JWSS - Journal of Water and Soil Science

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In order to investigate the diversity and relationship between agronomical traits with seed yield components in barley, twenty advanced barley lines were evaluated in a randomized complete block design with 3 replications at Research Center of Agriculture in Sistan in 2006. Each plot consisted of six rows spaced 20 cm apart and 5 meters long. In this research, 24 Agronomic traits were measured on five randomly selected plants in the central rows of each plot. Analysis of variance showed that there were significant differences among the lines for most of the traits. Line No.7 had the highest (406 grs/m²) and line No.5 had the lowest (309 grs/m²) seed yield. There were high correlation between seed yield and number of panicle/m2. Factor analysis results indicated that 7 independent factors explained 82 percent of the total variation. The first two factors, namely yield components and tillering capacity, explained 41 percent of the total variation. Therefore, it can be concluded that the traits are related to seed yield and tillering capacity, i.e., number of seed per main panicle. 1000 seed weight, number of seed per plant, number of days to physiological maturity and days to heading are the most important characteristics in selecting lines with high seed yield. Number of fertile tiller, total number of tillers and peduncle length were also next set of important traits. Number of days to emergence, nodule number and number of panicle per m² were also important as selection criteria. Seed weight per plant, biological yield, awn length and the traits that were related to flag leaf had lower importance for selection of lines with high seed yield.

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To study relationship between grain yield and important agronomic traits of rapeseed in paddy fields as second culture, fourteen varieties of spring rapeseed were grown in a randomized complete block design of experiment with three replications at Rice Research Institute of Iran, Rasht, during 2005-2006. Analysis of variance showed that there were significant differences between varieties for most of traits. Broad sense heritability ranged from 0.29 for pod length to 0.99 for days to maturity. Phenotypic and genotypic coefficients of variation for days to maturity and the number of pods in secondary branches were the lowest and highest, respectively. Moreover, genetic advance with 5% of selection intensity varied from 3.68% (0.25 cm) for pod length in main branch to 31.48% (915.58 Kg.ha^-1) for grain yield. Results from genotypic correlation coefficients demonstrated that there were positive significant correlations between grain yield and the number of secondary branches, the number of pod in main and secondary branches, pod length in secondary branches, pod diameter in main and secondary branches, 1000-grain weight and oil percentage, and negative significant correlations between grain yield and days to 90% of flowering and days to maturity. Path analysis on genotypic correlations for grain yield as a dependent variable and the other traits as independent variables showed that the 1000-grain weight and the number of pods in secondary branches had the highest direct effects and days to 90% of flowering had low and negative direct effect on grain yield. Therefore, indirect selection for increasing 1000-grain weight and the number of pods in secondary branches are recommended for improving grain yield in rapeseed as second culture in paddy fields.

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In order to investigate the relationship among seed yield and its components in safflower, path and factor analysis were conducted using the agronomic and morphological traits of 32 genotypes. Genotypes were evaluated on the Research Farm of Isfahan University of Technology, using a randomized complete block design with three replications. The correlation coefficients showed that number of seeds per capitula and number of capitula per plant had the highest positive correlation with both seed yield and seed yield per plant. The results of regression analysis showed that number of capitula per plant explained 43.6%, and along with seeds per capitula and plant height 60% of the phenotypic variations for seed yield. The regression analysis for seed yield per plant also revealed that seeds per capitula, capitula per plant and seed weight in order had more contributions to the variation of seed yield per plant and explained 81.2% of its variation. Path analysis showed that capitula per plant had the most direct positive effect on both seed yield and seed yield per plant however, this effect was decreased by the indirect and negative effect of seed weight. Results of factor analysis recognized 3 main factors which explained 81.81 % of total variation of the data. These factors were named the seed yield and its components, phenological traits and branching. In general, it can be concluded that seeds per capitula, capitula per plant and seed weight in order contributed more to the seed yield of safflower genotypes. In conclusion, these yield components can be used as selection criteria in breeding programs.

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Overtopping is one of the main factors responsible for dam failure. To avoid overtopping, dam is equipped with one or some spillways to release the water impounded in the reservoir. The number and size of these spillways are determined on the basis of design flood. Determination of design flood of dam spillway can be formulated as a multiobjective risk problem. This problem can be solved by Quantitative Risk Analysis Methods. Here, four economical design methods which are based on risk analysis including, United States National Research Council (NRC), US Civil Engineering, Unit Curve and Partitioned Multiobjective Risk (PMR) were studied. In order to compare these methods, Risk Analysis was performed for re-determining design flood of Pishin Dam Spillway. This Dam has been constructed on the Sarbaz River. Owing to the fact that the integrals of the expected damage relations in the two methods, i.e., Civil Engineering, and Partitioned Multiobjective Risk are analytically unsolvable, Romberg numerical integration technique and Excel software were utilized for the related calculations and drawing graphs. Also, in order to select suitable distribution, the flood analysis was done using Smada software. The findings of the study indicated that design flood determined by the three methods, i.e., Civil Engineering, National Research Council and Unit Curve was almost the same, and that the amount of flood was less than the 10,000-year-old flood while design flood determined by Partitioned Multiobjective Risk Method, was larger than the 10,000- year-old flood.

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Soil erosion is an important economical, social and environmental problem requiring intensive watershed management for its control. In recent years, modeling has become a useful approach for assessing the impact of various erosion-reduction approaches. ِDue to limited hydrologic data in mountainous watersheds, watershed modeling is, however, subject to large uncertainties. In this study, SWAT2000 was applied to simulate runoff and sediment discharge in Beheshtabad watershed, a sub-basin of Northern Karun catchment in central Iran, with an area of 3860 km2. Model calibration and uncertainty analysis were performed with SUFI-2. Four indices were used to assess the goodness of calibration, viz., P-factor, d-factor, R2 and Nash-Sutcliffe (NS). Runoff data (1996-2004) of six hydrometery stations were used for calibration and validation of this watershed. The results of monthly calibration p-factor, d-factor, R2 and NS values for runoff at the watershed outlet were 0.61, 0.48, 0.85 and 0.75, respectively, and for the validation, these statistics were 0.53, 0.38, 0.85 and 0.57, respectively. The values for calibration of sediment concentration at the watershed outlet were 0.55, 0.41, 0.55 and 0.52, respectively, and for the validation, these statistics were 0.69, 0.29, 0.60 and 0.27, respectively. In general, SWAT simulated runoff much better than sediment. Weak simulation of runoff at some months of the year might be due to under-prediction of snowmelt in this mountainous watershed, model’s assumptions in frozen and saturated soil layers, and lack of sufficient data. Improper simulation of sediment load could be attributed to weak simulation of runoff, insufficient data and periodicity of sediment data.

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Understanding distribution of soil properties at the field scale is important for improving agricultural management practices and for assessing the effects of agriculture on environmental quality. Spatial variability within soil occurs naturally due to pedogenic factors as well as land use and management strategies. The variability of soil properties within fields is often described by classical statistical and geostatistical methods. This research was conducted to study what factors control the spatial variability of soil nutrients using an integration of principal component analysis and geostatistics in Appaipally Village, Andra Pradesh, India. 110 soil samples were randomly collected from 0-30 cm and prepared for laboratory analyses. Total N, available P, Ca, K, Na, Mg, S, B, Mn, Fe, Zn were measured using standard methods. Statistical and geostatistical analysis were then performed on raw data. The results of PCA analysis showed that 4 PC's had Eigen-value of more than 1 and explained 71.64 % of total variance. The results of geostatistical analysis revealed that three PC's had isotropic distribution based on surface variogram. Spherical model was fitted to all PC's. Ranges of model were 288 and 393 m for PC1 and PC3 respectively. On the other hand the range for PC2 was significantly different (877m). The most important elements in PC2 such as Fe, Mn, and Zn probably had similar range of effectiveness (700-900m). The comparison of PC's distributions indicated that PC1 and PC3 including total N, available Mg, K, Cu, Ca and P, were in accordance with farming plots dimensions and management practices. Therefore, it is necessary to improve the appropriate fertilizers used by farmers. The pattern of PC2 distribution was not consistent with farmer's plots, but had the best concordance with soil acidity. Therefore, the most correlated elements with this PC including Fe, Mn, and Zn are mainly controlled by soil acidity and not affected by management practices. However, spatial variability of these elements in areas lower than critical values should be considered for site-specific management.

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Women as extension help-agents could play an important role in rural extension programs. This study was aimed at evaluating the success of women as extension help-agents, and factors affecting their success in Isfahan province, Iran. The data was randomly collected from 156 extension help-agents, using Cochran formula. The validity and reliability of constructed questioners were checked, using Cronbach Alfa and K.M.O. criteria. The results showed that 6.5 percent of the agents gained a success score of more than or equal to the average score, 50.3 percent equal to the average score, and 43.2 percent gained less than the average success score. The Effective factors are categorized as the degree of their familiarity with the problems of the population, the degree of their involvement in extension-educational programs, and factors such as their access to rural libraries, financial and occupational background and contribution to community and team work. According to the regression analysis results, participation activities of the villagers, financial status of the help-agents and educational programs provide most influential factors for successful extension programs.

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This experiment was conducted to investigate the genetic diversity, identify traits explaining yield variation, recognize relationships between traits and classify accessions in a Iranian and forign germplasem of tall fescue. Forty six Iranian and foreign tall fescue accessions were surveyed for Phenological, morphological and agronomical characters in a randomized complete block design with three replications in field for 2 years. Significant differences were observed for all of the characters, indicating broad variation in this germplasm. Iranian accessions had a better performance for most of the traits in both years indicating their high potential for developing commercial varieties in breeding programs. Basis on stepwise regression analysis, crown width justified the majority of hay yield variation, followed by establishment rate, percentage of dry matter, height and number of stem. Hence, these characters could be used for selecting high yielding cultivars. Factor analysis revealed 4 factors which explained more than 80 percent of the total variation and confirmed the results of regression analysis. Using UPGMA method, cluster analysis revealed five groups. Accessions with similar country of origin or same ecological conditions were grouped in same cluster. Regarding the morphological characters the best accessions were identified to be used in the further breeding projects.

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To study general combining ability(GCA) and gene action for resistance to sunn pest(Eurygaster integriceps) six lines of bread wheat numbered 7214 ,6412,c-75-4,18,14,12 plus azadi variety werw crossed in a half-diallel system. Seven parents and twenty-one hybrids were evaluated in a randomized complete block design with 3 replication at Tehran University Research Station during the years 2005-2006. Analysis of variance indicated that among all of traits except for weight of sunn pest damaged kernel, difference existed at 1% level of significant. Results of combining ability analysis showed that in all traits, additive and non- additive variances in inheritance is important. Considering GCA for resistance to sunn pest line 7214 was the best and line 18 was the worst. Considering specific combining ability(SCA) for resistance to sunn pest damage with regard to all of traits the azadi×c-75-4 hybrid was the best and the 18×12 hybrid was the worst. Study of Hayman genetics parameters confirmed the results of Griffing GCA analysis indicating that in all traits additive and non- additive components are effective in inheritance except 50 kernel weight damaged, sunn pest seed damaged percent and height. But dominant variance is more important in these traits. For all traits except awn length exist over dominance, furthermore in all traits nonsymmetrical distribution of negative and positive effects and environment effect is also important.

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Factor and principal component analyses are widely used in different sciences especially in agricultural science. To determine the factors that create variation between potato cultivars, in normal (non-stress) and water deficit (stress) conditions, two experiments were conducted in the form of randomized complete block design with three replications in summer 2002. Stepwise regression analysis showed that in normal conditions, stem length, number of stems/plant and leaflet width contributed significantly to yield. In stress condition, other than stem length and number of leaves/main stem, leaflet length also entered the model. As is evident, stem length had a detrimental effect on tuber yield in both stress and non-stress conditions. So, this trait could be used as an important criterion for the selection of high yielding genotypes. Principal component analysis revealed that number of stem, leaf length and leaf width were important traits creating variability between potato cultivars, especially number of stem that had high coefficients in the first principal for both environments. Factor analysis distinguished two factors in normal environment named leaf surface and structural attitude factors, and also two factors in stress environment called photosynthetic surface and structural attitude. Therefore, these factors should be intervened and attended to in breeding programs.

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The main purpose of this research is to study the success of rural cooperative companies and evaluate different factors which may influence such a success. For this purpose, sample companies were randomly selected based on multistage and stratification sampling with proportional allocation method,. Finally, 18 rural co-operative companies were studied using questionnaires filled out by 328 ordinary members and 93 members of board of directors and managers of these rural co-operative companies. Inter-organization and intra-organization and structure variables which affected the companies performance and achievement were studied. The findings showed poor performance of these companies. According to the current research, some variables such as membership experience, co-operative structure, co-operative investment, enhancement of member share, education, participation and level of training, awareness (ability) of members have a direct influence on companies' success. The other factors involving function of rural co-operative organization, use of managers’ misuses and abuses in co-operatives have a direct, but negative influence. Share mean value of each member, management experience knowledge and age of people have also indirect positive effect on success and the size has indirect negative influence on co-operative companies .

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Climate change has important effects on earth environment and human life. Therefor, investigation and study of climate change is very essential. This study investigated rainfall, temperature, relative humidity and wind variability by analyzing data for annual and monthly climatic factors collected at 13 synoptic stations (industrial cities of Iran) by using Mann-Kendall test. The results of monthly rainfall trends showed that most of synoptic stations have significant positive and negative trends in winter and spring months. About 23% and 1.7% of stations have significant negative and positive trends, respectively, in annual trend of this factor. The results of monthly number of rainy days showed the major number of significant trends occurs in spring. In autumn (September, October and November) like as summer most of the stations have no significant trends. Analyzing the annual number of rainy days trends also showed that 4 stations have significant positive trends and 2 stations negative trends. Trend of greatest daily precipitation is low throughout the year, so there is not any significant trend in winter. Annual investigations confirm the seasonal investigations. The major number of significant trends in monthly mean maximum temperature occurs in summer but there are not any significant trends in winter and March. The trend of mean minimum temperature is approximately high in all of the seasons and the major number of significant trends occurs in summer and autumn and then in spring and ultimately in winter. In annual investigation, most of the stations showed positive trends and only Oroomieh station has negative trends. Trend of mean temperature is high except for winter. Most of the stations showed positive trend, indicating increasing trends in this factor. Annual studies vertify the positive trends and about 63% of stations have significant positive trends.

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Low flow estimation and its characteristics play an important role in hydrologic studies. However, some low flow events are ignored compared with the lowest annual low flow that may have high risk. These events are taken into consideration by the use of partial duration or peak over threshold models. In this study, a 7-day low flow was applied for frequency distribution and threshold, and the lower events were considered as the number of low flow event ( ) to study seasonal variation of low flows together with two graphical methods. The results showed two major low flow seasons, and for other times of the year, the low flow events are negligible. At last, the region was divided into homogeneous groups based on seasonal variation of low flows.

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In order to study gene action in rice for traits related to quality (gelatinization temperature, gel consistency and amylose content), four varieties of rice (Sang-e-Tarrom, Gerdeh, IRRI2 and IR229) were investigated. Ten different generations including P1, P2, F1, RF1, BC1, RBC1, BC2, RBC2, F2 and RF2 were evaluated using generation mean analysis. In generation mean analysis, one of non-allelic interaction components, [i], [j]1, [j]2, [l]1, [l]2, [l] was significant indicating the genetic model of these characters were described by additive-dominance model with non-allelic gene interaction (except for gelatinization temperature trait in Sang-Tarrom × Gerdeh cross). The cross IRRI2×IR229 showed duplicate epistasis for gel consistency trait. Cytoplasmic effects and interactions between cytoplasmic and nuclear effects in two crosses were significant for amylose content and gel consistency traits. The estimation of narrow and broad-sense heritability for two crosses were 0.77 to 0.99 and 0.05 to 0.93, respectively. The predominantly additive nature of the genetic variability was further revealed by the variance components. Component D was detected significant in all the crosses. The covariance component and , however, showed indirectly that dominance contributed significantly to variability at the variance level. Therefore, according to the obtained results, selection can be effectively done in later segregation generations for gel consistency and amylose content and in early generation for traits.

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Discrimination function analysis is a method of multivariate analysis that can be used for determination of validity in cluster analysis. In this study, Fisher’s linear discrimination function analysis was used to evaluate the results from different methods of cluster analysis (i.e. different distance criteria, different cluster procedures, standardized and un-standardized data). Furthermore, Hotelling T2, CCC plot and multivariate analysis of variance were used to support the results. To achieve the goals, 8 rapeseed genotypes were planted in a randomized complete block design with three replications in Rice Research Institute of Iran, Rasht, durin 2005-2006, and 14 characteristics were measured. Analysis of variance based on the randomized complete block design showed significant differences between genotypes for all the studied traits. Comparison of means between genotypes indicated that the genotype Hyola401 for grain yield and most of the measured characteristics was better than the other genotypes. Evaluation of phenotypic and genotypic coefficient of variations showed that most of the traits had high variability in the population. Discrimination function analysis showed that the Euclidean distance criterion was better than others and a desirable clustering was obtained by this criterion. Also, all of the data standardization methods produced similar clusters and were better than un-standardized data. Based on evaluation of dendrograms derived from different methods of cluster analysis determined that the UPGMA, complete linkage and Ward’s minimum variance methods were better than the other methods, and grouped the genotypes into three clusters. Fisher’s linear discrimination function analysis showed that UPGMA and Ward's minimum variance methods with clustering validity of 87.5 percent, was more suitable than other cluster analysis methods however, discrimination analysis grouped genotypes into two clusters. Tests of Hotelling T2, CCC plot and multivariate analysis of variance supported the results from the discrimination function analysis. It seems that the UPGMA and Ward's minimum variance procedures based on Euclidean distance criterion of standardized data function better in grouping genotypes, yet, the use of discrimination function analysis is recommended to confirm the results and determine the actual groups.

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Because of the fact that alternative agricultural policies cannot be examined in a laboratory, the potential effects of policies must be analyzed before policy setting, and during or after the policy implementation using mathematical programming (MP) models. In this context, the consequences of increasing the acreage of colza at representative farm (RF) level of Namdan plain were analyzed using positive mathematical programming (PMP), which were improved to overcome normative character of optimization models. The main aim of PMP is to give as true a picture as possible of the situation and then simulate the behavior of farmers as parameters in which the object of agricultural policy intervention is shifted. Based on the results of this study, reduction in the acreage of wheat and bean and increase in the expected profit of RFs are the consequences of increasing acreage of colza. But, as variance of profit increases, the net impact of policy on the expected utility of RFs is not perfectly known. The results also indicated that the use of pesticide increases through introducing colza into a cropping pattern. The effect of policy on water use is different among RFs and they can't take this policy as a water demand management policy.

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Canola (Brassica napus L.) as one of the oilseed crops has recently received lots of attention due to its suitable oil quality and high oil percentage. To evaluate the effect of water deficit stress on canola genotypes with regard to yield, drought tolerance quantitative indices and identify drought tolerant genotypes, an experiment was conducted in research farm of seed and plant improvement Institute (Karaj) using completely randomized block design with four replications, and 25 genotypes under two stress and non stress conditions. Genotypes were evaluated regarding drought tolerance. Drought tolerance quantitative indices include MP, GMP, STI, SSI and TOL. Result of variance analysis showed significant differences among genotypes as regards all indices and seed yield of genotypes in both conditions at 1% probability level. MP, GMP and STI indices were selected as the best indices for selection of tolerant genotypes in regard to analysis of correlation between seed yield in two stress and non stress conditions and drought tolerance indices. Results indicated that Genotype Vectra was the best genotype, because of its proper mean yield in both stress and non stress conditions. Multivariate biplot figure display showed that MP, GMP and STI indices had high correlation coefficient with each other, and tolerant genotypes were located near tolerance indices. Also results of three dimensional scatter plot and cluster analysis indicated that genotypes Modena, Jura, Eshydromel, Vectra, Dante, Zarfam, Esbetty and Olano were tolerant to drought stress, and genotypes Olpop, GKHelena, RG4504 and Olphi were sensitive genotypes.

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In this study the causality relationship among variables in chicken and beef markets were investigated based on annual data from 1974 to 2004 in the I.S. of Iran. For this purpose, causality algorithms emerging from directed acyclic graphs were used in two cases, one based on co- integration analysis and innovation correlation matrix of Vector Error Correction Model (VECM) and the other using data directly. In the investigation of causality process, PC algorithm based on partial correlations and GES (Greedy Equivalent Search) based on Bayesian network model were used. The results revealed that there is no specific causality relation between chicken and beef consumption and their price indices. Thus it seems other variables and government interventions are effective factors in these markets. Therefore, this model is an instrument for forecasting changes of these variables. In these markets there are causality relations among price indices, quantities of consumption and other variables. Beef price index as endogenous variable, is under the effect of chicken price index, non meat food price index, non food price index, per capita expenditure and quantity of chicken consumption. The quantity of beef consumption is predetermined and isn’t under the effect of other variables. Chicken price index is endogenous and under the effect of beef price index, per capita expenditure and non food price index. Also chicken quantity consumption is endogenous and under the effect of beef quantity consumption and non meat food price index. With respect to the findings, for effectiveness, policy initiatives aimed at improving in meat industry should be different for different meat markets and the method of directed graphs can be used a s a guideline.

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In order to study the correlation of agronomic, morphologic and physiologic traits and their effects on grain yield of rice genotypes in two environments (favorable irrigation and water stress), 49 genotypes were evaluated using a completely randomized block design with 3 replications in two experimental conditions. All practices and conditions were the same for the two experiments with the exception of irrigation, where under stress conditions no irrigation was applied at tillering stage. Comparison of means showed significant differences between genotypes in each environment. Also, differences between yield and yield components of each genotype under two conditions were significant. The results of phenotypic correlations showed that the highest positive and significant correlation with grain yield belonged to number of panicle per plant (0.95) in irrigation conditions and to number of filled grains per panicle (0.92) in water stress conditions. Stepwise regression analysis for grain yield introduced number of panicle per plant, relative water content (RWC), flag leaf length and number of spikelet per panicle, respectively, as effective traits in grain yield in irrigation conditions, however, in stress conditions, number of filled grain per panicle, number of panicle per plant and relative water content were effective traits in yield. The results of path analysis showed that the number of panicle per plant had the highest positive and direct effect on grain yield in the two environments. Factor analysis introduced four factors in the two conditions named yield and crop production, phenologic, harvest index and plant shape and appearance quality of grains factors. Therefore, to select high yield and drought tolerant genotypes, we need to consider number of filled grain per panicle, number of panicle per plant and relative water content. In addition, traits such as panicle length, number of spikelet per panicle, flag leaf length and width that showed significant correlations with grain yield in stress conditions should also be considered important and second to the above mentioned traits.

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Monitoring Land Use and Land Cover Changes have a significant role in environmental programming and management. Satellite data is an essential tool for detecting and analyzing environmental changes. Many change detection techniques have been developed which have advantages or disadvantages. Change Vector Analysis (CVA) technique is one such a method. This method is based on radiometric changes between two dates of satellite imagery. Main advantage of this method is that it provides direction and magnitude image of change. The aim of this study was to describe change vector analysis technique and it applies to detect land cover change in Isfahan area during an 11-year period. The data used for this study were two images Landsat: TM 05 June 1987 and 03 June 1998. Correction radiometric was not carried out because of the similar sensor and acquisition time of the remote sensing data. After geometric correction, the study area was selected from Landsat images. Change vector technique was applied to analyze magnitude and direction of change. The change map showed Kappa and overall accuracy coefficient of 63.19% and 74.4%, respectively. The results showed that the changed land cover was 3340 ha during this period. Overall, the results show that 1325 hectares (especially agricultural lands) have been converted into urban areas, agricultural areas were increased up to1385 hectares, and 435 hectares of agricultural areas were converted to other land use over the period of study. This study showed that CVA is a robust approach for detecting and characterizing radiometric change in multi-spectral remote sensing data sets.