جستجوی مقالات مرتبط با کلیدواژه « نقشه حرارتی » در نشریات گروه « زراعت »

Barley (Hordeum vulgare L.) has been known as one of the most adapted cereal crops to various environmental conditions. In addition, this cereal crop ranks fourth in the world in terms of economic importance after wheat, rice, and corn. Identification of high-yielding genotypes with highly desirable growth traits is one of the most important goals in barley breeding programs. However, the present study aimed to select superior genotypes based on grain yield and some morpho-phenological traits using different selection models.

To identify superior genotypes of barley for cultivation in the warm and dry conditions in the southern regions in the Fars province, 21 genotypes were investigated in a randomized complete block with three replications during the 2021-2023 cropping seasons. During the plant growth and development, several morpho-phenological traits were recorded. Agronomic traits measured were days to spike emergence (DHE), days to maturity (DMA), plant height (PLH), grain filling period (GFP), grain yield (GY), 1000-kernel weight (TKW), spike length (SL), spike density (SD), awn length (AL), spike weight (SPW), number of grains per spike (NGS), row type (RT) and number of spikes per m2 (NS). After collecting experimental data, statistical analyses were computed using SAS and R softwares 

Results of the analysis of variance (ANOVA) showed that there is a significant difference among the investigated genotypes in terms of all the measured traits (except for Awn length and spike length). Based on the multi-trait genotype-ideotype distance index (MGIDI), genotypes G1 (Oxin), G7, G3, and G14 with the lowest values were identified as superior genotypes. Moreover, the ideotype design via the best linear unbiased prediction (FAI-BLUP) model identified genotypes G20, G8, G2 (Golchin) and G1 (Oxin) as the desirable genotypes compared with other genotypes. The Venn diagram revealed that genotypes G1 and G7 were selected using both selection indices, simultaneously. The MGIDI index showed a positive and significant correlation with DHE (0.58**), while it negatively and significantly correlated with GFP (-0.51*) and GY (-0.74 **). The FAI-BLUP index showed a significant positive relationship with SL (0.50 *) and SPW (0.45*), while it negatively and significantly correlated with DHE (-0.47 *). To better evaluate and group the investigated genotypes, principal components analysis (PCA) was used. Results of the PCA showed that the first and second principal components explained 30.5 and 20.6 percent, respectively. Moreover, the results of the PCA showed that the selected genotypes based on MGIDI and FAI-BLUP indices are located in the 1st and 2nd regions of the PCA diagram. In total, the results showed that there is a relative compliance between the MGIDI and FAI-BLUP indices with PCA. Additionlly, there was a high agreement between the MGIDI indices and the first principal component, and on the other hand, the second principal component showed a high agreement with the FAI-BLUP index.

In conclusion, the genotype G7 with the highest grain performance and the relative superiority in terms of MGIDI and FAI-BLUP indices was identified as a superior genotype compared to all reference genotypes. Hence, this genotype can be recommended for further comprehensive evaluation before commercial release in the southern regions in Fars province.

This study was conducted to investigate the phenotypic diversity of 108 pure barley lines in Augment design with four controls in six blocks in the farm of Darab Agricultural Research Station (2020-2021). In order to select the best lines in terms of yield and other traits, SIIG index was used. The results of restricted maximum likelihood (REML) analysis showed that the lowest and highest levels of broad-sense heritability were related to the number of days to the spike emergence of (0.21) and the number of days to maturity (0.97), respectively. Based on the SIIG index, the studied lines were grouped into five categories. The results of SIIG index showed that L86 and L38 lines with the highest (0.640) and lowest (0.249) values of SIIG were the best and weakest lines, respectively. The results of grouping the lines based on SIIG index showed that group one and two lines and lines L22, L62, L80, L64, L51, L10, L96, L73, L81, L100, L69 and L97 from group three, were the best in this study and can be considered for further investigations. Overall, the results showed that SIIG index was able to classify genotypes based on grain yield, spikes/m2, seeds/spike, spike weight and plant height simultaneously, and also there was a relative compliance between the SIIG index and principal component analysis. Therefore, SIIG index is a suitable method for selecting ideal genotypes based on several traits simultaneously.