Soil Salinity Indicators Research Articles

Long-term effects of combining reclaimed and freshwater on mandarin tree performance

The current scenario of water scarcity leads to a seek for new irrigation strategies that maintain agricultural productivity while reducing the pressure on freshwater resources, especially in the Mediterranean region, which has a structural deficit of water sources. In this study, the effects of irrigation with water from different origins were assessed over six consecutive years in soil salinity indicators, tree physiology and yield of mandarin trees in south east Spain. The following treatments were considered: i) freshwater from the “Tagus-Segura” water-transfer canal (TW or Control); ii) reclaimed water from a wastewater-treatment plant (RW); iii) irrigation with TW, except in stage II of fruit development when RW was used (TWc); and iv) irrigation with RW except in stage II when TW was used (RWc). Soil salinity indicators increased in all treatments with respect to Control, except for TWc in winter. However, this increase only led to reductions in net photosynthesis and stomatal conductance in the RW treatment, while gas exchange parameters of trees from TWc and RWc were not negatively affected. Tree vegetative growth was only affected in the long-term, with trees from the RWc treatment having greater canopies. Yield was increased in the TWc and RWc treatments by 23.1 % and 29.5 %, respectively; while it was reduced by 23.5 % in RW when compared to TW. Likewise, differences among treatments in fruit quality traits were also detected. Our results suggest that combining water from different sources could be a viable alternative for irrigating mandarin trees under semiarid conditions with savings of approximately 50 % of fresh water, although the sustainability of soil health should be ensured. In addition, further research is needed to adapt these strategies to other species and cultivar-rootstock combinations.

Evaluating machine learning performance in predicting sodium adsorption ratio for sustainable soil-water management in the eastern Mediterranean

Soil salinization is a critical global issue for sustainable agriculture, impacting crop yields and posing a threat to achieving the Sustainable Development Goal (SDG) of ensuring food security. It is necessary to monitor it in detail and uncover its underlying factors at a regional scale. In this context, the present study aimed to evaluate soil health in the eastern Mediterranean region by using the Sodium Adsorption Ratio (SAR) as an indicator of soil salinity in three distinct soil horizons. The main objective of the research was to evaluate the performance of four machine learning (ML) models, including Random Forest (RF), Nu Support Vector Regression (NuSVR), Artificial Neural Network-Multi Layer Perceptron (ANN-MLP), and Gradient Boosting Regression (GBR), for accurate prediction of SAR following the Recursive Feature Elimination (RFE) as a feature selection method. Moreover, SHapely Additive exPlanations (SHAP) was applied as sensitivity analysis to identify the most influential covariates. Main findings of the research revealed that the average clay content in the surface horizon (H10-25cm) was 50.5% ± 10.4, which significantly increased to 57.5% ± 8.7 (p < 0.05). No significant mean differences were detected between the studied horizons for SAR and Na+. ML output revealed that NuSVR outperformed other algorithms in accurately predicting outcomes during both the training and testing stages. Moreover, Scenario 2 (SC2) with seven selected features from the RFE method facilitated highly accurate SAR predictions. Overall, the performance of ML models is ranked as NuSVR > GBR > ANN-MLP > RF. Lastly, SHAP sensitivity analysis identified CEC, Ca+2, Mg+2, and Na+ as the most influential variables for SAR prediction in both the training and testing stages. Hence, the research yielded valuable insights for efficient agricultural soil management at a regional level using state-of-the-art technology.

Bespoke cultivation of seablite with digital agriculture and machine learning

Climate change has driven agriculture to alter farming methods for food production. This paper presents a new concept for monitoring, acquisition, management, analysis, and synthesis of ecological data, which captures the environmental determinants and direct gradients suited to a particular requirement for specific plant cultivation and sustainable agriculture. The purpose of this study is to investigate a smart seablite cultivation system. A novel digital agricultural method was developed and applied to digitised seablite cultivation. Machine learning was used to predict the future growth conditions of plants (seablites). The study identified the illustrative maps of seablite origins, a conceptual seablite smart farming model, essential factors for growing seablite, a digital circuit for cultivating seablite, and digital data of seablite growth phases comprised the digital data. The findings indicate that: (1) An indicator of soil salinity is a quantity of sodium chloride extracted from a seablite sample indicating its origin of environmental determinants. (2) Saline soil, saline water, pH, moisture, temperature, and sunlight are essential factors for seablite development. These factors are dependent on climate change and were measured using a smart seablite cultivation system. (3) Digital circuits of seablite cultivation provide a better understanding of the relationship between the essential factors for seablite growth and seablite growth phases. (4) Deep neural networks outperformed vector machines, with 86% accuracy at predicting future growth of seablites. Therefore, this finding showed that the essential seablite development factors can be manipulated as key controllers for agriculture in response to climate change and agriculture can be planned. Basic digitisation of specific plants aids plant migration. Digital agriculture is an important practice for agroecosystems.

Inversion of coastal cultivated soil salt content based on multi-source spectra and environmental variables

Soil salinization seriously hinders the development of efficient ecological agriculture in coastal areas. The use of Landsat, Sentinel series and hyperspectral data is an ideal way for assessing soil salinity indicators. However, environmental data (e.g. climate, terrain and parent material) are important factors for estimating such indicators. It is necessary to find the advantages and limitations of a combination of satellite images, hyperspectral data and environmental variables (ENVI) for assessing soil salinity accurately. Various data or their combinations ([I] remote sensing [RS], i.e. bands and salinity indices of Landsat 9 and Sentinel 2; [II] ENVI, including soil attributes, climate and topography; and [III] RS + ENVI) were used to construct the salinity inversion model using random forest (RF) and extremely randomized trees (ERT) for cultivated areas in the coastal plain of Dongtai City, China. The hyperspectral data were also resampled to match the range of the image bands. RF performed better than ERT for all types of analyzed data, and RS + ENVI exhibited the best performance for Sentinel 2 (R2 = 0.86). Compared with the RS data alone, Landsat 9 and Sentinel 2 provided higher salinity simulations (41% and 126%, respectively) after combination with ENVI, and salinity mapping was closer to the actual soil salinity measurements. The variables of slope, salinity index (SIT), difference index and SIT had the highest contribution in Landsat 9, Sentinel 2 and resampled hyperspectrum based on Landsat 9 and Sentinel 2, respectively. In conclusion, RS + ENVI based on Sentinel 2 data is the recommended approach for monitoring the salt content of coastal cultivated soil.

Coupling of machine learning and remote sensing for soil salinity mapping in coastal area of Bangladesh

Soil salinity is a pressing issue for sustainable food security in coastal regions. However, the coupling of machine learning and remote sensing was seldom employed for soil salinity mapping in the coastal areas of Bangladesh. The research aims to estimate the soil salinity level in a southwestern coastal region of Bangladesh. Using the Landsat OLI images, 13 soil salinity indicators were calculated, and 241 samples of soil salinity data were collected from a secondary source. This study applied three distinct machine learning models (namely, random forest, bagging with random forest, and artificial neural network) to estimate soil salinity. The best model was subsequently used to categorize soil salinity zones into five distinct groups. According to the findings, the artificial neural network model has the highest area under the curve (0.921), indicating that it has the most potential to predict and detect soil salinity zones. The high soil salinity zone covers an area of 977.94 km2 or roughly 413.51% of the total study area. According to additional data, a moderate soil salinity zone (686.92 km2) covers 30.56% of Satkhira, while a low soil salinity zone (582.73 km2) covers 25.93% of the area. Since increased soil salinity adversely affects human health, agricultural production, etc., the study's findings will be an effective tool for policymakers in integrated coastal zone management in the southwestern coastal area of Bangladesh.

Spectral prediction of soil salinity and alkalinity indicators using visible, near-, and mid-infrared spectroscopy

Drought and the impacts of climate change have led to an escalation in soil salinity and alkalinity across various regions worldwide, including Iran. The Chahardowli Plain in western Iran, in particular, has witnessed a significant intensification of this phenomenon over the past decade. Consequently, modeling of soil attributes that serve as indicators of soil salinity and alkalinity became a priority in this region. To date, only a limited number of studies have been conducted to assess indicators of salinity and alkalinity through spectrometry across diverse spectral ranges. The spectral ranges encompassing mid-infrared (mid-IR), visible, and near-infrared (vis-NIR) spectroscopy were employed to estimate soil properties including sodium adsorption ratio (SAR), exchangeable sodium ratio (ESR), exchangeable sodium percentage (ESP), pH, and electrical conductivity (EC). Five distinct models were employed: Partial Least Squares Regression (PLSR), bootstrapping aggregation PLSR (BgPLSR), Memory-Based Learning (MBL), Random Forest (RF), and Cubist. The calibration and assessment of model performance were carried out using several key metrics including Ratio of Performance to Deviation (RPD) and the coefficient of determination (R2). Analysis of the outcomes indicates that the accuracy and precision of the mid-IR spectra surpassed that of vis-NIR spectra, except for pH, which exhibited a superior RPD compared to other properties. Notably, in the prediction of pH utilizing vis-NIR reflectance spectra, the BgPLSR model exhibited the highest accuracy and precision, boasting an RPD value of 2.56. In the domain of EC prediction, the PLSR model yielded an RPD of 2.64. For SAR, the MBL model achieved an RPD of 2.70, while ESR prediction benefited from the MBL model with an impressive RPD of 4.36. Likewise, the MBL model demonstrated remarkable precision and accuracy in ESP prediction, garnering an RPD of 4.41. The MBL model's efficacy in forecasting with limited datasets was notably pronounced among the models considered. This study underscores the valuable role of spectral predictions in facilitating the work of soil surveyors in gauging salinity and alkalinity indicators. It is recommended that the integration of spectrometry-based salinity and alkalinity predictions be incorporated into forthcoming soil mapping endeavors within semi-arid and arid regions.

Partial least-squares regression for soil salinity mapping in Bangladesh

Estimating the salinity of the soil along the coast of south-western Bangladesh is the focus of this study. Thirteen soil salinity indicators were computed using the Landsat OLI images, and 241 soil salinity samples were gathered from secondary sources. In this research, partial least-squares regression was used to estimate soil salinity. Soil salinity zones were then divided into five classes based on Jenks natural breaks. There is a strong relationship between soil salinity and several indices, including B2 (Blue band), B5 (Near-Infrared band), SI_1 (Salinity index 1), and SI_2 (Salinity index 2). The correlation between B2 and soil salinity is particularly high, hovering around 0.92. The majority of other factors, however, have a negative or barely positive correlation of −0.260 to 0.032. Very high and high levels of salinity are estimated to cover 29.02% and 13.45% of the study area, respectively, based on the soil salinity map. Finally, cross-validation has been used to see how well the model works to predict soil salinity for a specified weight. Iteration has been done three times in order to assess the model's performance on various subsets of data and produce a more accurate prediction. In the third iteration, PRESS, RSS, and Q2cum values were 1.844826, 3.354687, and 0.9705131, correspondingly, which indicates a higher predictive accuracy. The findings of the study will be useful as a tool for policymakers in the south-western coastal area of Bangladesh who are responsible for integrated coastal zone management.

Characterizing of Sandy Loam Soil Electrical Conductivity and Cone Index Relationship at Varying Depths in Punjab Region

Soil constitutes a fundamental resource for the production of food and fiber, thereby meeting diverse human needs and sustaining global ecosystems. Soil compaction and electrical conductivity are key soil properties that can significantly impact soil health and contribute to environmental challenges. The findings of the study shed light on the severity of soil compaction, with the highest cone index 4495 kPa value recorded at a depth of 225 mm in sandy loam soil. This highlights the potential limitations on root growth and nutrient uptake, which can have implications for plant productivity and the overall ecosystem. Furthermore, the study examined the electrical conductivity of the soil, which serves as an indicator of soil salinity. Elevated electrical conductivity levels can lead to adverse effects such as reduced crop yields and environmental stress. Bulk density was increased (1.4 to 1.73 Mg/m3) as well as the moisture content of the soil was decreased (7.6 to 4.3 %). Understanding the range of electrical conductivity values observed in the study (11 to 29 mS/m) helps to assess the extent of salinity-related challenges in the studied region. By establishing regression equations between cone index and soil properties like bulk density and moisture content, the study provides valuable insights into the underlying relationships that contribute to soil compaction and its environmental implications. The coefficient of determination (R2) between the bulk density with moisture content and cone index was 0.99 and 0.95, respectively. These findings can assist in the development of strategies to mitigate soil degradation, promote sustainable land management practices, and address environmental concerns related to soil health.

Calcium-modified biochar rather than original biochar decreases salinization indexes of saline-alkaline soil.

We investigated the improvement effects of herbaceous (corn) and woody (oak sawdust) biochar with their calcium modification on saline alkali soil. The addition of unmodified biochar regardless of types had no significant effect on the soluble cations (Na+, Ca2+, and Mg2+) and the main indicators of soil salinity and alkalinity (pH, sodium adsorption ratio (SAR), exchangeable sodium percentage (ESP), and total alkalinity (TA)), but the addition of calcium modified biochar decreased these soluble cations and indicators, especially the addition of modified woody biochar (PBM). Compared to CK, TA decreased by 70.02% and 89.25% in PBM with 2% and 4% addition, respectively. Soil ESP and SAR showed a significantly positive correlation with pH and TA, which indicated that soil salinization and alkalization were synchronized. These results showed that the calcium modified biochar, especially the modified woody biochar, instead of the original biochar could be potential soil amendments for the improvement of saline-alkali soil.

Canopy temperature: as an indicator of soil salinity (a case study in Syrdarya province, Uzbekistan)

A shift in the temperature of the canopy may signify stress in the plants. In laboratory and greenhouse trials, using canopy temperature for the measurement salt stress in certain agricultural crops was thoroughly examined; however, its potential application in landscape-level investigations employing remote sensing methods has not yet been investigated at different time series. A satellite thermography for measuring the soil salinity of agricultural areas at the provincial level was the subject of our investigation. The research area was the irrigated, semi-arid, and salt-affected agricultural land appertain to Syrdarya province in Uzbekistan, which was mostly planted with wheat and cotton. The provincial soil salinity map was considered as a ground truth data and the moderate-resolution imaging spectroradiometer satellite (MODIS) data were perceived as an indication for canopy temperature in this study. We investigated the relationships between the soil salinity, the normalized difference vegetation index, and canopy temperature, using analysis of variance. The findings indicated a strong inverse correlation between canopy temperature and soil salinity, although this relationship changed throughout the experimental years. For cotton, the highest correlation was shown in September. In comparison to the other variables looked at, canopy temperature had higher computed F values. Our findings indicate that soil salinity may be detected at the landscape level using satellite thermography in regions where crops are being grown.

Salt accumulation in soils under furrow and drip irrigation using modified waters in Central Iran

The objectives of this study were i) to characterize the water and soils under different managements, ii) to evaluate the sustainability of using hypersaline soils and water, and iii) to assess possible solutions to prevent more degradation of soil and water resources. Field and laboratory analysis of the samples using eight pedons and 128 surface samples taken from grid in four pre-determined land uses; pistachio orchard abandoned, pistachio orchard with furrow irrigation, wheat and maize cropping with furrow irrigation, pistachio orchard with drip irrigation. The study area, 170 ha, comprised two distinct soil parent materials including marls (max. ECe &gt;100 dS/m) and alluviums (max. ECe &gt;60 dS/m). Abandoning lands caused salinity increasing due to lack of leaching by irrigation water. The maximum increase of soil salinity was in the abandoned land use (EC e =98 dS/m), where trees had been removed and there is no irrigation, followed by pistachio plantation land use (EC=11 to 34 dS/m), and wheat and maize cropping land use (EC=11-19 dS/m). The minimum rise in soil salinity was in the drip irrigation due to mixing freshwater with saline water and therefore better water quality (EC=3 dS/m at surface layer and 17 dS/m in next layer). Land use change to agriculture increased the need for irrigation and because of arid climate it mainly supplied by groundwater from deep wells. Using deep groundwater due to rock-water reaction and increasing salinity, decreased water quality in furrow irrigation and therefore it had more significant effect on soil salinity compare to drip. Comparison of the mean values of soil salinity indicators in 2018 showed that salinity has increased by 3-6 times in the furrow irrigation and at least two-fold in the drip irrigation, compared to 2002. The calculated salinity indicators also proved the soil and water resources had been degraded and present land use types are not sustainable. Possible solutions could be to minimize land use change to agriculture, to use drip irrigation with mixed saline and freshwater, and to remove salt crusts from the soil surface.

Modelling the Percentage of the Exchangeable Magnesium Based on the Adsorption Ratio in the Some Saline Soils Central Iraq

The study aims at estimating the percentage of exchangeable magnesium in the Some Saline Soils Central Iraq, where the percentage of the exchangeable magnesium is often estimated using hard and time-consuming lab tests, so simpler methods have been developed using soil salinity indicators. The study was conducted on the Some Saline Soils Central Iraq11 sites in depth (30-0 cm deep root area), which included regions of the Middle Euphrates Governorates (Babylon, Karbala, Najaf, Diwaniyah).The results showed a linear relationship between MgAR and EMgR with MgAR ranging between 0.55-0.99 Cmol.kg-1and EMgR ranged from 0.51-0.64 Cmol.kg-1 Also, there was a linear relationship between SAR and ESR. SAR ranged from 2.74-9.66 ( Cmol.kg-1)0.5 and ESR values ranged from 0.84- 5.00Cmol.kg-A linear regression equation was proposed for the percentage of EMgP in the soil based on its adsorption ratio EMgP = 1.114 * MgAR + 0.095 The results of the study Showed that EMgP values ranged between 0.72- 1.22 Cmol.kg-1 in study soil samples.

Analysis of spatial-temporal variation of the saline-sodic soil in the west of Jilin Province from 1989 to 2019 and influencing factors

Soil salinization is a serious land degradation phenomenon that significantly affects regional economies and agricultural development. Since the 1980s, the state of soil salinization has been constantly changing due to various climatic conditions, human activities, and the application of soil improvement measures. In this study, the Landsat-5 TM and Landsat-8 OLI satellites, launched in 1984 and 2013, respectively, were used to track changes in soil electrical conductivity (EC), a key indicator of soil salinity. A remote-sensing inversion model for the EC of saline-sodic soil was established (R = 0.5), and the soil EC in western Jilin Province from 1989 to 2019 was inverted. Then, the temporal and spatial variation characteristics of the soil salinization degree were counted, and the related variables and influencing factors were analyzed. The results are in good agreement with the conclusion that meteorological conditions are the most relevant and important factors in soil salinization. The area of saline soil increased continuously in the natural state (from 1989 to 1999), and the implementation of government policies (1999 to 2019) reversed the saline soil expansion, converting large areas of saline soil to grassland and cropland. This study is the first to quantitatively analyze the degree of soil salinization and its influencing factors in western Jilin Province from 1989 to 2019; this work also successfully predicted and monitored the variation in soil salinization. This provides data support for the study of the dominant elements in soil salinization changes and the rational development of land.

The Effect of Biochar Obtained from Municipal Pruning Wastes at Different Pyrolysis Temperatures on Maize Yield and Some Soil Properties

The pot experiment was set up in a randomized plot design with three replications. Biochar was obtained at three different pyrolysis temperatures (300, 500, 700°C) by using municipal pruning wastes. In response to the biochar applications at five different doses (0, 10, 20, 30 and 60 t ha-1), some biological (soil respiration, dehydrogenase activity) and chemical properties of the soils (pH, electrical conductivity-EC, organic carbon, cation exchange capacity-CEC, N, P, K, Ca, Mg, Na, Fe, Cu, Zn, Mn and B) and yield (fresh plant biomass) were investigated. The parameters analyzed showed significant changes between -48% and 141% when compared with the control soils (P&lt;0.05). It was determined that the biochar obtained at 300°C pyrolysis temperature can be biodegraded more easily than the biochar obtained at 500 and 700°C. Similarly, available phosphorus and boron had the most positive effect from biochar application at 300°C pyrolysis temperature. Moreover, the available boron concentration achieved the highest increase with the application of 60 t biochar ha-1. It has been determined that biochar has the potential to increase the pH value of soils between 0.5-6.2% in 75 days. The EC value, which is an indicator of soil salinity, decreased with 700°C and 20, 30 and 60 t ha-1 applications. An increase potential of 31.3% was determined in the CEC. There was no determinable effect on available Mn, Fe and Cu by independent variables. An increase of 71.4% was detected in the maize yield with the application of 700 °C and 60 t ha-1.

Агроэкологическая оценка засоления почв

In the conducted studies, the influence of salinization on the soil-plant system on the soils of Russia and Vietnam was evaluated. It is shown that the nature and degree of soil salinization vary over time and in space, including on individual elements of meso- and microrelief. For soils, plants and biota, it is advisable to allocate their optima and maximum permissible salinity concentrations. They differ for different soils, for individual plant species and microorganisms. It is shown that optimal and acceptable indicators of soil salinity differ from a combination of parameters of plant life factors and soil functioning: temperature, humidity, combination of soil properties, phases of soil and plant development, etc. It was found that the composition of the soil extract differs significantly from the composition of the soil solution not only in the concentration of salts, but also in their composition, which must be taken into account in the agroecological assessment of salinity. The possibility of increasing the resistance of plants to soil salinization when they are fed with biophilic elements, stimulants, complexons is shown. Keywords: SALINIZATION, PLANTS, MAXIMUM PERMISSIBLE CONCENTRATIONS, OPTIMIZATION

Soil salinity indicators and salinity build-up on saline water irrigation in seed spices

Context Soil salinity is a major constraint in crop production in arid and semiarid regions where saline water is the main irrigation source affecting soil quality and crop productivity. Aims A pot experiment was conducted to study the impact of alternate application of saline water (electrical conductivity (EC): 6.0 dS m−1) and fresh water on soil salinity indicators and salinity build-up under four seed spices (Anethum graveolens L., Nigella sativa L., Pimpinella anisum L. and Trachyspermum ammi L.). Methods Soil samples were collected from a pot experiment (0–15 cm depth) conducted at ICAR-Central Soil Salinity Research Institute, Karnal, Haryana, and analysed for different physicochemical properties (pH and EC), cationic (Ca2+, Mg2+, Na+ and K+) and anionic (CO32−, HCO3−, Cl−, SO42−) concentration and their contribution to salinity build-up in soil. Key results Soil EC varied between 0.45 and 8.27 dS m−1 (EC1:2) and between 1.20 and 24.90 dS m−1 (ECe) irrespective of the seed spice species. The application of saline water in the early stages of growth followed by fresh-water irrigation resulted in a comparatively low ECe, and cation and anion concentrations over continuous saline-water irrigation irrespective of the seed spice crop. ECe, sodium adsorption ratio, potassium and pHs are the important indicators identified by principal component analysis and better explained soil salinity under this situation. Conclusions Keeping these in view, there is a need for cyclic use of saline and fresh water in growing seed spice crops to prevent soil degradation. Study recommends that these four seed spice crops should be irrigated with fresh water during crop establishment and flowering stage, whereas, in between, saline water can be applied. Implications Such management of saline water irrigation could help to reduce salinity and maintain soil health for sustaining crop productivity, specifically for seed spices, in arid and semiarid regions.

Effects of Equilibrium Time on Electrical Conductivity Measurements Using Soil-Water Extracts and Soil Saturated Paste

Electrical conductivity (EC) is a robust indicator of soil salinity. In South Korea, EC of 1:5 of soil:water extracts (EC1:5) and soil saturated paste extracts (ECe) are often used. The standard shaking time is 0.5 hr for EC1:5 and standing time after soil saturation is 16 hrs for ECe. In this study, we investigated the effects of equilibrium time (shaking and standing time for EC1:5 and ECe, respectively) to test if the standard time is enough for precise and accurate measurement of EC. Two soils (silt loam and sandy loam) with seven levels of EC were prepared by adding salt solutions (1, 2, 4, 8, 12, 16, and 24 dS m-1) to the soils. The soils were used for experiments after 2-months aging. The EC1:5 was measured after shaking soil-water mixture for 0.5, 1, 2, and 3 hrs, and ECe was determined after standing the saturated soils for 4, 8, 16, 24, and 48 hrs. The results showed that 4 hrs of standing time for ECe are sufficient for most saline soils regardless of soil texture. However, occasional occurrence of large variations in ECe up to 25% indicates a necessity of longer (e.g., 24 hrs) standing time for some soils. Therefore, for long-term monitoring of ECe, it may be necessary to determine site-specific standing time for ECe. Unlike ECe, the variations in EC1:5 with shaking time was negligible. However, for soils treated with solution of high EC (24 dS m-1), the variability increased up to 6% variations. Therefore, it may be necessary to increase shaking time to 1 hr to ensure a precise measurement of EC1:5 particularly for high-saline soils. For precise measurement of EC by achieving equilibrium of ions, shaking for 1 hr is required for EC1:5 and standing for 24 hrs is appropriate for ECe.

Evaluation of soil treatment techniques on remediated brine spill sites in semi-arid rangelands

Unconventional oil and gas development (UOG) generates high volumes of flowback and produced water, byproducts of hydraulic fracturing operations, that are often released or spilled on the soil surface. Soil contamination with these wastewaters, commonly referred to as brine, has the potential to inhibit vegetation growth indefinitely. Natural attenuation of brine is not expedient in arid and semi-arid regions where most United States UOG developments are located, including the Bakken region of North Dakota. In situ (at-site) and ex situ (off-site) soil treatment techniques are commonly employed to remediate brine-contaminated soils in the Bakken. However, little is known regarding each technique's efficacy despite differences in application, cost, and efficiency. We selected 10 sites previously remediated with chemical amendments (in situ) and 11 sites with topsoil excavation (ex situ) in the United States Forest Service Little Missouri National Grasslands. We paired each remediated site with a reference to examine the ability of each strategy to return brine-contaminated sites to conditions reflective of the current state of the surrounding semi-arid rangeland ecosystem. At each site, we quantified soil electrical conductivity (ECe) as an indicator of soil salinity and measured vegetation cover, biomass production, bare ground, and litter. The difference between paired reference and remediated sites was used for analysis. Brine contamination was still evident as soil ECe was similarly increased on chemical amendment and topsoil excavation remediated sites over paired references at all soil depths tested. Due to the nature of the topsoil excavation treatment, elevated ECe in the 0–15 cm depth suggested resalinization of the new topsoil. Remediation techniques also resulted in similar plant community composition marked by an increase in exotic forb biomass, largely due to the invasion of kochia (Bassia scoparia) which was absent from reference sites. However, remediation techniques differed substantially in vegetation establishment. We found 15% more bare ground on sites remediated with chemical amendment treatment than paired references and 55% more with topsoil excavation. Our results indicate that in situ strategies may be more suitable than ex situ strategies for brine-spill remediation in semi-arid rangelands like the Bakken in North Dakota as they cause less disturbance and likely require less post-remediation management to establish adequate vegetation cover to protect the soil from further erosion.

Assessment and Mapping of Soil Salinization Risk in an Egyptian Field Using a Probabilistic Approach

The assessment of soil salinization risk at the field scale requires modeling of the spatial variability of soil salinity. This paper presents a probabilistic approach to estimate and map a risk index using all available auxiliary information. A probabilistic methodology is proposed to estimate the conditional probability of exceeding the assigned threshold value of a generic indicator of soil salinity. A geostatistical non-parametric technique, probability kriging, was used to assess the risk of soil salinization and delineate different hazard zones within a field. The technique relies on indicator coding of information. The approach was applied to soil electrical conductivity measurements collected in an experimental field located in the Nile Delta region in Egypt, and submitted over time to trials with different fertilization treatments. The application of the method allowed delineation of a north-eastern zone in the field with a high risk of soil salinization due to its lack of cultivation for a long time and nearness to buildings that prevent water infiltration. The method proved to be quite promising from the perspective of precision agriculture and it is easily extendable to any sort of remote and proximal sensing auxiliary information, including information on the deepest layers of soil.

Salinity stress detection in rice crops using time series MODIS VI data

ABSTRACTSoil salinity is one of the abiotic stresses that constrains rice crop growth in the extensive coastal regions of India. Monitoring soil condition on such a large scale is time-consuming and expensive and alternative approaches have been proposed. Crop condition can be used as a reference to detect salinity-affected soils in rice crops instead of direct detection of soil condition. Vegetation indices derived from high temporal resolution satellites like the Moderate Resolution Imaging Spectroradiometer (MODIS) have the potential to detect soil salinity in a cost- and time-efficient manner. This study analysed the temporal trend of salinity in the kharif (wet) season and the behaviour of rice crop phenological indicators under different levels of salinity. We tested the applicability of MODIS time series Enhanced Vegetation Index (EVI) data for soil salinity detection in rice crops in the coastal region of Odisha, India. We used smoothed time series profiles of MODIS EVI 8 day composite data from 2011 to 2015 to extract rice crop phenological parameters. A total of 350 soil samples were periodically collected from 80 sites during the kharif season. Results showed that soil salinity was negatively correlated with EVI (r = – 0.76). Among all the rice crop parameters, the seasonal integral (SI) and amplitude of the EVI signal were the most promising indicators of soil salinity and its intensity. We observed consistently low SI and amplitude values of EVI in homogeneous rice pixels at high saline levels from 2011 to 2015 (P > 0.050). Our study showed that the extraction of phenological parameters from MODIS EVI is a promising method for detecting soil salinity in rice growing areas at regional scales even at low salinity levels.