فهرست مطالب somayyeh sanchouli

Rice is one of the most important crops in Asian countries, which is cultivated in more than half of the continent's agricultural lands. Environmental conditions are different and uncontrollable even in different parts of an area, so the response of rice cultivars to these conditions will be different. Therefore, it is necessary to perform performance comparison experiments to achieve high quality, quantity, consistency and stability in different regions. More than 50 percent of human food is supplied from cereals, and rice is a cereal that has a high crop after wheat, but more than wheat and others in terms of energy production per hectare Cereals are important. By using the GGE biplot method, by using multivariate methods, in addition to proper data analysis, the work facilitates the interpretation of the results. The aim of this study was to evaluate the stability of the lakes by using GGE biplot analysis and selecting and introducing superior lanes for stability and response in underwater stress conditions and flooding.

In this experiment, the eight lines with the control cultivar of the region and IR29 cultivar during 2014 and 2015 with the desirable qualitative and qualitative characteristics and suitable growth period in a completely randomized block design with three replications in two regions of Gonbad Kavous and Ali-Abad were cultivated. Ten plants of 15 cultivated plants were randomly selected and separated from the soil at a depth of 50 cm. After removing the bushes from the soil using the shovelomics method, the plants were first immersed in water for seven days. Then the root and part of the air organs were separated. To record the root characteristics, each root of the plant is separated and the number of roots is less than 5 cm, the number of roots is 7-6 cm, the number of roots between 20-8 cm, the number of roots 21-30 cm and the number of roots greater than 30 cm, root volume and root dry weight were measured. Using the aerial parts, related traits such as panicle number, plant height, stem weight, straw weight, panicle length, number of filled grains, weight of grains and cluster number were recorded.

The results of analysis of variance showed that the difference between locations for days to days of traits, number of roots, total length of roots, root number between 7 to 20 cm, stem weight, panicle weight, root dry weight, straw weight and seed number Poke was significant, and the difference between years was significant for number of pancakes, root number was significant. Comparison of mean of studied sites and years showed that grain yield per hectare had no significant difference, but the mean comparison of this trait in terms of waterlogging and stress conditions indicated that flooding conditions had a higher yield than tension. Separation of the interaction of location × planting time × irrigation conditions with different cultivars by biplot method showed that the cultivar 87.5.103 in all states related to Aliabad city had the highest yield. In irrigation stress conditions, IR55411, IR70360, 87.5.21, IR66424 and 87.110 lines had lower yields, but in terms of flooding, IR55411, IR70360, 87.5.21, IR66424 and 87.110 lines had the highest yield, respectively. In both cases, all of the cultivars had a higher yield than IR-29

Separation of the interaction of location × planting time × irrigation conditions with different cultivars by biplot showed that cultivar 87.5.103 in all states related to Aliabad city has the highest performance. In general, the cultivar 87.5.103 among all cultivars and the floodplain dome in the first year was the best environment for all the environments in terms of day.

Rice is an important crop that is considered a staple meal for 2.7 billion people worldwide. Therefore, the demand for it will increase with the increase of population. Environmental constraints always pose a serious threat to crop production, including rice. Drought is one of the most important challenges that limits the production of high-yielding cultivars in arid and rainfed areas. Global warming has also become a factor in limiting rice production in rain-dependent areas. Therefore, researchers are looking for a way to stabilize rice production in arid regions. In this study, informative markers related to the desired agronomic traits were identified in 59 rice genotypes using microsatellite marking system.

In order to evaluate the tolerance of rice genotypes to drought stress and to identify tolerant and sensitive genotypes, 59 genotypes received from the National Rice Research Institute and the International Rice Research Institute in a randomized complete block design with three replications in two separate conditions, without Stress (flood) and drought stress were performed in a research farm located in Aliabad Katoul city in 2013. In both conditions (normal and drought stress), the genotypes were planted in five rows of 25 × 25 cm in rows one meter long. Thirty days after planting in the nursery, healthy and strong seedlings were transferred to the main land. The required agronomic operations were carried out equally during the growth and development period of the plants under stress and normal conditions and only in terms of irrigation of the experimental field in both flood and stress environments, until the tillering stage of the cultivars were equally flooded. Then, to create stress, irrigation was done from 40 days after transplanting (maximum tillering stage) to the end of the growing season at 25-day intervals. Phenotypic values of grain yield and 1000-grain weight were measured under two conditions according to standard guidelines for evaluation of traits in rice. In order to investigate the relationship between agronomic traits and microsatellite markers with 59 rice genotypes out of 36 microsatellite molecular markers were performed in the Plant Breeding and Genetics Laboratory of Gonbad Kavous University, Faculty of Agriculture and Natural Resources. Young leaves of 21-day-old seedlings were extracted in four-leaf stage using CTAB method. Touchdown PCR reaction was studied and evaluated randomly using 36 microsatellite primers for 3 markers from each chromosome. To separate PCR products, 6% polyacrylamide gel electrophoresis was used and to reveal the banding pattern, silver nitrate staining method was used. The content of polymorphic information was calculated. The relationship between molecular data and traits of studied rice genotypes was investigated using multiple regression. Thus, each quantitative trait was considered as a dependent variable and microsatellite markers were considered as independent variables.

The average content of polymorphic corrections (PIC) was estimated to be 0.58, which showed RM 5647 with 0.81 the highest and RM 6022 with 0.32 the lowest polymorphism (PIC). The results of stepwise regression analysis showed that a total of 90 markers for normal conditions and 69 markers for drought stress conditions for morphological traits were identified. Under normal conditions, the number of spikes and the number of days to flowering with 9 markers and under drought stress, the weight of the cluster with 9 markers showed the most positive markers. The most explanation for variation in normal conditions is related to the total number of grains (0.83) by gene loci RM6324-E, RM5652-E, RM5761-D, RM6179-F, RM549-B, RM462-B, RM7420-D Explained. In drought stress conditions, the most explanation for variation related to panicle weight (0.70) by gene loci RM519-D, RM7545-A, RM6179-E, RM7118-G, RM3525-B, RM5761-B, RM38-C, RM7091-A, RM5647-B explained.

The results showed that some markers are associated with more than one trait, which indicates that these traits are very closely related to each other or may be influenced by multi-effect genes. To understand this, it is necessary to develop transgressive generations and linkage.

A mapping population of 96 inbred line derived by cross Neda (NAD) and Ahlamitaroum (ATM), was used to detect quantitative trait loci (QTLs) for fresh biomass (FB), root length (RL), shoot length (SL), root number (RN), leaf width (LW), root fresh weight (RFW), root dry weight (RDW) and K content (KC) under K deficiency condition in rice. Two parents and 96 inbred lines were phenotyped for the traits by growing them in K deficiency nutrient solution. In K deficiency, 16 QTL was able to explain a great deal of phenotypic variation in features. qSL-7a, qRL-6, qRL-10b, qRL-12b, qRN-6b, qRN-12b, qLL-6, qLL-7, qLW-6b, qLW-10b, qLW-12a, qLW-12b, qLW-12c, qKC-6b, qKC-10b and qKC-12c were on chromosomes 6, 7, 10 and 12. Their LOD are 4.732, 5.826, 5.01, 5.067, 11.346, 5.867, 5.973, 5.85, 7.077, 8.055, 5.577, 6.439, 8.012, 14.057, 6.432 and 858/6, respectively. In normal conditions, 5 QTL with a large effect were identified. qKCN-6c, qKCN-7a, qKCN-10, qKCN-12b and qKUN-6 were located on chromosomes 6, 7, 10, 12 and 6, respectively. Owing to the high percentage of explanation, the major QTLs can be a suitable candidate for marker assistance programs in recombinant Iranian rice lines after validation.

Rice is the world’s most important staple food and will continue to be so in the coming decades. Ferrous iron is essential for rice growth. A mapping population of 96 rice inbred lines derived by Neda (NAD) and Ahlemitarom (ATM) cross, was used to detect quantitative trait loci (QTLs) for fresh biomass (FB), root length (RL), shoot length (SL), root number (RN), leaf width (LW), root fresh weight (RFW), root dry weight (RDW) and Fe content (FC) under Fe toxicity condition in rice. Two parents and 96 inbred lines were evaluated for the traits by growing them under normal and Fe toxicity nutrient solution. Under stress condition, two QTLs were detected for FB on chromosome 10, with LOD of 2.859, and 2.465. Twelve QTLs were identified for RL on chromosomes 2, 4, 5, 6, 7, 8, 9, 10, and 12. Three QTLs were detected on chromosomes 6, 7, and 8 for RN, and two QTLs for RDW on chromosomes 2 and 9. One QTL controlling LW, RFW, and FC was located on chromosomes 10, 9, and 1, respectively. The other QTLs for FB, SL, and RN was located on chromosomes 12, 12, and 3 under normal condition, with respective contributions of 9.7, 10, and 9.9, respectively. qLWN-2, qLWN-7, and qLWN-12 were located for LW on chromosomes 2, 7, and 12. These QTLs, due to the high percentage of explanation after validation, are a good candidate for marker-assisted selection programs with the help of markers in the rice population.

In order to increase global wheat production, it is necessary to examine different ways of increasing yield. One of the solutions is to identify the genes that control different stress tolerance indices. This research aims to identify the genes controlling quantitative traits under normal conditions and salt stress in the germination stage. The experiments were carried out in a factorial form using a completely randomized design with 3 replications, on 107 lines resulted from the crossing of Gonbad and Zagros cultivars at Gonbad Kavous University, 2021. A linkage map was obtained using 519 SSR, 8 CAAT, 33 IJS, 47 iPBS, 3 IRAP, 17 RAPD, 8 SCoT and 12 ISSR markers on 21 wheat chromosomes. The length of the linkage map was 4918.94 cM and the distance between two adjacent markers about 5.55 cM. A total of 84 QTLs were detected in normal and salinity stress conditions (control, 6 dS/m, 12 dS/m), of which seven QTLs were related to control condition, 42 QTLs were related to 6 dS/m salinity stress and 35 QTLs were related to 12 dS/m stress condition. qLR-B3, qMGT-A5, qR/SDW-B2, qR/SDW-A3, qR/SDW-B7, qLS-A5 and qILVS-A5 were detected under control condition. qSLI-D6 was identified as a major QTL for SLI under 6 dS/m salinity stress by explaining more than 44% of the phenotypic variation of the trait. In the 12 dS/m salinity stress, several gene loci of large effect QTLs were detected, among which qIWVS-B3 explained more than 55% of the phenotypic diversity of the trait. After validation, the results of this research can introduce suitable candidates for marker-assisted selection programs in the population of Iranian wheat RILs.

In order to locate the QTLs associated with germination stresses and determine the contribution of each QTL in phenotypic variation, traits of 74 individuals RIL F8 of crosses of Ahlemi Tarom × Neda. The study was conducted at the University of Gonbad Kavous in 2017. The linkage map was based on 40 SSR markers, 16 ISSR markers (with 76 polymorph alleles), two IRAP markers (7 polymorph alleles) and an iPBS marker (with 3 polymorph alleles) assigned the markers to 12 groups of 12 rice chromosomes with a map length of 1491 cM and a distance between flanked markers of 13.07 cM. The components of germination including germination percentage, coleoptile length, radicle length and plumule length were recorded. Under normal conditions, nine genetic locations were detected. For the length of the roots of five genetic locations on chromosomes 3, 5, 6 and 7, and for the length of the stem, four gene locations were located on chromosomes 1, 5 and 7. Two genetic locations were detected in mannitol for Cleoptil length on chromosome 6. For the root length of the seven QTLs, they were located on chromosomes 3, 4, 6 and 7. For stem length, four gene locations were detected on chromosomes 4 and 6, which justified 8 to 11 percent of the phenotypic variation of the trait. Two genetic locations for root length were detected on chromosomes 5 and 8, while qRASA-8 was able to justify 14.9% of the phenotypic changes of the trait. There were also six genetic locations for stem length, which were located on chromosomes 5, 7 and 8, respectively: 8.75, 9.5, 11.25, 9.8, 13.6 and 15.7% respectively phenotypic changes justify the attribute. In the drought stress condition of polyethylene glycol, a gene site was detected for germination percentage on chromosome 2 and at 78 centimeters from the beginning of the chromosome, which justifies 10.3% of the phenotypic variation of the trait.

Rice (Oryza sativa L.) is a major source of food and energy for more than 2.7 billion people on a daily basis and is planted on approximately one-tenth of the earth's arable land. Rice is one of the most important cereal. Salinity is the second most problem next to drought, in rice growing areas of the world. Soil salinity is a abiotic stress in crop productivity worldwide. The aim of the present study is to identify QTLs related to salt tolerance by using an Iranian rice population and Comparison of different QTL mapping methods.

A F8 RILs population, derived from a cross between a salt tolerance Ahlemi Tarom (ATM) and salt sensitive Neda (NAD) which were used in this study. The early crosss and segregated generations in the University of Gonbad Kavous were developed. The genetic material involved 96 lines were used to evaluate the salt tolerance. This experiment was conducted at the faculty of agriculture, university of Gonbad-Kavos, in 2016 as hydroponics. The seeds were placed 50 C for 3 d to break dormancy, and then germinated at 25 C for 72 hours. Finally, the germinated seeds were sown in holes of the Styrofoam board with a nylon net bottom, which floated on water for 3 d, and after were transferred to float on Yoshida's nutrient solution for 11 d. two week after sowing, the seedling were transferred to nutrient solution with electrical conductivity 6 dSm-1 for 7 days, then NaCl concentration was increased to 12 dSm-1 for further 7 days. This experiment was conducted in a controlled condition with 16-h photoperiod, temperature of 29/21 C and minimum relative humidity of 70%. The culture solution was renewed weekly and the PH was adjusted daily to 5.5 by adding either NaOH or HCL. Chlorophyll content was measured using a SPAD device. 40 SSR primer pairs, 16 ISSR markers (76 alleles), two IRAP markers (7 alleles) and one iPBS marker (3 alleles) were appropriately distributed on 12 rice chromosomes. Finally, The genes controlling the chlorophyll content located using different QTL mapping methods in clouding SIM-MEL، SIM، CIM، MIM، PMLE، ICIM and STSIM. These methods detected different QTL.

In CIM-MLE method, five QTL were detected on chromosomes 3, 5, 6, 7 and 8 in normal condition, and three QTL were detected on chromosome 2 and 6 in salt stress condition. In SIM method, three QTL were identified on chromosomes 2, 3 and 8 in normal condition, and five QTL were identified on chromosomes 4, 6, 7 and 10, in salt stress conditions. In CIM method, three QTL were detected on chromosomes 2, 3 and 8, these QTLs justifying 13-23% of the phenotypic change of trait, in normal condition, but under salt stress condition, six QTL were detected on chromosomes 1, 2, 6 and 7. qCHLN-3, qCHLN-5 and qCHLN-6 were detected by MIM method in normal condition and qCHL-6a and qCHL-6b were detected on chromosome 6 in salt stress condition. Six QTL were detected by PMLE method in normal condition and two QTL detected on chromosomes 6 and 9 in salt stress condition. In ICIM method, three QTL were identified on chromosomes 2, 3 and 8 in normal condition, in salt stress condition were detected five QTL on chromosomes 4, 6, 7 and 10. qCHLN-3, qCHLN-6 and qCHLN-7 were detected by STSIM method in normal condition, andqCHL-6on chromosome 6 and has a LOD of 3.187 and an additive effect of -0.079.

ICIM, CIM and SIM has most closely in genetic location in normal and salt stress conditions. qCHL-6 was identified in six location method at 52 cM position in chromosome 6. qCHLM-3 and qCHLN-8 were detected in CIM, CIM and SIM on chromosomes 3 and 8 and explaining 18-22% of phenotypic variance chlorophyll content in normal condition. CIM, ICIM and SIM method were detected QTLs on chromosomes 6 and 7. Among the methods used, the CIM has the least error in estimating the original QTL effect and it can be done at any point in the genome that is covered by markers and the performance of the markers is higher in this method. Therefore, the effectiveness of using the markers introduced in this method will be higher. The results of this study can identify the better genotypes in term of chlorophyll content for marker selection programs after validation of QTLs.

In order to evaluate the effect of drought stress on yield and the other related traits, 14 rice genotypes (including five varieties of landrace and introduced varieties) in a randomized complete block design with three replications in two environments without stress (flooding) and drought stress in the field Research located in Aliabad (under the supervision of Gonbad Kavous University) was studied. Irrigation of experimental farm in both the flood and drought were flooded until maximum tillering stage. Irrigation was performed every 25 days until the end of the season from 40 days after maximum Tillerin). Analysis of variance showed significant differences among genotypes for all traits in both conditions. The average yield of the genotypes in the normal and stress conditions lR83752-BB-12-3 and was Sepedroud. Panicle weight (0.85**) under stress (flooding) and root volume (0.98**) in drought stress conditions, the highest positive correlation with grain yield were Significant. Path analysis results showed that different traits were involved in justifying grain yield under non-stress and drought stress conditions, so that in non-stress condition, number of panicle and in drought condition, number of filled grain per panicle had the highest direct and positive effect on grain yield. In grouping cultivars based on cluster analysis using ward method, cultivars were divided into three groups under normal conditions and stress. The results of cluster analysis showed that Pegaso, IR83752-B-B-12-3 and Sepidrood cultivars, which had more volume, fresh weight and root dry weight than other cultivars, were in the drought tolerant group.

In order to study the inheritance of some agronomic traits in rice, six parents named as Shahsavand, Ahlemi Tarom, Gharib, Neda, IR28 and Sepidrood were crossed to each other in a complete diallel design, respectively. Then, generations P1, P2, F1, and F1 were planted in Randomized Complete Block Design with three replications at the research farm in Gonbad-e-Kavous. Characteristics of flag leaf length and width, plant height, panicle number, the number of filled grain, Length of panicle exertion, panicle length and grain weight were determined. The results showed that the parents had a significant difference in all of the studied traits. The crossings evaluated showed a significant difference for all traits. Except for a few cases, all genetic parameters were significant at 1% probability level for all traits. Changes in b1 and c for grain number and b, b1, b2, b3 and d for leaf length, b1 and b3 for plant height, c and d were not significant for the number of panicles. Average heterozygosity (b1) for grain number, leaf length, and plant height was not significant. For most of the studied traits, Ahlemi Tarom was the best parent. In most of the cases, there was a positive and significant positive specific combining ability that one or both parents of the cross of the desired trait had high general combinig ability.