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ارزیابی پایداری عملکرد شکر سفید برخی ارقام چغندرقند آزادسازی شده تجاری در ایران از سال 1390 تا 1399 | ||
| نهال و بذر | ||
| دوره 38، شماره 3، مهر 1401، صفحه 339-364 اصل مقاله (1.46 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/spj.2023.362024.1305 | ||
| نویسندگان | ||
| سعید صادق زاده حمایتی* 1؛ علی صارمی راد1؛ مصطفی حسین پور1؛ علی جلیلیان2؛ مسعود احمدی3؛ حیدر عزیزی4؛ حسن حمیدی3؛ فرحناز حمدی1؛ فرشید مطلوبی اقدم1 | ||
| 1مؤسسه تحقیقات اصلاح و تهیه بذر چغندرقند، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرج، ایران. | ||
| 2مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان کرمانشاه، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرمانشاه، ایران. | ||
| 3مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان خراسان رضوی، سازمان تحقیقات، آموزش و ترویج کشاورزی، مشهد، ایران. | ||
| 4مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان آذربایجان‌غربی، سازمان تحقیقات، آموزش و ترویج کشاورزی، ارومیه، ایران. | ||
| چکیده | ||
| افزایش عملکرد و پایداری آن در طیفی از شرایط محیطی هدف، یکی از اصلی‌ترین اهداف برنامه های به نژادی محصولات زراعی است. در این پژوهش، پایداری عملکرد شکر نه رقم چغندرقند آزادسازی شده تجاری در ایران از سال 1390 تا سال 1399 به همراه یک شاهد خارجی در شش ایستگاه تحقیقات کشاورزی اردبیل، کرج، کرمانشاه، مشهد، میاندوآب و مغان در قالب طرح بلوک‌های کامل تصادفی با چهار تکرار به مدت چهار سال متوالی 1397-1396 تا 1400-1399 مورد بررسی و ارزیابی قرار گرفت. تجزیه واریانس مرکب داده ها نشان داد که اثر سال، مکان و ژنوتیپ و برهمکنش‌های سال × مکان، ژنوتیپ × سال، ژنوتیپ × مکان و ژنوتیپ × سال × مکان بر عملکرد شکر سفید 0.01) (p ≤ معنی دار بود. تجزیه اثر برهمکنش ژنوتیپ × محیط نشان داد که هفت مؤلفه اول معنی‌دار(p ≤ 0.01) بودند. بر اساس بای‌پلات AMMI1 ارقام آسیا، شکوفا و آرتا به ترتیب به عنوان پایدارترین ارقام شناخته شدند. نتایج مدل خطی مخلوط نشان داد که اثر ژنوتیپ و برهمکنش ژنوتیپ × محیط معنی‌دار (p ≤ 0.01)بود. بر اساس مدل BLUP ارقام پرفکتا، آسیا و شکوفا به ترتیب دارای بیشترین مقدار میانگین پیش‌بینی‌شده عملکرد شکر سفید بودند. بای‌پلات عملکرد شکر سفید با شاخص WAASB نشان داد که ارقام آسیا، آرتا و شکوفا علاوه بر پایداری عملکرد شکر سفید، ارزش عملکرد شکر سفید بیشتر از میانگین کل داشتند. بر اساس گروه‌بندی ارقام از نظر شاخص WAASB به عملکرد شکر سفید، ارقام آرتا، آسیا و شکوفا به‌عنوان ارقام با عملکرد شکر سفید بالا و پایدار شناخته شدند. رتبه‌بندی و گزینش همزمان ارقام برای شاخص پایداری WAASB و عملکرد شکرسفید نتایج مشابهی به همراه داشت، به‌طوری که رقم آسیا بیشترین شاخص WAASBY و پس از آن ارقام شکوفا و آرتا در رتبه های بعدی قرار گرفتند. بر این اساس ارقام آسیا، شکوفا و آرتا به‌عنوان ارقام با عملکرد شکر سفید بالا و پایدار شناخته شدند. | ||
| کلیدواژه‌ها | ||
| چغندرقند؛ آزمون درست‌نمایی؛ مدل خطی مخلوط؛ گزینش همزمان؛ مؤلفه اصلی | ||
| عنوان مقاله [English] | ||
| Evaluation of White Sugar Yield Stability of Some Commercially Released Sugar Beet Cultivars in Iran from 2011-2020 | ||
| نویسندگان [English] | ||
| S. Sadeghzadeh Hemayati1؛ A. Saremirad1؛ M. Hosseinpour1؛ A. Jalilian2؛ M. Ahmadi3؛ H. Azizi4؛ H. Hamidi3؛ F. Hamdi1؛ F. Matloubi Aghdam1 | ||
| 1Sugar Beet Research Institute, Agricultural Research, Education and Extension Organization, Karaj, Iran. | ||
| 2Kermanshah Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization, Kermanshah, Iran. | ||
| 3Khorasan-e-Razavi Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization, Mashhad, Iran. | ||
| 4West Azerbaijan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization, Urmia, Iran. | ||
| چکیده [English] | ||
| High yield and yield stability across a range of environmental conditions is one of the main objectives of crop breeding programs. In this study, white sugar yield stability of nine sugar beet cultivars commercially released in Iran from 2011 to 2020 with an introduced foreign cultivar as check was evaluated using randomized complete block design with four replications in six research field stations; Karaj, Ardabil, Kermanshah, Mashhad, Moghan, Miandoab, in four cropping seasons 2017-2021. The combined analysis of variance revealed that effects of genotype, year, location, and year × location, genotype × year, genotype × location and genotype × year × location were significant (p ≤ 0.01) on white sugar yield. Analysis of genotype × environment interaction showed that the first seven components were significant (p ≤ 0.01). Based on AMMI1 analysis, cv. Asia, cv. Shokoufa and cv. Arta had white sugar yield stability. Linear mixed model analysis showed that the effects of genotype and genotype × environment interaction were significant (p ≤ 0.01) on white sugare yield. Based on the BLUP method, cv. Perfecta, cv. Asia and cv. Shokoufa had the highest predicted mean value of white sugar yield. Biplot of white sugar yield with the WAASB index showed that cv. Asia, cv. Arta and cv. Shokoufa had higher sugar yield as well as higher white sugar yield stability. Ranking of sugar beet cultivars based on WAASB/white sugar yield ratio identified cv. Arta, cv. Asia and cv. Shokoufa as high yielding with white sugar yield stability. Simultaneous ranking and selection of cultivars based on 50:50 WAASB/white sugar yield ratio resulted the same. Therefore, cv. Asia followed by cv. Shokoufa and cv. Arta had relatively higher WAASBY indices. Considering the results of this study, cv. Asia, cv. Shokoufa and cv. Arta were identified as high yielding with higher white sugar yield stability. | ||
| کلیدواژه‌ها [English] | ||
| Sugar beet, likelihood test ratio, linear mixed model, simultaneous selection, principle component | ||
| مراجع | ||
|
Abamu, F., and Alluri. K. 1998. AMMI analysis of rainfed lowland rice (Oryza sativa) trials in Nigeria. Plant Breeding 117 (4): 395-397.
Ahakpaz, F., Abdi, H., Neyestani, E., Hesami, A., Mohammadi, B., Mahmoudi, K. N., Abedi-Asl, G., Noshabadi, M. R. J., Ahakpaz, F., and Alipour, H. 2021. Genotype-by-environment interaction analysis for grain yield of barley genotypes under dryland conditions and the role of monthly rainfall. Agricultural Water Management 245: 106665.DOI:10.1016/j.agwat.2020.106665.
Annicchiarico, P., Russi, L., Piano, E., and Veronesi, F. 2006. Cultivar adaptation across Italian locations in four turfgrass species. Crop Science 46 (1): 264-272.Anonymous. 1999. Agribusiness Handbooks. Volume 4. Sugar Beets/ White Sugar.
Arshad, M., Hussain, T., Iqbal, M., and Abbas, M. 2017. Enhanced ethanol production at commercial scale from molasses using high gravity technology by mutant S. cerevisiae. Brazilian Journal ofMicrobiology48:403-409.DOI:10.1016/j.bjm.2017.02.003.
Barbosa, M. H. P., Ferreira, A., Peixoto, L., Resende, M., Nascimento M., and Silva, F. 2014. Selection of sugar cane families by using BLUP and multi-diverse analyses for planting in the Brazilian savannah. Genetics and MolecularResearch13:1619-1626.
Baretta, D., Nardino M., Carvalho, I. R., de Oliveira, A. C., de Souza, V., and da Maia, L. C. 2016. Performance of maize genotypes of Rio Grande do Sul using mixed models. Científica 44 (3): 403-411.
Bartlett, M. S. 1937. Properties of sufficiency and statistical tests. Proceedings of the Royal Society of London. Series A-Mathematical and Physical Sciences 160 (901): 268-282.
Basafa, M., and Taherian, M. 2016. Analysis of stability and adaptability of forage yield among silage corn hybrids. Journal of Crop Breeding 8 (19): 185-191.
Benakanahalli, N. K., Sridhara, S., Ramesh, N., Olivoto, T., Sreekantappa, G., Tamam, N., Abdelbacki, A. M., Elansary H. O., and Abdelmohsen, S. A. 2021. A Framework for Identification of stable genotypes based on MTSI and MGDII Indexes: An example in Guar (Cymopsis tetragonoloba L.). Agronomy 11 (6): 1221. DOI:10.3390/agronomy11061221.
Cárdenas-Fernández, M., Bawn, M., Hamley-Bennett, C., Bharat, P. K. V., Subrizi, F., Suhaili, N., Ward, D. P., Bourdin, S., Dalby, P. A., Hailes, H. C., Hewitson, P., Ignatova, S., Kontoravdi, C., Leak, D. J., Shah, N., Sheppard, T. D., Ward J. M., and Lye, G. J. 2017. An integrated biorefinery concept for conversion of sugar beet pulp into value-added chemicals and pharmaceutical intermediates. Faraday Discussions 202: 415-431. https://doi.org/10.1039/C7FD00094D.
Cook, D., and Scott, R. 1993. The sugar beet crop: science into practice. Champan and Hall Press. 675 pp.
Dohm, J. C., Minoche, A. E., Holtgräwe, D., Capella-Gutiérrez, S., Zakrzewski, F., Tafer, H., Rupp, O., Sörensen, T. R., Stracke, R., Reinhardt, R., Goesmann, A., Kraft, T., Schulz, B., Stadler, P. F., Schmidt, T., Gabaldón, T., Lehrach, H., Weisshaar B., and Himmelbauer, H.,. 2014. The genome of the recently domesticated crop plant sugar beet (Beta vulgaris). Nature 505 (7484): 546-549. https://doi.org/10.1038/nature12817.
Duraisam, R., Salelgn K., and Berekete, A. K. 2017. Production of beet sugar and bio-ethanol from sugar beet and it bagasse: a review. International Journal of Engineering Trends and Technology 43 (4): 222-233. DOI: 10.14445/22315381/IJETT-V43P237. European Commission. 2018. A sustainable bioeconomy for Europe. Strengthening the connection between economy, society and the environment. Updated Bioeconomy Strategy. Luxembourg. DOI: 10.2777/792130.
European Commission. 2021. Sugar. https:// https://agriculture.ec.europa.eu/farming/crop-productions-and-plant-based-products/sugar_en.
FAO. 2021. Crops production and area harvested. http://www.fao.org/faostat/en/#data/QCL. Fathi Sadabadi, M., Ranjbar, G. Zangi, M., Tabar S., and Zarini, H. N. 2018. Analysis of stability and adaptation of cotton genotypes using GGE biplot method. Trakia Journal of Sciences 16 (1): 51-61. DOI: 10.15547/tjs.2018.01.009.
Gauch, H. 1992. Statistical analysis of regional yield trials: AMMI analysis of factorial designs. Elsevier Science Publishers. 278 pp.
Gumienna, M., Szambelan, K., Jeleń H., and Czarnecki, Z. 2014. Evaluation of ethanol fermentation parameters for bioethanol production from sugar beet pulp and juice. Journal of the Institute of Brewing 120 (4): 543-549. https://doi.org/https://doi.org/10.1002/jib.181.
Huang, X., Jang, S. Kim, B. Piao, Z. Redona E., and Koh, H.-J. 2021. Evaluating genotype× environment interactions of yield traits and adaptability in rice cultivars grown under temperate, subtropical and tropical environments. Agriculture 11 (6): 558. DOI: 10.3390/agriculture11060558.
Kang, M. S. 2004. Breeding: genotype by environment interaction. Pp. 218-221. In: Goodman, R. M. (ed.) Encyclopedia of Plant and Crop Science (Print). CRC Press. New York.
Kang, M. S. 1997. Using genotype-by-environment interaction for crop cultivar development. Advances in Agronomy 62 (1): 199-252.
Koundinya, A., Ajeesh, B., Hegde, V., Sheela, M., Mohan C., and Asha, K. 2021. Genetic parameters, stability and selection of cassava genotypes between rainy and water stress conditions using AMMI, WAAS, BLUP and MTSI. Scientia Horticulturae 281: 109949. DOI: 10.1016/j.scienta.2021.109949.
Kunz, M., Martin D., and Puke, H. 2002. Precision of beet analyses in Germany explained for polarization. Zuckerindustrie 127 (1): 13-21.
Lin, C. and Binns, M. 1988. A method of analyzing cultivar × location × year experiments: a new stability parameter. Theoretical and Applied Genetics 76 (3): 425-430.
Lin, C., Binns M., and Janick J. 2010. Concepts and methods for analyzing regional trial data for cultivar and location selection. Plant Breeding Review 12: 271-297.
Macholdt, J., Piepho H.-P., and Honermeier B. 2019. Mineral NPK and manure fertilisation affecting the yield stability of winter wheat: Results from a long-term field experiment. European Journal of Agronomy 102: 14-22.
Mohr, A., and Raman, S. 2013. Lessons from first generation biofuels and implications for the sustainability appraisal of second generation biofuels. Energy Policy 63: 114-122.
Mutari, B., Sibiya, J., Gasura, E., Kondwakwenda, A., Matova, P. M., and Chirwa, R. 2022. Genotype × environment interaction and stability analyses of grain yield and micronutrient (Fe and Zn) concentrations in navy bean (Phaseolus vulgaris L.) genotypes under varied production environments. Field Crops Research 286: 108607. DOI: 10.1016/j.fcr.2022.108607.
Nicodème, T., Berchem, T., Jacquet N., and Richel, A. 2018. Thermochemical conversion of sugar industry by-products to biofuels. Renewable and Sustainable Energy Reviews 88: 151-159.
Olivoto, T., Lúcio, A. D. C., da Silva, J. A. G., Marchioro, V. S., de Souza, V. Q., and Jost, E. 2019a. Mean performance and stability in multi‐environment trials I: combining features of AMMI and BLUP techniques. Agronomy Journal 111 (6): 2949-2960. DOI: 10.2134/agronj2019.03.0220.
Olivoto, T., Lúcio, A. D. C., da Silva, J. A. G., Sari, B. G., and Diel, M. I. 2019b. Mean performance and stability in multi‐environment trials II: Selection based on multiple traits. Agronomy Journal 111 (6): 2961-2969. DOI: 10.2134/agronj2019.03.0221.
Olmos, J. C., and Hansen, M. Z. 2012. Enzymatic depolymerization of sugar beet pulp: Production and characterization of pectin and pectic-oligosaccharides as a potential source for functional carbohydrates. Chemical Engineering Journal 192: 29-36.
Omrani, S., Omrani, A., Afshari, M., Saremirad, A., Bardehji ,S., and Foroozesh, P. 2019. Application of additive main effects and multiplicative interaction and biplot graphical analysis multivariate methods to study of genotype-environment interaction on safflower genotypes grain yield. Journal of Crop Breeding 11 (31): 153-163 (in Persian).
Piepho, H.-P. 1994. Best linear unbiased prediction (BLUP) for regional yield trials: a comparison to additive main effects and multiplicative interaction (AMMI) analysis. Theoretical and Applied Genetics 89: 647-654.
Piepho, H.-P., Möhring, J., Melchinger, A., and Büchse, A. 2008. BLUP for phenotypic selection in plant breeding and variety testing. Euphytica 161 (1-2): 209-228. DOI:10.1007/s10681-007-9449-8.
Rajabi, A., Ahmadi, M., Bazrafshan, M., Hassani, M., and Saremirad, A. 2022. Evaluation of resistance and determination of stability of different sugar beet (Beta vulgaris L.) genotypes in rhizomania-infected conditions. Food Science & Nutrition 11: 1403-1414. https://doi.org/https://doi.org/10.1002/fsn3.3180.
Rodrigues, P. C., Monteiro, A., and Lourenço, V. M. 2016. A robust AMMI model for the analysis of genotype-by-environment data. Bioinformatics 32 (1): 58-66.
Sabaghnia, N., Dehghani, H., Alizadeh, B., and Mohghaddam, M. 2010. Genetic analysis of oil yield, seed yield, and yield components in rapeseed using additive main effects and multiplicative interaction biplots. Agronomy Journal 102 (5): 1361-1368.
Salazar-Ordóñez, M., Pérez-Hernández, P. P., and M. Martín-Lozano, J. 2013. Sugar beet for bioethanol production: An approach based on environmental agricultural outputs. Energy Policy 55: 662-668.
Saremirad, A., Bihamta, M. R., Malihipour, A., Mostafavi, K., and Alipour, H. 2022. Evaluation of seedling stage resistance of commercial bread wheat cultivars to black rust disease using GGE biplot method. Journal of Crop Breeding 14 (42): 186-196 (in Persian).http://jcb.sanru.ac.ir/article-1-1304-fa.html.
Saremirad, A., and Mostafavi, K. 2021. Using AMMI and biplot graphical analysis multivariate methods to evaluate the effect of genotype-environment interaction in cotton genotypes. Iranian Journal of Cotton Researches 8 (2): 127-144 (in Persian).. https://doi.org/10.22092/ijcr.2021.353002.1163
Saremirad, A., and Taleghani, D. 2022. Utilization of univariate parametric and non-parametric methods in the stability analysis of sugar yield in sugar beet (Beta vulgaris L.) hybrids. Journal of Crop Breeding 14 (43): 49-63 (in Persian). http://jcb.sanru.ac.ir/article-1-1324-en.html.
Smith, A., Cullis, B. R., and Thompson, R. 2005. The analysis of crop cultivar breeding and evaluation trials: an overview of current mixed model approaches. The Journal of Agricultural Science 143 (6): 449-462.
Taleghani, D., Saremirad, A., Hosseinpour, M., Ahmadi, M., Hamidi, H., and Nemati, R. 2022. Genotype × environment interaction effect on white Sugar yield of winter-sown short-season sugar beet (Beta vulgaris L.) cultivars. Seed and Plant Journal 38 (1): 53-69 (in Persian). https://doi.org/10.22092/spj.2022.360021.1275.
Tohidi, B., Mohammadi-Nejad, G., Nakhoda, B., and Saboori, H. 2015. Evaluation of grain yield stability of recombinant inbred lines in bread wheat (Triticum aestivum L.) based on AMMI method. Journal of Plant Production 22 (2): 189-202.
Tomaszewska, J., Bieliński, D., Binczarski, M., Berlowska, J., Dziugan, P., Piotrowski, J., Stanishevsky, A., and Witońska, I. A. 2018. Products of sugar beet processing as raw materials for chemicals and biodegradable polymers. RSC Advances 8 (6): 3161-3177. DOI: 10.1039/C7RA12782K.
Vineeth, T., Prasad, I., Chinchmalatpure, A. R., Lokeshkumar, B., Kumar, S., Ravikiran, K., and Sharma, P. C. 2022. Weighted average absolute scores of BLUPs (WAASB) based selection of stable Asiatic cotton genotypes for the salt affected Vertisols of India. Indian Journal of Genetics and Plant Breeding 82 (01): 104-108.
Yan, W., and Kang, M. S. 2002. GGE biplot analysis: A graphical tool for breeders, geneticists, and agronomists. CRC press. 288 pp. https://doi.org/10.1201/9781420040371. Zobel, R. W., Wright, M. J., and Gauch Jr., H. G. 1988. Statistical analysis of a yield trial. Agronomy Journal 80 (3): 388-393.
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