Spatial prediction of saline and sodic soils in rice‒shrimp farming land by using integrated artificial neural network/regression model and kriging

In the context of widespread saline and sodic soil, mapping and monitoring spatial distribution of soil salinity and sodicity are important for utilization and management in agriculture lands. In this study, two-stage assessment was proposed to predict spatial distribution of saline and sodic soils. First, artificial neural network (ANN) and multiple linear regressions (MLR) model were used to predict sodium adsorption ratio (SAR) and exchangeable sodium percentage (ESP) based on soil electrical conductivity (EC) and pH. Then, the Kriging interpolation method combined with overlay mapping technique was used to perform saline spatial predictions in the study area. The model accuracy level is evaluated based on coefficient of determination (R²) and root mean square error (RMSE). In the first stage, the values of R² and RMSE of SAR and ESP were 0.94, 0.17 and 0.94, 0.24 for ANN, and 0.35, 0.52 and 0.34, 0.76 for MLR, respectively. Similarly, in the second stage, the RMSE of ANN-Kriging were much closer to 0 and relatively lower than MLR-Kriging and Kriging. The results show that ANN-Kriging can be used to improve the accuracy of mapping and monitoring spatial distribution of saline and sodic soil in areas that develop the rice-shrimp cultivation model.     

The interactive effects of biochar and cow manure on rice growth and selected properties of salt-affected soil

Salt-affected soil induces detrimental influences on paddy rice (Oryza sativa L.) growth and ameliorating the influences could be done with organic amendments, such as animal manure and biochar. The aims of the current study are: (1) to examine the interactive effects of biochar and cow manure on rice growth and on selected properties of salt-affected soil, and (2) to identify potential mechanisms related to the amendments. Saline-sodic soil was used for a net house experiment with two experimental factors: biochar (no-biochar, rice-husk, and -straw biochar) and cow manure (with and without cow manure). Without the manure, addition of both rice-hush and – straw biochar significantly increased rice growth, whereas a combination of individual biochar with manure did not show a positive synergistic effect. The interactive effect of two factors was not significant on available P and exchangeable K concentrations, but the main effects of the two factors were significant. Biochar addition resulted in higher soil cation exchange capacity (CEC) (28.8 to 29.0 cmol_c kg^?1) than the control (25.6 cmol_c kg^?1), but manure addition did not. Improved nutrient availabilities such as P and K, as well as CEC are among the potential mechanisms accounting for the enhanced rice growth with biochar.     

Contrastive nutrient leaching from two differently textured paddy soils as influenced by biochar addition

Journal of Soils and Sediments - The aims of the current study were to (1) examine the interactive effects of biochar addition and differently textured soils (clayey and sandy soils) on nutrient...     

The impact of rainfall in remobilising particulate matter accumulated on leaves of four evergreen species grown on a green screen and a living wall

Green walls have recently been identified as a green infrastructure (GI) solution to the problem of particulate matter (PM) air pollution. Green wall systems mostly use evergreen plants as the leaves are retained throughout the year; however, researchers have argued that evergreen foliage becomes saturated with PM and fails to capture more due to a long retention time on the leaves. This study evaluated the potential of (simulated) rainfall to remobilise these captured PM and renew the capture ability of the leaf surfaces of four evergreen species (Heuchera villosa Michx, Helleborus × sternii Turrill, Bergenia cordifolia (Haw.) Sternb., Hedera helix L.) used in a living wall and a green screen located along a busy road in Stoke-on-Trent, UK. The approach used compared PM densities on pre- and post-rain exposed leaf surfaces (using leaf halves of the same leaf) and using a paired t-test to identify any significant reduction in PM due to the rainfall. An Environmental Scanning Electron Microscope (ESEM) and ImageJ image analysis software were employed to quantify the PM densities on leaves. The reduction of PM on leaves, following exposure to 16 mm hr⁻¹ simulated rain in six different rainfall durations was estimated in all four species in order to evaluate any variable impact of rainfall on different species of plants. PM wash-off levels on leaves of H. helix by 41 mm hr⁻¹ rain was also evaluated, using the same rainfall durations, to assess any differential impact of rainfall intensity on PM wash-off. This study revealed a significant impact of rainfall in washing the particles off the leaves in all rainfall durations used. A one-way Anova in a Generalised Linear Model showed a differential impact of rainfall in remobilising PM on different species of plants. The rainfall with higher intensity (41 mm hr⁻¹) showed a significantly higher impact on PM wash-off compared to 16 mm hr⁻¹ rain. The results of this study demonstrated the potential of green walls to act as good PM traps throughout the year by recycling their capture surfaces.     

Stabilization of toxic metals in three contaminated soils by residual impact of lime integrated with biochar and clays

Journal of Soils and Sediments - Mine and smelter soil contaminations with Pb, Cu, Cd, and Zn have become serious problems in China, because of anthropogenic activities. Little is known about the...     

Research on the horizontal reduction effect of urban roadside green belt on atmospheric particulate matter in a semi-arid area

In this study, the horizontal abatement effects of green belts on atmospheric particulate matter at different horizontal distances and plant community structures were investigated in urban roadside green belts in semi-arid areas.We collected mass concentrations of six types of atmospheric particulate matter (PM) per unit time of PM_0.3, PM_0.5, PM_1.0, PM_2.5, PM_5.0, and PM₁₀ and various meteorological indices, to compare the horizontal reduction efficiency of different distances and plant community structures on different particle sizes, and to establish a support vector machine model. The results showed that 1) the horizontal abatement efficiency of green belts was different for six particle sizes, while the horizontal abatement rate strengthened as the particle size increased. The abatement rate was significantly correlated with microclimatic factors such as temperature and humidity, but less correlated with wind speed. 2) The horizontal abatement rate of roadside green belts on atmospheric particulate matter varies with the increase of horizontal distance in a "single-peak" or "double-peak" pattern, with the best abatement effect of green belts on each particle size at a horizontal distance of 45m. Among the four types of plant community structures, the strongest abatement ability was in the arbor-shrub-herb structure. 3) The correctly tuned prediction model, based on Support Vector Machines, resulted in better horizontal abatement ability of green belts on atmospheric PM. The prediction results showed that the average horizontal abatement rate has the best abatement effect at 45–55 m, peaked at 50 m, and formed stagnant dust at 65 m. In the design of urban road green spaces in semi-arid areas, to achieve the best dust retention effect, the green belt width should be ≥40 m and it is desirable to choose arbor-shrub-herb structures. This study provides a design basis and theoretical support for urban road green space planning in semi-arid areas.