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

The increased demand for cereals that are consumed by humans and livestock can be met through the development of planting drought-tolerant genotypes. Due to the interaction of genotypes × environment, the best genotype in one environment may not be the best in other environments, and therefore, this interaction provides valuable information about the yield of each genotype in different environments and plays an important role in evaluating yield stability. Genetic modification of drought tolerance in crops is one of the most stable and cost-effective approaches to increase production and yield stability. Examining the compatibility and stability of grain yield based on various parametric and non-parametric stability statistics and evaluating tolerance to drought stress based on stress indices in promising barley genotypes of the country's temperate climate are among the goals of this research.

To assess grain yield adaptation and stability and to select high-yielding barley genotypes suitable for terminal drought stress in the temperate climate of Iran, 16 barley genotypes were cultivated during two crop years 2021-2023 in a randomized complete blocks design with three replications in three research stations including Varamin, Birjand, and Yazd under two none-stress and drought stress conditions at the end of the season (12 environments). After determining the grain yield, stress indices, including MP, GMP, TOL, HARM, STI, YI, YSI, RSI, and SSI, and the correlation of each with grain yield were calculated in this study. Stability statistics included Nassar and Huehn’s stability statistics (S(1-6)), Thennarasu’s stability statistics (NP(1-4)), deviation from regression (S²dᵢ), regression coefficient (b), Shukla’s stability variance (σ²ᵢ), environmental variation coefficient (CV), variance component (θᵢ), coefficient of variance (θ(i)), Wricke’s ecovalence (Wᵢ²), and Kang’s sum of ranks (KR). Their relationships were calculated based on Pearson’s correlation. Analysis of variance (ANOVA), mean comparison, and simple correlation were calculated using the SAS-9.0 program, stability statistics were calculated using STABILITYSOFT and principal component analysis (PCA). Stress indices and the correlation of each of these indices with grain yield were calculated using iPASTIC. The three-dimensional distribution diagram of genotypes in the ranges of A, B, C, and D was drawn using Grapher software.

The results of the combined ANOVA indicated the significance of the genotype × environment interaction. According to S(1-2) statistics, G7, G10, G11, and G3, and according to S(3-6) statistics, G7, G3, and G9 were the most stable genotypes. Among the non-parametric Thennarasu’s stability statistics according to the NP(1) criterion of G9, G3, and G5, according to NP(2) G5, G3, and G8, and according to NP(3) and NP(4) criteria, G7, G3, and G9 were recognized as the most stable genotypes. Based on Wricke (W²) and Shukla (σ²) equivalency stability statistics, G3, G9, and G13 were the most stable genotypes. Based on Eberhart and Russell's regression method, G9, G7, and G3 genotypes, with high yields, had general compatibility and good yield stability. Based on Francis and Kannenberg (CVi), genotypes G1, G2, and G15 had the lowest coefficients of environmental variation. Based on the average rank of each genotype in all stress indices (AR), G2, G7, and G3 genotypes were the most tolerant, and G14, G11, and G10 were the most sensitive genotypes to drought stress at the end of seasons, respectively. In the drought stress conditions at the end of the season, grain yield had positive and significant correlations with YI, HM, GMP,  STI, MP, YSI, and RSI indices and negative and significant correlations with the SSI index. In non-stress conditions, grain yield had positive and significant correlations with MP, GMP, STI, HM, and YI indices, but no significant correlations were observed between grain yield and SSI, TOL, YSI, and RSI indices. The PCA revealed that the first and the second principal components explained 69.71% and 30.27% of the variance of the main variables, respectively. The first main component had a positive and high correlation with yield in both stressed and non-stressed environments, as well as MP, STI, GMP, and HM indices. The second component showed a positive and high correlation with grain yield in the non-stressed environment and TOL and SSI indices; it also had negative and high correlations with RSI and YSI indices. Based on the biplot diagram, G3, G7, G8, G9, G12, and G13 presented higher grain yield potential and are more tolerant to drought stress.

In this study, grain yield had negative and significant correlations with NP(3), KR, NP(2), NP(4), S(6), and S(1) statistics, respectively, therefore these statistics can be used for identifying stable genotypes. G3, G7, and G9 with averages of 6732.9, 6730.6, and 6608.1 kg/ha, respectively, not only produced the highest grain yield but also showed the highest grain yield stability and tolerance to terminal drought stress among the studied genotypes based on the total ranking of all studied stability statistics and stress indices. Therefore, they can be used as new cultivars in drought-affected regions in temperate climates or as desirable genetic materials in barley breeding programs for drought tolerance.

Water and nitrogen are known as the two main limiting factors in achieving maximum yield in agriculture. Therefore, the use of nitrogen fertilizer plays a significant role in increasing agricultural production. However, the indiscriminate use of these fertilizers may be due to their low efficiency. Therefore, investigating the challenges of water scarcity and improper nitrogen use management is crucial. In this study, considering the challenges of water scarcity and improper nitrogen use management, an investigation has been conducted.

This experiment was conducted in the research farm of the Agriculture Faculty of Shirvan Azad University during the agricultural year of 1399-1400. The experiment was carried out in the form of strip plots in three replicates and in a complete randomized block design. Three levels of 50%, 75%, and 100% of plant water requirement were considered as the main plot and five levels of zero (N0), 100 (N100), 200 (N200), 300 (N300), and 400 (N400) kilograms of nitrogen per hectare were considered in the subplots.

Most attributes related to yield components had higher values during the second year in comparison with the first year of the experiment. Moreover, plant height, grain weight, rows per cob and kernels per row in the 100% water requirement were greater compared with the 50% water requirement treatment. As a result of 400 kg/ha nitrogen fertilizer application, plant height, rows per cob, kernels per row, 100-grain weight and biological yield increased by 24.4%, 24.6%, 23.8%, 24% and 24.2% compared with the nitrogen-free treatment, respectively.

Increasing nitrogen fertilizer application leads to an improvement in yield and yield components. The only factor that decreased with increased water supply was water use efficiency. Grain yield under 100% water requirement was 38% more than that of 50% water requirement treatment. The difference between 100% and 50% water requirement treatments in terms of the biological yield was 49.5% in the first and 24% in the second year. Also, increased nitrogen fertilizer application led to increased grain yield. However, no significant difference was observed between 300 and 400 kg/ha nitrogen fertilizer treatments, especially in the second year. Therefore, a 300 kg/ha fertilizer application is recommended to achieve a suitable yield. Fertilizer use efficiency was affected by water requirement so that nitrogen use efficiency under 100% water requirement supply at a given nitrogen fertilizer level (e.g. 100 kg/ha) was significantly higher than that of 50% water requirement conditions. Under 100 kg/ha nitrogen application, nitrogen use efficiency in 100% water requirement treatment was 111.5 and in 50% water requirement treatment was 74.3 kg/ha. Thus, nitrogen use efficiency declined by 33% due to decreased water use. Therefore, it can be concluded that in conditions where sufficient water is not available for irrigation, farmers should not use excessive amounts of nitrogen fertilizer because it has little efficiency and only leads to environmental pollution and capital waste.

The importance of rapid, non-destructive, and accurate estimation of leaf area (LA) in agronomic and physiological studies is well known. The need for such estimates of leaf area in particular for leaves with unusual shapes has led to studies relating leaf dimensions and leaf area. However, a search of literature revealed that little information is available for short day onion (Allium cepa L.). The objective of this study was to develop a statistically validated regression model for leaf area prediction from simple non-destructive measurements for onion cultivated in the South of Iran.

In order to prepare the required plant samples to measure leaf area parameters, an experiment was conducted in the cropping year 2020-2021 in a field of 300 square meters located in Hormozgan province-Rahdad district. The short-day cultivar Takii was used because of its wide planting area in the province. 40 leaves were chosen at random from plants growing in the farm, half were left for validation of the model and the other 20 leaves were subjected to measurement and calibration. The standard method (LICOR LI-3000C) was used for measuring the actual areas of the leaves. Before measuring the leaf area, two main parameters related to leaf length and width were accurately determined for each leaf. To select the best variables and the regression model for estimating leaf area, the fitted simple and multiple linear regression were subjected to the stepwise elimination method. Finally, the accuracy of the selected models was validated using the root mean square error (RMSE) and coefficient of determination (R2).

All linear models for estimation of leaf area with width (W) and length (L) as well as models including both variables were subjected to regression analysis. The results indicated that leaf length (L) is more appropriate variable for estimating onion leaf area and leaf width (W) was not able to properly describe leaf area. Based on the findings of this research leaf area estimation model with both W and L had the best prediction, with RMSE% = 8.64, RMSE= 1.76 and R2= 0.91. (Area= 0.121 + 0.01537 L2 + 0.3225 L×W).

As the understanding of plant growth and development has been increasing, such mathematical models will be very useful tools for the prediction of leaf area for many plants without the use of expensive devices. Thus, the models from the present study will enable researchers of plant growth modeling to predict leaf area non-destructively with the equations developed.Keywords: Leaf Area, Leaf Length, Leaf Width, Regression, Coefficient of determination.

Under oxidative stress conditions, the quantity of some biochemical traits, including antioxidants, increases in the plant. Now this question is raised that incremental change in these traits can always increase drought resistance (higher biomass) in drought-stressed conditions? In this regard, conflicting results have been published, which are mostly based on their correlation coefficient. The purpose of this study was a deeper contemplation into these complexities in quinoa; so, the nonlinear, synergistic, and antagonistic relationships of several biochemical traits with biomass were investigated, using multiple nonlinear regression based on stepwise selection. Finally, the resulting regression function was maximized (optimized). The split-plot experiment based on a randomized complete block design was carried out in three replications and two locations (Damghan and Dibaj). Factors were deficit irrigation levels [control (Supplying 100% of the plant's water requirements), moderate deficit irrigation (70%), and severe deficit irrigation (40%)] in the main plots and sodium nitrophenolate spraying levels [the control (spraying tap water on the plant), foliar spraying at the stem elongation stage and foliar spraying at the flowering stage] in the subplots. The data of biomass (dependent variable; Y or drought resistance) and several regressors (superoxide dismutase, anthocyanin, soluble sugars, proline, carotenoid, superoxide dismutase, ascorbate-peroxidase, and catalase; X or independent variables), obtained under severe deficit irrigation conditions, were used for the present analysis. The results of the regression analysis showed that the relation of carotenoid, catalase, anthocyanin, and ascorbate peroxidase with drought resistance was quadratic, however, the mentioned relationship was very different. Over low concentrations (activities), the effect of catalase, anthocyanin, and ascorbate peroxidase was statistically not significant (with increasing concentration, drought resistance showed no change), but over higher concentrations, their impact was decreasing; the effect of ascorbate peroxidase appeared to be increasing just over its higher concentrations. On the contrary, over both low and high concentrations, the effect of carotenoid was positive; the positive effect of higher carotenoid concentrations tended to be slightly stronger than that of low concentrations. Proline and catalase had the highest effect on drought resistance when their increase was considered simultaneously (synergistic relationship). The effect of soluble sugars was negative; particularly, the simultaneous increase in soluble sugars and proline concentrations led to a sharp decrease in drought resistance (antagonistic relationship); hence, it seems that the simultaneous increase of these two traits should not be used for breeding purposes. The results regarding optimization (maximization) of the regression function indicated that a combination of 600 Unit SOD.g-1FW superoxide dismutase, 8.58 μmol ascorbate min-1.mg-1FW ascorbate peroxidase, 2.28 μmol H2O2.min-1FW catalase, 6.65 mg.g-1FW soluble sugars, 2.15 mg.g-1FW anthocyanin, 6.79 mg.g-1FW proline, and 18.8 mg.g-1FW carotenoids could be led to an increase in biomass by 9% higher than the maximum observed value of biomass.

Rapeseed with the scientific name (Brassica napus L.) are one of the most important oilseed crops in the world. Considering the country's increasing need to import oil and the importance of rapeseed among oilseeds plants, increasing the yield and percentage of oil is very important. Therefore, identification the traits that increase seed yield, play an important role in the success of breeding programs. Yield is a complex trait that is affected by many factors. direct selection of a variety for yield is not often very effective, so investigating the relationships between grain yield and other traits for indirect selection may be effective. In such a situation, correlations may not clarify the relationships well, therefore, multivariate statistical methods and path analysis can help in understanding the nature of the relationship between traits for direct or indirect selection and improve the efficiency of selection in breeding programs plants become useful.

In this study, eight genotypes of spring rapeseed (SPN-202, SPN-204, SPN-206, SPN-207, SPN-217, SPN-225, SPN-227, SPN-182) along with 56 rapeseed hybrids (F1) were planted in a randomized complete block design with three replications in 2019-2020 crop years in the research farm of Gorgan Agricultural Research Station. During the experiment, phenological, morphological traits, yield and yield components were recorded. The normality of the data was evaluated based on the Kolmogorov-Smirnov method. In order to understand the relationships between traits and to identify the traits affecting on grain yield, at first, correlation coefficients were estimated, then stepwise regression were done. Then, path analysis based on correlation coefficients was used to determine the direct and indirect effects of traits. Finally, all traits were grouped based on principal component analysis.

The results of analysis of variance indicate a significant difference for all studied traits at the probability level of one percent. The coefficient of variation (C.V) for the traits varied from 1.37 (physiological maturity) to 10.31 (number of sub-branches), which indicates that there is sufficient accuracy in conducting the research. The results of correlation evaluation showed that number of pod per lateral branches (0.864), number pods per Plant (0.865), number of lateral branches (0.466), physiological maturing (0.329) and number of grain per pod (0.358) had a positive and significant correlation with grain yield (P<0.01). Stepwise regression analysis showed that the traits of number of pods per plant, number of grain per pod and 1000-grain weight has a decisive role on grain yield. Based on the results of path analysis, number of pods per plant had the most direct effect (0.881) and number of grain per pod had the most indirect effect (0.053) through the number of pods per plant on grain yield. In order the results of principal component analysis, four components were able to explain 75.91% of the changes in the measured data of 64 rapeseed varieties.

Results of the path analysis showed that number of pods per plant, number of grain per pod and 1000-grain weight had a direct positive effect on grain yield. Also, the stepwise regression analysis showed that the stated traits have the highest regression coefficients. Therefore, selection for these traits can be very effective in achieving high performance

Seed yield in soybean is a complex trait and is influenced by multiple genetic and environmental factors. In this research, 13 cultivars and advanced genotypes with two control cultivars (Sari and Katul) were cultivated in three warm and humid regions of the north of Iran, including Gorgan, Sari and Mughan, during the two cropping years of 2013 and 2014 in a randomized complete block design with three replications. Mean comparison in different years and locations showed that G15 genotype had the highest seed yield, followed by G2, G4 and G9 genotypes. Correlation coefficients   showed that the number of seeds per m2 (R² = 0.77) and the number of pods per plant (R² = 0.66) had a positive and significant correlation with seed yield, while the correlation between 100 seed weight and seed yield was insignificant (R²=0.02). G2 and G3 genotypes had the highest percentage of charcoal rot and G4 genotype showed the lowest percentage of infection. Regarding the percentage of infection with charcoal rot disease, all the investigated genotypes are below 10% and are considered tolerant genotypes for this disease. GGE biplot analysis indicated that the first and second principal components explained 70.6% of total yield variation. According to which-won-where pattern of GGE biplot, G4, G15, G12 and G7 genotypes had the longest distance from the origin of the biplot and were placed in the group of reactive genotypes to the environment and G2, G3 and G1 genotypes were classified as stable group. Genotype G4 performed well in Mughan1 and Mughan2 whereas, G15 genotype showed the best performance in Sari1 and Gorgan2, and G12 genotype was the best genotype in Sari2 environments. According to the genotypes comparison with ideal genotype, G15, G9, G2 and G1 genotypes had the shortest distance from the ideal genotype and were, hence, included in the group of stable genotypes. The simultaneous GGE biplot of seed yield and stability displayed graphically that G2 genotype had superior performance and broad adaptation to the diverse environments. Sari1(Sari 2013) was the most discriminating and representative environment and is classified as the superior environment and Gorgan 2 (Gorgan 2014) was ranked second. Based on tolerance to charcoal rot disease, average seed yield, as well as different GGE biplot graphs, G2 genotype incorporated both high mean yield and yield stability and can be released as a new soybean variety.

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.

For identification of correlations and relations among different traits in bread wheat, 30 genotypes were investigated based on a split plot experiment in the form of randomized complete block design with three replications under normal and moisture stress conditions during the 2016-2017 crop season. The results of the analysis of variance showed that the effect of genotypes was significant for most of the studied traits. The interaction effect of drought stress and genotype was significant for all examined traits except 1000-grain weight. Genotypes c-94-3 and Heidari were recognized as the best genotypes with average grain yield of 437 and 427 g/m2, respectively, in normal conditions. Under drought conditions, genotypes cd-4 and cd-6 had the lowest grain yield with an average of 104 and 108 g/m2, respectively. Based on the results of multiple regression in field experiment, under optimal irrigation conditions, flag leaf area, spike density,  number of days to 50% flowering and spike length, (R2= 0.466) and under stress conditions, grain filling rate, grain filling period and straw yield (R2= 0.736) were considered as important traits affecting grain yield.  Path analysis of grain yield under normal conditions indicated that the flag leaf area, spike density and spike length, and under stress conditions, the grain filling rate, grain filling period and straw yield, with the greatest direct positive effect were the important and effective traits in grain yield. Based on the results of the analysis of the grain yield correlation coefficients under normal condition, the number of kernels per spike, 1000-grain weight, flag leaf area, spike density and spike length, and under stress conditions, the number of kernels per spike, the grain filling rate, grain filling period and straw yield, were considered as the traits affecting grain yield.

By progeny trial after a diallel crossing, the function of genes and inheritance of quantitative traits can be estimated. This increases the ability to select parental lines, to participate in crosses, and determines the management of generations in the segregating populations.

In order to understand the genetic structure of grain yield and yield components, seven bread wheat cultivars including Bolani, Falat, Mehrgan, Meraj, Kalateh, Frontana and MV17 were planted in the crossing block and direct crosses were performed among the seven cultivars to produce F1 generation. The progenies of the crosses with their parents were evaluated based on a randomized complete block design (RCBD) with three replications in Gorgan agricultural research station in 2020-2021 cropping season.

The results of this experiment showed a significant difference among the genotyps in all the studied traits. Estimation of genetic parameters using Heiman's method showed that the values of dominance variance were significantly greater than the additive variance in grain yield, biological yield, 100-kernel weight, number of grains per spike, grain weight per spike and peduncle length. There was a small difference between the values of dominance and additive variance in the plant height and days to anthesis. In biological yield, 100-kernel weight and days to anthesis, the proportion of dominant alleles and was greater than the recessive alleles in parents, while the proportion of dominant and recessive alleles was equal in the other traits. High broad-sense heritability in grain yield (89%) and biological yield (93%) and grain weight per spike (74%) and their low narrow-sense heritability (10%, 10% and 13, respectively) showed a greater share of the dominance effect in controlling these traits. The results of graphical analysis showed that grain yield, biological yield and 100-kernel weight were controlled with dominance gene action and number of grains per spike, peduncle length and plant height were controlled with incomplete dominance gene action. The distribution of parents around the regression line showed that Falat and Mehregan cultivars carried the most dominant genes and MV17 and Frontana cultivars carried the most recessive genes in controlling grain yield and biological yield.

The results of this study indicated that due to the greater share of non-additive effects of genes in grain yield, biological yield and yield components, it is better to select among the progenies of the crosses be postponed to more advanced generations. While, genetic improvement in plant height and days to anthesis can be achieved in the early generations.

The objective of this study was to investigate the effect of soil and land characteristics on saffron yield, their rating for preparation of a soil requirements table of this plant for land suitability studies by FAO method. First, 124 saffron farms in Khorasan Razavi, South Khorasan, Fars, Golestan, Markazi and Kerman provinces were selected. In each field, suitable soil samples were taken and a land use questionnaire was completed during three years from 2017 to 2019. Soil samples were tested to determine their physicochemical properties. After preparing the data, they were statistically analyzed. Multivariate regression between yield as a dependent variable and salinity, percentage of exchangeable sodium, soil reaction, gypsum, lime, clay, sand, silt, gravel and available potassium and phosphorus of soil were studied as independent variables by stepwise method. By examining the relationships of simple regression between important and effective land characteristics and yield, land characteristics rating was performed. Then, the saffron soil requirements table prepared. The proposed table was verified with the data of 21 new farms. The results showed that available potassium, sand, soil salinity, percentage of exchangeable sodium and lime had the highest and the reaction of soil and organic carbon had the lowest range of changes. Regression results showed that the independent variables of soil salinity, ESP, lime, gypsum, gravel and available potassium and phosphorus, respectively, affect yield. Coefficient of determination of multivariate regression showed that the variables entered in the model were able to determine 95% of the variance related to the dependent variable. The largest contribution to the reduction of saffron yield is related to soil salinity, gravel, exchangeable sodium percentage and the amount of lime. Validation results showed that the yield correlation coefficient with the soil index of the proposed table for saffron is about 0.92, which indicates the acceptable accuracy of the proposed table.

In order to study some agronomic characteristics and identify the desirable genotypes using different statistical methods, 15 lentil genotypes received from ICARDA were evaluated in a randomized complete block design with three replicates under rainfed conditions at Sarab-Changai Research Station, Lorestan Agricultural and Natural Resources Research and Education Centre, Khorramabad, Iran during 2019-2020 cropping year. According to the results, analysis of variance indicated significant genotypic diversity between the studied genotypes in terms of most traits. The correlation between grain yield per plant and most of the traits, especially yield components, was positive and significant. Genotypes 13, 14, 7 and 1 were known as the best and genotypes 15, 12 and 4 were identified as the weakest genotypes based on the results of various statistical methods including mean comparisons, cluster analysis, “genotype × trait” biplot and SIIG index. The highest direct positive effect on “seed yield per plant” belonged to the “number of one-seeded pods per plant” (0.580) and “hundred seed weight” (0.437), respectively, and therefore these traits can be applied as selection criteria. Moreover, genotype 7 was the best genotype in terms of important agronomic traits and has the potential to be used in the future breeding and subsequent agronomic research programs.

In Iran, the majority of research has been done on the depth of burial on crops and the effect of these factors on weeds resulted from seeds and rhziomes of seedlings has been less studied. Strangle Wort weed is one of the most problematic weeds in sugarcane fields, orchards, especially pistachio orchards, barren lands, and roads. Thus, this study aimed to find out the effect of different levels of burial depth and flood stress on the extent and quality of seedlings resulted from its rhizomes.

Two separate greenhouse experiments based on completely randomized design with four replications were conducted in the experimental farm of Sugarcane Research Institute of Khuzestan province at 2014-2015 growing season. Flooding stress treatment included 7 levels of flooding stress (4, 8, 16, 24, 48, 72, and 96 flooding hours) and 6 levels of seed burial (1, 3, 5, 7, 10, and 15 cm) and rhizome burial depth treatment included 7 levels (1, 3, 5, 7, 10, and cm 15).

With increasing levels of flood stress, all studied traits including root length, stem height, total dry matter biomass, shoot dry weight and root dry weight of strangle wort rhizomes decreased at 96 hours of waterlogged stress compared to the control by 63, 70, 59, 98 and 74 percent, respectively. Also, in second experiment, buried rhizomes of this weed with a length of 5 cm at a depth of 15 cm were not able to produce new seedlings.

According to these results, for proper management of this weed, highly contaminated areas in the field should be identified (especially in sugarcane fallow fields because at this time there is no weed control limitation). Then, the weed should be turned it into pieces smaller than 5 cm with plowing tools. Also, if possible, with suitable tools, transfer rhizomes should be transferred to a depth of more than 15 cm and integrated weed control management with drought and flooding stress and burial depth with at least two times of plowing could be implemented to control weed and prevent its expansion.

1-Germination characteristics of strangle wort seedlings under burial depths and flooding stress were evaluated.2-The effect of burial depth and flooding stress on control and management of strangle wort was examined.3-Precise determination of weed biology and weed germination behavior will lead to proper execution of weed control program.

Salt stress is one of the most important abiotic stresses, which decreases crop yields and limits the land use. Three hectares of agricultural land are being destroyed in each minute by salinity In Iran, salinity is one of the most important factors which limit the agriculture. Salt stress studies were very important in the most sugar beet planting regions of Iran based on sugar beet experts and farmers opinions producing sugar beet salt tolerant varieties, improving planting methods, irrigation and fertilizer use are the most Sugar Beet Seed Institute (SBSI) strategies for optimizing the production in saline lands.

In order to investigate the effect of salinity stress on quantitative and qualitative characteristics of sugar beet genotypes in normal and salinity stress conditions an experiment was conducted in a completely randomized block design with three replications in Miandoab Agricultural and Natural Resources Research Station at 2016-17 Crop seasons. In this research, 16 sugar beet genotypes were tested under two normal conditions and saline conditions with salinity of 12.1 dS/m. In this research, root yield, sugar content, white sugar content, sugar yield, the amount of sodium, nitrogen, and potassium of root, white sugar yield percent of sugar extraction was measured.Data was analyzed by using SAS 9.2 software.

The results showed that the effect of the environment on all studied traits wasn’t significant. Differences between genotypes were significant for all traits except for white sugar content, also the interaction of genotype in environment on all traits except the white sugar content was significant. Results showed that the highest root yield, white sugar yield and sugar yield in normal and salinity conditions were allocated to genotypes 14 and 15. Based on the results of correlation analysis in both environmental conditions, white sugar yield had positively and significantly correlated with root yield, white sugar content and sugar yield. Based on the results of stepwise regression analysis, root yield, percent of sugar extraction and sugar yield in normal conditions with justified 93 percent of white sugar yield variation and in salt stress condition with justified 84 percent of the total white sugar yield variation were identified as the most effective traits on white sugar yield.

In the present study, there was no significant difference between two years in term of studied traits. It can be concluded that climate conditions in the two years studied did not have a significant difference in terms of effect on the studied traits. In the present study, there wasn’t significant difference between normal and salinity stress conditions in terms of all traits and salinity Therefore, it can be said that sugar beet genotypes were resistant to salinity. In both environmental conditions, genotypes number 14 and 15 had the highest root yield, white sugar yield and sugar yield, Selection of these genotypes is recommended for future breeding programs. In both normal and salinity conditions root yield, percent of sugar extraction and sugar yield recommended as Criteria for selecting high-yielding genotypes.

This research was carried out to investigate the relationship between agronomic traits and yield using 132 spring wheat genotypes. Genotypes were cultivated in alpha-lattice design with two replications at the research farm of Gonbad Kavous agricultural research station. The highest correlation was observed respectively between yield with seed filling rate (0.84), harvest index (0.84), biomass (0.86), 1000 grain weight (0.58), grain weight per spike (0.44), and weight of the spike (0.41). Yield had a negative and significant correlation with days to maturity (-0.57) and days to heading (-0.45). Seed filling rate and days to heading were introduced into the model as important traits affecting grain yield and 78.1% of yield variation was justified. Grain filling rate and days to heading had direct effects of 0.73 and -0.285 respectively. The traits of seed filling rate and days to heading can be used as selection indices in dryland conditions. The results of cluster analysis showed that genotypes 38, 39 and 52 were in 7 group. This group was superior to other groups in terms of average traits. Genotypes in groups 5 and 7 can be used to aggregate performance-related traits.

To evaluate the soil residue of imazthapyr effects on cotton and maize, an experiment was carried out based on completely randomized design at greenhouse of Agricultural Faculty of University of Gonbad Kavous. Different doses of imazthapyr (0, 4.5, 23, 45, 68, 91, 140, 180, 230, 340 and 450 μg.kg-1 soil) were applied on maize and cotton and finally the dry weight of root and shoot, shoot height, root length and leaf area of crops were measured. Three and four Log- logistic curves were fitted to the data. Results indicated that almost all data fitted better to the three parameter log- logistic curve. Results of separate fitting of the data showed that the residue of imazthapyr always reduced dry weight of crops. For 50 percent reduction of shoot dry weight of cotton and maize, 13.26 and 21.91 g.a.i.ha-1of imazthapyr were needed respectively. To reduce 50 percent of root dry weight of cotton and maize, 72.75 and 25.19 g.a.i. ha-1of imazyhapyr were needed. Generally, the results of data simultaneously fitting showed that maize root and shoot sensitivity to imazthapyr were always more than cotton. Shoot height, root length and leaf area were decreased as imazthapyr residue was increased.

To study the effect of drought stress on some physiological characteristics of corn, an split-plot experimental based on randomized complete block design with three replications in the School of Agriculture and Natural Resources Mugan were administered. Three irrigation regimes full irrigation, deficit irrigation at flowering and grain filling stage irrigation, the main plot and seven commercial hybrids of corn (Single cross 260, 301, 302, 400, 500, 704 and 370 double-cross) were the subplots. Analysis of variance showed that the effects of stress, tension hybrid and hybrid interaction was significant for most traits. Under normal conditions, stress during flowering and grain filling, hybrid single cross 704, respectively, 14.53, 9.38 and 10.53 tons per hectare accounted for the highest yield. Results of correlation analysis showed, the number of grains per corn, number of rows per corn, thousand grain weight, plant height, maize height deployment, total leaf area and flag and biological function positively correlated with grain yield and hectoliter weight and number of tassel branches have were negatively correlated. The highest correlation to the number of rows per maize was allocated by a factor of 0.908. Results Regression analysis also showed that the number of rows per maize and thousand kernel weight and number of branches has a significant effect tassel and grain yield were entered into the equation changesgradient of grain row has the highest effect. In general, hybrid 704 was observed compared to other hybrids are corn tolerant to drought stress during flowering led to a further reduction in yield.

Nowadays, herbicides are crucial for modern agricultural systems. Yet, improper application of herbicides will have dangerous consequences. For example, there will be crop damage due to long lasting herbicide residue or herbicide drift on neighbor crops if applied on non-target species. The purpose of this experiment was to determine the sensitivity of wheat and barley to pendimethalin residue by using bioassay.

In order to evaluate soil residue of pendimethalin, experiments with completely randomized designs were carried out in the greenhouse at Faculty of Agricluture and Natural Resources of Gonbad Kavous University. In this experiments, different doses of pendimethalin (0, 1, 5, 10, 15, 20, 30, 40, 50, 75 and 100 % of recommended dose) were applied on wheat and barley in order to measure the dry weight of root and shoot of crops. The concentration of herbicides was made by dilution of stock solvent. Three/four-parameter log-logistic curves were used for fitness of the data.

Results indicated that almost all data followed the three-parameter logarithm logistic curve. Results of separate fitness of data showed that the residue of pendimethalin always caused reduction of dry weight of the evaluated crops. Fifty percent reduction of shoot dry weight of wheat and barley was obtained from 400.5 and 623.05 g.a.i.ha-1 of pendimethalin and the ED50 values of root dry weight of wheat and barley were 65.88 and 269.66 g.a.i.ha-1 of pendimethalin, respectively.

Generally, the results of simultaneously fitted data shows that wheat is always more sensitive to pendimethalin rather than barley. Based on results, even the very little portion of herbicides can cause damage to crops, thus, considering the sensitive crop in rotation is so important since this residue can stay active even in two-season crops and make crop injuries. The selection between 3 and 4 Logarithm logistic model was based on: 1. Lack of fit test significance 2. Significance of estimated parameters, especially the parameters and the way of distribution of data around the curve (graphical observation).

Providing the main food for people in arid and semi-arid regions such as Iran, bread wheat is considered one of the most important crop plants in the world. The present study was aimed to assess the genetic diversity and the relationship between morphological and agronomic traits under normal and drought stress conditions among thirty-five wheat genotypes. A split-plot in RCB consisting of three replicates was carried out. The morphological and agronomic traits, which were measured, included plant height, awn length, spike length, leaf length, internode length and number of nodes, number of tillers, number of leaves, number of spikelets per a spike, number of grains per spike, number of spikes per plant, grain yield, 100 grain weight and biological yield. And phonological traits containing of number of days to germination and heading were calculated too. According to anova, there were statistically significant differences among various wheat genotypes for the all of traits evaluated. Correlation coefficients among traits demonstrated that in normal conditions, there was a positive correlation between grain yield and spike length, spike weight, number of nodes, number of spikelets per spike, number of grains per spike and number of spikes per plant. However, under stress conditions, grain yield showed a positive correlation with spike weight, number of nodes, number of leaves, number of spikelets per spike, number of grains per spike and number of spikes per plant. In regression analysis, the four variables entered the model in normal conditions, included 100 grain weight, number of spikes per plant, number of grains per spike and awn length. And the six variables under stress conditions which have been entered the model contained number of grains per spike, number of spikes per plant, spike weight, 100 grain weight and leaf length. Path analysis revealed that under normal and stress conditions, 100 grain weight and spike weight had the highest role in increasing grain yield, respectively. In the analysis of the main components, the three main factors justify 77/5٪ of the total variation in normal condition.According to the results, the genotype D1(genomeAA(and Sabalan cultivar,which in all their high performance conditions,can be recognizwd as the most tolerant.