mohammad barzali

Oilseeds are one of the most important sources of energy in the world. As one of the most important oilseed plants in the world, rapeseed has nutritional and economic value, with its seeds containing 40% oil and its oilseed meal containing more than 35% protein. This plant has higher adaptability to diverse climatic conditions. Given the need to supply edible oil in Iran, the selection of high-yielding genotypes with desirable characteristics in this plant is very important for sustainability in production. Moreover, examining the relationship between yield and other agronomic traits improves the efficiency of breeding programs by determining appropriate selection criteria, such that some breeders prefer to select varieties indirectly using yield-related traits to achieve high yield. Among the methods that provide more appropriate characteristics of the status of genotypes and traits are graphical methods that allow for visual examination of correlations and relationships between traits and evaluation and identification of desirable genotypes based on the values of yield-trait combinations. Therefore, this study aimed to compare different genotypes of rapeseed in terms of several traits and analyze the correlation between their different traits, as well as to select superior rapeseed genotypes based on the combination of agronomic traits with oil yield using genotype × trait biplot and genotype × yield × trait biplot methods.

In this study, 15 new lines along with two cultivars were evaluated in a randomized complete block design with three replications in the Agriculture Research Station of Gonbad during 2019-2020. The phonological, morphological characteristics, and yield components including days to flowering starting, days to physiological maturity, plant height, the number of lateral branches, the number of pods per plant, the number of seeds per pod, 1000-seed weight (TSW), oil content, and oil yield were measured in the sample plants. In this study, the genotype × trait (GT) and genotype by yield × trait biplot (GYT) methods were used to identify interrelationships between different traits and selection of the best rapeseed genotypes.

The results showed that the genotype by yield × trait biplot method was more efficient than the genotype × trait biplot method. Based on the biplot and the genotype by yield × trait biplot index, a positive correlation was observed between all yield-trait combinations, and genotype 17, followed by genotypes 11 and 4, respectively, were identified as the best genotypes in the combination of oil yield with days to flowering, days to physiological maturity, plant height, the number of lateral branches, the number of pods per plant, the number of seeds per pod, TSW, and oil percentage. Genotypes 2 and 15, respectively, were identified as the weakest genotypes by being located at the end of the horizontal axis of the average tester coordinate diagram. Moreover, the results of the genotype by yield × trait biplot showed a high positive correlation between oil yield × the number of lateral branches, oil yield × the number of seeds per pod, oil yield × TSW, oil yield × plant height, oil yield × oil percentage, oil yield/days to flowering, and oil yield/days to physiological maturity, indicating the usefulness of combining the number of lateral branches, the number of seeds per pod, TSW, oil percentage, plant height, and maturing time (the number of days to flowering and maturing) with oil yield to increase the production of genotypes in rapeseed.

In general, the results showed that the genotype by yield × trait biplot method was a more suitable tool for investigating relationships between traits and evaluating, comparing, and selecting different rapeseed genotypes in terms of multiple traits than the genotype × trait biplot method. Based on the display of the average tester coordinates of the genotype × yield × trait biplot, genotypes 17, 11, and 4 were identified as the best genotypes with genotype × yield × trait biplot values of 2.22, 1.33, and 1.01, respectively, and genotypes 2 and 15 were identified as the weakest genotypes with genotype × yield × trait biplot values of -1.78 and -1.01, respectively, in terms of oil yield and other agronomic traits. Furthermore, the number of lateral branches, the number of seeds per pod, TSW, plant height, and maturing time (the number of days to flowering and maturing) are the traits that can be used as appropriate indicators in breeding programs to select high-yielding genotypes in rapeseed.

To identify and introduce cultivars with high yield and stability, evaluating the genotype-by-environment interaction in multi-environment trials is of paramount importance. One of the most significant challenges for lentil breeders is selecting genotypes with high yield across diverse environments. Assessing genotypes in different environments can lead to the identification of genotype(s) with high yield potential and yield stability. Genotypes exhibiting lower responses to environmental effects typically have lower genotype × environment interactions and are desirable selections for breeders, especially if they demonstrate higher yield potential compared to control. Various univariate and multivariate methods exist for evaluating genotype-environment interactions. This study evaluated the grain yield stability of advanced lentil genotypes in tropical climates using two methods: univariate (non-parametric statistics) and multivariate (additive main effects and multiplicative interaction, AMMI).

 Fifteen selected genotypes along with two check cultivars, Gachsaran and Sephar, were evaluated in a randomized complete block design with three replications across eight environments (four warm and semi-warm regions: Gachsaran, Khorramabad, Moghan, and Ilam over two years). Each experimental plot consisted of four rows, 6 meters long, with a plant density of 200 plants per square meter. Grain yield was measured after harvest. To analyze the data, after ensuring the homogeneity of error variances across environments using Bartlett's test, a combined analysis of variance was performed for the eight studied environments. The METAN package in the Rver4.3.2 statistical software was used for AMMI analysis, calculation of non-parametric statistics, and plotting AMMI1 and AMMI2 biplots. 

The results of the combined analysis of variance for grain yield (based on the AMMI model) showed that the effects of environment, genotype and the interaction of genotype × environment were significant at the statistical probability level of 1%. Considering the significance of the genotype × environment interaction, it is possible to perform stability analyze on data in order to interpret this interaction. Stability analysis using the non-parametric TOP statistic, identified genotypes 12 and 10 as the most stable genotypes. The Kang statistic also ranked genotypes 12 and 13 as the best genotypes. Among the non-parametric statistics, TOP and Kang placed high-yielding genotypes at the top of the yield stability rankings, whereas the rankings based on Thennarasu , Nassar and Huehn statistics favored genotypes with lower mean yields. According to the Thennarasu statistic, genotypes 5, 6, and 2 were identified as the most stable, but all three had low mean yields. Similarly, genotypes with high yield stability based on the Nassar and Huehn statistics, had low mean yields. By calculating the SIIG statistic and considering all non-parametric statistics, genotype 12 was found to have the best combination of yield and yield stability. In addition to this genotype, genotypes 13, 1, and 11 could also be considered desirable, considering both yield and stability. One of the most widely used multivariate methods for stability analysis is the additive main effects and multiplicative interaction (AMMI) model. The results of the combined analysis of variance for grain yield based on the AMMI model showed that the first three principal components of the genotype × environment interaction were significant, explaining approximately 40.4%, 26.5%, and 19.8% (a total of 86.7% for the first three components and 67.1% for the first two components) of the genotype × environment interaction, respectively. The AMMI1 biplot, considering mean grain yield and the value of the first principal component, identified genotypes 10, 11, and 12 as superior genotypes. The AMMI2 biplot, considering the values of the first and second principal components, also identified genotype 12 as the most stable in terms of grain yield. The two check cultivars, Gachsaran and Sephar, showed lower yield stability than the superior genotypes based on both biplots. Genotypes positioned at the center of the biplot exhibit general stability and are recommended for cultivation across most of the investigated environments. In contrast, genotypes located farther from the center and closer to specific environments demonstrate stability exclusive to those environments. In this regard, genotype 3 exhibited specific adaptation to environment 4, while genotypes 13 and 14 showed specific adaptation to environments 2 and 1, respectively.

Based on the obtained results, genotype 12 stands out as the best performer in terms of both high yield and stability. Therefore, it can be considered a promising candidate for release as a new cultivar in warm and semi-warm regions.

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.

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.

In this study, 12 promising lentil genotypes along with Kimia and Gachsaran cultivars were cultivated during three cropping years (2010-2011) in Gachsaran, Gonbad, Khorramabad and Moghan regions. AMMI analysis of variance showed that the effects of environment, genotype and their interaction and the first six main components were significant. Genotype 4 (1196 kg / ha) had the highest grain yield. 8, 11, 14, 10 and 6 genotypes based on ASV index, 9, 14, 11, 10 and 12 genotypes based on SIPC index, 9, 12, 14, 10 and 4 genotypes based on EV index, selected genotypes 14, 11, 9, 8 and 6 based on ZA index and 11, 14, 8, 6 and 1 genotypes based on WAAS index were selected as the most stable genotypes. The best genotypes in terms of yield and stability were  4, 8, 6, 10 and 7 genotypes based on ssiASV, 9, 4, 6 and 8 genotypes based on ssiSIPC index, 4, 9 and 12 genotypes based on ssiEV index, 6, 8, 9 and 4 genotypes based on ssiZA index and 4, 6 and 8 genotypes based on ssiWAAS index.  Based on AMMI1 biplot, 6, 4 and 8 genotypes with mean grain yield higher than total yield average and lowest values of IPCA1 were identified as stable genotypes with high general compatibility. In AMMI2 biplot, 4, 8, 9 and 12 genotypes high general stability and higher grain yield than the total average. The most stable genotypes in the superiority index were 4 and 6 genotypes. In general, based on different indices, 4, 6 and 8 genotypes had high yield in many environments and in the most methods, had good stability and could be candidates for introduction of new cultivars.

of different planting densities was conducted in Golestan province in 1398‎‏.‏‎ This research ‎was carried out in a factorial experiment on randomized complete block design (RCBD) ‎with four replications‏ ‏in which the cultivar (Latif and Sajedi) and plant density (55000, ‎‎75000, 95000 and 115000 per ha) were considered as factors‎‏.‏‎ The results showed that the ‎duration of growth period was significantly higher in Sajedi cultivar than Latif cultivar and ‎by increasing the density up to 95000 plants per ha, the amount of number of bolls per ‎area unit, seed cotton yield and lint yield traits was increased significantly in both ‎cultivars‏.‏‎ There was no significant difference in seed cotton yield and lint yield between ‎densities of 75000 to 115000 plants per ha in both cultivars‏.‏‎ In both cultivars, the highest ‎accumulation of plant vegetative and total biomass was observed at a density of 115000 ‎plants per ha. Based on modeling of plant biomass at different densities, the highest ‎average and maximum rate if reproductive organs biomass belonged to 95000 plants per ‎ha treatment with 73.4 and 83.7 kg/ha, respectively‏.‏‎ In this study, the highest seed cotton ‎yield was obtained in Sajedi cultivar under a density of 95000 plants per ha with 4464 ‎kg/ha.‏

In order to investigate the effect of genotype, environment and the interaction of genotype × environment, for seed yield, 18 lentil genotypes in a randomized complete block design with three replications in four environments (Khorramabad, Gachsaran, Ilam and Gonbad) during three years (2014-2017) Were evaluated. Based on combined analysis of variance, the main effects of location, genotype and genotype × location and genotype × location × year interactions were significant. The results of combined analysis of variance for different environments showed that environment, genotype and genotype × environment interaction for Showed seed yield. According to the results of AMMI analysis of variance, the environment was the main source of change, which accounted for 83.6% of the total squares. The results of AMMI analysis of variance also showed that the four-component interaction effect on seed yield was significant. The first two main components of the AMMI model accounted for 52.56% of the total yield variance. The results of AMMI stability values (ASV) showed that for seed yield, the genotypes including G8, G7, G9, G18, G6, G16 and G17 had the lowest values and therefore the highest general stability to all experimental environments. Genotype G5 with ASV value of 33.7 was highest private stability to the second environment (E2). Among the genotypes with the lowest ASV, only G6 and G16 genotypes had higher average seed yield than the average of all genotypes. Based on the values of the first two principal components, AMMI (ASV) stability value and genotype stability index (GSI), the genotypes G6, G1 and G13 were identified as the most stable genotypes, and the closest genotypes to the ideal genotype.

In this research, 12 selective advanced genotypes of lentil with Kimia and Gachsaran checks were grown for three growing years (2010-2013) in four locations including Gachsaran, Gonbad, Khoramabad and Moghan using randomized complete block design with three replicates in each location. The heatmap plot indicated the variation of seed yield of genotypes in different environments. 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 17.53% and 82.47%, respectively. The likelihood ratio test (LRT) indicated that the effect of GEI was significant on seed yield and therefore for evaluation of stability of genotypes, singular value decomposition (SVD) was performed on the matrix of best linear unbiased prediction (BLUPs) of GEI. The screet test showed that the first five principal components had a significant contribution in the GEI matrix derived from BLUP, as the first and second principal components explained only 32.28% and 26.95% of the GEI variation, respectively. The bilot of first principle component (PC1) of the environment versus nominal yield showed that genotypes 8, 4, 3, 14 and 7, due to the lowest scores of the PC1, had a small share in the GEI and were more stable. Biplot of seed yield versus WAASB (weighted average of absolute scores) placed genotypes in four regions, so that the genotypes 4, 6, 8, 9, 10 and 12 in the fourth region 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 (weighted average of the stability (WAASB) and mean performance (Y)) showed genotypes 4, 6, 8 and 9 as high yielding and stable, and therefore can be candidate for cultivar introduction.

This study was set up as a factorial split block design in RCBD layout with 4 replications at a research farm in south east of Ali Abad Katool district in 2013 to investigate different tillage methods (T), soil initial moisture (M) and trifluralin application (W) effects on weed control and cotton lint yield. The main factors were two tillage methods (disk plow (T1) and moldboard plow (T2)) in a factorial combination with two soil moisture levels at 24 cm soil depth (12-14% (M1) and 17-19% (M2)). The sub-factor consisted of six trifluralin herbicide application times (hand hoeing (W1), PPI application of trifluralin at 1.5 lit/ha rate at the time of the first irrigation plus the herbicide application rate of 1 lit/ha with irrigation (herbigation) six weeks after planting (W2), PPI trifluralin application at 1.5 lit/ha and the application at 1 lit/ha with soil incorporation by cultivator six weeks after planting (W3), trifluralin application at 2.5 lit/ha after planting and prior to irrigation (W4) PPI application of trifluralin at 2.5 lit/ha with soil incorporation(W5) and check treatment where no weed control was undertaken (W6)). The data from the experiment were subjected to variance analysis using SAS statistical software and means were compared with least significant difference (LSD) test at the 5% probability level.The results indicated significant differences amongst the main effects of treatments and their interaction for many of the measured traits in cotton. Slicing of interactions of soil initial moisture in each tillage level indicated that majority of difference between soil initial moisture levels was related to their response to tillage type and M2 in comparison to M1 was significantly superior in secondary mean weight diameter of soil aggregate, weed control efficiency (WCE) at the 4th and 8th week after planting as well as boll numbers per plant under disk plow. Dheyab (2017) reported that with increasing soil moisture and reducing the size of soil aggregates up to 30 cm depth, the effectiveness of chemicals in weed control increased significantly. Meanwhile it must be noted the existence of more soil moisture content in moldboard plows plots creates a wet and homogeneous substrate with high seed contact with soil that allows seeds to quickly germinate, leading to the uniform emergence of cotton seedlings. Under different M levels, W5 treatment resulted in the highest WCE and total lint yield in the 4th week since planting as compared to W2 and W3. The most significant and positive correlation coefficients among the studied traits was found between total lint yield and WCE at the 4th week after platning (R2=0.88**) and WCE at the 8th week after platning (R2=0.85**). The highest total lint yield was produced by W5 in different trifluralin application levels. In current study, among the different treatments, T2M2W5 with 3730 kg/ha gave the highest lint yield, which had no significant difference with treatments involving hand hoeing under different tillage types and soil initial moisture levels.

In order to evaluate the effects of different tillage methods and Treflan application manner on cotton yield and weeds control, this study was conducted in a research farm in south east of Ali Abad Katool district in 2014. This study arranged in split block design under RCBD with 4 replications. Main factors were tillage (Moldboard plow (T1), disk plow (T2) and chisel plow (T3)), and Treflan application manner as sub factor (hand hoeing (H1), Treflan application by PPI methods with 1.5 lit/ha plus using of Treflan at the sixth weeks after planting with application rate of 1.5 lit/ha with irrigation (herbigation) (H2), Treflan application by PPI methods with 1.5 lit/ha plus using of Treflan at the sixth weeks after planting with application rate of 1.5 lit/ha with soil incorporated by cultivator (H3), 3 lit/ha Treflan application after sowing by PPI before irrigating (H4) and 3 Lit/ha Treflan application before sowing by PPI before irrigating (H5) and no weed control (H6)). In current study, the most relative abundance percent was belonged to redroot pigweed (Amaranthus retroflexus L.) and board leaf weeds. Results revealed that tillage factor had no significant effect on total lint yield and total weed dry weight except of boll number per plant. The highest total lint yield was produced by hand hoeing among different herbicide levels., Total dry weight of weed in forth week after cotton sowing had main role for the maximum total lint yield production, meanwhile the most significant and positive correlation among yield with yield components was seen with boll number per plant. Investigation of traits interaction effects showed that in case of no application of hand hoeing in cotton cultivation, it could be recommended to apply 3 Lit/ha Treflan before sowing.

In order to investigate the effect of different levels of plant density and irrigation on some morphologic and physiologic responses and antioxidants activities in chickpea cv. Hashem, an experiment was conducted at Goonbad-e-Kavoos region during 2012-2013 cropping season. Treatments were arranged as split plot in randomized complete block design with four replications. Different irrigation regime levels (rainfed without irrigation, irrigation at flowering stage, and irrigation during pod filling stage) were assigned to main plots and sub-plots comprised of different plant densities (20, 30, 50, and 70 plants/m2). Results showed that different irrigation regimes significantly affected traits except for days to 50% flowering and plant height but plant density factor had significant impacts only on days to 50% flowering, plant height, days to physiologic maturity, seed yield, biologic yield, and leaf chlorophyll index. Results showed that irrigation caused significant increase in leaf relative water content and leaf chlorophyll index while it decreased grain protein content, superoxide dismutase and catalase activities. Maximum and minimum grain protein contents were produced in no-irrigation and irrigation during pod filling stage, respectively. Meanwhile, the maximum and minimum grain yield was gained by irrigation at flowering stage and rainfed without irrigation, respectively. Under plant density levels, the maximum and minimum grain yield was produced in 70 plants/m2 and 20 plants/m2, respectively. Irrigation at flowering stage × 70 plants/m2 had the highest grain yield among all treatments of the study.

In order to evaluate of picking time and vertical distribution of bolls effects on some seed germination and vigor traits and seedling field emergence of cotton (Gossypium hirsutum L.) Sahel cultivar, two experiments were conducted in Golestan province during 2011-2012 in farm and a laboratory phases. In first, farm phases were carried out in factorial experiment based on a complete block design (RCBD) with four replications and two factors. Factors levels was boll picking time (first and second picking) and vertical distribution of bolls (lower, middle and upper parts). Germination and vigor tests was surveyed on these seeds and in second farm study, same seeds was planted under first year farm experiment procedure in order to evaluating of seedling filed emergence percentage, main root length of seedling and seedling vigor index under field condition. Analysis of variance of the first year of the study showed that picking time and vertical distribution of boll had only significant effect on the percent of seed germination but their interaction effect on germinated seed percentage after accelerated ageing test, seed electrical conductivity, germination percent in cool germination test, germination percent in warm germination test and cool warm vigor index were significant. Mean comparison of studied traits indicated that seed volume and seed bulk density of first picking bolls in compare to second picking bolls had effectively more values and seed germination percentage of first picking and middle-located bolls promoted the highest germination percentage. Evaluating of the seed vigor tests results under factors interaction (picking time × vertical distribution of bolls) revealed in accelerated ageing test, the seeds of lower and middle bolls had better germination percentage in compare to other treatments. Results of seedling filed emergence percentage under filed circumstance was indicated that seeds of middle bolls had highest seedling filed emergence percentage but there was no significant different between lower and upper boll seeds on seedling root length size. The highest correlation among germinated seed percentage under field condition was seen with cool germination results. Outcomes of this study were indicated in order to produce high quality seed germination and seed vigor of cotton Sahel cultivar, seed can be removed in first picking and middle bolls

German chamomile (Matricaria chamomilla) is one of the most important industrial medicinal plant. In this experiment the effects of foliar application of salicylic acid in different levels (0، 10-2، 10-4، 10-6) were investigated on the floral traits and essential oil content of three ecotypes (Italian،Tehran، Zabol) of German chamomile. The results showed that maximum percentage of essential oil (1. 5 weight percent) was obtained from Zabol ecotype as sprayed with 10-4 M of salicylic acid and Maximum oil yield (0. 085 gr per pot) was obtained from Italy ecotype in which treated with 10-2 M salicylic acid. Among the identified sesquiterpenes of essential oil، Chamazulene، E-β Farencene and α-Bisabolol oxide A were significantly varied based on the experimental treatments. The highest amount of Chamazulene (3. 789 percent) was recorded in Italian ecotype in which treated with 10-2 M salicylic acid، and the same ecotype produced highest amount of farencene (2. 264 percent) when treated with 10-2 M Salicylic acid. The application of salicylic acid in concentrations of 10-4 and 10-6 M increased the α-Bisabolol oxide A (respectively، 70. 907 and 73. 482 percent) Compare with control. Generally، contrary to essential oil yield component، only the essential oil yield and its constituent were affected by salicylic acid application and the responses of experimental ecotypes was varied.

This research was carried out considering the Pathology of the acts, and regulation related to transferring land in the country beside the suggestion of related solutions for controlling study-research solutions for controlling corruption by using adaptive studies method, and relying on official statistics, and resources of executive, and supervisory apparatuses, and reviewing the acts, and regulations related to this subject in 2010. The damages of such transfers, and the solutions are discussed in this research by taking a look at legislative, administrative, and judicial aspects of this subject. The result showed if some amendments are exerted in the acts, and regulations of natural recourses, and land affairs, and present impediments are removed, the possibility of controlling unlawful possession and transfer of national lands will increase highly specially considering the solutions presented herein.

A filed experiment was conducted to determine the effects of urea starter fertilizer and post emergence herbicide effects on weed interference in early season cotton production and some agronomic traits of cotton plants in Gorgan region (northeast Iran) in 2008. Experiment was carried out as factorial based on a randomized complete block design with four replications. First factor was weed management at four levels including early post emergence application of Methazole + Envoc in early cotton growth, late post emergence application of Methazole + Envoc in 15-20 cm cotton plant height period, early hand weeding and late hand weeding in cotton growth season, and four levels of starter fertilizer as the second factor were 0, 50, 100 and 150 percent of recommended dosage of urea starter fertilizer (175 kg.ha-1), respectively. Planted cotton cultivar was Siokra, one of commercial verities in Golestan province. Late hand weeding and late herbicide application resulted in the highest amounts of whole weed biomass 10 day after weed control (W1) and a month after weed control (W2). Increasing urea starter fertilizer level increased weed biomass 10 day after weed control, but decreased it a month after weed control.

To evaluate the effects of Glycine Betaine (GB) on yield and seed quality of some cotton (Gossypium hirsutum L.) cultivars in response to drought stress, a field experiment was conducted in northern part of Iran, Gorgan. The experiment was a factorial on the basis of a randomized complete block design with four replications. Field research survey consisted of two factors; GB concentration and cotton cultivars. Glycine Betaine was applied to filed-grown cotton at three rates: 0 kg/ha foliar application of GB at the first flowering + application at one and two weeks later, 3 kg/ha foliar application of GB at the first flowering + application at one and two weeks later (two times GB application) and 6 kg/ha foliar application of GB at the first flowering + application at one, two, three and four weeks later (four times GB application). Four cotton cultivars were selected based on their proportional susceptibility to drought stress including Tabladilla and Nazili, considered as drought tolerant, and Varamin and Sahel, considered as drought sensitive cultivars. The results showed that spraying with GB at different levels (3 and 6 Kg/ha) enhanced cotton yield, boll numbers, sympodia branch numbers, early maturity, plant height, final node on main stem, boll retention at 2nd position and boll abortion, seed bulk density, but had no significant effect on monopodia branch numbers. This research could ultimately result in higher yield production and improved tolerance to water deficit condition with applying following treatments: Tabladilla and Nazili with 3 kg/ha foliar application of GB at the first flowering + application at one and two weeks later for tolerant cultivars and Varamin and Sahel with 6 kg/ha foliar application of GB at the first flowering + application at one, two, three and four weeks after first flower.