Iranian Journal of Soil Research
Year:2017 | Volume:31 | Issue:1 |Papers Count:14

In order to study the effects of nitrogen application management on nitrogen use efficiency (NUE) in rainfed wheat, a field experiment was conducted as split plot experiment, based on randomized complete block design, included five rates of nitrogen (0, 30, 60, 90 and 120 kgN ha-1) using urea (labeled with 8% and 6% atom excess 15N) as the main plot in Kurdistan province during 2012-13. The sub-plots were consisted of three application times such as T1: Total nitrogen in the fall, T2: 2.3 nitrogen in the fall+1.3 nitrogen in the spring, and T3: 1.2 nitrogen in the fall+1.2 nitrogen in the spring (at tillering stage). The isotopic (15N) technique was applied to estimate fertilizer N recovery efficiency based on recovery of labeled urea-15N in the aboveground portion of wheat (grain and straw). Results showed that the effects of nitrogen timing on grain yield and total N uptake were significant at 5% level. The effect of nitrogen rates were significant on grain yield, total N uptake, fertilizer N uptake, fraction of N in the plant derived from the15N-labeled fertilizer (Ndff%), and NUE of 15N (p<0.05), and grain protein content (p<0.05).Compared with the check, nitrogen timing increased grain yield and grain protein content by 35% and 33%, respectively. By increasing nitrogen from 30 to 120 kg.ha-1, fraction of Ndff % was increased from 11% to 39%, corresponding to NUE range from 21% to 28%. The maximum NUE (33%) was obtained in 90 kg N.ha-1. About 70% of nitrogen uptake in the aboveground portion of wheat was belonged to grain wheat. Optimum grain yield with maximum NUE in different nitrogen application timings of T1, T2 and T3 were estimated at 69, 60, and 51 kg N.ha-1, respectively.

Regarding the necessity of increasing organic matter in agricultural land (through application of crop residues to the soil) and due to net immobilization and temporary shortage of nitrogen sources in the soil, conducting more research on utilization of N resources is inevitable. In order to evaluate the effects of nitrification inhibitor DMPP on soil nitrate and ammonium accumulation and wheat yield production and fertilizer use efficiency, a greenhouse experiment was carried out in a factorial randomized complete block design (CRD) in three replications during two consecutive years (2014-2015). The first factor was three levels of wheat straw (zero, five and ten tons per hectare) and the second factor was three fertilization treatments (nofertilizer, labeled 15N ammonium sulfate, and 15N fertilizers containing nitrification inhibitor). The results showed that DMPP could delay conversion of ammonium to nitrate for more than a month. The use of wheat straw (treated with ammonium sulfate) reduced yield and nitrogen fertilizer use efficiency. But, the addition of DMPP (mixed with ammonium sulfate fertilizer) changed the above process and led to increased yield and nitrogen fertilizer use efficiency. By the application of DMPP inhibitor, ammonium concentration in the soil (derived from fertilizer) increased and, consequently, soil microorganisms benefitted from ammonium nutritional source. Considering that absorption of ammonium is preferred by these microorganisms, it seems that presence of large amounts of ammonium affect microorganisms growth and, consequently, their competition with plant will decrease. The outcome of the above actions is over in favor of the plant and reduction in yield and nitrogen use efficiency (due to the net immobilization) partially adjusted (or neutralized).

A three-year study was carried out to evaluate the effects of different levels and times of nitrogen (N) application on qualitative and quantitative yield of almond (Prunus dulcis Mill.), cv."Mamai". The experiment used a factorial arrangement in a randomized complete block design, with three replications.The treatments of this research consisted of four rates of N (N1=150, N2=300, N3=450 and N4=600 gram net N per tree, from ammonium nitrate) and four times of N application (T1=one, T2=Two, T3=Three and T4=Four splits).The analysis of variance results revealed that the effect of year was significant on 100 fruit weight, tree diameter, initially and finally fruit set percentage, and tree yield. The effect of nitrogen rate application was significant on the studied parameters. The maximum rate of 100 fruit weight, tree yield, 100 kernel weight, initial and final fruit set percentage, and protein percentage were obtained from N3 and were, respectively, 403.6 g, 3.78 kg, 120.5 g, 34%, 23% and 7.20%. The maximum of branch length, branch diameter and tree diameter were, respectively, 31.5, 0.92, and 7.8 mm which were obtained in N4 treatment. Different times of N application showed a significant effect on the studied parameters. The maximum of these parameters were obtained from T3. The interaction effect of rate and time of application was significant on 100 fruit weight, 100 kernel weight, tree diameter, and protein percentage. The maximum of the studied parameters was obtained from N3T3. Considering the results of this experiment, the N3T3 treatment is suggested for almond orchards of Chaharmahal-va-Bakhtiari province and regions with the same soil and climate condition.

Proper exploitation of available resources in agriculture is possible by examining relationships between plants, soil, and environmental factors. Effective strategy for the development of sustainable agriculture requires maps of the spatial variability of soil nutrients and plant distribution. In this context, the present study is to evaluate the spatial structure with Semivariogram models in nutrient: P, K, Zn, Cu, Mn, and B in the soil and leaves as well as leaf nitrogen and organic carbon in the soil in vineyards of Khodabande in Zanjan Province. After preparing the Best Semi-variogram model, zoning maps were prepared by using geostatistical methods such as Kriging and Co-Kriging also IDW (powers: 1 to 5) and Kernel model of the interpolation methods. Results showed most of parameters in soil and the leaves in vineyards had high coefficients of variation. Soil content of Mn and P in leaves samples had the highest correlation radius. The nutrients in soil samples had higher average correlation radii than those of leaves. The results of the evaluations by geostatistical and interpolation methods with root mean square error (RMSE) values, mean absolute error (MAE) and efficiency coefficient showed that the Co-Kriging had the best performance for estimating hydraulic properties and Co-Kriging for soils and vineyards leaves nutrients. The best estimates were obtained with Co-Kriging in soil Zn and Kernel method showed similar results for leaves Zn. The results of the spatial variation of soil and leaf nutrients in the area showed that most of the vineyards were deficient in soil organic carbon, phosphorus, iron, and zinc.

To evaluate the effect of different sources of sulfur and manure inoculated with Thiobacilluson sulfur and phosphorus availability in the soil and corn grown in a calcareous soil, a greenhouse experiment was conducted based on randomized complete block design with 3 replications. The treatments included S (elemental sulfur), SIB (domestic bentonite sulfur), SDB (foreign bentonite sulfur), S+M (elemental sulfur+manure) and IN (control) at 3 levels of fertilizer, i.e. S0, S1 and S2 (0, 25 and 50 mg S.kg-1 soil), respectively. After imposing the treatments, corn seeds were planted and harvested after 8 weeks. The results showed that S+M and S treatments significantly increased soil sulfate and phosphorus availability compared to the control. The main effect of all treatments increased corn plant yield and sulfur uptake as well as phosphorus, chlorophyll content, and leaf area index compared to the control. The results showed that the use of sulfur along with an organic matter source in the calcareous soil sample used in the experiment significantly (P<0.01) increased the availability of soil phosphorus and sulfur, and improved growth of corn.

Soil saturated hydraulic conductivity (Ks) is one of the important factors involved in water, soil, and agricultural sciences. Ks measurement is important for solute and water movement modeling and, in turn, is costly and time consuming. It is also impractical to spatially and temporarily measure the Ks in large scale studies. Therefore, it would be wise to predict Ks using indirect methods such as pedotransfer functions (PTFs). The objective of this study was to use the regression and artificial neural networks methods as an alternative method to estimate the saturated hydraulic conductivity. Therefore, 80 undisturbed soil samples in three replications were collected in Mazandaran province, northern Iran, and analyzed by laboratory methods. Data was divided into two categories including the training (80%) and testing dataset (20%). In order to predict the soil saturated hydraulic conductivity, the multiple linear regression models (MLR), multilayer perceptron (MLP) and radial basis function (RBF) methods were used. To test the performance of the three methods, the correlation (R2), mean square error (RMSE) and consistent correlation coefficient (CCC) statistics between actual and predicted values were measured. The results showed that MLP with two hidden layers by sigmoid activation function was the best method for Ks estimation. R2, RMSE and CCC statistics were 0.871, 1.02 cm/h and 0.869, respectively, for the best predicted method. The sensitivity analysis showed that the soil bulk density, pH and porosity had the highest impact on Ks, while soil salinity affected the Ks slightly. Therefore, use of MLP with two hidden layers efficiently can predict Ks in the study area and could be introduced as a promising method for Ks estimation. Considering the slightly low sampling data, this research can be considered as a starting step for future comprehensive studies with high intensive sampling sites that would enhance the reliability of these results.

Arsenic (As) is one of the heavy metals whose contamination of soil and groundwater is common and disturbing. Therefore reducing the concentrations of labile fractions of arsenic is very important, because these fractions are bioavailable. This study was carried out to evaluate the effects of hematite nanoparticles (a-Fe2O3) and maleic anhydride - styrene - acrylic acid copolymer on motility of arsenic in contaminated soils and arsenic distribution in different fractions in soil. For this purpose, an experiment was conducted using a completely randomized design and three replications. Two types of adsorbents including hematite nanoparticles and maleic anhydride- styrene- acrylic acid copolymer were applied at the rate of 0.2 percent in a soil contaminated with 96 mg As/kg. The structure and properties of the nanoparticles were determined using x-ray diffraction, scanning electron microscopy, and transmission electron microscopy. The effects of adsorbents on arsenic mobility and distribution in soil were assessed by sequential extraction method and using ICP devices. Imaging of hematite nanoparticles showed that the mean diameter of the particles was 32.69 nm and their morphology was spherical. The results showed that the effects of adsorbents on the concentrations of non-specifically and specifically sorbed arsenic, amorphous and poorly-crystalline hydrous oxides of Fe and Al, well-crystallized hydrous oxides of Fe and Al and residual phases were significant. Application of adsorbents decreased the concentrations of non-specifically and specifically sorbed arsenic and hematite nanoparticles had more efficiency. The reduction in the concentrations of non-specifically sorbed (available) arsenic at the application rate of 0.2% of the hematite nanoparticles and acrylic copolymer were 65.31% and 62.54%, respectively. A sharp increase was observed in the concentrations of poorly and well-crystalline hydrous oxides of Fe and Al by application of hematite nanoparticles. Also, the concentration of residual arsenic sharply increased by application of acrylic copolymer. Hematite nanoparticles and acrylic copolymer immobilized arsenic in the soil by changing its distribution.

In the recent decades, application of geostatistic for mapping salinity and sodicity of soil and investigation of their changes has developed. The purpose of this study is to use Indicator Krijing to make probability maps of soil salinity and sodicity. In order to do this, in 178 points of the study area, 356 soil samples from two depths, i.e.0-30 and 30-60 cm, were taken in Ghorveh soils, Kurdistan Province, using hypercube method. Then, electrical conductivity, pH, Na, Ca, Mg, and Sodium Adsorption Ratio characteristics were measured. Using Indicator Krijing, probability maps of soil salinity and sodicity were prepared for both depths based on two threshold indices of 4 dS/m for salinity and 13 (mmoll-1)0.5 for SAR. Results showed that probability maps of 0-30 cm depth for soil salinity and sodicity, respectively, with 0.53 and 0.94 Kappa index, had moderate and excellent accuracy levels, while in 30-60 cm depth, with 0.64 and 0.8 Kappa index, respectively, had good and excellent accuracy levels. Central part of the area had higher probability of salinity and sodicity compared to the other parts. This part of the area had lowland physiography and somewhat unsuitable water table near the soil surface.

A novel method of monitoring soil pollution by heavy metals is using magnetometry as a cost-effective, simple, efficient, and fast method. In the present study, the potential of the magnetometric method to monitor the plant nutrient elements such as iron, manganese, zinc, and copper in the bioavailable fraction was evaluated. Also, their relationship with the greenhouse soil physicochemical characteristics of two towns of Isfahan Province (Dehaqan and Shahreza) were investigated. A total of 40 topsoil samples were collected (0-20 cm depth) from the study area in summer 2014 and the magnetic susceptibility (c) of the collected samples was measured at both low and high frequencies (clf and chf) using the Bartington MS2 dual frequency sensor. The results indicated that there were no significant correlations between the soil magnetic susceptibility and the bioavailable forms of Cu, Zn, and Mn in the whole of the study area. This could be probably due to lack of penetration of these elements into the lattice structure of the ferromagnetic materials. The negative correlations between magnetic susceptibility and Fe showed that the increase of the bioavailable concentration of Fe is not necessity related to its high contents in the soil magnetic structure, so, it would not affect magnetic susceptibility positively. In addition, there was positive significant correlation between the magnetic susceptibility and clay and silt content as well as a negative significant correlation with sand content. Also, the low values of soil magnetic susceptibility in the bioavailable fraction (30.30±7.89 10-8 m3 kg-1) reflects the dominant effect of the bedrock and the inherent characteristics of soil on metals concentrations in the region. Based on this study, it can be concluded that in order to monitor the heavy metals in the bioavailable fraction of soils, and also in areas that are not affected by certain sources of pollution, monitoring techniques based on magnetic susceptibility do not have high efficiency.

Competition between potassium and ammonium fixation was studied in some soils of the northern and southern Khuzestan province. Samples were taken from 0-30 and 30-60 cm depths in cultivation systems such as sugarcane, rotational cropping, date palm, and uncultivated lands. Then, 0, 50, 100, 200, 400, 600, 800, 1000 mg kg-1 concentrations of potassium and ammonium were added to these soil samples. The statistical analysis showed that with increasing concentrations from zero to 1000 mg kg-1 potassium and ammonium for all treatments, potassium and ammonium fixation increased in all systems and depths. In cultivated system, ammonium was fixed from 2007.50 to 1952.08 mg kg-1 in the surface and subsurface layers, respectively, while uncultivated land fixed 1880.62 to 1941.87 mg kg-1 ammonium. In contrast to ammonium, the same trend was not observed for potassium. Ammonium has also more tendency to fixing than potassium due to the presence of smectites and depleted illite clay minerals in these regions. The correlation coefficients between physicochemical properties and fixation showed that organic matter and clay content had negative effect on these cation fixations, but these effects were not statistically significant. Also, soil pH, the amount of lime and sand had insignificant and negative effect on ammonium fixation -0.034, -0.029 and -0.021 and cation exchange capacity and silt percentage show negative effect that was insignificant at -0.068 and -0.017, respectively.

Investigation of heavy metal distributions in environment is vital for the remediation of contaminated soils and environmental monitoring. In this study, the Pb-concentration variation in surface soils surrounding the Dezful-Ahvaz highway and the soil parameters influencing it were investigated. For this purpose, soil sampling was performed from depth of 0-10 cm of adjacent lines with the highway by collecting three identical samples at intervals of 15, 40, and 100 m from the road side (a total of 135 samples). Then, the total Pb-concentration, organic matter, calcium carbonate equivalent (CCE), pH, electrical conductivity (EC), and soil particle size distribution (clay, silt, sand, fine sand, and very fine) were measured in each soil sample. Modeling analyses were performed using support vector machine (SVM) and multiple linear regression (MLR) methods. To investigate the model performances, some statistical indicators including the correlation coefficient (r), root mean square error (RMSE), and model efficiency factor (MEF) were calculated between the measured and the predicted values. The variable selection results using the SVMs method indicated that the CCE was the most effective factor on Pbadsorption by soil particles at the distance of 0-15 m from the road side and the clay % and pH (followed by sand %) parameters had the highest importance coefficients at the distances of 15-45 and 45-100 m, respectively. The r coefficient and MEF values for estimation of Pb using the SVM model were 0.86 and 0.70 %, while they were 0.62 and 0.38 % for the MLR models. Therefore, according to the obtained results in this study, it appears that it would be possible to use support vector machines for determining the factors influencing the Pb- adsorption by soil surrounding the road sides and for modelling heavy metal concentration variations in soils.