Developing selection criteria based on an ontogenetic path analysis approach to improve grain yield in barley

Abstract

ABSTRACT. We used correlation and path coefficient analysis based on an ontogenetic approach to develop selection criteria in barley (Hordeum vulgare L.) for an early production system in Ethiopia. A total of 100 genotypes using 10 x 10-simple lattices with two replications were used to perform the experiment at Ambo and Asasa. The combined analysis of the measured traits showed significant differences among genotypes for all traits. A positive correlation was observed between grain yield and spike/m 2 , kernel number/spike, and 1000-kernel weight. The path analysis showed that spike/m 2 , 1000-kernel weight, and kernel number per spike had significant positive direct effects on grain yield, which shows that these traits can be used as selection criteria to improve grain yield. The significant positive correlation of spike/ m 2 , 1000-kernel weight, and grain-filling period and the positive direct effect on grain yield indicated the potential of these traits as indirect selection criteria to improve grain yield in the early production system in Ethiopia. This study also showed that the path coefficient analysis based on an ontogenetic model was efficient and produced results that can be interpreted clearly.

Path Coefficient Analyses for Growth and Yield Traits of Barley Under Different Seeding Rates

“…And all direct effects of the characteristic of spike length on grain yield achieved varying negative values The highest direct negative effect (very high) (5749.11-) for the seed rate (160 kg.ha -1 ) followed by the seed rate (130 kg.ha -1 ), which amounted to (-4612.3), then the seed rate (190 kg.ha. -1 ) which recorded a direct negative effect (high only) (-0.4414) compared to the negative significant genetic correlation values for the three seed rates, while the trait of the number of shrivels had low direct negative effects on seed rates (130 kg.ha -1 ) and direct positive effects medium for seeding rate (190 kg.h -1 ), compared with the positive significant genetic correlation values for the number of tillers in all three seed rates.All of the above-mentioned results are consistent with what was mentioned by [12][13][14][15][16][17][18], as they found direct positive and negative effects for the characteristics of biological yield, harvest index, number of days to physiological maturity, number of spikes, number of spikes, and the number of spikes. The grains per spike, the weight of 1000 grains, and the length of the spike in grain yield.…”

“…8288) in spite of the negative indirect effect of the characteristics of the number of grains per spike, the weight of 1000 grains, the growth rate of the crop and the harvest index (-0.0186, -0.0605, -0.9322, -0.1839) respectively, the trait of the harvest index achieved a direct positive effect (0.4745), as were its total effects. Positive (0.1987), due to the positive indirect effects across the characteristics of the number of spikes, the weight of 1000 grains and the growth rate of the crop (0.0056, 0.0525, 0.3934), and the negative effect did not affect it through the characteristics of the number of grains per spike and biological yield (-0.0021, -0.7253), respectively..The results were close to what was found by [12][13][14][15][16][17][18] and [16], as they found direct positive and negative effects for the characteristics of biological yield, harvest index, number of days to physiological maturity, number of spikes, and number of grains per spike. The weight of 1000 grains and the length of the spike in grain yield., it is clear from table (3) that the ratio of the effect of other unstudied factors amounted to 0.342 and 0.542.…”

Correlation and path analyses of the performance elements in spring barley cultivars

“…Since the effectiveness of selection in breeding depends on mutual influence of traits, a lot of researchers study plant performance and yield capacity by correlation and path analyses. In studies on barley, the most common positive significant correlations are found between the yield capacity and the kernel number per spike, 1000-kernel weight, the number of spikes/m 2 , yield index and biological yield (Budacli Carpici and Celik, 2012;Setotaw et al, 2014;Mohtashami, 2015;Arpali, & Yagmur, 2015;Bocianowski et al, 2016;Tawfiq et al, 2016;Mirosavljević et al, 2016;Shrimali et al, 2017;Amardeep et al, 2017;Marzougui and Chargui, 2018;Đekić et al, 2019;Fatemi et al, 2019;Madić et al, 2019;Matin et al, 2019;Tanaka and Nakano, 2019;Rajičić et al, 2021). There is also evidence of negative correlations between the yield capacity and 1000-kernel weight (Budacli Carpici and Celik, 2012;Gebru et al, 2018), kernel number per spike (Marzougui and Chargui, 2018), and plant height (Gebru et al, 2018).…”

“…As to path analysis, the direct positive effects of the kernel and spikelet numbers per spike, yield index, the spike number/m 2 , productive tillering capacity, biological yield, and 1000-kernel weight (Budacli Carpici and Celik, 2012;Setotaw et al, 2014;Arpali and Yagmur, 2015;Mohtashami, 2015;Bocianowski et al, 2016;Dimitrova-Doneva et al, 2016;Tawfiq et al, 2016;Kompanets et al, 2016;Shrimali et al, 2017;Amardeep et al, 2017;Demydov et al, 2017;Malik et al, 2018;Fatemi et al, 2019;Sandhya et al, 2019), on the barley yield capacity and performance are most often observed. Analysis of the yield capacity and performance fulfillment paths revealed that the direct effect of biological yield is enhanced by the indirect effects of the 1000-kernel weight, number of spikes/m 2 , plant height, the kernel weight per spike and spike length (Tofiq et al, 2015;Tawfiq et al, 2016;Kohan et al, 2016;Sandhya et al, 2019).…”

Assessment of genetic diversity in chinese hulless barley accessions for qualitative traits

“…Moreover, the nature of genetic variation will provide information of variability in the cultivars and the significance between the cultivars which are accessible and utilized in breeding programs. It has been observed that there are several approaches to assay genetic diversity in which one of them is the use of linear relationship between variables, conferring correlation analysis which is an useful tool for indirect selection of complex characters such as yield and quality traits ( Setotaw et al 2014;Kaur et al 2016). The coefficient of correlation shows the variations of common traits interactions under the study and does not imply causation (Mohtashami 2015).…”