discriminant function

Drought stress is one of the most important environmental stresses that has limited the production of wheat and has significantly reduced the production efficiency of this plant in arid and semi-arid regions. The aim of this study was to investigate the response of 15 spring wheat genotypes to drought stress and to introduce the best genotypes for cultivation in areas with water shortage.

In order to study of genetic diversity in advanced spring wheat genotypes based on some morphological, physiological and agronomic traits, 15 spring wheat genotypes by using randomized complete block design (RCBD) in three replications under two non-stress and water-deficit stress conditions were examined at research field of Islamic Azad University, Tabriz Branch, Iran during 2016-2017 and 2017-2018 crop years. The morphological, physiological and yield component were assayed.

Based on results of the combined variance analysis, the interaction effects of genotype, irrigation conditions and year were significant for most of the studied traits, except chlorophyll index, carotenoid and chlorophyll b, that indicating the different reactions of genotypes under two non-stress and water-deficit stress conditions. Also, the interaction effects of genotype and water conditions were significant on studied characters except of spike number, spike length and carotenoid. Under water deficit stress the genotypes of URBWYT4, URBWYT1 and ERWYT4 showed the highest seed yield, and URBWYT3, Gonbad, Karim, and Morvarid showed the maximum seed yield in normal condition. Cluster analysis using Ward method based on studied traits with standardized data led to the allocation of genotypes in two separate clusters in both non-stress and water-deficit stress conditions. Also the results of the discrimination function analysis confirmed the genotypes grouping of cluster analysis. According to cluster analysis the genotypes of ERWYT1, URBWYT3, URBWYT5, URBWYT4, ERWYT5, URBWYT1, URBWYT2, ERWYT4 and ERWYT2 in non-stress condition and Morvarid, ERWYT1، ERWYT2، URBWYT2، ERWYT3 and URBWYT4 under water-deficit stress were elite genotypes.

according to obtained results the two genotypes of Morvarid and ERWYT3 can be introduced as the best genotypes for both studied conditions.

Chickpea is one of the most important pulses in the world and it plays a key role in feeding of people in Iran. Iran is one of the original centers of chickpea. This plant has a significant genetic diversity and has favorable conditions for breeding and introducing new cultivars in Iran. According to FAO reports (2014), the average grain yield of chickpea in Iran with a cultivated area of 565 thousand hectares and an average yield of 557 kg per hectare is very low compared to the average of the major countries producing chickpeas. In order to solve this problem, identification of the initial variety of lines and cultivars to start the program is of particular importance. In fact, three factors of heredity, high diversity and severity of selection are effective factors in increasing the response to selection. In this regard, there are various methods for estimating genetic variation in plant species, one of the most important of these methods is multivariate analysis. It is necessary to use these methods for the classification of germplasm and the analysis of the genetic relationships existing between the modifying materials. Among the various methods of multivariate analysis, the principal component analysis, factor analysis, cluster analysis and decomposition function analysis are among the most important of these methods. In fact, one of the important goals of an outbreak is to select the best genotypes. In order to achieve this goal, the studied population should have a significant variation in terms of the characteristics studied, which knowledge of this diversity requires evaluation of the germplasm.

This study included 20 chickpea (Cicer arietinum L.) promising lines were planted in a randomized complete block design with three replications in 2013 growing season. To evaluate the diversity of lines based on important agro-morphological traits and to achieve the desired goals, various statistical methods including principal component analysis, factor analysis, cluster analysis and decomposition function analysis were used. Genotypic coefficient of variance (GCV) and phenotype coefficient of variance (PCV) using genotype variance and phenotype variance, respectively, were calculated. All calculations and statistical analyzes such as component analysis, cluster analysis, and decomposition function analysis were performed using SPSS and Minitab software.

The lowest and the highest coefficient of genotypic changes were related to day physiology (2.66%) and 100-seed weight (49.76%). The results showed that the three main first components explained 76% of the total variance of the traits totally. The first and second components were named as "component of grain yield increase" and "vegetative growth component". The results of factor analysis were consistent with the results of the principal component analysis. In order to cluster the studied lines and for their grouping based on the studied traits, Ward method and Pearson’s square distance matrix were used. According to the cluster analysis, the genotypes were located in three distinct groups, so that the second and third clusters had the highest genetic distance and they were recognized as “yield clusters" and "vegetative growth cluster", respectively. Therefore, crossing between the two above mentioned-groups, will result to high artificial diversity in the future generations. According to the cluster analysis, the lines were divided into three separate clusters. Discriminant function analysis confirmed the cluster grouping completely. In a study, nineteen chickpea lines and five genotypes of wild chickpea (Cicer veticulatum) were classified into three distinct groups using cluster analysis, so that wild-type genotypes in one group and two lines of cropping lines were grouped separately from other lines. Ganjali et al, (2009), applied the multivariate bi-plot analysis to evaluate the variation in chickpea germplasm for drought resistance.

There was a remarkable genetic diversity for the current germplasm, therefore existence a high level of genetic diversity and a remarkable GVC for the trait of 100-seed weight indicating that this trait can be used as a suitable character for yield improvement in the germplasm and under this experiment conditions. Finally, selection for high levels of biomass, pod number per plant and seed number per plant, and a low amount of main branches number, will increase the grain yield in the future breeding program.

In order to investigate the effects of chilling stress on saffron plant, a completely randomized design as pot cultivation with four replicates on five ecotypes of saffron including: (Torbat Heydariyeh, Ferdows, Gonabad, Ghaen, Kashmar) was performed. Two temperature conditions 25° C (non-stress conditions) and 5° C (stress conditions) were applied to the ecotypes in the growth chamber (germinator) and twelve morphological traits were measured. The results showed that there was a significant difference in the most studied traits at 1% level. The results of factor analysis showed that three components totally explained 84.6% of the studied traits under non-stress conditions and four components totally explained 86.12% of the studied traits under stress conditions. Cluster analysis under non-stress conditions revealed that Ghaen, Gonabad, Kashmar and Ferdows ecotypes were in the first cluster and Torbat Heydariyeh ecotype in the second cluster. Similarly, Cluster analysis under stress conditions revealed that the Torbat Heydariyeh and Ferdows ecotypes were in the first cluster, the Kashmar and Gonabad ecotypes were in the second cluster, and the Ghaen ecotype was in the third cluster. The discriminant analysis showed a function with Eigenvalue (61.51%) explained 100% of ecotypes variation under non-stress condition while two functions explained 100% of ecotypes variation under stress condition, with only the first function had a specific eigenvalue greater than 1. According to the group membership prediction table, the ecotypes in each of the three groups of dendrogram cuts were correctly divided by 100% probability. It can be concluded that the ecotypes of Torbat-e-Heydariyeh and Ferdows which grouped in one cluster and had the highest biologic yield under chilling conditions can be recommended for cultivation in cold regions.

Due to wheat cultivation in semi-arid regions of the world, much attention is paid to the production these days and introduction of cultivars that do not have a significant yield reduction during drought condition. The healthy and developed roots increase the efficiency of water and nutrients absorption that increase the yield. In this research, root length and number, total length of roots and shoot in embryo stage,  root fresh and dry weights, volume, length, shoot length and weight, number of leaves and tillers and also root traits like fineness, diameter, water content, root length, length density, specific mass, texture density, mass density, surface area and density indices in seedling were measured. Descriptive statistics of traits estimated and genotypes were classified into nine groups based on the root depth and texture density. Also, analysis of variance of different groups was estimated. Based on the results, 20 genotypes had dense and deep roots, the best indices for drought tolerance. Most traits, except embryonic stage traits, showed significant differences among genotypes. Discriminant function was performed and the amounts of functions were calculated for each group.

To investigate the genetic diversity of differential cultivars wheat rusts (yellow (Puccinia striiformis), stem (P. graminis) and leaf (P. recondite) rust) for phenological, morphological and agronomical traits 58 differential cultivars along with 5 control genotypes (Karem, Yavaros, Rejab, Saji and Zardak), an experiment base on  augmented  design at the the research field of Ilam University during 2017 was carried out. The studied traits were phenological traits (from days to emergence, days to shooting, days to booting, days to heading, days to flowering, days to physiological maturity) and morphological traits (plant height, peduncle length, node number per shoot, tiller fertill nounmber per plant, tiller number per plant, awn length, spike length, spikelet no. per spike, 100 seed number, biological yield, grain yield and harvest index). The range of trait changes from 134 to 188 days was variable and respectively for differential cultivars SR17 and SR13. The range of plant height changes varied from 47.4 cm in the differential cultivar YR8 to 100.3 cm in the differential cultivar LR12. Cluster analysis by Ward method divided the differential cultivars yellow, stem and leaf rusts were studied in three, three and five groups, respectively. LR1, LR9, LR12 and LR13 were identified as the best differential leaf rust varieties in terms of yield and yield components. YR4 and YR11, which are among the yellow rust differential cultivars, had high grain yield and harvest index. SR1, SR13, SR6, SR14, SR7, SR8 and SR3 were identified as superior stem rust genotypes. Therefore, it can be concluded that these varieties are of great importance in selection and breeding programs to achieve higher yield.

In this research a total of 49 accessions in oat collection of National Plant Gene Bank of Iran belonging to four species of Avena barbata, A. wiestii, A. fatua and A. sativa were studied. The experiment was performed in an observatory designin research field of Seed and Plant Improvement Institute located in Karaj, Iran. The agromorphological traits were measured according to IBPGR descriptor. Based on Shannon index, lemma color had the largest variety (1.42). In principal component analysis, the first seven components comprised 68.43% of total variation. Two accessions of 439 (A. barbata) and 449 (A. barbata) were the nearest and two accessions of 447 (A. wiestii) and 115059 (A. wiestii) and were the farthest based on genetic distance. The studied accessions were placed  in five groups by K means clustering method. Three discriminant functions were developed justifying total variance in data. The first and second discriminant functionsmaintained the most distinction between two species of A. barbata and A.sativa and between two species of A. fatua and A. wiestii, respectively. Total results indicated presence of suitable diversity in the studied population based on the measured traits which could be exploited in oat breeding in future. In addition, the discriminant functions developed in this research could be used in distinguishing the relating oat species.

In order to investigate the relationship among amylose content, gelatinization temperature and paste viscosity properties and identifying the discriminator parameters of intermediate and high amylose groups in rice genotypes, 40 pure lines and cultivars from two groups of high and medium amylose content were evaluated in a randomized complete block design with three replications at Rice Research Institute of Iran during 2015 and 2016. Physicochemical characteristics of grain (amylose content, gelatinization temperature) and paste viscosity properties including peak viscosity, trough viscosity, breakdown viscosity, final viscosity, setback viscosity and consistency viscosity were measured by Rapid Visco Analyser in RVU unit. Results of cluster analysis with WARD method showed that all selected rice genotypes from high and intermediate amylose groups were classified into two groups based on paste viscosity characteristics and gelatinization temperature. The first group consisted of 19 genotypes with high amylose content and the second group included 21 genotypes with intermediate amylose content. In fact, the classification of the genotypes based on the viscosity parameters and gelatinization temperature was similar to the classification of the cultivars based on the amylose content of grain. Discriminant function analysis showed that only the final viscosity and gelatinization temperature completely (100%) separate the high and intermediate amylose groups and identified as the best and most effective discriminators (with an eigenvalue of 56.35) that are easier, less cost and time consuming, compared to direct measurement of amylose content of grain. Evaluation of starch viscosity characteristics and gelatinization temperature of rice grain in combination with multivariate statistical methods may be considered as an easier and less costly method suitable for the classification of rice genotypes and may be used in breeding programs to identify good grain quality rice cultivars. According to the fitted regression model, it is possible to predict the intermediate to high amylose groups of rice genotypes in regard to final viscosity value between 290.50 and 493.25 RVU with an adjusted R square of 97%.

Evaluation of the grain yield and yield components and also morphological traits can help to identify strategies for selection of resistant cultivars and increased crop yield production under heat stress condition. To this end, in experiment to evaluate of grain yield and yield components on 42 rice lines tolerant to heat stress from International Rice Research Institute (IRRI).
An experiment was conducted based on randomized complete block design (RCBD) with three replications plus Hoveizeh cultivar as control during 2013-2014 in Agricultural Research and Natural Resources Center of Khuzestan province, Iran. The evaluated eight characters were: degree of maturity, growth period, number of tillers per square meter, number of grains per panicle, number of hollow grains per panicle, 1000 grain weight, fertility percentage and grain yield measured on the basis of the standard system of International Rice Research Institute. As the degree of maturity of the product of two traits of fertility percentage and 1000 grain weight were measured in each trial.
Research simple of traits variance analysis showed there were significant differences in effect of cultivar for all traits except number of tiller per square meter that this result showed that diversity of the lines and cultivar for using in the study. Number of tillers per square meter and number of grains per panicle in front of grain yield imported in regression equation that these traits of positive and significant correlation with grain yield. Path analysis showed that the number of grains per panicle with correlation coefficient of 0.337 had the higher direct effect than number of tillers per square meter with correlation coefficient of 0.186 on grain yield. Principal component analysis of the first two components explained 74% of the total diversity. In this analysis desirable characters such as fertility percentage, grain yield and degree of maturity in the first component have positive coefficient traits which increase first component, and also 1000 grain weight with negative factor coefficient in second component, so first component increased and second component decreased. Cluster analysis, case of study cultivar and lines resolved in three groups. In the first cluster, lines such as: 1, 34, 54, 35, 39, 21, 33, 40, 25, 28, 9, 10, 15, 29, 32, 31, 30, 36 and 44 as well as in the second cluster, lines such as: 3, 24, 4, 19, 5, 7, 6, 55, 14, 23, 16, 17, 18 and 20 and in the third cluster, lines such as: 8, 50, 52, 43, 41, 45, 11, 13, 12 and 26 were included. In the first cluster desirable lines were 40, 35, 34 and 25 and in second cluster undesirable lines were 11, 12, 13, 26, 41 and 45.
Significant differences in effect of cultivar for all traits except number of tiller per square meter that this result showed that diversity of the lines and cultivar for using in the study. According to graph of biplot the first cluster was as best cluster and third cluster was as worse. Discriminant function showed truth grouping at 95%.

Implementation of management policies and sediment control is considered requirements. And requires of these programs, identifying and obtaining information about the relative importance of sediment sources and there by identifies critical areas within the watershed. In this study, Sediment deposited in the behind of sediment control structures were studied that constructed on the main channel watershed Nokandeh in Golestan Province. The purpose of this studyis separation of sediment sources with using the tracing method. The first, accordingto available information and maps of the lithology was determined land use, erosion and slope map units and in every work unit were taken the number of three sediment samples.Then samples being analysis in laboratory with X-ray diffraction and 9 clay minerals recognized based on statistical methods Kruskal Walis and were evaluated of discriminate function. The results indicate the proper separation of sediment sources, so that 2 clay minerals the Muscovite and Quartz could have %100 separation sediment sources. Average of low relativeerror, the high performance coefficient above model and also good consistent results with field observations show that tracers method was utilized in this study, a method is valuable for the separation of sediment sources in the watershed. The reesults also showed that using a combination of tracers characteristics, sediment sources separation rates are significantly higher than any of the individual properties.

In order to evaluate the relationship between SDS-sedimentation value and breadmaking quality of wheat (Triticum aestivum L.), glutenin subunits of different genotypes (foreign and Iranian cultivars) were analyzed by reverse-phase high performance liquid chromatography (RP-HPLC). SDS-sedimentation value was used as an indirect criterion for breadmaking quality. Correlation coefficients revealed a closer relationship between low molecular weight (LMW) glutenin subunits and variation in SDS-sedimentation value. Principal component analysis confirmed the presence of association between some of the glutenin subunits and SDS-sedimentation value. Based on the stepwise regression analysis, two LMW and four high molecular weight (HMW) peaks (subunits) were selected which accounted for 70.2 and 18.7% of variability in SDS sedimentation values, respectively. On the basis of the results of the stepwise regression analysis, a discriminant function was developed. The great efficiency of discriminant function in correct classification of completely different genotypes (Iranian landraces and cultivars) showed that the observed relationship between glutenin subunits and SDS-sedimentation value has a genetic basis and the effects of LMW and HMW glutenin subunits on SDS-sedimentation value are additive. Therefore, it seems that this method based on more protein components (rather than only on HMW glutenin subunits) can be used to predict breadmaking quality of wheat against many genetic backgrounds.