Iranian Journal of Soil Research
Year:2019 | Volume:33 | Issue:2 |Papers Count:11

Rainfed lands located on slopes are subjected to higher water erosion types, particularly rill erosion. Rill erosion rate can be varied in various growth stages. This study was carried out to find the effect of wheat straw on soil loss resulting from rill erosion in wheat growth stages. A field experiment was done in rainfed wheat land with 10% slope and seven wheat straw levels (0, 25, 50, 75, 100, 125 and 150% of ground cover), using the randomized complete block design with three replications. The land was plowed down slope and various straw amounts were separately incorporated into the soil. A 0. 5 kg wheat straw per square meter was used in the 100% straw level. Three rills/furrows with 20-cm width and 10-m length were installed by the the planter machine similare to cultivation furrows for each straw level and rill erosion measurements were done using a discharge flow of 2 L. min-1 at five growth stages (planting, emergence, tillering, stem elongation, grain filling ). Based on the results, soil loss by rill erosion was significantly affected by straw amount (P< 0. 05). Soil loss by rill erosion in 100% straw level was the lowest amount at all wheat growth stages and was about 79% lower than that in the control treatment. There was no significant difference between this treatment and higher straw levels (150% and 125% ground cover) in rill erosion. The emergence stage in each straw level, which was associated with poor vegetation cover and higher soil water content in this period, was the most susceptible stage in the rill erosion among all growing stages. In this stage, application of 100% wheat straw equal to 5 ton per hectare could decrease rill erosion by 71% (0. 009 g m-2 equal to 0. 34 ton per hectare for each 1-h rainfall event) in the cultivated furrows.

A comprehensive assessment of agricultural soils quality is essential for making wise decision that leads to sustainable production and environmental preservation. Many studies have shown that soil quality index, based on a combination of soil characteristics, can describe soil conditions better than individual soil characteristics. In this study, to evaluate the soil quality, first, a minimum data set was made by using the principal component analysis. Then, quantitative characteristics of the soil were quantified through scoring. Finally, by weighting each attribute (through the principal component analysis), soil quality index (SQI) was obtained and converted to map by GIS software. The results showed that most of the soils (97% of soil samples) had electrical conductivity less than 2 dS/m and they were in the group of natural soils without limitation. Most of the studied paddy soils had a good pH and no special limitation. The average of available phosphorus concentration was more than the critical level (12 mg/kg) because of the presence of a number of high concentration samples, while there was a poor phosphorous situation in more than 50% of the region. Nearly 76 percent of the studied regions had total nitrogen higher than 0. 2 percent, which indicated the relative adequacy of this nutrient element in most of the land. The concentration of available potassium in most of the studied areas was less than the critical level. Mapping of the soil quality index showed that some paddy fields had poor fertility quality because of the limitation of soil intrinsic and static properties such as clay, organic carbon, and cation exchange capacity. However, most of the studied lands had moderate fertility quality and the most important limiting factor was the shortage of available phosphorus and potassium, while the use of phosphorus and potassium fertilizers was low in this area.

One of the ways to use and exploit saline lands and water is to use salinity resistant cultivars, such as Quinoa plant. Managing nutrients such as phosphorus in saline soils can reduce the negative effects of salinity on plant growth and yield. In order to investigate the effect of irrigation water salinity (ECW) and phosphorus levels on some of the characteristics of Quinoa plant in greenhouse conditions, a factorial experiment was conducted in a completely randomized design with three replications. The treatments consisted of ECW at six levels: control (0. 7), 3, 6, 9, 12, and 15 dSm-1, and phosphorus was added to the soil from the source of triple superphosphate at four levels, 0, 50, 100, 200 kg per hectare (equivalent to 0, 0. 01, 0. 02 and 0. 04 grams of phosphorus in the pot from source of triple superphosphate). The results showed that with increasing ECW up to 15 dS m-1 (equivalent to ECe= 30. 44 dSm-1), plant height decreased by 18. 65% and panicle length by 52. 4%. ECe reached 182. 72 dSm-1 and sodium concentration in plant increased by 18. 5%, compared with the control. Increasing phosphorus application to the soil up to 100 kg/ha increased plant height by 12. 3%, panicle length by 8. 8%, and phosphorus concentration in plant aerial parts by 12. 5%, compared to the control. Comparison of the average interactions between salinity of irrigation water and phosphorus on plant height showed that salinity of 3 dS/m, at all levels of phosphorus, increased the plant height by 15. 1%, in comparison with the control. Adding phosphorus up to 100 kg triple-super phosphate per hectare reduced the effect of salinity stress in the plant, but at 200 kg it exacerbated the effects of salinity and led to a sharp decrease in plant height and panicle length. The highest concentration of phosphorus was observed at 100 kg/ha phosphorus and 15 dS/m salinity, which increased by 92. 9% compared to the control. Based on the results, quinoa can be a very good choice for saline and low-yielding lands.

Phosphorus availability in soils is controlled by sorption/desorption reactions. These reactions are also affected by the physical and chemical properties of the soil. In this research, effects of organic carbon, active calcium carbonate and clay on the phosphorus sorption behavior of calcareous soils were studied along Negar-Lalezar and Baft Orzouyeh transects in Kerman Province, Iran. The phosphorus sorption in soil was determined using batch experiment and Langmuir, Freundlich and Van Hay isotherms. The results showed that, based on the coefficient of determination (0. 978) and standard error of estimate (0. 027), Langmuir model fitted well with experimental data. Maximum sorption of phosphorus (qmax) and maximum buffering capacity of P increased when the active calcium carbonate increased. Results showed that qmax increased from 655 to 1025 mg. kg-1, and the maximum buffering capacity of P increased from 114 to 243 L. kg-1 when calcium carbonate increased from 7. 68% to 18. 25%. However, qmax decreased from 701 to 535 mg. kg-1 and maximum buffering capacity of P decreased from 90 to 44 L. kg-1 with increasing organic carbon in the studied soils (from1. 74% to 7. 8%). Using the equation of Van Hay, the required standard phosphorus calculated at a concentration of 0. 3 mg P. L-1 showed a significantly positive correlation with clay (r = 0. 80**) and active calcium carbonate (r = 0. 77**) contents, but significantly negative correlations with organic carbon (r =-0. 63*) and available phosphorus (r =-0. 61*). The maximum buffering capacity and the required standard phosphorus were found in the soils of Orzouyeh and Negar regions due to their highest percentage of active calcium carbonate and clay. However, in the soils of Lalezar and Baft regions, due to the presence of more organic carbon and less active calcium carbonate, the lowest standard phosphorus requirement was obtained. It could be concluded that phosphorus sorption in soils is influenced by soil properties such as clay, active calcium carbonate, and organic carbon contents.

Rice is one of the strategic agricultural crops in Iran and provides a high percentage of dietary calorie and protein of the people. With a rapidly increasing population and the consequent increase in food demands, it is necessary to know the relationship between rice yield and soil conditions. Therefore, the objective of the present study was to evaluate the relationships between yield and soil quality (SQ) in order to provide guidance regarding proper soil management practices for the sustainable development of Goldasht region paddy fields, in Mazandaran province. One hundred and twenty-eight surface soil samples were collected to measure some physical and chemical soil properties. At harvest time, grain yield was measured at 14% moisture content. Firstly, to determine the SQI, the characteristics with the highest effect on the SQI of the region were identified by the Principal Component Analysis. Fuzzy logic method was used to convert quantitative soil properties to qualitative ranking and, finally, the indices were combined using the concept of coefficient of variation. The average recorded rice grain yield of the studied area was 3498 kg ha-1 and SQI varied between 0. 47 and 0. 97. Contrary to expectation, there was no significant correlation between performance and SQI in the studied area (reasons are explained in discussion section). According to the results of SQI classification, available phosphorous is the most important limiting factor for soil quality in the area. Direct comparison among maps of yield and soil quality indices indicate that proper soil conditions along with proper management of the farm can be an effective solution to maximize rice yield. Thus, proper management by the farmers can remedy shortcomings and somewhat inappropriate soil conditions. However, in many cases, proper soil quality alone cannot compensate for crop management shortcomings.

Soil texture is a static soil property that has great effects on soil physicochemical properties. Therefore, global demands are increasing for a high spatial resolution map of soil texture. Lack of intrinsic soil data can lead to wrong policies regarding management and degradation of soil and water resources. Iran has many scattered soil data that have been collected at great cost. These data can be useful in a wide range of applications if presented accurately in digital map format. In this study, Ordinary Kriging, Pixel-Based Classification (PBC), and Inverse Distance Weighted (IDW) methods were investigated using 4665 soil surface samples collected from croplands and orchards to map Guilan soil texture groups (fine, medium and coarse) and soil mineral particles. MBE, NRMSE, KIA, R2 and Pa statistics were used for verification. The results indicated that IDW could provide higher accuracy for clay (R2 = 0. 64 and NRMSE = 0. 22) and sand (R2 = 0. 67 and NRMSE = 0. 25) particles prediction, but PBC had higher accuracy for predicting fine, medium and coarse soil texture groups according to KIA and Pa of 0. 46 and 0. 73, respectively. However, superiority of PBC was minor (KIA = 0. 43 and Pa = 0. 71) compared to Ordinary Kriging. PBC used auxiliary soil data as inputs for Artificial Neural Network to predict soil mineral particles of unvisited pixels. For more certainty regarding efficiency of PBC in predicting soil texture groups, it is recommended to test the mentioned methods in areas with more physiographic diversity.

While land resource management needs detailed and accurate information about soil properties and distribution, this kind of data is limited in Iran. In this research, we tested performance of three digital soil mapping (DSM) approaches including Multiple Linear Regression (MLR), Cubist (CU) and Random Forest (RF) to map the spatial 3D distribution of soil organic carbon (SOC) in Saadat Shahr plain in Fars Province. Latin hypercube sampling (LHS) was used to determine locations of soil profiles in the field. The soil profiles were sampled and SOC was measured. Different environmental covariates including terrain attributes, remote sensing auxiliary variables, and maps of soil, geoform and distance from rivers were used in this research as auxiliary data. According to the link of the environmental covariates and soil organic carbon contents in the framework of each model in combination with equal-area spline algorithm, soil organic carbon maps were produced at five standard depths of soils in the whole study area. Model performance was evaluated by root-mean-square error (RMSE), mean error (ME) and normalized root-meansquare error (NRMSE). Among the used models, RF model showed the highest performance to predict organic carbon in depths of 0-5 and 60-100 cm. Meanwhile, MLR and CU had the lowest error for prediction in depths of 5-15 and 15-30 cm, respectively. In spite of these results, RF model was considered as the best model for its power to explain the spatial distribution of soil organic carbon in all soil depths in the study area.

Increasing aridity caused by the drought phenomenon in recent years has led to a decrease in the threshold wind speed for removing dust particles from the soil surface of arid areas. The accumulation of salts on the surface of the soil, due to low precipitation and high evaporation, causes disintegration of the aggregates. Salt particles along with the soil particles are removed by wind and they fall over a distance. If the target area is agricultural land, dust containing salt results in the transfer of salt to the affected area, which leads to a decrease in the yield of the crops. In this research, EM38 electromagnetic induction instrument was used in horizontal orientation in order to provide a cheap and fast map for surface soil salinity (0-30 cm) in the area that are sensitive to wind erosion in northern Yazd-Ardakan plain. The ESAP-95 2. 35R Software package was also used to determine the number and location of soil samples in order to determine the regression model and to prepare a map of the salinity pattern in the area. With the transfer of EM38 data to the ESAP-RSSD program, the standard number and soil sampling points for 12 sites were identified. Subsequently, a Multiple Linear Regression model (MLR) was developed using ESAPCalibrate and using Electrical Conductivity data of the saturated extract (ECe), saturation percentage (SP) and percentage of water content (WC). The results showed that the proposed regression model could model the soil surface salinity from EM38 readings with a determination coefficient (R2) of 0. 37 and Root Mean Square Error (RMSE) of 80. 55. The results of using the ESAPSaltMapper program for mapping soil surface salinity showed that the southeast of the study area had higher salinity and the possibility of harvesting salt by the wind was much higher. The results also showed that the use of the EM38 device should be made in areas with homogeneous and standardized moisture at field capacity level and ESAP software can be used to map soil salinity.

Limitations in conventional soil identification methods and the advances made in information technology in soil science have attracted more attention to new approaches to soil mapping in order to improve the purity of soil maps. The present study was carried out in a part of Ilam province to identify and prepare soil maps of this region. At first, 46 profiles were identified. Then, based on the morphological characteristics of each profile, soil samples were taken from all genetic horizons and analyzed for chemical and physical properties. Then, the soils were classified based on the Soil Survey Staff keys (2014). A multinomial logistic regression model was used for spatial prediction of soil taxonomic classes. The geomorphometric features were extracted from digital elevation model with a resolution of 30 m2 by SAGAGIS2. 2 software. The classification results of each soil control profile in the studied area showed that, in general, the soils were in three order categories: Mollisols, Inceptisols, Entisols, and six classes at the family level. The correlation between the features of digital elevation model showed that the parameters of the mid slope position, spatial solar radiation, index of moisture content, ground roughness index, surface curvature, and profile curvature had the most effect on the formation of soil family classes. The overall accuracy and Kappa index of spatial prediction map from the regression model was 60% and 0. 38 at the familial level, respectively. Finally, the results of this study showed that geomorphometric variables had a significant influence on the prediction of soil classes. Therefore, it is suggested that in future studies, other covariates derived from remote sensing data should also be used to improve the quality and accuracy of soil maps.

To manage the positive and negative aspects of application of nanomaterials to natural systems, it is necessary to know the distribution and fate of these materials in such systems. In this regard, the nanoparticle background concentration is one of the factors affecting the transfer process. In this study, in order to investigate the effect of background concentration on the transport of titanium dioxide nanoparticles, transport of TiO2 nanoparticles was first investigated in undisturbed soil columns under different flow rates. The flow rates were equal to the saturated hydraulic conductivity (Ks), 0. 9 Ks, 0. 7 Ks, and 0. 5 Ks (unsaturated flow) applied by peristaltic pump (BT100-1F) to the different soil columns. Then, in order to investigate the effect of the first experiment (background concentration after the first experiment) on subsequent experiments, in a column after the saturation flow test and measuring the outflow and determining the concentration of TiO2 nanoparticles as a function of time, flow rates at unit volume of 540, 420, and 300 μ L/min, respectively, are 0. 9, 0. 7 and 0. 5 times the saturated hydraulic conductivity, respectively. Parameters explaining the transport of nanoparticles using measured data of breakthrough curves based on one-site sorption model and one kinetic site sorption model were estimated. At 540 μ L/min, the amount of TiO2 nanoparticles in outflow from the column was lower relative to the absence of the background concentration due to the increase in the concentration of nanoparticles and, therefore, the possibility of more collisions and formation of larger aggregates that caused trapping (straining) them in the pores of the soil. By decreasing the flow rate from 540 to 420 and then 300 μ L/min, there was no background concentration in the soil column due to the increase of the nanoparticles in the soil column and the lack of sorption site for more nanoparticles were introduced into the outlet from the column. Therefore, due to the effect of TiO2 NPs background concentration on the transfer of these particles in the soil, it is necessary to determine their background concentration in the contaminated soil and water where TiO2 NPs are used for remediation of contamination. Also, effect of background concentration on the transfer process depending on the influent flow rate should be considered. In the one kinetic sorption site model, taking into account the detachment coefficient of TiO2 nanoparticles, the results of estimation the nanoparticles transport through soil column were significantly improved (R2>0. 89, ME, and RMSE were also much lower than the one site sorption model at all flow rates).