JOURNAL OF WATER AND SOIL RESOURCES CONSERVATION
Year:2018 | Volume:7 | Issue:3 |Papers Count:9

Simulation models that illustrate the effects of water on crop yield are useful tools to optimize water productivity and improve farm level water management. In this study, the performance of Aqua Crop model to simulate canopy cover, biomass and yield of the sugar beet and soil water content under six irrigation treatments was evaluated. The irrigation treatments were consisted of full irrigation (T1) as control, removing last irrigation (T2), applying irrigation water at 10% greater than control (T3), applying irrigation water at 10 (T4), 20 (T5) and 30% (T6) less than control. The experiment was conducted at Aleshtar in Lorestan province during growing seasons of 2014 and 2015, using a randomized complete block design. The first and second year’s data were used for calibrating and validating of the model, respectively. Evaluation of the model was performed using the coefficient of residual mass, root mean square error, normalized root mean square error, index of agreement and coefficient of determination. According to the results, the normalized root mean square error in the canopy cover and soil water content simulations for calibration was 5.18 to 9.41 percent and 9.91 to 17.23 percent, respectively and for validation was 6.64 to 9.2 percent and 12.36 to 25.77 percent, respectively. Also, the normalized root mean square error in the yield and biomass simulations for calibration was 7.3 and 8.67 percent and for validation was 7.69 and 9.82 percent, respectively. The results indicated a good performance of the Aqua Crop Model in simulating the canopy cover development, biomass and yield of sugar beet and soil water content under different irrigation managements. Therefore, the Aqua Crop model can be used to explore management scenarios to improve the sugar beet water management over the study region.

A pot experiment carried out to study the effects of date palm leaves bio char on WUE and some other performance components of wheat (Triticum aestivum L.) under conjunctive water and salinity stress conditions. The experiment was factorial based on a randomized complete design (RCD) with three replications during one growing season (2015-16). The treatments included bio char application rates (0 and 3% w/w), irrigation interval levels (3, 7 and 12 days) and water salinity levels (1.1, 3.8, 8 and 12 dS/m). The results showed significant (P<0.05) effects of bio char on wheat WUE and yield components under conjunctive water and salinity stress conditions. Mean values of grain yield (GY), shoot dry weight (SDW) and root dry weight (RDW) increased 22%, 38% and 20% respectively, due to bio char addition. Also, mean values of evapotranspiration (ET), water use efficiency (WUE) and soil gravimetric water content before irrigation increased 18.6%, 8.7% and 23% respectively. Values of crop yield response factors to water stress (Ky) for GY, SDW and RDW noticeably decreased in bio char treated pots, indicating reduced crop sensitivity to water deficiency. In addition, bio char addition reduced the slops of yield reduction and thresholds of soil solution salinity for GY, SDW and RDW indicating the increased crop tolerance to salinity. Therefore, bio char of date palm leaves, might be an effective conditioner to improve crop performance and water productivity particularly under water and salinity stress, although, further studies under field conditions is recommended to verify these results.

Estimating the time of concentration is one of the most important concepts in physiographic and hydrologic studies in watersheds, and relatively correct estimation of it greatly influences calculation of hydrologic parameters, especially flood peak discharge. Construction of check dams in watersheds leading to residential areas is one of the strategies for changing time of concentration and reducing channel slope, which are substantially effective in controlling floods and reducing flood peak discharge. This research intended to evaluate the effects of corrective structures on hydrologic behavior of watersheds based on flood index. In this study Changes in time of concentration and flood discharge were compared between 1384-1389 years from construction of corrective structures by studying the region, topographic conditions, and protective measures. The return period between 1.25 to 100 years was selected. The parameters of the numbers and heights of the check dams, hydrologic soil groups, channel slopes, time of concentration, regional hydrology, and area of each watershed parcel were studied for this comparison. Results indicated that construction of corrective structures increased time of concentration in I28, I13, I12, I10, I9, I8, I6, I4 parcels amount of 4, 8.74, 2.68, 1.43, 1.31, 2.01, 0.51 and 11.78 respectively. The lowest percentage increase in the concentration of Parcel No. I13 with a 1 percent. The maximum time to focus on I28, with 11.78 percent.

Rice is the main product of northern provinces of IRAN. Rice, as a strategic product that plays an important role in agricultural planning, needs to monitor annual production and crop area. Today, management practices can be improved using remote sensing technology and vegetation indices. The present study was conducted in order to determine the regression correlation between local and high-yielding varieties of rice in 2012, with NDVI, SAVI, DVI and RVI indices extracted from landsat7 ETM+images, and to identify the most appropriate index in paddies Shaft. The highest coefficient of determination between yield and indices belonged to the flowering period, among these indices, NDVI is the most suitable indices for estimating the yield of local and high yielding rice varieties in the region. The statistical criteria show a good ability to simulate the model. Modeling efficiency (EF) were for local and high yielding varieties in crop year 2013 to the equivalent of 0.60 and 0.41 respectively, which represents a high efficient model in predicting product yield is acceptable percent. Modeling results indicate that the simulated yield level is well fitted with observed values. The results of the statistical test show that there is no significant difference between the observed and simulated values (P>0.05). The correlation coefficient of determination 0.70 and 0.66 between the observed and estimated yields in the evaluation of the models might be related to the changes in planting conditions and agricultural management, plant pests and diseases, climatic condition of the area and multiple local variables.

The Correct management watersheds is one of the most important methods is the optimal use of soil and water resources. To do this, it’s need for comprehensive information of different methods administrative management. Recent decades, correct and timely assessment of quantitative and qualitative runoff is considered one of the concerns of the country's macro management. This project is modeling and evaluation and efficiency of the SWAT model to simulate the monthly runoff, sensitivity analysis, optimization of critical parameters, to evaluate the effect land use during the past four decades on Quantity of Maroon River watershed at the departure station (Idanak) in Kohgiloyeh and Boyerahmad. For this purpose, were used from semi-distributed SWAT model and SUFI2 program in the form of bundles SWAT CUP for sensitivity analysis, calibration, validation and uncertainty analysis. In this research first were prepared changes in land use map available as well as soil and vegetation in the watershed Maroon map within four ten-year period from 1980 to 2010 by using TM and ETM sensor of Landsat satellite. Observations Data of hydrometric Idenak station as base station was used during 1970 to 2010 in four ten-year period to observe changes in runoff during the calibration and validation. Compare the effect of land use management options on the different components of the hydrological cycle shows different amounts of runoff. Shows a the pessimistic scenario occurred in four decades. With continuing damage to the state in land use regression various amounts of runoff increases and decreases permeability and gullies on the surface and deep aquifers.

oil structure is of great importance from both crop production and water resources management point of views. Since soil structure is often expressed qualitatively, the so-called fractal geometry, as a novel method, can be used to describe the soil structure in a quantitative manner. Using fractal concept and its comparison with the classical aggregate stability methods can assist to better understanding of soil structure. This research was aimed to quantitatively assess the soil aggregate stability by using some fractal and classical models. To attain this purpose, a number 30 soil samples were collected from topsoil of an agricultural area. Then, the mean weight diameter MWD and geometric mean diameter GMD of soil samples were determined by using wet and dry sieving method. The fractal dimensions of soil samples were determined for four fractal models including the number-size and mass-size of Rieu and Sposito, number-size of Mandelbrot, and mass-size of Tyler and Wheat craft. Results indicated that the range of fractal dimensions for mass-size model of Rieu and Sposito in dry condition varies from 2.86 to 2.92 and in wet condition from 2.90 to 2.99., this range for Tyler and Wheat craft model was 2.52 to 2.78 and 2.24 to 2.55, for dry and wet conditions, respectively. Results further showed that for the number-size model of Rieu and Sposito the fractal dimension varied from 2.77 to 3.59 in the dry and from 2.35 to 3.18 in wet conditions. These ranges for Mandelbrot model were obtained to be 2.89 to 3.72 and 2.21 to 3.22 for the dry and wet sieves, respectively. The largest standard deviation was obtained for MWD, while the lowest belonged to the mass-size model of Rieu and Sposito. The obtained results further indicated that by increasing the fractal dimension, aggregate stability decreases and aggregate instability tend to increase. It can be then concluded that by using fractal dimensions one can more precisely describe the aggregate stability compares to the classical methods.

In this research evaluated production functions has been modified by Rase, 2004 And Taftehet al.2013, this methods evaluated to estimate rapeseed yield under deficit irrigations with in the monthly intervals, quadruple growth stages and total growth period in the Qazvin plain. For this purpose, randomized complete block design was applied. The irrigation treatments (60-90-120-150 mm evaporation from pan class A) with three replications were used during 2010 - 2012. The results showed that Treatments T11 and T12 with 2390 and 1320 yield (kg/ha) respectively have a maximum and minimum yield. Also results show that the best estimation was occurred in the total growth stage and other intervals were decreased the accuracy of the models. So the minimum error was in the total growth stage in the both methods. Statistical analysis shown that Tafteh et al. (2013) method in the total growth stage has the lowest root mean squared error (RMSE) and Normalized root mean squared error (NRMSE) respectively equal to 423 and 0.211. Also this method has a maximum of agreement index (d) and correlation coefficient (R2) Respectively equal to 0.76 and 0.645. Therefore Tafteh et al. (2013) method is suitable way for estimation of rapeseed yield in deficit irrigations.

One of the most challenging quantities in analyzing the transfer of contaminant in the porous medium is the determination of longitudinal dispersivity. In this research, the effects of the porous media particle shape on the longitudinal dispersivity were investigated by experiments on three column of soil (broken texture, river sand, spherical texture). Flow and contaminant (NaCl) were injected at five levels of velocities and the Breakthrough curve was extracted at five points along the column by Self-Potential Method, Then, by simulating the results with FEFLOW software and the inverse analysis with CXTFIT2 software, longitudinal dispersivity was determined. The results indicate that, for constant velocity, the medium with spherical texture has a longitudinal dispersivity greater than the other two medium and in the sandy medium it is more than the broken texture. Thiscan be due to the increase in the contact surface as well as the path’s tortuosity, with the particle coming out of the spherical shape. With increasing velocity, the longitudinal dispersivity decreases in all three medium. Also, as the particles are closer to the spherical shape, the scale effect will be greater, and with increasing velocity, the decrease in the longitudinal dispersivity in these medium will be less.

Saturated hydraulic conductivity (Ks) is one of the essential input for water flow and solute transport modelling, irrigation and drainage design, groundwater modeling and environmental processes. Direct measurement of Ks is possible, but that is usually time consuming, tedious, expensive and impractical for larger scale studies. Also, these methods are partly unreliable because of soil heterogeneity and experimental errors. One solution to govern this problem is using indirect methods such as pedo-transfer functions (PTFs). Since PTFs have not yet been developed to soils in the study area, this study evaluates and describes neural network and statistical regression PTFs to predict Ks from limited or more extended sets of the readily available soil properties. For this purpose, Ks from 95 points of Sirwan-Chardawel sub-basins in Ilam province were measured using Guelph permeameter. Also, some of the readily available soil parameters were obtained. The accuracy and reliability of the derived PTFs were evaluated using root mean square error (RMSE), mean error (ME) and Pearson correlation coefficient (r). The highest correlation coefficients of 0.58 and 0.56 were found between Ks and geometric mean particle diameter and sand content, respectively. The results indicated that artificial neural network and regression PTFs can predict Ks with relatively good accuracy even if a few readily available soil properties are measured (rR-val=0.85, RMSER-val=6.81 mm/hr and rANN-test=0.87, RMSEANN-test=10.80 mm/hr). However, based upon results, the prediction accuracy of ANN model at both training and testing stages increased if more readily available soil properties are used (rtrain=0.92, RMSEtrain=4.36 mm/hr and rtest=0.89, RMSEtest=7.17 mm/hr). In general, it was found that ANNs method had better performance than linear regression model in predicting Ks.