Variability Of Soil Salinity Research Articles

Spatial and temporal heterogeneity of soil salinity and ionic coupling relationship under the water-saving renovation of a typical irrigation district in arid and semi-arid areas

Soil salinization is a global environmental problem. To investigate the spatiotemporal heterogeneity of soil salinity and the coupling relationship of soil salinity ions under water-saving renovation conditions, we chose Shenwu Irrigation Area, where water-saving renovation projects are being conducted comprehensively. Specifically, for our field experiments, we chose 46 soil sampling points. Specifically, we used different spatial interpolation methods and coupling coordination models were applied to study the spatial and temporal distribution of soil salinity and soil salt-based ion coupling relationship changes during different water-saving periods in the irrigation area between 2015 and 2022. The results showed that (1) the emergence of soil salinity extremes after water-saving renovation project led to an increase in the variability of soil salinity. (2) Compared with the Inverse Distance Weighted, Radial Basis Function, and Universal Kriging interpolations, the Mean Absolute Error of the Ordinary Kriging interpolation was reduced by 12.68 %, 43.96 %, and 38.77 %, the Root Mean Square Error was reduced by 14.34 %, 60.85 %, and 36.53 %, reflective of a significant improvement in accuracy, and thus confirming this method to be optimal for elucidating soil salinity. (3) Compared with pre-water-saving renovation project, the average soil desalination rates in the 0–50 cm soil layer during and after project implementation were 5.71 % and 35.67 %. The impact of the water-saving renovation project was highest in channelside areas, followed by croplands, and lowest in lakeside areas. (4) The growth rates of soil salt-based ion coupling degree and coupling coordination degree after project implementation, compared with before implementation, were 17.07 % and 10.81 %. The implementation of water-saving renovation project improved soil quality. This study provides scientific support for the prevention and control of soil salinization in irrigation areas.

Optimal groundwater depth and irrigation amount can mitigate secondary salinization in water-saving irrigated areas in arid regions

Secondary salinization poses a significant threat to the sustainable development of water-saving irrigation districts. This study aims to explore the spatial and temporal variations in soil salinity and the factors influencing these changes in water-saving irrigation areas in the inland arid regions of Northwest China. The Manas River Irrigation District was selected as the study area. A grid measuring 10 km × 14 km grid was designed to determine the latitude and longitude coordinates of the grid centers, resulting in 66 sample points. Soil samples were collected from these points in 2013, 2014, 2020, and 2021 from the 0100 cm layer to obtain salinity data. Based on existing research and practical conditions in water-saving irrigation areas, 11 factors influencing soil salinity changes were identified, including irrigation area and irrigation amount. Classical statistical methods and interpretable machine learning techniques were employed to analyze the distribution characteristics of soil salinity and the influencing factors. This analysis proposes effective solutions to mitigate potential secondary salinization in irrigation areas. The results revealed that soil salinity in the irrigation area belonged to moderate variation (Cv = 46.74 %51.80 %). The horizontal direction of the irrigation area shows higher salt content in the upstream and downstream areas, and a gradual decrease in variability with increasing depth characterizes the vertical direction. From 2013–2021, soil salinization in the irrigation area gradually decreased. In 2013 and 2014, the area was predominantly covered by mild saline-alkali soil, accounting for 75.1 % and 76.6 % of the total area, respectively. However, in 2020 and 2021, non-saline soils became dominant, covering 60.9 % and 66.5 % of the total irrigation area, respectively. In order of importance, the factors affecting the spatial and temporal evolution of soil salinity are groundwater depth, annual water surface evaporation, water-saving irrigation area, underground water diversion amount, mineralization of groundwater, irrigation amount, surface water diversion amount, and annual rainfall. In the oasis irrigation area, maintaining a groundwater depth of 4.06.0 m and an irrigation amount of 55006000 m3 ha−1 can alleviate the problem of secondary salinization that may result from large-scale development of water-saving irrigation. The findings of this study provide a basis for the prevention and control of soil salinization in water-saving irrigation areas and the development and management of saline land in oasis areas.

Future changes in soil salinization across Central Asia under CMIP6 forcing scenarios

AbstractSoil salinization is a critical environmental and socio‐economic concern with global implications, and its severity is expected to amplify under changing climate. The impact of climate change on salinization in Central Asia is still not fully understood. This study addresses this gap by employing a digital soil mapping (DSM) framework. Cubist, random forest (RF), and quantile regression forests (QRF) are utilized to project variations in soil surface salinity (0‐10 cm) in Central Asia from 2025 to 2100 under two shared socio‐economic pathways (SSPs): SSP2‐4.5 and SSP5‐8.5. These models are developed using data from 20 global climate models (GCMs) obtained from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The results reveal that the RF model exhibits superior predictive capability in estimating soil salinity. RF performed on the calibration set with a coefficient of determination (R2) of 0.86, root mean square error (RMSE) of 9.84 and 9.90 dS m−1, ratio of performance to interquartile distance (RPIQ) of 3.09 and 3.07, and a Nash–Sutcliffe efficiency (NSE) of 0.86. The multi‐GCM ensemble means revealed the potential for varying degrees of salinization in Central Asia, with higher levels predominantly observed in the southeast and southwest of the study area, particularly downstream of the river and in the lakeside areas. Temporal analysis of soil salinity evolution reveals an overall increase in salinity across the region, with more notable changes projected under SSP5‐8.5. Specifically, the projected increase rate in soil salinity for Central Asia was 0.0005 dS m−1/year under SSP2‐4.5 and 0.01 dS m−1/year under SSP5‐8.5. Turkmenistan is notable for possessing the highest regional average of soil salinity, with the exception of a declining trend observed within this area. The remaining regions of Central Asia exhibit an upward trend in average soil salinity, particularly noteworthy under the SSP5‐8.5 scenario, where variations in soil salinity are more obvious. These findings hold significant potential in enhancing our understanding of how Central Asia responds to global change, advances toward sustainable development, and enhances comprehension of the dynamics within the region.

Effect of three-year amendment measures on coastal saline-alkali soil conditions during the growing season

Seawater intrusion and fluctuations in the water table in coastal areas lead to seasonal variations in soil salinity and pH, which greatly limit the development of coastal protection forests. In a three-year field study, the impact of five soil amendment measures were evaluated on soil conditions in coastal areas. Amendments included biochar, biochar with arbuscular mycorrhizal fungi (AMF), straw with AMF, straw alone, and AMF alone, compared to a control (CK) with no additive. Results indicated that combinations of straw, biochar, and AMF reduced soil pH across various layers and seasons, with electrical conductivity mainly decreasing in spring. During the summer, at the 0-20 cm soil depth, microbial biomass carbon notably increased due to these mixtures. Additionally, AMF alone and biochar with AMF significantly improved enzyme activities in the 0-40 cm layer in spring, while in fall, AMF alone notably increased nutrient availability in the same layer. Linear regression analysis revealed a negative correlation between electrical conductivity, microbial biomass carbon, enzyme activity, and nutrient availability with pH. The biochar–AMF mixture emerged as the most effective soil amendment, suggesting that using it in conjunction with seasonal management could optimize soil health and promote silviculture in coastal regions.

SALINE MAPS OF SOME SOILS OF MUTHANNA GOVERNORATE

performed based on data collected from 15 soil samples from 0 to 20 cm deep. We used salinity of the irrigated soils of Al-Muthana governorate in southern Iraq. These maps were 20% of the world's land. This study was made in order to map the spatial distribution of soil or anthropogenic processes, which is certainly an environmental problem that affects more than environment and agricultural production. The main causes of this salinization come from natural It is important to study the problem of salinization, soil salinity has adverse effects both on the ordinary kriging (OK) to analyze the spatial variability of soil salinity, The results confirmed that the use of saline maps was supportive in giving a clear vision of the of 12.11 dS m-1, for developing probability maps to determine the distribution of risk areas. showed the different classes of salinity with the highest value of 23.17 dS m-1 and lowest value map predicted by the electrical conductivity (EC) values using the ordinary kriging (OK) method while indicator kriging (IK) was used to analyze salinity versus threshold values . The salinity salinity of the soils for the study area

Assessment and mapping of soil salinity and groundwater quality in Nijhum Island, Hatiya Upazila, Noakhali

Crop yield is heavily influenced by soil salinity. The objective of the research is to assess the depth wise soil salinity distribution pattern and groundwater quality by measuring physico-chemical parameters in the agricultural land of Nijhum Island, Noakhali, Bangladesh. This study also explicates the spatial variation of soil salinity in the study area. Soil samples were collected from three different depths to estimate the Electrical Conductivity (EC) using a paste of 1:5 soil and distilled water suspension (1:5 weight-to-volume method). Spatial analyst tool of ArcGIS was used and observed that the land of Nijhum Island was affected with severe salinity in almost 85% of sampling plots and 15% of sampling plots were extremely saline. The soil salinity process has been noticed due to seawater intrusion from the Bay of Bengal through the Meghna estuary which threatens crop production and soil fertility, threatening the population's livelihood. This study provides a baseline understanding of soil salinity in Nijhum Island to help decision-makers and smallholder farmers improve their livelihoods. Thus, groundwater depth did not affect soil salinity significantly. The result contradicts most studies that found a strong influence on soil salinity. Because the study area is coastal, most of the salt comes from seawater inundation and capillary rise, which reduces groundwater availability.

Spatio-Temporal Variation Analysis of Soil Salinization in the Ougan-Kuqa River Oasis of China

In order to investigate the mechanism of environmental factors in soil salinization, this study focused on analyzing the temporal-spatial variation of soil salinity in the Ogan-Kuqa River Oasis in Xinjiang, China. The research aimed to predict soil salinity using a combination of satellite data, environmental covariates, and advanced modeling techniques. Firstly, Boruta and ReliefF algorithms were employed to select variables that significantly affect soil salinity from the Sentinel-2 satellite data and environmental covariates. Subsequently, a soil salinity inversion model was established using three advanced strategies: comprehensive variable analysis, a Boruta-based variable selection algorithm, and a ReliefF-based variable selection algorithm. Each strategy was modeled using a Light Gradient Boosting Machine (LightGBM), an Extreme Learning Machine (ELM), and a Support Vector Machine (SVM). Finally, the Boruta-LightGBM strategy was proven to be the most effective in predicting soil electrical conductivity (EC), with a coefficient of determination (R2) of 0.72 and a Root Mean Square Error (RMSE) of 12.49 ds/m. The experimental results show that the red-edge band index is the foremost variable in predicting soil salinity, succeeded by the salinity index and soil attribute data, while the topographic index has the least influence, which further demonstrates that proper variable selection could significantly improve model functionality and predictive precision. Furthermore, the Multiscale Geographically Weighted Regression (MGWR) model was utilized to reveal the influence and temporal-temporal-spatial heterogeneity of environmental factors such as soil organic carbon (SOC), precipitation (PRE), pH value, and temperature (TEM) on soil EC. This research offers not just a viable methodological framework for monitoring soil salinization but also new perspectives on the environmental drivers of soil salinity changes, which have implications for sustainable land management and provide valuable information for decision-making in soil salinity control and mitigation efforts.

Potential of solar-induced chlorophyll fluorescence (SIF) to access long-term dynamics of soil salinity using OCO-2 satellite data and machine learning method

The accumulation of soil salt becomes a worldwide widespread phenomenon, being a major threat to global production. As an environmental stress, soil salinity can reduce the vegetation photosynthetic activity. Solar-induced chlorophyll fluorescence (SIF) is an electromagnetic signal actively released by vegetation during photosynthesis. SIF not only can capture lower vegetation photosynthetic activity due to environmental stress promptly, but also is less affected by atmosphere and soil background. However, the ability of SIF observation to detect soil salinity remains unclear. Here, we use standardizedsolar-induced chlorophyll iluorescence index (SIFI) from SIF observation time series (2000 ∼ 2020) of global OCO-2 based SIF product (GOSIF) to develop soil salinity model. The results show that: SIF observation can identify salt affected soil (EC ≥ 2 ∼ 4 dS m−1) and salinity class a global scale. The SIFI calculated from SIF observation at May ∼ August (hereafter SIFI5-8) is the optimal sensitivity indices for rainfed cropland, herbaceous cover, irrigated cropland, shrubland, grassland. SIFI10-11 is the optimal sensitivity indices for forest and sparse vegetation; (2) By comparison, the ovrerall classification accuracy of predicted salinity class is above 70 %. The order of classification accuracy is rainfed cropland > irrigated cropland > bare area > forest > grassland > shrubland > herbaceous cover > sparse vegetation; (3) During at least three-quarters of the period from 2000 to 2020, the area of salt affected soil (EC ≥ 2 ∼ 4 dS m−1) was 4.9 Mkm2; (4) The annual change rate of global soil salt content is generally between −0.05 and 0.05 dS m−1 yr−1. Soil salinity in South Africa and West Asia increased greatly with an annual change rate of 0.02 ∼ 0.03 dS m−1 yr−1. These results demonstrate the ability of SIF observation to estimate soil salinity, providing a new perspective for explaining and evaluating soil salinity variation.

Analysis of spatiotemporal variations of drought and soil salinity via integrated multiscale and remote sensing-based techniques (Case study: Urmia Lake basin)

In this study, the spatiotemporal variations of drought and salinity were assessed for the Urmia Lake basin, which is a very critical and challenging ecosystem problem. The lowering of water level in the Urmia Lake and the associated risk of becoming a completely saline land has become an important issue in Iran. In the first step of the study, using the ground- and satellite-based datasets, a new multiscale technique based on Empirical Wavelet Transform (EWT) and Differential Symbolic Entropy (DSE) was proposed to monitor the variations of drought in the selected region. In the next step, variations in the water level and salinity of Urmia Lake and its impacts on the environment were investigated using satellite datasets. Results showed that the Southern parts of the basin were more prone to severe droughts, and there was a direct relationship between entropy and drought intensity. Based on the results, it was concluded that climate change had an impact on the shrinkage of the lake; however, these changes were not solely responsible of causing the dramatic water loss of the Lake. Variations of the Salinity Index (SI) showed that saline lands were mostly seen in the Eastern and Southern parts of the lake, causing negative impacts on air quality and agricultural activities in these parts. It was found that the proper management of water resources would play an effective role in the restoration of the lake.

Precision agriculture predictive modeling and sensor analysis for enhanced crop monitoring

This research investigated the application of the Internet of Things (IoT) in precision agriculture and crop monitoring through a two-fold research methodology. A simulated dataset was generated, mirroring real-world IoT sensor readings of soil temperature, salinity level, soil moisture, and conductivity. Employing the Pandas and Matplotlib libraries in Python facilitated exploratory data analysis, including time series analysis through line plots to illustrate temporal variations in these critical parameters. The study then explored the evaluation of predictive models for soil moisture levels, extending the dataset to include simulated predicted values. Performance metrics such as Mean Squared Error (MSE) and R-squared (R2) were computed using the sci-kit-learn library, providing a comprehensive evaluative framework. Visual representations of actual versus predicted soil moisture levels, accompanied by the analysis of residuals, offered nuanced insights into the model’s efficacy. The results highlight dynamic variations in soil temperature, salinity, soil moisture, and conductivity, emphasizing the importance of continuous monitoring in precision agriculture. Fluctuations observed in these parameters are attributed to climatic conditions, agricultural practices, and soil properties. The study contributes valuable insights for stakeholders, emphasizing the significance of IoT technologies in providing actionable data for sustainable and adaptive farming practices. The visual representations offer practical tools for decision-making, while the performance evaluation of predictive models enhances the reliability of data-driven approaches in agriculture. The findings presented herein contribute to the ongoing discourse on precision agriculture, emphasizing the role of accurate predictions for efficient resource utilization and improved crop yield.

Spatiotemporal variation in soil salinity under irrigated fields at Bochessa catchment in Central Ethiopia

AbstractSoil salinity and sodicity problems are one of the major challenges to the permanence of irrigated agriculture in Ethiopia. This manuscript, therefore, concerns its spatial and temporal variation under irrigated fields and suggests possible management options. For this investigation, eight monitoring locations were selected based on the irrigation intensity that farmers practised in the area. With each location, three irrigated farmers' fields were randomly selected for sampling purposes. Likewise, six farmers' fields from the rain‐fed system were also selected for comparison purposes. Sampling was performed at the beginning and end of each cropping season for three consecutive years from 2017 to 2019. The major physical and chemical properties of the soil were analysed in accordance with standard laboratory procedures. A linear model of two‐way analysis of variance was used to analyse parameters across time and space. The results indicated that the majority of the soil properties studied showed significant differences (p < 0.05) over time. This implies that the change is in accordance with the seasonal soil property, possibly due to irrigation practices. Similarly, approximately 90% of the soil properties studied showed noticeable differences (p < 0.05) across locations. Almost all salinity indicators showed an increasing trend in irrigated fields compared to their situation in rain‐fed fields. For instance, the electrical conductivity (EC) and exchangeable sodium percentage (ESP) values across the fields ranged from 0.54 to 0.82 dS m−¹ and 8–1%, respectively, with maximum values observed in irrigated fields. This implies that irrigation practices influence soil properties in the area. In addition, the ESP values approaching the maximum permissible limit suggest that sodicity may cause more problems than salinity in the area. Therefore, agronomic practices (e.g. residue management, deep tillage, salt‐tolerant crops and periodic fallowing), irrigation and drainage management practices, and amendments may help farmers mitigate salinity and sodicity problems in the area.

Towards the Improvement of Soil Salinity Mapping in a Data-Scarce Context Using Sentinel-2 Images in Machine-Learning Models

Several recent studies have evidenced the relevance of machine-learning for soil salinity mapping using Sentinel-2 reflectance as input data and field soil salinity measurement (i.e., Electrical Conductivity-EC) as the target. As soil EC monitoring is costly and time consuming, most learning databases used for training/validation rely on a limited number of soil samples, which can affect the model consistency. Based on the low soil salinity variation at the Sentinel-2 pixel resolution, this study proposes to increase the learning database’s number of observations by assigning the EC value obtained on the sampled pixel to the eight neighboring pixels. The method allowed extending the original learning database made up of 97 field EC measurements (OD) to an enhanced learning database made up of 691 observations (ED). Two classification machine-learning models (i.e., Random Forest-RF and Support Vector Machine-SVM) were trained with both OD and ED to assess the efficiency of the proposed method by comparing the models’ outcomes with EC observations not used in the models´ training. The use of ED led to a significant increase in both models’ consistency with the overall accuracy of the RF (SVM) model increasing from 0.25 (0.26) when using the OD to 0.77 (0.55) when using ED. This corresponds to an improvement of approximately 208% and 111%, respectively. Besides the improved accuracy reached with the ED database, the results showed that the RF model provided better soil salinity estimations than the SVM model and that feature selection (i.e., Variance Inflation Factor-VIF and/or Genetic Algorithm-GA) increase both models´ reliability, with GA being the most efficient. This study highlights the potential of machine-learning and Sentinel-2 image combination for soil salinity monitoring in a data-scarce context, and shows the importance of both model and features selection for an optimum machine-learning set-up.

Spatial and Temporal Variation in Soil Salinity and Correlation with Groundwater Depth in the Karamay Irrigation District of China

The secondary salinization of irrigated areas poses a direct threat to both the sustainable development of oasis agriculture and ecological stability in arid regions. In this study, we conducted an experiment to examine alterations in groundwater levels and soil salinity within the plow layer, as well as their combined impact, in arid regions following extended reclamation in standard diversion irrigation areas. For this experiment, the Karamay irrigation district was selected. Four different years, namely, 1996, 2006, 2016, and 2021, were selected for soil sampling and groundwater monitoring data. Descriptive statistics, along with the use of GIS technology and Pearson’s correlation, were employed to analyze the data in order to discern the patterns of soil salinity and groundwater depth within the plow layer. Additionally, this approach helped establish the correlation between these factors over the last 25 years of reclamation in the Karamay irrigation district. The results showed that, (1) due to an increase in the reclamation duration, the groundwater depth in the irrigation area decreased year by year, and the salinity of the arable soil showed an overall decreasing trend, but it increased in local low-lying areas; (2) the influence of the groundwater depth on the salinity of the arable soil had a threshold value. It decreased from 3.1 m in 2016 to 2.4 m in 2021, and a significant negative correlation was observed between salinity and the depth of groundwater. When the groundwater depth was shallower than the threshold value, the soil salinity in the plow layer was negatively correlated with the groundwater depth. In the arid irrigation zone, inadequate drainage facilities resulted in a significant rise in the groundwater table due to the excessive amount of irrigation water. This created secondary salinization of the arable soil. It is thus concluded that implementing adequate drainage systems in arid irrigation regions will help prevent secondary salinization and promote the sustainable development of agriculture in these areas.

Spatio-Temporal Variation in Soil Salinity and Its Influencing Factors in Desert Natural Protected Forest Areas

Soil salinity is a crucial parameter affecting soil health. Excessive surface salt accumulation degrades soil structure, inhibits vegetation growth, and diminishes plant diversity. Such increases in salinity can accelerate desertification, leading to soil resource loss, hampering agricultural progress, and compromising ecological security. However, the vastness of arid regions and data acquisition challenges often hinder efficient SSC monitoring and modeling. In this study, we leveraged remote sensing data coupled with machine learning techniques to investigate the spatio-temporal dynamics of SSC in a representative desert natural forest area, the Alxa National Public Welfare Forest. Utilizing the geodetector model, we also delved into the factors influencing SSC. Our results underscored the effectiveness of the Convolutional Neural Networks (CNN) model in predicting SSC, achieving an accuracy of 0.745. Based on this model, we mapped the spatial distribution of SSC, revealing hydrothermal conditions as pivotal determinants of salt accumulation. From 2016 to 2021, soils impacted by salinity in the research area exhibited a rising trend, attributed to the prevailing dry climate and low precipitation. Such intensified salinity accumulation poses threats to the healthy growth of protective forest vegetation. This study can provide a theoretical reference for salinization management and ecological protection in desert natural forest areas.

Assessment and Mapping of Soil Salinity Using the EM38 and EM38MK2 Sensors: A Focus on the Modeling Approaches

Soil salinization and its detrimental agricultural, environmental, and socioeconomic impact over extended regions represent a major global concern that needs to be addressed. The sustainability of agricultural lands and the development of proper mitigation strategies require effective monitoring and mapping of the saline areas of the world. Therefore, robust modeling techniques and efficient sensors that assess and monitor the spatial and temporal variations in soil salinity within an area, promptly and accurately, are essential. The aim of this paper is to provide a comprehensive and up-to-date review of the modeling approaches for the assessment and mapping of saline soils using data collected by the EM38 and EM38MK2 (MK2) sensors at different scales. By examining the current and latest approaches and highlighting the most noteworthy considerations related to their accuracy and reliability, the intention of this review is to elucidate and underline the role of the EM38 and the MK2 type in the recent needs of detecting and interpreting soil salinity. Another aim is to assist researchers and users in selecting the optimal approach for future surveys and making well-informed decisions for the implementation of precise management practices. The study’s findings revealed that the integration of the EM38 and MK2 sensors with remote sensing data and advanced methods like machine learning and inversion is a promising approach to the accurate prediction and mapping of the spatiotemporal variations in soil salinity. Therefore, future research focused on validating and expanding such sophisticated modeling applications to regional and global scales should be increased.

Temporal and Spatial Assessment of Soil Salinity Post-Flood Irrigation: A Guide to Optimal Cotton Sowing Timing

Flood irrigation is often applied in the arid regions of Northwest China to facilitate the leaching of salts accumulated in the soil during cotton growth in the previous season. This will, in turn, affect the temporal and spatial patterns of soil salinity, and thus cotton germination. To reveal the salinity of the two soil layers (0–20 cm and 20–60 cm), so as to determine the optimal cotton sowing timing, an electronic ground conductivity meter (EM38-MK2) was employed to measure the soil apparent electrical-conductivity (ECa) on different days: 4 days prior to flood irrigation, and, respectively, 6, 10, 15, 20, and 45 days after flood irrigation. Moreover, geostatistical analysis and block kriging interpolation were employed to analyze the spatial-temporal variations of soil salinity introduced by flood irrigation. Our results indicate that: (1) soil salinity in the two layers on different days can be well inverted from binary first-order equations of ECa at two coils (i.e., ECa1.0 and ECa0.5), demonstrating the feasibility of applying EM38-MK2 to estimate soil salinity in the field; and (2) soil salinity in the 0–20 cm layer significantly decreased during the first 15 days after flood irrigation with the greatest leaching rate of 88.37%, but tended to increase afterwards. However, the salinity in the 20–60 cm layer was persistently high before and after flood irrigation, with merely a brief decrease during the first 10 days after flood irrigation at the highest leaching rate of 40.74%. (3) The optimal semi-variance models illustrate that, after flood irrigation, the sill value (C0 + C) in the 0–20 cm layer decreased sharply, but the 20–60 cm Range of the layer significantly increased, suggesting that flood irrigation not only reduces the spatial variability of surface soil salinity, but also enhances spatial dependence in the 20–60 cm layer. (4) The correlation of the soil salinity between the two soil layers was very poor before flood irrigation, but gradually enhanced during the first 15 days after flood irrigation. Overall, for the study year, the first 15 days after flood irrigation was an optimal timing for cotton sowing when the leaching effects during flood irrigation were most efficient, and overrode the effects of evaporation and microtopography. Although not directly applicable to other years or regions, the electromagnetic induction surveys and spatiotemporal analysis of soil salinity can provide a rapid and viable guide to help determine optimal cotton sowing timing.

Perceptions and attitudes of farmers and landowners on soil salinity management and use of elemental sulphur in Oman

AbstractSoil salinity threatens agricultural sustainability globally and is a significant challenge in Oman. Previous studies in Oman focused on examining the causality and spatiotemporal variation of groundwater and soil salinity, neglecting farmers' perceptions and attitudes towards salinity management. Therefore, this study evaluates (i) perceptions and attitudes of landowners and farmers towards soil salinity and its management, and (ii) determinants of willingness to use elemental sulphur to alleviate soil salinity in Oman. A questionnaire survey (122 responses) assessed perceptions, attitudes and knowledge using Likert scales and qualitative questions. Binary Logistic Regression analysed determinants of willingness to use elemental sulphur. Results reveal variations in perceptions, attitudes, knowledge and management practices. Most of the respondents were aware of adequate nutrient and water requirements for crops (p < .05). Respondents rely on social media (38.5%), fellow farmers (32.1%) and personal experiences (11.5%) for knowledge acquisition about soil salinity management. Half of the respondents did not monitor soil salinity, while the other half relied primarily on visual observation. A substantial number (62.6%) of the respondents did not implement any techniques to alleviate soil salinity, while among those who acted, a majority preferred cultivating date palms (55%) or fodders (29.4%). Awareness of the national salinity management strategy and commercial products is lacking. However, when introduced to elemental sulphur as a soil amendment for salinity, 74.8% of the respondents demonstrated a willingness to utilize it. Most (82.4%) preferred elemental sulphur products within the lowest price range. Being a full‐time farmer, owning a farm, awareness of the national strategy and low‐price preferences were positive determinants, while <1 year of farming experience was a negative determinant of willingness to use elemental sulphur. These findings inform future research on socio‐economic perceptions of salinity in Oman, the Gulf region and similar arid areas facing food production limitations.

Fine root dynamics of Kandelia obovata, Rhizophora stylosa and Bruguiera gymnorrhiza in a mangrove environment in Okinawa, Japan

The present study investigates the fine root dynamics including comparison of fine root production, mortality and decomposition of Kandelia obovata (S., L.) Yong, Rhizophora stylosa Griff. and Bruguiera gymnorrhiza (L.) Lamk. in the Manko Wetland of Okinawa, Japan considering the importance of fine root to the ecosystem carbon dynamics. Our objective was to investigate seasonal dynamics of fine root of the three species and explore whether there is variation among species using sequential core and ingrowth core methods. The root systems were separated into living and dead mass fractions and these were subsequently separated as very fine roots (<0.5 mm) and fine roots (0.5—2 mm). Total fine root production of K. obovata, R. stylosa and B. gymnorrhiza ranged from 4.7 to 7.1 Mg ha −1 y−1, 1.7 to 3.6 Mg ha −1 y−1 and 2.9 to 4.9 Mg ha −1 y−1, respectively whereas mortality ranged 2.9 to 5.9 Mg ha −1 y−1, 0.6 to 2.3 Mg ha −1 y−1 and 0.8 to 3 Mg ha −1 y−1. The mean fine root turnover of K. obovata, R. stylosa and B. gymnorrhiza were estimated as 3.42, 2.78 and 3.37 y−1 having a longevity of 3.46, 4.32 and 3.56 months, respectively. The amount of decomposition through sequential core and ingrowth core methods for K. obovata, was estimated at 47 and 28 g m−2y−1, respectively, whereas for R. stylosa and B. gymnorrhiza these were 6 and 11 g m−2y−1, and 15 and 19 g m−2y−1, respectively. Significant variation of soil salinity, soil N, soil pH among three species plots was observed although the result was insignificant in different seasons. Using ingrowth core method, we observed significant correlations of fine root production with soil salinity and soil pH, but the results were insignificant with sequential core data. Finally, the significant effect of soil pH was found in the regression model as the predictor of fine root production. Although the seasonal pattern was absent in fine root production, the seasonal change in the biomass and necromass was obvious. The high amount of fine root necromass available can be a great source of organic matter in the forest for carbon and nutrient cycling.

Salinity Temporal Evolution Assessment under Mediterranean Conditions

Background: The present study aims to study the temporal evolution of the salinity of three profiles of Solonchaks which are in the region of Rélizane (Algeria). The aim is to determine the temporal changes in salinity between 2010 and 2014, by comparing saline profiles to determine the direction and intensity of variation in soil salinity. Methods: The experiment was carried out on three soil profiles (Solonchaks) which are located in the perimeter of the Mina which is located in the Bas-Chélif. Result: The study of the temporal evolution of salinity showed that the majority of horizons underwent desalination between the 2010 and 2014 campaign. This desalination occurred at a speed between 3.67 and 10.74 dS m-1 per year. This would be due to a partial leaching of salts from the surface horizons during the rainy season. The soils studied are marked by a higher salinity during the 2010 compared to the 2014 campaign. The principal component analysis (PCA) reveals that the chemical elements of the soil solution have the most weight on the variability of salinity, and the Wilcoxon test reveals that the difference between the EC of the 2010 campaign and that of 2014 is significant.

Assessment of soil salinity by geo-spatial technology in Ballia district of Uttar Pradesh

Soils vary in their characteristics from one place to another place due to interactions of soil forming and processes. Assessment of spatial and temporal variation of characteristics is needed for any mean or use it's crucial to take into account the diverse spatial differences in soil properties, which has long in this context. Geographic information system (GIS) technology has enormous potential in the field of soil's assessment for upgrading soil statistic systems for an accurate and cost-effective means of understanding changes in the landscape. The future agricultural monitoring of the nutritional quality of the soil in diverse regions and villages is crucial. In this paper assessment of spatial variation of soil salinity using GIS techniques around the Ballia District of Uttar Pradesh is presented from the year 1994 to 2019. Normalized Difference Salinity Index (NDSI) was used. It showed increasing trend in salinity from 1994 to 2019 for each block. Using Landsat (TM/OLI) imageries with a 30 m spatial resolution, the spatial distribution of salinity from 1994 to 2019 was determined and low negative NDSI value was observed from - 0.143 to -0.117 in the southeast part of Ballia which can be an important explanation for soil degradation due to increasing soil salinity.