پژوهشنامه اصلاح گیاهان زراعی
پیاپی 52 (زمستان 1403)

Due to the presence of nitrogen-fixing bacteria in its roots, the lentil plant causes biological nitrogen fixation, and in addition to meeting the plant's need for this substance, they also add some pure nitrogen to the soil each year. Therefore, it has made the soil fertile, especially in dry areas, and in this sense, it is considered a suitable rotation for rainfed cereals. Lentils are a significant source of food proteins, minerals (potassium, phosphorus, iron, and zinc), carbohydrates, and vitamins in human nutrition and have high health benefits due to their low fat content and glycemic index. The low yield of lentil genotypes is caused by various factors, including poor soil fertility, lack of high-yielding improved cultivars, severe moisture stress, diseases, pests, weeds, and inadequate crop management skills. In recent years, global climate change, especially due to rainfall and environmental changes, has significantly impacted lentil production. In Iran, lentils are usually cultivated in spring under rainfed conditions while autumn cultivation increases grain yield due to increasing rainfall efficiency compared to spring cultivation under rainfed conditions. In addition, to maximize yield and control phenotypic expression, breeders must select specific genotypes that are stable or adapted to a specific environment. Therefore, the identification of high-yield genotypes with adaptation to a wide range of environments is one of the major goals in crop breeding programs. In multi-environment experiments, lentil yield is influenced by the genetic structure, environment, and genotype × environment interaction. To better interpret the genotype × environment interaction, the additive main effects and multiplicative interaction (AMMI) model is one of the most common methods in the study of multi-environment experiments. The current study aimed to investigate the genotype ×environment interaction effect on lentil genotypes and to identify stable, high-yielding genotypes compatible with the climatic conditions of temperate rainfed regions in Iran.

In this study, 12 promising lentil genotypes along with “local”, “Kimia”, and “Gachsaran” cultivars were cultivated in a randomized complete block design for three consecutive cropping years (2019-2022) in Lorestan/Khoramabad, Ilam/Chardavel, and Kermanshah/Sararood. In the field, each plot consisted of 4 m planting rows with a distance of 25 cm and a density of 200 seeds per square meter. Stability analysis was performed using the AMMI multivariate method. Statistical analyses were performed using Metan and GGE packages of multi-environment experiments in R software.

The AMMI analysis of variance showed the significant effects of the environment, genotype, the genotype × environment, and the first seven main components. Therefore, the significance of genotype × environment interaction allows for the stability analysis of these data. According to AMMI analysis, the first and second main components of the genotype-environment interaction accounted for 45.6 and 19% of genotype × environment interaction variations, respectively. The effect of the first seven main components was significant and in total explained 99.5% of the variations of genotype × environment interaction. The shares of the environment, genotype, and interaction of genotype × environment in the sum of total squares were 54.56, 5.45, and 16.9%, respectively. Among the studied genotypes, the highest grain yield belonged to Genotype 10 with 850 kg/ha, followed by Genotypes 12, 6, and 4. Based on the ASV stability index, genotypes 3, 5, and 1, based on the SIPC index, genotypes 3, 1, 7, and 10, based on the EV index, genotypes 1, 10, and 3, and based on Za and WAAS indexes, genotypes 3, 1, 5, and 10 were the most stable genotypes. Based on the simultaneous selection index of ssiASV, genotypes 10, 4, 5, and 1, based on the ssiSIPC index, genotypes 10, 6, 7, and 4, based on the ssiEV index, genotypes 10, 6, 1, and 7, based on the ssiZA index, genotypes 1, 10, 6, and 7, and based on the ssiWAAS index, genotypes 1, 10, 6, and 3 were the best genotypes in terms of yield and stability. Based on the AMMI1 biplot, genotypes 1, 6, 10, and 11 with mean grain yield higher than the overall average and lowest values of IPCA1 were identified as stable genotypes with high general compatibility. In the AMMI2 biplot, genotypes 9, 11, 1, and 10 produced higher grain yields than the overall average, in addition to high general stability. In addition to the AMMI indices, Lin and Binn's superiority index was also used to identify the best genotypes, and based on this, genotypes 1, 10, 9, and 15 were the most stable genotypes in the studied environments. Using the AMMI distance parameter, genotypes 1, 3, 5, and 7 were recognized as genotypes with stable yields.

In general, genotypes 10 (09S96510-13) and 6 (ILL2261) produced high yields in most of the environments based on different indices and showed good stability in most methods. Therefore, they could be candidates for the introduction of new cultivars.

Cumin (Cuminum cyminum L.) is one of the most important and economic medicinal plants from the Apiaceae family with a considerable distribution in different regions of Iran. The cumin plant is one of the most important medicinal plants in Iran, which needs a lower irrigation to complete the growth stages. Therefore, cumin is one of the most valuable plants for cultivation in arid and semiarid regions of the world. The variety of cultivars is considered one of the important factors affecting the performance of plants. Therefore, the accurate and correct identification of the genotypes and landraces is very useful, in addition to being necessary for crop improvement programs of these plants; it is also very effective in preserving genetic reserves. The objectives of this research were to identify the superior cumin ecotypes based on grain yield and some agronomical traits using the selection index of ideal genotype (SIIG) method, classify cumin ecotypes using the cluster analysis method, and determine the most important traits affecting the essential oil yield trait using stepwise regression.

To evaluate cumin ecotypes using the SIIG, an experiment was conducted on 12 cumin ecotypes based on a randomized complete block design with three replications in the 2022-2023 cropping season. The studied ecotypes were collected from Yazd (Ardakan, Abarkouh, Ashkzar, Bafgh, and Khatam), Fars (Shiraz and Abadeh), Isfahan (Isfahan and Semirom), and Kerman (Kerman, Rafsanjan, and Sirjan). Agronomical traits, including plant height, number of branches per plant, number of umbels per plant, number of seeds per umbel, 1000-seed weight (TSW), grain yield, biological yield, harvest index, essential oil percentage, and essential oil yield, were measured in this research. The SIIG method and cluster analysis were used to select superior cumin ecotypes.

The results of the analysis of variance (ANOVA) revealed that ecotypes significantly affected all studied traits at the 1% probability level, except for TSW. Mean comparisons showed that the Sirjan ecotype produced the maximum value of grain yield (642.3 kg/ha), followed by Kerman, Khatam, and Ardakan ecotypes (634, 614.2, and 594 kg/ha, respectively). The minimum values of grain yield were seen in Ashkzar (456.3 kg/ha) and Bafgh (467.11 kg/ha) ecotypes. Mean comparisons showed that Sirjan, Khatam, Kerman, and Ardakan ecotypes attained the highest values in the most traits. The minimum values of plant height were recorded in Kerman and Sirjan, and the maximum value belonged to Bafgh and Ashkzar ecotypes. Sirjan, Kerman, Khatam, and Ardakan ecotypes showed the minimum distance from the ideal genotype (di+), the maximum distance from the non-ideal genotype (di-), and the maximum SIIG value (0.937, 0.926, 0.769, and 0.667, respectively); thus, they were introduced as favorable ecotypes. Ashkzar, Bafgh, and Shiraz showed the maximum distance from the ideal genotype (di+), the minimum distance from the non-ideal genotype (di-), and the minimum SIIG value (0.059, 0.094, and 0.166, respectively); hence, they were introduced as the weakest studied ecotypes. Cluster analysis on significant traits in ANOVA based on the Ward method and using Euclidian distance classified the studied ecotypes into three groups. Sirjan, Kerman, Khatam, Abadeh, and Ardakan ecotypes were placed in the first group, Ashkzar, Shiraz, and Bafgh ecotypes in the second group, and Abarkouh, Isfahan, Semirom, and Rafsanjan in the third group. Grain yield and the most studied traits were higher in the first group ecotypes than in the other groups and the whole mean. Ecotypes of the second group produced the least grain yield and the other studied traits, except for the plant height trait. The third group ecotypes showed average values of the studied traits. The result of the distance between centers of groups obtained from cluster analysis showed that the maximum genetic distance was between ecotypes in the first and second groups. Thus, it could be claimed that the cross between ecotypes in the first and second groups probably produces higher heterosis and genetic variation. For stepwise regression, the essential oil yield trait was selected as the dependent variable, and the other studied traits as independent variables. The result of stepwise regression showed that four traits, including the number of umbel per plant, the number of branches per plant, the number of seed per umbel, and essential oil percentage, were entered the stepwise regression model and could explain 99.5% of the variability among the essential oil yield trait.

The results of cluster analysis using the studied traits are similar to those of ecotype ranking based on the SIIG method. The present study showed that the SIIG method could properly classify the ecotypes. The result of cluster analysis and the SIIG method showed that Sirjan, Kerman, Khatam, Abadeh, and Ardakan were the best ecotypes, which can be used in breeding programs and the cultivation development of this plant. Ashkzar, Shiraz, and Bafgh were the worst ecotypes according to cluster analysis and SIIG values.

In recent years, the use of medicinal plants has increased due to the side effects, lower cost, patient compatibility to these drugs, and the harmful effects of chemical drugs on human health. Zataria multiflora Boiss. is an important medicinal plant species in the Lamiaceae family, native to Iran, Pakistan, and Afghanistan. The essential oil of this plant has antioxidant, antifungal, and antimicrobial properties and is used as a preservative in the food industry. Water scarcity is a significant limiting factor for crop growth and production. Reduced efficiency of chemical fertilizer uptake under drought conditions and their negative environmental effects necessitate using sustainable agricultural practices. Studies have shown that using biofertilizers, including mycorrhiza fungi, can reduce the effect of drought stress. The effect of mycorrhiza fungi inoculation with plants under drought stress is due to physical, nutritional, physiological, and cellular effects. Considering the medicinal and nutritional value of Z. multiflora, it is very important to identify ecotypes with high production to achieve high economic productivity due to existing drought; this importance can only be investigated through research on the genotype ×environment interaction. The performance of ecotypes largely depends on the genotype, environment, and the genotype × environment interaction. Due to the time-consuming stability and compatibility tests, it is usually possible to obtain some fixed or random effects of year and place from different agricultural managements, such as different planting dates, to reduce the length of the evaluation period. This study aims to investigate the mutual effect of Z. multiflora ecotypes under drought stress conditions and the use of mycorrhizal fungi in three planting dates using different stability parameters. Each of the stability parameters has strengths and weaknesses in identifying high-yielding and stable ecotypes. In this study, therefore, the combined aforementioned methods were used to determine the stability of Z. multiflora ecotypes.

This study was conducted on eight Z. multiflora ecotypes collected from Safashahr, Neyriz, Khanekhore, and Fasa from Fars Province, ‎ Fooladshahr, Baharestan, and Kolahghazi from Isfahan Province, and Qasreghand from Sistan and Baluchestan Province. The seeds of the above-mentioned ecotypes were evaluated on three planting dates, including March 1, 2021, March 24, 2021, and April 22, 2022. In this study, the combination of the three mentioned planting dates, at two levels of non-stress and drought stress and two levels of application and non-application of mycorrhiza were determined as 12 different environments, and plant dry weights of the ecotypes were used for stability analysis. After examining the homogeneity of test errors in the environment, data were subjected to the combined analysis of variance (ANOVA) using SAS9.4 software. The stability analysis parameters were measured using SPSS26, R, Minitab18, and Excel software.

The results of ANOVA ‎ indicated a very significant effect of the environment, ecotype, and the environment × ecotype interaction on plant dry weight‎. Drought stress reduced plant dry weight in all environments. The yield increased with mycorrhiza application. Mean comparisons showed significant differences among the ecotypes in each medium. The sowing date of 1 March 2021 was the best sowing date at a dry weight of 1.93 g. In this date, Safashahr ecotypes produced the highest yield in all treatment combinations.  In Eberhart & Russell's method, ecotype Khanekhore (b = -0.11) had the closest slope to one; besides, this ecotype had a low regression deviation (S2d = 1.80). Based on this parameter, it is considered the most stable ecotype. The lowest rank mean belonged to the Fasa ecotype (R = 2.67), followed by the Khanekhore ecotype (R = 3.50). The lowest standard deviation rank belonged to the Baharestan and Fooladshahr ecotypes (0.28 and 0.50, respectively), indicating the stability of these ecotypes based on these indicators. Baharestan (CV = 26.57) and Fooladshahr (CV = 43.43) ecotypes were considered stable ecotypes based on the coefficient of variance. According to environmental variance, Baharestan and Fooladshahr (Si2 = 0.08 and Si2 = 0.25, respectively) were identified as stable ecotypes. Based on Wricke's ecovalence, Fasa (Wi2 = 92.80) and Kolahghazi (Wi2 = 92.49) ecotypes were recognized as stable ecotypes. Based on Shukla's stability variance, Fasa (σi2 = 5.38), Kolahghazi (σi2 = 5.34), Fooladshahr, (σi2 = 5.65), and Khanekhore (σi2 = 5.93) were considered stable ecotypes.

According to the results of comparing the averages, the best planting date for Z. multiflora was March 1. The results of the mean ranking, standard deviation, coefficient of variation, environmental variance, Wricke's ecovalence method, and Shukla's stability variance were similar in identifying stable ecotypes. As such, Safashahr and Neyriz ecotypes were identified as unstable ecotypes, and the others showed moderate stability. Based on Eberhart & Russell's method, Khanekhore was the most stable ecotype.

Genotype-environment interaction, reflecting how genotypes respond differently across environments, significantly impacts breeding progress. This phenomenon poses challenges for breeders in selecting and evaluating superior genotypes. Understanding the genotype-environment interaction enables breeders to meticulously assess and select genotypes that align well with target environment conditions. Moreover, it offers opportunities to identify genotypes that exhibit positive interactions with specific environments or demonstrate robust performance in diverse environments. By cultivating genotypes in varied environments, breeders can observe genotypic responses, facilitating the selection of superior and stable genotypes. A thorough exploration of the genotype-environment interaction necessitates the application of robust statistical methods. In this study, a series of sugar beet hybrids were grown in diverse environments to investigate the impact of the genotype-environment interaction on the quantitative production potential of root yield and white sugar yield, assess their adaptability, and employ different statistical approaches to identify successful hybrids.

This research involved the cultivation of 18 sugar beet genotypes that encompassed 15 hybrids and three control varieties previously developed to enhance resistance against rhizomania, rhizoctonia, and cyst nematode diseases. Phenotypic evaluations were conducted over two consecutive crop years (2022 and 2023) at two agricultural research stations in Karaj, Alborz, Iran, and Kermanshah, Kermanshah, Iran, utilizing a randomized complete block design with four replications. Standard agricultural practices, including weed control, irrigation, fertilizer application, and other farm management activities, were implemented, throughout the cropping season. Statistical analyses were performed on the collected data related to root yield and white sugar yield.

The analysis of genotype main effects revealed a significant difference in both root yield and white sugar yield. The interaction effects of year-location and genotype-year-location at the 1% probability level for both root yield and white sugar yield traits and genotype-year at the 5% probability level on white sugar yield made a significant difference. These findings suggest that fluctuations in environmental conditions and genetic factors can impact genotype performance across diverse environments, leading to marked variations in root yield and white sugar yield among genotypes. Using the GGE biplot method, it was found that the first and second principal components of the genotype-environment interaction explained 93.44 and 3.85%, respectively, and a total of 97.29% of variations in the root yield. Regarding the white sugar yield, the first and second principal components of the genotype-environment interaction explained 95.10 and 2.54%, respectively, and a total of 97.64% of variations. Considering that the first and second principal components played an important role in the variance of the data, the respective biplots can well explain the genotype variations and the genotype-environment interaction. The association biplot illustrated positive correlations between experimental environments, with a strong positive correlation observed between Karaj in 2023 and Kermanshah in 2022 for the root yield. Similarly, a positive correlation between the environments of Karaj and Kermanshah was identified in terms of the white sugar yield. Genotypes 18, 15, 12, 14, and 8 exhibited superior root yields across locations, while genotypes 18, 15, and 14 excelled in the white sugar yield across the four studied environments. A combined assessment of stability and yield indicated that genotypes 18, 15, 12, and 14 excelled in the root yield, and genotypes 18, 15, and 14 performed exceptionally well in the white sugar yield and stability, being recognized as top-performing genotypes. Through the analysis of a hypothetical ideal genotype, genotypes 15, 12, and 14 were identified as the best yields in terms of root and white sugar yields, considering their proximity to the ideal genotype. By employing a multi-trait stability index with a selection pressure of 20%, genotype 16 emerged as the top-ranking genotype, followed by genotypes 10, 9, and 6. A comparison of trait values in the selected genotypes revealed increased root yield and sugar content, alongside decreased levels of potassium, alpha-amino nitrogen, and sodium, showcasing overall improvements across all studied traits with high heritability.

The results indicate that interactions between environmental factors and genetic makeup can influence the performance of different genotypes across varying conditions, leading to substantial variations in their outcomes. Overall, genotypes 18, 15, 14, and 10 were identified as the most stable genotypes.

The production of new hybrids with high production potential, adaptability to climatic conditions, resistance or tolerance to stresses, and stability in a wide range of environmental conditions have special importance to increase the yield per unit areaTo avoid creating and evaluating a large number of crosses in maize breeding programs based on hybrid production, it is necessary to assign maize lines to heterotic groups. Considering that the heterotic groups have not been determined for the selected maize inbred lines in Iran, this research aims to investigate genetic diversity and determine the heterotic groups in the selected maize lines to make maximum use of heterosis for future breeding programs. Moreover, this research also seeks to classify the selected Iranian maize inbred lines into early, medium, and late ripening groups based on the phenological traits.

Aiming at producing hybrid maize and determining the heterotic groups in this research, 50 selected maize inbred lines produced in Seed and Plant Improvement Institute (SPII) were chosen and evaluated based on their kernel yields, and morphological and phenological traits in a randomized complete block design with three replications in the research field of the Seed and Plant Improvement Institute. Variance analysis and cluster analysis were performed using Ward's method and principal component analysis.

In general, significant differences were observed between the traits studied in these selected maize inbred lines. The comparison of the average kernel yield of the inbred lines showed that the inbred line KE77008/1 (from the medium ripening group) produced the highest yield with 4.708 tons per hectare and the inbred line K615/1 (from the early ripening group) produced the lowest yield with 1.016 tons/ha. Moreover, the range of variation was significant for the kernel yield per hectare and other traits among these inbred lines. Therefore, high diversity was observed among the studied lines in terms of kernel yield and the other traits. Selected maize inbred lines based on the phenological traits were classified into three groups, early ripening (105.5-110 days), medium ripening (111-116 days), and late ripening (117.5-122 days). The yield range of the early group differed from 1.01 to 2.74 tons per hectare, the medium group from 1.18 to 3.09 tons per hectare (except for the medium ripening group containing the line KE77008/1 with a performance of 4.70 tons per hectare), and the late group from 1.21 to 3.28 tons per hectare. In genetic analysis, the low difference between the percentage of phenotypic and genotypic coefficients of variation of the traits showed less effects of environmental factors on these traits. The lowest difference was observed between the percentage phenotypic and genotypic coefficients of variation in days to physiological ripening (0.24) and ear height (0.94), which indicates the great effect of genetic factors on the control of these traits. The highest percentage genotypic coefficient of variation was obtained for kernel yield (31.77%), ear height (20.07%), percentage of cob wood (19.96%), the number of kernels per row (17.35%), and 1000-kernel weight (16.8%) traits. In this research, therefore, these traits are valuable in examining the genetic diversity and grouping of maize inbred lines. However, if the percentage of the genetic variation coefficient is higher, more genetic diversity and less environmental influence are observed for that trait. Based on the results of the principal component analysis, the first component explained 25.67% of the total variation. In this component, the highest positive coefficients are attributed to the number of kernels per row (0.518), ear diameter (0.771), leaf number (0.718), plant height (0.668), cob diameter (0.607), ear height (0.50), and kernel moisture at harvest (0.563). The second component explained 19.22% of the total data variance. In this component, the largest positive coefficients belonged to 1000-kernel weight (0.723) and plant height (0.531) traits, and a negative factor coefficient belonged to the number of kernels per row (-0.755). Based on the results of clustering by Ward's method, the selected maize inbred lines were classified into six separate groups. The percentage of deviation from the total average was positive for the yield trait in the first and fourth groups. These two clusters based on the days to physiological ripening trait were medium ripening. The highest days to physiological ripening trait for the lines was recorded in the second cluster) late ripening), it was medium in the first, third, fourth, and sixth clusters (medium ripening), and the lowest value belonged to the fifth cluster (early ripening) compared to the other clusters. Furthermore, the distribution of the lines based on the first and second principle component analysis and cluster analysis were in significant agreement with each other.

In general, significant differences in yield and yield components were observed in this set of selected maize inbred lines. Also, a high level of genetic diversity was identified in kernel yield and morphological and phenological traits. Based on the evaluated traits, 50 selected maize inbred lines were generally classified into six groups. Additional investigations based on the produced single cross hybrids (KSC 704, KSC 647, KSC 604, KSC 700, KSC 703, KSC 705, KSC 706, KSC 500, and KSC 260) showed the correct classification of inbred lines and identification of heterotic groups. The heterotic group 5 identified in this study has not played a role in the production of famous single-cross hybrids. Therefore, it is worth paying attention to using the potential of the heterotic group 5 and the other inbred lines attributed to heterotic groups that have not played a role in the production of single-cross hybrids in the production of new hybrids.

Sugar beet stands as a cornerstone crop in global agriculture, holding a pivotal position in sugar production and significantly shaping the sweetener industry. Given its fundamental role, enhancing traits crucial for sugar beet growth and yield is imperative to meet the escalating sugar demand forecasted for the future. Consequently, sugar beet breeding has long been a critical domain of research and development, focusing on enhancing yield, quality, and resistance to diseases of this essential commodity. Breeders have effectively cultivated sugar beet varieties boasting elevated quantitative and qualitative yields, alongside enhanced disease resistance, achieved through the meticulous selection and crossbreeding of diverse sugar beet lines. The current study undertakes the comprehensive evaluation of sugar beet genotypes across various traits encompassing quantitative and qualitative performance, aiming to pinpoint and select superior genotypes for future breeding initiatives.

This research was conducted on 49 meticulously chosen sugar beet genotypes, comprising 44 novel hybrids and five control varieties, encompassing three domestic and two foreign cultivars. These innovative hybrids emerged from a cross between 22 pure pollinator lines of sugar beet as the paternal parent and two commercial monogerm male-sterile single crosses as the maternal parent in 2020 at the Sugar Beet Seed Institute (SBSI), Karaj, Alborz, Iran. The phenotypic assessment of these experimental genotypes was conducted throughout the 2021 crop year at the Hamedan Agricultural Research Station utilizing an augmented randomized complete block design. The genotypes were cultivated in eight-meter rows spaced 50 cm apart within five incomplete blocks. Following data collection, variance analysis was carried out using R software. Subsequently, trait values for each hybrid were adjusted based on the augmented design implementation and the variance analysis outcomes for the various studied traits. The corrected trait averages were then employed for subsequent statistical analyses.

The results obtained from the variance analysis of various evaluated traits, such as white sugar yield, root yield, sugar content, sodium, potassium, and alpha-amino nitrogen, revealed that the adjusted block effect non-significantly influenced all the studied traits. This non-significance indicates the homogeneity of the incomplete blocks concerning controllable or uncontrollable environmental conditions. Notably, the adjusted treatment effect proved significant for all traits, except for root yield and potassium content at a 1% probability level. When dissecting the treatment effect into hybrid and control impacts, it emerged that only the sugar content trait exhibited a significant hybrid effect while all the investigated traits showed a significant control effect. The analysis of the hybrid versus control effect unveiled a significant disparity solely in terms of sodium and alpha-amino nitrogen content at a 1% probability level, with no notable difference observed for the other traits. Among the experimental hybrids and controls, two controls, 8K801 and BTS 1885, along with genotypes 33, 12, and 24, demonstrated commendable average white sugar yields ranging from 9.15 to 10.52 tons per hectare. These hybrids notably outperformed domestic controls, e.g. Hosna, Kimia, and Shokoufa, indicating the superiority of the new improved hybrids over internal control varieties. Genotype 42 exhibited the least favorable condition in white sugar yield and ranked lowest. The phenotypic diversity coefficient ranged from 4.99% for sugar content to 22.53% for sodium content while the genetic diversity coefficient varied from 1.96% for potassium content to 14.23% for sodium content. Traits such as alpha amino nitrogen, sodium, and root yield displayed moderate genetic advance whereas potassium, sugar content, and white sugar yield showed lower genetic advance. Notably, high heritability was observed alongside favorable genetic advance for alpha-amino nitrogen, suggesting additive genetic effects. The SIIG index results highlighted genotype 12 as the closest to the ideal genotype and farthest from the non-ideal genotype, earning it the highest SIIG value of 0.82, designating it as the best genotype. Following this, control variety 8K801 and genotypes 35 and 33 secured subsequent ranks with SIIG values of 0.72, 0.71, and 0.71, respectively, denoting them as suitable genotypes.

The findings underscore significant genetic diversity evident in the majority of traits among the experimental hybrids, highlighting substantial genetic alterations within hybrid breeding endeavors. This rich diversity within the studied germplasm is attributed to the diverse paternal line, resulting in the assignment of experimental hybrids into distinct groups based on all evaluated traits. Overall, the results firmly establish the superior performance of three key genotypes, namely 12, 35, and 33, surpassing other tested genotypes in the study.

Oilseeds are among the most important sources of energy all over the world. Soybean (Glycine max L.) is an important crop and its oil has nutritional and high economic value. As an annual, self-pollinating, diploid plant belonging to the Leguminosae pea family, soybean falls into the most important oil plants in the world, containing 18-22% oil and 40-50% protein, depending on the genotype and environmental factors. Soybean has been the food of Asian people, especially China, for centuries, and Chinese people consume it along with rice as their main food. The United States of America is the largest producer of soybeans and produces almost two-thirds of the world's crop. Improving seed yield is always a major goal in soybean breeding programs. The economic performance of soybeans can be increased by using new and high-yield varieties. It is essential to evaluate promising advanced soybean genotypes under different environmental conditions for identifying and selecting superior genotypes with high and stable yield potential. Genotype × environment interaction effects are important limiting factors in the introduction of new cultivars. The genotype × environment interaction is a major challenge in the study of quantitative characters because it reduces yield stability in different environments and complicates the interpretation of genetic experiments, making predictions difficult. Therefore, it is crucial 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 GGE-biplot graphic method is a technique with suitable efficiency to investigate the genotype × environment interaction effect and provides good information about the studied genotypes and environments graphically. This study aimed to investigate the genotype × environment interaction effect using the GGE-biplot graphic method to evaluate genotypes, environments, and relationships between genotypes and environments. Finally, this research seeks to identify stable soybean genotypes with high grain yields under different environmental conditions.

In total, 27 new soybean lines along with Saba and Amir cultivars were evaluated under different environmental conditions in a randomized complete block design with three replications in four experimental field stations (Karaj, Gorgan, Sari, and Moghan) during the 2022 cropping season. The plots consisted of four rows of 5 m in length with 50 cm spacing between the rows. The GGE biplot statistical method (the genotype effect + genotype × environment interaction) was used to study the stability of genotypes in the studied environments. Plants were harvested at maturity, and then the seed yield was recorded for each genotype at each test environment.

The results of the combined analysis of variance indicated that the effects of environments (E), genotypes (G), and genotype × environment (G×E) interaction were significant for seed yield, suggesting that the genotypes responded differently in the studied environmental conditions, making the stability analysis possible. The results of the genotype × environment interaction analysis using the GGE-biplot method indicated that the two first and second principal components of the GGE-biplot explained 84.8% of the total seed yield variation, indicating the high validity of the biplot in explaining the variations of genotypes and the genotype × environment interaction (G + GE). This study identified two mega-environments, the first of which included Gorgan and Mughan, and the second mega-environment included Sari and Karaj. Based on the polygon view of the biplot, the genotype G1 in Sari and Karaj environments, and the genotypes G21 and G22 in Gorgan and Moghan environments were superior genotypes with high specific adaptation. The results of the average environment coordinate biplot showed that the G1, G22, G5, and G9 genotypes produced the highest seed yield, respectively. On the other hand, the G28, G25, G16, and G19 genotypes produced the lowest seed yield, respectively. Based on the hypothetical ideal genotype biplot, the G22, G5, G16, G12, G14, and G9 genotypes were better than the other genotypes for seed yield and stability and showed high general adaptation to all environments. Moreover, the Karaj and Moghan environments were the nearest environments to the ideal environment with the highest discriminating ability and representativeness. Therefore, the Karaj and Moghan environments can be used as a suitable test location for selecting superior soybean genotypes.

Based on the results of this study, the G22, G5, G16, G12, G14, and G9 genotypes are superior for seed yield and stability in this study. Therefore, these hybrids can be used for further testing, including adaptation tests. Besides, the results show that the Karaj and Moghan environments can be used as suitable test locations for selecting superior soybean genotypes. Generally, our results demonstrate the efficiency of the GGE-biplot graphical method to investigate the G × E interaction effect and provide good information about the studied genotypes and environments.

Rice (Oryza sativa L.) serves as the primary food source for over half of the world's population. The disease caused by Rhizoctonia solani has significantly impeded rice production, resulting in substantial economic losses and posing a threat to food security. Currently, the most suitable method to control this disease is the use of commercial fungicides. However, the use of fungicides results in increased costs and harm to the environment and human health. Therefore, the utilization of biocompatible chemical compounds is regarded as an innovative and effective strategy for integrated product management and combating various stressors. One such compound is potassium phosphite, which works to reduce disease by directly affecting the pathogen and indirectly stimulating the host's defense responses.

The seeds of local Tarom and Khazar cultivars were prepared and disinfected to produce identical, pathogen-free seedlings. Subsequently, the seeds were germinated and transferred to plastic pots filled with sterile soil. Finally, the pots were moved to the growth chamber. Some of the seedlings were treated with potassium phosphite while others served as the positive control. All the plants were then infected with R. solani. Leaf tissues from the treated and control seedlings were sampled at 0, 24, 48, 72, and 96 hours after infection. The leaf extract was then obtained to measure enzyme activity, including catalase (CAT), polyphenol oxidase (PPO), and superoxide dismutase (SOD). Disease severity and enzyme activity were studied in a factorial design using a completely randomized design with two cultivars, Tarom (resistant) and Khazar (susceptible), and two treatments of potassium phosphite and R. solani, each with three replications.

The results of the analysis of variance showed that the effects of the treatments were significant in all sources of changes in the severity of the disease and the activity of PPO and SOD enzymes. However, the effects of the treatments were not significant in the treatment number for the CAT enzyme activity but were significant in other sources. The severity of the disease was more pronounced in the control plants than in the plants treated with potassium phosphite, and in the sensitive variety (Khazar) than in the resistant variety (Tarom). Additionally, in both the control and treated plants, the disease progressed more by the 10th day after infection than on the 21st day. Enzyme activity was significant in both the resistant (Tarom) and sensitive (Khazar) varieties, as well as between the two potassium phosphite treatments in the presence of the pathogen. The highest level of enzyme activity was associated with the Tarom variety and potassium phosphite treatment in the presence of the pathogen while the lowest level was linked to the Khazar variety and R. solani treatment. The CAT enzyme activity increased in both treatments and cultivars at 24 h after infection, and then gradually decreased at 48 and 72 h. At 96 hours, however, it reached the highest level of activity compared to the initial time. The PPO enzyme activity increased in all time periods while the activity of SOD increased gradually, reaching its peak at 72 h, which then decreased at 96 h.

CAT catalyzes the conversion of hydrogen peroxide into water and oxygen, protecting the plant against oxidative damage caused by pathogens. In diseased plants, the presence of potassium phosphite led to an increase in enzyme activity compared to infected control plants. This suggests that potassium phosphite enhances enzyme activity in plants to combat pathogens. PPOs catalyze the oxidation of phenol to quinone, creating unfavorable conditions for pathogen development. It induces resistance in potassium phosphite treatment, leading to increased quinone production and creating a toxic environment for the pathogen. The spread of the disease decreased compared to the treatment of R. solani. SOD plays a crucial role in plant physiology by serving as signals in transmission pathways and as a defense against cell damage caused by excessive oxygen production. In plants treated with potassium phosphite, the enzyme exhibited maximum activity at 72 h. In the control plants, this peak occurred at 96 h after contamination. This suggests that potassium phosphite has reduced oxidative stress, thereby impeding the progression of the disease and its associated symptoms. In this study, the activities of CAT, PPO, and SOD enzymes were lower in the sensitive variety (Khazor) than in the resistant variety (Tarom). This suggests that the level of reactive oxygen species was higher in the Khazor variety, leading to an increase in the spread and symptoms of the disease. R. solani induces cell death in plants by secreting enzymes and toxins. Potassium phosphite inhibits cell death by enhancing the activity of antioxidant enzymes, such as CAT, PPO, and SOD, thereby impeding the progression of the fungus. Therefore, potassium phosphite can be used as a biocompatible chemical compound instead of fungicides in the integrated management of the disease.

Sugars are one of the important components in the human diet, along with fats and proteins, which provide about 60-70% of their energy. Stevia is one of the most important medicinal plants with high antioxidant properties, obvious sugar characteristics, and without calories, which can replace sugars extracted from other sources. One of the features of this sweetener is that it is very useful for diabetics due to its lack of absorption in the digestive system. On the other hand, it is suitable for the diet of obese people due to its non-caloric nature. Sucrose is the transportable form of photosynthetic products, i.e. carbohydrates, in plants. Sucrose foliar spraying is important for the quick supply of compounds needed by plants.

This factorial experiment was carried out based on a randomized complete block design (RCBD) with four replications at Sari Agricultural Sciences and Natural Resources University from 2014 to 2015. Evaluated treatments consisted of different concentrations of a sucrose solution (0, 2.5, 5, and 10%). Thus, the mentioned material was diluted in solvents such as methanol 15%, boric acid (3 ppm), and acetic acid (1 ppm). Treatments were sprayed on plants at 10-day intervals until plants started the flowering stage. Some parameters, such as total chlorophyll, carotenoids, total phenol, total flavonoid, antioxidant activity, steviozide, and ribodiozide contents were measured at the beginning of flowering.

The results of the analysis of variance showed that the simple effect of the solvent type and sucrose concentration on all studied traits at the probability level of 0.01 and the interaction effect of the treatments on all traits, except for the amount of ribodiozide, were significant at the probability level of 0.05 and on the amount of steviozide at the probability level of 0.01. The average data comparison results showed that the amount of total chlorophyll in 10% sucrose concentration was significantly higher than the other sucrose concentrations. There was a significant difference between different concentrations of sucrose in terms of carotenoid content so that the highest (10.72 mg/g fresh weight) and the lowest (9.56 mg/g fresh weight) levels were recorded at concentrations of 5% and 0% sucrose, respectively. The amount of carotenoid in the methanol solvent (11.99 mg/g fresh weight) was significantly higher than in the other solvents, namely boric acid, acetic acid, and distilled water (its lowest amount). The amount of flavonoid was significantly different between different concentrations of sucrose and also in different solvents. Five percent sucrose concentration (10.82 mg of quercetin per gram of dry weight) and zero percent sucrose concentration produced the lowest (9.31 mg of quercetin per gram of dry weight) flavonoids. Based on the results of the data, the highest and lowest flavonoid contents were obtained in methanol (11.51 mg quercetin per gram of dry weight), boric acid (10.74 mg quercetin per gram of dry weight), acetic acid (9.42 mg quercetin per gram of dry weight) ), and distilled water (8.32 mg of quercetin per gram of dry weight) solutions, respectively. The amount of total phenol in the 5% sucrose concentration (19.54 mg of gallic acid per gram of dry weight) was significantly higher than in other concentrations. The results of comparing the average data showed a significant difference between different solvents in terms of the amount of total phenol so that the methanol solvent (23.07 mg of gallic acid per gram of dry weight) and the distilled water solvent (13.47 mg of gallic acid per gram of dry weight) contained the highest the lowest total phenol contents, respectively. The results obtained from this research showed a significant difference between different concentrations of sucrose in terms of the amount of antioxidants so that the highest (85.71%) and lowest (62.38%) amounts were found in five and zero percent concentrations, respectively. Based on variance analysis data, there was a significant difference in the amount of antioxidants in different solvents, and the highest and the lowest amounts were observed in methanol (90.59%) and distilled water (69.87%) solvents, respectively. Based on the obtained results, the methanol solvent had the highest (14.01%) and the distilled water solvent produced the lowest (11.27%) amounts of steviozide. Based on the comparison of average data, the lowest (5.77%) and the highest (13.01%) amounts of steviozide were measured at concentrations of 10% and 5% sucrose, respectively. According to the results of comparing the average concentrations of different sucrose levels on the amount of ribodiozide, the concentrations of 0% and 5% sucrose led to the lowest and the highest amounts of ribodiozide, respectively. According to the effect of the solvent, the highest and lowest amounts of ribodiozide belonged to methanol and distilled water solvents, respectively. Based on the mean comparison analysis, the most optimum amount of photosynthetic and phytochemical parameters were related to the concentration of 5% sucrose while the other concentrations were in the next orders. Moreover, the highest and the lowest optimum amounts were recorded in the methanol (15%), boric acid (3 ppm), acetic (1 ppm), and distilled water (control) solvents, respectively.

The studied carbon sources had a significant effect on many investigated traits. An increase in the concentration of assimilated carbohydrates as a result of sucrose application at a concentration of 5% with the methanol solvent may increase the availability of respiratory intermediates, which results in the reduction of non-photochemical energy loss. The occurrence of these biochemical phenomena caused by the consumption of intermediate substances ultimately increases the photosynthetic efficiency and improves the accumulation of photosynthetic substances, as well as the synthesis of secondary metabolites. Finally, it can be concluded that the application of sucrose with its appropriate solvent (methanol) could decrease or replace the absolute requirement for carbon source in the environment.

Salinity stress is one of the environmental stresses that significantly affects the growth of crops, especially in arid and semi-arid regions. With the expansion of salinity, using saline lands for agricultural production has become a significant challenge. Lentil grows in various climatic conditions; however, this plant is sensitive to saline stress. Using saline-tolerant lentil cultivars can help increase production and efficiency in saline soils due to biological nitrogen fixation. This study aimed to investigate the diversity of salinity tolerance in 215 lentil genotypes and select superior genotypes tolerant to salinity.

This study was conducted in the research greenhouse of the Research Center for Plant Sciences, Ferdowsi University of Mashhad, in 2017. In this experiment, 215 lentil genotypes were studied using a completely randomized design with three replications under hydroponic conditions at 12 dS.m-1 sodium chloride salinity. Salinity stress was applied one week after germination. The irrigation method was closed-circuit trickle irrigation. The nutrient solution was replaced weekly, and the salinity level of the nutrient solution was monitored and adjusted daily. Four weeks after the saline treatment, the following parameters were measured and recorded in the vegetative stage: plant height, the number of branches per plant, plant survival rate, the survival percentage of leaves, the percentage of shed leaves, shoot fresh and dry weights, and sodium and potassium contents of shoot. After conducting the data normality test and homogeneity of variances, the means were compared using the Least Significant Difference (LSD) test at a 5% probability level. Cluster analysis using the Ward method, factor analysis, correlation between traits, and the by-plot were performed on the data. The performed grouping was validated through a multivariate analysis of variance and discriminant function analysis. Additionally, to examine the differences between groups in terms of various traits, the means of the groups were compared for the investigated traits.

The evaluation of the frequency distribution of lentil genotypes in survival ranges showed that 12% of the genotypes (25 genotypes) had a survival rate of upper 80%, 15 genotypes of which had a survival rate of 100%. On the other hand, 36% of the genotypes (78 genotypes) had a survival rate of less than 20%, with 39 genotypes showing no survival four weeks after 12 dSm-1 salinity stress. In the range of 100-81% survival, six genotypes were at the podding stage. Plant height decreased with a decrease in the survival percentage due to salinity stress. The highest percentage of remaining leaves and the lowest amount of leaf shedding were observed in the three survival ranges of 100-81%, 80-61%, and 60-41%. With a decrease in the survival percentage from 100-81% to 80-61%, 60-41%, and 20-0%, the dry weight of the plant decreased by 13.5%, 22.7%, 36.6%, and 2.03 times, respectively. The tissue water content only significantly decreased in the survival range of 20-0%, and there was no significant difference in this parameter in the other survival ranges. With a decrease in the survival percentage from 100-81% to 80-61%, 60-41%, and 20-0%, sodium concentrations increased by 36%, 48%, 62%, and 2.8 times, respectively. Based on factor analysis under salinity stress conditions, three factors were selected that altogether accounted for 74.4 % of the total data variance. Factor analysis showed that the first factor explained approximately 45.98 % of the variance, which included the percentage of survival, growth stage, plant height, branches number per plant, shoot fresh and dry weights, relative water content with negative loading, sodium concentration, and sodium to potassium ratio with positive loading. The second factor explained approximately 20.1% of the variance, which included the survival percentage of leaves with a positive loading and the percentage of shed leaves with a negative loading. The third factor accounted for 4.8% of the variance, which included potassium concentration with a positive loading. Cluster analysis results showed that the 215 lentil genotypes were grouped into eight plotted groups. The results of discriminant function analysis showed that 92.6% of genotypes were correctly classified, and the success rate of the discriminant function was 100% in groups six, seven, and eight. Genotypes of the cluster sixth, including MLC25, MLC47, MLC64, and MLC77, had the highest values in all studied traits, including survival percentage and growth stage, plant height, branches number per plant, leaf survival percentage, shoot fresh and dry weights, relative water content, and potassium concentration, while they had the lowest values in the percentage of shed leaves, sodium concentration, and the sodium to potassium ratio.

Generally, genotypes MLC25, MLC47, MLC64, and MLC77 had the highest values for all studied traits, including survival percentage, growth stage, plant height, the number of lateral branches, the percentage of remaining leaves, fresh and dry weights of shoot, tissue water content, and potassium concentration, while they had the lowest percentage of leaf shedding, sodium concentration, and the Na/K ratio. Considering the superiority of these genotypes in the studied traits, genotypes belonging to this group can be used to study the superior traits in complementary salinity stress studies.

The process of selecting and introducing compatible genotypes with high yield potential requires evaluation in different years and places. Due to the severe and rapid changes in climate, which confront crops with all kinds of stress, especially drought stress, it is expected that the cultivated area of crops, such as sunflower, which is highly desirable for planting due to its special characteristics, will decrease. Undoubtedly, improving drought tolerance and developing high-yielding cultivars is one of the most important goals in breeding programs. On the other hand, in the selection and introduction of varieties, performance, the most important feature of the breeding program, due to its polygenic nature, is strongly affected by biotic and abiotic stresses. Therefore, taking into account the quantitative control, and the effects of the environment and the interaction of the genotype × environment, selection for this trait is complex, costly, and time-consuming. Therefore, understanding the genotype × environment interaction is essential for the development of high-yielding and stable genotypes. Crop stability in the agricultural concept means no deviation from the expected product response. Based on this, several methods have been introduced for selection with optimal efficiency and high accuracy. Stability indices are divided into two main groups: parametric and non-parametric stability indices. Each of these two groups has advantages and disadvantages. Thus, if parametric methods are more capable of evaluating the interaction effects of the genotype × environment and analysis of stability, non-parametric methods have a higher ability to analyze non-crossed interactions. However, it seems that the purpose of the plant breeder and the size of the studied sample are decisive in the superiority of these statistics. If the purpose of the plant breeder is only to rank genotypes among environments, non-parametric statistics are more suitable. If the sample size is small, the use of parametric statistics will be more appropriate than non-parametric ones, but if the sample size is large, the efficiency of both types of stability indices will be equal. It seems that using both stability indices helps in selecting genotypes with stable performance. In this research, it was tried to obtain comprehensive information about the studied genotypes using both groups of stability indices.

One hundred oilseed sunflower genotypes were evaluated in a 10 × 10 simple lattice design under two normal and drought stress (irrigation limitation) conditions during 2012 and 2013 (four environments) in terms of seed yield in Qezeljeh village in West Azerbaijan, Iran. For this purpose, cultivation was done in lines with 5-meter long. The distance between the lines was 60 cm, and the distance between the plants on the lines was 50 cm. The criterion for applying the treatment was the rate of evaporation from the class A evaporation pan. In both years, the field was irrigated up to the 8-leaf stage in both normal and limited irrigation experiments after 90 mm of evaporation from the Class A evaporation pan. From the 8-leaf stage onwards in the normal irrigation experiment, irrigation continued in the same way until the end of the growing season. In limited irrigation, irrigation was done after 180 mm of evaporation from the Class A evaporation pan. Parametric and non-parametric stability indices were used to select genotypes with high and stable performance. In this regard, the analysis of variance (ANOVA) was done with a mixed linear model, considering the environment and genotype as fixed effects and the year and replication as random effects. SAS software version 4.9 was used for ANOVA, and the STABILITYSOFT program under the R environment was used for stability analysis. Stability analysis was done with seven different parametric methods (based on ANOVA and regression analysis) and 11 non-parametric methods. Moreover, the STABILITYSOFT program was used to show the relationship between different stability indices in the form of a heat map plot.

Based on the results of combined ANOVA, the effects of the genotype and genotype × environment were significant. Considering the variability observed among genotypes and the different reactions of genotypes from one environment to another, stability analysis was done with different parametric and non-parametric methods to select stable genotypes. Based on the correlation results, the average yield (MY) with S(3) statistics at the 5% probability level and with S(6), NP(2), NP(3), NP(4), GE (θ(i)), and Kang statistics at the 1% probability level showed a negative correlation and with NP(1), Wi2, σi2, Reg, MV (θi) and Sdi2 statistics at the 1% level showed positive and significant correlations. In particular, the three Shukla's statistics (σi2), Wick's equivalence (Wi2), and MV (θi) parameters showed a positive correlation with yield. Based on all parametric and non-parametric stability parameters, the AS613 genotype was introduced as a genotype with high yield and stability.

The stability indices, which evaluate the stability of genetic materials, can be beneficial to a large extent in the optimal and efficient selection of parental genotypes for developing high-yielding and stress-tolerant cultivars.

The faba bean (Vicia faba L.) is considered one of the important legumes and is cultivated as a source of cheap protein. This plant is very suitable for agriculture due to having suitable agricultural characteristics, including nitrogen fixation and the possibility of being placed in crop rotation with fall crops, especially cereals. Bean plant fodder can also be ensiled pure or mixed with cereal plants. Therefore, studies on the genetic diversity of this plant help breeders to identify the genetic potential and capacity of different cultivars, as well as to find out the relationship between different traits with fodder and seed yield and determine the most important morphological characteristics affecting yield.

To investigate the yield and related traits of some low-tannin fodder genotypes of faba bean, an experiment was performed based on a randomized complete block design with three replications in the research field of the Gharakheyl Agricultural Research Station (Mazandaran Agricultural and Natural Resources Research and Education Center) in 2022. The treatments included 11 bean cultivars (nine low-tannin bean lines from ICARDA along with a low-tannin control variety (Mahta) and a tannin-rich control (Shadan) from Iran). Each genotype was cultivated in six lines of 6 meters. The distance between the rows was 60 cm, and the distance between the plants on the row was 8 cm. The seeds were sown by hand at a depth of 5 cm in the soil in November. From the time of planting to harvesting during agricultural operations, necessary notes were taken at different stages of growth and development. Analysis of variance (ANOVA) was performed on recorded yield data and yield components, and the mean of treatments was compared with the least significant difference (LSD) method using SAS 9.0 software. Bean genotypes were grouped using cluster analysis based on Euclidean distance and Ward's method. The correlation between traits was calculated by Pearson’s method and stepwise regression analysis using SPSS 18 software.

The results of ANOVA for the data showed a significant difference between the broad bean cultivars in terms of leaf dry weight, stem dry weight, days to germination, fodder dry weight, seed yield, leaf fresh weight, pod dry weight, fodder fresh weight, and the harvest index. The mean comparison of traits showed that the Mahta cultivar produced the highest value of grain yield (5.24 tons/ha) and the highest value of fodder dry weight (8.19 tons/ha). The highest fodder fresh weight belonged to the FLIP03-07FB cultivar at the rate of 35.76 tons/ha, which was not significantly different from the Mahta and s2008-033 cultivars. The highest values of stem dry weight and pod dry weight belonged to the Mahta genotype (2.69 and 3.73 tons/ha, respectively), and the highest leaf fresh and dry weights belonged to the s2008-033 genotype (7.20 and 1.78 tons/ha, respectively). Cluster analysis placed the studied genotypes into three groups. The s2008-033 genotype was included in the first group. This genotype showed the highest values in most of the studied traits, and in some traits such as the harvest index, fodder fresh weight, and grain yield, there were no significant differences with the highest values of the genotypes. In the second group, there were five genotypes WRB1-5, WRB1-3, Shadan, s2009-167, and Mahta, and except for two harvest index and grain yield traits with high values, they were in an average condition in the rest of the studied traits. In this group, the Mahta genotype showed high values in most traits and was placed in the same group with another Iranian genotype (Shadan) with a high harvest index. In the third group, there were five genotypes BPL4104, s2008-034, FLIP03-07FB, ILBxZV-1509-39, and s2008-96, which showed the lowest values in most studied traits. The correlation analysis showed a positive and significant relationship between grain yield and pod fresh weight, and a positive correlation with the harvest index. There was a positive and significant correlation between fodder dry weight and fodder fresh weight, leaf dry weight, stem dry weight, and pod dry weight. Regression analysis showed that the harvest index, stem dry weight, and leaf fresh weight were three traits that entered the regression model of yield and could justify 0.97 of variances.

In the case of targeting the seed production of the studied cultivars, cultivars with a higher harvest index than the other cultivars that can produce more biological mass should be included in the selection program. However, cultivars that produce more fresh and dry weights of stems, leaves, and pods should be selected if the goal is to produce dry fodder from the studied cultivars. Among the genotypes studied in this experiment, Mahta, FLIP03-07FB, and s2008-033 genotypes have the highest mean values for traits related to fodder dry weight, the highest values of fodder dry weight, the highest values of grain yield, and the acceptable level of the harvest index. Accordingly, these genotypes can be used in breeding programs to increase yield and fodder production.