Journal of Crop Production and Processing

fa گزینش ژنوتیپ های برتر جو بدون پوشینه برای اقلیم گرم جنوب فارس Selection of Hull-Less Barley Superior Genotypes for the Warm Climate of Southern Fars in Iran عمومى General كاربردي Applicable <div style="text-align: justify;">جو (Hordeum vulgare L.) یکی از مناسب‌ترین گیاهان سازگار برای کشت در شرایط مختلف ایران است. بنابراین هدف از این مطالعه انتخاب ژنوتیپ‌های برتر جو بدون پوشینه بر اساس عملکرد دانه و برخی صفات مورفولوژیک بود. به همین منظور تعداد 69 ژنوتیپ جو بدون پوشینه در قالب طرح حجیم‌شده بدون تکرار آگمنت با سه بلوک ناقص و با سه ژنوتیپ شاهد جو لخت (لوت، EH-85-9 و EH-87-4) در ایستگاه تحقیقات کشاورزی داراب طی سال زراعی 1400-1399 ارزیابی شدند. شاخص های فاصله ژنوتیپ- ایدئوتیپ چند صفتی (MGIDI) و طراحی ایدئوتیپ از طریق پیش بینی نااریب بهترین خط (FAI-BLUP) برای انتخاب ژنوتیپ‌های برتر با استفاده از 17 صفت مورفولوژیک محاسبه شدند. هیچ کدام از ژنوتیپ های شاهد جزء ژنوتیپ های برتر بر مبنای هر دو شاخص MGIDI و FAI-BLUP نبودند. نتایج نشان داد که شاخص MGIDI بیشترین همبستگی منفی و معنی دار را با عملکرد دانه (**0/91-)، تعداد سنبله در متر مربع (**0/91-)، وضعیت ساقه (**0/37-) و دوره پر شدن دانه (*0/25-) دارد.  نتایج همبستگی بین شاخص FAI-BLUP و صفات مورد بررسی نشان داد که FAI-BLUP همبستگی مثبت و معنی داری با تعداد سنبله در متر مربع (**0/86)، عملکرد دانه (**0/84) و طول دوره پر شدن دانه (**0/38) دارد و از طرفی همبستگی معنی دار، اما منفی با تعداد دانه در سنبله (**0/31-) داشت. همچنین همبستگی بالا و معنی دار، ولی منفی بین شاخص MGIDI و FAI-BLUP (**82/0-) مشاهده شد. نتایج نشان داد که شاخص‌های MGIDI و FAI-BLUP پتانسیل ایده‌آلی برای شناسایی ژنوتیپ های پرمحصول با صفات مطلوب دارند. از این رو، استفاده از این شاخص ها می تواند در غربال گری ژنوتیپ‌های برتر در مراحل اولیه برنامه اصلاحی مفید باشند. در مجموع نتایج هر دو شاخص MGIDI و FAI-BLUP مشابه بودند و ژنوتیپ‌های یکسانی را به-عنوان ژنوتیپ‌های برتر شناسایی نمودند و در نهایت ژنوتیپ های L24، L23، L37، L59، L18، L32، L29، L41، L61، L27، L38، L16، L66، L39 انتخاب شدند.</div> <div style="text-align: justify;">Barley (Hordeum vulgare L.) is one of the widely-adapted crops for cultivation in the diverse conditions around Iran. Moreover, this cereal crop ranks fourth in the world in terms of economic importance after wheat, rice, and corn. Simultaneous application of several traits in selection of superior high-performing genotypes can be a difficult task, as each genotype can be superior in terms of some traits. With increase in number of traits, it becomes difficult to select the appropriate genotype, necessitating reliance on selection indices. Using the selection indices, all traits become one index and it becomes easier to rank and identify superior genotypes. Thus, the aim of this study was to select superior hull-less barley genotypes based on grain yield and some morphological traits using different indicators. Materials and Methods To select superior barley genotypes, 69 genotypes of hull-less barley were evaluated in the non-repeating Augment design with three incomplete blocks along with three hull-less barley check genotypes (Loot, EH-85-9 and EH-87-4) in Darab Agricultural Research Station, south of Iran, during the 2020-2021 cropping season of. The studied genotypes were planted in six lines 6 m at length with a distance of 15 cm from each other. Seeding was done in 400 seeds m-2. Seeds were sown using an experimental plot planter (Wintersteiger, Ried, Austria). Fertilizers were used as 150 kg ha-1 nitrogen (twice), and di-ammonium phosphate and potassium sulfate in 100 and 50 kg ha-1, respectively (before planting). All experimental plots were harvested with an experimental grain harvester (Wintersteiger, Ried, Austria). The studied traits included days to spike appearance (DHE), days to maturity (DMA), plant height (PLH), thousand kernel weight (TKW), length of grain filling period (GFP), grain yield (YLD), spike length (SL), awn length (AL), grains/spike (NGS), awn type (AT), peduncle length (PL), spike density (SD), spike weight (SPW), spikes m-2 (NSP), stem situation (SS), spike situation (SPS) and row type (RT). The multi-trait genotype-ideotype distance index (MGIDI) and ideotype design via best linear unbiased prediction (FAI-BLUP) were calculated using 17 morphological traits to select superior genotypes. Results and Discussion According to the results of analysis of variance, a significant difference was observed between the studied lines for all traits (except TKW) at the probability levels of 5 and 1%. The results of factor analysis for the 17 studied traits identified five hidden factors that explained 72.7% of the total variance of the data. The results showed that low values of the NGS, SD and RT and high values of the TKW, LS and DHE were effective factors in selecting superior genotypes using the first factor. Based on this factor, L66 was superior. In the second factor, high values of the DMA, SPW and GFP were the main factors in selecting genotypes, and based on this factor, L27 genotype was ideal. The third factor selected genotypes based on high values of both trait YLD and NSP. L24 genotype was the superior one based on the third factor. Fourth factor selected genotypes based on low Al, PL, AT and SPS (L38 genotype was superior), and fifth factor selected genotypes with high value of PLH and SS and based on this factor L38 and L61 genotypes were ideal. Based on the MGIDI index, genotypes L24, L23, L37, L59, L18, L32, L29, L41, L61, L27, L38, L16, L66, L39 and L46 with the lowest values were identified as superior genotypes. Moreover, FAI-BLUP index identified genotypes L24, L37, L23, L32, L59, L29, L33, L27, L44, L41, L66, L46, L61 and L43 as the desirable genotypes compared with other genotypes. None of the check genotypes were among the superior genotypes based on both MGIDI and FAI-BLUP indices. The results showed that the MGIDI index has the most negative and significant correlation with grain yield (-0.91**), spikes m-2 (-0.91**), stem situation (-0.37**) and grain filling period (-0.25*). The results also showed that FAI-BLUP has a positive and significant correlation with spikes m-2 (0.86**), grain yield (0.84**), and grain filling period (0.38**), and on the other hand, it had a significant but negative correlation with grains/spike (-0.31**). Finally, a highly significant negative correlation was observed between MGIDI index and FAI-BLUP index (-0.82**). Conclusions In general, the results showed that the two-row genotypes were on average superior to the six-row genotypes in terms of the DHE, DMA, NSP, TKW, SL and PL. On the other hand, the six-row genotypes were superior to the two-row genotypes in terms of PLH, GFP, YLD, NGS, and SPW. Our results revealed that the MGIDI and FAI-BLUP indices have ideal potential to identify the high-yielding genotypes with desirable traits. Hence, the use of these indices can be useful in screening the superior genotypes in the early steps of  breeding programs for barley. The results of both MGIDI and FAI-BLUP indices were similar and identified the same genotypes as superior genotypes and finally, L24, L23, L37, L59, L18, L32, L29, L41, L61, L27, L38, L16, L66, L39 genotypes were identified as superior genotypes.</div> تجزیه به عامل ها, شاخص های مبتنی بر چند صفت, صفات مورفولوژیک , عملکرد دانه Factors analysis, Grain yield, Indices based on multiple traits, Morphological traits 1 20 http://jcpp.iut.ac.ir/browse.php?a_code=A-10-3290-3&slc_lang=fa&sid=1 H. Aflatooni حامد افلاطونی hamed.aflatooni@ut.ac.ir No Respectively, Department of Plant Production and Genetics, Faculty of Agricultural Sciences and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran. گروه مهندسی تولید و ژنتیک گیاهی، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران O. Sofalian امید سفالیان sofalian@gmail.com No Respectively, Department of Plant Production and Genetics, Faculty of Agricultural Sciences and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran. گروه مهندسی تولید و ژنتیک گیاهی، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران H. Zali حسن زالی hzali90@yahoo.com Yes Crop and Horticultural Science Research Department, Fars Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Darab, Iran. بخش تحقیقات علوم زراعی و باغی، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی فارس، سازمان تحقیقات، آموزش و ترویج کشاورزی A. Asghari علی اصغری ali_asgharii@yahoo.com No Respectively, Department of Plant Production and Genetics, Faculty of Agricultural Sciences and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran. گروه مهندسی تولید و ژنتیک گیاهی، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران