The creation of salt-tolerant wheat genotypes can provide a basis for sustainable wheat production in areas that are particularly sensitive to the impacts of climate change on soil salinity. This study aimed to select salt-tolerant wheat genotypes that could serve as a genetic resource in breeding for salinity tolerance. A two-year experiment was established with 27 wheat genotypes, grown in salinity stress and non-stress conditions. Agronomic parameters (plant height, spike weight, number of grains per spike, thousand grain weight, and grain yield/plant) were analyzed in the phenophase of full maturity, while biochemical parameters (DPPH radical scavenging activity and total phenolic content) were tested in four phenophases. Grain yield/plant was the most sensitive parameter to salinity, with a 31.5% reduction in value. Selection based on salt tolerance indices (STI, MP, and GMP) favored the selection of the genotypes Renesansa, Harmonija, Orašanka, Bankut 1205, KG-58, and Jugoslavija. Based on YI (1.30) and stability analysis, the genotype Harmonija stands out as the most desirable genotype for cultivation in saline conditions. The presence of positive correlations between grain yield/plant and biochemical parameters, in all phenophases, enables the selection of genotypes with high antioxidant activity and high yield potential, even in the early stages of plant development.
Through choosing bread wheat genotypes that can be cultivated in less productive areas, one can increase the economic worth of those lands, and increase the area under cultivation for this strategic crop. As a result, more food sources will be available for the growing global population. The phenotypic variation of ear mass and grain mass per ear, as well as the genotype × environment interaction, were studied in 11 wheat (Triticum aestivum L.) cultivars and 1 triticale (Triticosecale W.) cultivar grown under soil salinity stress (3S) during three vegetation seasons. The results of the experiment set on the control variant (solonetz) were compared to the results obtained from soil reclaimed by phosphogypsum in the amount of 25 t × ha−1 and 50 t × ha−1. Using the AMMI analysis of variance, there was found to be a statistically significant influence of additive and non-additive sources of variation on the phenotypic variation of the analyzed traits. Although the local landrace Banatka and the old variety Bankut 1205 did not have high enough genetic capacity to exhibit high values of ear mass, they were well-adapted to 3S. The highest average values of grain mass per ear and the lowest average values of the coefficient of variation were obtained in all test variants under microclimatic condition B. On soil reclaimed by 25 t × ha−1 and 50 t × ha−1 of phosphogypsum, in microclimate C, the genotypes showed the highest stability. The most stable genotypes were Rapsodija and Renesansa. Under 3S, genotype Simonida produced one of the most stable reactions for grain mass per ear.
Various statistical methods were applied in this research: analysis of genetic parameters, Pearson's correlation, genotypic and phenotypic correlations, and Path analysis, with the aim of creating a selection criterion for increasing wheat grain yield. A two-year experimental study was conducted with twenty-seven wheat genotypes, grown on two localities: Rimski Šančevi (Bačka, Vojvodina), on Chernozem soil type; and Kumane (Banat, Vojvodina), on Solonjec soil type. The highest values of phenotypic coefficient of variation (CVp) had the grain weight per plant (17.44% on Chernozem and 13.81% on Solonetz), while the lowest value of CVp had the thousand grain weight (8.12% on Chernozem and 5.47% on Solonetz). On Chernozem, the value of the genotypic coefficient of variation (CVg) ranged from 1.51%, in the number of grains per spike, to 9.17% in the spike length, while on Solonetz, grain weight per plant had the lowest value of CVg (0.36%) and plant height the highest one (11.15%). At both localities, grain yield was in highly significant and positive correlations with all analyzed traits, except with plant height and spike length. In favorable environmental conditions (Chernozem), Path analysis revealed that grain yield directly depends on grain weight per spike (0.317**), number of grains per spike (0.232**) and spike weight (0.209**), and other analyzed traits have a positive indirect effect on grain yield over mentioned traits. Under salinity stress conditions, the grain weight per plant had the highest direct effect on grain yield (0.891**), which makes this trait a good selection criterion in breeding for salinity stress tolerance.
In order to evaluate the variability and relationship between different wheat yield components, a randomized complete block design experiment with ten genotypes of wheat had been carried out during three growing seasons (2010-2012). The number of spikelet per spike and grain weight per spike had low genotypic and phenotypic variability, while plant height had the highest one. High heritability was observed for plant height (h 2 =93.1%), spike length (h 2 =92.3%) and spike density (h 2 =92.9%). The low heritability was found for grain weight per spike (h 2 =35.6%). Grain weight per spike was in significant positive genotypic and phenotypic correlation with all the traits (plant height, spike height, number of spikelet per spike, number of grain per spike and spike weight) except spike density. The spike weight had the highest phenotypic (r p =0.988), while number of spikelet per spike had the highest genotypic correlation with grain weight per spike (r g =0.981). Path coefficient analysis revealed that all the traits had highly significant direct effect on grain weight per spike, except spike length. The stepwise regression revealed that 87.1% of the grain weight per spike variation was explained by model which excludes spike length. Spike weight and plant height had the highest shared and unique contribution to grain weight per spike.
Bread wheat is one of the most represented field crops whose level and stability of yield is very important for the food security in Republic Serbia. In the paper was investigated stability of yield expression of 15 winter bread wheat genotypes in different agroecological conditions of Serbia, using the Additive main effects and multiplicative interaction (AMMI) model and GGE-biplot method of analysis. Aim of investigation was to determine which of applied analysis is superior in identification of the most desirable genotypes for cultivation in given environments. Analysis of variance showed that genotype and genotype-environment (G×E) interaction represent highly significant sources of variability in expression of grain yield. AMMI and GGE analyses were point out similar results and an indisputable conclusion is that multienvironment trials, besides routine usage of analysis of variance, must be analized with one of this two models, which combine analysis of variance and PCA analysis. AMMI analysis is simpler for interpretation and closer to the concept of view of the agronomical trial, while GGE analysis is more complex and gives more precise interpretation of "which-won-where", i .e. for defining of narrowly adapted genotypes in given environments. Thus, G11 as the genotype with highest average yield is narrow adapted to the environment Sombor and can be recommended, as well as in the environment 2 (Kruševac), while genotypes with modest requests (G2) rather can be recommended for cultivation in the environment 1 (Kragujevac), which is characterized by less fertile soil and a smaller amount and uneven distribution of precipitation.
The aim of this study was to investigate phenotypic variability of yield components for different spelt wheat genotypes (Triticum spelta L.). Six genotypes of winter spelt wheat (Nirvana, KG-37-8/3, KG-54-7/3, KG-54-8/1, KG-54-4/2, and KG-54-2/3) were grown during two growing seasons (2011/2012 and 2012/2013) at certified organic trial parcel in the Municipality of Čačak, Serbia. Through variance analysis, highly significant differences in mean values for both investigated yield components (number of grains per spike and grain weight per spike) were established. Higher values of coefficient of variability for grain weight per spike (CV = 12.8%) than grain number per spike (CV = 10.2%) were determined. The highest average value for number of grains per spike had genotype KG-54-7/3 (46.22). Genotype KG-54-2/3 (1.94 g) had significantly higher mass of hulled grains per spike compared to other investigated genotypes. Phenotypic analysis of variance indicated that ecological factors had higher impact on the expression of grain weight per spike, but genetic factors had higher impact on the expression of number of grains per spike.
This research was conducted with some spike traits of twenty winter six-row barley genotypes in six environments. The aim of this study was to determine the significance and take advantage useful genotype by environment interacton (GEI) by applying AMMI-1 model. High statistical significance GEI was determined. Wide adaptability genotypes were J-29, J-33, J-9 and J-21 for spike length (SL) as Grand and Ozren for grain number per spike (GNS). The winner genotypes in all environments were Ozren and Grand for SL as Ozren for GNS. All the examined environments can be considered as one megaenvironment, which indicates that unpredictable interactions dominate in this research.
The present study was carried out to investigate the variability and heritability ofwheat quality components and to evaluate the stability of ten wheat genotypes indifferent environmental conditions. The experiment was conducted in the Centerfor Small Grains in Kragujevac, Serbia during two growing seasons (2010 and2011). Thousand grain weight had the highest value of GCV and PCV (7.13 and7.7%), while test weight had the lowest PCV (3.24%) and protein content had thelowest GCV (1.82%). The highest heritability was observed for thousand grainweight (H2=85.37%), while the lowest one was found for protein content(H2=19.56%). The AMMI analysis showed significant effect of the G×Einteraction, where first main component was significant for all components.Genotypes KG-56, Arsenal and Osječanka are close to the average values for allcomponents and expressed the highest stability. Genotypes with the highest orlowest average values for analyzed traits, such as Norin 10, Mironovskaya 808,Gruža and Spartanka, showed moderate to high instability. Cluster analysiscategorized the genotypes into four groups. The genotype Norin 10 showed thehighest distance from other genotypes, whereas the stable genotypes grouped together.
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