JWSS - Journal of Water and Soil Science

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In order to study the genetic variation among local varieties of onion in Iran, an experiment was conducted in the Research Center, Faculty of Agriculture, Tabriz University. Sixteen populations were evaluated for agronomic characteristics and also total seed proteins via SDS-PAGE. Cluster analysis and principal component analysis were used to group the onion populations under study.

 Analysis of variance showed significant differences among varieties for leaf color, leaf length, texture tightness, onion yield per plant, and number of edible layers. No significant differences were observed for the number of twin onions, bulb diameter, and onion dry weight. Hamadan (98-148), Arak (98-95, 98-96, 98-97, 98-103), and Zanjan (98-223) populations acquired the highest onion yield per plant. The significant differences between populations for the majority of characteristics proved the existence of genetic variation in the Iranian onion germplasm. The results from cluster analysis for agronomic characteristics were the same as those from the cluster analysis for the onion yield per plant. The 16 populations were divided into 4 groups. Cluster analysis for the electrophoresis banding pattern resulted in two groups, which was not similar to the dendrogram of agronomic traits. Using principal component analysis, the first principal components determined 97.57% of the total variation. Onion yield per plant was the most important trait in the first principal component and onion dry weight was the second trait in the rank.

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In order to evaluate the morphological and agronomic characteristics of 19 land races of onions and one foreign cultivar (Yellow Sweet Spanish), an experiment was conducted in 1998 at the Research Farm of the College of Agriculture, Isfahan University of Technology.

 Analysis of variance showed significant differences among genotypes for all of the morphological and agronomic traits. Dry weight and number of days to emergence had the highest and lowest coefficients of genetic variability, respectively. Plant fresh weight, yield of 30 plants and total yield also had high coefficients of variability. Broad sense heritability estimates were high for all of the traits, indicating low environmental affects them. Based on cluster analysis and plot of the first two canonical variables, the genotypes were classified in four groups with different agronomic traits. Canonical discriminant analysis based on nine agronomic traits introduced three canonical variables which justified 99.9 percent of the total variation among characters. Principal component analysis revealed four components while factor analysis showed three factors which explained 87.3 and 95.5 percent of the total variation among characters, respectively. The first and second factors were related to adaptation and assimilate translocation, respectively. Bulb diameter, bulb height and bulb weight in positive directions and sensitivity to Fusarium in negative direction had greater loads in the third factor.

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Since the fluctuations of the Persian Gulf Sea Surface Temperature (PGSST) have a significant effect on the winter precipitation and water resources and agricultural productions of the south western parts of Iran, the possibility of the Winter SST prediction was evaluated by multiple regression model. The time series of PGSSTs for all seasons, during 1947-1992, were considered as predictors, and the time series of MSSTs during 1948-1993, as the prrdictand. For the purpose of data reduction and principal components extraction, the principal components analysis was applied. Just the scores of the first four PCs (PC1 to PC4) that accounted for the total variance in predictor field were considered as the input file for the regression analysis. For finding the dependency of each principal component to the first time series of the PGSST, the Varimax rotation analysis was applied. The results have indicated that PC1 to PC4 respectively are the indicator of temperature changes during winter, autumn, Spring and Summer. According to the regression model, the components of PC1, PC2 and PC4 were significant at 5% level. But the components of PC3 was insignificant. The results indicated that the significant variables are held accountable for the 33.5% of the total variance in the winter PGSSTs. It became obvious that for the prediction of the winter PGSST, the PGSST during the winter of the last year has a particular importance. At the next stage, autumn and summer temperature have also a role in prediction of winter PGSST.

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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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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 aim of the study was to investigate the effects of land use on soil quality parameters using multivariate statistical analysis. Soil samples (0-25 and 25-50 cm depths) were taken from three land uses in forest area of Marivan including forest, rangeland, and cultivated land. Soil characteristics of pH, EC, sand, silt, clay and CaCO3 content, water-stable aggregates and their organic carbon content were measured. Principal component, cluster and discriminant analyses were used to evaluate the soil quality. Principal component analysis classified soil properties into five factors. The most important factors were soil aggregates organic carbon content and aggregate stability indices. Schematic distribution of factors and also cluster analysis showed the same pattern. Soil aggregates organic carbon content, water-stable aggregates and aggregate stability indices were the most sensitive factors to land use changes. These soil properties and factors had the same pattern in forest and rangeland, but significantly reduced in the cultivated land use. Land use change from forest to cultivated land resulted in significant decrease of aggregates organic carbon content, water-stable aggregates and also an increase of pH. The results showed the usefulness of multivariate statistical methods for integration of the soil properties and determination of different soil quality indices.

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In this study the effect of temperature, evaporation or evapotranspiration, precipitation, hillside direction and altitudinal classes, texture and acidity of soil on organic carbon content in the depths of 15 and 45 cm were evaluated. Paired t-test results showed that there is a significant difference between measured parameters in two soil depths. After preparing required data and processing them, outlier's data were removed. Then, base maps for each of the information layers were prepared by Arc GIS9.3 software and all relatd information fit together by overlapping them. Pearson correlation between environmental factors and soil organic carbon values were calculated and it was found that in the depth of 15 cm, the correlation between soil organic carbon values and two environmental factors including temperature and altitude were significant at the level 0.01. As well the results of statistical analysis by using principal component analysis (PCA) method showed that the factors temperature, evaporation (1%), and silt and clay (5%) have had a significant effect on the amount of soil organic carbon. The first, second, and third axes with eigenvalues of 98/4, 78/3 and 92/1, respectively, explained the values 0.33, 0.25, and 0.13 % of correlation between organic carbon and environmental data.

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