHOR
Awasthi, O. P., and T. A. More. 2009. Genetic diversity and status of Ziziphus in India. Acta Hort. 840: 33-40.
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4
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25