جستجوی مقالات مرتبط با کلیدواژه « سازگاری » در نشریات گروه « زراعت »

It is important to attend the physical and biochemical characteristics of the fruit, especially in newly introduced cultivars that may be unknown to the consumer. Nutritive compounds in fruit are usually influenced by variety, rootstock and post-harvest conditions. In this article, the results related to the evaluation of the fruit quality of three varieties of oranges on two rootstocks have been examined at harvest time and during storage.

Fruit of Thomson Novel, Fukumoto, and Navelate oranges on two rootstocks including Swingle Citrumelo and Citrange C-35 were harvested when TSS/TA reached about 6.5-7. Fruits were evaluated at the harvest time and then stored in cool storage (5oC, RH=85%) for 60 days. The fruit quality were checked on the 30th and 60th days of storage for two years. Evaluated Characteristics included fruit lenght, width, thickness; arithmetic, geometric, equivalent diameter and harmonic means ; fruit aspect ratio, sphericity, surface area, true volume, apparent volume, volume error, density, weight, weight loss, peel thickness, juice percentage, peel color indices (L*, a*, b*, hue angle, chroma and CCI), total soluble solid (TSS), titratable acidity (TA), technological index (TI), total phenol, ascorbic acid, antioxidant capacity and organoleptic evaluation.

The fruit of Navelate orange on Citrumelo rootstock and Fukumoto orange on C-35 rootstock had smaller physical characteristics than Thomson Novel. In all treatments, the shape of the fruit was balanced and without elongation or indentation. The fruit of Navelate on Citrumelo (aspect ratio: 96.96) was slightly elongated. Navelate variety on C-35 produced larger and heavier fruit similarly Thomson fruit. The volume error in Fukumoto and Navelate varieties and on both rootstocks was less than the control. At the time of harvest, the fruit of the Fukumoto had a thicker peel (more than 4 mm) on both rootstocks than the other cultivars. The amount of juice percentage was not significantly different than control at harvest time, but at the end of storage, the fruit of Navelate on the C-35 had the lowest (35.73%) of juice percentage. Fukumoto and Navelate fruits on both rootstocks had higher TI index than Thomson at harvest time. The TSS at harvest time was the lowest (9.22 and 9.78 respectively) only in the fruits of Thomson and Navelate on Citrumelo rootstock. Navelate and Thomson fruits had higher TA on C-35 rootstock. In all treatments, the amount of TA decreased during storage. Fukumoto fruit on C-35 had the highest TSS/TA ratio, which was preserved during storage. The fruit of Navelate had a lower TSS/TA level at the harvest time on both rootstocks (7.12 and 7.5). At the time of harvest, the average ascorbic acid in all three cultivars on C-35 rootstock was higher than the same cultivars on Citrumelo rootstock. The value of a*, hue angle, and CCI of the skin of Navelate orange with a negative value were different from the other cultivars on Citrumelo and C-35 rootstocks (-1.12 and -0.61 respectively). The antioxidant capacity of Fukumoto fruit was 43.99% on Citrumelo and 62.12% on C-35 rootstock at the harvest time. The amount of antioxidant capacity and total phenol increased during storage. The organoleptic evaluation showed that the Fukumoto as Thomson, scored better in relations to peel and pulp appearance, sourness, aroma and taste than the fruit of the same cultivars on the C-35 rootstock. Navelate fruit produced higher score in terms of taste and sweetness (7) on Citrumelo rootstock than C-35 (6) rootstock.

Based on the TSS/TA ratio, it was determined that Fukumoto and Navelate oranges are early and late ripening respectively. Citrumelo rootstock increased late ripening in Navelate fruit and contrary C-35 rootstock increased early ripening in Fukumoto fruit. According to the a* and CCI indices, the Navelate fruits did not have suitable coloring at the time of harvest, and Citrumelo rootstock delayed the color change of the fruit skin. In general, the organoleptic characteristics of the fruit on C-35 were better than Citrumelo. Finally, it seems that the combination of Navelate/C-35 is more suitable than Navelate/Citrumelo. Fukumoto/C-35 combination is suitable for having early ripening fruit.

In order to produce varieties of barley that tolerate drought and cold, Artan variety was entered into internal tests of the cold and temperate cold stations of the Dryland Agriculture Research Institute through the international center of ICARDA in the form of the of segregating populations (F3 generation) in 1997-98. Genetic purity was achieved by the modified bulk method. Then, during several years, it was evaluated in Maragheh, Shirvan, Ghamlo, Sararoud, Zanjan, Urmia and Ardabil stations in yield trials and yield stability experiment. The Artan variety with an average seed yield of 2.142 Kgha-1 had 11% higher seed yield than the Abidar check variety. Artan had relatively early maturity, height and high thousand-seed weight. Artan variety has general adaptability and good yield stability, facultative growth habit, resistance to seed shattering, tolerance to cold and drought, relative early maturity, high plant height (83 cm) and high thousand-seed weight (42 gr). It has also good level of resistant to yellow rust and brown rust diseases of barley. Artan variety was selected and released as a new cultivar due to its high yield and favorable agronomic characteristics for planting in the cold and moderately cold regions in rainfed areas of the country.

Barley is highly variable in adaptation to marginal environments. The main objective of barley breeding programs has been on improving yield by developing new cultivars with high grain yield and quality characteristics for different climates. Ansar is a new high yielding and drought tolerant cultivar of barley with facultative growth habit. It was released in the year 2013 for rainfed cold areas of Iran. Ansar originated from a cross Yea168.4/Yea605.5//Yea206-4A-3 (a high yielding line received from ICARDA). Based on regional yield trials, the average graind yield of Ansar was 3025 kgha-1, which was 55 kg higher than Sahand (2970 kgha-1). In comparison with Sahand (check), Ansar had 1-2 days early maturity, 4-6 cm taller stem, 1-2 g higher TKW. In onfarm trials, Ansar had 34, 12, 11% more grain yield than Sahand in Miyaneh, Oshnaviyeh and Piranshar regions, respectively. Ansar is tolerant to cold, drought, grain shedding, lodging, two-rowed variety with 73 cm plant height. Ansar is resistance to leaf strip, scald and powdery mildew and rust. It can be concluded that Ansar is not only a high-yielder, possesses better quality traits and tolerant/resistant to all diseases, but is also best suited in cold rainfed areas of Iran.Barley is highly variable in adaptation to marginal environments. The main objective of barley breeding programs has been on improving yield by developing new cultivars with high grain yield and quality characteristics for different climates. Ansar is a new high yielding and drought tolerant cultivar of barley with facultative growth habit. It was released in the year 2013 for rainfed cold areas of Iran. Ansar originated from a cross Yea168.4/Yea605.5//Yea206-4A-3 (a high yielding line received from ICARDA). Based on regional yield trials, the average graind yield of Ansar was 3025 kgha-1, which was 55 kg higher than Sahand (2970 kgha-1). In comparison with Sahand (check), Ansar had 1-2 days early maturity, 4-6 cm taller stem, 1-2 g higher TKW. In onfarm trials, Ansar had 34, 12, 11% more grain yield than Sahand in Miyaneh, Oshnaviyeh and Piranshar regions, respectively. Ansar is tolerant to cold, drought, grain shedding, lodging, two-rowed variety with 73 cm plant height. Ansar is resistance to leaf strip, scald and powdery mildew and rust. It can be concluded that Ansar is not only a high-yielder, possesses better quality traits and tolerant/resistant to all diseases, but is also best suited in cold rainfed areas of Iran.

Genotype × environment interaction is the main limiting factor in identifying superior genotypes in plant breeding programs. This research was conducted with the aim of investigating the genotype × environment interaction and selecting high-yielding and stable quinoa genotypes using AMMI (Additive Main effects and Multiplicative Interaction) and GGE (Genotype plus Genotype by Environment interaction) biplot methods. A number of 30 different quinoa genotypes prepared from the IPK Institute of Germany with different origins were cultivated as plant materials of this experiment in the form of randomized complete block design in two environments, Buin Zahra and Takestan, during the two crop years of 2022-2023. The results showed that the variance caused by the effects of genotype, environment and genotype × environment interaction was significant for grain yield and this trait was more affected by genotypic diversity. The variation of genotype × environment interaction in the AMMI method was explained by the Two principal components. Using this method, genotypes G14, G11, G12, G23, G1, G5 and G13 were recognized as high-yielding and stable genotypes, and the Takestan environment in the second year was introduced as a stable and high-yielding environment. Also, the first two main components in GGE biplot method explained about 92% of the variation of genotype and genotype × environment interaction for grain yield. In this method, the studied environments were placed in two mega-environments. All environments had high differentiation ability for grain yield in the studied genotypes. G11 and G14 genotypes were identified as ideal genotypes. Finally, based on both AMMI and GGE bi-plot methods, genotypes G5, G11, G12, G13, G14 and G23 among the quinoa genotypes studied were identified as high-yielding and stable genotypes, and Buin Zahra environment was introduced as an ideal environment.

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.

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 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.

In order to study the adaptability and determine the cultivation value of new corn hybrids, 16 forage maize genotypes of local and exotic origin were evaluated along with three common genotypes (KSC704, Bolson and Massil) in four different regions (Karaj, Maghan, Jovein and Dezful) using randomized complete block design with four replications in two years (2020 and 2021). Important traits such as days to flowering, plant height, ear height, ear-to-plant ratio and wet silage yield were recorded, based on the manual of value for cultivation and use test. To evaluate the yield stability, Francis and Kannenberg's environmental coefficient of variation was used. The results of two-years mean comparison of yield showed that Egean, Sy-Lavaredo, ZP873, Zarin2 and Zarin3 hybrids had the highest forage yield with 65.4, 64.8, 64.7, 63.8 and 62.3 tons per hectare, respectively, While the average yield of check varieties was 60 tons per hectare. Sy-Andromeda, Sy-Lavaredo, Egean, Jeta poly, Zarin 1, Zarin 2, Zarin 3 and ZP873 hybrids can be introduced with general adaptability, due to their higher yield and yield stability in comparison with the check varieties. Sy-Bilbao and BT-6470 hybrids are recommended in Karaj and Jovein regions and Ajeeb and ZP600 hybrids are also recommended in Jovein region with specific adaptability. ZP707, RGT-Joxxlin, RGT-Coruxxo and Exxupery hybrids do not deserve to release as a new variety because of low forage yield and yield adaptability in comparison with the check varieties.

The methods used to analyze genotype-environment interaction and determine the stability and adaptability of genotypes are continuously evolving to improve the accuracy of evaluating genotypes and studying environmental interaction components. Using a combination of several methods provides a more comprehensive understanding of the genotype-environment interaction from multiple dimensions. In this study, the genotype-environment interaction was investigated using the AMMI method, along with the development of appropriate statistical techniques.

For this purpose, 25 sugar beet genotypes, consisting of 18 new hybrids and seven controls, were cultivated in seven environments: Karaj, Mashhad, Shiraz, Miandoab, Kermanshah, Hamadan, and Khoi. The experiment followed a randomized complete block design with four replications during the year 2020. After checking the uniformity of the variance of the experimental errors using Bartlett's test, the stability of the genotypes was analyzed based on the AMMI model. In this study, 12 statistics obtained from the AMMI model, including ASTAB, ASI, ASV, AVAMGE, DA, DZ, EV, FA, MASI, MASV, SIPCi, and Za, were used to identify stable genotypes. To further investigate the stability of the experimental genotypes, the SIIG index was also estimated, taking into account the criteria provided by each index based on the AMMI model.

The results obtained from Bartlett's test confirmed the homogeneity of the variance of experimental errors in experiments from different regions. Therefore, a combined variance analysis was performed based on the AMMI model. The combined analysis of variance, based on the AMMI model, indicated the significance of the additive effects of the environment and genotype, as well as the multiplicative effect of genotype-environment interaction at the 1% probability level. Analyzing the interaction effects into principal components showed that the first four components were significant at the 1% probability level and explained 93.50% of the variations related to genotype-environment interaction. According to the AMMI1 biplot, hybrids 3 and 9 were recognized as the most stable genotypes due to having a sugar yield higher than the overall average and a low value of the first interaction component. According to the AMMI2 biplot, hybrid 3 and BTS 335 with the Miandoab environment, Lexia and 4 with the Mashhad environment, Lexia with the Hamedan environment, 14 and 15 with the Kermanshah environment, 13 and 17 with the Shiraz environment, 11 with the Karaj environment, and Baloo with the Khoi environment have considerable specific adaptability. Hybrid 9, followed by 6, 2, and 8, had general adaptability. The SIIG index, calculated based on AMMI model statistics, identified 2, 9, 10, and 8 as the most stable genotypes with optimal yield.

Based on the obtained results, the environment and its interaction with the genetic structure of different genotypes have played a significant role in the phenotypic expression of white sugar yield and have caused the genotypes to provide different responses in terms of white sugar yield according to the conditions of different environments. In general, based on the results of the present study, hybrid 9 and then hybrids 6, 2, 8, 7, and 10 had high white sugar yield and yield stability. Generally, real progress in sugar beet breeding will be obtained if the genetic factors controlling the sensitivity of sugar beet genotypes to a variable environment are identified.

This experiment was conducted to investigate the agricultural value of two new varieties of beans: Sepehr and Sembol, which were introduced by the private sector with the aim of registering the named varieties in the national list of plant varieties.

These cultivars (Sepehr and Sembol) along with 4 domestic control cultivars were studied in 3 locations (Karaj, Zanjan, and Khomein) based on complete randomized block design in 3 replications during two crop seasons (2019-2020 and 2020-2021).

Results showed that flowering occurred significantly earlier in candidate cultivars (Sepehr and Sembol) than control cultivars (8 and 4 days respectively). As the same way, physiological seed ripening in candidate cultivars occurred earlier than control cultivars (8 and 10 days earlier than mean of other cultivars respectively). The number of seeds in the pods of Sepehr and Sembol candidate cultivars did not significantly differ from the control cultivars. Sepehr and Sembol cultivars produced larger seeds and hundred seeds weight of these cultivars was greatly higher than other cultivars (9.7 and 24.1 percent higher than mean of other cultivars respectively), but the seed yield in these two cultivars was significantly lower than control cultivars (19.89 and 18.27 percent lower than mean of other cultivars respectively).

Finally, the candidate cultivars Sepehr and Sembol can play an effective role as new germplasms in the country's bean production due to their erect type and ease of harvesting, early maturity and consumption of one to two times less water, marketability and production of larger seeds.

Water is the most significant limiting factor for sugar beet cultivation in Iran. Autumn sowing of sugar beet in dry climates like Iran consumes less water compared to spring sowing and can be a more suitable option for taking advantage of autumn and winter rains and dealing with the water shortage crisis. It seems that the transfer of sugar beet cultivation from spring to autumn will cause that in addition to consuming much less water in this type of cultivation and increasing the efficiency of water consumption, it will have a significant economic benefit for farmers and will spread quickly. But autumn sowing is facing problems in many areas. Therefore, the current research was conducted to investigate the effect of winter sowing on the quantitative and qualitative characteristics of sugar beet. The experiment was conducted in the form of a randomized complete block design with four replications on 11 early maturity sugar beet cultivars at Jovein, Torbat-Jam, and Moghan agricultural research stations for one, two, and three crop years, respectively. In order to study the effect of genotype-environment interaction and to identify genotypes with general and specific stability, AMMI and MTSI stability analysis methods were used. The combined analysis of variance results based on the AMMI model confirmed the significant effect of the main effects of environment and cultivar at the one percent probability level. The interaction between them also showed a statistically significant difference at the one percent probability level. The analysis of the multiplicative effects of the AMMI model confirmed that the first two components are significant at the one percent probability level and together explain 75.20% of the interaction variation. The bi-plot obtained from the mean white sugar yield and the first principal component of the interaction confirmed the superiority of FDIR19B4028 and Modex cultivars due to their high white sugar yield and stability. According to the bi-plot results obtained from the first two components, there was no appreciable specific adaptability between cultivars with Moghan environment in 2021 and 2020, but on the other hand, a very high specific adaptability was observed between Moghan environment in 2019 with Modex cultivar, Torbat-Jam in 2021 and 2020 respectively with Asia and SVZD2019 cultivars, and Jovein with SVZC2019 cultivar. Cadmus cultivar has general adaptability because it is somewhat close to the origin of the coordinates. Based on the results of the MTSI index, SVZD2019 was ranked first, and FDIR19B4028, Dravus, and FDIR19B3021 were placed in the next ranks of the most ideal sustainable cultivars in terms of all studied traits. In general, four cultivars of SVZD2019, FDIR19B4028, Dravus and FDIR19B3021 are recommended for winter cultivation. The obtained results show that the development of winter sugar beet sowing is certainly one of the important strategies for using seasonal rains and saving water consumption; In this regard, in the winter sowing of sugar beet, choosing the suitable cultivar plays a very vital role, so that it affects most of the quantitative and qualitative characteristics.

To identify salinity tolerant cultivated genotypes, 50 barley genotypes originated from Iran were evaluated in a field experiment during 2019-2020 and 2020-2021 cropping seasons in Yazd; Optimum experiment at Yazd central field station, and stress experiment in Ardakan field station, Iran. Six improved barley cultivars; Mehr and Khatam (Salinity tolerant), Nosrat, Nimrooz, Yousef and Gohran were also included in the experiments. The experiments were carried out using 8×7 rectangular lattice design with three replications. Phenological traits, grain yield and 1000-kernel weight were measured in all genotypes under optimum and salinity stress conditions. The electrical conductivity of soil and irrigation water in optimum condition was 3.4 and 3.6 dS m-1, and in the salinity condition, it was 14 and 10 dS m-1, respectively. The stress intensity index (SI) was 0.21 and 0.32 in 2019-20 and 2020-21 cropping seasons, repectively, which indicated higher stress intensity in the second year in the stress experiment. The mean grain yield under optimum and salt stress conditions in 2019-20 was 215 and 174 g m-2, respectively. In 2020-21 cropping season, mean grain yiled under optimum stress conditions were 271.9 and 184 g m-2, respectively. Combined analysis of traits showed that the of year × salinity stress × genotype interaction effect was significant on evaluated traits. Based on the results of principle component analysis and stress indices in 2019-20 cropping season, genotypes no. 50 (TN5008), 33 (TN4247) and 38 (TN4357) were superior genotypes. While in 2020-21 cropping season genotypes no. 24 (TN3474) and 27 (TN3643) together with salinity tolerant barley cultivars; Khatam and Mehr had higher grain yield. The collecting site of these two genotypes is Kerman province and Fars province, respectively. Based on the results of this research, tolerant cultivated barley genotypes were identified for further studies.

Local rice cultivars have decreased their purity, and yield due to continuous cultivation. Recognition and production of high and stable yield cultivars are one of the goals of achieving new cultivars (Gravois, et al., 1991). The reaction of the different genotypes in different environments, and thus evaluation of genotype interaction in the environment helps plant breeders to evaluate genotypes more accurately and select the best one (Finlay & Wilkinson, 1963). Due to severe drought, late ripening, disease susceptibility, and very low yield of local rice cultivars, achieving new high-yielding cultivars that are cold tolerant and adaptable, and stable in cold regions has been one of the important goals of this activity. The introduction of new 53 line can be suitable for cold and temperate cold regions of the country.

The 97 rice lines were planted in 2008 with local control cultivars in Yasuj Agricultural Research Station in a preliminary experiment. 15 lines were selected based on important agronomic characteristics. Then they were compared in a randomized complete block design, and were selected 4 lines 53, 71, 32, and 39. The lines were evaluated in two regions for two years for compatibility and performance stability. A combined analysis of variance was performed to determine the genotype interaction. Before combined analysis, the Bartlett test was used to correct the homogeneity of variance of years and places. From the method proposed by Eberhart & Russell (1966) and environmental variance stability criteria, Wricke (1962), Shukla stability variance (1972), and Francis & Kannenberg (1987) environmental change coefficient, stability analysis was performed to determine compatible cultivars.

A simple analysis of variance showed genetic differences among the genotypes. The combined analysis of variance was performed after the Bartlett test, the combined analysis of variance indicated the significant effects of genotype, location and interactions of genotype × location, and genotype × year × location. The mean grain yield of genotypes showed that out of 97 studied genotypes, 4 genotypes produced higher yields than the average yield of genotypes in all environments. So that the highest grain yield was seen in genotypes 53 and 71, followed by genotypes 32 and 39. According to the calculation of the mean of genotypes, the highest grain yield was related to genotypes 53 and 71. The lowest environmental variance had the control cultivar and genotype 53. Control cultivar and genotype 53 had the lowest coefficient of environmental change, so these genotypes had the maximum stability of grain yield. The lowest stability variance and as a result the highest compatibility and yield stability were related to control cultivar and genotypes 53. The results showed that genotype 53 had the lowest mean squared deviation from the regression line, the lowest stability variance, and the lowest environmental variance among the promising lines and this genotype had the highest stability in different environments compared to other genotypes. In general, based on all methods of genotype 53 stability parameters, due to high average yield in different regions, yield stability and suitable environmental adaptation are identified as superior genotypes. The results of recording blast disease showed that 53 line was resistant and local cultivars were sensitive. In the research-extension design, the average yield of line 53 in all regions was higher than other lines and control cultivars. Line 53 for one week to ten days earlier than the control cultivar. The results show that line 53 is among the very good quality lines in terms of grain cooking quality. And having 72.4% of total conversion and 52.2% of healthy rice has good conversion quality.

The new genotype (line 53), gave a high average grain yield and was also stable. This line has always been selected as one of the top lines under all experimental designs and conditions such as preliminary, advanced, compatibility, and stability. In general, line 53 with high yield, early ripening, blast disease resistance, stability in the study areas, and very suitable quantitative and qualitative characteristics of grain, was introduced to the agricultural community as a variety of Setayesh rice in 2021.

The yield and quality of sugar in this plant, like other plants, are strongly influenced by environmental factors, and high modifications occur in its quantitative and qualitative performance. Therefore, the present research was designated and implemented to study the effect of genotype-environment interaction on the quantitative and qualitative production potential of sugar beet cultivars and also determine their adaptability to a wide range of environments with different conditions.

Seven cultivars of sugar beet, consisting of four domestic and three foreign cultivars formed the genetic material of the present study. The plant materials under investigation were cultivated in the randomized complete block design with four replications in four regions of Borujerd, Piranshahr, Jovein, and Shirvan in 2021. After harvesting and estimating root yield, sugar content, white sugar yield, and extraction coefficient of sugar for each of the experimental cultivars, analysis of the stability of root yield and white sugar yield were done by using the additive main effect and multiplicative interaction model and simultaneous analysis of the stability of four measured characteristics with a multi-trait stability index.

The additive effects analysis results of AMMI's model showed that genotype, environment, and genotype-environment interaction were significant at the 1% probability level. Analyzing the multiplicative effect of AMMI's model into principal components showed that in both studied traits, only the first two components of the interaction were significant at the 1% probability level. According to the biplot of the mean yield against the weighted average of absolute scores of the principal components, the Dena in terms of root yield and Dena and Sina in terms of white sugar yield was known as stable cultivars with suitable yield. In the biplot of the first and second principal components of the genotype-environment interaction, the Dena in terms of root yield and Dena, Ekbatan, and Sina in terms of white sugar yield was recognized as cultivars with suitable general adaptability. The studied areas were divided into three mega-environments in terms of each root yield and white sugar yield, so Jovein and Shirvan were identified as the first mega-environment, Borujerd as the second mega-environment, and Piranshahr as the third mega-environment. Based on the results of the multi-trait stability index, the Perfekta was the most ideal cultivar at the same time in terms of all four traits of root yield, white sugar yield, sugar content, and extraction coefficient of sugar, and after that, Flores and Sina were the ideal cultivars.

According to the obtained results, it can be concluded that the environmental conditions and their interaction with the genotype play a significant role in influencing the phenotypic expression of traits in different sugar beet genotypes and cause yield fluctuations from one environment to another. Therefore, it is necessary to be more careful when releasing genotypes to introduce genotypes that are adaptable to the conditions of the target environment and produce suitable products.

The new barley cultivar Golchin was derived from cross between  P.sto/3/Lb.iran/Una80//Lignee640/4/Bllu/5/Petunia1 and Nik cultivar as female and male parents, respectively. The initial cross between parents of this cultivar and selection among segregating generations till achieving genetic purity were done in Cereal Research Department at Seed and Plant Improvement Institute, Karaj, Iran. Observational evaluations and preliminary and advanced yield trials were performed at Ahvaz, Darab, Zabol and Gonbad agricultural research stations and the desired line was selected according to its high yield, desirable agronomic characteristics and  comparison  with yields of the check cultivars. To evaluate grain yield stability of 17 pure lines selected from advanced experiment as well as Zahak/Sahra and Nimrooz as check cultivars were studied in Ahvaz, Darab, Zabol, Gonbad and Moghan agricultural researches stations in 2015-2017 cropping seasons. Selection of lines with desirable and stable grain yield was performed using the results of grain yield mean comparisons, Lin and Binns superiority coefficient and GGE biplot analysis. Line WB-94-3 (Golchin cultivar) with an average grain yield of 4311 and 4502 kgh-1 in south and north warm regions of Iran respectively, had 9% and 8% yield superiority over Zahak and Sahra check cultivars. According to the results it was determined that line WB-94-3 in addition to desirable performance and agronomic characteristics had general adaptability to the warm regions of the Iran and therefore was released as Golchin cultivar. Golchin is a six-rowed variety and exhibits lodging resistance and non-shattering rachis. The growth type of this cultivar is spring and its protein content is 11.3%. Golchin is earlier than commercial check cultivars and has high tolerance to salinity stress and moderate tolerance to drought stress. Its reaction to powdery mildew is in the range of semi-resistant to semi-sensitive.

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

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

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

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

Due to the increase in population and unfavorable environmental conditions, especially various stresses and the problem of water scarcity, evaluation of plant genotypes in various environments is inevitable. The interaction effect of the genotype and environment can be used as a mean by plant breeders to select the best genotypes/cultivars in plant growth environments (in one or more years).

In order to evaluate the stability of grain yield, this experiment was performed on 108 chickpea genotypes in the cropping years 2017-2018, 2018-2019 and 2019-2020 in the form of a randomized complete block design. By carefully planting, holding and harvesting operations, during the growing season and postharvest, seed yield, 100-seed weight, plant height, number of days to 50% flowering, number of days to maturity, number of pods per plant, number of main branches was recorded.

The results of combined analysis of variance showed that there was a significant difference between the genotypes in terms of plant height, grain yield, number of pods per plant, number of main branches. Comparison of mean grain yield showed that genotype number 10 had the highest yield (54.038 gram).significancy of GE interaction effect showed the response of genotypes within various environments so then it is possible to perform the stability analysis of genotypes and for this reason, the GGE biplot was used in parallel with some findings of AMMI model and mixed AMMI model (AMMI in conjunction with BLUP) which GGE biplot resulted that first 2 components accounted for 76.3 percent of total variation of grain yield and in this context varieties 1, 26, 60 and 101 acconted as ideal ones and so genotypes 32, 69, 76, 89, 90, 94, 98 and 108 mentioned as suitable genotypes compared to other genotypes and that 4th enfironment (year 3-Kermanshah) mentioned as ideal environment.

Considering the superiority of some chickpea cultivars in this study in terms of both grain yield and yield stability, in 2 temperate and cold regions in terms of stability and high grain yield, these cultivars can be candidates to introduce the cultivar or be used as parents of crosses in future breeding programs of this plant.

Considering the requirement of the country to meet the nutritional needs of the society and the nutritional needs of the existing livestock in order to meet the nutritional needs, it is necessary to introduce cultivars that can be superior to the existing cultivars in terms of adaptability and stability in arid and semi-arid regions. The use of forage plants such as Grass pea can play an important role in crop rotation, soil protection, reducing weeds and diseases due to high adaptability to dry and semi-arid climates and high yield potential, nitrogen fixation, tolerance to drought and salinity. This research was carried out with the aim of evaluating the interaction effect of genotype × environment and the stability of forage and grain yield of Grass pea genotypes in different regions of the country.

This study was carried out with 16 advanced Grass pea lines in Gachsaran, Mehran, Shirvan Cherdavel and Kohdasht stations for three crop years in the form of a randomized complete block design with three replications. Each genotype was cultivated in 6 lines with a length of 7.03 meters and a distance of 0.25 cm from each other. After analyzing the composite variance for different years in different regions, mean comparison was done using the minimum significant difference method at 5% and 1% probability levels. To analys the stability and compatibility of lines, were used the stability methods of Francis and Kanenberg, Eberhut and Russell, Rick's equivalence, Shokla's stability variance, Plasted and Patterson's stability parameter, Finley and Wilkinson, Perkins and Jinks, Gang's total ranking, and the single-variable, non-parametric method, Nassar- Han and Tanazaro.

The results of composite variance analysis of forage yield and grain yield showed that the simple effect of year, genotype, location and the genotype × location interaction effect for both traits were statistically non-significant, and the interaction effect of year × genotype was significant for forage yield and non-significant for grain yield. It was meaningful. The three-way effect of year × location × genotype was not significant at statistical probability levels for forage; but it was significant for grain yield. The forage yield for kholer lines from 12839 kg ha-1 for line number 5 to 16680 kg ha-1 for genotype number 10 with 11.5% drop and 15% superiority compared to Naghadeh, respectively. Also, in terms of grain yield, Grass pea lines fluctuated from 1239 kg ha-1 for line No. 7 to 1723 kg ha-1 for Line No. 10 with a 2% drop and 36.3% superiority compared to Naghadeh. The results of the stability analysis of forage yield showed that in terms of the Coefficient of variation parameter, genotypes 16, 5 and 10, in terms of Phenyl Wilkinson genotypes 15, 2, 13, in terms of Shokla genotypes 14, 9, 6, and in terms of Rick's equivalence, genotypes 14, 9, 6, and 13 were the most stable genotypes. The analysis of grain yield stability by univariate method showed that in terms of Coefficient of variation, genotypes 11, 12, 3, in terms of Phenyl Wilkinson, genotypes 13, 12, 4, 5, 6, in terms of Shukla's, genotypes 7, 12, 5, 6, and in terms of Rick's equivalence, genotypes 7, 12, 5, 6, and 8 were determined to be the most stable Grass pea genotypes.

Based on the obtained results, in terms of forage yield lines No. 1, 10, 3, 9 and 15 and in terms of grain yield genotypes No. 12, 15, 8, 10 and 7 had high stability and yield compared to other genotypes. In general, considering all parameters of stability and compatibility, lines number 1, 10, 12, 15, 8, 7 and 9 were selected as the most stable lines, and genotypes No. 10 and 15 were suitable for both forage and seed.

In order to evaluate and compare 15 different hybrid genotypes of greenhouse tomato (Solanum lycopersicum L) in Gorgan, Golestan, north of Iran, an experiment was carried out in randomized complete block design with 3 replications at two Golestan province greenhouses (greenhouse of Gorgan Agricultural and Natural Resources Research and Education Center and a private greenhouse in Gorgan).  Investigated traits were fruit yield per plant, number of fruits per plant, number of fruits per cluster, average fruit weight, fruit diameter, fruit height, stem length, total soluble solids, pH, pericarp thickness, total phenolics content and flavonoids content. Statistical analyses were suggestive of significant difference in all of the studied traits between different genotypes in both greenhouse experiments. In both of the greenhouse experiments, the highest fruit yield per plant was observed in 4129, Omagna, and then Izmono, 10161, Hirad and Sakhia genotypes. Dafnis and Hiva cultivars were intermediate in terms of fruit yield compared to other genotypes. In traits such as fruit number, fruit weight, fruit size, plant height, pericarp thickness, total soluble solids, pH, phenolics content and flavonoids great differences were observed between the studied genotypes. Positive correlations of fruit yield with number of fruits per plant, mean fruit weight and fruit diameter traits were observed in both greenhouse experiments. Therefore, selecting based on the latter three attributes may improve fruit yield of tomato in seed and variety breeding programs.

The sustainability of landscapes, especially in arid and sub-tropical areas with limited water resources, requires the use of suitable plants and adapted to the climatic conditions of these areas. Therefore, the study of species tolerant to salinity and drought stress for the construction of landscapes with the approach of optimal use of water resources and increase its productivity, the study of plants tolerant to drought and salinity stress is important. This study was performed to investigate the effects of salinity and drought stress on germination of four turfgrass species. In order to investigate the effects of drought stress (0, -2, -4, -8 and -16 Bar) and salinity (0, 2, 4, 8 and 16 dS/m) on four species of warm-season turfgrass (Buchloe dactyloides, Cynodon dactylon, Zoysia japonica and Paspalum vaginatom) were two separate experiments designed and performed in a completely randomized design with four replications in the Horticulture Physiology Laboratory of Agricultural Sciences and Natural Resources University of Khuzestan in 2020. The results showed that in conditions without stress and moderate salinity and drought stress, there was no difference between the four studied tropical grass species in terms of the studied characteristics, while in severe salinity and drought stresses, P. vaginatom ratio It excelled in three other ways. P. vaginatom had the highest germination rate with an average of 3.8 and 4.6 per day in the most severe drought and salinity levels. Also, in severe salinity and drought stress, B. dactyloides had the lowest percentage and germination rate, the lowest root and stem growth. Analysis of alpha-amylase activity among different species and salinity and drought stress showed that the highest activity of this enzyme was observed in all species at the control level (without stress). Alpha-amylase activity was lower in B. dactyloides at all levels than in other species. Also in severe levels of species strain P. vaginatum showed higher activity of alpha-amylase enzyme. Therefore, it will give it a better chance to establish the plant and start exploiting the facilities of the environment in conditions of drought and salinity stress.