Effects of saline water mulched drip irrigation on cotton yield and soil quality in the North China Plain

Digital mapping of Philip model parameters for prediction of water infiltration at the watershed scale in a semi-arid region of Iran

Significant research has been conducted on the effects of fertilizers or agents on the sustainable development of agriculture in salinization areas. By contrast, limited consideration has been given to the interactive effects of microbial fertilizer (MF) and salinity on hydraulic properties in secondary salinization soil (SS) and coastal saline soil (CS). An incubation experiment was conducted to investigate the effects of saline soil types, salinity levels (non-saline, low-salinity, and high-salinity soils), and MF amounts (32.89 g kg−1 and 0 g kg−1) on soil hydraulic properties. Applied MF improved soil water holding capacity in each saline soil compared with that in CK, and SS was higher than CS. Applied MF increased saturated moisture, field capacity, capillary fracture moisture, the wilting coefficient, and the hygroscopic coefficient by 0.02–18.91% in SS, while it was increased by 11.62–181.88% in CS. It increased soil water supply capacity in SS (except for high-salinity soil) and CS by 0.02–14.53% and 0.04–2.34%, respectively, compared with that in CK. Soil available, readily available, and unavailable water were positively correlated with MF, while soil gravity and readily available and unavailable water were positively correlated with salinity in SS. Therefore, a potential fertilization program with MF should be developed to increase hydraulic properties or mitigate the adverse effects of salinity on plants in similar SS or CS areas.

Interactive Effects of Microbial Fertilizer and Soil Salinity on the Hydraulic Properties of Salt-Affected Soil

Predicting saturated hydraulic conductivity by pedo-transfer function and spatial methods in calcareous soils

The direct estimation of soil hydraulic conductivity (Ks) requires expensive laboratory measurement to present adequately soil properties in an area of interest. Moreover, the estimation process is labor and time-intensive due to the difficulties of collecting the soil samples from the field. Hence, innovative methods, such as machine learning techniques, can be an alternative to estimate Ks. This might facilitate agricultural water and nutrient management which has an impact on food and water security. In this spirit, the study presents neural-network-based models (artificial neural network (ANN), deep learning (DL)), tree-based (decision tree (DT), and random forest (RF)) to estimate Ks using eight combinations of soil data under calcareous alluvial soils in a semi-arid region. The combinations consisted of soil data such as clay, silt, sand, porosity, effective porosity, field capacity, permanent wilting point, bulk density, and organic carbon contents. The results compared with the well-established model showed that all the models had satisfactory results for the estimation of Ks, where ANN7 with soil inputs of sand, silt, clay, permanent wilting point, field capacity, and bulk density values showed the best performance with mean absolute error (MAE) of 2.401 mm h−1, root means square error (RMSE) of 3.096 mm h−1, coefficient of determination (R2) of 0.940, and correlation coefficient (CC) of 0.970. Therefore, the ANN could be suggested among the neural-network-based models. Otherwise, RF could also be used for the estimation of Ks among the tree-based models.

/pdf/saturated-hydraulic-conductivity-estimation-using-artificial-1xc3rd0b.pdf

Saturated Hydraulic Conductivity Estimation Using Artificial Intelligence Techniques: A Case Study for Calcareous Alluvial Soils in a Semi-Arid Region

Saturated soil hydraulic conductivity (Ksat) and the soil water characteristic curve have been predicted from soil texture, bulk density, and other soil physical properties. These previous functions do not account for soil clay mineralogy or soil solution composition. The objectives of this study were to: (1) analyze the performance of previously developed pedotransfer functions (PTFs) on saline and sodic soils, (2) predict hydraulic properties of saline and sodic soils with a range of clay mineralogy, and (3) develop PTFs that account for clay mineralogy and solution composition. Most PTFs do not perform well on soils with smectitic clay mineralogy and usually over-estimate Ksat and under-estimate soil water characteristic on smectite clay dominant soils. Accounting for soil cation exchange capacity (CEC) as an indicator of soil clay types and solution composition improved the prediction of Ksat which had a geometric root mean squared error of 3.28 cm day−1 compared to 6.04 cm day−1 for the best performing PTF from the literature. A PTF we developed to predict soil water characteristic curve was able to predict soil water characteristic curve with a root mean squared error of 0.032 g g−1 compared to 0.082 g g−1 for the best performing previously published PTF. The PTFs developed in this study improved prediction of soil hydraulic properties on salt affected soils by using soil CEC to account for soil clay behavior and responsiveness to the soil solution composition. The PTFs reported here can easily be used to predict the Ksat and the soil water characteristic curve of soils in response to changes in irrigation water quality.

Development of functions to predict soil hydraulic properties that account for solution sodicity and salinity

Irrigation water quality is a global agricultural management issue. High solution sodium adsorption ratio (SAR) and low electrical conductivity (EC) of irrigation waters decrease both hydraulic con...

The Effects of Soil Solution Electrical Conductivity and Sodium Adsorption Ratio on Soil Liquid Limit and Soil Strength

Analysis of pedotransfer functions to predict the effects of salinity and sodicity on saturated hydraulic conductivity of soils

Soil hydraulic properties are important for water management in salt and sodium (Na) affected soils. The soil saturated hydraulic conductivity (Ksat) has been found to decrease when high soil solution Na adsorption ratio (SAR) is coupled with low solution electrical conductivity (EC). Soil clay type plays an important role in changes to soil Ksat when the soil solution composition has a high SAR and low EC. Little is known about how soil water retention changes from solution EC and SAR. The objective of this study was to determine the impact of a range of solution EC and SAR values on soil Ksat, and water retention for soils containing swelling and nonswelling clay minerals. The soil Ksat and water retention were measured on four smectitic soils, a kaolinitic soil, and an illitic soil. Smectite swelling caused large changes in soil hydraulic properties in smectitic soils at high exchangeable Na percentage (ESP) and low EC levels, whereas there was a reduced effect on soils with little smectite clay. Increasing soil solution ESP and decreasing EC level caused the Ksat to decrease and the water content at a given pressure head to increase on soils that contained smectite. A narrower range of EC and SAR of waters suitable for use on smectitic soils are required to prevent changes in soil water flow rates and soil water retention.

Influence of exchangeable sodium and clay mineralogy on soil water retention and hydraulic conductivity

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Development of functions to predict soil hydraulic properties that account for solution sodicity and salinity

The Effects of Soil Solution Electrical Conductivity and Sodium Adsorption Ratio on Soil Liquid Limit and Soil Strength

Analysis of pedotransfer functions to predict the effects of salinity and sodicity on saturated hydraulic conductivity of soils

Influence of exchangeable sodium and clay mineralogy on soil water retention and hydraulic conductivity