| تعداد نشریات | 284 |
| تعداد نشریات سازمان | 82 |
| تعداد شماره‌ها | 3,036 |
| تعداد مقالات | 33,971 |
| تعداد مشاهده مقاله | 45,672,384 |
| تعداد دریافت فایل اصل مقاله | 76,769,037 |
تحلیل پایداری عملکرد، وراثت‌پذیری و مشخصه‌یابی لاین‌های امیدبخش جو (Hordeum vulgare L.) در نیشابور | ||
| پژوهش های کاربردی زراعی | ||
| دوره 35، شماره 3 - شماره پیاپی 136، مرداد 1402، صفحه 45-28 اصل مقاله (833.42 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/aj.2023.358117.1596 | ||
| نویسندگان | ||
| مجید طاهریان* ؛ حمید رضا نیکخواه | ||
| استادیار، بخش تحقیقات علوم زراعی و باغی، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی خراسان رضوی، سازمان تحقیقات، آموزش و ترویج | ||
| چکیده | ||
| جو (Hordeum vulgare L.) بعد از گندم از لحاظ سطح زیر کشت در ایران مقام دوم را دارد. به‌منظور شناسایی و گزینش ژنوتیپ‌های مطلوب جو، نوزده لاین امیدبخش، به همراه رقم به‌رخ به عنوان شاهد در قالب طرح بلوک‌های کـامل تصادفی در سه تکرار در ایستگاه تحقیقات کشاورزی نیشابور طی سه سال زراعی 98-1395 کشت و مورد مطالعه قرار گرفتند. صفات اصلی شامل تعداد روز تا ظهور سنبله، تعداد روز تا رسیدگی، طول دوره پرشدن دانه، وزن هزار دانه، ارتفاع بوته و عملکرد دانه اندازه‌گیری شدند. از تجزیه GGE بای‌پلات، تجزیه بای‌پلات صفت×ژنوتیپ و برآورد وراثت‌پذیری و سود ژنتیکی جهت بررسی اهداف این پژوهش استفاده شد. بر اساس نتایج تجزیه پایداری، ژنوتیپ‌های G12 و G3 به عنوان لاین‌های دارای عملکرد مطلوب و پایدار بوده و نزدیک‌ترین ژنوتیپ‌ها به آن‌ها، ژنوتیپ‌های G9، G11، G15 و G16 بودند. در این پژوهش مشخص شد که ژنوتیپ‌های دارای عملکرد بالا، از دوره پرشدن دانه طولانی، زمان گلدهی کوتاه‌تر و ارتفاع بوته کمتر برخوردار بودند و با توجه به وراثت‌پذیری عمومی و سود ژنتیکی بالا برای صفات مذکور می‌توان از آن‌ها برای گزینش لاین‌های پرعملکرد بهره گرفت. در مجموع براساس نتایج این پژوهش بهترین ژنوتیپ‌ها در منطقه نیشابور براساس عملکرد دانه و پایداری آن، وزن هزار دانه، طول دوره پرشدن دانه و زودرسی ژنوتیپ‌های 12، 3 و 9 بودند. | ||
| کلیدواژه‌ها | ||
| اثر متقابل ژنوتیپ×محیط؛ ژنوتیپ مطلوب؛ سود ژنتیکی؛ صفت تاثیرگذار | ||
| عنوان مقاله [English] | ||
| Analysis of yield stability, heritability and characterization of barley promising lines in Nishabour | ||
| نویسندگان [English] | ||
| majid Taherian؛ Hamid Reza Nikkhah | ||
| Assistant Professor of Horticulture Crop Research Department, Khorasan Razavi Agricultural and Natural Resources Resaerch and Education Center, AREEO, Mashhad, Iran. | ||
| چکیده [English] | ||
| Introduction Barley (Hordeum vulgare L.) with a cultivated area of nearly one and a half million hectares and with a production of about three million tons per year after wheat is the main crop in the country. Of this amount, about 600,000 hectares with a production of approximately two million tons are related to irrigated barley and about 900,000 hectares with a production of approximately one million tons are related to dryland barley (Ahmadi et al., 2020). In recent years, the shortage of barley production has been felt more than ever and in the comprehensive plan of the country's fodder, It has planned to increase its production in the long run. Similar to other crops, insufficient yield stability in barley is recognized as a one of the factors responsible for the gap between actual yield and potential yield (Cattivelli et al., 2008). In breeding programs, the identification of superior genotypes is difficult due to environmental variability of target locations and the interaction of these variabilities with the investigated genotypes. Therefore, it is important to evaluate the advanced agronomic lines across various environments and over multiple years to ensure their yield stability and production (Yan & Rajcan, 2002). The main objectives of this study were to evaluate grain yield stability and adaptability in some promising barley lines and characterization of barley inbred lines based on multiple traits under irrigation conditions. Materials and Methods Nineteen promising barley lines (G1-G19) along with one check cultivar (Behrokh), were studied during 2016-2019 at Nishabour Agricultural Research Stations. The experimental design at was a randomized complete block with three replications. Several main traits i. e., days to heading, days to maturity, plant height, thousand kernel weight and grain yield were recorded for all genotypes. GGE biplot and genotype by trait (GT) biplot methods were used to yield stability assessment of genotypes and characterization of barley inbred lines based on multiple traits. Combined analysis of variance for grain yield and other traits was analyzed using ADEL-R software. The GGE biplot and GT biplot methodologies were employed to analyze GxE interaction and characterization of barley inbred lines based on multiple traits using GEA-R software (Yan, 2001). Results and discussion The yield combined analysis of variance showed that the effects of year, genotype and genotype×year were significant at 1% probability level. The results also showed that approximately 23.45% of total variance was appertained to year effect, 30.72% to genotype effect and 21.37% to genotype × year interaction. Whole the mean grain yield of the evaluated lines in all years varied from 4.446 to 6.946 ton /ha and the G17 and G12 lines had the lowest and highest grain yield, respectively. Based on the biplot of average-environment coordination (AEC) for simultaneous selection of grain yield and stability of barley genotypes, genotypes G12 and G3 with the high grain yield were the most stable genotypeS. According to resulting from biplot of barley promosing lines in comparison with ideal genotype, G12 and G3 were identified as the ideal genotype. Also the closest genotypes to them were G9, G11, G15 and G16. Based on GT-Biplot polygon, G12, G3 and G9 lines were displayed high grain yield, grain filling period and lowest days to heading. The vector view of GT biplot showed high positive correlation between grain yield with grain filling period and negative correlation with days to heading. The high heritability along with advance genetic for the grain filling period, days to heading and plant height is encouraging from a standpoint of increasing the selection efficiency. In conclusion, the GT biplot offers a useful analytic tool for examining the variation among sets of lines, for exploring multiple trait data and for aiding in multi-trait selection. Conclusion: It was found that the genotypes with the highest grain yield had high the duration of the grain filling period, early in flowering time and Medium to low plant height under irrigated conditions. Based on the results, lines G12, G3 and G9 were the most stable high-yielding genotypes that had high duration of the grain filling period and thousand kernel weight, early in flowering time and low plant height in Nishabour condition. | ||
| کلیدواژه‌ها [English] | ||
| Genetic advance, Genotype×environment interaction, Ideal genotype, Effective trait | ||
| مراجع | ||
|
Ahmadi, K., Ebadzadeh, H. R., Hatami, F., Abdshah, H., and Kazemian, A.2020. Agricultural statistics. Ministry of Agriculture-Jahad. Vol. 1. 95 pp. (In Persian). Ahmadi, J., Mohammadi, A., and Najafi Mirak,T.2012. Targeting promising bread wheat (Triticum aestivum L.) lines for cold climate growing environments using AMMI and SREG GGE biplot analyses. Journal of Agricultural Science and Technology, 14: 645-657. Allard, R.W., and Bradshaw, A. D. 1964. Implication of genotype–environmental interaction in applied plant breeding. Crop Science, 5: 503–506. Becker, H.C., and Leon, J. 1988. Stability analysis in plant breeding. Plant Breeding, 101:1–23. Blanche, S.B., and Myers,G.O. 2006. Identifying discriminating locations for cultivar selection in Louisiana. Crop Science, 46: 946–949. Cammarano, D., Hawes, C., Squire, G., Holland, J., Rivington, M., Murgia, T., Roggero, P.P., Fontana, F., Casa, R., and Ronga, D. 2019. Rainfall and temperature impacts on barley (Hordeum vulgare L.) yield and malting quality in Scotland. Field Crops Research, 241: 1-11. Dimitrios, B., Christos, G., Jesus, R., and Eva, B. 2008. Separation of cotton cultivar testing sites based on representativeness and discriminating ability using GGE Biplots. Agronomy Journal, 100: 1230–1236. Ebadi-Segherloo, A., Mohammadi, S.A., Sadeghzadeh, B., and Kamrani, M. 2015. Study of heritability and genetic advance of agronomic traits in barley (Hordeum vulgare L.) and graphic analysis of trait relations by biplot. Jordan Journal of Agricultural Sciences, 12(1), 299-310. Fan, X.M., Kang, M. S., Chen, H., Zhang, Y., Tan, J., and Xu, C. 2007. Yield stability of maize hybrids evaluated in multi-environment trials in Yunnan, China. Agronomy Journal, 99: 220–228. Gabriel, K.R. 1971. The biplot graphic display of matrices with application to principal component analysis. Biometrika, 58: 453–467. Gholizadeh, A., and Dehghani, H. 2016. Graphic analysis of trait relations of Iranian bread wheat germplasm under non-saline and saline conditions using the biplot method. Genetika, 48(2), 473-486. Hudzenko, V., Demydov, O., Voloshchuk, H., Sardak, H., and Ishchenko, V. 2019. Genotype by environment interaction and yield stability of barley breeding lines in multi-environment trials. Agriculture and Forestry, 65(1), 201-210. Karahan, T., and Akgün,I. 2020. Selection of barley (Hordeum vulgareL.) genotypes by GYT (genotype × yield × trait) biplot technique and its comparison with GT (genotype × trait). Applied Ecology and Environmental Research, 18(1),1347-1359. Kendal, E. 2020. Evaluation of some barley genotypes with genotype by yield*trait (GYT) biplot method. Agriculture and Forestry, 66 (2), 137-150. Kendal, E., Karaman, M., Tekdal, S., and Dogan, S. 2019. Analysis of promising barley (Hordeum vulgare L.) lines performance by AMMI and GGE biplot in multiple traits and environment. Applied Ecology and Environmental Research, 17(2), 5219-5233. Koocheki, A. R., Sorkhilaleloo, B., and Eslamzadeh Hesari, M.R. 2012. Yield stability of barley elite genotypes in cold regions of Iran using GGE biplot. Seed and Plant Improvement Journal, 28 (4), 533-543 (in Persian with English abstract). Kroonenberg, P. M. 1995. Introduction to biplots for G×E tables. Department of mathematics, research report 51. University of Queensland. Maniruzzaman, M.Z., Begum, F., Khan, M.A.A., Amiruzzaman, M., and Hossain, A. 2019. valuation of yield stability of seven barley (Hordeum vulgare L.) genotypes in multiple environments using GGE biplot and AMMI model. Open Agriculture. 4:284-293. Merrick, L.F., Glover, K.D., Yabwalo, D., and Byamukama, E. 2020. Use of genotype by yield*trait (GYT) Analysis to select hard red spring wheat with elevated performance for agronomic and disease resistance traits. Crop Breeding, Genetics and Genomics, 2(2), 1-18. Moayyedi, A., Najafi Mirak, T., Taherian, M., Sasani, S., and Amin Azarm, D. 2020. Evaluation of grain yieldstability of durum wheat (Triticum turgidum L. var. durum) promising lines in moderate regions of Iran. Journal ofAgroecology, 12(2), 345-358 (in Persian with English abstract). Mohammadi, R., and Amri, A. 2011. Graphic analysis of trait relations and genotype evaluation in durum wheat. Journal of Crop Improvement, 25: 680-696. Mohammadi, M., Hosseinpour, T., Armion, M., Khanzadeh, H., and Ghojogh,H. 2016b. Analysis of genotype, environment and genotype × environment interaction in bread wheat genotypes using GGE biplot. Agricultural Communications, 4(3), 1-8. Mohammadi, R., Armion, M., Sadeghzadeh, B., Golkari, S., Khalilzadeh, G.H., Ahmadi, H., Abedi-Asl, G.H., and Eskandari, M. 2016a. Assessment of grain yield stability and adaptability of rainfed durum wheat breeding lines. AppliedField Crops Research, 29 (4), 25-42. (In Persian with English abstract). Mortazavian, S.M.M., Nikkhah, H.R., Hassani, F.A., Sharif-al-Hosseini, M., Taheri, M.,and Mahlooji, M. 2014. GGE Biplot and AMMI Analysis of Yield Performance of Barley Genotypes across Different Environments in Iran. Journal of Agricultural Science and Technology,16: 609-622. Omrani, S., Mohammad Naji, A., and Esmaeilzadeh Moghaddam, M. 2017. Yield stability analysis of promising bread wheat lines in southern warm and dry agroclimatic zone of Iran using GGE biplot model. Journal of Crop Breeding, 23(9), 157-165. Pacheco, A., Rodríguez, F., Alvarado, G.,and Burgueño, J. 2017. "ADEL-R. Analysis and design of experiments with R for Windows. Version 2.0", https://hdl.handle.net/11529/10857, CIMMYT Research Data & Software Repository Network, V3. Pacheco, A., Vargas, M., Alvarado, G., Rodríguez, F., Crossa, J.,and Burgueño, J. 2015. "GEA-R (Genotype x Environment Analysis with R for Windows) Version 4.1", https://hdl.handle.net/11529/10203, CIMMYT Research Data & Software Repository Network, V16. Panse, V.G. 1957. Genetics of quantitative characters in relation to plant breeding. Indian Journal of Genetics, 17, 318-328. Pržulj, N., Mirosavljević, M., Čanak, P., Zorić, M., and Boćanski, J. 2015. Evaluation of spring barley performance by biplot analysis. Cereal Research Communications, 43(4), 692-703. Rahmati, M., Ahmadi, A., Hossein Pour, T., Hamidiyan, K., andReisvand, M. 2021. Evaluation of yield potential of barley genotypes and identification of traits related to improving grain yield under rainfed conditions. Iranian Journal of Dryland Agriculture, 10 (1), 57-71 (In Persian). Ramazani, S.H.R., and Abdipour, M. 2018. Statistical analysis of grain yield in Iranian cultivars of barley (Hordeum vulgare L.). Journal of Agricultural Research, 8: 239-246. Rose, I.V.L.W., Das, M.K., and Taliaferro, C.M. 2008. A comparison of dry matter yield stability assessment methods for small numbers of genotypes of bermudagrass. Euphytica, 164: 19–25. Roy, D. 2000. Plant breeding analysis and exploitation of variation. Alpha Science. Oxford, UK. Samonte, S.O.P.B., Wilson, L.T., McClung, A. M.,and Medley, J. C. 2005. Targeting cultivars onto rice growing environments using AMMI and SREG GGE biplot analysis. Crop Science, 45: 2414–2424. Stansfield, W.D. 1991. Theory and Problems in Genetics. McGraw-Hill, New York, USA. Taherian, M., Bihamta, M.R., Peyghambari, S.A., Alizadeh, H., and Rasoulnia, A. 2019. Stability analysis and selection of salinity tolerant barley genotypes. Journal of Crop Breeding, 11(29), 93-103. Taheripourfard, Z., Izadi- Darbandi, A., Ghazvini, H., Ebrahimi, M., Mortazavian, S. M. M., and Abdipour, M. 2017. Identifying superior barley (Hordeum vulgare L.) genotypes using GGE-biplot across warm and moderate environments under irrigated conditions in Iran. Crop Breeding Journal, 7 (1 & 2), 23-35. Vaezi, B., Pour-Aboughadareh, A., Mohammadi, A., Armion, M., Mehraban, A., HosseinPour,T.,and Dorii, M. 2017. GGE Biplot and AMMI Analysis of Barley Yield Performance in Iran. Cereal Research Communications, 45(3), 500-511. Xu, N., Fok, M., Li, J., Yang, X., andYan, W. 2017. Optimization of cotton variety registration criteria aided with a genotype- by-trait biplot analysis. Scientific Reports, 7: 17237. Yan, W. 2001. GGE biplot–A Windows application for graphical analysis of multi-environment trial data and other types of two-way data. Agronomy Journal, 93: 1111–1118. Yan, W., and Kang, M.S. 2003. GGE Biplot Analysis: A graphical tool for breeders, geneticists, and agronomists. CRC Press, Boca Raton, FL. Yan, W., and Rajcan, I. 2002. Biplot analysis of sites and trait relations of soybean in Ontario. Crop Science, 42: 11-20. Yan, W., and Tinker, N. A. 2006. Biplot analysis of multi-environment trial data: Principles and applications. Canadian Journal of Plant Science, 86:623–645. Yan, W., Hunt, L.A., Sheng, Q., and Szlavnics, Z. 2000. Genotype evaluation and mega-environment investigation based on the GGE biplot. Crop Science, 40: 597–605. Yan,W., Fregeau-Reid, J., Mountain, N., and Kobler, J. 2019. Genotype and management evaluation based on genotype by yield×trait (GYT) analysis. Crop Breeding Genetics and Genomics, 1(1), 1-21. Zali1, H., Barati, A., and Jabari, M. 2021. Evaluation of variation at barley inbred lines (Hordeum vulgare L.) using SIIG index. Journal of Crop Breeding, 13 (39), 179-194. | ||
|
آمار تعداد مشاهده مقاله: 1,339 تعداد دریافت فایل اصل مقاله: 664 |
||