1- Department of Plant Production and Genetics engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran
2- Crop and Horticultural Science Research Department, Fars Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Darab, Iran
3- Seed and Plant Improvement Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran
2- Crop and Horticultural Science Research Department, Fars Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Darab, Iran
3- Seed and Plant Improvement Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran
Abstract: (470 Views)
Extended Abstract
Background: Barley (Hordeum vulgare L.) is one of the important cereal crops, which is ranked fourth globally after wheat, rice, and maize. Superior genotypes must be accurately identified in any breeding program. Improvement of some traits, such as grain yield, may be efficient through indirect selection pathways due to their high correlations with other traits. Therefore, this study aimed to select the best barley genotypes with desirable agronomic traits by using and finally comparing a combination of different selection indices.
Methods: To evaluate some superior barley genotypes using multi-trait selection indices, an experiment was conducted at the Darab Agricultural and Natural Resources Research Station, Darab, Iran, in the 2020-2021 cropping year. The plant genetic materials consisted of 51 barley genotypes, along with nine check genotypes. The experiment was carried out based on a randomized complete block design with three replications. The studied genotypes were planted in six lines of 5 m long and 15 cm space between them. Seed density was determined as 300 seeds per square meter, and seeds were sown using an experimental plot planter (Wintersteiger, Austria). Before seed sowing, the fertilizer composition was 150 kg ha-1 nitrogen (twice), and di-ammonium phosphate and potassium sulfate were 100 and 50 kg ha-1, respectively. After removing the border effect, all experimental plots were harvested using an experimental combine (Wintersteiger, Austria). Three selection indices, including selection of ideal genotype (SIIG), multi-trait genotype-ideotype distance index (MGIDI), ideotype design via best linear unbiased prediction (FAI-BLUP), and Smith-Hazel (SH), were estimated based on 12 morpho-phenological traits to select superior genotypes. The measured traits included grain yield, the number of spikes per square meter, the number of grains per spike, weight of spike, thousand-grain weight, grain filling period, spike length, plant height, the number of days to heading, the number of days to maturity, grain filling rate, and the spike type.
Results: The results of the likelihood ratio test (LRT) showed that the genotype effect was significant for all measured traits at the 1% probability level. The results of restricted maximum likelihood (REML) showed that the lowest heritability was recorded for the grain filling period (0.505), grain yield (0.611), and grain filling rate (0.649), while the highest values were observed for thousand-grain weight (0.884) and the number of days to heading (0.877). The results of comparing the adjusted mean using the REML-BLUP model revealed that the highest grain yield belonged to genotypes 3, 1, 50, and 30, respectively. The grain yields of these genotypes were higher than all the check genotypes. Based on the MGIDI index, genotypes 39, 14, 43, 49, 6, 35, 19, 32, 41, 50, 55 (Auxin), 38, 44, 60 (Nimrooz), 28, 42, 40, and 34 with the lowest values were identified as superior genotypes. Genotypes 39, 35, 43, 14, 32, 6, 41, 44, 50, and 42 with the highest FAI-BLUP values were selected as the best genotypes. The results of the Smith-Hazel index showed that the genotypes 30, 55, 1, 2, 3, 40, 38, 12, 10, and 13 with the highest values were identified as superior genotypes. Moreover, genotypes 3, 50, 30, 1, 55, 10, 13, 2, 11, 39, 12, 38, 31, 35, 36, and 44 with the highest SIIG index were superior genotypes in terms of most measured traits. The results of the correlation analysis showed that grain yield was positively and significantly correlated with the number of days to maturity (0.35*), plant height (0.37*), grain filling period (0.26*), the number of spikes per square meter (0.61**), and grain filling rate (0.96**). All selection indices were significantly correlated with grain yield, and the highest correlation value was found between grain yield and the SIIG (0.92**) and Smith-Hazel (0.78**) indices. FAI-BLUP and MGIDI indices were significantly associated with all traits, except for thousand-grain weight, length of ripening period, spike length, and the number of spikes per square meter traits. Only the thousand-grain weight and spike length traits did not show significant correlations with any of the selection indices.
Conclusion: In conclusion, genotypes 3, 50, 30, 1, 55, 10, 13, 2, 11, 12, and 38 were identified as superior genotypes using both SIIG and Smith-Hazel indices, and genotypes 39, 35, 50, 38, and 44 were identified as superior genotypes by FAI-BLUP and MGIDI indices. Finally, our results revealed that SIIG and Smith-Hazel indices were better than FAI-BLUP and MGIDI indices to identify superior genotypes.
Background: Barley (Hordeum vulgare L.) is one of the important cereal crops, which is ranked fourth globally after wheat, rice, and maize. Superior genotypes must be accurately identified in any breeding program. Improvement of some traits, such as grain yield, may be efficient through indirect selection pathways due to their high correlations with other traits. Therefore, this study aimed to select the best barley genotypes with desirable agronomic traits by using and finally comparing a combination of different selection indices.
Methods: To evaluate some superior barley genotypes using multi-trait selection indices, an experiment was conducted at the Darab Agricultural and Natural Resources Research Station, Darab, Iran, in the 2020-2021 cropping year. The plant genetic materials consisted of 51 barley genotypes, along with nine check genotypes. The experiment was carried out based on a randomized complete block design with three replications. The studied genotypes were planted in six lines of 5 m long and 15 cm space between them. Seed density was determined as 300 seeds per square meter, and seeds were sown using an experimental plot planter (Wintersteiger, Austria). Before seed sowing, the fertilizer composition was 150 kg ha-1 nitrogen (twice), and di-ammonium phosphate and potassium sulfate were 100 and 50 kg ha-1, respectively. After removing the border effect, all experimental plots were harvested using an experimental combine (Wintersteiger, Austria). Three selection indices, including selection of ideal genotype (SIIG), multi-trait genotype-ideotype distance index (MGIDI), ideotype design via best linear unbiased prediction (FAI-BLUP), and Smith-Hazel (SH), were estimated based on 12 morpho-phenological traits to select superior genotypes. The measured traits included grain yield, the number of spikes per square meter, the number of grains per spike, weight of spike, thousand-grain weight, grain filling period, spike length, plant height, the number of days to heading, the number of days to maturity, grain filling rate, and the spike type.
Results: The results of the likelihood ratio test (LRT) showed that the genotype effect was significant for all measured traits at the 1% probability level. The results of restricted maximum likelihood (REML) showed that the lowest heritability was recorded for the grain filling period (0.505), grain yield (0.611), and grain filling rate (0.649), while the highest values were observed for thousand-grain weight (0.884) and the number of days to heading (0.877). The results of comparing the adjusted mean using the REML-BLUP model revealed that the highest grain yield belonged to genotypes 3, 1, 50, and 30, respectively. The grain yields of these genotypes were higher than all the check genotypes. Based on the MGIDI index, genotypes 39, 14, 43, 49, 6, 35, 19, 32, 41, 50, 55 (Auxin), 38, 44, 60 (Nimrooz), 28, 42, 40, and 34 with the lowest values were identified as superior genotypes. Genotypes 39, 35, 43, 14, 32, 6, 41, 44, 50, and 42 with the highest FAI-BLUP values were selected as the best genotypes. The results of the Smith-Hazel index showed that the genotypes 30, 55, 1, 2, 3, 40, 38, 12, 10, and 13 with the highest values were identified as superior genotypes. Moreover, genotypes 3, 50, 30, 1, 55, 10, 13, 2, 11, 39, 12, 38, 31, 35, 36, and 44 with the highest SIIG index were superior genotypes in terms of most measured traits. The results of the correlation analysis showed that grain yield was positively and significantly correlated with the number of days to maturity (0.35*), plant height (0.37*), grain filling period (0.26*), the number of spikes per square meter (0.61**), and grain filling rate (0.96**). All selection indices were significantly correlated with grain yield, and the highest correlation value was found between grain yield and the SIIG (0.92**) and Smith-Hazel (0.78**) indices. FAI-BLUP and MGIDI indices were significantly associated with all traits, except for thousand-grain weight, length of ripening period, spike length, and the number of spikes per square meter traits. Only the thousand-grain weight and spike length traits did not show significant correlations with any of the selection indices.
Conclusion: In conclusion, genotypes 3, 50, 30, 1, 55, 10, 13, 2, 11, 12, and 38 were identified as superior genotypes using both SIIG and Smith-Hazel indices, and genotypes 39, 35, 50, 38, and 44 were identified as superior genotypes by FAI-BLUP and MGIDI indices. Finally, our results revealed that SIIG and Smith-Hazel indices were better than FAI-BLUP and MGIDI indices to identify superior genotypes.
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