A B S T R A C TCation exchange capacity (CEC), as an important indicator for soil quality, represents soil's ability to hold positively charged ions. We attempted to predict CEC using different statistical methods including monotone analysis of variance (MONANOVA), artificial neural networks (ANNs), principal components regressions (PCR), and particle swarm optimization (PSO) in order to compare the utility of these approaches and identify the best predictor. We analyzed 170 soil samples from four different nations (USA, Spain, Iran and Iraq) under three land uses (agriculture, pasture, and forest). Seventy percent of the samples (120 samples) were selected as the calibration set and the remaining 50 samples (30%) were used as the prediction set. The results indicated that the MONANOVA (R 2 = 0.82 and Root Mean Squared Error (RMSE) = 6.32) and ANNs (R 2 = 0.82 and RMSE = 5.53) were the best models to estimate CEC, PSO (R 2 = 0.80 and RMSE = 5.54) and PCR (R 2 = 0.70 and RMSE = 6.48) also worked well and the results were very similar to each other. While the most influential variables for the various countries and land uses were different and CEC was affected by different variables in different situations, clay (positively correlated) and sand (negatively correlated) were the most influential variables for predicting CEC for the entire data set. Although the MANOVA and ANNs provided good predictions of the entire dataset, PSO gives a formula to estimate soil CEC using commonly tested soil properties. Therefore, PSO shows promise as a technique to estimate soil CEC. Establishing effective pedotransfer functions to predict CEC would be productive where there are limitations of time and money, and other commonly analyzed soil properties are available.

No abstract

Determination of soil cation exchange capacity (CEC) in lab is cumbersome, time consuming, and costly. Accordingly, this article attempted to formulate pedotransfer functions for predicting it using some soil physical and chemical properties e.g., sand (SA), silt (SI), clay (CL), organic matter (OM) and calcium carbonate (CC). This research included four steps: preparing soil database; selecting independent variables which are related to CEC value; formulating models using NCSS 12.0.2 software, and the last step is to achieve specific objective of the research which is the comparsion among models by a series of efficiency criteria: root mean square error (RMSE), Nash-Sutcliffe model efficiency coefficient (EF), average absolute percent error (AAPE), and percentage of improving model efficiency (PIME). The statistical results of the research indicated that CEC of calcareous soils could be predicted from models that have one variable (CL), two variables (CL and OM), and three variables (CL, OM, and CC) with slight decrease in the RMSE (2.95402, 2.81180, and 2.79268) respectively, and slight increase in the EF (0.887360, 0.898448, and 0.90023) respectively. While the reliable models to predict soil CEC are formulated from the fewer number of independent variables with having the lowest points of the standardized residual of CEC that greater than +2 cmolc kg-1).

The measurement of soil hydraulic properties is tedious, time-consuming, and costly. An alternative approach is to formulate models that utilize the physical and chemical properties of the soil as input variables to predict soil saturated hydraulic conductivity (Ks). However, the previous studies have not paid attention to the calcium carbonate content in their models; it can lead to reducing the size and number of the pores in the soil which, in turn, can lead to reduction Ks . Here we evaluated the ability of the Soil, Plant, Atmosphere, and Water (SPAW) model to predict Ks under different states of compaction for calcareous soils with wide-ranging textures sampled along a precipitation gradient in northwestern Iraqi Kurdistan. The results revealed that the best match occurred under loose to normal state of compaction for these soils. Among the soil properties, sand content was high significantly correlated with Ks followed by CaCO3, clay, organic matter content, silt and Electrical conductivity. A pedotransfer function (PTF) was proposed using these data and its results were compared to these from the SPAW model. Root mean square error (RMSE) and coefficient of variation (CV) for the comparison between measured Ks values and those predicted by the SPAW model were very high 2.7×10-4 cm s−1 and 166% respectively, that due to the values of Ks predicted by the SPAW model are overestimated for calcareous soils, for these reasons the accuracy of the SPAW model was improved via calibration. The RMSE and CV of the calibrated SPAW model were dropped to 9.8×10-5 cm s−1 and 61.2%, respectively. Additionally, the accuracy of our best PTF that constructed from sand, clay, and CaCO3 was slightly higher than the calibrated SPAW model. Therefore, it is recommended to use the calibrated SPAW model for predicting Ks in calcareous soils.

The excessive use of irrigation water led to thinking about creating new irrigation techniques to take full advantage of water input, whereby the agricultural drought will be reduced. For this purpose the current study was carried out to adopt modern irrigation techniques by manipulating traditional drip irrigation technique at depth 0, 5, 15, and 30 cm (DI, SDI5, SDI15, SDI30) and basket technique at depth 5, 15, and 30 cm (BT5, BT15, and BT30) for irrigating corn plant under two levels of irrigation for receiving 100% and 65% of depleted water (I1 and I2, respectively). The grain yield and the amount of consumed water were estimated to calculate irrigation water use efficiency (IWUE) and yield response factor (Ky). The cumulative depth of irrigation with precipitation for I1 and I2 were 781 and 606 mm, respectively. The grain yield of corn has been significantly (p ≤ 0.05) influenced by level and technique of irrigation; the level I1 and the technique SDI15 showed the highest values. Also the maximum IWUE among all techniques in both irrigation levels was for SDI15, while the minimum IWUE was for DI. The yield response factor showed no significant difference (p ≤ 0.05) among all techniques except the DI which gave the highest value 1.381, while the lowest value of 1.093 recorded for BT5. In this study, Ky values in all drip irrigation techniques were bigger than one; In this case using degree of soil water stress (Ks) less than 0.74is not preferable and mitigating drought impact in corn cultivation is unsuccessful.