Control Soil Salinity Research Articles

Preferential flow improves root-soil system on a small scale: A case study of two ecotypes of Phragmites communis

The effects of preferential flow on plant root and soil have not been deeply studied. Research on the combination of wetland vegetation, hydrology, and soil can provide a theoretical basis for wetland vegetation restoration and soil salinization control. We integrated preferential flow, root, and soil to study the distribution characteristics of the root architecture and soil nutrient of two different ecotypes of Phragmites communis (P. communis) in the Yellow River Delta on a small scale. We found that both root architectures included root length, width, surface area, volume, and biomass and soil nutrients included soil organic carbon, organic matter and total nitrogen decreased with the increase of soil depth. Large P. communis (LP) not only had significantly larger root length, width, surface area, but also had significantly higher soil organic carbon, organic matter, total nitrogen, and available phosphorus than small P. communis (SP). The root architectures of SP in the preferential flow area (PFA) were significantly greater than those in the matrix flow area (MFA). The contents of soil organic carbon, organic matter, and total nitrogen in LP grown in the PFA were obviously higher than those in the MFA. Based on correlation analysis, root architectures had remarkably positive correlations with soil organic carbon, organic matter, and total nitrogen. These findings suggest that preferential flow improves the root-soil system. By artificially adding peat and biochar and changing irrigation methods, the possibility of preferential flow can be increased, plant root growth and soil nutrient can be improved, so as to resist the damage of soil salinization. In addition, different ecotypes of P. communis should be planted to improve soil salinization. LP and SP are suitable for slight and heavy soil salinization areas respectively.

Experimental research on the laws of acidification and salinization under the conditions of Ionic rare earth leaching and water Leaching

In the ionic rare earth mining,ammonium sulfate is used as a leaching agent for in-situ leaching, the concentration of ammonium sulfate is 2%∼3%, and the pH is 4∼5, leading to soil acidification and increased salt content. In this paper,taking a rare earth mining area in Guangdong as the research object, carried out soil column simulation test research. After leaching with water for the soil column that complete leaching, in condition of using different leaching agents of pH values and different water leaching process characteristics, the variation of pH and total salt content was studied. The results show that, after leaching ,the mineral sample changed from mild acidification to non-acidification, and the the salt content of the mineral sample was effectively reduced, the salt leaching rate is above 57%. The research conclusions laid a theoretical foundation for soil acidification and salinization control in ion-type rare earth mining areas.

Effects of ice cover on soil water, heat, and solute movement: An experimental study

In cold regions, the surface cover plays a pivotal role in understanding the soil water and energy balance during winter. Although the effects of other types of surface cover on soil freezing-thawing process have been studied for decades, the effects of ice cover derived from irrigation remain unclear. To estimate the effects of ice cover on soil water, heat, and solute movement, ice coverage and field observation data were collected for three winter periods from 2013 to 2016 in Inner Mongolia, China. The collected data was then analyzed using the principal component analysis (PCA) method. The dynamics of the water, heat, and solute transport were also investigated using in-situ plot experiments conducted in four lysimeters, which were controlled by manipulating the thickness of the ice cover. The thickness of the ice cover was controlled by the amount of water irrigation in the treatment plots, and no irrigation was performed in the control plot. The results of the field-scale survey and PCA indicated that the ice cover had positive effect on soil salinization control and moisture preservation after autumn irrigation (AI). An unique air space created during the ice formation might enhance the heat insulation effect of ice cover. Furthermore, the results of the in-situ plot experiment revealed that the freezing of the irrigated soil could be divided into the unstable freezing period (UFP) and stable freezing period (SFP) when considering the ice cover. During the UFP, the upward movement of the solute that was leached by the irrigation water was reduced as the development of the freezing front was hindered by the irrigation water. During the SFP, the penetration of the freezing front was further reduced by the combination of the ‘inherited’ thermal regime from the UFP and the input solar energy reduced by the remaining ice cover and air space, which decreased the water and solute transport. The study findings aid in achieving an improved understanding of ice cover on agricultural fields in cold regions, and have important implications for water conservation and salinization control.

Foliar Nourishment with Nano-Selenium Dioxide Promotes Physiology, Biochemistry, Antioxidant Defenses, and Salt Tolerance in Phaseolus vulgaris.

Novel strategic green approaches are urgently needed to raise the performance of plants subjected to stress. Two field-level experimental attempts were implemented during two (2019 and 2020) growing seasons to study the possible effects of exogenous nourishment with selenium dioxide nanoparticles (Se-NPs) on growth, physio-biochemical ingredients, antioxidant defenses, and yield of Phaseolus vulgaris (L.) plant growing on a salt-affected soil (EC = 7.55–7.61 dS m−1). At 20, 30, and 40 days from seeding, three foliar sprays were applied to plants with Se-NPs at a rate of 0.5, 1.0, or 1.5 mM. The experimental design was accomplished in randomized complete plots. The data indicate noteworthy elevations in indicators related to growth and yield; pigments related to effective photosynthesis, osmoprotectant (free proline and soluble sugars), nutrient and Se contents, K+/Na+ ratio, cell integrity (water content and stability of membranes), all enzyme activities; and all features related to leaf anatomy induced by Se-NPs foliar spray. Conversely, marked lowering in markers of Na+ content-induced oxidative stress (superoxide radical and hydrogen peroxide) and their outcomes in terms of ionic leakage and malondialdehyde were reported by foliar nourishment with Se-NPS compared to spraying leaves with water as an implemented control. The best results were recorded with Se-NPs applied at 1.0 mM, which mitigated the negative effects of soil salinity (control results). Therefore, the outcomes of this successful study recommend the use of Se-NPs at a rate of 1.0 mM as a foliar spray to grow common beans on saline soils with EC up to 7.55–7.61 dS m−1.

Spatial modelling of soil salinity: deep or shallow learning models?

Understanding the spatial distribution of soil salinity is required to conserve land against degradation and desertification. Against this background, this study is the first attempt to predict soil salinity in the Jaghin basin, in southern Iran, by applying and comparing the performance of four deep learning (DL) models (deep convolutional neural networks-DCNNs, dense connected deep neural networks-DenseDNNs, recurrent neural networks-long short-term memory-RNN-LSTM and recurrent neural networks-gated recurrent unit-RNN-GRU) and six shallow machine learning (ML) models (bagged classification and regression tree-BCART, cforest, cubist, quantile regression with LASSO penalty-QR-LASSO, ridge regression-RR and support vectore machine-SVM). To do this, 49 environmental landsat8-derived variables including digital elevation model (DEM)-extracted covariates, soil-salinity indices, and other variables (e.g., soil order, lithology, land use) were mapped spatially. For assessing the relationships between soil salinity (EC) and factors controlling EC, we collected 319 surficial (0-5 cm depth) soil samples for measuring soil salinity on the basis of electrical conductivity (EC). We then selected the most important features (covariates) controlling soil salinity by applying a MARS model. The performance of the DL and shallow ML models for generating soil salinity spatial maps (SSSMs) was assessed using a Taylor diagram and the Nash Sutcliff coefficient (NSE). Among all 10 predictive models, DL models with NSE ≥ 0.9 (DCNNs was the most accurate model with NSE = 0.96) were selected as the four best models, and performed better than the six shallow ML models with NSE ≤ 0.83 (QR-LASSO was the weakest predictive model with NSE = 0.50). Based on DCNNs-, the values of the EC ranged between 0.67 and 14.73 dS/m, whereas for QR-LASSO the corresponding EC values were 0.37 to 19.6 dS/m. Overall, DL models performed better than shallow ML models for production of the SSSMs and therefore we recommend applying DL models for prediction purposes in environmental sciences.

Evolution of soil salinity and the critical ratio of drainage to irrigation (CRDI) in the Weigan Oasis in the Tarim Basin

Soil salt accumulation and salt leaching are very important issues for a long-term irrigation-dominated oasis with shallow groundwater in arid land. The critical ratio of drainage to irrigation (CRDI) was defined and determined to help the guide drainage and irrigation practices and maintain sustainable oasis agricultural development. This study aimed to identify the soil salinity evolution under long-term irrigation and to determine the threshold of irrigation to drainage with land-use change and groundwater depth by regression, water and salt balance, and geostatistical analysis. The results showed that the soil salinity in 0–20 cm soil depth has decreased greatly in the Weigan Oasis over the past 60 years, with an annual average decrease of 0.68 g·kg−1 in the Weigan Oasis. The soil salt content generally increases from inside to outside of the region and varies from the upperstream to downstream. In the past 30 years, land-use change has caused a sharp reduction in the salinity in the region, and the process of irrigation and drainage has made a decrease in the total salt of the irrigated district of 207.38 × 105 t. Meanwhile, shallow groundwater depth and salinity are the main factors influencing soil salinization in this fluvial oasis. A CRDI is determined to be 8.20% in the Weigan Oasis. In conclusion, our study provides scientific support for the prevention and control of regional soil salinization, as well as a managerial basis for the rational allocation of water resources in long-term irrigation-dominated oasis.

Characteristics of Seasonal Frozen Soil in Hetao Irrigation District under Climate Change

Seasonal freezing and thawing is a major cause of serious soil salinization in Hetao irrigation area under climate change. In this paper, the Hetao irrigation area has been selected as an example and field monitoring experiments were used to analyze and study the dynamics of soil water, salt, and heat during freezing and thawing periods. The research results show that the unfrozen water presents a “concave” shape change, and as the degree of freezing deepens, the total water content continues to increase; the salt content also shows an increasing trend, with the 0 ~ 100 cm soil layer salt rate being 43.73%, of which 0 ~ 10 cm soil layer is 91.56%, followed by the 10 ~ 20 cm soil layer, which is 41.78%. The surface aggregation effect is obvious. The temperature data obtained by analysis shows that the freezing process is one-way freezing and the melting process is two-way melting. The above analysis results can provide a scientific basis for scientific planting and soil salinization prevention and control in irrigation areas.

Effects of Shallow Saline Groundwater Table Depth and Evaporative Flux on Soil Salinity Dynamics using Hydrus-1D

Soil salinization is a major environmental problem and critical concern in arid and semiarid regions. Hydrus-1D model was used to simulate effects of shallow saline groundwater table depth and evaporative flux on soil salinity movement in a saline environment. After successful calibration and validation with recorded soil moisture and soil salinity data, the model was used to evaluate two hypothetical scenarios for managing soil salinity, i.e., with 5 groundwater table depths (WTD) (WTD as on 2015, 25% and 50% rise; 25% and 50% decline in WTD based on the 2015 reference depth) and 3 different evaporative flux conditions (25, 50 and 75% reduction in evaporative flux). The model was calibrated, validated and run for scenarios with 2015 weather and WTD condition for the periods of 288 days. During calibration periods, root mean square error (RMSE) value of soil moisture content was 0.023 cm3 cm−3 and for soil salinity was 2.58 dS m−1, while during validation period RMSE of 0.023 cm3 cm−3 and 1.5 dS m−1, respectively, was recorded for soil moisture and soil salinity. Simulation results indicated that summer season (March to May, 60–150 Julian days) is the most important time to control soil salinity in this region. Considerable upward salt movement occurred during this period with 25 and 50% rise in groundwater table depth. Average root zone soil salinity increased by 6 and 12 dS m−1 when WTD raised by 25 and 50%, respectively, but negligible change in soil salinity was observed when groundwater table declined by 25 and 50%. The effective way to control this upward salt movement was reducing evaporative demands. Simulation study indicated that reducing evaporative flux of 25, 50 and 75% reduced profile soil salinity by 3.53, 6.95 and 12.38 dS m−1, respectively, during peak summer period.

Soil Salinity and Moisture Control the Processes of Soil Nitrification and Denitrification in a Riparian Wetlands in an Extremely Arid Regions in Northwestern China

Soil nitrification and denitrification are key nitrogen (N) removal processes in riparian wetlands in extremely arid regions, but the driving factors of the two N processes in these wetlands are still unclear. We measured soil nitrification and denitrification rates and related environmental properties in a typical riparian wetland in the middle reaches of the Heihe River, northwestern China. Our results showed that rates of soil nitrification and denitrification exhibited moderate variability, ranging from 52.77 to 221.18 μg kg−1 h−1 and 91.25 to 428.26 μg kg−1 h−1, respectively. Soil salinity was high, with mean electrical conductivity (EC) of 6.8 mS cm−1. Soil salinity and moisture were the key factors influencing nitrification and denitrification in this riparian wetland in an extremely arid region. Soil salinity exerted significant inhibitory impact on soil nitrification when EC was > 4.05 mS cm−1. Soil nitrification increased with an increase in soil moisture when soil water content < 27.03% and decreased with an increase in soil moisture when soil water content > 27.03%. Denitrification had a significantly negative relationship with soil salinity, and significantly positive relationship with soil moisture. The interaction of soil salinity and moisture played a central role in regulating soil denitrification. Based on these results, we propose that water consumption of riparian wetlands, and the planting of halophytes, should be increased to reduce soil salinity and increase soil moisture, which is essential for sustaining soil N removal function in riparian wetlands in extremely arid regions.

Estimation of soil salt content using machine learning techniques based on remote-sensing fractional derivatives, a case study in the Ebinur Lake Wetland National Nature Reserve, Northwest China

AbstractSoil salinity is a common global environmental problem that severely restricts industrial, agricultural and human development. In Northwest China, soil salinity is a problem affecting the Lake Ebinur area and needs to be monitored and addressed. The use of optical remote sensing technology to for timely and accurate soil salinity monitoring has great potentials and can be crucial for industrial and agricultural development. Optical remote sensing technology is an important data source for monitoring of soil salinization because of its rich spectral information and high-resolution. Based on the HJ-1/HSI data of fractional derivative transformation, this paper calculated the parameters, such as the difference index (DI), normalized difference index (NDI), ratio index (RI) and salt index (SI), that can invert the spatial distribution of salinization and then calculates a particle swarm optimization support vector machine (PSO-SVM). These index parameters were used to invert the spatial distribution information of salinized soil to explore the influence of fractional derivatives on the estimation accuracy of the spatial distribution of salinized soil. The results showed the following: (1) The optimal model based on different fractional derivatives and exponential transformations was the DI transformation under the fractional derivative of 1.2-order. R2, RMSE and RPD were 0.66, 13.81 g/kg and 2.59, respectively. These data show that the model had a good prediction effect. (2) The application of fractional derivatives in the processing of hyperspectral images could highlight the spectral details of the images and enrich their pretreatment methods, which is of great significance for the inversion of the distribution of salinized soil by hyperspectral images. (3) In the lakeside area of Lake Ebinur, the soil is mainly saline to severely saline. In the northern mountainous region of Lake Ebinur, the soil is mainly non-saline soil to mildly saline soil. Finally, in the Kuitun riverbank area in the southeast of Lake Ebinur, the soils are mainly non-saline soils to mildly saline soils. The results of this study demonstrate the potential of spectral index optimization and prediction models to monitor soil salinization based on fractional derivatives. This technique highlights the spectral detail information and enriches the preprocessing methods of hyperspectral imagery, resulting in the mapping of the spatial distribution of the degree of soil salinization. This has practical importance for the prevention and control of soil salinization and its spatial extent in arid and semi-arid regions.

Optimization-Based Water-Salt Dynamic Threshold Analysis of Cotton Root Zone in Arid Areas

Threshold levels of soil moisture and salinity in the plant root zone can guide crop planting and farming practices by providing a baseline for adjusting irrigation and modifying soil salinity. This study describes a method of soil water and salinity control based on an optimized model for growing cotton in an arid area. Experiments were conducted in Akesu Irrigation District, southern Xinjiang, northwest China, to provide data for cotton yield and soil water content and salinity in the root zone at different growth stages. The sensitivity of cotton to soil water content and salinity was predicted for different growth periods using a modified Jensen model. An optimization model with 480 boundary conditions was created, with the objective of maximizing yield, to obtain the dynamically varying water and salt threshold levels in the root zone for scenarios that included three initial soil moisture content values (W0), eight irrigation quantities (M), five initial soil salt content values (S0), and four irrigation water salinity levels (K). Results showed that the flowering–boll stage is the crucial period for cotton yield, and the threshold levels of soil water content and salinity in the cotton root zone varied with the boundary conditions. The scenario chosen for the research area in this study was W0 = 0.85θfc (θfc is field capacity), S0 = 4 g kg−1, M = 400 mm, K = 0 g L−1. The predicted threshold levels of soil water for different growth stages (seedling, bud, flowering–boll, and boll-opening) were respectively 0.75–0.85θfc, 0.65–0.75θfc, 0.56–0.65θfc, and 0.45–0.56θfc. Corresponding threshold levels of salt were 4–4.16, 4.16–4.39, 4.39–4.64, and 4.64–4.97 g kg−1 when no action was taken to remove salt from the root zone. This study provides an innovation method for the determination of dynamically varying soil water content and salt thresholds.

Modelling of soil water and salt dynamics and prediction of salinity risks in Lezíria (Portugal) in response to different qualities of irrigation water

The control and mitigation of soil salinization and/or sodicization phenomena are considered one of the main challenges of irrigated agriculture. In Portugal, Leziria Grande of Vila Franca de Xira its a region with increased salinization and/or sodicization risks due to its costal location with tidal influence. The aim of this study was to evaluate soil water and salt dynamics in a irrigated Fluvisol with a crop rotation of maize and annual ryegrass, and to predict irrigation-induced risks of soil salinization. We first calibrated the Hydrus 1D model for predicting soil water flow and solute transport with observed field data, afterwards we predict future risks of soil salinization performing a scenario analyses considering irrigation with different water qualities (ECw of 1.5, 3 and 5 dS m-1). We observed that when the ECw increases to values of 5 dS m-1, the average solute concentration in the root zone rises to levels above the threshold tolerated by maize. Hydrus 1D successfully simulated soil water content (RMSE= 0.019 m3m-3 (0.23-0.58 m3m-3) and R2= 0.94), the total dissolved solids in the soil (TDS with RMSE= 0.212 g L-1 (0.35-2.47 g L-1) and R2= 0.77) and the reduction in water uptake by the roots due to osmotic stress, and is a very useful tool in the management and planning of irrigation and in predicting soil salinity risks.

Evaluating the effects of layered soils on water flow, solute transport, and crop growth with a coupled agro-eco-hydrological model

PurposeSoil salinization and degradation in the arid and semiarid areas are a worldwide phenomenon. Soil capping with capillary barriers is a potential practice to hydraulically isolate contaminated soils, which may improve the soil environment for plant growth. This study aims to investigate the influences of soil capping on crop growth and soil salinization control in the arid area with shallow groundwater tables.Materials and methodsA one-dimensional agro-eco-hydrological model, LAWSTAC, capable of simulating water and solute transport in layered soil coupled with crop growth, was applied for simulating sunflower growth under field condition in Arid Northwest China. The model was calibrated and validated with the experimental data of 2012 and 2013 crop seasons. The calibrated model was then used to explore how the soil capping consisting of combinations of fine soil (10, 15, 17, 19, and 20 cm thick) and coarse sand (10, 5, 3, 1, and 0 cm thick correspondingly) would influence the soil water and salt dynamics, and seed yield.Results and discussionSimulation results by LAWSTAC compared well with the observed soil water content, salt concentration, leaf area index, and seed yield. Further scenario simulations showed that a sand layer in the soil capping could greatly affect the water and salt distribution in the soil above and below the sand layer. Though soil capping could decrease the water storage (WS) in the root zone, it caused no obvious increase in water stress to root uptake for sand thickness of 1–3 cm and also considerably reduced the root zone salt content (SC) in crop season compared with that without soil capping. The average WS during the crop season showed a negative correlation with the thickness of sand layer in the soil capping. The average SC from planting to harvest was significantly lower for thicker sand in the soil capping. To soils with high background salinization, the increase of sand thickness would be helpful for enhancing seed yield, until it reached a critical value.ConclusionsCoarse soil layer in the soil capping could prevent salt moving into the root zone, while fine soil could supply water to plant once water in coarse soil was low. Thus, in a long run, the soil capping consisting of combinations of fine and coarse soils with certain thicknesses would be an alternative practice for saline soil reclamation and improving crop production in arid area with shallow groundwater tables and soil salinization.

Soil salinity control and cauliflower quality promotion by intercropping with five turfgrass species

Greenhouse vegetable production contributes 60% of the economic value of the world vegetable industry. However, the secondary soil salinization and nitrate accumulation in vegetable products have become critical concerns in greenhouse vegetable production system. Here we show that cauliflower intercropped with five turfgrass species not only reduces the salinity stress and nitrate accumulation of cauliflower but also improves cauliflower curd quality. Effect of soil salinity control and cauliflower nitrate reduction was associated with the capacity of salt and nitrate uptake and accumulation of turfgrass species. Paspalum vaginatum performed the most significant effects on soil salinity control, reduced 37.8% of nitrate content, increased 50.7% of vitamin C and 21.1% of soluble protein contents in cauliflower curd. Our results demonstrated that intercropping with Paspalum vaginatum could provide a significant help for the sustainable development in the intensive greenhouse vegetable production.

Optimizing irrigation and drainage by considering agricultural hydrological process in arid farmland with shallow groundwater

Agricultural production in arid and semi-arid area faces a global problem of water resources shortage and land salinization. Irrigation and drainage are the important measure to enhance crop yield and control soil salinity. Generally, at field scale only irrigation is optimized to pursue the higher water use efficiency and crop yield, while drainage is difficult to optimize owing to controlled by dynamic groundwater levels. Here, we develop a new collaborative optimization model of irrigation and drainage to improve irrigation water use efficiency and to control soil salinity. The model is formulated by integrating simulation of physical processes of field water - salt balance and a genetic algorithm-based optimization model. The new model is to search optimized irrigation and drainage strategic decision for enhancing field economic benefit with the condition controlling salinity with limited water resources. Then, a case study on optimally allocating irrigation and drainage water to different growth stages of maize field in the Hetao Irrigation District, arid area of northwest China shows enhanced applicability of the developed model. Five groundwater depth levels (1 m, 1.5 m, 2 m, 2.5 m and 3 m) and five groundwater salinity levels (2 g/L, 2.25 g/L, 2.5 g/L, 2.75 g/L and 3 g/L) are provided to show and compare the solutions of the optimal irrigation and drainage water allocation. Results indicate the developed model can supply reasonable field monthly irrigation and drainage decision with considering field hydrology, especially contribution of groundwater to crop water demand and groundwater role to soil salt accumulation. The contrary relationship between system benefit and irrigation water use efficiency was described successfully by the developed model. And compared with traditional single irrigation optimization model, the developed irrigation-drainage collaborative optimization model can enhance drainage function to keep the optimal groundwater levels and improve the system benefit by 0–8%. Overall, the developed model can provide more applicable irrigation water and drainage strategies to the sustainable development of irrigation agriculture.

A sustainable irrigation water management framework coupling water-salt processes simulation and uncertain optimization in an arid area

Field irrigation water management depends on interactions among crop yield, soil water/salt and groundwater/salt in arid irrigation area with shallow-saline groundwater. This paper presents a novel uncertainty simulation-optimization framework for irrigation water allocation and sustainable agricultural environment, which integrates simulation of physical processes of soil-groundwater water and salt balance into an uncertainty-based optimization model. The impacts of crop evapotranspiration, soil water and salt and groundwater levels are interactively involved in the simulation model. Uncertainties (economic and crop parameters, available water amount) presented as fuzzy boundary intervals and probability distribution functions are considered in the optimization model. This field irrigation water allocation framework emphasizes the role of field soil water and salt movement processes to decision-making of irrigation water allocation. Then, the proposed simulation-optimization framework was applied to a case study in the Hetao Irrigation District, an arid area of northwest China where soil salinity is a serious environmental problem induced by irrigation and shallow groundwater. Therefore, optimal irrigation water allocation solutions can be generated for providing decision makers with reliable decision options where the maximum system benefits resulting from sustainable agricultural production are desired. Furthermore, the results can support analysis of interrelationships among system benefits, water allocation planning and groundwater depth, soil salt content constraints. Scenario analysis (groundwater table depth (GTD) = 1, 1.5, 2, 2.5, 3 m and no groundwater exchange consideration) showed that the maximum net benefit could be [27469, 44818] Yuan with the groundwater table depth of 1.5 m. Also, the irrigation water allocation changed when the salt constraint was considered, which indicates that the results obtained by the developed framework can alleviate soil salinization to a certain degree. Therefore, this framework can provide more effective information for the irrigation water management and soil salinization control, which is meaningful for the sustainable development of irrigation agriculture.

Potential of irrigation technology to control soil salinity and greenhouses pepper yield production improvement in sandy soil of Southern Tunisia

Localised surface drip irrigation (DI) is used to irrigate pepper crop in a greenhouse with two irrigation treatments 100% (T1) and 50% (T2) of the plant needs. The DI system is compared with a new irrigation technique called buried diffuser (BD). Irrigation treatment, soil depth, soil electrical conductivity (EC) and pepper yield production were used to compare between the irrigation systems using generalised additive mixed model (GAMM). The results indicated that yield production increased from 23,047 kg/ha under DI to 23,945 kg/ha under BD using T1 treatment. By using T2 treatment, the yield production was 13,164 kg/ha under DI and 15,703 kg/ha under BD. Moreover, BD helped to eliminate the salinity in the root zone for the T1 treatment. While it has the same effect on soil salinity as the T2 treatment of the DI. Under T1 and T2, the yield obtained from BD was significantly higher that DI.

Potential of irrigation technology to control soil salinity and greenhouses pepper yield production improvement in sandy soil of Southern Tunisia

Potential of irrigation technology to control soil salinity and greenhouses pepper yield production improvement in sandy soil of Southern Tunisia

Optimizing water resources allocation and soil salinity control for supporting agricultural and environmental sustainable development in Central Asia

In this study, a stochastic-fuzzy-based fractional programming (SFFP) method is advanced for optimizing water-resources allocation and soil-salinity control under uncertainty. The developed method can address ratio objective optimization problems of complex system in association with stochastic and fuzzy uncertainties, which can help gain in-depth analysis of the interrelationships between marginal effectiveness and system reliability. Then, SFFP is applied to an irrigation region in the lower reach of Amu Darya River basin, where linear crop yield-salinity functions and salt-leaching functions are introduced into the modeling formulation for reflecting the complicated interactions among water resources, soil salinity, arable land, and electricity supply. Solutions under 96 scenarios related to different irrigation efficiencies, water availabilities, and electricity supplies have been obtained. Our findings are: i) increased water availability, electricity supply, and irrigation efficiency result in high marginal benefit; ii) irrigation efficiency is the key factor influencing water allocation patterns for crop irrigation and salt-leaching, promotion of which can facilitate mitigating economic and environmental losses in the water-deficit and soil-salinized region; iii) leaching water allocation patterns for soil-salinity washing is related to salinity characters of crops and regions, and boosting drought- and salt-tolerance crop can be effective in adaption to risks of water scarcity and land salinization. Compared to the conventional approaches, SFFP can generate more flexible alternatives and achieve higher marginal effectiveness. These findings can provide effective decision support to identify desired water management strategies under multiple uncertainties for supporting agricultural sustainability in arid regions.

Monitoring the seasonal dynamics of soil salinization in the Yellow River delta of China using Landsat data

Abstract. In regions with distinct seasons, soil salinity usually varies greatly by season. Thus, the seasonal dynamics of soil salinization must be monitored to prevent and control soil salinity hazards and to reduce ecological risk. This article took the Kenli District in the Yellow River delta (YRD) of China as the experimental area. Based on Landsat data from spring and autumn, improved vegetation indices (IVIs) were created and then applied to inversion modeling of the soil salinity content (SSC) by employing stepwise multiple linear regression, back propagation neural network and support vector machine methods. Finally, the optimal SSC model in each season was extracted, and the spatial distributions and seasonal dynamics of SSC within a year were analyzed. The results indicated that the SSC varied by season in the YRD, and the support vector machine method offered the best SSC inversion models for the precision of the calibration set (R2>0.72, RMSE < 6.34 g kg−1) and the validation set (R2>0.71, RMSE < 6.00 g kg−1 and RPD > 1.66). The best SSC inversion model for spring could be applied to the SSC inversion in winter (R2 of 0.66), and the best model for autumn could be applied to the SSC inversion in summer (R2 of 0.65). The SSC exhibited a gradual increasing trend from the southwest to northeast in the Kenli District. The SSC also underwent the following seasonal dynamics: soil salinity accumulated in spring, decreased in summer, increased in autumn and reached its peak at the end of winter. This work provides data support for the control of soil salinity hazards and utilization of saline–alkali soil in the YRD.