نشریه پژوهشهای خاک (علوم خاک و آب)
سال سی و پنجم شماره 4 (زمستان 1400)

Meeting the demand for rice in the country as a strategic product is of special importance. The objective of this study was to investigate the effect of soil properties on rice yield and their rating to prepare a table of soil and landscape requirements of this crop for use in land suitability assessment studies. For this purpose, 99 rice fields in Mazandaran, Guilan, and Khuzestan provinces were selected. In each farm, a soil profile was studied and a land use questionnaire was completed. After the necessary physical and chemical analyses, a simultaneous multivariate regression between yield and different soil properties was investigated. Then, in order to prepare a table of soil and landscape requirements for rice, simple regression relationships between effective soil characteristics and yield were investigated and rated for different land suitability classes. Verification of the proposed table was performed using data from 18 rice fields (about 18% of the whole data). The results showed that the range of yield, lime, sand, clay and silt in selected fields was extensive, but the percentage of exchangeable sodium and soil salinity were in a more limited range. In multivariate regression, the variables of salinity, ESP, lime, soil reaction, percentage of clay and sand, respectively, were efficient. The coefficient of determination of the proposed equation was about 0.38. Also, the value of the coefficient of determination between yield and soil index for verification data was about 0.71, which indicates the reliability of the proposed table in this study.

The present study was performed to evaluate compost effects on growth components and uptake of some macronutrients by Momordica Charantia L. under water stress. The greenhouse experiment was conducted in 2021 as factorial in a completely randomized design with three replications. Treatments included five levels of compost (0, 15%, 30%, 45%, and 60% by weight) and three levels of water stress (irrigation at 80% FC (control), 60%FC (moderate water stress) and 40%FC (severe water stress). The results showed that water application at 60% and 40%FC resulted in reduction of, respectively, 35.8% and 60.8% in shoot dry weight, 30.7% and 56.3% in plant height, 15.9% and 42.3% in fruit weight, 18.1% and 27.2% in number of fruits, 29.4% and 46.7% in fruit length, 13% and 30.7% in N, 21.7% and 52.1% in P, and 28.2% and 36.7% in K concentration compared to the control. The use of different levels of compost as a culture medium for bitter melon had a significant effect on growth characteristics of the plant. Maximum shoot dry weight (93.99 g.pot-1), plant height (351.38 cm), fruit weight (23.56 g.pot-1), number of fruits (16.44), fruit length (13.48 cm), N (2.01%), P (0.2%) and K concentration (2.2%) were observed in culture medium with 45% compost. Thus, the best level of compost application was 45% by weight of compost for Bitter melon. Based on the results, culture medium containing 45% by weight of compost reduces the negative effects of water stress on bitter melon under stress conditions.

Nitrogen is one of the most important nutrients in crop production, but its efficiency in urea fertilizer is low, especially in paddy fields. Nitrogen yield can be increased by reducing the dissolution of urea in the soil. Therefore, the present study was conducted to investigate the effect of polymer-coated urea fertilizer on growth and yield of rice cv.Tarom Hashemi. The experiment was conducted in a randomized complete block design with three replications in two locations. Experimental treatments included T0: control, T1: two splits of ordinary urea (OU), T2: coated urea (CU), T3: OU (30%)+CU (70%)(use after transplanting) and T4: OU (30%)+CU (70%). CU was used at the time of transplanting and OU at heading. The results showed that the location effect was significant only on tiller number and head number/heap. However, the type of urea fertilizer and application method significantly affected tiller number, head number, plant height, grain number, straw yield, grain yield, Harvest Index, and N recovery. Average of the maximum numbers of tillers (24.5) and heads (20.33) in T3 treatment in the two sites showed, respectively, 52% and 47.5% increase compared to the control treatment. Average grain yields in T3 treatment in the two fields were 43.1%, 17%, 18.1%, and 18.1% higher than, respectively, the control treatments, T1, T2, and T4. For Tarom Hashemi cultivar, while application of polymer CU mixed with OU was effective in increasing nitrogen recycling and reducing its losses in both places, the effectiveness of this treatment in the field with loamy sand soil was more. Therefore, this can be the most appropriate method of fertilizer application in these soils.

Proper soil hydraulic functions that accurately predict soil water characteristic curve and hydraulic conductivity are essential for studies of unsaturated flow in soils. In this study, 8 soils from different texture classes of UNSODA soil bank were selected and the optimal validity of shape factor (n) of the RETC software (using retention curve function of van Genuchten-Mualem (VGM) model) was investigated for predicting the value of unsaturated hydraulic conductivity at different moisture contents. Since the predicted results of this model were poor, we attempted to investigate a separate shape factor such as  for the hydraulic conductivity-moisture (K-θ) function of VGM Model. To this end, 24 soil samples from different texture classes of UNSODA were selected and their measured parameters were analyzed by regression analysis to find a suitable pedotransfer function for estimating . The developed function confirmed the relationship of  with both saturated moisture (θs) and organic matter contents with a correlation coefficient of r = 0.745 at significant level of P = 0.0005. Also, to validate the developed pedotransfer function, the hydraulic conductivity values ​​corresponding to the measured moistures for the 8 selected soils in the validation section were calculated based on the two shape factors obtained from the pedotransfer function ( ) and the RETC (n) and the results were compared with the measured values. The statistical indices of root mean square error of model (RMSEM) and Nash-Sutcliff model efficiency coefficient  (NSE) showed that the shape factor of the developed pedotransfer function compared to the RETC, had a better performance in predicting unsaturated hydraulic conductivity values.

Modeling and mapping of plants nutrient elements in soil has importance in increasing the productivity of agriculture and achieving sustainable development. The aim of this research was to prepare digital maps of two nutrients, namely, available phosphorus (Pav) and exchangeable potassium (Kex) using machine learning models (MLM) i.e., random forest (RF), cubist (CB), support vector regression (SVR) and k-nearest neighborhood (k-NN) at two depths of 15-30 and 0-15 cm in a part of Abyek Plain. In this regard, 278 soil profiles were dug, sampled from the desired horizons, and samples were analyzed. MLM performance was implemented by 10-fold cross-valuation. The modeling results demonstrated that the RF model had the highest accuracy and minimum error compared to the other three models in spatial estimation of available Pav and Kex at the two studied depths. According to the results, for Pav at a depth of 0-15 cm, CCC statistics values of 0.84, 0.74, 0.48 and 0.35 and NRMSE values of 0.38, 0.54, 0.70, and 0.80 belonged to RF, CB, k-NN, and SVR, respectively. For Kex at the same depth, CCC values were 0.82, 0.72, 0. 70, 0.47 and NRMSE 0.25, 0.34, 0.36 and 0.45, by RF, CB, SVR, and k-NN models, respectively. Similar results were obtained for 15-30 cm layer. The relative importance of environmental variables showed that soil covariates had a more effective role in the spatial estimation of Pav and Kex than other environmental variables. According to the estimated maps of the two elements and the predominance of agricultural land uses, major parts of the area are Pav deficient based on standard amounts. Therefore, to increase productivity and improve management of soil fertility, use of phosphate fertilizers is recommended under the supervision of soil experts.

Measurement of soil hydraulic properties such as soil saturated hydraulic conductivity is one of the most important physical properties of soil. In this study, adaptive neuro fuzzy inference system (ANFIS) and pedo-transfer functions (PTFs) are used to estimate soil saturated hydraulic conductivity. The model inputs included soil texture, the percent of silt, clay, and sand. To evaluate the performance of the model, parameters of root mean square error (RMSE), percentage of relative error (ε), mean absolute error (MAE) and the coefficient of determination (R2) were used, which for (ANFIS) model were determined as 0.557, 0.627%, 0.844, and 0.997, respectively. Accuracy of PTFs methods decreased from Ferrer-Julià (2004), Roseta (UNSODA(lab)-SSC), Dane (1994), Cosby (1984), Puckett (1985), and Campbell (1994). Among PTFs methods, Ferrer-Julià (2004) had more accuracy with a regression coefficient of (R2 =0.89) and (RMSE =2.1). Performance evaluation of the models showed that the ANFIS model compared with PTFs was able to predict soil hydraulic conductivity with more accuracy.

One of the most important signs of climate change is the increase in soil temperature, but compared to other meteorological parameters, it has received less attention by researchers. The objective of this study was to investigate soil temperature trends at 5, 10, 20, 30, 50, and 100 cm depths in selected regions of Khuzestan Province over 25 years (1994-2018). To this end, hourly soil temperature data were collected from 10 selected synoptic stations located in Khuzestan. The modified Mann-Kendall non-parametric test and Sen’s slope estimation method were used to determine soil temperature change trend and the slope of temperature gradient, respectively. The results of soil temperature investigation in all regions showed that with increasing soil depth, soil temperature decreased  in warm seasons (spring and summer); while in cold seasons (winter and fall) the soil temperature increased slightly. The results also revealed that soil temperature trend increased annually and seasonally in all profile depths in most regions of Khuzestan Province. Furthermore, the highest annual increase in soil temperature was observed in winter, summer, and spring, respectively, and the lowest increase occurred in the fall season. The results also showed that the annual temperature of soil profile increased by 1.28 °C during the study period