Iranian Journal of Soil Research
Year:2023 | Volume:37 | Issue:2|Papers Count:6

Tissue analysis is a useful tool for evaluation and optimizing nutrients for sugar beet using Deviation from Optimum Percentage (DOP) and Compositional Nutrient Diagnosis (CND) methods. In order to evaluate the nutritional status of sugar beet farms in Kermanshah Province, this project was conducted in two cropping seasons (2018-21). In each year, 30 different farms with different ranges of soil properties were selected in each region. The project database for the first and second seasons was completed and analyzed and DOP and CND indices were calculated for each nutrient element. Compared with the critical nutrient concentration in Kermanshah Province, the results showed that 85%, 10%, 97. 5%, 67%, 93%, 100%, and 5% of farms were deficient in phosphorus, potassium, iron, manganese, zinc, boron and copper, respectively. The average sugar beet yield in this region was about 67. 6 t/ha. Selected sugar beet farms were divided into two groups of high and low yields. The results of CND and DOP indices in the Kermanshah province showed that Mg, P, N and K deficiencies were more prevalent among macro-elements and Fe, Mn and Zn deficiencies were observed among micro-elements. However, there was a difference between the two methods in terms of the content of N, Mn, and Fe among the deficient elements. There was a significant correlation (0. 48) between nutritional balance index and yield at 1% probability level. The results obtained in this study can be used to increase the yield and improve the quality of sugar beet in each of the regions.

In order to evaluate the effect of iron and zinc on maize grain yield and the contribution of current photosynthesis and photosynthate redistribution under water stress conditions, this study was conducted in two seasons (2018-2020) in research farm at northwest of Ahvaz. The experiment was conducted in split split-plot design with randomized complete block design with three replications. Main plots consisted of water stress at three levels including complete irrigation, cut-off irrigation at vegetative growth stage (CI-VGS), and cut-off irrigation at early seed stage (CI-ESS). Sub-plots consisted of foliar application of zinc sulfate with three concentrations (0, 0. 5 %, and 1 %) and sub sub-plots received foliar application of iron sulfate at three concentrations (0, 0. 3 % and 0. 6 %). The results showed that cut-off irrigation at CI-ESS decreased grain yield, biological yield, current photosynthesis rate, and current photosynthetic contribution, significantly (p<1%). But the redistribution of storage materials was the highest in this treatment. Also, 1000-grain weight in the CI-ESS was 26. 9% lower than that of complete irrigation. The highest biological yield and current photosynthesis contribution were obtained by leaf foliar application of iron sulfate. Foliar application of zinc sulfate with 1% concentrations in CI-VGS resulted in significant increase in grain yield of 33. 7%, a 7. 6% share of current photosynthesis and a decrease in the redistribution of storage materials. The highest grain yields (7062 and 6735 kg. ­ha-1) and current photosynthesis rate (536. 6 g. m-2) were obtained in the foliar application of 1% ZnSO4 and 0. 6% iron sulfate. In general, water stress reduced the contribution of current photosynthesis and, consequently, decreased grain yield of maize cv. 704 hybrid, which could be compensated by foliar application of 1% zinc sulfate and 0. 6% iron sulfate.

Fodder production for livestock using highly saline soil and waters is a part of halo-culture projects. So, the present study aimed to elucidate the effect of nitrogen fertilizer amount on some fodder characteristics and performance of salicornia (Salicornia persica Akhani) irrigated with the Persian Gulf seawater with the electrical conductivity of 64. 5 dSm-1. A field experiment with four levels of urea fertilizer including zero, 100, 150, and 200 kg ha-1 in the form of randomized complete block design with three replications was carried out at seashore of Delwar City, Bushehr Province, in 2017. The results showed that fodder production and fertilizer application had a linear and increasing relationship. As the rate of urea fertilizer increased (100, 150, and 200 kg ha-1), the dry fodder production rose by 96. 7%, 144. 5%, and 188. 4%, respectively, compared with the control treatment. In addition, results showed that urea application increased plant height, spike length, spike diameter and spike weight. Moreover, application of urea fertilizer at a rate of 200 kg ha-1 increased water use efficiency by three times compared to the control. Hence, to optimize the quantity of fodder production from Salicornia and water use efficiency, urea application at a rate of 200 kg ha-1 is recommended. It is suggested to determine the effect of saline water on soil in future research.

Digital soil mapping (DSM), which uses machine learning (ML) algorithms and environmental covariates, is used worldwide for predicting soil properties and classes, due to being time-effective and cost-saving. This research was conducted to compare ML models and their efficiency in predicting the spatial distribution of soil subgroups in part of Zanjan Province. For this purpose, sampling was carried out through a regular pattern with 500 meters intervals, and 148 soil profile samples were randomly collected and classified. The soils of the region at the subgroup level were categorized in five classes, including Typic Calcixerepts, Typic Haploxerepts, Gypsic Haploxerepts, Typic Xerorthents, and Lithic Xerorthents. Environmental covariates included geomorphological and geological maps, digital elevation model (DEM), and remote sensing (RS), selected by principal component analysis (PCA) and expert knowledge approaches. Fifty-seven environmental variables were extracted as representatives of soil forming factors. Modeling of the soil-landscape relationship was performed using three algorithms, namely, multinomial logistic regression (MNLR), random forest (RF), and boosted regression tree (BRT) in Rstudio software. The results of evaluation metrics such as overall accuracy and Kappa index were 65% and 0. 32 for the RF algorithm, 60% and 0. 35 for the BRT, and 65% and 0. 41 for the MNLR. Referring to the importance of environmental variables, results showed that the four factors of valley depth, LS-Factor, channel network distance, and multiple resolution ridge top flatness index (MRRTF) were the most important variables in MNLR algorithm. Also, the results of two statistics of user’s accuracy (UA) and producer’s accuracy (PA) showed that the MNLR model performed better in the spatial prediction of soil at subgroup level. Soil classes with higher frequency had higher accuracy. The results of the prediction accuracy of soil classes showed that the model prediction is more accurate for the more frequent types in the observations.

The purpose of this research was to investigate the impact of pH, EC, and organic carbon (OC) on the amount of total nitrogen in the soil of the Zarin Shahr Region using statistical analyses (borderline analysis and step-by-step regression). In the Zarinshahr region, a comprehensive study was conducted for a duration of one year across all four seasons. The study employed GPS technology to select a total of 60 points in agricultural, tree plantations, and bare soil areas while ensuring an average distance of 546 meters between each point. The plots areas were 50x50x20 cm in length, width, and height. The various soil samples collected from these plots were analyzed. Then, the maximum reaction of a biogeochemical process to a certain environmental factor was determined using borderline analysis. According to the findings, the soil's EC exhibited higher readings during the hotter seasons of the year, in comparison to other seasons. Specifically, during the summer, the maximum value of this parameter was approximately 1587 dS/m, while in the winter, the values were nearly 1140 dS/m. Also, pH levels of the soil was generally alkaline. The seasonal variations in soil OC and organic matter percentage were evident, with notably lower values (ranging from 0. 04-2. 52) in the autumn and winter, followed by a marked increase during the summer and spring (0. 12-2. 39). Boundary line analysis was used to investigate the relationships between variables and the values of indicators that caused the maximum increase in soil nitrogen. It was ascertained that OC percentage and soil nitrogen percentage exhibited a significant linear correlation in all land uses. Hence, increase in OC percentage resulted in an increase in the total nitrogen content in the soil. The step-by-step regression analysis in a systematic manner revealed that, within the variables under investigation, the percentage of total nitrogen in the soil can be determined based on the impact of two key parameters, namely, EC and the percentage of OC. The results suggest that use of boundary line analysis and step-by-step regression represent a viable approach for determining the effects of the significant factors on the levels of nitrogen content in the soil.

This study aimed to estimate the spatial distribution of soil properties including field capacity (FC), permanent wilting point (PWP) and total available water (TAW) using ordinary kriging (OK) and random forest (RF) in agricultural lands of the southern Sistan Plain, covering an area of approximtely 147000 hectars. FC, PWP, and TAW and soil texture components were measured for a total of 200 surface soil samples (0-30 cm). Performance evaluation of the two methods based on the percentage of normalized root mean square error (nRMSE) revealed that the conventional OK with 5% less error in estimating FC, 3% less error in estimating PWP, and 5% less error in estimating TAW performed slightly better than RF. Comparing Bias values showed that OK underestimates both FC and PWP and overestimates TAW, while RF overestimates all three parameters. Spatial distribution maps of FC, PWP, and TAW produced by OK model showed that the highest amount of FC (23%) and TAW (14. 4%) were in the west and northeast of the region, which had heavier texture and lower altitude from the sea level. In the southern and southeastern regions, which have lighter soil texture, the amount of available water was less compared to the western and northeastern regions. In the RF model, the most important variable extracted from satellite images was Digital Elevation Model (DEM), and all three features had higher values in areas where DEM was lower. It seems that the flatness of the study area and the inadequacy of auxiliary variables caused the lower accuracy of the RF method.