علی اکبر اسدی

This study was carried out in order to investigate the interaction effect of genotype × environment and to identify the stable genotypes of wheat in the saline areas of the country.

18 selected genotypes from wheat yield comparison experiments along with two control varieties, Narin and Barzegar, in five regions including Birjand, Kerman, Yazd, Zabul and Isfahan on bais randomized complete block design with 3 replications and during two years crop (2019-2021) was evaluated. For grain yield, combined analyses of variance were performed and stability analysis was performed using AMMI multivariate method and GGE biplot analysis in order to identify genotypes that have high yield potential, high yield stability and general compatibility.

Based on the first and second main components of AMMI stability analysis, G19, G5 and to some extent G10 genotypes with the least amount of interaction were recognized as stable genotypes. Based on GGE biplot analysis, G18 and G10 genotypes were among the superior genotypes in terms of yield and yield stability. Also, G18 and G3 genotypes were located at a short distance from the ideal genotype, respectively. This analysis divided the environments into three environmental groups and the genotypes into five genotypic groups. Yazd 1 and 2, Kerman 1 and 2, Birjand 2 and Zabul 2 were in the first group, Isfahan 1 and 2 and Birjand 1 were in the second group and Zabul 1 were in the third group. In the first group, G3, G18, G8, G9, and G10, in the second group, G1 and G2, and in the third group, G16, G17, and G7 genotypes had the highest yield. Therefore, it is possible to introduce G3, G8 and G18 genotypes in Yazd and Kerman regions and G1 and G2 genotypes in Isfahan region as genotypes with private adaptation.

Considering grain yield and yield stability, G18 (Elvira/Milan//Arg) and G3 (DH-209-1557 F3,Vee"s"/Nac//1-66-22/3/Dove"s"/Buc"s"//2*Darab) with a yield of 4.89 and 5.3 tons per hectare respectively, were selected as superior genotypes that can be introduced as new cultivars for regions with saline conditions in the country.

Oil revenues in the form of rentier economy have been one of the main factors of political stability in Saudi Arabia. The decline in oil revenues in Saudi Arabia in the coming decades and the possibility of an economic crisis in this country has been one of the main factors behind the decision of the Saudi leaders to compile and implement the Vision 2030 during the reign of King Salman. Considering the centrality of economic goals and programs in the 2030 vision document, the main objective of this research is to evaluate the effects and consequences of the new Saudi economic trends on political stability. The main question is how the new economic trends and developments of Saudi Arabia in the era of King Salman affect the political stability in this country? In this regard, the operational perspective of David Saunders has been adopted as the conceptual framework of the research. Also, the new economic condition of Saudi Arabia has been analyzed with emphasis on five components, which are: economic growth and prosperity; unemployment rate; Housing situation; Inflation rate and consumer spending index and; the situation of poverty and inequality. The main finding of this study is that the new policies and programs of the Saudi government in the light of 2030 vision have caused a relative progress in economic conditions and leads to Maintaining political stability in the country. However, the social and political changes caused by the development of the non-oil economy construct grounds and conditions that will lead to new instabilities.

Considering the limitation of arable land, the most effective factor in increasing the production of beans is to conduct research in the field of agronomic and breeding in order to increase the yield per unit area. In order to determine the effect of genetic or environmental factors on a trait, different genotypes should be studied in multiple environments. The varying responses of genotypes in different environments, coupled with the interaction effects of genotype in the environment, render the selection of genotypes from one environment to another challenging. Therefore, examining genotypes in diverse environments holds significant importance in determining the appropriate breeding strategy for the release of adapted cultivars to the target environments. Considering the role of genetic diversity in the advancement of breeding programs, the study of morphological and phenological characteristics that determine yield is a suitable method to achieve selection criteria for improving yield and improving and introducing compatible and high-yielding cultivars. Seed yield is a complex trait that is controlled by a large number of genes, and selection based on yield alone is often not successful. For this reason, one of the ways to identify high-yielding genotypes is to study traits that have a significant relationship with seed yield, so that by selecting or removing them, the accumulation of desirable genes in improved cultivars can be done. Considering the climatic conditions of different regions of Iran, this research was conducted in order to investigate the yield and yield components of red beans (Phaseolus vulgaris L.) in 4 major bean growing regions in the country.

14 red bean genotypes along with Yaghout, Ofogh and Dadfar varieties (Controls) were studied in randomized complete block design with three replications in four research stations of Khomein, Broujerd, Shahrekord and Zanjan for 2 crop years (2018-2019). At the time of harvest, each plot was harvested separately and the yield of each plot was weighed after threshing. After collecting data related to yield and its components, combined variance analysis, simple variance analysis related to each location and mean comparisons were performed. Also, correlation analysis and step-by-step regression were used to investigate the relationship between yield and its components.

The results showed that there is a significant statistical difference between the studied locations, years and genotypes in terms of all traits at the probability level of 1%. The significance of genotype × location interaction for all traits made it necessary to analyze the variance separately in each investigated location. The significance of double interactions indicates the relative instability of the traits of different genotypes in different times and places. The highest seed yield was observed at Khomein station in G12, Yaghot and G4 genotypes, Borujerd in G14 and G13 genotypes, Zanjan in Yaghot and G12 genotypes and Shahrekord in G12 and G16 genotypes. Based on the days to maturity the Ofogh variety, G9, G16 and to some extent G4 genotypes, and based on its yield and yield components, the G12 genotype and Yaghuot and Dadfar varieties were introduced as desirable genotypes. Correlation analysis showed that there is a positive but non-significant correlation between seed yield and number of pods per plant, number of seeds per pod and number of seeds per plant. Regression analysis showed that the traits of number of seeds per pod, days to maturity, number of pods per plant and 100 grain weight are included in the regression model as effective traits and among these traits, days to maturity with a negative coefficient and the number of pods per plant with a positive coefficient were more effective in seed yield.

This study showed that according to the yield and its components in red beans, it is better to introduce a specific variety for each region. Based on the number of days to maturity, Ofogh (check) and G9, G16, and to some extent G4 genotypes, and based on its yield and components, genotypes G12 and G5 can be reported as favorable genotypes. Besides the seed yield, the yield components including the number of pods per plant, the number of seeds per plant, and the 100 grain weight also played a role in selecting better lines; therefore, indirect selection through the selection of these traits can be effective in increasing seed yield. Finally, it can be concluded that apart from seed yield, yield components including the number of pods per plant, the number of seeds per plant, and the weight of 100 seeds can also be effective in selecting superior genotypes; therefore, indirect selection through these traits can be effective in increasing grain yield.

Genotype × environment interaction is one of the complex issues in plant breeding programs to introduce high yielding and stable genotypes, which is evaluated using multi-regional experiments before the release of new cultivars. The presence of genotype × environment interaction causes the yield of cultivars to be affected by the environment and leads to differences in the yield of cultivars in different environments. AMMI and GGE-Biplot models are very important among the multivariate methods and have high resolution in identifying high yielding and stable genotypes. The objective of this study was to evaluate the stability of promising bread wheat genotypes and to identify high yielding and stable genotypes in the cold climate of the Iran.

Fourtheen wheat genotypes with winter and intermediate (facultative) growth type along with Mihan, Heydari, Zarrineh and Zare varieties as controls (a total of 18 genotypes) were investigated in randomized complete block design with three replications in research stations of Karaj, Hamadan, Mashhad, Jalgarokh, Miandoab, Ardabil, Arak, Eqlid, Tabriz and Qazvin. To analyze the data, first analysis of variance was seprately done in each year and location, and then combined analysis of variance was performed for grain yield after confirming the homogeneity of the variances of experimental errors. AMMI and GGE-Biplot methods were used to investigate the stability of the studied genotypes. AMMI stability parameters and simultaneous selection indices were also calculated based on these parameters.

The results of combined analysis of variance showed that the main effect of environment and genotype and the interaction of genotype × environment accounted for 47.2, 9.8 and 28.3 percent of the total sum of squares, respectively. Genotypes G7, G8, G12, G2 and G1 had the highest grain yield and genotypes G15, G18, G10, G13, G14 and G16 had the lowest grain yield among the studied genotypes respectively. The results of AMMI analysis showed the existence of significant differences between environments, genotypes and their interactions. The first 12 significant principal components of AMMI analysis explained 98% of the genotype × environment interaction variance, and the first and second principal components explained a total of 46.27% of this variance. Based on the AMMI1 biplot, genotypes G8, G3, G1 and G4 and environments E9 and E5 with the higher grain yield than average grain yield and the lowest value of the first principal component were recognized as the most stable genotypes and environments. AMMI2 biplot did not identify a specific genotype as the genotype with general compatibility, however, G3 and G4 genotypes showed somewhat better general compatibility than the others. The simultaneous selection indices based on AMMI parameters identified G8, G12, G1, G4, and G3 genotypes with the lowest total rank as the stable and high yielding genotypes, respectively. The results of GGE-Biplot method based on biplot of the average yield and stability, introduced G8, G4, G3 genotypes followed by G1 as the most stable genotypes, due to grain yield higher than the average of the studied genotypes. Which-won-where biplot pattern divided the studied genotypes and environments into five and three groups, respectively, so that G12, G11, G3 and G4 genotypes in Karaj and Miandoab and G5, G7 and G8 genotypes in Jalgerokh and Mashhad showed better adaptation in both years. According to the biplot of the ranking of genotypes, there was no ideal genotype, but G8, G3, G5, G7 and G4 genotypes with the smallest distance from the hypothetical ideal genotype were identified as the best genotypes.

The results of this study showed that there is a little difference between AMMI and GGE-Biplot analyzes and both methods presented the same genotypes as superior genotypes. However, it is more logical to select genotypes using simultaneous selection indices based on AMMI analysis parameters, because all significant components are included in the calculation of these parameters. Therefore, based on simultaneous selection indices, genotypes G8, G12, G1, G4 and G3 with the lowest total rank are introduced as stable and high yielding genotypes.

This research was conducted in order to evaluate the reaction of different barley genotypes and to understand better the genotype × environment interaction effect in different areas of the country's cold climate, and to select stable genotypes.

Four barley genotypes, including Jalgah, Mehtab, Bahman, CB-96-10, along with four imported genotypes in five stations of Karaj, Zanjan, Jalgarokh, Ardabil, and Miandoab in the form of complete randomized block design with three replications in two consecutive years of evaluation became.

In Eberhart and Russell method, G1, G4, and G6 genotypes; in parametric methods G1, G4, G6, and to some extent G7 genotypes and; in nonparametric methods G1, G4, and G6 genotypes were identified as stable genotypes. According to the comparison of average performance, sustainability criteria and agricultural characteristics and general compatibility using AMMI analysis, G6, G1, and G7 genotypes were recognized as the most suitable and most compatible genotypes, respectively and can be considered as climate compatible cultivars in all cold regions of the country. Genotypes G2, and G3 are also considered varieties with private compatibility.

According to the results, G6, and G1 genotypes can be considered as the most compatible genotypes in all cold regions of the country, and G2, and G3 genotypes can also be considered as genotypes with high private compatibility in high potential regions.

Rainfed barley is mainly cultivated in cold and cold temperate areas of Iran, and a large part of the barley cultivation area is facing the problem of lack of precipitation and lack of proper distribution. The cold is one of the main limiting factors in barley production in drylands of cold regions of the country, which prevents it from increasing its cultivation area. Considering that different cultivars show different reactions to environmental conditions, therefore, evaluation of cultivars' reactions in exposure to environmental changes is an important issue in selecting breeding cultivars. Genotype × environment interaction is the main reason for the differences in the adaptation of cultivars in different environments (Clevland, 2001). The various methods used to investigate the interaction can be traced back to AMMI and GGE biplot (Khamari et al., 2018). The AMMI analysis identifies genotypes and environments about each other and the studied environments by locating genotypes and environments on the biplot. In the GGE biplot method, the effects of genotype and genotype interaction × environment are graphically investigated; Also, genotypes can be evaluated based on yield in separate environments, all environments, stability and yield composition, and private and general adaptation. (Yan & Tinker, 2005). This study aimed to analyze the interaction of genotype × environment in barley genotypes using multivariate methods to evaluate genotypes, environments, relationships between genotypes and environments, and also to determine stable genotypes in terms of yield. In this study, 12 promising barley genotypes along with 3 cultivars of control Ansar, Abidar, and Sararud1 were studied in rainfed conditions in a randomized complete block design with four replications in dryland research stations in cold and temperate cold regions of Iran for three years (2018 to 1420). After determining grain yield composite analysis of variance was performed. AMMI and GGE biplot analysis were used to evaluate the stability of genotypes. After performing AMMI analysis, stability analysis parameters and simultaneous selection indices were calculated. Combined analysis of variance showed that simple and interaction effects were significant at a 1% probability level. This was the reason for the difference in environmental conditions in the stations and the years under test. The main environmental effect and genotype × interaction had the highest share of total squares observed in the experiments with 83.7% and 8.2%, respectively. AMMI analysis showed that genotypes G14, G10, and G9 had low interaction and with a near-average yield could be introduced as genotypes with general adaptation. In contrast, G1, G2, G4, and G13 genotypes with the highest yield were introduced as genotypes with private adaptation. Based on stability indices based on AMMI analysis and simultaneous selection index in a total of the calculated parameters, genotypes G1, G11, G2, G13, and G3 have the lowest total and can be selected as stable genotypes with high yield. Selection of control genotypes G1, G2, and G3 in this method shows the accuracy of the calculations and estimations. Based on the GGE biplot method, the G9 genotype had the highest general stability and in the next stage, G11, G2, G4, G1, and G13 had the highest yield with relatively low stability. This method introduced genotypes G9, G2, and G11 as compatible genotypes. These genotypes were introduced as desirable genotypes with high mean yield and high yield stability. In the next step, genotypes G1, G3, and G13 were included. According to the results of two analyses, genotype G9 can be introduced as the most stable genotype, and genotypes G1, G11, G2, G13, and G3 as genotypes with high adaptability and yield. Considering that a lot of time and money is spent on cultivar breeding, this process requires that the best method be used to analyze the stability and adaptation of cultivars to select the high-yield genotypes with the least interaction with the environment and if there is a specific adaptation, certain genotypes are introduced for specific regions. Therefore, due to the multiplicity of stability analysis methods, it is better to examine the results of experiments by several methods to be more confident about identifying and introducing superior genotypes and just doing one particular method does not seem reasonable.

 Yield under stress conditions has never been an accurate criterion for selecting drought-tolerant genotypes, and the goal of preparing drought-tolerant cultivars has always been to create cultivars that relatively tolerate stress better compared to other genotypes and in the same agricultural conditions, they show less yield loss. In most studies, only the seed yield is considered for the field selection of crops, while some researchers believe that in order to be more efficient in breeding compatible and superior cultivars in arid and semi-arid regions, the indicators that are effective in identifying the stability of cultivars under drought stress conditions should be identified and used them as selection index in addition to seed yield, therefore, the relative uield status of genotypes in drought stress conditions and water conditions is a starting point for identifying and selecting genotypes for improvement in dry environments. In semi-arid regions where the distribution of rainfall is not proportional, the yield potential in stress conditions is not considered the best criterion for drought tolerance, but in addition to yield stability, the comparison of yield in stress and optimal conditions is a more suitable criterion for the reaction of genotypes to moisture stress. Due to the fact that most of the studies related to indicators in crop species and especially wheat are done only in one place and one year and finally in a specific environment and the results are generalized to all environments and the variable effects of year and place are not taken into account in the calculation of indicators. In this regard, this research was carried out to evaluate the genotypes of autumn wheat in terms of drought tolerance, to select the best drought tolerance indices and to identify drought stress tolerant cultivars in different regions of the country's cold climate.

In this research project, 20 genotypes under normal irrigation and water deficit conditions in the form of randomized complete block design (RCBD) with three replications in the research stations of Karaj, Mashhad, Miandoab and Arak, in the crop years of 2017-2018 and 2018-2019 were investigated. In order to investigate the drought tolerance of the genotypes, different tolerance and stress sensitivity indices were determined for the genotypes under investigation and the genotypes were grouped based on the sensitivity and tolerance to drought by each of these indices. Principal component analyzing was also used in order to summarize the data and draw a diagram based on the first two components and identify the desired indicators. Finally, biplot analysis was used to group genotypes and indices based on drought tolerance criteria.

Stress decreased the Yield of genotypes, but the amount of reduction was different in different genotypes. The average grain yield for all genotypes under normal irrigation conditions and drought stress at the end of the season was 7.002 and 5.215 tons per hectare, respectively, which showed that the stress conditions caused a decrease of about 25% in grain yield compared to normal irrigation conditions. The environmental and agricultural conditions of different regions and the studied years caused differences in the estimated indices so that the ranking of genotypes changed based on different indices in different years and regions therefore, it is better to carry out studies on stress tolerance indices in several places and several years so that the process of functional changes of different cultivars and genotypes is correctly determined and as a result, there is more confidence in the calculated tolerance and sensitivity indices. Genotypes G4, G2, G16 and G5 showed higher yield in both conditions. STI, MP, GMP, MSTI1, MSTI2, YI, HM, and RR had a positive and significant correlation with grain yield under normal and stress conditions, and therefore, they can be used for more favorable selection of drought tolerant genotypes.  Principal component analysis showed that indicators can be grouped using the first two components. The first principal component explained 53.16% of the changes in the total data and had a positive and significant correlation with yield in normal and stress conditions as well as MP, STI, GMP, YI, MSTI1, MSTI2, HM and RDY indicators. Due to the high correlation of this component with yield under normal and stress conditions, this component was named as the yield potential component under normal and stress conditions. The second estimated component justified 42.33% of the total data changes and showed a positive and significant correlation with yield in normal conditions and SSI, TOL, ATI, SSPI and RR indices. It seems that this component is able to identify genotypes that have high yield under stress conditions. Biplot analysis showed that HM, MP, STI, MSTI1, MSTI2, GMP, RDY, YI, and SNPI indices had a positive and high correlation with performance under stress and normal conditions. So that the sharp angles between these indices showed their positive and very high correlation with each other. RDI and YSI indices had a negative correlation with performance in normal conditions and RR, SSI, TOL and SSPI indices had a negative correlation with performance in stressful conditions.

 In order to achieve varieties resistant to drought stress by using indices, in order to prevent the mutual effects of the environment in the genotype, the experiments should be carried out in many years and places so that the effects of the environment in the calculated indices can be reduced and more reasonable results can be obtained. Finally, G4, G2, G16, G5 and G17 genotypes can be selected as genotypes with high performance under normal and stress conditions.

The evaluation of the genotype × environment interaction effect provides valuable information regarding the yield of plant cultivars in different environments and plays an important role in evaluating the stability of the yield of breeding cultivars. Genotype × environment interaction effect, especially in stressful environments, are important limiting factors in the introduction of new cultivars; therefore, it is very important to know the type and nature of the interaction effect and reach the verities that have the least role in creating interaction effects. Various methods have been introduced to evaluate the interaction effect, each of which examines the nature of the interaction effect from a specific point of view. The results of different methods may not be the same, but the best result is obtained when a genotype with different evaluation methods shows similar results in terms of stability. The purpose of this research was to evaluate the genotype × environment interaction effect in experiments conducted in different environments, to determine the relationships between genotypes and environments and to introduce the most stable red bean genotypes.

In this research, 14 red bean lines along with Yakut, Ofog and Dadfar control cultivars were cultivated in the form of a randomized complete block design with three replications in Khomein, Borujerd, Shahrekord and Zanjan research stations for 2 crop years under the same conditions. After combined variance analysis, according to the significance of genotype × environment interaction, AMMI and GGE-Biplot analysis methods were used to determine the compatibility and stability of genotypes. After AMMI Analysis, the stability parameters of AMMI were calculated. In addition to the AMMI stability parameters, the simultaneous selection index was also calculated for each of the indices, which was the sum of the rank of the genotypes based on each of the AMMI stability indices and the average seed yield rank of the genotypes in all environments.

The significance of the double and triple interaction effects of genotype with year and place (environment) in this study showed that genotypes showed different responses in different environments, and in other words, the difference between genotypes is not the same from one environment to another, and in these conditions, the stability of grain yield can be evaluated. The contribution of about 2.5 times the interaction effect of genotype × environment from the total sum of squares, compared to the effect of genotype, indicated the possibility of the existence of mega-environmental groups in which some genotypes show their maximum performance potential in those environmental groups. Genotypes G12, G5 and G17 had the highest seed yield among the genotypes with yields of 3288, 3136 and 3111 kg per hectare, respectively. AMMI analysis showed that the first to seventh main components were significant at the 1% probability level, and despite the significance of all model components, the first and second main components had the largest contribution to the expression of genotype × environment interaction (66.5%). Based on AMMI1 biplot Genotypes G4, G5, G16, G17 and G12 had the highest values (positive and negative) of IPCA1. In contrast, genotypes G8, G3, G2, G7 and G11 had IPCA1 values close to zero. However, only the genotype G11 showed a performance higher than the average total yield and therefore it can be introduced as a stable genotype with high general compatibility. Based on AMMI2 biplot, genotypes G2, G7, G3 and to some extent G8 and G13 were introduced as stable genotypes, but only G13 genotype had a higher yield in all environments, so this genotype can be introduced as a stable genotype with good yield.; also, every two years, the same place under investigation had a high correlation with each other, so that Bro1 and Bro2 environments on the one hand and Kho1 and Kho2 environments and finally Zan1 and Zan2 showed a high positive correlation (the same effect) to create a mutual effect. In total of the simultaneous selection indices calculated based on AMMI analysis, genotypes G11, G17, G7, G13 and G12 were introduced as stable genotypes with high yield. GGE-Biplot analysis based on average yield and stability showed that genotypes G1, G2, G3, G8 and G7 had the highest general stability compared to other genotypes despite having the lowest yield. On the other hand, G12, G5 and G17 genotypes had the highest yield with less stability. No ideal environment was observed. But Kho1, Kho2 and Sha1 environments are closer to the ideal environment than other environments and they can be used to distinguish the studied genotypes to some extent. On the other hand, G12 genotype can be considered as a desirable genotype that has high average yield and high yield stability. In the same way, G17, G5 and G11 genotypes were in the next stage compared to the ideal genotype and to some extent they can also be considered as desirable genotypes.

According to all the results, G12 genotype can be considered as a desirable genotype that has a high average yield and also has yield stability, and G17, G5 and G11 genotypes were in the next stage.

In this study, various statistical methodologies, including Additive Main effects and Multiplicative Interaction (AMMI) and Best Linear Unbiased Prediction (BLUP), were employed to identify high-yielding rainfed barley genotypes that are suitable for the cold and rainy regions of Iran. The experimental design comprised 25 barley cultivars and lines, along with three check cultivars, arranged in a randomized complete block design with four replications over three crop years (2017-2020). The AMMI analysis revealed that certain genotypes, specifically G15 and G21, demonstrated stability and adaptability across diverse environments, consistently yielding higher than other genotypes. Following the estimation of best linear unbiased predictions and conducting a stability analysis via the AMMI method, it was found that the highest yields were recorded in genotypes G6, G7, G15, G21, and G22, whereas the lowest yields were associated with genotypes G12, G25, G26, G27, and G28. According to the BLUP indices, genotypes G6, G15, G21, G20, G22, G17, G7, G9, and G19 were identified as superior in terms of grain stability and yield relative to the other genotypes. In the stability assessment utilizing a third-type biplot (yield versus WAASB (Weighted Average of Absolute Scores of the Best) index), it was noted that genotypes G2, G9, G10, G14, G16, G17, G19, G20, and G22 exhibited both high yield and stability. Furthermore, genotypes G4, G62, G7, G9, G10, G15, G16, G17, G19, G20, G21, and G22, which demonstrated the highest WAASBY (Weighted Average of Absolute Scores of the Best Yield) values, were classified as stable and high-yielding. Ultimately, when the first principal components in the AMMI analysis or GGE Biplot account for a lower percentage of genotype-environment interaction, it is advisable to employ methodologies that incorporate all significant principal components to effectively identify superior genotypes.

In recent years, there has been a notable trend in Iran's environment, characterized by China's increasing presence and influence in the Middle East, as well as its endeavors to strengthen relations with the Persian Gulf countries. Given America's longstanding presence in the Persian Gulf and its strategic partnerships with Arab countries, China's involvement has significantly contributed to the complexity of regional issues. For instance, Saudi Arabia, the largest Arab country in the Persian Gulf, has expanded its interactions with China focusing on energy, and in practice, China has become Riyadh's first commercial and economic partner.The main aim of this article is to provide a comprehensive analysis of the essential factors and significant changes that have shaped Saudi Arabia's foreign policy towards China during the reign of King Salman. It addresses the following questions: what are the determining elements of Saudi Arabia's foreign policy regarding China in the years after 2015, and how have these factors led to the prioritization of extensive cooperation and a comprehensive strategic partnership between the two countries?

Methods and Conceptual Framework: 

To identify and explain the underlying factors behind Saudi Arabia's shift in foreign policy toward China, this study adopts the conceptual framework of Neoclassical Realism and uses an explanatory methodology. Structural realists emphasize the importance of changes in the international system and the balance of global power in shaping countries' foreign policies. However, neoclassical realists emphasize the significance of internal factors in understanding a government's foreign policy. Therefore, a substantial part of this research focuses on investigating the impact of domestic variables on the strategic choices made by the Saudi government towards China.

The findings of this research indicate that the rise of China and its consequences at international and regional levels are the main and independent variables shaping Saudi Arabia's new foreign policy towards China. However, while international factors have a substantial impact, there are also specific domestic factors that serve as intermediate variables in Saudi Arabia's strategies toward China. At this level, several crucial factors contributed to the development of Saudi cooperation with China during King Salman's era. These factors included the capacities of national power, the rise of authoritarianism, unity and consensus among Saudi elites, the emergence of new Saudi nationalism, the centralization of authoritarian development, and evolving perceptions of Saudi leaders regarding recent international and regional transformations.Saudi Arabia and China's relations have shown a positive and steadily growing trend over the past three decades. However, it is during the reign of King Salman that these relations have entered a new stage, known as a comprehensive strategic partnership. This partnership is characterized by increased cooperation in the commercial, economic, and political spheres. The emergence of a new generation of Saudi leaders, led by Muhammad bin Salman, has significantly influenced the transformation of Saudi policies. These leaders possess a deep and comprehensive understanding of international trends and conditions, which has played a pivotal role in reshaping Saudi Arabia's approach. Particularly noteworthy is their realistic understanding of the shift of power from West to East, their pragmatic approach in relations with regional and global actors, and the new definition of national interests.

In the new circumstances, despite the persistence of some uncertainties regarding the overall direction of Saudi Arabia's foreign policy, the continuation of the country's military-security cooperation with the United States remains evident. However, the prioritization of authoritarian development by Saudi leaders is a significant driver that will lead to the continuation and development of Saudi Arabia-China relations in the coming years.

Qatar and the United Arab Emirates stand as pioneers in the cultivation of soft power and national branding within the Persian Gulf region. Their leadership recognizes the pivotal role of branding in safeguarding and advancing national interests and security and continues to invest significant efforts in this domain. Recently, Saudi Arabia has also turned its attention towards this sphere, emerging as a unique and somewhat complex case study in terms of branding capacities and policies. Especially since the coming to power of the current king of Saudi Arabia in 2015 and the subsequent power consolidation of Crown Prince Mohammed bin Salman, Riyadh has embarked on a new trajectory of domestic and foreign policies. Changing Saudi Arabia’s image and its national branding is one of those. For this purpose, this paper aims to explore the fundamental question: “What role does national branding play in Saudi Arabia’s overarching policies, and how has this evolved during Salman’s reign?”

This research is theoretically and conceptually grounded in Simon Anholt’s six-dimensional conceptual framework for national branding, which encompasses Exports; Governance; Culture; People; Tourism; and Immigration and Investment. Methodologically, the study adopts a descriptive-explanatory approach. Utilizing a qualitative method, data has been collected through library research and online resources.

Over the past decades, Saudi Arabia has carved out a distinctive international reputation, effectively establishing its national brand on the global and regional stages. This image is primarily shaped by three key facets. Firstly, the nation’s substantial capacities and capabilities in oil production and export, with Saudi Aramco leading as the world’s largest exporter of this commodity. Secondly, Saudi Arabia’s significant religious standing within the Islamic world, which extends its influence globally. Thirdly, its financial and economic aid to various countries, particularly within the Islamic world. This assistance is often manifested in the form of humanitarian or developmental aid. Thus, the triad of traditional elements shaping Saudi Arabia’s reputation and national brand, thereby crafting a unique anddistinctimage of the country on the international stage, encompasses its oil industry, religious influence, and humanitarian and developmental contributions.Under the reign of King Salman, Saudi leaders have initiated a mission to reshape the past image, striving to project a new and positive depiction of the nation. While some previous policies have been maintained, significant changes have ushered in a new era for national branding. Central to this is the Vision 2030 document, which serves as the primary framework guiding efforts to effect a fundamental shift in Saudi Arabia’s position and global image. The document, aimed at diversifying Saudi Arabia’s oil-dependent economy, represents a strategic endeavor to shift the traditional perception of Saudi Arabia as merely an oil-rich country. Its three core tenets - fostering a vibrant society, building a thriving economy, and cultivating an ambitious nation - encapsulate the renewed image that Saudi leaders aspire to project, underscoring their commitment to transformative change.The Saudis have gradually come to the conclusion that the traditional religious-conservative image and the presentation of Saudi Arabia as an oil country cannot contribute much to the soft power and the country's capacities. For this, the introduction of a new image as a country with a diverse economy, an open and moderate society, and efficient and modern governance, is considered an important part of the efforts of Saudi statesmen. In this context, a variety of strategies have been implemented with the goal of advancing to a new level of national branding. The most significant of these include economic branding, along with the promotion of Saudi Arabia as a nation with a diversified economy; cultural branding and social reforms, aimed at presenting Saudi Arabia as a moderate and non-extremist society; The development of tourism and branding that leverages cultural heritage; Enhancing governance to fortify the national brand; Sports branding; and Urban branding and the creation of new cities. These initiatives are crucial in achieving this objective.Saudi leaders recognize the necessity of shifting global public opinion towards their country, including through national branding. They are striving to craft a positive, modern, and distinguished image of Saudi Arabia. These efforts involve acknowledging past weaknesses and negative perceptions, while also considering Riyadh’s regional competition with smaller neighbors such as the United Arab Emirates and Qatar. In this context, traditional capacities and structural barriers are also deemed significant.  Indeed, the political determination of the Saudis and their efforts to implement new policies and significant innovations are crucial.

Despite some accomplishments and positive outcomes in terms of national branding, there are several challenges that Saudi Arabia faces. These include:The absence of a clear definition of branding as a distinct issue in national strategies;The continued dominance of oil in Saudi Arabia’s economic structure, perpetuating the country’s image as an oil-based economy;The potential escalation of internal conflicts and divisions due to cultural and social reforms, which could lead to new crises; and.The increasing political authoritarianism within the Saudi government and the limited participation of civil society in national branding.

It is necessary to develop new sustainable high-yielding cultivars in drought-prone areas. The yield of cultivars in different environments is different and their yield rating varies from one environment to another. Reduction of interaction effects and production stability in different environments is one of the goals of breeding program and introducing cultivars in different regions. The purpose of this study was to evaluate the genotype ×environment interaction effect in different cold climate regions of Iran and to determine the superior genotypes and introduce the most stable wheat genotype in these conditions.

To investigate of the stability of 17 wheat genotypes along with Mihan, Heydari and Zarineh cultivars (check cultivars) under water deficit conditions, these genotypes were tested in a randomized complete block designwith three replications in the research stations of Karaj, Hamedan, Mashhad, Jalgarokh, Miandoab and Ardabil in the two crop years 2018 to 2019. In order to check the stability of genotypes, AMMI and GGE-biplot analysis were used.

AMMI analysis showed that the first ten main components were significant and in total explained nearly 97% of the changes in the genotype × environment interaction and the two main components, the first and the second, contributed 46% to the expression of the genotype × environment interaction. Based on SSiASV and SSiWAAS indices G2, G19 and G4 were identified as the best genotypes, respectively. The G3 was the most stable genotype. GGE-biplot analysis showed that G16, G1, G3 and G7 had the highest general stability compared to other genotypes. G16 can be considered as a desirable genotype that has high average yield and high yield stability. G12 and G9 genotypes were in the next ranks. On the other hand, the ideal environment was not observed, but the environments of Mashhad, Ardabil and Karaj in the first year can be introduced as the closest environments to the ideal environment for the selection of superior wheat genotypes in water deficit condition in the cold climate of the Iran.

G3 was the most stable genotype, and then G16, G1 and G7 had the most general stability compared to other genotypes, so that G16 was identified as the desired genotype with high average yield and high yield stability. The environments of Mashhad, Ardabil and Karaj in the first year can also be used to select superior genotypes.

Studying the nature of the genotype×environment interaction provides the possibility of identifying stable and compatible genotypes for breeders, and it has always been one of the important issues in the production and release of new stable and high-yield cultivars in breeding projects. The current research was conducted with the aim of identifying the response of genotypes in each of the studied areas, based on AMMI and GGE biplot models and better understanding of the genotype×environment interaction and determining the level of public and private stability of cultivars. Twenty facultative and winter wheat genotypes were cultivated and evaluated in nine regions (18 environments) during the two crop years of 2017 to 2019 in a randomized complete block design with three replications. In order to analyze the compatibility and stability of genotypes, AMMI and GGE biplot models were used. The results of AMMI analysis showed that the main effect of genotype, genotype×environment interaction effect and the first six principal components were significant at the 1% probability level. The first and second principal components explained about 51% of the sum of squares of the genotype×environment interaction effect. Based on SSiASV and SSiWAAS simultaneous selection indices, genotypes G13, G1, G3, G2, G6, G16, and G7 were identified as the best genotypes. Biplot analysis showed that the genotypes G1, G3, and G6 with average yield had the highest general stability compared to other genotypes. None of the genotypes can be considered as desirable genotypes that have high average yield and high yield stability, but genotype G16 and in the next stage the genotypes G1 and G13 were close to the ideal genotype. Polygon biplot model divided environments into two environmental groups (macro environment) and genotypes into four groups. The first environmental group included Kar1, Kar2, Qaz1, Qaz2, Ham1, Ham2, Mia2, Egl1, Jol2, Ard2, and Ara1 environments, with G16, G17, and G4 genotypes having the highest yield. The second environmental group included Mas1, Mas2, Mia1, Ard1, Jol1, Ara1, and Egl2 environments, and in this macro environment G2, G11, G7, and G12 genotypes had the highest yield. Finally, the examination of the relationships among the environments showed a very high correlation among the investigated environments, which indicated the similar behavior of the genotypes in the most of the tested environments.

In order to investigate the effect of plant density on yield and physiological traits of two cultivars of lentils Kimia and Bilesvar, a factorial split plot experiment with a randomized complete block design with three replications was conducted at Khodabandeh Dryland Research Station in Zanjan in two cropping years 2015 to 2017. Three spacing between rows (15, 20 and 25 cm) and six levels of seed density (150, 175, 200, 225, 250 and 275 seeds per m2) were considered. Between the two years of the experiment, a significant difference was observed in terms of total yield at the 1%. In terms of the parameters of leaf temperature, stomatal conductance, substomatal CO2, photosynthesis rate at 5% and total yield at 1%, significant difference was observed between planting intervals. Significant difference was observed between the studied genotypes in terms of leaf temperature and total yield at 1% and water use efficiency at 5%. As the distance between the rows increased, the leaf temperature increased, so that the highest and lowest temperatures were observed at the distance between the rows of 25 and 15 cm, respectively. The highest and lowest stomatal conductance were obtained at the distance between rows of 15 and 25 cm, respectively. Increasing the row spacing from 15 to 25 cm decreased the total yield. At 15 cm row spacing, the lowest leaf temperature and the highest stomatal conductance, substomatal CO2 and photosynthesis rate were observed, and the highest yield was also obtained in this row distance; Therefore, by choosing this row distance, in terms of these physiological traits, the farm will be in more favorable condition and eventually a higher yield will be obtained. The highest and lowest yields were related to seed densities of 225 and 150, respectively. Of course, the density of 225 was not significantly different from the density of 200. Therefore, for the cultivation of dry lentils, the best density was 200 seeds per square meter and the best row spacing for planting was 15 cm.

Various theories have been proposed to analyze behaviors in the international arena. Some theories, such as mainstream theories, are influenced by the linear approach of classical science, considering the international system as mechanical, closed, and far from interaction. Others, such as complex systems theory, considered the concept of nonlinear relationships and interaction used in the analysis of biological systems to explain the behavior of the international system and tried to explain human behavior as a biological system. Interaction is the main concept in explaining the behavior of natural and living systems that causes chaotic behaviors. Because the concept of interaction in human systems, as the highest example of biological systems due to the power of thinking, is a different concept. The purpose of this article is to introduce the interaction in the international system with features; Flow-creation and interpretation of information, purposefulness, sensitivity to the initial condition and impact and effectiveness. Studies show that due to the differences between human interaction and the interaction of other living organisms, Chaotic behaviors in the international system have characteristics; Simplicity in complexity, certainty in uncertainty, and order in disorder.

Existence of genotype × environment interaction for quantitative traits such as grain yield can limit the selection of superior genotypes for the development of improved cultivars. In order to calculate the interaction of genotype × environment, breeders evaluate genotypes in several environments to identify genotypes with high yield and stability. This experiment was performed to investigate the interaction of genotype in the environment on 11 bean genotypes using parametric and nonparametric methods and GGE bioplot model to evaluate genotypes and environments, determine the relationship between genotypes and environments and identify the ideal genotype.   

In this experiment, 9 lines of pinto beans along with Ghaffar cultivars and Cos16 lines (11 lines in total) were performed in a randomized complete block design with three replications in order to achieve high yield and marketable bean cultivars. Parametric and non-parametric methods were used to select stable genotypes with high yield and GGE bioplot analysis was used to select superior genotypes that are compatible with regional environments.

There was a significant interaction between genotype and environment, indicating significant differences in the response of genotypes to different environments of the experiment. In parametric methods G4, G8, G9 and to some extent G2 genotypes and in nonparametric methods G2, G8, G3, G4 and to some extent G9 genotypes were introduced as stable cultivars. Biplot analysis showed that G1 genotype in Zanjan in first and Second years and G2 genotype in Khomein n first and second environments showed the highest yield. G11 and G7 genotypes with the longest distance to the ATC line had low yield and low yield stability. None of the genotypes were found to be desirable genotypes with average yield and high yield stability, but G2 genotype and later, G4 genotype was a short distance from the ideal genotype. None of the studied environments is close to the ideal environment and therefore none of them can be considered as representative of environments for genotype segregation.

Zanjan in first and Second years and G2 genotype in Khomein n first and second environments have the highest yield. No ideal genotypes were observed, but two genotypes, G1 and G4, can be introduced as superior genotypes.

The most important factor limiting plant growth in rainfed conditions is water, and since most of the land in Iran is located in arid and semi-arid regions, determining the relative drought tolerance in crops is of particular importance. By evaluating the genotypes of each plant that are able to provide relatively acceptable yield under low water conditions, they can be grown with more confidence in arid and semi-arid regions.

In order to investigate the effect of drought stress and selection of drought tolerant genotype in barley, 15 different genotypes under two conditions of normal irrigation and rainfed conditions in randomized complete block design with four replications in 2020-2021 in Khairabad and Khodabandeh research stations Of Zanjan province were studied.

The results of combined analysis showed that there was a significant difference between genotypes in terms of grain yield between normal irrigation and rainfed conditions and rainfed conditions reduced grain yield by more than 50% compared to normal irrigation conditions. Under normal irrigation conditions, the highest yields belonged to G3, G10 and G11 genotypes, respectively, and under rainfed conditions, despite the lack of significant differences between genotypes, the highest yields belonged to G3, G6 and G2 genotypes, respectively. There was a significant difference between the studied genotypes in terms of all quantitative indices of drought tolerance (except YI and SNPI) as well as yield under stress. Correlation analysis of indices with yield under normal irrigation and rainfed conditions showed that there was a significant correlation between MP, STI, GMP, MSTI2, HM and RDY indices with yield under normal irrigation and rainfed conditions. Correlation of indices with yield under normal irrigation and rainfed conditions showed that there was a significant correlation between MP, STI, GMP, MSTI2, HM and RDY indices with yield under normal irrigation and rainfed conditions. Principal component analysis based on indices showed that the first component and second components explain 79.95% and 14.049% of the total changes. The bioplate diagram of the components showed that genotypes G1, G2, G3, G9, G10, G11, G12 and G13 were in the positive part of the first component diagram and these genotypes had good adaptation in normal irrigation conditions. Among these genotypes, G2, G3 and G13 genotypes were in the positive part of the second component diagram, so these genotypes can be introduced as high-yield genotypes in both irrigated and rainfed conditions. Cluster analysis showed that G1, G2, G3, G9, G10 and G11 genotypes were in the same group and these genotypes can be considered as drought tolerant genotypes in terms of calculated indices.

There was a significant correlation between MP, STI, GMP, MSTI2, HM and RDY indices calculated with yield under normal irrigation and rainfed conditions. Therefore, these indices can be used to select stress-tolerant genotypes. Grouping of barley genotypes based on calculated quantitative and qualitative indices showed that G2, G3 and G13 genotypes were identified as superior genotypes in full irrigation conditions and the most tolerant barley genotypes to low water stress.

The alpine ultramafic-mafic Sargaz-Abshour complex in the SW of Baft, as a part of the ultramafic-mafic/ophiolitic intrusives of the Hajiabad-Esfandagheh region, comprises of tectonite units (foliated harzburgite and porphyroclastic dunite), layered ultramafic-mafics, large isotropic gabbro bodies and scattered microgabroic-diabase dykes. In this complex, olivines are the main mafic residual phase in porphyroclastic dunites and harzburgites, as well as magmatic cumulate phase in chromitites, ultramafic and layered mafics. In most cases, composition of olivine is chrysolite and in chromitite is forsterite. Their compositions are consistent with mantle peridotites, abyssal peridotites, alpine massives and magmatic cumulative series. Due to the refractory nature in porphyroclastic dunites and primitive crystal phases in chromite and layered cumulative dunites, percentage of forsterite content is high, and will reduced with the progress of differentiation in the mafic layered rocks. These changes may indicate the residual nature of olivine and evolution of Mg-rich mantle-derived magma. Different source and origin of magmas  have been compareable with other ultramafic-mafic complexes of Esfandagheh region and alpine ultramafic-mafic intrusions which distinct from ophiolites.

 

Considering the development of cultivation and production of legumes and the importance of genetic studies in plant breeding, identifying the genetic potential of these plants is very important. Knowledge of genetic diversity and relationships between genotypes is important for understanding available genetic variability and the potential to use it in breeding programs. According to previous studies on lentils, it was found that improving the yield potential per unit area can be one of the important criteria for increasing the production of this plant. Increasing the yield per unit area is possible mainly by modifying and creating high-yielding cultivars, improving the characteristics and increasing the quantitative and qualitative potentials. Breeders and plant physiologists believe that in order to be more productive in improving compatible cultivars in areas with limited water resources, recognizing the agronomic traits affecting grain yield under stress conditions will be of great importance; Therefore, indirect selection based on physiological traits has been proposed as a complement to the selection of cultivars with high yield potential. Considering the different reactions of photosynthetic indices under stress conditions, it is important to know the genotypic diversity of photosynthetic indices under culture conditions and their relationship with grain yield. Despite numerous studies on the role of physiological traits in drought tolerance in crops, recent studies on lentils, especially in Iran, are limited. This study was designed and carried out with the aim of determining genotypes with desirable physiological and yield traits and the relationship between these traits and photosynthesis under rainfed conditions.

 

In order to study the photosynthetic and yield parameters of lentil plant in rainfed conditions, selected advanced lentil lines with control cultivars were studied in a randomized complete block design with 3 replications in Khodabandeh dryland research station in Zanjan Province in two cropping seasons 2018 to 2020. Physiological traits included photosynthesis per unit leaf area, stomatal conductance, transpiration rate, photosynthetic activated radiation, sub stumatal CO2, leaf temperature, photosynthetic water use efficiency, mesophilic conductivity and water use efficiency. Plant height, 100-seed weight, number of pods and yield (kg/ha) were also measured for each genotype in each plot. Analysis of variance and comparison of means were performed using Duncan's test at 5% probability level. Finally, correlation analysis and stepwise regression analysis were performed with the variables of photosynthesis rate and total yield.

 

The results showed that there was a significant difference in photosynthetic active radiation, leaf temperature, photosynthetic water use efficiency, mesophilic conductivity, water use efficiency, 100-grain weight and yield between two years of experiment. The studied genotypes showed significant differences in leaf temperature, photosynthesis, plant height, 100-seed weight and yield, which indicated the high genetic diversity of these genotypes in terms of these traits. The interaction effect of genotype per year was not significant in all studied traits, which shows that the trend of changes in these traits between genotypes during the two years was the same. Among the studied genotypes, G5 genotype is a genotype with superior agronomic characteristics that can be recommended as a cultivar with high yield potential. Photosynthesis rate showed a significant negative correlation with leaf temperature and significant positive correlation with transpiration rate, stomatal conductance, mesophilic conductivity, water use efficiency and 100-grain weight. 100-seed weight showed a significant negative correlation with leaf temperature and a significant positive correlation with stomatal conductance, photosynthesis, mesophilic conductivity and water use efficiency. These results show that drought and lack of water in the soil have the greatest effect on reducing photosynthesis and plant yield at different phenological stages (seedling, flowering and podding) of the plant. Finally, regression analysis showed that stomatal conductance and sub stumatal CO2 explained the changes in photosynthesis.

 

The results showed that for lentils, two traits of stomatal conductance and sub stumatal CO2 concentration may explain the changes in photosynthesis. Genetic diversity is very important for crop breeding and higher diversity of genotypes provides a better chance of producing a variety of desirable cultivars. The observed genetic diversity in traits can help select superior genotypes based on phenotypic expression and can be used in breeding programs to improve economically important traits. Finally, among the studied genotypes, G5 genotype was found to be a genotype with superior agronomic characteristics that could be recommended to the farmers to improve lentil yield.

In order to determine the genetic parameters and heritability of different traits of sugar beet and also the relationship between traits, full-sib and hybrid genotypes were evaluated in separate trials at Miandoab Agricultural Research Station under salinity and normal conditions in 2017. In full-sib trial performed under salinity condition, root yield, sugar yield, white sugar yield, leaf relative water content, root dry weight, and root/shoot ratio, and under normal condition, root yield, sugar yield, root/shoot ratio and specific leaf weight had higher genetic variance than environmental variance and consequently had higher broad-sense heritability. In hybrid trial performed under salinity condition, for white sugar yield, potassium content, potassium/sodium ratio, nitrogen content, and leaf area, and under normal condition, for root yield, sodium content, potassium/sodium ratio and nitrogen content, genetic variance was high and as a result, broad-sense heritability was above 0.5. Therefore, these traits can be considered in breeding and selection programs. Under salinity condition, the narrow-sense heritability ranged from 0.16 to 0.66. The highest narrow-sense heritability was related to molasses sugar with 0.66, nitrogen content with 0.43, sugar yield with 0.38 and leaf relative water content with 0.38, respectively. Therefore, it can be concluded that these traits are partially controlled by additive effects and can be used for selection of different degrees. Under normal condition, the narrow-sense heritability ranged from 0.01 to 0.29 and the relative water content with 0.29 showed the highest heritability. Under salinity stress, heritability of root yield and sugar yield was moderate; so yield could be improved by long-term selection and hybrid production. The results of regression and path analysis showed that under salinity and normal conditions, when the root yield was considered as dependent variable, specific leaf weight, leaf area, and relative water content explained the variation among which the leaf area had the highest positive direct effect on root yield.

Improving crop yield under drought stress conditions is one of the most important goals of plant breeding. Drought tolerance is usually evaluated by plant yield under low water stress, but due to the influence of other traits under stress conditions, this trait alone cannot indicate drought tolerance of genotypes. Therefore, it is better in breeding programs to identify drought tolerance traits of genotypes and guide programs based on yield and other important physiological and biochemical traits. Determining the values of genetic variance and the additive part and the dominance of genetic variance in controlling traits under both normal and stress conditions is the basis for deciding how to use germplasms in different conditions, which researchers use based on different methods to estimate them. The aim of this study was to estimate the genetic parameters of quantitatively yielding and qualitative traits of sugar beet under drought and normal stress conditions for use in breeding programs.

To determine the genetic parameters of different quantitative and qualitative traits of sugar beet, two series of genotypes full-sib and hybrid (resulting from the meeting of some of full-sibs as paternal lines and two lines SC C2 and SC 261 as maternal lines) were studied under both drought and normal conditions, in Motahari's Research, Karaj in 2017. After testing, different physiological and qualitative traits of genotypes were measured in two environments and finally genetic parameters were calculated.

In full-sib experiment under stress conditions, sodium content, potassium/sodium ratio, nitrogen content, alkalinity coefficient, syrup purity, molasses sugar, leaf area, shoot fresh weight, shoot dry weight, root dry weight, root mass ratio, and root/stem ratio had more genetic variance than environmental variance, so they also had higher broad sense heritability. Therefore, due to high genetic variance, these traits can be used in breeding and selection programs. Under normal conditions, the amount of genetic variance was low in all traits and consequently, the general heritability was low. In hybrid experiment under stress conditions for sugar yield traits, sodium content, sodium/potassium ratio, nitrogen content, and leaf area, and normal conditions for sugar yield and sodium/potassium ratio there was high genetic variance and broad-sense heritability was observed above 0.5. Therefore, these traits can be considered for breeding and selection programs. In drought conditions, the highest heritability was related to potassium content 0.77, molasses sugar 0.65, extractable sugar percentage 0.48, syrup purity 0.44, and sodium content and relative water content with 43, respectively. So, these traits were controlled to varying degrees by additive effects. Under normal conditions, pure sugar with 0.4 and sodium with 0.44 showed the highest heritability.

The results showed that the genetic parameters calculated in the stress environment cannot be generalized to the non-stress environment. As a result, studying the genetic characteristics of lines and hybrids under different environments is inevitable so that the estimation of gene function can be more accurate.  In both environments, the narrow-sense heritability of root yield was low but in sugar yield was moderate. So, in improving root yield, hybrid products can be used, and in sugar yield, selection can be used in long and term generations, as well as hybrid production.