پژوهشنامه اصلاح گیاهان زراعی
پیاپی 50 (تابستان 1403)

As one of the most important cereals, bread wheat is an essential part of food security in the world, which supplies one-fifth of the total calories of the world population. Nowadays, the yield of wheat has been affected by climate change-driven drought as one of the most important abiotic stresses that has become an important threat to food security in the world. Root and stomatal traits are especially important in breeding plants to withstand drought stress. Stomata play a key role in controlling carbon dioxide uptake and water loss through transpiration. Therefore, stomatal characteristics are used as indicators of water status and plant growth, especially in drought stress conditions. Having a wide range of physiological and morphological characteristics, roots play an essential role in absorbing water and nutrients. They are also the first organ that sends signals to control the stomata in response to dryness. Therefore, the difference in the structure of the root system can cause the difference between the performance in different cultivars. This study was conducted to investigate stomatal characteristics and their relationship with the root system and plant performance in 24 bread wheat lines and cultivars.

To investigate the relationship between stomatal dimensions and density with the root system, an experiment was conducted on 24 bread wheat genotypes in the form of a randomized complete block design with three replications in the rainfed conditions of the research farm at Zanjan University Faculty of Agriculture in the crop year 2018-2019. In this experiment, PVC pipes were used to study the root system. Twelve seeds were planted in each tube, which were thinned to seven after germination. In each experimental unit, there were two tubes for each genotype, one of which was used to evaluate traits and final yield, and the second tube was used for root studies. Stomatal traits, including the length and width of stomata and number of stomata per unit area, root traits including root length, root diameter, root volume, root surface, and root biomass, and seed yield were measured in the end. The resulting data from the measured traits were analyzed in the form of a randomized complete block design, and the averages were compared using the LSD method. The data were analyzed using multivariate statistical analyses, including regression analysis, path analysis, and factor analysis, and cluster analysis was used to group genotypes. Statistical calculations were done using SAS 9.0 and SPSS 21 software.

The results of analysis of variance and mean comparison showed high variability among genotypes for all measured traits. The results of the mean comparison of genotypes showed that genotypes 2, 5, 8, and 16 had the highest yield, and genotype 23 had the lowest yield among the examined genotypes. The highest number of stomata on the upper and lower leaf surfaces belonged to genotypes 5 and 2. In terms of root traits, the highest diameter, volume, length, root surface, and root dry weight at a depth of 0-25 cm were recorded for genotypes 2, 3, 18, and 5, respectively. There was a high and significant correlation between the yield and the number of stomata on the upper and lower leaf surfaces, the length and width of  the stomata on the upper leaf surface, diameter, volume, dry weight, and root surface at a depth of 0-25 cm in the soil. Based on the results of stepwise regression analysis, two variables, the number of stomata on the lower leaf surface and root dry weight at a depth of more than 25 cm explained 91.4% of the changes in grain yield. According to the results of the causality analysis of the number of stomata on the lower leaf surface, the most direct effect had a positive effect on seed yield. The results of factor analysis grouped the studied traits into three factors with 82.48% variability justification. The shares of the first, second, and third factors to explaining data changes were 48.86%, 24.62%, and 8.99%, respectively. Based on the plot obtained from factor analysis, genotypes 2, 5, 8, and 16 had high values for the first and second factors. According to the coefficients of the factors, it can be claimed that the genotypes located in this area have high performance, a high number of stomata, and strong root traits, which were found at the soil depth of 0-25 cm. For this reason, these are the genotypes that could produce high yields by absorbing water from the surface layers of the soil by having a large number of stomata and carrying out more photosynthesis. Moreover, the investigated genotypes were divided into three groups from cluster analysis by the ward method and Euclidean distance. Genotypes 2, 5, 8, and 16 were placed in the first group and had the highest mean values for grain yield traits, number and width of stomata on the upper and lower leaf surfaces, and root traits including diameter, volume, and dry weight at a soil depth of 0-25 cm, and root diameter at a depth greater than 25 cm. The lowest values for stomatal length were observed in both leaf surfaces. These were the best genotypes for cultivation in dry conditions.

A strong superficial root system can provide the plant with water from scattered rains that occur with low frequency at the end of the growth period. On the other hand, the increase in the number of stomata along with their smaller size reduces leaf pores and enables a faster response of the stomata, and the rapid response of the stomata maximizes water use efficiency. Therefore, having a strong superficial root system along with high stomatal density can increase seed yield in dry conditions.

Maize is an important crop that is cultivated in many parts of the world. The evaluation of genotypes in breeding programs often faces two important challenges, the genotype × environment interaction effect for the target trait and unfavorable relationships between the target traits. Even though many methods have been offered for stability analysis, especially graphical tools and their relatively good efficiency in interpreting the results, it seems that the best linear unbiased predictions (BLUP) method estimates the means with high accuracy, especially in mixed models, in multi-environmental trails (MET). Therefore, the stability index of weighted average absolute scores (WAASB), which is estimated from the integration of the two stability methods of additive main effect and multiplicative interaction (AMMI) and best linear unbiased predictions, can be used in METs to estimate more accurately the stability of genotypes. Maize breeding programs prioritize high grain yields and earliness as important traits. The multi-trait stability index (MTSI) is a valuable tool for the simultaneous selection of multiple traits. It is estimated based on the average performance and simultaneous stability of genotypes in different traits and environments. Therefore, the current research aimed to identify stable and high grain yield maize hybrids along with the optimal level of grain moisture percentage at harvest time and days to physiological maturity using the integration of AMMI and BLUP methods with WAASB, WAASBY, and MTSI indices.

This study involved the evaluation of seven promising maize hybrids along with four commercial check varieties, including SC647, TWC647, SC704, and SC715, in maize METs based on a randomized complete block design with four replications across 10 regions (Karaj, Moghan, Shiraz, Kermanshah, Kerman, Mashhad, Dezful, Miyandoab, Jiroft, and Mazandaran) during two cropping seasons of 2019-2020. The recorded traits were grain yield adjusted at 14% moisture content, grain moisture percentage at harvest time, and days to physiological maturity. The WAASB was used to estimate genotypic stability for each genotype. It was computed from the singular value decomposition (SVD) of the matrix of best linear unbiased predictions of genotype vs. environment interaction effects generated by a linear mixed-effect model. The WAASBY index for simultaneous selection based on grain yield (Y) and stability (WAASB) was  estimated by assigning different weights to grain yield and stability. The simultaneous selection for grain yield and stability based on several traits was conducted using the scores obtained from an exploratory factor analysis (MTSI).

Based on the grain yield across 10 environments over two years, promising hybrid NO. 3 had the highest grain yield with 12.80 tons per hectare. According to the likelihood ratio test (LRT), the genotype-by-environment interaction was significant for the traits of grain yield, grain moisture percent at harvest time, and the days to physiological maturity. Therefore, BLUP analysis can be performed on these data due to the significant genotype by environment interaction. The BLUPs performed for hybrids were followed by stability analysis using the AMMI method on these BLUPs. The results indicated that the first and second components justified 27.7% and 24.6% of the hybrid by environment interaction variances, respectively. The highest predicted grain yield by the BLUP method belonged to hybrids No. 3, 2, 4, and 1, with higher than average predicted grain yields. Based on the biplot for the first principal component of the environments against the nominal grain yield, hybrids 2, 6, 3, and 1, having the lowest scores of the first principal component (coefficient b or line slope), had a negligible contribution to the hybrid by environment interaction and were distinguished stable. To enable simultaneous selection based on both grain yield and stability, the WAASBY index was estimated by integrating grain yield (Y) and the WAASB stability index. Considering the 50% contribution of each of the two grain yield and yield stability components, five hybrids (1, 2, 3, 6, and 4) showed above-average WAASBY. Among these, hybrids 1, 2, and 3 had significantly higher WAASBY than the other hybrids. All four control cultivars SC647, TWC647, SC704, and SC715 had lower-than-average WAASBY. Based on the MTSI, hybrid 3 was selected as the best hybrid. In addition, the estimated variance components by restricted maximum likelihood (REML) for grain yield indicated that 75.72% and 7.57% of the phenotypic variance were explained by the environment and GEI variances, respectively, whereas the contribution of residual variance to the phenotypic variance was 16.77%.

Based on the results, hybrid 3 (K47/2-2-1-4-2-1-1-1× MO17) was identified as a high-yielding hybrid, which can be introduced to farmers as a new superior maize hybrid. It seems that the use of the ratio of the WAASB stability index to grain yield (WAASB/Y) and the selection of superior genotypes based on the MTSI could identify hybrids with high grain yields, stability, and desirable levels of important agronomic traits.

The production of peanuts (Arachis hypogaea L.) as one of the vital industrial plants is affected by the environment, genotype, and their interaction. Therefore, the environment × genotype interaction on peanut yield should be evaluated before the introduction of cultivars. Evaluation of the genotype × environment interaction provides valuable information regarding the performance of plant cultivars in different environments. It plays a crucial role in evaluating the stability of the performance of breeding materials. This experiment evaluated the stability and yield of superior peanut genotypes in three regions of Guilan province, Iran, in the 2018 and 2019 crop years.

In this study, the top 10 peanut genotypes (130, 140, 113, 115, 128, 176, 178, 192, 201, and 208 from ICRISAT) along with the NC2 variety as a control were assessed in a complete randomized block design trial with three replications across three regions Rasht, Masal, and Talash. Each plot comprised six lines, each with 5 m long, 50 cm row spacing, and 20 cm plant spacing. Upon reaching physiological ripeness, a 5-m2 area was harvested from the middle four rows of each plot after removing 0.5 m from both ends to eliminate marginal effects. The plant height (cm), average number of sub-branches per plant, number of pods per plant, and number of seeds per pod were randomly recorded and counted from 10 plants. After drying, biomass, pod, and seed yields were calculated in kg/ha. Following seed separation from the shell, five random samples of 100 seeds were taken from each plot to measure the 100 seed weight (HSW). Additionally, the length and width of peanut pods and seeds were recorded (in mm) using a digital caliper. To determine seed oil percentage, 150 g of peanut seeds were randomly selected from each plot, and their oil percentage was measured using the Soxhlet method after grinding the samples. Composite variance analysis was conducted after ensuring the uniformity of experimental error, and the mean traits were compared using the least significant difference (LSD) method. The stability of peanut genotypes was assessed using the GGE bi-plot analysis.

The variance analysis revealed that the interaction of location × genotype significantly affected the peanut plant's height, sub-branches, and pod diameter at a one percent probability level. Additionally, the year × location × genotype interaction significantly affected other traits such as pods per plant, seeds per pod, HSW, pod yield, seed yield, seed oil percentage, oil yield, shell yield, pod length, and peanut seed length and width at the 1% probability level. Notably, genotype 208 in the Rasht region exhibited the tallest average plant height (103.5 cm), which was not significantly different from line 201. Furthermore, the highest number of peanut pods (31.72 pieces) was observed for genotype 128 in the Rasht region in the first crop year, showing no significant difference with line 128 in the first and second crop years. Significant differences were also noted in the number of seeds per pod across different genotypes and regions. For instance, the second crop year in the Rasht region and genotype 113 yielded the highest peanut HSW (71.45 g), which was not significantly different from some lines in the two crop years in the Masal and Rasht regions. Furthermore, the pod yield of genotype 192 in the first crop year was superior in Rasht (5583 kg/ha), Masal (5233 kg/ha), and Talash (4166 kg/ha) regions compared to the other genotypes. Genotype 192 exhibited the highest seed yield (3777 kg/ha) in the first crop year in Rasht, representing a 133% increase compared to the control (NC2). These results underscore the significant influence of climatic conditions on peanut seed yield and the genetic potential variations among different genotypes in diverse regions. Additionally, genotype 192 in the first cropping year and Rasht region attained the highest peanut oil yield (1841 kg/ha), aligning with findings from other researchers regarding varying oil yields among different peanut lines.

Based on the findings of this study, all traits measured in peanuts were impacted by the interaction of genotype and environment. Line 192 displayed significantly superior quantitative and qualitative performance of peanut seeds to the NC2 variety, known as the Goli native variety, and the other studied lines. The increase in the number of pods per plant, seeds per pod, and the peanut HSW were important agronomic indicators in improving the performance of line 192 in the Guilan region. The results indicated that the interaction effect of genotype and environment led to changes in the yield components, resulting in changes in the yield of peanut seeds and pods per unit area, with the oil yield increasing in parallel with the grain yield. Using the GGE bi-plot method to evaluate performance stability, peanut line 192 was identified as a high-yielding line with high performance and stability in all environments. Therefore, groundnut genotype 192 is recommended for achieving the highest seed yield in the region's climatic conditions.

Lentil is a popular legume crop in the Mediterranean region, widely grown for its nutritious seeds and improving soil fertility. Interest in legumes is increasing as a protein source to replace meat in the future. Identification of high-yield genotypes with adaptation to a wide range of environments is one of the major goals in crop and lentil breeding programs. Combining the best linear unbiased predictions (BLUP), additive main effects, and multiplicative interaction (AMMI) methods in multi-environment experiments and multi-trait stability selection (MTSI) helps to better evaluate plant genotypes and achieve more accurate results. Additive main effect and multiplicative interaction (AMMI) and BLUP are two methods for analyzing multi-environment trials. The linear mixed effects model (LMM) and the restricted maximum likelihood (REML) estimator methods are among the important methods that have been proposed to analyze the data of multi-environmental experiments. In this regard, the BLUPs obtained from the interaction of genotype and environment are performed with principal component analysis or single value analysis on the matrix. This method uses the stability index of the weighted average of absolute scores of the best unbiased linear forecasts (WAASB), the weighted average of the stability index of WAASB, and the dependent variable (WAASBY). Researchers have also proposed an MTSI based on factor analysis, in which grain yield, other traits, and the stability of each are simultaneously used to identify stable genotypes. This research aimed to identify stable and high-yielding lentil genotypes in autumn cultivation.

To evaluate the seed yield stability of 12 lentil genotypes along with three check genotypes, including Kimia, Bileh Sawar, and local landrace, an experiment was conducted as a randomized complete block design with three replications at Agricultural Research Stations of Khorramabad (Lorestan), Zanjireh (Ilam), and Sararoud (Kermanshah) in three cropping years (2019-2022). Each plot consisted of four lines with a length of 4 m and a distance of 25 cm from each other. iIn addition to the usual crop care such as weeding and pest control, the desired traits and characteristics, such as the number of days to 50% flowering, plant height, and number of days to maturity, were measured during the growing season. Hundred-seed weights and the yield of each plot were measured after the maturity and harvesting of experiments. Combined analysis of variance (ANOVA) was performed using SAS software, and the average traits of the treatments were compared using the LSD test. For statistical analyses, the Metan Ver.1.9.0 (multi-environment trial analysis) package was used in the R software environment. To estimate stability quantities, singular value decomposition (SVD) was applied to the matrix of BLUPs obtained from genotype-by-environment interactions with an LMM. Variance components were estimated by the REML method. After analyzing the variance of the data, the stability parameters of WAASB and WAASBY (for simultaneous selection based on average performance and stability) were estimated using the eigenvalues obtained from the AMMI analysis on BLUP, and the best genotypes were selected with these two indicators. Genotypic stability values were obtained from the Harmonic Average of the Genotypic Values (HMGV) index. The compatibility of genotypes was evaluated based on the relative performance index of genotypic values (RPGV). The harmonic mean index and relative performance of genotypic value (HMRPGV) were used to simultaneously evaluate stability, compatibility, and seed yield.

The effect of environment, genotype, and genotype × environment interaction were significant on seed yield, plant height, days to flowering, days to maturity, seed filling period, seed filling ratio, seed yield formation rate, rainfall efficiency, and single seed weight. The genotype effect was significant on all traits, except for the seed-filling period. Based on the biplot analysis, genotypes 4, 6, 7, 9, and 10 had higher yield stability in addition to the highest seed yield. The Scree test showed that the first three principal components explained 45.41, 19.13, and 14.34% of the genotype × environment interaction variation obtained from BLUP for grain yield, respectively; in total, they justified 78.87% of the variation. Based on a weighted average of absolute scores of WAASB, genotypes 6, 10, and 12 were high-yielding and stable. Genotypes 1 and 10 were superior based on the (MTSI). The harmonic mean and HMRPGV introduced genotypes 10, 9, 4, and 12 as the genotypes that had high stability and compatibility in addition to high seed yields.

Based on all the analyses, genotype 10 was the most stable genotype, which, in addition to seed yield, was superior to other genotypes in terms of the other measured traits and can be a candidate for introduction as a new cultivar.

As one of the medicinal plants from the Orchidaceae family, Orchis simia tubers are traded all over the world. The unmarketable parts are usually discarded (e.g., inflorescence, leaves, and stems) and produce a large amount of unused biomass. Medicinal effects of plants usually result from the composition of their secondary metabolites. Identification and quantification of secondary metabolites will play an important role in exploiting the medicinal potential of the plant. Orchids grow in different environments and habitats, mainly due to the presence of a unique set of secondary metabolites that help these plants tolerate stressful conditions. Therefore, following traditional approaches, these plants have been proposed as an important source for biological exploration. A number of compounds obtained from different parts of the orchid indicate biological activity. Alkaloids, flavonoids, phenanthrenes, terpenoids, bibenzyl derivatives, and other biologically active compounds have been reported in orchids. The present study identifies the phytochemical substances in Orchis simia orchid.

To investigate the phytochemistry of the inflorescence, leaf, and stem of Simia orchid separately, the samples were collected from nature, washed with water, and dried in the shade. Each of the organs was then powdered separately, and methanolic extract was extracted by ultrasonic and centrifugation methods. Total phenol was evaluated by the Folin-Ciocalteau method. Antioxidant capacity was determined by the FRAP method. The main compounds were estimated quantitatively and qualitatively by high-performance liquid chromatography (HPLC) and liquid chromatography-mass spectrometry (LC-MS) methods.

HPLC and LC-MS analyses of O. simia samples led to the isolation of 28 secondary metabolites (four compounds of bibenzyl derivatives, eight phenolic and flavonoid compounds, and 16 alkaloid compounds). Phenolic and flavonoid compounds identified in Simia orchid included benzoic acid, caffeic acid, m-coumaric acid, rutin, luteolin, kaempferol, quercitrin, and quercetin. In parallel, the total phenol content and total antioxidant capacity of the extracts were also measured in the extract. The results of variance analysis of the obtained data showed that the leaf, stem, and inflorescence samples were significantly different from each other at the 1% level in terms of total phenol content and total antioxidant capacity. Among the studied organs, inflorescence extract was superior to the other studied tissues in terms of 28 secondary metabolites as well as total phenol and antioxidant capacity. The average comparison results showed that the total phenolic compounds and antioxidant capacity in different organs of the O.s simia plant were not the same, and they were placed in three different groups. The highest total phenol content (138.65 mg GAE/g dry weight) was observed in orchid inflorescences. The highest and lowest levels of antioxidant activity were obtained from inflorescence (77.58 μmol/g) and stem (53.24 μmol/g) samples, respectively.

A proper knowledge of the chemical composition of a plant leads to a better understanding of its potential medicinal value. In this research, therefore, the changes in biochemical compounds and antioxidant capacity of the extracts were studied in leaf, stem, and inflorescence organs. According to the results of this research, the inflorescence of O. simia is a potential source of antioxidant capacity, phenol, and alkaloids, which will have an important medicinal role. The secondary metabolites obtained from this plant are reported for the first time and will be useful for new medicinal developments and applications in the future. Moreover, this study will help reduce the waste of this orchid in industrial production because its aerial organs can be exploited for medicinal purposes.

The reduction of genetic diversity in tomatoes (Solanum lycopersicum L.), caused by domestication and breeding, has necessitated the use of all available genetic resources. Limiting cultivated tomato cultivars to modified cultivars and reducing genetic diversity are not desirable and cause production vulnerability and instability. Native populations can be used as genetic resources to improve and introduce superior crop varieties. Local populations are one of the most important genetic resources that are often non-uniform and consist of different and mostly pure genotypes. Breeding tomatoes means transferring desirable traits from parents to progeny and causing the variety and quality of tomatoes. Considering the great variety of tomato cultivars found in Iran, they can be used to select high-quality cultivars and produce desirable hybrids. This research aimed to select accessions for purposeful crossings to investigate the heritability of fruit traits, and the amount of heterosis in the F1, and to select progeny with small fruit and non-determinate growth.

In this research, seeds were collected from nine identified local accessions of native cherry tomatoes in Iran, including Kafshgiri from Gorgan, 2) Darklate, 3) Kurdistan, 4) Varamin, 5) Rasht, 6) Gorgan, 7) Rafsanjan, 8) Sari, and 9) Kermanshah. These accessions were subjected to 17 crosses, and the results of their first generations were analyzed afterward. After collecting pollen from the male parent and removing the stamens of the female parent flowers, the pollen was placed on the stigma of the female in the early morning before opening the flowers. In each mass, 6-10 plants were selected for crossing. In one cluster, 2-4 flowers were considered for crossing. Crossing was repeated in the case of no successful pollination and no fruit formation. After crossing and to control and prevent unwanted crossing by insects, the bushes were completely enclosed with a thin net (mosquito net). The formed fruits were harvested 30-40 days after mating. The fruits obtained in the laboratory were evaluated in terms of fruit volume, fruit weight, fruit length, fruit diameter, number of seeds in the fruit, Brix, acidity, and vitamin C. The experiment was based on a randomized complete block design with 17 treatments (17 crossings) in three replications. One-way analysis of variance (ANOVA) for the measured traits was performed using SAS software version 9.1. Other calculated parameters were variance components, general heritability, phenotypic, genotypic, environmental diversity coefficients, and the degree of trait heterosis.

The results of ANOVA for different fruit traits showed a significant difference between the studied crosses in terms of fruit volume, fruit length, fruit weight, fruit diameter, number of fruit seeds, Brix, acidity, and vitamin C at the probability level of 1%. The comparison of the average traits showed that fruit weight, fruit diameter, and fruit length traits were the highest in the progeny of the Kermanshah × Rasht cross with values of 28.16 g, 3.29 mm, and 2.71 mm, respectively. The highest fruit volume (18.8 ml) was recorded in the offspring of the Sari × Rasht cross. The highest number of seeds per fruit was counted at 141 and 140, respectively, in the cross between Gorgan × Varamin and Shoghgiri × Kurdistan. The highest Brix (8.67%) belonged to the crossbreed of Rafsanjan × Kurdistan, the highest acidity (11.03 mg/100 ml of water) to the crossbreed of Rafsanjan × Rasht, and the highest vitamin C (1.6 mg per 100 ml of water) to the progeny of Kafshgiri × Rafsanjan.  In the fruit size, fruit weight, number of fruit seeds, Brix, acidity, and vitamin C traits, the genetic variation coefficient was higher than the phenotypic variation coefficient, indicating the lesser effect of environmental factors on these traits. In fruit length and diameter, the phenotypic diversity coefficient was higher than the genetic diversity coefficient. The estimated heritability (h2) of traits revealed that fruit volume, fruit weight, acidity, and vitamin C traits had general h2 from 80 to 99%. The Brix value showed the lowest general h2 of 59%. The results showed positive heterosis in fruit diameter and volume. Negative heterosis was observed in fruit length, fruit weight, and number of seeds. All crosses showed positive and high heterosis regarding fruit size, and the highest fruit size heterosis with 10.71 was obtained in the progeny of the Kurdistan × Kafshgiri cross. The superior cross in terms of fruit diameter was identified in the Kermanshah × Rasht cross, with a heterosis of 7.53.

The obtained results showed that the highest level of general h2 and genetic progress were found for the vitamin C, fruit acidity, fruit weight, and fruit volume traits, respectively, which were found in the crossings of Kafshgiri × Rafsanjan, Rafsanjan × Rasht, and Kermanshah × Rasht. In terms of fruit size, fruit weight, number of fruit seeds, soluble solids, acidity, and vitamin C, the genetic variation coefficient was higher than the phenotypic variation coefficient, indicating less influence of environmental factors on these traits. In other words, high heritability, genetic progress, and genetic diversity for quantitative and qualitative traits can help breeders choose the best combination and reach an optimal level of performance potential.

Cumin (Cuminum cyminum L.) is an aromatic, annual herbaceous plant from the Apiaceae family. Cumin is one of the tolerant medicinal plants to water deficit conditions with a short growth period, which can produce acceptable and economic yield under water deficit conditions. This plant is currently the second most used spice in the world after pepper (Pepper nigrum), suggesting its high importance. This experiment aimed to evaluate ecotypes, planting dates, and relationships between ecotypes and planting dates and to identify high-yield cumin stable ecotypes using AMMI and GGE bi-plot methods.

The stability of the grain yield of seven cumin ecotypes (Bardascan, Birjand, Taibad, Davarzan, Ferdos, Salehabad, and Nehbandan) was investigated in an experiment based on randomized complete blocks design with three replications in four planting dates (November 6, December 6, January 5, and February 5) at the research farm of the Southern Kerman Agricultural and Natural Resources Research and Education Center during 2020-2021 crop year. The seeds were planted by hand on the rows at a depth of 1 cm with a distance of 5 cm from each other. The distance between the rows was 20 cm. Seeds were irrigated with the drip method, and weeds were controlled by hand. The plants were harvested after physiological maturity, and the grains were separated from other organs and recorded as the grain yield of each plot. The yield stability of the ecotypes was analyzed using the AMMI model, and the first and second interaction components of AMMI (IPCA1 & IPCA2) were used as stability parameters for the ecotypes and the planting dates (environments). The GGE bi-plot method was used to analyze the obtained data, interpret the ecotype and planting date interaction, and determine mega-environments.

The results of compound variance analysis showed significant effects of the environment (planting date), ecotype, and the interaction of planting date × ecotype. Due to the significance of the environmental effect and the justification of 80% of the variation by this effect, as well as the significance of the planting date × ecotype, stability analysis of grain yield was conducted for ecotypes in different planting dates. The results of AMMI analysis showed that the two components, IPCA1 (AMMI 1) and IPCA2 (AMMI 2), included 93.56% of the total variance of the genotype × environment interaction. The AMMI stability value (ASV) was used for the simultaneous use of all components. The ASV statistic indicated that the Nehbandan ecotype with the lowest value (1.91) was the most stable ecotype, and the Ferdos and Salehabad ecotypes with the highest ASV value were the most unstable ecotypes. The results of the GGE biplot method revealed that the first and second principal components accounted for 93% of the total variation related to the ecotype and planting date interaction, which indicated the validity of the GGE-biplot analysis. Based on GGE biplot results, planting dates of November 6 and December 6 were in the same megaenvironment and produced the highest grain yield. Similarly, the two planting dates of January 5 and February 5 were in the same megaenvironment and produced the lowest grain yield. The graphs showed that the Ferdos ecotype had high special adaptability with the planting dates of November 6 and December 6, and the Birjand and Davarzen ecotypes had high special  adaptability with the planting dates of January 5 and February 5. The Taibad, Salehabad, and Nehbandan ecotypes had no special compatibility with the studied planting dates. Ferdos and Birjand ecotypes had a higher average grain yield than the other ecotypes, but they were placed in the group of ecotypes with low stability due to their distance from the AEC line. The Nehbandan was the most stable ecotype due to its adjacency to the AEC line, but it produced a low grain yield. In addition, the results of the GGE bi-plot showed that the Birjand was the closest ecotype to the ideal genotype hence it was considered the most desirable ecotype. Bardascan, Ferdos, and Daverzan ecotypes were the ecotypes in the next ranks in terms of desirability, and Salehabad and Taibad ecotypes were identified as undesirable ecotypes due to their greatest distance from the ideal genotype.

The results of compound variance analysis showed significant effects of the environment (planting date), ecotype, and the planting date × ecotype interaction effect. The biplot results of AMMI analysis showed that the Nehbandan ecotype was the most stable and Ferdos and Salehabad ecotypes were the most unstable ecotypes. This was also confirmed through the ASV. The results of the GGE bi-plot indicated that the planting dates of November 6 December 6 with the highest average grain yields were located in a mega-environment, and the two January 5 and February 5 planting dates, with the lowest average grain yields, were also located in the same mega-environment. This can indicate the determination of the time range of the planting date to obtain an acceptable yield, although the yield decreased with a delay in planting. Finally, the figure showed that the Nehbandan was the most stable ecotype with below the average yield, and Birjand was the most ideal ecotype. Therefore, it can be concluded that the Birjand ecotype cultivation not only produces a high yield but also has high relative stability.

Wheat is a plant that is cultivated in different environments around the world and provides approximately 20% of the energy and 25% of the protein needs of the world population. Durum wheat (Triticum turgidum L. var durum) is an industrial product that is mainly used in the pasta production industry. In addition, the difference in the price of durum wheat in the world markets compared to bread wheat has prompted some countries to increase the area under cultivation and production of this crop and start its export to supply their bread wheat needs through imports, thereby saving large sums of foreign currency. Although it is often believed that durum wheat produces less yield than bread wheat, the results of national uniformity tests conducted in recent years have shown that not only the yield of durum wheat is not lower than that of bread wheat but also the promising lines of durum wheat has an increase in yield in the conditions of the tests. If we consider a 20% increase in the price of its grain, the development and promotion of its cultivation in tense areas can provide more economic benefits for the producers and the country. The production of crop cultivars with appropriate characteristics, high yields, and stable production is one of the main goals of wheat breeding programs. Therefore, the identification of suitable lines and cultivars for cultivation in each geographical region is of special importance. It is possible to achieve such a goal by evaluating the set of new lines of each plant in each region. According to the special program of the Seed and Plant Improvement Institute (SPII), which plans to increase the self-reliance coefficient of wheat for the introduction of new varieties, this study also aims to obtain more productive and promising durum wheat genotypes in the northern region of Khuzestan province.

To identify the best durum wheat lines in terms of yield and other agricultural traits, an experiment was carried out during two 2020-2021 and 2021-2022 crop years in the research farm of Safiabad Agricultural and Natural Resources Research Center, Dezful. New durum wheat lines (n = 76) were evaluated observationally along with three control varieties Aran, Hana, and Mehregan in the first year and 37 best lines of the first year were evaluated in the form of an alpha lattice design, along with three control varieties, in the second year. In addition to seed yield, other important agricultural traits, including heading date, physiological maturity date, length of seed filling period, lodging rate, plant height, and 1000 kernel weight, were calculated and considered in the final selection. For the results of each year, statistical parameters (indices of central tendency) were measured using Excel software. The correlation between traits was measured using SPSS software. In addition, MetaR software was used to analyze the variance of the Alphalatis design. To group the tested lines, a dendrogram was drawn using the Ward method and SPSS software.

The results of the trait analysis in the first year showed that the tested lines had a very high variety so that their yield ranged from 5883 kg/ha for the weakest line to 8350 kg/ha for the best line, and the average yield of all tested lines was 7231 kg/ha. In addition, the average yield of 37 selected lines in the second year was about 6423 kg/ha, which was about one ton less than the yield of the same lines in the first year (7397 kg/ha). One of the main reasons for this decrease in yield can be attributed to the increase in the temperature of March in the second year (20 °C) compared to the first year (16 °C), which caused the average grain filling period from 49.3 days in the first year to reach 45.1 days in the second year. Finally, according to the total measured traits of each line during two crop years and cluster analysis, lines 6, 38, 57, 58, 59, and 73 were selected as the best lines. The yields of these lines were 8350, 8303, 7917, 7537, 8000, and 8130 kg/ha in the first year, and 6933, 6917, 6775, 7045, 7178, and 6815 kg/ha in the second year, respectively. The average yields of Aran, Hana, and Mehregan controls were 7207, 7148, and 7109 kg/ha in the first year, and 5698, 6208, and 5927 kg/ha in the second year, respectively. The dendrogram drawn using the total of the measured traits placed the 40 studied lines in three different groups, each of which had two subgroups. An important point about the cluster analysis is that the selected top lines (6, 38, 57, 58, 59, and 73) are placed in one group, and only line 61 is added to them.

In the pedigree study of the top lines, a common parent named PLATA was found in the pedigree of the four lines, which probably made these lines superior to the other tested lines. According to these results, it is possible to propose and implement the use of this line to improve the wheat breeding program in the hot and dry areas of the south.

Khuzestan, as one of the most important corn-producing provinces of Iran, lacks improved cultivars compatible with extremely hot and dry conditions and relies on foreign cultivars, including the old single cross 704 variety. To prepare suitable cultivars for Khuzestan, lines should be produced and evaluated in the region that can be used in the production of hybrid and open-pollinated cultivars.

To identify and improve the important and effective traits on maize grain yield, 289 corn lines, which were bred in Khuzestan, were crossed with the hybrid single cross 704 and evaluated in two cropping seasons (summer 2016 and spring 2017). The experiment was carried out with a simple lattice square design of 17 × 17 and two replications at the Safi-Abad of Dezful Agricultural Research Center in 2016 and 2017. The traits measured from the growing stage to harvest were plant height, tassel length, tassel branch number/plant, stem diameter, number of leaves / plant, ear diameter, grain depth, ear length, number of rows/ear, grain number/row, 100-grain weight, grain yield, biological yield, and harvest index based on the guidelines of the corn and fodder plant department of the Iranian seed breeding and preparation research institute. The yield based on 14% seed moisture along with other traits was measured and calculated from the two middle lines of each experimental plot. The genetic parameters in this research were calculated using the mathematical expectation of the mean square as the basic design of random complete blocks in the form of genetic relationships. To analyze the correlation between the traits and to identify the common factors affecting the studied traits, decomposition into factors was used with the principal component analysis and varimax rotation. Eventually, the analysis of variance (ANOVA) of data and calculation of correlation coefficients between variables were done using SAS Ver 9.20 software and analysis into factors with StatGraphics Ver 19.0 software.

The results of ANOVA showed an acceptable genetic diversity among promising maize lines in terms of all studied traits, except for ear length, number of rows/grain, and number of grain/row. The results of the composite analysis in two cropping seasons revealed that the effect of genotype was significant on all studied traits at the level of 1%, suggesting significant genetic diversity among the studied genotypes for all traits. The significance of the genotype × season interaction effect for all studied traits at the 1% level indicates the different responses of genotypes in summer and spring. The highest and the lowest genetic and phenotypic variances in summer and spring belonged to biological yield and seed depth traits, respectively. The ranges of genetic diversity in the studied traits in summer and spring ranged from 1.96 to 32.72 and from 7.63 to 29.88, respectively. Among the studied traits, the highest genetic and phenotypic diversities were observed in the number of tassel branches/plant trait (32.72 and 33.35, respectively). The highest heritability rates in spring were observed for biological yield (97.69%), grain yield (97.43%), grain depth (97.06%), ear length (96.30%), and plant height (95.74%). On the other hand, the highest amount of heritability along with the highest amount of genetic improvement was observed for the biological yield and grain yield traits, both of which can be considered the most important criteria for selecting parental lines in breeding programs based on this study. The results of the analysis of correlation coefficients for the studied traits in summer indicated that the grain number/row (0.81), biological yield (0.72), ear diameter (0.62), and grain depth (0.52) produced medium to high grain yields among the studied traits. In spring, the biological yield (0.86), 100-grain weight (0.65), grain number/row (0.64), ear length (0.64), and ear diameter (0.63) had medium to high effects on the seed yield.The results of analysis in the factor analysis in summer and spring revealed that five hidden and independent factors justified 71.40 and 72.27% of the total data changes, respectively. Five hidden factors for summer were the share of grain yield from the total dry matter (33.10%), plant height factor (15.80), ear diameter component factor (8.80), tassel height factor (7.50), and grain weight factor (7.20).

The two factors, namely the share of grain yield from total dry matter and the plant height factor, account for more than 61% of the total justifiable variation of 14 traits in 289 lines. These two factors enable us to define and recognize differences and similarities between the lines that grow in spring conditions in Khuzestan.

Salinity is one of the major abiotic stresses and the most limiting factor in agricultural production worldwide, affecting the growth, development, and final yields of crops. Rapeseed is one of the most important sources of oilseeds in the world, and its seeds contain more than 40% of oil. Moreover, the meal obtained from oil extraction has more than 35% protein, hence it currently ranks third among oil crops in the world after soybean and oil palm, making it necessary to identify the genotypes that tolerate salinity stress. The development and improvement of rapeseed cultivars with salinity tolerance and acclimation offer promising prospects for improving sustainable production in this area. Therefore, the current study aimed to investigate the responses of rapeseed genotypes to salinity stress through analyses of agronomic and biochemical traits.

The genetic diversity between rapeseed lines in terms of agronomic, morphological, and physiological traits in saline soils was investigated in an experiment based on a randomized complete block design with 17 autumn rapeseed genotypes with three replicates in the research farm of East-Azarbaijan Agricultural and Natural Resources Research and Education Center. The measured traits were plant height, the number of fertile pods, number of seeds per pod, pod length, pod area, plant growth rate, 1000-seed weight (TSW), seed yield, oil content, and oil yield. The relationships between yield, yield components, and morphological traits were explored using the analysis of variance (ANOVA), comparison of averages, correlation analysis, cluster analysis, and biplot to understand the relative importance of traits affecting the yield of the studied genotypes.

The studied genotypes were significantly different from each other in pod length, pod area, number of fertile pods, number of seeds per pod, plant growth rate, seed oil percentage, plant height, TSW, grain yield, and oil yield. However, there were no significant differences between the studied genotypes in terms of harvest index and number of actual pods to potential pods. According to the mean comparisons, genotypes 5, 11, and 15 can be introduced as salinity-tolerant lines, and genotypes 2, 4, 6, 9, and 12 can be considered salinity-sensitive lines. According to the other traits, genotype 11 produced a high pod length, number of fertile pods, oil percentage, and oil yield, genotype 5 had a high growth rate and oil percentage, and genotype 15 presented a high height and number of fertile pods. According to the cluster analysis, the second and third groups contained tolerant and susceptible genotypes, respectively. The genotypes in the second group had the highest percentage of positive deviation from the overall mean for grain yield, plant height, harvest index, seed oil percentage, pod length, pod area, and number of fertile pods. Based on the biplot analysis, Karaj 8 and 14 genotypes had a strong relationship with the number of  fertile pods, number of seeds per pod, pod length, pod area, and plant growth rate. Based on the obtained results, the plant height, TSW, seed yield, and oil content traits were closely correlated with Karaj 5, 7, 11, 10, and 15 genotypes. Based on the results of correlation analysis, the correlation coefficient of seed yield was positive and significant for three traits, i.e., plant height, oil percentage, and number of fertile pods, and the highest correlation coefficient (r = 0.879) was obtained for seed yield with seed oil percentage. Positive and significant correlations were measured for the number of seeds in pods with pod length (r = 0.699), pod area (r = 0.555), number of fertile pods (r = 0.678), and number of actual pods. Therefore, genotypes characterized by longer and more abundant pods play a crucial role in improving seed quantity, a key component of grain yield in saline environments. Consequently, the size and number of pods per plant serve as indicators of high-yield potential under such conditions. Based on the results of the principal component analysis (PCA), the first and second components had the highest relative variances, accounting for 44.66% and 31.22% of the total variance, respectively. Together, these two components accounted for 75.88% of the total variance. Factor loadings showed that traits such as number of fertile pods, seed yield, oil yield, and seed oil content had the highest factor loadings in the first component. Similarly, the plant growth rate had the highest factor load in the second component among all the studied traits. Cluster analysis divided the genotypes into four groups, and its dendrogram showed that all the studied genotypes were divided into four separate groups based on all the measured traits. The first group comprised three Karaj 1, 13, and 16 genotypes, the fourth group (like the first group) contained three Karaj 5, 11, and 15 genotypes, and the third group had five genotypes, namely Karaj 3, 7, 8, 10, and 14. The remaining genotypes were assigned to the second group.

The results of the present study demonstrate acceptable genetic diversity among rapeseed genotypes in terms of the evaluated traits in saline lands. This shows the importance and the possibility of using these genetic resources to achieve promising and superior genotypes in breeding programs.

One of the main challenges for wheat farmers and breeders worldwide is the limitation of growth, development, and yield in the face of environmental stresses. Dehydration is one of the most important stresses, leading to decreased efficiency and production of this agricultural product. As a classic adaptation mechanism, drought escape allows the plant to complete its life cycle before occurring drought stress. This issue is particularly important in areas where drought occurs at the end of the growing season. In addition to controlling the growth habit of wheat, photoperiod genes play a key role in the flowering time and earliness of wheat and are of interest in drought tolerance research. This research aimed to investigate drought stress tolerance in wheat isogenic lines using quantitative indices of stress tolerance.

In previous research, earliness was transferred from an Australian early-heading variety, Excalibur, to Roshan and Kolhaidari using the backcrossing method to develop the BC3F2 generation. In the present research, the early-heading plants of this generation were crossed with the recurrent parents (Roshan and Kolhaydari) in 2017 to obtain the first generation from the fourth backcross. In this generation, parents and offspring were evaluated for photoperiod-controlling genes. During the first to fourth generations of the fifth backcross, only heterozygous plants (Ppd-D1a/Ppd-D1b) were selected using specific markers for the Ppd-D1 locus. Five generations of backcrossing and four generations of selfing were performed to have different alleles of Ppd-D1 in the same genetic background. Both homozygous genotypes (Ppd-D1a/Ppd-D1a and Ppd-D1b/Ppd-D1b) were selected in the fifth generation of the fifth backcross to generate isogenic lines for Ppd-D1. In this generation, homozygous lines were selected for final field trials. To investigate the effect of photoperiod genes on drought tolerance, four isogenic lines were created in experiments with a randomized complete block design and four replications in the crop year 2018-2019 in the rainfed conditions of Sepidan and in the well-watered conditions of Kerman in the crop year 2019-2020. The number of days to flowering, number of days to maturity, grain-filling period, number of spikes per square meter, number of seeds per spike, 1000-grain weight, and grain yield were evaluated in this research. The drought tolerance of isogenic lines was evaluated using eight indices, including productivity mean, yield index, stress tolerance index, geometric mean of production, stress sensitivity index, yield stability index, and stress tolerance score.

Unlike Ppd-D1b, which is a photoperiod-sensitive allele, Ppd-D1a as a photoperiod-insensitive allele, effectively improves early flowering under dryland and well-watered conditions. The Ppd-D1a allele reduced the number of days to flowering in the Roshan genetic background by 3.75 and 4.00 days, and in the Kolhaydari genetic background by 5.08 and 4.7 days in rainfed conditions of Sepidan and well-watered conditions of Kerman, respectively. These results were also reflected in the number of days to maturity, in which Ppd-D1a improved this trait by 7.04 and 8.02 days in the Roshan and Kalhaydari genetic backgrounds, respectively. Ppd-D1a improved earliness in both genetic backgrounds, with better performance in the Kalhaydari genetic background. These findings confirm the interaction between genetic background and the Ppd-D1 gene. Despite the positive effect of Ppd-D1a on earliness in both genetic backgrounds under all environments, there was an interaction between genetic backgrounds and Ppd-D1a alleles for earliness, implying that genetic backgrounds determine the extent of the response to selection. Among the studied isogenic lines under drought stress conditions, the Ppd-D1a allele significantly improved grain yield in the Roshan and Kalhaydari genetic backgrounds, respectively (96 kg/ha and 99 kg/ha). However, there were no significant differences between isogenic lines in both genetic backgrounds under well-watered conditions. These results highlight the importance of marker-assisted selection and backcrossing for Ppd-D1a in breeding programs for dryland conditions. According to yield correlation (0.952) in drought stress and normal environments, selection in both conditions can show high-yielding isogenic lines with good yield stability. In normal conditions, there was a high correlation between the geometric mean of production, stress tolerance index, yield index, stress sensitivity index, and grain yield. This correlation was high and significant for the geometric mean of productivity, stress tolerance index, and yield index under drought stress conditions. The stress tolerance score showed that Ppd-D1a isogenic lines in both Roshan and Kolhaydari genetic backgrounds, which had the photoperiod insensitivity allele, had the highest stress tolerance score. Principal component analysis showed that Ppd-D1a in the Roshan background was the most drought-tolerant isogenic line.

The results showed that Ppd-D1a not only improved earliness in the isogenic lines but also improved drought tolerance using the drought escape mechanism.

The increased demand for cereals that are consumed by humans and livestock can be met through the development of planting drought-tolerant genotypes. Due to the interaction of genotypes × environment, the best genotype in one environment may not be the best in other environments, and therefore, this interaction provides valuable information about the yield of each genotype in different environments and plays an important role in evaluating yield stability. Genetic modification of drought tolerance in crops is one of the most stable and cost-effective approaches to increase production and yield stability. Examining the compatibility and stability of grain yield based on various parametric and non-parametric stability statistics and evaluating tolerance to drought stress based on stress indices in promising barley genotypes of the country's temperate climate are among the goals of this research.

To assess grain yield adaptation and stability and to select high-yielding barley genotypes suitable for terminal drought stress in the temperate climate of Iran, 16 barley genotypes were cultivated during two crop years 2021-2023 in a randomized complete blocks design with three replications in three research stations including Varamin, Birjand, and Yazd under two none-stress and drought stress conditions at the end of the season (12 environments). After determining the grain yield, stress indices, including MP, GMP, TOL, HARM, STI, YI, YSI, RSI, and SSI, and the correlation of each with grain yield were calculated in this study. Stability statistics included Nassar and Huehn’s stability statistics (S(1-6)), Thennarasu’s stability statistics (NP(1-4)), deviation from regression (S²dᵢ), regression coefficient (b), Shukla’s stability variance (σ²ᵢ), environmental variation coefficient (CV), variance component (θᵢ), coefficient of variance (θ(i)), Wricke’s ecovalence (Wᵢ²), and Kang’s sum of ranks (KR). Their relationships were calculated based on Pearson’s correlation. Analysis of variance (ANOVA), mean comparison, and simple correlation were calculated using the SAS-9.0 program, stability statistics were calculated using STABILITYSOFT and principal component analysis (PCA). Stress indices and the correlation of each of these indices with grain yield were calculated using iPASTIC. The three-dimensional distribution diagram of genotypes in the ranges of A, B, C, and D was drawn using Grapher software.

The results of the combined ANOVA indicated the significance of the genotype × environment interaction. According to S(1-2) statistics, G7, G10, G11, and G3, and according to S(3-6) statistics, G7, G3, and G9 were the most stable genotypes. Among the non-parametric Thennarasu’s stability statistics according to the NP(1) criterion of G9, G3, and G5, according to NP(2) G5, G3, and G8, and according to NP(3) and NP(4) criteria, G7, G3, and G9 were recognized as the most stable genotypes. Based on Wricke (W²) and Shukla (σ²) equivalency stability statistics, G3, G9, and G13 were the most stable genotypes. Based on Eberhart and Russell's regression method, G9, G7, and G3 genotypes, with high yields, had general compatibility and good yield stability. Based on Francis and Kannenberg (CVi), genotypes G1, G2, and G15 had the lowest coefficients of environmental variation. Based on the average rank of each genotype in all stress indices (AR), G2, G7, and G3 genotypes were the most tolerant, and G14, G11, and G10 were the most sensitive genotypes to drought stress at the end of seasons, respectively. In the drought stress conditions at the end of the season, grain yield had positive and significant correlations with YI, HM, GMP,  STI, MP, YSI, and RSI indices and negative and significant correlations with the SSI index. In non-stress conditions, grain yield had positive and significant correlations with MP, GMP, STI, HM, and YI indices, but no significant correlations were observed between grain yield and SSI, TOL, YSI, and RSI indices. The PCA revealed that the first and the second principal components explained 69.71% and 30.27% of the variance of the main variables, respectively. The first main component had a positive and high correlation with yield in both stressed and non-stressed environments, as well as MP, STI, GMP, and HM indices. The second component showed a positive and high correlation with grain yield in the non-stressed environment and TOL and SSI indices; it also had negative and high correlations with RSI and YSI indices. Based on the biplot diagram, G3, G7, G8, G9, G12, and G13 presented higher grain yield potential and are more tolerant to drought stress.

In this study, grain yield had negative and significant correlations with NP(3), KR, NP(2), NP(4), S(6), and S(1) statistics, respectively, therefore these statistics can be used for identifying stable genotypes. G3, G7, and G9 with averages of 6732.9, 6730.6, and 6608.1 kg/ha, respectively, not only produced the highest grain yield but also showed the highest grain yield stability and tolerance to terminal drought stress among the studied genotypes based on the total ranking of all studied stability statistics and stress indices. Therefore, they can be used as new cultivars in drought-affected regions in temperate climates or as desirable genetic materials in barley breeding programs for drought tolerance.