dr. rahmatollah karimizadeh

Crop development and breeding are critical for ensuring food security, improving nutritional quality, and mitigating the impact of changing climate. However, these processes are inherently complex, requiring the evaluation and selection of genotypes based on multiple traits. In this context, the Multi-traits Genotype-Ideotype Distance Index (MGIDI) and the Selection Index of Ideal Genotype (SIIG) have emerged as robust and versatile tools for identifying superior genotypes across a range of traits. These multivariate selection indices consolidate information from various agronomic characteristics into a single value, allowing for the effective ranking of genotypes based on their distance from an ideal genotype. The present study aimed to identify promising bread wheat genotypes using the MGIDI and SIIG indices, with a focus on yield and other key agronomic traits.
 In this study, 14 promising wheat lines - selected from the wheat breeding program of the Rainfed Agriculture Research Institute and provided by the ICARDA international research center – along with two check cultivars, were evaluated using a randomized complete blocks design (RCBD) with three replications. The experiment was conducted at the Sarab-Chengaei research station during the 2021-2023 cropping seasons. Each experimental plot consisted of eight cropping rows, each 6 meters long, spaced 15 cm apart, with a seeding density of 350 seeds per square meter. Fertilization was applied based on soil chemical analysis. The traits measured included days to heading, days to maturity, plant height, 1000-kernel weight, grain filling period, peduncle length, grain filling rate, awn length, number of spikelet per spike, spike density, number of grains per spike, grain weight per spike and grain yield. All plots were harvested using an experimental grain harvester.
Combined variance analysis revealed that genotype effects were significant for days to heading, days to maturity, grain filling period, spike length, awn length, spike density and 1000-kernel weight. Genotypes G15, G14, G2, G10, and G8 recorded the highest grain yield, averaging 4169, 3911, 3907, 3870 and 3809 kg ha-1, respectively. Based on the MGIDI index, lines G2 and G15 were identified as desirable genotypes. Factor analysis extracted five factors that collectively explained 88.9% of the total phenotypic variation among genotypes. According to the SIIG index, genotypes G2, G15 and G9 were superior, exhibiting the highest SIIG values. A positive and significant correlation was observed between grain yield and number of grains per square meter, grain filling rate, and peduncle length, indicating that these traits play a key role in enhancing yield performance. Cluster analysis classified the genotypes into three main clusters: Class 1 included early-maturing genotypes with low yield; Class 2 comprised genotypes with later maturity and low yield; and Class 3 contained early- maturing genotypes with high yield potential.
Line G15 and the Qaboos cultivar, which had the lowest MGIDI values and yields above the average of all genotypes, were identified as ideotypes based on the MGIDI index. According to the SIIG index, lines G15 and G9, along with the Qaboos control cultivar, were recognized as the top-performing genotypes. Therefore, line G15 is recommended for use in future breeding programs or as a promising candidate for cultivar release. The high yield performance of the Qaboos cultivar further supports its suitability for cultivation in regions with climatic conditions similar to those of the experimental site.

Wheat is one of the most important crops and an essential source of calories and protein in the world. Food security depends on agricultural production to supply the growing world population with certain nutrients. Plants are often exposed to stressful conditions. Stress can occur within minutes, days, or even months. Temperature is the most important environmental variable that affects the growth, development, and final productivity of agricultural grain products. Cold or freezing temperatures cause a lot of damage to agriculture, especially in cereal crops in subtropical and temperate regions. Most of the world's wheat fields are often under low-temperature stress. The ability of plants to tolerate cold without damaging their growth cycle is called cold tolerance. Crop plants, including wheat, tend to overcome cold stress through cold adaptation. In winter cereals, low-temperature stress in the vegetative stage causes chlorosis and wilting of leaves and eventually leads to necrosis and growth inhibition. Wheat needs an ideal temperature range for growth and ideal performance, and any deviation from it affects the natural growth process. In general, winter cereals like wheat have two varieties, sensitive to cold and resistant to cold. Numerous studies show that the intraspecies genetic diversity of wheat has not yet been fully exploited. The purpose of this study is to investigate the diversity of durum wheat lines under conditions of freezing stress, identify cold-resistant lines in durum wheat based on some morphophysiological traits, and determine the relationship between durum wheat traits and cold tolerance.

Freezing and laboratory experiments were carried out in the greenhouse and plant breeding laboratory of Mohaghegh Ardabili University in 2019 on 45 durum wheat genotypes as a factorial experiment in the form of a randomized complete block design with three replications. In this experiment, four control temperature treatments (without freezing), -8, -10 and -12 degrees Celsius were investigated. LT50 of several applied temperatures (-8, -10, and -12), and by analyzing Probit was calculated for each genotype. The mean comparison was done with the LSD method at the five percent probability level. Lines grouping was done by Ward's clustering method using the square measure of Euclidean distance.

The highest amount of LT50 trait was observed in genotype number 44, and the lowest amount was observed in genotype number 36. LT50, survival percentage, height, fresh weight of shoots, dry weight of shoots, saturated weight of shoots, number of leaves, relative water content, and electrolyte leakage were investigated. The correlation was calculated separately in stress levels. Lines with high height in terms of LT50 trait were also included in the group of resistant lines. Multivariate variance analysis based on unbalanced one-way variance analysis was used to determine the most favorable groups. The lines were placed in four groups in the control conditions, the stress of -8, and -12°C, and in five groups in the -10°C stresses. The traits of shoot weight, number of leaves, relative leaf water content and electrical conductivity had an important role in the average of the groups. Decomposition into factors was done in separate stress levels. Four factors were selected in control levels, and -8°C stress, and three factors were selected in the -10°C, and -12°C stress levels.

In general, lines 35, 40, and 44 were recognized as lines with higher averages in all clusters, and lines 4, 5, 6, and 20 were placed in groups with lower averages. The traits of shoot weight, number of leaves, relative leaf water content, and electrical conductivity had an important role in the average of the groups. According to the final results of the study, lines 17, 24, 27, 35, 38, and 40 were recognized as resistant lines. Lines 3, 12, 18, 20, and 44 were found to be sensitive.

Drought stress is one of the most important abiotic factors that can limit plant growth and yield. The response of plants to water limitation has been evaluated based on genetic, biochemical, and morpho-physiological traits. Plants are constantly affected by drought stress and re-irrigation. Therefore, rapid and efficient recovery from water deficit stress may be one of the key determinants of drought adaptation in plants. The aim of this research was the evaluation of drought stress tolerance and recovery in lentil cultivars after stress conditions.

In order to evaluate the response of lentil cultivars to drought stress and re-irrigation, a factorial split-plot experiment based on a randomized complete block design with three replications was conducted in the greenhouse. Drought stress was applied at the flowering stage. The factors include 4 lentil cultivars (Namin landrace and Sepehr, Gachsaran, and Kimiya cultivars), drought stress (control (irrigation at 80% FC), medium stress (irrigation at 55% FC) and severe stress (irrigation at 30% FC)) and 3 sampling times (three and six days after drought and recovery (two days after re-irrigation)). All the plants were allowed to grow until the flowering stage (50 days after sowing) under well-watered conditions (80% FC (field capacity) of soil). Afterward, the plants were randomly assigned to three different groups and were exposed to different irrigation regimes including the control (well-watered and maintained at 80% FC), medium stress (watered and maintained at 55% FC), and severe drought stress (watered and maintained at 30% FC). The moisture content of the soil was controlled and maintained within a defined range using the weight method. Stress conditions were kept until the crop maturity and harvesting stage. The leaf samples from 5 seedlings of each pod were collected at 3 and 6 days after drought stress exposure, and two days after re-irrigation and used for physiological and biochemical analysis. The samples immediately were frozen in liquid nitrogen and stored at −80°C until analysis.

The results showed that adaptation to drought stress was closely related to the recovery ability of plants. Drought stress caused a decrease in chlorophyll a, chlorophyll b, total chlorophyll, carotenoid, protein and proline, yield, and yield components. The reduction of these traits was more remarkable at six days after stress. However, during the recovery time remarkable increase was observed in these traits. The results showed that the correlation between H2O2 and MDA was significant and positive. Furthermore, drought stress increased the amount of H2O2 and MDA, which increased the activity of antioxidant enzymes (catalase, polyphenol oxidase, and peroxidase). An increase in the intensity and duration of the drought stress also caused an increase in proline (63%), H2O2, (19%), and MDA (110%) content, and the activity of CAT (33%), PP0 (56%), and POX (24%) compared to the control treatment. An increase in the intensity and duration of the drought stress also caused an increase in H2O2 and MDA content and the activity of antioxidant enzymes. In addition, in the recovery conditions, a significant reduction in the destructive effects of stress (H2O2, MDA content) and the activity of antioxidant enzymes was visible. The results of the present study indicated that the effects of drought stress on lentil cultivars' yield and yield components (seed numbers, number of pods, 100-seed weight, and seed yield) were varied. Drought stress at the flowering stage decreased the number of seeds (20%) and pods per plant (37%), and 100-seed weight (16%), which led to 29% yield losses. Although the Gachsaran cultivar had the highest yield under normal conditions. However, under drought stress conditions Gachsaran and Sepehr cultivars showed the highest plant yield. On the other hand, the Namin landrace exhibited the lowest yield (40%) under stress conditions.

The water stresses markedly increased the reactive oxygen species (ROS) level and impaired the biosynthesis of the photosynthetic pigment, resulting in the reduction of plant growth and yield with fewer seeds and pods number per plant. However, re-irrigation (recovery) remarkably improved plant growth and reduced the negative effects of drought stress, such as reducing the amount of MDA and H2O2 and improving the activity of antioxidant enzymes and proline content. In conclusion, based on physiological traits Gachsaran, and Sepehr cultivars seem to be suitable cultivars for culture in the regions challenged with water deficit stress.

Legumes are common crops in arid and semi-arid regions. This study was conducted to investigate three levels of salinity stress on 18 lentil cultivars in the greenhouse and laboratories of Mohaghegh Ardabili University in 2016-2016 in a factorial manner in the form of a randomized complete block design with three replications. The results of comparing average lentil genotypes were done with Duncan's method at the five percent probability level. Correlation was done in separate stress levels. The dendrogram of cluster analysis divided the genotypes into three groups in control and 60 mM conditions and two in 120 mM conditions. Genotypes 7 and 5 were in the top group in all groupings. The relationship between ISSR molecular markers and physiological traits was calculated among genotypes. All traits in three levels of salinity stress had a significant correlation with some ISSR markers. A total of 22 positive markers for physiological traits were identified at the control level, 9 positive markers at the moderate stress level, and 23 positive markers at the severe stress level. Among the 21 studied ISSR primers, the P8A11 marker had the largest number of gene loci related to the studied physiological traits.

Plant breeders are constantly evaluating genetic diversity resources to lay a foundation for crop improvement. Tissue culture techniques have been applied successfully in many species to generate genetic variation in plants. Researches on lentil genetic diversity and parameter estimation under tissue culture conditions are relatively limited. Therefore, this research was carried out in three separate experiments for genetic parameters estimation and to understand the response of 14 lentil genotypes to callus induction using kinetin (Kin) and 2,4-dichlorophenoxyacetic acid (2,4-D) growth regulators; shoot production using thidiazuron (TDZ), 2-Isopentyladenine (2iP), benzylamino purine (BAP), and Kin growth regulators; and rooting the plants using NAA growth regulator. Results showed that the best treatments were 1 mg/L Kin and 2 mg/L 2,4-D for callus induction, 3 mg/L BAP, 4 mg/L Kin, and 2 mg/L TDZ for shoot regeneration, and 3 mg/L NAA for rooting. The 09S 83259-14ILL6994/ILL5480 and FLIP2010-40L-10770-ILL8119/ILL7686 genotypes were the best in term of callus, shoot, and root induction. The broad sense heritability of most of the traits was high demonstrating lower contribution of environmental factors in phenotypic variances. Overall, large diversity identified among genotypes in vitro conditions and high heritability estimates indicated that selection based on the measured traits including callus, shoot, and root induction will be efficient in tissue culture conditions.

Drought stress is a significant constraint in arid and semi-arid regions, such as Bushehr province, Iran, and it has a detrimental impact on wheat production.Given the scarcity of rainfall and recurrent droughts, comparing wheat cultivars is crucial to identify those with higher yield and resilience under drought stress conditions. Eight wheat cultivars suitable for cultivation in Dashtestan, Bushehr province, were planted under both irrigated and dryland conditions using a randomized complete block design (RCBD) and evaluated for various Agronomic traits in 2022-2023 years. The comparison of various traits under both irrigated and dryland conditions revealed the detrimental impact of drought stress on the evaluated wheat cultivars and the presence of genotype-by-environment interactions for a many of traits.While no significant statistical differences were observed for flag leaf length and the number of leaves per plant between the two cultivation environments, their absolute values were generally higher under dryland conditions. The average grain yield of the wheat cultivars was 4929.4 kg/ha under irrigated conditions and 1347.6 kg/ha under dryland conditions. Upon assessing yield-related traits and their components under irrigated conditions, Chamran, Mehregan and Aftab cultivars exhibited superior productivity. The yields of Chamran, Mehrgan and Aftab cultivars in irrigated cultivation were 3589.7, 3539.1 and 3315.8 kg per hectare, respectively. Among all the evaluated cultivars, Koohdasht exhibited the most favorable productivity in terms of the majority of yield-related traits and their components, including the number of productive tillers, the number of spikelets, the number of grains per spikelet, grain yield per plant, and grain yield per hectare. Additionally, this cultivar demonstrated remarkable resilience to drought stress and exhibited yield stability. Under dryland conditions, the cultivar exhibited a yield reduction of only 19.4% compared to irrigated conditions.

Rapid and efficient recovery from water deficit stress may be one of the key determinants of drought adaptation in plants. The present study was designed to investigate drought stress tolerance and recovery in promising lentil lines at the flowering stage. For this, a factorial experiment based on the completely randomized design was conducted with three replications. The factors included 6 lentil lines, drought stress (control (irrigation at 80% FC or 20% moisture depletion), medium stress (irrigation at 55% FC or 45% moisture depletion), and severe stress (irrigation at 30% FC or 70% moisture depletion)), and three sampling times (three and six days after drought, and recovery (two days after re-irrigation)). Drought stress caused a decrease in chlorophyll a, chlorophyll b, total chlorophyll, carotenoid, protein, yield, and yield components. The reduction of these traits was more remarkable at six days after stress. However, during the recovery time remarkable increase was observed in these traits. The results showed that the correlation between H2O2 and MDA was significant and positive. Furthermore, drought stress increased the amount of proline, H2O2, and MDA, which resulted in an increase in the activity of antioxidant enzymes (catalase, polyphenol oxidase, and peroxidase). An increase in the intensity and duration of the drought stress also caused an increase in H2O2 and MDA content and the activity of antioxidant enzymes. In addition, in the recovery conditions, a significant reduction in the destructive effects of stress (H2O2, MDA content) and the activity of antioxidant enzymes was visible. The results of the present study indicated that the effects of drought stress on lentil lines yield and yield components (seed number, number of pods, 100-seed weight, and seed yield) were varied. Drought stress at the flowering stage decreased the number of seeds and pods per plant, and 100-seed weight, which led to yield losses. Although line 2 had the highest yield under normal and drought stress conditions, line 1 exhibited the lowest yield under stress conditions. Based on the results of this experiment, line 2 seems to be a suitable line for culture in the regions challenged with water deficit stress.

 Water deficit stress is a serious threat to food security worldwide. Association mapping is a suitable method to identify the location of quantitative traits based on linkage disequilibrium, which is highly effective in describing complex genetic traits. This study aimed to evaluate the population structure and the SNP markers association morphological traits of spring wheat genotypes under water deficit stress conditions.

In this research, genome-wide association mapping was done for 111 spring wheat genotypes. Phenotyping evaluation was done in the form of a simple lattice design with two replications in the agricultural year 2020-2021 under non stress and water deficit stress conditions in the experimental field of Gachsaran Rain-fed Agricultural Research Station in Iran. Genotyping of the samples was done using SNP markers (15 K SNP array) in Trait-Genetic company in Germany. Each genotype was evaluated using 15 thousand SNP markers. The obtained SNP data were filtered in terms of MAF indices (minor allele frequency) and missing data (Missing data >10%). From each of the 21 pairs of bread wheat homologous chromosomes, seven SNP markers with no missing data and with suitable distribution were selected (147 markers in total) to determine the structure of the studied population (Q matrix) using STRUCTURE 2.3 software and the number of groups (K) was identified. The studied traits were flag leaf area, number of nodes, first internode length, number of tiller per plant, number of fertile tillers per plant, number of infertile tiller per plant, and spike weight. Genome-wide association mapping using TASSEL 5.0 software and the general linear model (GLM) method with Q matrix and decomposition into principal components with more than 80% justification (PCA) with 52 components was used.

The results of the analysis of variance showed that there was an acceptable genetic variation in terms of all studied traits and also the reaction of genotypes was different in facing water deficit stress.
In the water deficit stress condition, chromosome 2A had the maximum significant correlation with 39 and chromosome 5A had the minimum association marker with the trait with four. In the of water stress conditions, three bread wheat genomes (A, B, and D) accounted for 53, 36, and 11% of the frequency of marker-trait association, respectively. A and D genomes of bread wheat under water deficit stress conditions ratio to non-stress conditions assigned a more prominent role for the examined traits. In total in the non-stress and water deficit stress condition 180 and 111 marker trait association (MTA) were identified respectively. In the water deficit stress conditions, the number of correlations with markers for flag leaf area, first internode length, tiller of number per plant, and spike weight were 17, 24, 10 and 19, respectively. In the water deficit stress condition, the frequency of significant marker association more significant than in non-stress condition for traits of first internode and fertile tiller of number per plant. Three bread wheat genomes showed an unequal contribution for the studied traits in terms of the number of identified marker-trait associations (MTA). Also, genome A had a significant contribution to grain yield under water stress conditions with 74% of significant correlations between the marker and the spike weight trait.

 Finally, the MTAs identified in the present study can be used in the development of wheat breeding programs under water deficit conditions especially gene pyramiding or marker-assisted selection.

Wheat bread is one of the most important food products in the world. In terms of area under cultivation and production it ranks second among different products. Therefore, Genetic advances in sustainable wheat production can play a large role in global food security. The average wheat production in the world is reported Nearly 3.425 and its average production in Iran is 2.164 tons per hectare. Given the importance of wheat, Production of this product should be increased by cultivating modified genotypes with high grain yield. Wheat grain yield is affected by environmental conditions, genetic potential and its interaction. Identifying genotypes that have good performance and stability in different environmental conditions seems to be complex due to the strong interaction of genotype and environment. The change that occurs in the relative performance of genotypes in different environments is called genotype × environment interaction.Genotype × environment interaction is one of the most important issues in plant breeding which is of great importance in introducing and releasing modified varieties. Cultivation of genotypes in test environments during different years and places it has determined the stability of performance. And genotypes with less genotype × environment interaction are selected. Usually in breeding programs, Genotypes are known as compatible that the variance of their interaction with the environment is small. Among the multivariate methods, GGE biplot method is one of the most important methods for investigating the interaction of genotype × environment and determining stable genotypes.Water scarcity is the most essential limiting element of agricultural production, particulary in arid and semi-arid areas throughout the world. Evaluation of the bread wheat genotypes under different environmental conditions would be useful to identify stable and high yield potential genotypes.

15 new bread wheat lines along with Aftab cultivar were evaluated in a randomized complete block design with three replications in four experimental field stations (Gachsaran Khoramabad, Moghan and Gonbad) during three crop seasons (2017-2020). GGE biplot statistical method (genotype effect + genotype × environment interaction) was used to study stability of genotypes in the studied environments.

Results of combined analysis of variance indicated that the effects of environments, genotypes and genotype × environment interaction were significant. The results indicated that 91.49, 1.54 and 5.03 percent of total variation were related to the environment, genotype and genotype × environment interaction effects, respectively. The polygon-view of GGE biplot recognized five superior genotypes and four mega-environments so that the best genotypes within each environment were determined. Based on the hypothetical ideal genotype biplot, the line G7 with 3818 Kg ha-1 grain yield was the better genotype than other genotypes. Also this genotype showed the most stability and had the high general adaptation to all environments. Biplot of correlation among environments revealed that environmental vectors of Gachsaran and Gonbad were near to 90◦ so, these locations were different environments. The results showed that all environments had high discriminating ability so that could able to show differences between genotypes.

Generally, the results indicated that the line G7 with suitable mean seed yield and high broad adaptability was selected as superior line for further investigation to introduce the new commercial wheat cultivar under dryland conditions. Also, the Moghan environment was the nearest environment to ideal environment that had the highest discriminating ability and representativeness.

In order to achieve more high- yielding chickpea genotypes than the existing cultivars that have suitable traits such as seed yield, more number of pods per plant, coarseness of seeds , early maturity and other desired agricultural traits, 16 advanced chickpea genotypes selected from advanced tests comparing crop year yield 2015-2016 along with Adel and Azad witness figures for three crop years (2016-2019) in Gachsaran, Gonbad, Khoramabad and Ilam were planted in the form of a completerandomized block design with three replications. Composite variance analysis showed a significant effect of genotype, environment and genotype interaction in the environment (GEI)., Therefore, Biplot method was used to analyze the genotype × environment interaction. The first two principal components explained 32/50 percent (26.12 and 24.2%, respectively) of the total GEI changes. The polygon view of Biplot showed that genotypes 18, 9, 17 and 16 with higher than average performance and near the origin of Bioplate were genotypes with high general stablity. Also genotypes 5, 12 and 11 showed adaptation to many environments. The average tester view of Biplot also showed that genotypes 12, 18 and 9 were the closest genotypes to the ATC axis and therefore the most stable and also had high average yield in different environments. The ideal genotype view of Biplot showed that genotypes 5 and 12 at the closest distance from the Biplot origin were the best genotypes and genotypes 1, 2 and 13 were the most unfavorable genotypes in terms of seed stability and yield.According to the results, genotypes 5, 9, 12 and 16 were selected as promising genotypes and candidates for introduction.

Given the growing rate of population and its consequences, such as hungrier people and more demand for food, the Food and Agricultural Organization (FAO) has introduced potato (Solanum tubersum L.) as a food security plant. Thus, the need for expanding potato production is globally felt to manage the increase in food demands and food security (4, 10). The interaction between the genotype and the environment creates complexity in yield prediction and is a challenge for plant production and breeding programs. This study was conducted to achieve a stable high-yielding genotype that is adaptive to climatic conditions of potato-producing regions in Iran.

A total of 20 potato hybrides along with five commercial varieties (Savalan, Agria, Caesar, Luta and Satina) were evaluated in a randomized complete block design with three replicates in the agricultural research stations of five locations (Ardabil, Hamadan, Isfahan, Karaj, and Razavi Khorasan) in Iran, for two years (2016 and 2017). Each of the hybrids and control cultivars were planted in two rows with six meters long. The rows with inter row spacing of 75 cm and plant spacing of 25 cm was taken. The genotype yields were measured after the harvest. Combined analysis of variances was done and comparison of means was done by LSD at one percent probability level. The principal coordinate analysis was used to analyze yield stability.

The results of combined analysis of variance indicated that the effect of genotype, year, location and year – location, location – genotype, year – genotype and year - location – genotype interactions were significant at 1% level of probability. Therefore, the analysis of genotype - environment interaction was performed using multivariate analysis of principal coordinates. Compared to the grand mean, 10 environments under study were divided into two groups including three environments with higher performance than the total average and seven environments with lower performance than the total average. The most stable genotypes based on the MST (Minimum Spanning Tree) and distance from the center of plots were hybrids 1, 8 and Savalan cultivar in low cycles and hybrid 5 was identified in high cycles.

The hybrids 1 (35.57 ton/ha), 8 (35.05 ton/ha) and Savalan cultivar (33.52 ton/ha) which could be recommended for environments with the yield lower than the average mean of all studied environments. Also, hybrid 5 (41.21 ton/ha) was identified for environments with higher performance than the total average.

The main goal of genome-wide association study (GWAS) is to identify genes associated with a specific trait. In this mapping method, researchers compare the whole genomic DNA sequence of people in the community to find single nucleotide differences between them. Identifying and mapping effective genes in response to drought stress, in addition to understanding the molecular and physiological mechanisms, can provide breeders with a better understanding of the genetic structure of population and the genetic control of stress and it’s breeding in the studied population. In this experiment, the mapping genes controlling important agronomic traits of bread wheat under two non-stress and drought stress conditions using genome wide association analysis method was performed. The objective of the experiment was to analyze QTLs related to the response to water deficit and to identify markers related to some important and effective traits in bread wheat.

The plant materials of this experiment were 121 spring bread wheat genotypes, including 111 lines obtained from local spring bread wheat varieties originating from 28 countries from five different continents and 10 spring bread wheat genotypes from Iran and Pakistan, which were evaluated under two non-stress and water deficit stress conditions in the field. Genotyping for the samples was done using SNP markers (15 K SNP array) at TraitGenetic Company in Germany, and each genotype was evaluated using a set of SNPs. To determine the population structure, 147 SNP markers with no missing data and with suitable distribution on 21 homologous chromosome pairs of bread wheat (seven markers per chromosome) were used. To determine the possible sub-populations and studying the population structure, Bayesian method and Structure software V 2.3.4 were used, and then the average fixation index (Fst) and membership matrix (Q) were calculated with the same software. Genome-wide association analysis with the general linear model (Q+PCA) method was used to identify the markers related to the studied traits under non-stress and drought stress conditions with average data in TASSEL 5.0 software.

The results of analysis of variance showed that there was an acceptable genetic diversity in terms of all the studied traits between the genotypes and reaction of the genotypes to water deficit stress was different. Based on genome-wide association mapping, in total, 511 and 469 significant marker-trait relationships were identified under non-stress and water deficit stress conditions, respectively. The most significant marker-trait association under water deficit stress was revealed for plant height, flag leaf area, peduncle length, and spike yield on chromosomes 1A, 2A, 3B, and 2A, respectively. Also, five SNP markers at positions of 113.30, 25.02, 13.90, 43.10, and 71.97 cM on chromosomes 2A, 2A, 5A, 6A, and 6B, respectively, showed the highest significant associations with flag leaf length and area, plant height, peduncle length, and spike yield under water deficit stress conditions, respectively. Multi-trait loci were also identified on chromosome 2A for flag leaf length, width and area, plant height and spike yield under water stress conditions. Finally, 21, 12, 14, 44, 92 and 14 significant marker-trait associations (QTLs) were found for flag leaf length, width and area, plant height, peduncle length and spike yield under water deficit stress conditions, respectively, using genome-wide association study.

The results of this study provided valuable information on the genetic basis of the studied traits under water deficit stress conditions, which can be used in bread wheat breeding programs, including marker assisted selection (MAS).

Water scarcity is the most essential limiting element of agricultural production, particulary in arid and semi-arid areas throughout the world. The line × environment interaction is a major challenge in the study of quantitative characters because it reduces yield stability in different environments and also it complicates the interpretation of genetic experiments and makes predictions difficult. In this regard to analysis of line × environment interaction and determine the yield stability of bread wheat genotypes, 15 new bread wheat lines along with Aftab cultivar were evaluated in a randomized complete block design with three replications in four experimental field stations (Gachsaran, Khoramabad, Moghan and Gonbad) during three cropping seasons. Results of combined analysis of variance indicated that the effects of environments, line and line × environment interaction were significant, suggesting that the lines responded differently in the studied environment conditions. So, there was the possibility of stability analysis. According to mean rank of nonparametric stability parameters, the lines G1, G3, G7 and G15 with the lowest value for mean rank were stable, whereas lines G2, G6, G8 and G10 with highest values were unstable. Also, the results indicated that the nonparametric statistics Si(3), Si(6), NPi(2), NPi(3), NPi(4), KR and Top were associated with mean seed yield and the dynamic concept of stability. Therefore, these methods were suitable for selecting stable and high yielding lines in bread wheat. Finally, G7 line with average grain yield and high general stability was the top line of this test, which after additional tests can be a candidate for a new cultivar to introduce.

In this study, 12 promising lentil genotypes along with Kimia and Gachsaran cultivars were cultivated during three cropping years (2010-2011) in Gachsaran, Gonbad, Khorramabad and Moghan regions. AMMI analysis of variance showed that the effects of environment, genotype and their interaction and the first six main components were significant. Genotype 4 (1196 kg / ha) had the highest grain yield. 8, 11, 14, 10 and 6 genotypes based on ASV index, 9, 14, 11, 10 and 12 genotypes based on SIPC index, 9, 12, 14, 10 and 4 genotypes based on EV index, selected genotypes 14, 11, 9, 8 and 6 based on ZA index and 11, 14, 8, 6 and 1 genotypes based on WAAS index were selected as the most stable genotypes. The best genotypes in terms of yield and stability were  4, 8, 6, 10 and 7 genotypes based on ssiASV, 9, 4, 6 and 8 genotypes based on ssiSIPC index, 4, 9 and 12 genotypes based on ssiEV index, 6, 8, 9 and 4 genotypes based on ssiZA index and 4, 6 and 8 genotypes based on ssiWAAS index.  Based on AMMI1 biplot, 6, 4 and 8 genotypes with mean grain yield higher than total yield average and lowest values of IPCA1 were identified as stable genotypes with high general compatibility. In AMMI2 biplot, 4, 8, 9 and 12 genotypes high general stability and higher grain yield than the total average. The most stable genotypes in the superiority index were 4 and 6 genotypes. In general, based on different indices, 4, 6 and 8 genotypes had high yield in many environments and in the most methods, had good stability and could be candidates for introduction of new cultivars.

Lentil as a legume crop is one of the most important food crops in developing countries (Karimizadeh & Mohammadi, 2010). In most cases, the interaction between environment and genotype occurs, complicating selection for improved yield among genotypes (Sabaghpour et al., 2004). A cultivar or genotype is considered to be more adaptive or stable if it has a high mean yield but a low degree of fluctuation in yielding ability when grown in diverse environments (Finlay & Wilkinson, 1963). The response of different genotypes in different environments and thus the evaluation of genotype interaction in the environment are of particular importance to researchers in plant genetics and breeding, which can help plant breeders to evaluate genotypes more accurately and select the best one. The purpose of this study was to identify and introduce superior genotypes in terms of yield and yield stability among the lines obtained from preliminary yield test.

Sixteen advanced lentil genotypes along with the control genotypes i.e. Kimia and Gachsaran selected from the advanced yield trial of the 2012-13 cropping year were used as planting material in Gachsaran, Khorramabad and Ilam areas for three years (2013-2016) in a randomized complete block design with three replications. Analysis of variance was performed separately in each environment and then Bartlett test was used to evaluate the homogeneity of experimental errors. Then the combined analysis of variance was performed on seed yield. Stability analysis were performed using environmental variance (S2i), coefficient of variation (CVi), Shukla's variance (2i), Wrick equivalence (Wi), Plaisted statistic ( ), Plaisted and Peterson statistic ( ) and superiority index (Pi) and nonparametric methods, , , , TOP and mean of rank.

Simple analysis of variance showed genetic differences among the genotypes. The combined analysis of variance was performed after Bartlett test, which confirmed variance homogeneity of experimental errors (χ2 = 9.5; P = 0.33). The combined analysis of variance indicated the significant effects of genotype, year, location and interactions of year × location, genotype × location and genotype × year × location. The mean seed yield of genotypes showed that out of 18 studied genotypes, seven genotypes produced higher yields than the average yield of genotypes in the all environments (1566.39 kg.ha-1), so that the highest seed yield were seen in the genotypes 15 and 16, followed by genotypes 8, 12, 11, 5 and 2. Based on environmental variance (S2i), the genotypes 3, 7, 6 and 13 and based on the coefficient of environmental variation (CVi), the genotypes 3, 7, 6 and 15 were identified as stable genotypes. Plaisted and Plaisted and Peterson methods identified the genotypes 4, 9, 2, 10 and 3 as stable genotypes. Wrick equivalence and Shukla variance also introduced the genotypes 4, 9, 2, 10, 3 and 12 as stable genotypes. The Lin and Binns superiority index identified the genotypes 16, 8, 15, 12 and 11 as the most stable genotypes. The genotypes 4, 2, 3, 10 and 9 were the most stable genotypes based on nonparametric index. Based on the index, the genotypes 1, 2, 3, 4, 6, 7, 9, 10 and 18 and based on statistics, the genotypes 3, 4, 10, 9, 1, 7, 2 and 6 were stable genotypes. Based on Fox nonparametric index, the genotypes 16, 11, 2, 8, 12, 13 and 14 were stable genotypes. The genotypes 12, 2, 9, 16, 11, 8, 4 and 3 were more stable based on the total Kong rank. The principal component analysis to evaluate the relationship between seed yield and stability indices showed that seed yield had the highest correlation with MID, TOP and PI. Therefore, these three indices can be used as the best indices to identify superior genotypes in terms of seed yield and stability.

In general, the genotypes 2, 5, 8, 11, 12, 13, 15 and 16 gave higher average yield or equal to Gachsaran control seed yield and were also stable. The genotype 16 and 11 were the most stable genotypes based on MID, TOP and PI and also had the highest seed yield and could be candidates to be released as new cultivars.

In order to evaluate durum wheat lines in terms of frost stress tolerance, 45 promising durum wheat lines resulting from crosses between different parents were assessed using stress tolerance indices. This experiment consisting of greenhouse and laboratory tests was performed in the Faculty of Plant Breeding of MohagheghArdabili University in the 2017-18 crop year. In this experiment, wheat lines at seedling stage were studied. Durum wheat lines were planted in a factorial design based on randomized complete blocks with three replications. Plants up to 3 to 5 leaf stage were kept in greenhouse conditions. The pots were then taken to a cold room at 4°C to prepare for stress. Based on biological performance, tolerance indices (TOL), stress sensitivity (SI), average production (MP), stress tolerance index (STI) Geometric mean of production (GMP), Harmonic mean of production index (HARM), Yield reduction index (Yr) and Root and shoot tolerance index (TI) were calculated. The results of analysis of variance showed that the studied lines had significant differences in terms of MP, GMP and HARM traits and Yr and Ti indices were significant in stress. The interaction of the line in stress was insignificant for all indices. Due to the correlation between performance and indices, the most appropriate indices were MP, GMP, HARM and TOL. Comparison of mean lines and stresses were examined separately. The maximum correlation coefficient was related to the relationship (0.999) between STI index with SI, Yr with SI and Yr with STI. In general, GMP, MP and HARM indices are the most appropriate indices to evaluate the stress tolerance of lines at all levels. The study and TOL index were suitable for higher stress levels. Study of lines by indices using cluster analysis, detection function analysis and factor analysis showed similar results. The results of cluster analysis and factor analysis with the first three factors introduce lines 25, 26, 27, 28, 31, 36 and 39 as the most tolerant lines and lines 11, 13, 15, 17, 18 and 19 as the most sensitive. They were identified. In factor analysis, the first three factors explained 97.52% of the available variance and the indices of STI, SI, Yr, Ti aerial parts, MP, GMP and HARM They had the highest value and were the most effective indicators in the distribution of lines. According to the three-dimensional diagram of line distribution, the highest factor coefficients based on the first three factors were related to lines 5, 7, 25, 26, 27, 28, 31, 36 and 39.

Cold tolerance in wheat is one of the most important factors effective in the field of winter damage in Iran. Freezing and laboratory tests were carried out in the greenhouse and plant breeding laboratory of Mohaghegh Ardabili University in 2018-20 to achieve the set goals. The plant materials used included 45 promising durum wheat lines. Durum wheat genotypes were planted in a randomized complete block design with three stress levels. The results of the variance analysis of LT50 showed a significant difference between the genotypes at the probability level of 1%. LT50 varied between -0.754 and -26.609 values. The survival percentage of plants decreased with increasing stress. In clustering based on the LT50, the genotypes were divided into 5 groups. The dendrogram obtained from the cluster analysis based on all the traits divided lines in the control level, -8 °C, -10 °C, and -12 °C into 8, 6, 9 and 7 different groups. Four factors were identified in the control level, 5 in the first stress, 6 in the second stress, and 5 in the third stress level. To evaluate the relationship between the measured traits and RAPD molecular markers, stepwise multiple regression analysis was performed and significant relationships were observed. LT50 showed a correlation with 9 markers. Finally, according to the tests conducted, lines 1, 2, 3, 4, 5, 6, 7, 8, 10, and 23 were recognized sensitive lines and lines 11, 12, 13, 14, 15, 17, 18, 19, 21, 29, 31 and 27 were recognized as resistant.

Durum wheat (Triticum turgidum L.), like most other crops, is affected by various stresses. Therefore, cultivars that, in addition to the ability to produce higher yields, can maintain their yield potential in different years and locations are considered superior cultivars. In order to obtain high-yielding and stable genotypes of durum wheat, 16 lines with two control cultivars Dehdasht and Seymareh were evaluated in four locations of Gachsaran, Gonbad, Khorramabad and Moghan based on randomized complete block design with four replications in three cropping seasons (2013-2016). Combined analysis of variance indicated a significant effect of genotype, environment and genotype by environment interaction. Genotypes G6 and G18 had the highest and lowest grain yield, respectively. Based on parametric methods, genotypes G3, G5, G15, G13 and G16 and based on non-parametric methods, genotypes G1, G3, G4, G5, G15 and G3 were the most stable genotypes. The most stable genotypes based on the total Kang sum-rank were genotypes G15, G5, G6 and G1. The Selection index of ideal genotype (SIIG) was used to integrate all indices into one index, based on which genotypes G5 and G15 were the superior genotypes with the highest SIIG index and grain yield. Based on all indices, genotypes G5 and G15 were the most stable genotype in terms of grain yield and can be used in cultivar introduction processes.

There are different methods for study the genotype × environment interactions and determining stable genotypes such as parametric, non-parametric and multivariate methods. In this research, 19 selective genotypes from advanced trials of durum wheat at 2011-2012 agronomic year, have been cultivated with Dehdasht check cultivar for three growing years (2012-2015) in five locations (including Gachsaran, Gonbad, Khorramabad, Moghan and Ilam) in a randomized complete block design with four replications in each location. Combined analysis of variance indicated significant effects of genotype, environment and interactions of genotype × environment. In parametric uni-variate methods, genotypes 7, 12, 18 and 20 were determined as stable genotypes. In non-parametric uni-variate methods, genotypes 2, 7, 12, 13, 18, 19 and 20 had the lowest genotype × environment interaction and they were determined as stable genotypes. In AMMI method, genotypes 2, 7, 12, 19 and 20 had the lowest rank in different environments and highest grain yield, and these genotypes seems more stable genotypes. It can be concluded that genotypes 7, 12, 18 and 20 could be considered as promising genotypes and candidate for introducing new durum cultivar.

The aim of this study was to evaluate the efficiency of yield stability analysis models and to assess genotype × environment interaction effect on grain yield of 20 durum wheat genotypes for identifying high yielding and adapted genotypes by BLU and AMMI models using experimental data of four cropping cycles (2009-2013) in five filed stations in warm rainfed regions of Iran. The results of Likelihood ratio test (LRT) showed that the effect of genotype and genotype × environment interaction on grain yield was significant. Therefore, the best linear unbiased predictors (BLUPs) analysis was considered appropriate for these data. According to AMMI stability value (ASV) index, genotypes 11, 8, 14, 19 and 16 had more yield stability. Simultaneous selection index (ssiASV) based on ASV identified genotypes 8, 11, 4 and 10 in terms of grain yield and yield stability as superior genotypes. Given that by using these simultaneous selection indices, genotypes with different patterns for multivariate trials can be considered similar, the results can be misleading. Based on the first two main components, AMMI2 biplot diagram identified genotypes 19, 3, 14 and 11 as genotypes with yield stability. The results of the mosaic diagram showed that the contribution of genotype and genotype × environment interaction were 14.94% and 85.06% of the total variation, respectively. Based on weighted average of absolute scores (WAASBY) index using BLUP analysis, genotypes 4, 8, 11, 10 and 7 were identified as high yielding with yield stability. In general, usin mixed model as well as all the components in calculating the WAASBY index, it can be concluded that this index is superior to other indices.

Several methods have been proposed to analyze the interaction of genotype in the environment. In this research 16 selected durum wheat lines with two check cultivars (Dehdasht, Seimareh) were evaluated in randomized complete block design experiment with three replications in four regions of Iran including Gachsaran, Gonbad, Khoramabad and Moghan during three cropping seasons 2013-2016. Combined ANOVA indicated significant effect of year, location, genotype, year×location and genotype×year×location interactions at 1% probability level on durum wheat grain yield. Screet test indicated the first four principal components had high contribution of GEI, so that the PC1 and PC2 was explained 26.60% and 15.33% of GEI variation. Mosaic plot revealed that 20.76% of total sum of squares is illustrated by genotype sum of squares and 79.24% by genotype×environment interaction sum of squares. Heat-map plot was also indicated G1, G3, G6, G15 and G16 had high grain yield in many of environments. The polygon view of biplot indicated G10,G5,G6,G7 and G4 were as stable genotypes to the tested environments according to the closest distance to biplot origin, while G8 and G12 was not adaptable to any environments. The simultaneous studying of the effect of genotype and GE interaction by average tester coordinate view of biplot illustrated that G15, G13 and G6, in addition to high grain yield, were also more stable than other genotypes. Ideal genotype view of biplot indicated G15, G13, G16, G2 and G6 were as most desirable genotypes. In conclusion, G15, G13, G3 and G6 with high mean yield and stability performance can be used in selection/ recommendation process of cultivar.

In this research, 12 selective advanced genotypes of lentil with Kimia and Gachsaran checks were grown for three growing years (2010-2013) in four locations including Gachsaran, Gonbad, Khoramabad and Moghan using randomized complete block design with three replicates in each location. The heatmap plot indicated the variation of seed yield of genotypes in different environments. Mosaic plot showed that the portion of sum squares of genotype (G) and sum squares of genotype by environment interaction (GEI) in total sum of squares (TSS) were 17.53% and 82.47%, respectively. The likelihood ratio test (LRT) indicated that the effect of GEI was significant on seed yield and therefore for evaluation of stability of genotypes, singular value decomposition (SVD) was performed on the matrix of best linear unbiased prediction (BLUPs) of GEI. The screet test showed that the first five principal components had a significant contribution in the GEI matrix derived from BLUP, as the first and second principal components explained only 32.28% and 26.95% of the GEI variation, respectively. The bilot of first principle component (PC1) of the environment versus nominal yield showed that genotypes 8, 4, 3, 14 and 7, due to the lowest scores of the PC1, had a small share in the GEI and were more stable. Biplot of seed yield versus WAASB (weighted average of absolute scores) placed genotypes in four regions, so that the genotypes 4, 6, 8, 9, 10 and 12 in the fourth region were very productive and stabile due to the large value of response variable (high seed yield) and high stability (low values of WAASB). Identification of genotypes with WAASBY (weighted average of the stability (WAASB) and mean performance (Y)) showed genotypes 4, 6, 8 and 9 as high yielding and stable, and therefore can be candidate for cultivar introduction.

Nineteen durum wheat lines selected from preliminary yield trial along with Dehdasht as a control cultivar, were evaluated in a randomized complete block design with three replications at five regions in Iran including Gachsaran, Gonbad, Khoramabad, Moghan and Ilam during three cropping seasons of 2012-2015. Combined analysis of variance indicated significant effects of year, location, genotype and year × location, year × genptype and year × location × genotype interactions on durum wheat grain yield. Screet test indicated the first foure principal components had high contribution of geenotype × environmentinteractions (GEI), so that the PC1 and PC2 was explained 22.0% and 18.71% of GEI variation. Mosaic plot revealed that 11.54% of total variation is illustrated by genotype and 88.46% by GEI effects. Heatmap plot was also indicated G16, G19 and G20 had high grain yield in many of environments. The polygon view of biplot indicated G8, G7, G20 and G13 were as stable genotypes to the tested environments according to the closest distance to biplot origin, while  G18, G15 and G11 was not adaptable to any of environments. The simultaneous studying of the effects of genotype (G) and genotype-environment interactions (G×E) by average tester coordinate (ATC) view of biplot illustrated that G2, G7, G1 and G20, in addition to high grain yield, are also more stable to the tested environments than the other genotypes, and can be proposed as stable genotypes. G1 and G2 are placed close to the ideal genotype, are most desirable than the other genotypes. The vector view of GGE biplot indicated discriminating and representative environments (E5 and E3) are good environments for selecting generally adapted genotypes. Consequently, G7, G8, G20 and G13 with strong stability and high grain yield can be used in selection/ recommendation process of cultivar.

Knowlege of genotype × environment interactions helps breeders to select the best genotypes for different regions.The main objective of this research was to obtain high yielding durum wheat genotypes selected from several elite lines so that can be compatible to climatic conditions of the tropical and subtropical rainfed regions and superior to check varieties. In this research, 16 durum wheat genotypes selected from advanced yield trials along with two check cultivars, Dehdasht and Seimareh, were studied in a randomized complete block design with four replications for three cropping seosons (2013-2016) in four locations (Gachsaran, Gonbad, Khoramabad and Moghan). Combined analysis of variance indicated that main effects of genotype, year and location and interactions of year × location and genotype × year × location were significant at 1% probability level. AMMI analysis of variance also showed that the first four principal components were significant and explained about 81% of the total variance of genotype × environment interaction. The highest grain yield was observed in genotype 6 (3592 kg.ha-1) and the lowest in genotype 18 (3229 kg.ha-1). Environments 10, 8, 11 and 12 with long vectors had high resolution ability. The results of this study based on AMMI indices showed that genotypes 3, 5, 6, 13 and 15 with higher grain yield than the total mean in most environment were stable and superior genotypes in this experiments. Among them, genotype 13 with suitable general adaptability can be a promising genotype and a candidate to introduce a new cultivar for tropical and subtropical rainfed areas.

In order to evaluate the drought stress tolerance of durum wheat genotypes, an experiment was conducted at Gachsaran Dryland Research Center, Iran during 2017-18. A total of 18 durum wheat genotypes were studied in a randomized complete block design with four replications under rainfed and supplementary irrigation conditions. The results of combined analysis of variance showed that supplementary irrigation significantly increased grain yield. In supplementary irrigation condition, the genotypes 16, 17 and 18 and in rain fed condition the genotypes 4, 16 and 17 had the highest grain yield per hectare. The stress tolerance indices were calculated based on yield of genotypes in rain fed and supplementary irrigation conditions. With considering of significant positive correlation between grain yield in the rain fed condition (YS) with relative drought index (RDI), mean productivity index (MP), stress tolerance index (STI), yield index (YI), geometric mean productivity index (GMP), new drought resistance index (DI), improved stress tolerance index (MSTI), non-Stress Environmental Stress Index (SNPI) and harmonic Mean Index (HARM), these indices are suitable for identification of stress tolerant lines. Stress susceptibility index (SSI), yield stability index (YSI), abiotic stress tolerance index (ATI) and stress sensitivity percentage index (SSPI) did not play a significant role in the differentiation of genotypes. On the other hand, there was a significant positive correlation between grain yield in supplemental irrigation with TOL, MP, YI, MSTI, SSPI, ATI, SSI and STI. Grouping of genotypes using cluster analysis method showed that the genotypes were classified into four distinct groups and genotypes 4, 16 and 17 were in the fourth group with the highest tolerance indices compared to the other. Genotypes 7, 8 and 10, which had the lowest yield and also the lowest amount for the most indices, were identified as the most susceptible to drought stress. The results of factor analysis confirmed the above results.