پیام پزشکپور

 The integration of the two stability assessment methods of the best linear unbiased predictions (BLUP) and AMMI in multi- environment experiments based on the stability index of weighted average absolute scores (WAASB) helps to better evaluate plant genotypes. In the present study, the seed yield stability of 13 advanced lentil genotypes was evaluated in a multi-environment trials in three locations including; Khoramabad, Ilam and Kermanshahn in 2019-2022 cropping seasons. The experimental design was randomized complete block design with three replications. In order to evaluate genotype × environment interaction, AMMI and BLUP methods were combined by introducing WAASB and WAASBY indicators and the yield stability was evaluated by drawing various graphs. Considering the significant G×E interaction based on the results of the relative likelihood test (LRT), it was possible to perform BLUP analysis on the data. The highest predicted seed yield by BLUP method belonged to genotype no. 12 followed by genotypes no. 6, 4, 3, 5 and 9 which had higher than average predicted seed yield. To enable simultaneous selection based on both seed yield and yield stability, by combining seed yield (Y) and WAASB, a new index “WAASBY” was created. Considering 50% contribution of each of the two components of seed yield and yield stability, seven genotypes showed above average WAASBY. Genoypes no. 3, 6, 5 and 2 had considerably higher WAASBY when compared with other genotypes and was identified .

lentil is one of the legumes due to its protein percentage and high nutritional value, and it can be cultivated in the fall in rainy conditions. Due to the different reactions of crop genotypes in different environments, the evaluation of the genotype × environment interaction in the process of introducing new cultivars is fundamentally important. The development of high yielding cultivars with wide adaptability is the ultimate aim of plant breeders. However, attaining this goal is made more complicated by genotype-environment interactions. The genotype by environment interaction is a major problem in the study of quantitative traits because it complicates the interpretation of genetic experiments and makes predictions difficult, also it reduces grain yield stability in different environments. Multi-environment trials are often analyzed to assess the yield stability of genotypes. Combining features of the best linear unbiased predictions (BLUP) and additive main effects and multiplicative interaction (AMMI) throught “ Weighted average of absolute scores of best linear unbiased predictions ” (WAASB) index in multi- environment experiments may lead to more precise evaluation of genotypes and assessment of genotype × environment interaction. This statistical models has been widely used to explain complicate G×E interaction, to enhance selection efficiency and to ensure genetic gain from selection. The objective of this study was to investigate the response of the lines in studied locations and to identify lines adapted to the test environments. The objective of this study was to investigate the response of the lines in studied locations and to identify lines adapted to the test environments. In the present study, the seed yield stability of 18 advanced lentil genotypes was evaluated in a multi-environment trials in three locations including; Khoramabad, Ilam and Kermanshah, Iran in 2010-2013 cropping seasons. The experimental design was s randomized complete block design with three replications. Statistical analyzes were performed using multi-environment trials analysis. In order to evaluate genotype × environment interaction, AMMI and BLUP methods were combined by introducing WAASB and WAASBY indicators and the yield stability was evaluated by drawing various graphs. Considering the significant G×E interaction based on the results of the relative likelihood test (LRT), it was possible to perform BLUP analysis on the data. The results of the mosaic diagram showed that the contribution of genotype and genotype × environment interaction were 15.45% and 84.55% of the total variation, respectively. The highest predicted seed yield by BLUP method belonged to genotype no. 2 followed by genotypes no. 4, 19, 5 and 1 which had higher than average predicted seed yield. To enable simultaneous selection based on both seed yield and yield stability, by combining seed yield (Y) and WAASB, a new index “WAASBY” was created. Considering 50% contribution of each of the two components of seed yield and yield stability, fourteen genotypes showed above average WAASBY. Genoypes no. 19, 2, 4,6, 1, 3 and 13 had considerably higher WAASBY when compared with other genotypes and was identified. control Gachsaran cultivar (genotype 20) had lower than average WAASBY In conclusion, considering WAASBY index, genotypes 2, 4, 6, 1, 3 and 11 were identified as genotypes with high seed yield and yield stability, and can be considered for being released as new lentil cultivars.In general, usin mixed model as well as all the components in calculation the WAASBY index, it can be concluded that this index is superior to other indices.

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.

Considering the importance of Mosir as a medicinal-industrial plant, this research was conducted to investigate the different planting and harvesting methods’ effect on the plant’s quantitative and qualitative characteristics in the Lorestan province.

The experiment was conducted in the form of a completely randomized block design for three years (2017-2020) in the Aligudarz County. The methods of manual and machine planting, and planting distance of 10 and 15 cm were investigated. Additionlly, the economic performance of the different methods was evaluated.

The resulted demonstrated no significant difference in leaf width among the studied treatments, but displayed a significant difference in plant height at the level of 1%. The mean comparison showed that the wet and dry yield in different treatmenst had significant differences. The highest yield (847g/m2) was obtained in the second year with the machine method and the lowest yield (636g/m2) was obtained in the third year with manual planting. The dry matter yield was achieved at a planting distance of 10cm (185.633 g/m2) and a planting distance of 15cm (178.611g/m2) and the dry matter yield was obatiend in the machine planting method (199.91g/m2) and the manual planting method (164.33g/m2).The economic evaluation of the results indicated that the average costs are 19% lower in the machine cultivation method compared to the manual planting method.    

The overall results highlighted income increase and cost reduction in the planting and harvesting of Mosir with the machine method. Moreover, the mechanized planting and harvesting of Mosir was executed much faster compared to the manual planting and harvesting of this plant.

Lentil is a rich source of protein, fiber, minerals, antioxidants, folate, zinc, selenium, iron, and low amounts of fat and carbohydrates. Increasing water shortage and drought stress will be a major threat to global lentil production. The present study was conducted to determine the morphophysiological traits and grain of yield under normal and drought stress conditions, investigate tolerance indices and identify drought tolerance in some lentil genotypes.

In this experiment, twenty - four lentil genotypes were evaluated in the research farm of Shahrekord Faculty of Agriculture in two drought-stress environments and the normal conditions of the region in two separate randomized complete block designs with three replications experiments. In the 50% flowering stage, drought stress was done by completely stopping irrigation for the genotypes. Sampling was done at the end of the growing season. In this experiment, morphophysiological and yield traits were measured.

The results of composite data analysis showed that the effect of environment, genotype and the interaction effect of genotype × environment were significant on most traits. The share of these sources of variation in diversity of triats was different and environment placed on the most role in the diversity of seed yield (55.27%), genotype in the diversity of 100 seed weight (64.6%) and the number of single seed pods (53.9%). Seed yield in normal agricultural conditions has a positive correlation with most of the traits. In addition, under stress conditions, more traits had a significant correlation with grain yield. STI and GMP indices were highly correlated with yield in both environments and these indices can be used to select the superior genotype. Examining the main components of the indicators showed that the first component and two components justify 97.66% of the changes, the first component was introduced as a semi-tolerant component and the second component as a tolerant component. The results of the cluster analysis put the genotypes in three general groups. The results of cluster analysis based on STI and GMP indices put the genotypes in three separate groups.

One of the main goals of production programs in arid and semi-arid regions is the screening of germplasm in different stages of plant growth. The results of the present research showed that drought stress in the reproductive stage reduces the morphophysiological traits and yield of lentil genotypes. Based on the results of seed yield, Kimia genotype with the lowest yield loss is known as the tolerant variety and C113 variety with the highest yield loss is known as the sensitive variety. Based on the results of seed yield, Kimia genotype with the lowest yield loss is known as the tolerant variety and C113 variety with the highest yield loss is known as the sensitive variety.

The aim of study was to evaluate the efficiency of yield stability analysis models by BLUP and AMMI models.

In this research, nineteen advanced lentil genotypes along with three cultivars of check Kimia, Bileh Sawar and local mass in agricultural research stations of Khorramabad, Ilam and Sararoud in the form of complete blocks design. Randomly with three repetitions and for two crop years (2018-2020), were evaluated. To quantify the genotypic stability, singular value decomposition (SVD) was used with a linear mixed effect model (LMM).

The likelihood ratio test (LRT) indicated that the effect of GEI was significant on seed yield. Therfore, the best linear unbiased predictors (BLUPs) analysis was considered appropriate for these data. Mosaic plot showed that the portion of sum squares of genotype (G) and sum squares of genotype by environment interaction (GEI) in total sum of squares (TSS) were 6.75% and 34.36%, respectively. The bilot of first principle component (PC1) of the environment versus nominal yield showed that genotypes 13, 6, 16, 9 and 10, were more stable.

Biplot of seed yield versus WAASB Showed that the genotypes 10, 16, 1, 20, 15,4,13 and 7 were very productive and stabile due to the large value of response variable (high seed yield) and high stability (low values of WAASB). Identification of genotypes with WAASBY showed genotypes 16 and 1 high yielding and stable, and therefore can be candidate for cultivar inteoduction.

Lentils are one of the most important legumes, which have a high amount of protein and vitamins A and B, fiber, potassium and iron, and are an inexpensive food for low-income people. The objective of this study was the determination of variability, heritability, correlations between yield and yield components in 14 Lentil (Lens culinaris L.) genotypes using a randomized complete block design with three replications. The results showed that secondary stem per plant and productivity degree had the highest GCV and PCV but the lowest GCV and PVC was found for a number of pod/ m2 and number of the plant. Maximum heritability was estimated for 100-seed weight, number two-seed pod, biological yield, grain yield, productivity degree and plant height respectively. The high genetic advance and heritability were observed for the number of the two-seed pod and biological yield. The phenotypic correlation revealed that rainwater productivity, biological yield and plant height had the greatest, positive and significant association with seed yield. By performing path analysis based on stepwise regression, rainwater productivity had the most direct effect on seed yield, while the number of secondary stems per plant through rainwater productivity had the most indirect and negative effect on seed yield. That means with increasing number of the secondary stem, rainwater productivity is decreased which results in reduced grain yield. As a result, it was concluded that rainwater productivity and biological yield can be good selection criteria for improving seed yield per plant in Lens culinaris.

This study was conducted to evaluate the effect of different tillage systems including conservation and conventional on yield-related traits and seed quality of some autumn-seeded chickpea cultivars. The experiment was carried out as split plot based on a completely randomized block design during the 1399-1400 growing season. Tillage systems including conventional, reduced and no tillage were arranged as main factor and chickpea cultivars (Adel, Arman, Hashem, Mansour, Azad, Azkan, Aksu and Goksu) were arranged as sub factor. Among the traits under study only 100-seed weight was significantly affected by tillage systems and the interaction between tillage and cultivar. The highest 100-seed weight (42 g) was obtained for Aksu under no till system whereas, the lowest value was recorded for Arman and Hashem under all three tillage systems. However, the effect of cultivar on 100-seed weight, the number of pods per plant, harvest index, reproductive effort index and seed protein percent was significant. The highest harvest index (47%) and protein percent (19%) were observed in Adel and Mansour, respectively. Overall, the findings of this research showed that chickpea yield and related traits (excluding 100-seed weight) as well as grain quality were not significantly affected by tillage systems. There was no significant difference in grain yield between the studied cultivars, although the difference between them was significant in terms of some yield-related traits and grain protein percent.

Lentils are one of the most important crop plants in rotation.Lentil seed yield is strongly influenced by environments and breeders often determine the stability of high yield genotypes across environments before recommending a stable cultivar for release. Genotypial adaptability to environmental fluctuations is important for the stabilization of crop production over regions and years.Identification of high- yield genotypes with adaptation to a wide range of environments is one of the major goals in crop breeding programs. The challenge of the interaction of genotype × environment is one of the main issues in plant breeding. Various statistical methods to estimate the interaction of genotype × environment and choice the stable and productive genotype(s) have been introduced. One of the applications of Non-Parametric methods is determination of genotypes rank in different environments, which is also used as a measuring stability. A stable genotype shows similar ranks across different environments and has minimum rank variance in different environments. Non-Parametric Stability Statistics require no statistical assumptions about the distribution of the phenotypic values and are easy to use.

This study was conducted during two years (2019-2020 and 2020-2021) in two stations in the cold dry areas of the country (Qeydar Zanjan, Maragheh). The experiment consisted of 17 advanced lentil genotypes along with three control cultivars Kimia, Bilesvar and Senna (20 genotypes in total) which was performed in a randomized complete block design with 3 replications.

The combined analysis of variance indicated that the main effects of genotype (G), environment (E) and their interactions genotype and environmen (G×E) were highly significant (p < . ). The principal component analysis (PCA) based on rank correlation matrix indicated that the first two PCAs explained . of the variance of original variables. Based on bi-plot analysis, the stability parameters were classified into four groups. Clustering of the genotypes according to the mean yield and nonparametric stability statistics showed that there were two main clusters. Overall, according to mean rank of nonparametric stability parameters, G17 , G3 , G9 and G2 had the lowest variations and were recognized as the most stable genotypes. Genotypes G7 , G11 and G13 had the highest values of mean rank of parameters and therefore, would be considered to be the most unstable. According to the present study, the stability measures Ysi, and TOP were associated with mean yield (MY) and the dynamic concept of stability. Therefore, these procedures were suitable for selecting stable and high yielding genotypes

Therefore, these procedures were suitable for selecting stable and high yielding genotypes. Based on these parameters, genotypes G13 (340 t/ha) and G11 (305 t/ha) were identified as high yield stable genotypes.

This study was conducted to investigate the interaction of genotype × environment and to study the compatibility and yield stability of 20 lentil genotypes using incremental and multiplicative principal effects model (AMMI).

The present experiment was carried out in randomized complete block designs with 3 replications during 2018-2019, 2019-2020 under rain-fed conditions (in two regions of cold dryland areas including Zanjan, Maragheh (total of 4 environments). Each experimental plot consisted of 4 rows of planting 4 meters long with a density of 200 plants per square meter with a distance of 25 cm between lines. Planting was done manually. Combined analysis of variance was performed using SAS.V.9.1 statistical software assuming the effect of genotype and location were constant and the effect of year was random. AMMI analysis and calculation of main components of interaction for all genotypes and drawing of biplots and calculation of ASV stability index were done using CROPSTAT software.

The combined analysis of variance for grain yield showed significant differences for year, genotype, genotype × year, year × location effects. The first and second components in AMMI model accounted for 83.3% and 10.92% of the interaction sum of squares, respectively. Considering the numerical values of interaction for each genotypes and genotypes rank, genotypes G3، G17، G1، G7، G13، G14 and G18 were found to be more stable genotypes. Also, based on AMMI stability value (ASV), genotypes G11 ،G8 ،G14، G10، G20 ، G3 and G13 were determined as stable genotypes.

Among stable genotypes, genotypes G5, G8, G11, G13 and had higher mean grain yield. Therefore, these genotypes can be proposed for using in future breeding programs to introduce new cultivars.

Chickpea is one of the legumes due to its protein percentage and high nutritional value, and it can be cultivated in the fall in rainy conditions. Identification of high-yield genotypes with adaptation to a wide range of environments is one of the major goals in crop breeding programs. Combining features of the best linear unbiased predictions (BLUP) and additive main effects and multiplicative interaction (AMMI) in multi- environment experiments and multi-trait stability selection (MTSI) to better evaluate plant genotypes and achieve more accurate results It helps to be more precise. Additive main effect and multiplicative interaction (AMMI) and best linear unbiased prediction (BLUP) are two methods for analyzing multi-environment trials (MET). This research was done to identify stable and high-yielding chickpea genotypes in autumn planting.

In this study, seventeen advanced chickpea Genotypes were evaluated along with two check varieties (Adel and Azad) based on randomized complete block design with three replications at Sarab Changai agricultural and natural resources research station khoramabad lorestan for four crop years (2013-2017) were evaluated. To quantify the genotypic stability, the best linear unbiased predictions of the genotype by environment interactions (GEI) were estimated, and singular value decomposition (SVD), which is the basis of AMMI analysis, was performed on the resulting matrix.

The heat map plot indicated the variation of seed yield of genotype and sum squares of genotype by environment interaction in total sum of squares were 15.45 % and 31.26 % respectively. The likelihood ratio test (LRT) showed that the effect of GEI was significant on grain yield, 100 grain weight, plant height, grain – filling rate and grain– filling period. Therefore, due to the significant interaction of genotype by environment, BLUP analysis can be performed on this data. The screet test showed that the first four principal components had a significant Contribution in the GEI matrix derived from BLUP, as the first and second principal components explained only 34.31% and 31.38% of the GEI variation respectively. Based on the multi- trait stability index (MTSI), G7 was also selected as the best genotype in terms of grain yield, evaluated traits and stability of each trait.

In general, based on the results of all methods and simultaneous selection based on seed yield stability and all measured traits (MTSI), genotypes No. 7 (FLIP07-201C), and 4 (FLIP06-43C) were stable and superior genotypes, They were compared to the average of the total traits of the genotypes. Genotype No. 4 in terms of seed filling period (32 days), seed yield (2536.9 kg/ha), hundred seed weight (34.3 gr.), plant height (65.3 cm) and Genotype No. 7, with The average seed filling ratio (10.63 mg per seed per day) and seed yield (2250 kg/ha) were more than the average of the total traits of genotypes and controls (Azad and Adel) in this research

The aim of the present study was to evaluate and interpret the genotype × environment interaction for advanced rainfed lentil lines. Fifteen advanced rainfed lentil lines (genotypes) along with two commercial cultivars (cv. Gachsaran and cv. Sepehr) as check were evaluated using randomized complete block design with three replications in Gachsaran, Moghan, Khorramabad and Ilam field stations in Iran in 2019-20 and 2020-21 cropping seasons. The mean seed yield of trials was 891 kg ha-1. Genotype 10 with 1031 kg ha-1 had the highest mean seed yield, and genotype 16 (cv. Gachsaran) with 728 kg ha-1 had the lowest. Since genotype × environment interaction was significant, yield stability analysis was performed. Evaluation of the seed yield stability of genotypes by rank method showed that genotypes 12 and 10 had high seed yield and yield stability. In addition, yield stability analysis using WAASB index, as a multivariate method, was also performed. The seed yield × WAASB index biplot showed that most of the lentil genotypes had higher seed yield and yield stability than the check cultivars (cv. Gachsaran and cv. Sepehr). Genotypes 12, 13, 11, 10 and 1 had higher seed yield and yield stability than the superior check cultivar (cv. Sepehr). Considering the equal contribution for mean seed yield and yield stability in the calculation of WAASBY index, genotypes 10, 13, 11, 12, 1 were ranked as the most desirable genotypes for target environments.

Lentil is grown for its biological nitrogen fixation ability and high proteins as well as human and animal nutrition capacity. Terminal drought, lack of suitable varieties and quality seeds have challenged the efforts of breeders to increase its productivity. Assessment of genetic diversity for desired traits in germplasm collections plays a critical role in formulating crop enhancement strategies. The aim of this study was to investigate the agronomic, phenological and morphological traits of advanced lentil lines received from the International Center for Agricultural Research in the Dry Areas (ICARDA) and to identify the relationships between important and effective traits using different statistical methods.

This experiment was conducted on 15 promising lentil lines received from ICARDA along with two check cultivars (Gachsaran and Sepehr) in a randomized complete block design with three replicates under rain-fed conditions at Sarab-Changai Research Station, Lorestan Agricultural and Natural Resources Research and Education Centre, Khorramabad during 2019-2020 cropping year. Sowing was done by hand at four row plots, 4 m length, and 0.25 m row spacing as 200 seeds per square meter density. Fertilization with chemical fertilizers was performed based on soil test. The weeds control was manually performed twice. The total rainfall received was 542 millimeters in the cropping year. Different traits were measured according to standard guidelines for lentil during each stage.

According to the results, the “number of two-seeded pods per plant” followed by number of empty pods per plant, number of one-seeded pods per plant, seed yield per plant, weight of pods per plant and number of fertile pods per plant had the highest coefficient of variation (78.78%) and phenological traits had the least variability. The results of analysis of variance showed a significant genetic diversity between the studied genotypes in terms of most traits. The mean of seed yield per plant was 2.66 g plant-1. Genotypes 6, 14 and 17 (Sepehr) by 6.02, 5.06 and 4.15 g plant-1, respectively had the highest amount. The correlation between grain yield per plant and most of the traits, especially yield components, was positive and significant. The genotypes were classified in three clusters. According to cluster analysis using Ward method, the genotypes of third cluster had high yield and yield components in compare with other clusters. Based on the SIIG index, the genotypes 6, 11, 14 and 17 with the highest SIIG values (0.727, 0.584, 0.569 and 0.537, respectively) were known as the best genotypes. On the other hand, genotypes 15, 2, 9, 10 and 5 with the least amount of SIIG value (0.185, 0.284, 0.323, 0.324, 0.357 and 0.362 respectively) were known as the weakest genotypes under rain-fed conditions. The highest direct positive effect on “seed yield per plant” belonged to the “number of fertile pods per plant” and therefore this trait can be applied as selection criteria. Fifteen agronomic traits have been classified into five groups which expressed 90.82% diversity of the total variation according to the factor analysis.

In this study, the studied genotypes were significantly different for most of the studied traits. Genotypes 6, 14 and 17 (Sepehr) were known as the best genotypes based on the results of various statistical methods including mean comparisons, cluster analysis and SIIG index. Therefore, they have the potential to be used in the future breeding and subsequent agronomic research programs. The number of fertile pods per plant had the most direct positive effect on seed yield. Therefore, it can be considered as a criterion for selecting superior genotypes.

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

The interaction of genotype × environment causes significant differences between the reactions of genotypes in different environments. The yield and stability of cultivars are greatly affected by the interaction of genotype × environment. Numerous statistical methods have been introduced to estimate the interaction of genotype × environment and selection of stable and productive genotypes. This study was conducted to investigate the interaction between genotype × environment and grain yield stability and to introduce the best genotype and also to evaluate the efficiency of parametric methods in selecting stable genotypes with high yield.

This study was conducted during four cropping years (2016-2020) at Sarab Changai Agricultural Research Station in Khorramabad. The experiment consisted of 14 lentil genotypes along with two control genotypes of Gachsaran and Sepehr. The experiment was performed in a randomized complete block design with three replications.

Composite variance analysis showed significant effects on the interaction effect of year, genotype and genotype × environment. The importance of the interaction between genotype and environment can be inferred that the performance of cultivars in different environments is not the same and fluctuates, and stable genotypes can be identified. Considering this issue, the interaction effect of genotype × environment and the selection of stable genotypes were investigated. Based on statistics Wrick, Shukla variance, environmental coefficient of variation, coefficient of deviation from regression and coefficient of determination, genotype 5 was identified as a stable and high yield genotype. Also, based on the rank correlations, a positive and significant correlation was observed between Wrick, Shukla variance, environmental coefficient of variation and coefficient of deviation from regression. Based on the results of cluster analysis, parametric statistics were in three main clusters. Wrick, Shukla, coefficient of environmental changes and coefficient of deviation from regression were placed in one group and in the third cluster. According to the results of cluster analysis, lentil genotypes were divided into four main clusters based on average yield and parametric stability statistics of genotypes, with genotype 5 in the second group.

Based on the results of this study, the parametric stability methods except for regression coefficient and environmental variance statistics, were able to identify stable, high-yield genotypes. In general, according to the most of the parametric stability methods, genotype 5 (FLIP2014-032L) with a mean grain yield of 1574.68 kg ha-1 was introduced as a stable genotype and was proposed to cultivate in the similar environments climate.

Given the shortcomings regarding lentil cultivars compatible with temperate and semi-temperate rainfed regions of the country, identifying genotypes with potential to be introduced as cultivars is very important. Due to genotype × environment interaction, selection of genotypes with wide adaptation to different environments is difficult. In addition to high yield, these genotypes must have yield stability in different regions, in other words, general stability. In GGE model (G+GE), the selection of stable genotypes is based on both genotype (G) and genotype × environment (GE) interaction effects.

in order to evaluate yield stability of rain-fed lentil genotypes, the present study was conducted based on randomized complete block design with three replications with 14 advanced genotypes along with two local cultivars including G15 and G16 (Gachsaran and Sepehr respectively) over two years (since 2018). The experiment’s locations were Gachsaran, Khormeh Abad, Ilam and Moghan. After collecting data from eight environments, the genotype × environment interaction for grain yield was evaluated by using GGE biplot method.

Principal component analysis in the GGE biplot model showed that 47.1% of the variations caused by G and GE is justified by the first two components (PC1, PC2). The mosaic plot revealed that the contribution of diversity due to genotype × environment interaction was significantly higher than the diversity caused by genotype (G) alone. Using GGE biplot model and based on representativeness and discriminating ability, Gachsaran region was identified as a desirable environment to identify variation among genotypes. G11 was the closest genotype to the ideal genotype in terms of grain yield. This genotype had the highest yield among genotypes and was in a moderate position in terms of stability. Genotype 9 was in the second place in terms of yield but had high yield stability since was the closest genotype to the ideal genotype in terms of stability. Genotypes 10 and 1 were also in the next ranks considering both yield and stability. These four genotypes had considerable advantage in terms of both average yield and yield stability compated to control cultivars (G15 and G16). In addition, G5 showed the least yield stability among all genotypes. G15 and G16 as local cultivars showed relatively low grain yield and stability compared to the most of advanced genotypes.

G9, G11, G10, G1 were considered as genotypes with potential to be introduced as new cultivar(s) following further experiments in private farm lands.

In order to select genotypes with high and stable yield, recent research was conducted as uniform regional trials including 16 selected lentil genotypes in four regions of Gachsaran, Khorramabad, Moghan and Ilam, during two cropping years. Genotypes stability was assessed through using parametric and non-parametric statistics. Stable genotypes based on CVi had lower than average yields. Regarding regression coffecicient and deviation from regression, in total, genotype 10 was determined as the most stable genotype by considering yield. Rick equivalence and chokella variance statistics identified genotypes 12, 1, and 10 as stable genotypes. based on Hanson (Di) statistic, genotypes 12, 13 (low yield average) and 9, 10 (high yield average) were more stable and based on coefficient of determination, G9, G12 and G10 were more stable genotypes. Based on Kang index (YSi), genotypes 9, 10, and 2 were identified as stable genotypes. Regarding the results of nonparametric statistics of Nasar and Hyun (Si 1,2,3,6) and Tanarazo (NPi 1,2,3,4), more stable genotypes had lower yield than average. According to TOP statistic, genotypes 9, 11, and 5 were ranked in the top. The SIIG index also identified genotypes 4, 3, 13 and 10 as the most stable genotypes, of which only genotype 10 had higher than average performance.

Combinining features of the best linear unbiased predictions (BLUP) and additive main effects and multiplicative interaction (AMMI) through “Weighted average of absolutescores of best linear unbiased predictions” (WAASB) index in multi-environment experiments may lead to more percise evaluation of genotypes and assessment of genotype × environment interaction. In the present study, the seed yield stability of 14 advanced lentil lines was evaluated in a multi-environment trials in four locations including; Gachsaran, Khorramabad, Moghan and Ilam, Iran in 2018-2019 and 2019-2020 cropping seasons. The experimental design was s randomized complete block design with three replications. Statistical analyzes were performed using multi-environment trials analysis. Considering the significant G×E interaction based on the results of the relative likelihood test (LRT), it was possible to perform BLUP analysis on the data. The highest predicted seed yield by BLUP method belonged to genotype no. 9 followed by genotypes no. 11, 10, 5, which had higher than average predicted seed yield. To enable simultaneous selection based on both seed yield and yield stability, by combining seed yield (Y) and WAASB, a new index “WAASBY” was created. Considering 50% contribution of each of the two components of seed yield and yield stability, eight genotypes showed above average WAASBY. Genoype no. 9 had considerably higher WAASBY when compared with other genotypes and was identified as the best genotype followed by genotypes no. 10, 1, 2, 12, 14, 7 and 13 were ranked respectively. Both control cultivars (genotypes 15 and 16) had lower than average WAASBY. In conclusion, considering WAASBY index, genotypes 9, 10, 1 were identified as genotypes with highe seed yield and yield stability, and can be considered for being released as new lentil cultivars.

Identification of high-yield genotypes with adaptation to a wide range of environments is one of the major goals in crop breeding programs.

 In order to evaluate the yield stability of advanced lentil genotypes, 14 advanced genotypes along with two local cultivars (Gachsaran and Sepehr), were evaluated in four regions of Gachsaran, Khorramabad, Moghan and Ilam, during two cropping years 2018-2019 and 2019-2020. In order to evaluate the yield stability and genotype*environment interaction, the model of “additive main effects and multiplicative interactions” (AMMI) was used.

The results of AMMI analysis of variance showed very significant interaction between genotype and environment. The first two main components of the AMMI model accounted for nearly 70% of GE interaction. ASV statistic in AMMI model showed that genotypes 9, 8, 1 and 12 showed the lowest amount of ASV and therefore the highest stability. Among these genotypes, only genotype 9 , had higher yield than the superior control cultivar (Sepehr). AMMI 1 (PC1 and yield) and AMMI 2 (PC1 and PC2) biplot diagrams showed that genotypes 9 and 10 were identified as genotypes with higher yield and stability (copmared to Sepehr cultivar). Accordingly, genotypes 11 and 5 were the most unstable genotypes. High-yield genotype 11 lacked general adaptation and showed private adaptation to environment 1 and 2 that they belong to Gachsaran region. Genotype 5 showed private adaptation to Khorramabad region. Genotypes 12 and 8 showed lower than average yield despite suitable stability. Based on the biplot diagram of the first two main components, environments 8 and 7 (Ilam) along with 2 and 4 had the largest share in its GE interaction.

Overall, G9 and G10 genotypes due to higher grain yield and stability were determined as superior genotypes.

In order to study the responses of chickpea genotypes and super absorbent application under supplemental irrigation condition, an experiment was conducted base on split-split- plot randomized complete block design with four replications during 2014-2015 in the Agricultural Research Station of Sarab Changaei, Khorramabad. Suplimental irrigation (rainfed, suplimental irrigation at 50% flowering and supplemental irrigation at 50% flowering+ 50% poding) in the main plots, super absorbent polymer in subplot and cultivars (Arman, Azad, Hashem, Adel and ILC482) the sub-subplots were located. The highest grain yield and protein yield were obtained in supplementary irrigation in two stages (50% flowering + 50% poding) with super absorbent application, by avrege of 3890 and 870 kg.ha-1, respectively, which were 24% and 21% higher than control, respectively. The highest leaf greenness (39.22), pods per plant (22.1), seed weight (52.4) and biological yield (5413.5) were related to two-stage irrigation. The highest number of pods per plant (27.7) was observed in Hashem cultivar and superabsorbent application, which was 34% more than non- super absorbent in same cultivar. Results of genotype× super absorbent showed that the highest grain yield (4159 kg.ha-1), grain nitrogen percentage (3.69) and protein yield (960 kg.ha-1) were obtained under super absorbent and Adel cultivar conditions. Based on the results, application of supera bsorbent increased grain yield and protein yield of chickpea genotype in rain-fed conditions. By improving physiological traits, associated with drought tolerance, the use of supplemental irrigation method in areas that make this possible, especially if combined with the application of super absorbent polymers, can increase seed yield in chickpea.

The aim of this study was the determine effects of nitrogen fertilizer sources (biological, chemical and integrated) and weed interference on the yield and yield components of chickpea cultivars in Khorramabad conditions.

The experiment was conducted as split-split-plot based on randomized complete block design with three replications at the Experimental Farm of Pole Baba Hossein, Khorramabad during the 2017-2018 growing season. Nitrogen fertilizer as main factor (control (without fertilizer), Rhizobium bio-fertilizer, 100% required chemical fertilizer and integration of Rhizobium + 50% nitrogen chemical fertilizer), chickpea cultivars (Adel, Mansour and Arman) as sub-factor and weed interference (weed control and full-season weed interference) as sub-sub-factor were considered.

The effect of nitrogen fertilizer (especially integration of Rhizobium + 50% nitrogen chemical fertilizer) on all evaluated traits was positive and significant. The mean comparisons of cultivar × fertilizer showed that the highest number of pods with one seed, number of pods per plant, number of seeds per plant, straw yield and grain yield were achieved by Arman cultivar and with integrated application Rhizobium + 50% nitrogen chemical fertilizer. In weed control conditions, the highest plant height, number of pods with one seed, number of pods per plant and grain yield was related to Arman cultivar.

Arman cultivar can be considered as the best cultivar in Khorramabad climate conditions. Seeds inoculation with Rhizobium along with consuming 50% of the required nitrogen fertilizer and weeds control can significantly increase the yield of chickpea cultivars.

Chickpea is one of the most important legumes in Iran and accounts for almost 84% of dietary legumes. Chickpea seed yield is strongly influenced by environments, and breeders often determine the stability of high-yielding genotypes across different environments before being introduced as a cultivar. Accurate study of the nature of genotype × environment allows breeders to identify stable and compatible genotypes and has always been one of the important issues in the production and release of new stable and high-yielding cultivars in breeding programs. . Adaptation of chickpea genotypes to environmental conditions is important for crop production stability in different years and places. Existence of the interaction of genotype and environment affects the value of genotypes in different places. The present study was conducted to investigate the effect of genotype by environment interaction on grain yield of chickpea genotypes and cultivars in four environments and to identify stable and high-yielding genotypes under rainfed conditions as autumn planting.

In this study, twelve cultivars and advanced genotype of chickpea were planted during two cropping years (2016-2018) in a randomized complete block design with three replications in semi-warm (Kuhdasht) and temperate (Khorramabad) areas of Lorestan province. Various nonparametric methods such as non-parametric statistics of Si (1), Si (2), Si (3) and Si (6), Thennarasus statistics of NPi (1), NPi (2), NPi (3) and NPi (4), mean rank stability statistics (R), stability statistics of Ketata et al (σr, σmy), Kang stability statistics (Ysi), Fox stability statistics (TOP, MID and LOW) and Genotype Stability Index (GSI) were used for estimating the stability of genotypes. the principal component analysis method was used to better understand the relationships between different statistics..

The results of combined analysis of variance showed that the main effects of environment (including location, year and location ×year) and genotype ×environment were significant at the 1% probability level and the main effects of genotype, location × genotype and year × genotype were significant at the 5% probability level. The effects of genotype, environment and the genotype by environment interaction accounted for 6.48, 77.4 and 13.03% of the total squares, respectively.The biplot of the first principal component (PC1) versus the second principal component (PC2) classified the studied nonparametric stability statistics into three groups.Based on the statistics of NPi (1), NPi (2), NPi (3) and NPi (4), the genotypes with the lowest values are considered as stable genotypes. According to NPi (1) statistics, G1, G6 and G9 genotypes were identified as the most stable and G3 and G5 genotypes as the most unstable genotypes. Based on NPi (2) and NPi (4) parameters, G1, G10 and G9 genotypes were the most stable and G5, G12 and G4 genotypes were the most unstable. Based on the results, the first two principal components explained 68% (42% and 26%, respectively), of the variance of the main variables). Cluster analysis using mean seed yield and non-parametric statistics, placed chickpea genotypes in two main groups. The first cluster included mean seed yield, TOP, MID, σr and σmy. The second cluster consisted of four sub-clusters .

Based on Si (1), Si (2), Si (3) and Si (6) statistics, G1, G10 and G9 genotypes with the lowest values were identified as the most stable genotypes. G1 and G9 genotypes with the lowest GSI were recognized as the best genotypes in terms of grain yield and stability. Based on the parameters with the dynamics concept of stability, genotypes G1, G10 and G9 were identified as stable genotypes with high yield.

In this study, 14 advanced chickpea genotypes selected from regional experiments with Adel and Azad, as control cultivars, were cultivated in a randomized complete block design with three replications in eight environments (Gonbad, Gachsaran, Ilam and Khorramabad in three, two, two and one years, respectively) in Iran during 2017-2020 growing seasons. Combined analysis of variance showed that environment, genotype and genotype by environment interaction (GEI) explained 43.5, 19.8 and 36.7% of the total variation of grain yield, respectively. The first and second principal components accounted for 50.89% and 19.39% of variation, respectively. The polygonal view of biplot showed that genotypes G11, G14 and G9 were the most adaptable genotypes for environments E2, E3, E6, E7, E8, E4 and E5 and G8, G5 and G13 for E1. Based on average tester coordinate (ATC) view, genotypes G11, G5, G13 and G14 were the most stable and high-yielding genotypes. The GGE-biplot based on genotype-focused scaling showed that genotypes G11, G14, G8, G9 and G13, in the circles around the ideal genotype, were the most preferred genotypes. The priority index (PI) showed that in all environments and favourable environments (environments with higher than average grain yields), genotypes G11, G9, G16, G13 and G14 and in unfavourable environments (environments with lower than average grain yields), genotypes G11, G13, G14, G12 and G5 were superior genotypes. Based on different biplot views as well as priority index, genotypes G11, G14 and G5 had grain yields higher than average yield and yield of Adel and Azad cultivars. Further, given their grain yield stability in all environments they can be suitable candidates for introduction of new cultivars.

Chickpea (Cicer arietinum L.) is one of the most important legumes in the world after pea and bean and is rich in protein (21.7-23.4%), minerals (iron, phosphorus, calcium, zinc, potassium and magnesium), carbohydrates (41.1-47.4%) and vitamins (B1, B2, B3, B5, B6, B9, C, E, K). The interaction of genotype by environment, as a response of genotypes to the environmental variation is a source of complexity for breeding programs and preparation of high yielding and stable genotypes. One of the most important ways to discover the nature of genotype by environment interaction is stability analysis, which identified the stable or compatible genotypes. Different methods for investigation of genotype by environment interaction and determination of stable genotypes have been reported, which generally include uni-variate and multivariate methods. One of the multivariate methods is AMMI analysis. The purpose of present study is the evaluation of the stability of chickpea genotypes using AMMI indices and biplots. 

Eighteen selective advanced genotypes of chickpea from ICARDA with two check verities (Arman and Azad) evaluated across four locations (Gachsaran, Ilam, Gonbad and Khoramabad) at three growing seasons, in a completely randomized block design with three replications. The data of 3rd year in Gonbad were lost and therefore, the analysis of data performed on 11 environments. Average seed yield of genotypes estimated at each environment (combination of location and growing season) and used for analysis. Statistical analyses including simple analysis of variance, combined analysis of variance and stability analysis carried out by metan (Multi environment trial analysis) R package. Five AMMI stability indices including ASV (AMMI stability value), SIPC (Sum of IPCs scores), EV (Eigenvalue stability parameter of AMMI), Za (Absolute value of the relative contribution of IPCs to the interaction), WASS (Weighted average of absolute scores) and simultaneous selection index (ssi) of these parameters was used for stability evaluation of genotypes. 

Combined analysis of variance indicated environment, genotype and genotype by environment interaction accounted 79.2, 2.4 and 10.0% of the phenotypic variation of seed yield, respectively. The significant effect of genotype indicated the wide genetic background of genotypes, while the significant effect of genotype by environment interaction is indicated the diversity of genotypes in test locations and growing years and exhibited the necessary of evaluation of genotypes in multiple environments. According to the significant effect of genotype by environment interaction, AMMI analysis was carried out by principal components analysis and the results indicated the significant effects of five principal components on seed yield. The first two principal components contributed to 42.5 and 19.4% of genotype by environment interaction. Genotype G3 (1663 kg ha-1) followed by G1, G17, G20 and G5 had the highest seed yield. According to the ASV and WAAS indices, G3, G4 and G13; EV and ZA indices, G3, G14, G16 and G6; and SIPC, G14, G3, G11, G4 and G16 were the most stable genotypes. Selection of these genotypes was done only on stability aspect of genotypes, therefore simultaneous selection index (ssi) based on any of these parameters was used to evaluate the simultaneously selection of genotypes based on seed yield stability and performance. Genotypes G3, G1, G4, G13 and G16 selected as superior genotypes by ssiASV, ssiZA and ssiWAAS indices; while based on ssiSIPC and ssiEV, G3, G16 and G20 were as the best genotypes. The other applications of AMMI stability analysis are selection of best genotypes in any of environments. According to this procedure, genotype G3 was placed in the first order in E1, E5 and E7; in the second order in E2, E3 and E9; in the third order in the E6 and E7; and in the fourth order in the E5. The AMMI1 biplot (IPCA1 vs grain yield) identified G1, G3, G17 and G13 as high yielding and stable genotypes with seed yield higher than total mean and lowest IPCA1 values. This biplot was also indicated environments E1, E9, E5, E2 and E3 had the lowest contribution in genotype by environment interaction interaction. The first principal components explained only 42.5% of genotype by environment interaction, and therefore, it seems that using the AMMI2 biplot is more efficient to identify the superior genotypes. In AMMI2 biplot (IPCA1 vs IPCA2), genotypes G4, G3, G13, G1 and G10 had high general stability. The environments E3, E4 and E10 with long vectors, had high discriminating ability and can estimate the relative efficiency of genotypes well.

In general, based on different indices, G3, G1 and G13 had high yield in most of environments, and in most methods had good stability and could be candidates for introduction of new cultivars.

Improper distribution of rainfall and reduced rainfall are major factors in reducing lentil yield per unit area . Therefore, the use of genotypes adapted to adverse environmental conditions can play an important role in increasing yield in such conditions. Awareness of genotype × environment interactions helps breeds to be more accurate in evaluating genotypes and choosing the best genotypes.

This study was conducted during two years (2019-2020 and 2020-2021) in two stations in the cold dry areas of the country (Qeydar Zanjan, Maragheh). The experiment consisted of 17 advanced lentil genotypes along with three control cultivars Kimia, Bilesvar and Senna (20 genotypes in total) which was performed in a randomized complete block design with 3 replications.

The results of combined analysis showed a significant difference in the level of one percent probability for environment, genotype and genotype × environment interaction. The results of combined analysis of variance showed that the environment, genotype and genotype by environment interaction effects were 79.5%, 2.25% and 18.23% of total variance, respectively The results of GGE biplot indicate the existence of 42.1% of the total changes related to the first component and 26% of the total changes related to the second component, which together explain 68.1% of the total changes. According to the obtained results, there is a high correlation between E1 and E3 environments and between E2 and E4 environments and they can be introduced as similar environments. In biplot study, genotypes 7 (FLIP2013-29L), 13 (FLIP 2012-262 L) and 11 (FLIP 2012-207L) had higher performance and stability at the same time.

Genotypes 13, 7 and 11 were introduced as high yielding and stable genotypes and can be used to select or recommend a variety.