JWSS - Journal of Water and Soil Science

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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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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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Variance components and genetic parameters of body weight of Mehraban sheep were estimated by univariate and random regression models. This was done by using body weight records of 2746 Mehraban lambs related to flocks under supervision of the Agriculture Organization of the Hamadan province, collected between 1990 and 2005. In both methods, variance components estimates were obtained by restricted maximum likelihood (REML) using DFUNI and DXMRR programs, respectively, via DFREML 3.1 software package. Results showed that variance components obtained from RR models (except for residual variance) in some ages were higher than those obtained from univariate models. Direct heritability (h2) estimates from univariate and RR models were approximately equal to weaning age but, overall, RR estimates were higher than those obtained from univariate analyses. Maternal heritability estimates (m2) from RR models were higher than univariate models’ estimates, and showed a different pattern of variation with age. Correlations between predicted breeding values from univariate and RR models for birth weight and weaning weight were 0.72 and 0.70, respectively. Results showed that estimates of variance components and genetic parameters by RR models were affected by data structure and in case of the need for genetic parameters, especially those related to body weight late in lambs’ life, estimates of univariate analyses should be preferred.

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Soil fertility measures such as cation exchange capacity (CEC) may be used in upgrading soil maps and improving their quality. Direct measurement of CEC is costly and laborious. Indirect estimation of CEC via pedotransfer functions, therefore, may be appropriate and effective. Several delineations of two consociation map units consisting of two soil families, Shahrak series and Chaharmahal series, located in Shahrekord plain were identified. Soil samples were taken from two depths of 0-20 and 30-50 cm and were analyzed for several physico-chemical properties. Clay and organic matter percentages as well as moisture content at -1500 kPa correlated best with CEC. Pedotransfer functions were successfully developed using regression and artificial neural networks. In this research, it seemed that one hidden layer with one node was sufficient for all neural networks models. The best regression model consisting of organic matter and clay variables showed R2=0.81 and RMSE=7.2 while best corresponding neural network with a learning coefficient of 0.3 and an epoch of 40 had R2=0.88 and RMSE=0.34. Data partitioning according to soil series and soil depths increased the accuracy and precision of the functions. Compared to regression, artificial neural network technique gave pedotransfer functions with greater R2 and smaller RMSE.

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In mountainous basins, snow water equivalent is usually used to evaluate water resources related to snow. In this research, based on the observed data, the snow depth and its water equivalent was studied through application of non-linear regression, artificial neural network as well as optimization of network's parameters with genetic algorithm. To this end, the estimated values by artificial neural network, neural network-genetic algorithm combined method and regression method were compared with the observed data. The field measurement were carried out in the Samsami basin in February 2006. Correlation coefficient (r) mean square error (MSE) and mean absolute error (MAE) were used to evaluate efficiency of the various models of artificial neural networks and nonlinear regression models. The results showed that artificial neural network and genetic algorithm combined methods were suitable to estimate snow water equivalent. In general, among the methods used, neural network-genetic algorithm combined method presented the best result (r= 0.84, MSE= 0.041 and MAE= 0.051). Of the parameters considered, elevation from sea level is the most important and effective to estimate snow water equivalent.

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An extensive data collection on precipitation and runoff is required for development and implementation of soil and water projects. The unit hydrograph (UH) is an appropriate base for deriving flood hydrographs and therefore provides comprehensive information for planners and managers. However, UH derivation is not easy job for whole watersheds. The development of UH by using easily accessible rainfall data is then necessary. Besides that, the validity evaluation of different statistical modeling methods in hydrology and UH development has been rarely taken into account. Towards the attempt, the present study was planned to compare the efficiency of different modeling procedures in hydrograph and 2-h representative UH relationship in Kasilian watershed with concentration time of some 10h. The study took place by using 23 storm events occurred during four seasons within 33 years and applying two and multivariable regression models and 36 variables. According to the results, the median of estimated errors in estimation of 2-h UH dependent variables for verification stage varied from 37 to 88%. The results verified the better performance of cubic and linear bivariate models and logarithm-transformed data in multivariable model as well. The efficiency of multivariable models decreased when they were subjected to principle component analysis. The performance of backward method was frequently proved for estimation of dependent variables based on evaluation criteria, whereas the forward was found to be more efficient for time-dependent factors estimation.

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Soil temperature is one of the important variables in hydrology, agriculture, meteorology and climatology studies. Owing to the fact that soil temperature is only measured at synoptic stations, reconstruction of this variable in other places is of great importance for many relevant agricultural surveys. Using 10-year (1996-2005) daily meteorological observations, including: air temperature, global solar radiation, precipitation, relative humidity, vapor pressure, wind speed and air pressure data, different empirical relationships are suggested. At statistically significant level (P<0.05), the suggested regressions are reliable for estimating soil temperature in various depths (5, 10, 20, 30, 50 and 100 cm) and different climate types. Using soil temperature as the dependent variable and the other meteorological parameters as the independent variables, the multivariable relationships are classified accordingly. The results indicate that the impact of meteorological parameters on soil temperature is not the same. At statistically significant level (P<0.05), the mean daily air temperature presented the highest correlation coefficients with soil temperature for all climate types (on average, from R2>0.91 for warm semi-arid, to R2>0.85 for humid climates). Other results highlighted that the correlation coefficients decreased as the soil depth increased. The behavior of statistical validation criteria of the suggested relations are also discussed for all the mentioned climates.

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In this research, logistic regression analysis was used to create a landslide hazard map for Sajaroud basin. At first, an inventory map of 95 landslides was used to preduce a dependent variable, which takes a value of 0 for absence and 1 for presence of landslides. Ten factors affecting landslide occurence such as elevation , slope gradient, slope aspect, slope curvature, rainfall, distance from fault, distance from drainage, distance from road , land use and geology were taken as independent parameters. The effect of each parameter on landslide occurrence was determined from the corresponding coefficient that appears in the logistic regression function. The interpretation of the coefficients showed that road network plays the most important role in determining landslide occurrence. Elevation, curvature, rainfall and distance from fault were excluded from the final analysis because these variables did not significantly add to the predictive power of the logistic regression. After transferring final probability function into Arc/view 3.2 software, landslide susceptibility map was prepared. The results of accuracy assessment showed that overall accuracy of produced map is 85.3 percent. Therefore, 53% of the area was located in very low hazard, 18.3% in low hazard, 21% in moderate hazard and 7.7 % residual area is located in high hazard regions. Model and then susceptibility map verity was assessed using -2LL, Cox and Snell R2, Nagelkerk R2, and was validated.

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In this research, based on the observed data of Class A pan evaporation and application of non-linear regression (NLR), artificial neural network (ANN), neuro-fuzzy (NF) as well as Stephens-Stewart (SS) methods daily evaporation of Kerman region was evaluated. In the cases of NLR, ANN and NF methods, the input variables were air temperature (T), air pressure, relative humidity (RH), solar radiation (SR) and wind speed (U2) which were used in various combinations to estimate daily pan evaporation (Ep) defined as output variable. Performance of the methods was evaluated by comparing the observed and estimated data, using determination coefficient (R2), root mean square error (RMSE) and mean absolute error (MAE). Based on the observed data at Kerman meteorological station, the monthly and annual average evaporation values of the region were 272 and 3263 mm, respectively. The results of this study indicated that NF method is the most suitable method to estimate daily Class A pan evaporation. The statistics criteria of this model which is constituted based on the 5 input parameters were R2 = 0.85, RMSE=1.61 and MAE= 1.24 mm day-1. The sensitivity analysis of NF model revealed that the estimated EP is more sensitive to T and U2 (as the input variables), respectively. Due to weak accuracy of SS method, a new modification step of the model was also developed based on the SR and T in order to have a more exact daily evaporation estimation of the region. However, the result of the modified model was not acceptable

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Measuring the cation exchange capacity (CEC) as one of the most important chemical soil properties is very time consuming and costly. Pedotransfer functions (PTFs) provide an alternative to direct measurement by estimating CEC. The objective of this study was to develop PTFs for predicting CEC of Guilan province soils using artificial neural network (ANN) and multiple-linear regression method and also determine whether grouping based on soil textural class and organic carbon content improved estimating CEC by two methods. For this study, 1662 soil samples of Guilan province were used from soil chemistry laboratory database of Rice Research Institute. 1109 data were used for training (the development of PTFs) and 553 data for testing (the validation of PTFs) of the models. The results showed that organic carbon was the most important variable in the estimation of cation exchange capacity for total data and all classes in textural and organic C groups in both methods. ANN performed better than the regression method in predicting CEC in all data, and grouping of data only improved the prediction of PTFs in Sand and Sandy clay loam classes by ANN method.

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In this study, the remote sensing statistical approach was used to determine the global solar radiation from NOAA-AVHRR satellite data in southeast of Tehran. This approach is based on the linear correlation between a satellite derived cloud index and the atmospheric transmission measured by the clearness index on the ground. A multiple linear regression model was also used to convert the five AVHRR data channels and extraterrestrial radiation to global solar radiation. The results of this study showed that multiple linear regression model estimated the solar radiation with an R2 of 0.93 and a root mean square error (RMSE) of 5.8 percent, which was better than the statistical approach.

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Direct measurement of soil hydraulic characteristics is costly and time-consuming. Also, the method is partly unreliable due to soil heterogeneity and laboratory errors. Instead, soil hydraulic characteristics can be predicted using readily available data such as soil texture and bulk density using pedotransfer functions (PTFs). Artificial neural networks (ANNs) and statistical regression are two methods which are used to develop PTFs. In this study, the multi-layer perceptron (MLP) neural network and backward and stepwise regression models were used to estimate saturated hydraulic conductivity using some soil characteristics including the percentage of particle size distribution, porosity, and bulk density. Data of 125 soil profiles were collected from the reports of basic soil science and land reclamation studies conducted by Khuzestan Water and Power Organization. The results showed that MLP neural network having Bayesian training algorithm with the greater coefficient of determination (R2=0.65) and the lower error (RMSE =0.04) had better performance than multiple linear regression model in predicting saturated hydraulic conductivity.

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In this research, we estimated soil shallow depths temperatures using regression methods (Linear and Polynomial). The soil temperatures at soil depths (5, 10, 20, 30, 50 and 100 cm) were correlated with meteorological parameters. For this purpose, temperature data of Hamedan station (in the period 1992-2005) were employed. Soil temperature data were measured on a daily basis at 3 PM, 9 PM and 3 AM. MS Excel was used for deriving the regressions between soil temperature and meteorological parameters (air temperature, relative humidity and sunshine hours). The results showed that the highest coefficient of determination (R2) of the linear regression was between soil temperature in 20 cm soil depth and air temperature at 3 AM (R2= 98.15%) and the lowest value in 100 cm soil depth at 3PM (R2= 83.96%). Also, the highest R2 of non-linear regression was observed between soil temperature in 10 cm soil depth and air temperature at 3 AM (R2= 98.45%) and lowest value in 100 cm soil depth at 3PM (R2= 84.11%). The results showed that the highest and lowest values of R2 of linear relations between meteorological parameters (relative humidity and sunshine hours) and soil temperature were observed in 10 cm soil depth (at 3 AM) and in 100 cm soil depth, respectively. Correlations of soil temperature with air temperature were greater than those with the other two parameters. Moreover, R2 values of non- linear relation were higher than linear relation.

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Digital soil mapping includes soils, spatial prediction and their properties based on the relationship with covariates. This study was designed for digital soil mapping using binary logistic regression and boosted regression tree in Zarand region of Kerman. A stratified sampling scheme was adopted for the 90,000 ha area based on which, 123 soil profiles were described. In both approaches, the occurrence of relevant diagnostic horizons was first mapped, and subsequently, various maps were combined for a pixel-wise classification by combining the presence or absence of diagnostic horizons. Covariates included a geomorphology map, terrain attributes and remote sensing indices. Among the predictors, geomorphology map was identified as an important tool for digital soil mapping approaches as it helped increase the prediction accuracy. After geomorphic surfaces, the terrain attributes were identified as the most effective auxiliary parameters in predicting the diagnostic horizons. The methods predicted high probability of salic horizon in playa landform, gypsic horizon in gypsiferous hills and calcic horizon in alluvial fans. Both models predicted Calcigypsids with very low reliability and accuracy, while prediction of Haplosalids and Haplogypsids was carried out with high accuracy.

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Physicochemical characteristics of soil, land cover/use and human activities have effects on heavy metals distribution. In this study, we applied Classification and Regression Tree model (CART) to predict the spatial distribution of zinc in surface soil of Hamadan province under Geographic Information System environment. Two approaches were used to build the model. In the first approach, 10% of total data were randomly selected as test data and residual data were used for building model. In the second approach, all data were used to build and evaluate the CART model. Determination coefficient (R2) and Mean Square Error (MSE) were applied to estimate the accuracy of model. Final model included 51 nodes and 26 terminal nodes (leaf). Calcium carbonate, slope, sand, silt and land use/cover were determined by the CART model to predict spatial distribution of Zn as the most important independent variables. The regions of western Hamadan province had the highest concentration of Zn whereas the lowest concentration of Zn occurred in the regions of northern Hamadan province. The results indicate good accuracy of CART model using R2 and MSE indices.

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Quantitative evaluation of evapotranspiration on a regional scale is necessary for water resources management, crop production and environmental assessments in irrigated lands. In this study, in order to estimate ETo and because of few synoptic stations and also little recorded meteorological data in North Khorasan Province, Iran, with arid and semi-arid climate, 7 stations from neighboring provinces were used. Reference evapotranspiration was calculated using 6 different methods which required a small amount of input data, including Class A pan, Hargreaves-Samani, Priestly-Tailor, Turc, Makkink and the method proposed by Allen et al (1998) to estimate ETo with missing climate data. Besides, the standard FAO-Penman-Monteith was used (because there was no Lysimetric data in the region) to evaluate the applied formulas. Since there was no agreement over the appropriate method to calculate ETo in the selected stations, by using significance test of regression lines, a linear regression equation was computed for each month, in order to convert the best calculating method to FAO-Penman-Monteith formula. Evaluations of these equations showed their acceptable accuracy, in comparison with the previous researches, specifically for cold months (MAE values ranged from 0.3 to 1.4 mm/day).

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Hydrologic drought which usually affects wide regions can be studied through Low flow index. In this study, to predict hydrologic drought in North Karoon watershed, 14 stations with suitable and long enough duration data were recorded in the 1387-88 water year. Then 13 physiographic and climatic characteristics of the chosen stations were used to perform homogeneity test for cluster analysis. 7 day low flow series were calculated in each station and according to chi-square and Kolomogragh smirnov tests and parameter, 2 parameter gamma distribution was selected as the best regional distribution for this region. Therefore, a seven day low flow index was estimated using FREQ for 5,10,20,50,100 return periods. Regional analysis was performed using a multiple regression method. Moreover, flow duration curves were delineated to obtain Q95 index. Then, zoning maps for Q95، Q7,2 ،Q7,10, Q7,100 were prepared. The results of regional analysis indicated that the averages of height and slope were the two most effective parameters in low flow in this watershed. The investigation of zoning maps showed that southeastern part of this watershed experiences severe droughts compared with other parts.

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This study aimed to evaluate the performance of TM satellite data acquired in June 2009 to map soil salinity in southeast of Isfahan province. Ground salinity data (EC) was collected within 9 pixels, covering an area of approximately 8100 m2 using stratified random sampling technique at 53 sample sites. Spectral indices including TM bands, BI, SI1, SI2 and SI3, PC1, PC2, PC3 and also multiple linear regression modeling and maximum likelihood classification techniques were applied to the geometrically corrected image. Results of regression analysis showed that the TM band 4 had the strongest relationship with EC data (R2=0.48) and also the relationship of the modeling image using TM 3, TM 4, TM5 and PC3 was significant at the 99% confidence level. The accuracy assessment of the stratified TM4 and modeling image into five classes including 0-4, 4-20, 20-60, 60-100 and EC>100 ds/m indicated that there was more than 86% agreement with the field measurements of EC data. Therefore, it can be concluded that the discretely classified salinity maps have higher accuracy than regression methods for identifying broad areas of saline soils, and can be used as appropriate tools to manage and combat soil salinization.

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This research was conducted to prepare landslide susceptibility zonation (LSZ) map for the Dez-e-Ouliabasin using logistic regression model. For this purpose, at first, the most important factors affecting land sliding including slope, aspect, elevation, precipitation, the distance from road, the distance from fault, the distance from drainage, land use, and lithology were determined. Then, thelandslide inventory mapwas preparedby using field digital checks, GPS and satellite images. In the next step, the landslide susceptibility zonation mapwas preparedby usinglogistic regression method. According to the obtained coefficients for LSZ maps, the most important factor in the study area was elevation layer. The Receiver Operating Curve (ROC) index value was calculated (0.92), which indicates a very high level and suggests thatthe observed mass movements have a strong relationship with the logistic regression model.

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During the past few decades, the southern part of the Caspian Sea has more frequently experienced extreme climatic events such as drought and flood. Trend analysis of hydro-climatic variables was conducted using non-parametric Mann-Kendall test and regression test for Neka basin in the north of Iran.       Trends of precipitation and stream flow characteristics including maximum flow, mean flow and low flow indices were analyzed at the annual, seasonal and monthly time scales from 1358 to 1391 (34 years). Results showed a general decrease in annual and winter precipitation and decrease in daily maximum precipitation, with an increased trend in daily maximum precipitation of spring season. A decreasing trend was observed in 7-day low flow in summer for all sub-basins. Annual and monthly mean flows specifically in winter in all sub-basins decreased, but annual maximum flow increased from upstream to downstream. Land use changes showed that deforestation and urbanization increased during 34 years in the mid and downstream sub-basins. The analysis showed that low flow indices and mean flows are strictly sensitive to climate change. Overall, from hydrological perspective, these results indicate that the study region is getting dryer and facing more severe drought events. The results of this study can predict future droughts to make better decisions for irrigation planning and management of water resources.