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Net Primary Productivity (NPP) has been widely used to estimate the productivity of the farmland ecosystem (FES) and the carbon budget of the terrestrial ecosystem. Changes in the NPP of FES have knock-on effects on food security, sustainable agricultural development, carbon sequestration, and environmental changes. Existing studies mainly focused on the impact of climate change and urbanization on the spatial-temporal pattern of NPP, largely ignoring the roles of soil condition and agricultural management practices (AMPs). At present, the joint impact of “climate-urbanization-soil-AMPs” on the NPP of FES remains unknown. To fill this knowledge gap, we use the NPP dataset retrieved by the vegetation photosynthesis model (VPM), daily meteorological records, field-collected soil data, AMPs data collected from the Government Statistical Yearbook, and land cover dataset to study the joint mechanism of “climate-urbanization-soil” that drives spatial-temporal variations of NPP. Taking the northern slope of Tianshan Mountains as the study area, our results indicate that NPP had increased by 3.86 Tg C from 2000 to 2015 (11.93 Tg C in 2000 and 15.79 Tg C in 2015). From the proposed conceptual framework, we found that climate variables (accumulated temperature, precipitation, evapotranspiration) play a major role in driving the growth of NPP (+4.01 Tg C). The AMPs (i.e., mulching filming, drip irrigation, and fertilizer application) make a positive contribution to NPP increase (+0.98 Tg C). We also found that soil salinization (-1.07 Tg C) weakens the growth of NPP more significantly than urbanization (−0.16 Tg C). This study provides new insights on the mechanism of climate change, urbanization, and soil conditions on crops, benefiting stakeholders in designing better management plans for sustainable agriculture, ecosystem cycling, and food security.

• Biochar can alleviate soil salinity stress. • Biochar amendment does not modify the soil pH in salt-affected soils. • Biochar increases cation exchange capacity by 17.0% in salt-affected soils. • The impact of biochar addition on ECe depends on biochar feedstock and soil initial salinization level. Salinization remains a major issue in soil degradation, for which biochar is a potential solution. In this meta -analysis, using 660 paired observations from 99 peer-reviewed articles, we evaluated biochar's effect on salt-affected soils and identified the initial soil properties, biochar properties and experimental factors influencing its efficacy. The results showed that the addition of biochar had a significant ameliorating effect on salt-affected soils. The soil electrical conductivity of saturated paste extract (ECe) was significantly reduced by 13.2%, and such effect was significant in severe salinization soil (rather than slight and moderate salinization). Meanwhile, the soil cation exchange capacity was significantly increased by 17.0%. However, the addition of biochar had no significant impact on soil pH. Model selection analyses further indicated that the level of initial salinity and the type of biochar feedstock were the most important factors regulating the response of soil ECe to biochar addition. In summary, while our study highlights the potential of biochar in ameliorating salt-affected soils, particularly in severely salinized soils, it also underscores the need for more comprehensive research in this field. Additional research is necessary to comprehensively address the significant heterogeneity of biochars, including their thorough characterization. In addition, more studies are required to explore the impact of biochar on both salt-tolerant and non-salt-tolerant plants in salt-affected soils. [ABSTRACT FROM AUTHOR]

Soil salinization is one of the most important causes for land degradation and desertification and is an important threat to land management, farming activities, water quality, and sustainable development in arid and semi-arid areas. Soil salinization is often characterized with significant spatiotemporal dynamics. The salt-affected soil is predominant in the Ebinur Lake region in the Northwestern China. However, detailed local soil salinity information is ambiguous at the best due to limited monitoring techniques. Nowadays, the availability of Multi-Spectral Instrument (MSI) onboard Sentinel-2, offers unprecedented perspectives for the monitoring and mapping of soil salinity. The use of MSI data is an innovative attempt for salinity detection in arid land. We hypothesize that field observations and MSI data and MSI data-derived spectral indices using the partial least square regression (PLSR) approach will yield fairly accurate regional salinity map. Based on electrical conductivity of 1:5 soil:water extract (EC) of 72 ground-truth measurements (out of 116 sample sites) and various spectral parameters, such as satellite band reflectance, published satellite salinity indices, red-edge indices, newly constructed two-band indices, and three-band indices from MSI data, we built a few inversion models in an attempt to produce the regional salinity maps. Different algorithms including Pearson correlation coefficient method (PCC), variable importance in projection (VIP), Gray relational analysis (GRA), and random forest (RF) were applied for variable selection. The results suggest that both the newly proposed normalized difference index (NDI) [(B12 − B7) / (B12 + B7)] and three-band index (TBI4) [(B12 − B3) / (B3 − B11)] show a better correlation with validation data and could be applied to estimate the soil salinity in the Ebinur Lake region. The established models were validated using the remaining 44 independent ground-based measurements. The RF-PLSR model performed the best across the five models with R2 V , RMSE V , and RPD of 0.92, 7.58 dS m−1, and 2.36, respectively. The result from this model was then used to map the soil salinity over the study area. Our analyses suggest that soil salinization changes quite significantly in different seasons. Specifically, soil salinity in the dry season was higher than in the wet season, mostly in the lake area and nearby shores. We contend that the results from the study will be useful for soil salinization monitoring and land reclamation in arid or semi-arid regions outside the current study area. • The introduction of red-edge bands can enhance the sensitivities of the indices to soil salinity. • Three-band index [(B12 − B3) / (B3 − B11)] shows a best correlation (r = 0.544) with measured EC. • RF-PLSR model was proved a suitable method for soil salinity estimating and mapping. • The study shows a large variability in soil salinity in dry and wet seasons. [ABSTRACT FROM AUTHOR]

Soil sodification and salinization present a global soil degradation risk to agricultural production since they affect soil hydraulic properties. Soils containing smectite clays have been found to be most sensitive to sodicity although little research exists directly relating sodicity to clay swelling under varying levels of salinity and Exchangeable Sodium Percentage (ESP). The goal of this study is to determine whether soil swelling related measurements can be a proxy to determine soil sensitivity to exchangeable sodium. This study examined the response of three smectitic soils treated with salt solutions having concentrations ranging between 5 and 40 mmol c dm−3 and ESP levels between 0 and 50% to create differing degrees of sodicity. Whole soil Atterberg limits and the coefficient of linear extensibility (COLE) were measured prior to and after treatments. Liquid limit, plasticity index and the coefficient of linear extensibility increased for all three soils after treatment with a salt concentration of 5 mmol c dm−3 at an ESP of 25 and 50% and 10 mmol c dm−3 at an ESP of 50%. Comparing liquid limit of low salinity treatment at 50% ESP to low salinity 0% ESP revealed the presence of swelling smectite clay and the sensitivity of the soils to sodicity and a soil measurement to identify soil sodicity risk. • High soil solution ESP and low soil salinity increases soil liquid limit and shrinkage. • Smectite in smectitic soils swell much less than Wyoming bentonite. • Liquid limit best identified smectite presence and soil sodicity sensitivity [ABSTRACT FROM AUTHOR]

Abstract In this study, the variation of the soil water-heat-salt transport for one whole year of agriculture under different mulching treatments and autumn irrigation in the Hetao Irrigation District of Inner Mongolia has been investigated and the best mulching treatment with the optimal mulching amount for this district have been determined. Based on field tests conducted between October 2013 and October 2015, the field test data of October 2013~October 2014 have been used to calibrate the SHAW model. After the calibration, the model has been used to simulate the soil water-heat-salt transport in the freezing-thawing and growth periods for one whole year of agriculture from October 2014 to October 2015. The accuracy of the model simulations were validated by comparing the simulated results with the field test data. Further, the calibrated SHAW model was used to simulate the process of soil water- salt transport for one whole year of agriculture from October 2015 to October 2016 to assess different mulching treatments. According to the results: ① the simulated soil temperature by the SHAW model is highly accurate, with increasing soil depth, RMSE decreases and NS approaches 1. For the simulated soil moisture content, the variations of the simulated and measured soil moisture contents are close to each other. For the simulated soil salinity, the analyses of the simulated and observed soil salinities show that the mean RMSE for the F0.9 treatment is 0.358 g/kg, and the mean NS is 0.813. Thus, the soil salinity simulation for the straw mulching F0.9 treatment is slightly better than that other treatment. ② Based on a comprehensive analysis on the simulated soil water-salt movement, the best mulching treatment for the Shahao Canal Test Station in the Hetao Irrigation District of Inner Mongolia is the straw mulching with 0.9kg/m2 mulching amount. The results of this study can be used as a theoretical basis and technical reference for the applications of water-saving autumn irrigation techniques in the Hetao Irrigation District of Inner Mongolia. Highlights • SHAW model was used to simulate soil water-heat-salt transfer after autumn irrigation. • The best mulching treatment is the straw mulching with 0.9 kg/m2. • Results provide theoretical and technical reference for autumn irrigation scheme. • Results can be also used in future research on reducing soil secondary salinization. [ABSTRACT FROM AUTHOR]

Abstract A megaproject, "Gully Land Consolidation" (GLC), was launched on the Chinese Loess Plateau in 2011 to combat land degradation and create new farmlands. However, the newly-claimed farmlands have suffered soil salinization, and the mechanism of salinization has been inadequately addressed. In this study, a creek valley that completed the GLC project in 2014 was selected and a total of 17 new farmlands along the reshaped creek valley were investigated in 2016. Moreover, the hillslope farmlands without conducting the GLC project, the loess parent material that used to fill the stream channel, and the water samples from reservoirs, irrigation wells and spring outlets in the reshaped valley were sampled and chemically analyzed. We found that the new farmlands experienced a significant rise in the water table that played a key role on soil salinization. After GLC, >80% of the sampling sites (14 out of 17) showed a shallow depth (≤3 m) from the soil surface down to the groundwater influence zone. Moreover, the closer the downstream farmlands to the newly-built dams, the shallower the water table, indicating considerable impacts of constructing dams on the water table depth in the near-dam farmlands. At the study site, more than half of the shallow soil samples (20 cm depth) underwent light soil salinization, and the most significant soil salinity occurred in the upper stream farmlands. The interplay between a near-surface water table, a high evaporation rate, landform features, and the alkaline properties of the loessial soil has led to the significant soil salinization in the upper stream of the valley. To mitigate soil salinization and ensure the sustainability of the newly-created farmlands in GLC, drainage trenches in the downstream near-dam farmlands and the drainage canals on both sides of the reshaped valley are suggested, which improve the drainage efficiency and control the water table depth. Highlights • Valley reshaping and damming led to significant water table rise. • Soil salinity mostly occurred in the upper stream of the reshaped creek valley. • Salinization caused by the high water table and evaporation and alkaline properties of the soil. • The measures of drainage trench and canals are suggested to suppress soil salinization. [ABSTRACT FROM AUTHOR]

Abstract In cold/arid agricultural regions seasonal freezing/thawing of soils can result in soil salinization in winter; therefore, it is crucial to understand the mechanisms behind soil salinization during winter for better water management in agriculture. In Hetao Irrigation District of Inner Mongolia, northern China, we used the CoupModel (version 5) considering dynamical impacts of salt on soil freezing point to simulate soil salt dynamics and soil freezing/thawing in three winters during 2012–2015. The simulated soil temperature at different depths was improved by ~10% with respect to the Nash-Sutcliffe coefficient NSE R2 when dynamical salt impact on freezing point was taken into accounted. Simulations revealed that ice coverage on soil surface as well as water stored in drainage ditches during winter cause more severe salinization in spring due to improper AI (Autumn Irrigation) practices combining poor drainage systems. A new AI practice with earlier irrigation date (i.e. 10 d earlier than 2012/2013 winter regulation), longer irrigation period (i.e. 7 d instead of 3 d), but with less irrigation water (reduced by 20% from 2012/2013 winter regulation) was then proposed. The new AI practice can control groundwater level and salt accumulation better during winters. Our results highlight the importance of combining detailed field irrigation tests with a process-based model accounting for interactions between soil freezing/thawing and salinization to improve water management efficiency in cold/arid agricultural regions. Highlights • 3-year autumn irrigation and soil freezing/thawing experiments are conducted. • A relationship between osmotic potential and soil freezing point is developed. • Tight relationship between soil freezing/thawing and soil salinization is detected. • A proper autumn irrigation schedule is proposed based on process-based model. [ABSTRACT FROM AUTHOR]

Abstract Crop residue amendment is a usual practice in agroecosystem that simulates nitrous oxide (N 2 O) emission by providing available carbon and nitrogen sources, and offsets the benefit of this management practice in mitigating global warming by increasing soil organic carbon stocks. In the arid region, during the transformation of desert to oasis farmland, soil silt and clay contents and salinity tend to increase with cultivation years, and largely influence the impact of crop straw addition on N 2 O emission. However, the interaction effects of soil texture and salinity on N 2 O emissions after crop residue amendment remains unclear. In this study, microcosm experiments were used to investigate the effect of salinity (0.1%, 0.3%, 0.6% and 1.0%) on soil N 2 O emissions following cotton residue application in sandy clay loam soil and silty clay soil. In the absence of cotton residue, throughout the incubation period, low N 2 O emissions were observed from the two soils across different salt levels, and the cumulative N 2 O emissions increased with increasing salinity. The N 2 O emissions sharply increased following cotton residue amendment, and these emissions varied depending on soil texture and salinity. Increasing soil salinity greatly increased the cumulative N 2 O emissions from 46.81 to 780.69 μg N kg−1 due to the enhanced nitrogen mineralization and fungal denitrification rates in the sandy clay loam soil; further, in the silty clay soil, emissions increased from 11.81 to 60.74 μg N kg−1 due to the increased nitrification and bacterial denitrification rates. Overall, our results suggest that moderate soil salinization (≥0.6% salt) likely begins to cause a high risk of N 2 O emission in the field following cotton residue amendment, especially in soil with high sand content as a result of fungal denitrification. Highlights • We studied the interactive effect of soil texture and salinity on N 2 O emissions. • Increasing soil salinity highly increased N 2 O emissions after crop residue amendment. • High sand combined with high salinity increased the risk of N 2 O emission from soil. • The mechanism effect of soil salinity on N 2 O emissions varied with soil texture. [ABSTRACT FROM AUTHOR]

Abstract Soil salinization is one of the main reasons for soil health and ecosystem deterioration in most degraded arid and semiarid areas. To monitor its spatial variation as precise as possible over a large area, we collected 225 samples using traditional field experiment and laboratory analysis method from the southern part of the Xinjiang Province, China, affected by soil salinity under strong arid climate. Then, we constructed both Cubist and partial least square regression (PLSR) models on electrical conductivity (EC) (150 ground-based measurements as calibration set) using various related covariates (e.g. terrain attributes, remotely sensed spectral indices of vegetation and salinity from landsat8 OLI satellite) that are at the same time period corresponding to soil sampling. Two models were validated using remaining 75 independent ground based measurements and were then used to map the soil salinity over the study area. Finally, the validation results of two models were compared under different intervals of EC, soil moisture content and vegetation coverage. The results indicated that Cubist model could predict EC value with better accuracy and stability under variable environment than PLSR. The R2, RMSE, MAE and RPD of the Cubist model were 0.91, 5.18 dS m−1, 3.76 dS m−1 and 3.15 while corresponding values of the PLSR model were 0.66, 10.46 dS m−1, 8.21 dS m−1 and 1.56 in validation dataset, respectively. Additionally, the map derived from Cubist model revealed more detailed variation information of the spatial distribution of EC than that from PLSR model across the study area. Thus, Cubist model was recommended for mapping soil salinity using indices derived from satellite and terrain in other arid areas. Highlights • Soil salinity maps were produced using Cubist and PLSR models. • Cubist was proved a more suitable method for soil salinity mapping than PLSR. • Subregional modeling could improve the prediction results of soil salinity. • The study shows a large variability in EC across various land use. • Vegetation indices, soil salinity spectral indices and terrain attributes are important predictors of EC. [ABSTRACT FROM AUTHOR]

Water and salinity regulation measures have been widely taken to restore the ecological structures and functions of degraded coastal wetlands. To investigate the effects of different water and salinity regulation measures on soil carbon stocks in coastal wetlands, soil samples were collected to a depth of 50 cm in four types of wetlands (i.e., restored tidal salt marshes (RTSM, covered with Suaeda salsa ) and degraded tidal salt marshes (DTSM, bare land); freshwater restored wetlands (FRW) and degraded wetlands without freshwater inputs (DWFI), covered with Phragmites australis ) during the period from August to October in 2015 in the Yellow River Delta, China. Our results showed that the mean values of total carbon (TC), soil organic carbon (SOC), readily oxidizable organic carbon (ROOC) and dissolved organic carbon (DOC) were higher in restored wetlands (e.g., RTSM and FRW) than those in degraded wetlands (e.g., DTSM and DWFI). Along the whole soil profile, TC, SOC, ROOC and DOC were higher in RTSM soils than those in DTSM soils except for TC and SOC in the 0–10 cm soils and DOC in the 40–50 cm soils. FRW soils contained higher TC, SOC, ROOC and DOC than DWFI except for DOC in the 0–10 cm soils. Soil TC density (TCD), SOC density (SOCD), ROOC density (ROOCD) and DOC density (DOCD) in the top 50 cm soils in restored wetlands were higher compared with degraded wetlands. Multivariate analysis showed that soil carbon was significantly influenced by EC, soil base cations and anions (e.g., K + , Ca 2 + , Cl − and SO 4 2 − ), pH, TN, C/N ratio, bulk density (BD) and soil texture ( p < 0.05). The findings of this study indicated that these two water and salinity regulation measures are effective in increasing soil carbon stocks of coastal wetlands. [ABSTRACT FROM AUTHOR]

• A novel model, CIG, is proposed for soil EC retrieval using CYGNSS data. • CIG outperforms other models for soil EC retrieval. • The L-band signal of CYGNSS is pretty sensitive to soil EC. • CYGNSS can be an effective way for soil salinization monitoring on a large scale. Soil salinization, which occurs mainly in arid and coastal regions, hampers agricultural development, and threatens food security in these regions. Therefore, acquiring the spatial distribution of soil salinization with high accuracy is paramount. However, obstacles continue to hinder attempts to use satellite remote sensing. Cyclone Global Navigation Satellite System (CYGNSS) provides a new alternative opportunity for soil salinization retrieval. In this study, soil electrical conductivity (EC) was chosen as the proxy of soil salinity. A CYGNSS-based model for soil EC retrieval (CIG) was proposed, and has been tested in the Yellow River Delta, China. The CIG model consisted of two modules. First, we modified the geometrical optics model to derive the surface's Fresnel reflectivity from the incoherent scattering signal of the land surface with vegetation attenuation, and horizontal and vertical surface roughness calibrations. Second, soil EC was retrieved based on the gradient boost regression tree (GBRT) algorithm with the inputs of the surface's Fresnel reflectivity and other ancillary variables. The in-situ measurements were used to compare the CIG retrieved soil EC against the retrievals from other classical machine learning approaches and the coherent model using CYGNSS data, and the optical SVIs model. CYGNSS signal was found to be highly sensitive to in-situ soil EC, and the proposed CIG model outperformed other models, with R = 0.730, RMSE = 1.318 mS/cm, and MAE = 0.570 mS/cm. In addition, several issues related to the model were also discussed, including the interactions between the incidence angle of the CYGNSS signal and soil EC, and the advantages and limitations of CYGNSS. The result suggested that the proposed CIG model, along with new CYGNSS data, can provide a promising method for monitoring land salinization on a large scale. [ABSTRACT FROM AUTHOR]

• A discussion on the causes of soil salinization. • An overview of advances in water-salt transport research using temperature as a boundary. • A summary of experimental techniques in the field of water and salt transport research. Soil salinization is a global problem that severely restricts agriculture as well as the engineering and construction industry. Understanding water and salt transport laws is key to soil salinity improvement and prevention. This paper divides the development of the theory of water-salt migration into positive and negative temperature effects, taking temperature as the boundary. A review of the coupling of heat, salt, mechanics, and gas based on water-salt migration is included, and the generation of positive and negative temperature driving forces is analyzed along with the numerical models proposed in their development and the development of indoor experiments. In the end, a review of indoor tests and in situ soil salinity monitoring techniques is presented, and the shortcomings of soil salinity at this stage are summarized. Based on the above, an outlook on the future direction of soil salinization in a carbon–neutral environment is presented. [ABSTRACT FROM AUTHOR]

• Mixup-LGBM is suitable for soil salinity prediction with sparse samples. • The Mixup has potential for dealing with complex sample-sparsity regression tasks. • The Bayesian optimization algorithm can improve the adaptability of the model. • SHAP value can visualize the decision-making process of the black-box model. Soil salinization is a major environmental risk caused by natural or human activities especially in arid and semi-arid regions. Machine learning for rapidly monitoring large-scale spatial soil salinization becomes possible. However, machine learning often needs large training samples and obtaining extensive soil salinization information by field investigation is laborious and difficult. In practice, the field soil sampling datasets are often sparse and non-normally distributed. The intricacy of features extracted from remote sensing images increases the model complexity and often leads to degradation in the prediction performance. To solve this problem, an integrative framework is proposed to predict soil salt content (SSC) based on light gradient boosting machine (LGBM). In this model, we first introduce the data augmentation method (Mixup) to improve sample diversity and alleviate model overfitting by the sparsity of samples. To improve the generalization and robustness of the model in different spatial heterogeneity of soil salinization, the Mixup-LGBM model is adaptively and jointly optimized by combining hyperparameters and feature selection in a Bayesian optimization framework. Furthermore, model interpretability is improved using shapley additive explanations (SHAP) value based on the combination of the confidence of the synthetic data through model visualization and feature importance assessment. In addition, different cases are simulated to test the model performance. In Case I, the raw sample-sparsity model using the data augmentation algorithm has higher prediction accuracy than other unused models. In Case Ⅱ, the extreme sample-sparsity model still achieves satisfactory results while the other models can't learn any effective information after multiple iterations. The experimental results reveal that the proposed model can automatically find representative features in heterogeneous environments and has strong adaptability in different study areas. This finding indicates that digital elevation model (DEM) has a high influence on SSC in both study areas. Besides the DEM, soil salinization in the Manasi River Basin is more sensitive to human activities, while that in the Werigan–Kuqa River Delta Oasis is more sensitive to natural factors. The Mixup-LGBM model is suitable for predicting SSC in different sample sparsity scenarios while ensuring the high accuracy. The model has considerable potential for dealing with other complex sample sparsity regression tasks. [ABSTRACT FROM AUTHOR]

[Display omitted] • Soil bacterial diversity was positively related to multifunctionality in croplands. • The bacteria-driven multifunctionality depends on soil salinity and organic matter. • Sensitive bacteria were the major drivers of soil multifunctionality. Soil salinization is a widespread environmental problem adversely impacting global food production. Increasing soil organic matter (SOM) could alleviate salt stress, but soil salinity and SOM have differing effects on microbial diversity and activities. We explored how the relationships between soil biodiversity and multifunctionality were altered by soil salinity and SOM. We collected soils from the wheat-maize cropping system in the North China Plain and categorized soils according to salinity and SOM. Soil functions related to carbon, nitrogen, phosphorus, and micronutrient processing were measured as metrics of soil multifunctionality (SMF) characterization. We found significant positive relationships between SMF and bacterial diversity but not fungal diversity in soils with high SOM (>15 mg/kg) and low EC (<4 ds/m). The diversity and abundance of sensitive bacteria were more strongly correlated with SMF than those of non-sensitive bacteria. SOM directly and indirectly impacted SMF through changes in sensitive bacterial abundance, while soil EC impacted SMF via altered sensitive bacterial diversity. With respective to individual soil function, carbon and micronutrient cycling were predominantly determined by bacterial diversity. Our findings suggest coupling decreased salinization with the increase of SOM could increase soil multifunctionality by increasing diversity and abundance of sensitive soil microbes. These findings highlight the importance of sensitive microbial taxa to sustaining soil ecosystem functioning in croplands. [ABSTRACT FROM AUTHOR]

• EC e under bio-film mulching (BM) increased with increasing mulch degradation. • BM with a low disintegration rate (BM L) increased the leaching ratio of soil salts from the root zone. • BM L can better control soil salination in regions with shallow groundwater tables. • A positive linear relationship between EC e and the groundwater table depth was found for BM L. • The HYDRUS-2D model was calibrated and validated using experimental data. Soil salinization caused by shallow, saline groundwater represents a serious threat to field productivity, especially in arid regions with intense soil evaporation. Plastic film mulching (PM) has been increasingly applied to reduce soil evaporation and alleviate soil salinity stress. However, PM introduces into the soil a significant amount of plastic residues. Although biodegradable film mulching (BM) is an ideal alternative to PM due to the degradability of these films, unreasonably high disintegration rates may reduce the benefits of the proposed solution. Understanding the effects of these factors on soil salinity is essential for designing management options for improving water productivity. A two-year cornfield experiment was therefore carried out during 2019–2020 to evaluate differences in soil salt dynamics among treatments with BMs with low, medium, and high disintegration rates (BM L , BM M , BM H), one polyethylene film mulching (PM), and no mulching (NM). Additionally, the HYDRUS-2D model was used to evaluate the electrical conductivity of the saturation paste extract (EC e), soil salt fluxes, salt distributions, and salt mass balances in two-dimensional soil profiles under BM L , BM M , BM H , PM, and NM. The results showed that calibrated HYDRUS-2D could precisely simulate soil salinity under different mulching treatments. There were large differences between various treatments in the middle and late crop growth stages (Days After Sowing [DAS] 61–140). The highest EC e among different BM treatments occurred in BM H. Additionally, the two-dimensional distribution of soil salinity under BM was affected by irrigation events. The high soil salinity stress area (ECe > 3.80 dS m−1) occurred one day after irrigation (DAS 108) only under BM H among different BM treatments. Meanwhile, root water uptake (RWU) and crop yield (CY) under BM H were significantly reduced due to excessive accumulation of soil salinity in the root zone under intensive soil evaporation conditions. Compared with BM H and BM M , BM L increased CY and the leaching ratio of soil salts from the root zone due to its good performance in water conservation. Thus, BM with a low disintegration rate is more efficient in controlling soil salinization in soils with a shallow groundwater table than BMs with higher disintegration rates due to lower soil evaporation. The findings of this study improve the understanding of the mechanisms of salt dynamics under biodegradable film mulching with different disintegration rates. The study also recommends to the farmers and government a suitable disintegration rate of the biodegradable film that can be adopted to promote field productivity. [ABSTRACT FROM AUTHOR]

The Songnen plain in China has a typical soda-saline soil that frequently shrinks and cracks under natural conditions during water evaporation. Contrast (CON) is a typical gray level co-occurrence matrix (GLCM) texture feature, which can well represent the cracking degree of dried soil surface and is highly related with the salt content of soda-saline soils. In order to make a quantitative analysis of the effects of salt content on the cracking behavior of soda-saline soils, laboratory controlled and field verification experiments on the soda-saline soil of the Songnen plain were conducted in the present study. The laboratory experiment included 56 samples and was conducted to establish the relationships between the CON values of laboratory-generated crack patterns of soil samples and some primary salt parameters, such as the electrical conductivity (EC), Na + content, Cl − content, CO 3 2 − content, HCO 3 − content and total salinity. The field verification experiment included 48 samples and was conducted to establish the relationships between the CON values of field-generated crack patterns of soil samples and the salt parameters. For both laboratory experimental samples and field samples, the CON values were computed from the binary images of the cracked patterns and were significantly linearly correlated to the EC, Na + content and salinity (coefficient of determination [R 2 ] > 0.87); the relationship between the CON values of soil samples and the Cl − content was also quite linear (R 2 > 0.73). An online measurement method for rapid and accurate determination of salt contents of soda-saline soils was then proposed based on the results of the laboratory experiment. After that, the salt parameters of 48 field samples were estimated using the online method and fitted against the actual measured values. The fitting results relating the CON values and Na + content, Cl − content, EC and salinity showed high prediction accuracy (R 2 > 0.91, RPD > 4.49); the measurement method for CO 3 2 − content also had reasonable prediction accuracy (R 2 = 0.86 and RPD = 2.42) but with relatively poor stability and poor reliability. However, the measurement method for HCO 3 − content showed very poor prediction accuracy (R 2 = 0.51 and RPD = 1.31). [ABSTRACT FROM AUTHOR]

• Application of biochar plus corn stover improve soil water infiltration capacity. • Biochar enhance the salt leaching and restrain surface salinization. • Salt migration was associated with soil pores and cation exchange capacity. • This amendment is suitable for long-term saline-alkali regulation in cold areas. In midlatitude seasonally frozen areas, the freeze–thaw cycle affects the movement of soil water, which increases the uncertainty in salt transport and aggravates the risk of soil salinization. Based on this, a two-year field study was established on the Songnen Plain, a seasonally frozen area in northern China. Four treatments were set up: (i) control, (ii) corn stover, (iii) biochar, and (iv) combined (joint application of biochar and corn stover). The modified soil underwent seasonal freeze–thaw cycling, accompanied by the evaporation and infiltration of snowmelt water, and the soil salt migration and diffusion characteristics were determined. Both biochar and corn stover regulated the pore structure of the soil and enhanced the infiltration capacity of snowmelt water; notably, the maximum soil moisture contents in the corn stover, biochar and combined treatments increased by 9.33%, 22.12% and 16.37%, respectively, compared with that in the control group. However, biochar with abundant functional groups increased the cation exchange capacity and promoted the migration of salt ions (i.e., Na+, K+ and Cl−), and the surface soil salt content in the biochar treatment was 0.12–0.53 g/kg lower than that for the other three modes during the infiltration period. Additionally, soil water evaporation carried salts upwards, which greatly increased the salt content of the surface soil. Conversely, biochar presented a strong water-holding capacity, which reduced the ineffective evaporation of soil water, while stover acted as a barrier to hinder the return of salt. Among the treatments, the synergistic transport ability of soil salt and water was the lowest in the combined treatment. Overall, the combination of biochar and corn stover had advantages in terms of soil salt leaching and offered a healthy, effective strategy for soil salinization control in cold areas. [ABSTRACT FROM AUTHOR]

In arid and semi-arid regions, soil salinization is one of the most critical environmental problems due to its severe effects on agriculture productivity and long-term sustainable development. Monitoring, evaluating and predicting soil salinization are of utmost importance in those regions. The current study proposes an evaluating and predicting approach that is based on remote sensing (e.g., Landsat TM images) and near sensing technologies (e.g., electromagnetic induction device, EM38). We investigated seasonal and spatial changes of soil salinity in a Delta Oasis between the Werigan and Kuqa River in the northern rim of Tarim Basin, Xinjiang, China. Preliminary analysis suggests that apparent soil electrical conductivity obtained from EM38 is highly correlated with soil salinity, which is obtained from post-sampling laboratory tests. The study hence uses the apparent electrical conductivity as a surrogate for soil salinity to understand the spatial pattern of the latter. To understand soil salinity distribution pattern in the study region, we integrated spectral information derived from two Landsat TM images (acquired on April 15, 2011 for the dry season and September 22, 2011 for the wet season), and applied universal kriging, spectral index regression and regression–kriging approaches to obtain the pattern. Results suggest that regression–kriging with nested spherical model produces the closest fit of the observed soil apparent electrical conductivity. Since most previous studies often employ either one or the other approaches in soil salinity monitoring and evaluation, the study suggests that combining remote and near sensing technology provides a rapid and relatively accurate assessment of soil salinity in arid and semi-arid regions, which would be essential to manage and prevent further soil salinization and re-salinization. [ABSTRACT FROM AUTHOR]

• A global meta-analysis of microbial community to increased salinity. • Significant reduction of prokaryotic alpha-diversity with increased salinity. • High salt concentrations enhanced Bacteroidetes and Proteobacteria. • Saline environment promoted Ascomycota but restrained Basidiomycota. • Importance of high-salinity microbes in regulating nutrient and energy flow. Soil salinity can affect microbial activities and community structure, with broader implications for carbon storage and mineralization. However, a consensus on how soil microbial communities respond to elevated salinity has yet to be reached. With the increasing salinization of coastal lowlands, a comprehensive assessment of salinity-driven microbial community shifts is crucial for understanding and predicting shifts of biogeochemical processes in response to soil salinization. Here, we conducted a meta -analysis of 21 studies to investigate the changes in soil microbial communities along salinity gradients. We found a significant reduction of prokaryotic alpha-diversity indices with increasing salinity. Prokaryotic beta-diversity was altered by elevated salinity, but significant differences were only found between samples at < 1 and > 10 ppt salinity. Compared to non-saline conditions, high salt concentrations increased the relative abundance of Bacteroidetes and Proteobacteria by 17–19%, especially that of Alphaproteobacteria and Gammaproteobacteria, but generally inhibited the rest of the dominant phyla and classes. For fungi, Ascomycota was 1.3-fold more abundant in saline environments than in non-saline environments, but Basidiomycota was 21% less. This meta-analysis reveals significant changes of microbial community composition responding to environmental salinization. With strong biodiversity reduction under saline conditions, these dominant microbial groups with high-salinity niche preference will play a stronger role in regulating nutrient and energy flow, and constraining SOC dynamics. [ABSTRACT FROM AUTHOR]

• Climate change played a dominant role in driving the increase in NPP. • The impact of urbanization on NPP was overestimated in arid regions. • Soil salinization weakens NPP more strongly than urbanization in FES. • Quantified the joint impacts of "climate-urbanization-soil-AMPs" on NPP. Net Primary Productivity (NPP) has been widely used to estimate the productivity of the farmland ecosystem (FES) and the carbon budget of the terrestrial ecosystem. Changes in the NPP of FES have knock-on effects on food security, sustainable agricultural development, carbon sequestration, and environmental changes. Existing studies mainly focused on the impact of climate change and urbanization on the spatial-temporal pattern of NPP, largely ignoring the roles of soil condition and agricultural management practices (AMPs). At present, the joint impact of "climate-urbanization-soil-AMPs" on the NPP of FES remains unknown. To fill this knowledge gap, we use the NPP dataset retrieved by the vegetation photosynthesis model (VPM), daily meteorological records, field-collected soil data, AMPs data collected from the Government Statistical Yearbook, and land cover dataset to study the joint mechanism of "climate-urbanization-soil" that drives spatial-temporal variations of NPP. Taking the northern slope of Tianshan Mountains as the study area, our results indicate that NPP had increased by 3.86 Tg C from 2000 to 2015 (11.93 Tg C in 2000 and 15.79 Tg C in 2015). From the proposed conceptual framework, we found that climate variables (accumulated temperature, precipitation, evapotranspiration) play a major role in driving the growth of NPP (+4.01 Tg C). The AMPs (i.e., mulching filming, drip irrigation, and fertilizer application) make a positive contribution to NPP increase (+0.98 Tg C). We also found that soil salinization (-1.07 Tg C) weakens the growth of NPP more significantly than urbanization (−0.16 Tg C). This study provides new insights on the mechanism of climate change, urbanization, and soil conditions on crops, benefiting stakeholders in designing better management plans for sustainable agriculture, ecosystem cycling, and food security. [ABSTRACT FROM AUTHOR]

Abstract: Salinisation threatens the sustainability of irrigated paddy fields in western Africa. To reclaim saline soils and improve the management of irrigated paddy fields, it is necessary to evaluate and monitor soil salinity in space and time. Proximal soil sensing by apparent electrical resistivity (ER) measurements can help to survey soil salinity. This study aimed to assess changes in the spatial pattern of salinity in vertisols using high resolution 3D mapping at 2 dates on an 8-ha experimental site located at Kollo, Niger, along the Niger River. For each measurement campaign, the survey combined ER and electrical conductivity (EC) measurements of a 1:5 soil:water extract. This procedure was performed in May 2008 and again in March 2011 after a 100-year flood of the study area by the Niger River. EC measurements were performed on soil samples collected at 3 depths at 140 points. ER measurements were performed at 420 points at 2 and 4 depths for the first and second campaigns, respectively. We built an inference model of EC using a regression-tree method for 3 prediction depths. The predictive data were the two ER measurements, their ratio and a qualitative description of land use. The maps generated by ordinary kriging of predicted EC revealed strong lateral salinity gradients ranging from 0.3 to 5.0dS/m (RMSE=0.79–0.83dS/m). Vertical variability in salinity was also significant and its pattern could be explained by land use, mainly irrigation intensity. In 2011, salt stocks in the upper 60cm of the soil ranged from low stocks (<2kg/m2) to extremely high stocks along the levee border (5.5–8.0kg/m2). Comparisons of the 2008 and 2011 salinity maps at 3 depths indicated stable spatial patterns and highlighted the high resilience of the system. Consequently, desalinisation efforts can only minimise soil salinity in the upper cultivated horizon. [Copyright &y& Elsevier]

[Display omitted] • A novel framework was developed to monitor total salt content in the soil profile to a depth of 1 m. • Field experiment and times-series remote sensing data were used to learn spatio-temporal variability. • A total of 42 parameters were evaluated the correlation with soil salinity. • Random Forest-Temporal Convolution Network model was employed for estimation. • Influencing factors of salt content of soil profiles were revealed at field- and parcel-scale. Soil salinization causes a deterioration in soil health and threatens crop growth. Rapid identification of salinization in farmlands is of great significance to improve soil functions and to maintain sustainable land management. As salt moves in soil profiles during plowing and irrigation, the commonly used protocol for measuring and monitoring salt content in topsoil does not provide a thorough assessment. In order to quantify and comprehensively evaluate the salt content in deep soil, this study developed a novel framework for monitoring total salt content in the soil profile to a depth of 1 m by combining information from time-series satellite images and machine learning. The field experiments were conducted in Alar, Southern Xinjiang, with a total of 120 soil samples and 582 measurements of EM38-MK2 apparent electrical conductivity in 2019 and 2020 to quantify the vertical variation in the salt content. A total of 42 covariates derived from time-series Sentinel-2 images, including 20 salinity indices, 10 soil indices, and 12 vegetation indices were used for modeling salinity in the soil profile. From the total covariates, 22 were selected using the Random Forest. Soil salinity which was modeled using a Temporal Convolution Network in 2019 and 2020 and forecast for 2021. The model effectively revealed the spatial and temporal variability of the salt content in the soil profile with R2 of 0.71 and 0.65 for 2019 and 2020, respectively. The proposed new framework provides an effective method to estimate the salt content in the soil profile for precision agriculture in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

• There was an interaction between N addition and precipitation change on R s and R a. • R h was insensitive to N addition and precipitation change in saline-alkaline grassland. • R a was controlled by plants biomass, while R h was controlled by ST and soil pH. • Soil salinization regulated the effects of N addition and precipitation change on R s. The responses of soil total respiration (R s) and its components (soil autotrophic respiration (R a) and heterotrophic respiration (R h)) to nitrogen (N) deposition have been widely evaluated in non-saline-alkaline grasslands. However, their responses to N addition under drought and wet conditions, especially in saline-alkaline grasslands, remain unclear. A two-factorial experiment involving N addition and precipitation changes (decreased or increased 50% precipitation relative to ambient) was conducted in a saline-alkaline grassland of Northern China, while soil respiration was measured in different treatment plots during two growing seasons (2018, 2019). Results showed that the addition of N or changes in precipitation alone had no significant effect on R s , R a or R h in both years because root productivity and soil microbial biomass were not affected. However, N addition with increased precipitation synergistically augmented seasonal mean R s and R a by 25.7% and 46.8% in 2018, and by 42.4% and 89.3% in 2019, respectively, owing to the increase in plant productivity. The variation in R a primarily contributed to variation in R s , and the effect size of N addition on R s and R a were increased with precipitation. In addition, a structural equation model showed that the response of soil respiration to N addition and precipitation changes was regulated by soil salinization, in which the R a was regulated by soil based cations while R h was controlled by soil pH values. Our study highlights that N addition with increased precipitation preferentially affects plants rather than microorganisms, and that R h was not sensitive to N addition and precipitation changes in saline-alkaline grassland. [ABSTRACT FROM AUTHOR]

Abstract: Taking the Inner Mongolia Hetao Irrigation District (IMHID) agricultural production region as a background and based on field data and local meteorological data, the influence of antecedent soil water storage (ASWS) on water and heat status was simulated and analyzed using the SHAW model during the seasonal freezing–thawing period. The results showed that the amount of ASWS prior to soil freezing can influence the depth of freezing and penetration of low temperatures. When ASWS within the surface 1m is less than or equal to 150mm, soil water storage (SWS) was always increasing over the winter period. However, for ASWS greater than 150mm, SWS went through 3 phases: at first it decreased, later it increased, and eventually it decreased again. During soil freezing, the amount of upward water transfer made up the deficit caused by evaporation and percolation for ASWS less than or equal to 150mm. Conversely, the amount of percolation was greater than that of upward transfer for ASWS greater than 150mm. During soil thawing, water continued to transfer from lower soil layers to upper layers and overtook evaporation and percolation for ASWS less than or equal to 210mm. However, the amount of evaporation and percolation was greater than the upward transfer for ASWS larger 210mm. These results may be used to assist in appropriate irrigation scheme in autumn, agricultural irrigation water management and research on reducing soil secondary salinization. [Copyright &y& Elsevier]

Abstract: The European Union Site of Community Importance Pineta San Vitale (Ravenna, Italy) is located in a complex lagoon system nearby the Adriatic Sea. This area is affected by relevant environmental pressures related to subsidence, groundwater salinization and industrial contamination. The occurrence of rare earth elements (REE), and the characterization of processes that control their migration in the water–soil system were performed for implementing the knowledge of the present environmental status in the area. Nine soil profiles were opened in the Pineta San Vitale and each horizon was characterized. In the parent material, dominant quartz, followed by plagioclase, mica, chlorite and calcite were recognized by XRD. Topsoil samples were characterized by neutral or sub-acid pH, relatively high organic carbon content (LOI up to 43wt.%; TOC: 38÷61g/kg), and low carbonate (11÷20g/kg CaCO3). Subsoil horizons were sub-alkaline or moderately alkaline, rich in carbonate (up to 114g/kg CaCO3), and depleted in organic matter. The electrical conductivity (EC: 0.2÷4.2 mS/cm) values along the soil profiles did depend on the water table fluctuations and the salinity of groundwater. Sequential extraction tests on two soil profiles showed the highest Y and ΣREE occurrence in the residual fraction of soils, with values generally increasing at depth, and total amounts similar to values estimated in the upper continental crust (UCC). Poor fractionation of REE was observed when the bulk soil horizon was considered, but different REE patterns occurred in the organic, oxide, and carbonate soil fractions, suggesting REE fractionation during weathering and pedogenic processes. Physical–chemical parameters and concentrations of total and dissolved elements were determined in 44 water samples. Waters showed near-neutral or slightly alkaline pH (6.8÷8.0) and total dissolved solids mostly in the range of 2 to 24g/L, with values increasing with increasing seawater influence. Concentrations of ΣREE determined in waters filtered through 0.4μm pore size filters varied from 0.1 to 189μg/L (0.7·10−9 ÷1.3·10−6 mol/L ΣREE), with the highest values observed in soil waters. REE values were not related to salinity, nor to major ions, while were positively correlated to Al concentrations. Further filtration through 0.015μm pore size filters removed about 90% of REE compared to the 0.4μm filtered samples, i.e. the studied waters showed a small quantity of truly dissolved REE species. Speciation modeling results by Visual MINTEQ indicated that truly dissolved REE species are dominated by bi-dentate organic complexes in the soil waters rich in dissolved organic carbon (up to 74mg/L DOC); aqueous complex with the carbonate and bicarbonate ligands increased in waters having low DOC. UCC-normalized patterns of REE in the colloidal fraction of soil waters showed slight enrichments in the middle REE (MREE), likely due to preferential adsorption of the MREE onto <0.4μm particles. UCC-normalized patterns of REE truly dissolved in soil waters showed slight enrichments in the heavy REE (HREE), probably due to dominant low molecular weight DOC in the truly dissolved fraction. For evaluating the potential REE contribution from the atmosphere, 12 vegetation samples (ivy leaves) were submitted to leaching tests using acidulated water as a proxy of rain water. The REE leached from leaves were in the range of 0.5 to 10μg/kg ΣREE, suggesting a significant atmospheric contribution to the water–soil system. UCC-normalized patterns of REE leached from leaves were nearly flat, possibly indicating a dominant geogenic source of atmospheric REE. [Copyright &y& Elsevier]

Abstract: Inner Mongolia Hetao Irrigation District, in north of China, is typical of seasonal frozen soil areas in the region. Irrigation in autumn is required to leach soil salt and to provide a reserve of soil water for the next year''s crop. However, improper autumn irrigation results in the secondary salinization of soil. The objective of this study is to simulate soil water and heat dynamics during winter period with the one-dimensional Simultaneous Heat and Water (SHAW) model to assess its capability for simulating overwinter water storage. SHAW model soil parameters were calibrated by data of 1995–1996 and 2002–2004 and validated by data of 1996–2001 and 2005–2006 using field measured soil water contents and temperatures during freezing and thawing periods. Using calibrated and validated soil parameters, the paper simulates the process of soil freezing–thawing, and the dynamic variation of moisture–heat transfer, including soil water content, temperature, frost depth, soil evaporation, and water flux in the seasonal freezing–thawing period. These are useful to determine proper autumn irrigation management, and can be used in future research to address overwinter solute migration to reduce soil secondary salinization. [Copyright &y& Elsevier]

Abstract: Soil salinization is an important desertification process that threatens the stability of ecosystems, especially in arid lands. Quantifying and mapping soil salinity to monitor soil salinization is difficult because of its large spatial and temporal variability. There has been a growing interest in the use of hyperspectral reflectance as a rapid and inexpensive tool for soil salinity characterization in the recent past. However, as soil moisture often jointly affects soil reflectance, a moisture-insensitive reflectance model is needed to provide the base for soil salinity monitoring from soil reflectance. In this paper, we developed an exponent reflectance model to estimate soil salt contents inversely under various soil moisture conditions, based on a control laboratory experiment on the two factors (soil salinity and soil moisture) to soil reflectance. Main soil salt types (Na2SO4, NaCl, Na2CO3) with wide soil salinity (0% to 20%) and soil moisture (1.75% to 20%) levels (in weight base) from Western China were examined for their effects on soil reflectance through a model based approach. Moisture resistant but salt sensitive bands of reflected spectra have been identified for the model before being applied to inversely estimate soil salt content. Sensitive bands for Na2SO4 type of salt affected soils were identified as from 1920 to 2230nm, and 1970 to 2450nm for NaCl, 350 to 400nm for Na2CO3 type of salt affected soils, respectively. The sensitive bands focused on ranged from 1950 to 2450nm when all data were considered when ignoring salt types. The model was then applied to inversely estimate soil salt contents. High R2 of 0.87, 0.79, and 0.66, and low mean relative error (MRE) of 16.42%, 21.17%, and 27.16%; have been obtained for NaCl, Na2SO4 and Na2CO3, respectively. Performance of the inverse model dropped but remained significant when ignoring salt types with an R2 of 0.56 and a MRE of 33.25%. The approach proposed in this study should thus provide a new direction for estimating salinity from reflectance under various soil moisture conditions and should have wide applications in future monitoring of soil salinization. [Copyright &y& Elsevier]

Abstract: The Harmattan is a dry, dust-laden continental wind which has its origin in the Bodélé Depression in the Chad basin. In Ghana the Harmattan can be experienced from November to March, when the Harmattan replaces the dominant south westerly maritime Monsoon wind. The hypothesis of this study is that the majority of dust deposited in northern Ghana may not be from the original Harmattan source in the Bodélé Depression. The aim of this study is therefore to investigate the origin of deposited dust in Tamale, Ghana. This is examined by comparing wind data, grain size distribution, mineralogical and geochemical data from dust samples deposited during the Harmattan and Monsoon seasons, and topsoil. This study shows that despite a clear difference between the wind directions in the Harmattan and Monsoon seasons in Tamale, northern Ghana, no distinct differences are observed between the mineral or elemental compositions of the bulk dust samples. Traces of minerals in the clay fraction of the Harmattan period dust may have their origin in the Bodélé Depression or other saline environments. The Harmattan dust deposited in Ghana shows only little resemblance to dust from the Chad basin and with Harmattan dust deposited in Niger. This study therefore suggests that the dust deposited during the Harmattan period in northern Ghana is not under significant influence of sediments from the Bodélé Depression. Similarity in the mineral and elemental composition of the dust from both the Harmattan and Monsoon periods indicates that the dusts settled are mainly of local origin. [Copyright &y& Elsevier]

• Ice cover has positive effect on soil salinization control and moisture preservation after autumn irrigation. • The unique air space created during the ice formation enhanced the heat insulation of ice cover. • The development of freezing front was primarily prevented by infiltration water during unstable freezing period. • The upward solute flux in frozen soil was decreased by the combination of 'inherited' thermal regime and ice cover during stable freezing period. 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. [ABSTRACT FROM AUTHOR]

Abstract: Soils and sediments of the southern Aral Sea basin of Central Asia are commonly covered by salt-bearing surface crusts. These crusts represent a potential source of salt-rich dust that can contribute to salinisation of soils in a large area. Crust types include takyric crusts, surface crusts on Solonchaks, and salt crusts covering dry parts of the lake bed. The takyric crusts are structural crusts, showing features related to flooding and shrinkage. Gypsum, formed within the sediment matrix, is the only evaporite mineral they contain. One studied Solonchak crust is fine-grained, with a thin thenardite and bloedite coating along the irregular surface and halite coatings along the sides of pores. All salts in this crust formed by evaporation of solutions contained in the groundmass. A Solonchak crust with a sandy texture contains prismatic gypsum crystals that formed in a water-saturated surface layer, as well as bloedite, eugsterite and halite, formed by evaporation of interstitial brines. The lake bed crust is a pure accumulation of salt minerals (bloedite, hexahydrite, halite, kainite), with synsedimentary and diagenetic features. Salt mineral assemblages in the three crust types are those that form during successive stages of evaporation of Aral Sea waters, in agreement with published theoretical predictions. [Copyright &y& Elsevier]

• Comparing performance of different EMCI inverted from various EC a data. • Identify recharge and discharge zones using EM instruments and EM4Soil software. • Exploring potential to use of EC a from various EM instruments in predicting EC e. • Inversion software to create 2D-imaging information for salinity management. The introduction of irrigated agriculture through a network of canals has seen enormous growth in agricultural output in northeast Thailand. However, the canals are leaking causing recharge water to interact with an underlying sequence of geological units rich in salts, which has caused secondary soil salinization. Herein, we measure apparent electrical conductivity (EC a) from EM instruments (e.g. Geonics EM38, EM34 and DUALEM-421) along a section of the Kham-rean Canal of the Muang Pea project in Thailand. We inverted the EC a to generate electromagnetic conductivity images (EMCI) using EM4Soil and evaluate these 2-dimensional models of estimates of the electrical conductivity (σ) distribution with depth, against soil properties, including, measured electrical conductivity of saturated soil paste extract (EC e – dS/m). Using a linear regression (LR) calibration we determine the suitability of each instrument to predict EC e from σ. We found a moderate coefficient of determination between σ and EC e by inverting EC a using either a EM38 on the ground (R2 = 0.66) or a height of 0.5 m (0.65). This was also the case inverting DUALEM-1 (0.63) and DUALEM-2 (0.64) EC a. A strong R2 was achieved by inverting DUALEM-421 (0.72) EC a , with generally weak R2 achieved with DUALEM-4 (0.34) and EM34 (0.51). Using a leave-one-out cross validation, good agreement in EC e predictions were achieved by inverting DUALEM-421 (Lin's = 0.83), followed by moderate predictions when EM38 on the ground or a height of 0.5 m (0.79) and EM34 (0.66) were used. Our interpretation of predicted EC e suggests the approach can identify non-saline and highly saline areas. Our interpretation of the predicted EC e shows non-saline and highly saline areas, where the former represents a likely recharge area. In the centre of the study, the recharge area was characterised by large silt content (>70%), moderately sodic (6 < ESP < 12%) and slightly saline (EC e < 4 dS/m) conditions. The results have implications for Muang Pea project where 400 km of canals remain unsurveyed. We recommend using an EM38, DUALEM-421 or other shallow sensing EM can similarly potentially identify areas where further hydrological investigations can be undertaken and if recharge is confirmed, engineering solutions can be recommended to mitigate recharge and prevent soil salinisation. [ABSTRACT FROM AUTHOR]

• FGD gypsum can be used to reclaim sodic soil even under relatively dry conditions. • Proper leaching reduces the ion concentration leading to soil salinization. • FGD gypsum-sodic soil reaction is longer when the water input is insufficient. • Appropriate FGD gypsum application rates improve sodic soil reclamation. Flue gas desulfurization (FGD) gypsum has been widely used and commercialized in some areas to reclaim sodic soil. Previous studies mainly considered the reclamation efficiency after FGD gypsum application, whereas little information is available on the applicable conditions, such as soil moisture. Thus, a pot experiment was conducted with four FGD gypsum application rates (0, 0.5, 1.0 and 1.5% of soil) under four soil moisture conditions (25, 50, 75 and 100% of the water-holding capacity (WHC) at saturation) to investigate the soil salinity and sodicity changes over 90 days. For each moisture condition, the soil pH, sodium adsorption ratio (SAR), exchangeable sodium percentage (ESP) and CO 3 2− and HCO 3 − concentrations decreased with increasing FGD gypsum application. However, the opposite trend occurred for electrical conductivity (EC) due mainly to the increased soluble cation (i.e., Ca2+, Mg2+, Na+ and K+) and SO 4 2− concentrations. For each FGD gypsum application rate, the soil EC, ESP and Ca2+, Mg2+ and SO 4 2− concentrations decreased while the pH and SAR increased with increasing moisture content. Significant differences in these properties were found mainly between the 0% and 1.5% FGD gypsum application rates or 25% and 100% of WHC moisture conditions. Moreover, the reclamation efficiency caused by soil moisture was concentrated in the first 30 days and gradually decreased with time. Therefore, FGD gypsum can be used to reclaim sodic soil in areas with little water and its effectiveness can be enhanced by moderately increasing its application rate and/or its reaction time with soil. [ABSTRACT FROM AUTHOR]

• Soil-landscape relationship is dominant factor for accurate predicting soil salinity. • RF was recommended for mapping soil salinity in Xinjiang, China. • No algorithm that is superior for all soil depths. • Best linear and non-linear algorithms were LMSLR and RF. • Complexity of the algorithm did not always increase the stability of the algorithm. Soil salinization is one of the most predominant processes responsible for land degradation globally. However, monitoring large areas presents significant challenges due to strong spatial and temporal variability. Environmental covariates show promise in predicting salinity over large areas provided a reasonable relationship is developed with field measured salinity at few points. While simple regression-based approaches to complex data mining methods have been used in the prediction, a comprehensive comparison of their performances has not been explored, leading to uncertainty in which algorithms to select. This study compares thirteen popularly and non-popularly used algorithms and their performances following four criteria in predicting soil salinity from environmental covariates from Kuqa Oasis from Xinjiang, China. The environmental covariates used for the prediction include principal components of Landsat satellite images at multiple spectral bands, climate factors (referring to land surface temperature), vegetation indices, salinity and soil-related indices, soil moisture indices, DEM derived indices, land use, landform and soil type and categorized them under parameter categories of the SCORPAN (S, soils; C, climate; O, organisms, biotic factor; R, relief; P, parent material; A, age; and N, space) model. The predictive relationships were developed using the algorithms including some previously used ones such as Multiple Linear regression (MLR), Multi-Layer Perceptron-Artificial Neural Network (MLP-ANN), Stochastic Gradient Treeboost (SGT), M5 Model Tree (M5), Multivariate Adaptive Regression Splines (MARS), Classification and Regression Tree (CART), Random Forest (RF), and Support Vector Regression (SVR) and some that have not previously been used in predicting salinity such as Alternating Model Tree (ATM), Gaussian Processes Regression (GPR), Gaussian Radial Basis Functions (GRBF), Least Median Squared Linear Regression (LMSLR), and Reduced Error Pruning Tree (REPTree). Here, 5-fold cross-validation and an independent dataset (30% overall samples) at three depths, 0–10 cm, 10–30 cm, 30–50 cm, were used for parameter optimization and evaluating the performance of algorithms. The performances of these algorithms were compared against multiple criteria, including the parameterization, error level/fitting accuracy (determination coefficient, R2; root mean squared error, RMSE), stability (based on the Pearson correlation coefficient, R; mean absolute percent error, MAPE; root mean squared error, RMSE; Lin's concordance correlation coefficient, LCCC) and computational efficiency of the algorithms. Finally, the result showed that CSRI is most important parameter for the prediction of soil salinity at the 0–10 cm and 10–30 cm depths, whereas for the 30–50 cm depth interval, VD was the most important predictor. For depths of 0–10 cm, 10–30 cm and 30–50 cm across all models, the model R2 values ranged from 0.60 to 0.74, 0.15 to 0.31, and 0.30 to 0.47, and the RMSE values ranged from 18.87 to 23.49 dS m−1, 9.94 to 13.48 dS m−1 and 3.79 to 7.11 dS m−1. The optimal algorithms at three depths of 0–10 cm, 10–30 cm and 30–50 cm are RF, M5 and GRBF with considering accuracy and stability. After a comprehensive assessment of algorithm performance, we recommend RF for mapping salinity in an arid environment such as that of Xinjiang and elsewhere globally. However, there is no algorithm that can perform ideally for all datasets. Therefore, we suggest that the algorithm should be carefully chosen according to the purposes of the study. [ABSTRACT FROM AUTHOR]

• Plant-available water was assessed in a petrocalcic soil under two different land uses. • Rooting depths were estimated using a process-based numerical model. • Eucalyptus uptakes water mostly from the soil above the petrocalcic horizon. • Petrocalcic horizons partially disconnects shallow soil and the phreatic aquifer. • Grassland afforestation in these environments does not show soil salinization. Plant-available water (PAW) is one of the most important issues in productive landscapes around the world. Besides climatic conditions, it depends on several factors, among them, land uses, the rooting and water table depths, and the presence of soil heterogeneities (e.g., petrocalcic horizons). In the present work, PAW evolution was assessed over 441 days in a vertical 1D domain of a monitored Pampean petrocalcic soil under two different land uses and contrasted water demand (i.e., natural and afforested grasslands). For this purpose, rooting depths were estimated using a process-based numerical model of the aquifer-soil-plant-atmosphere continuum constrained by both in situ measured atmospheric demand and water table fluctuations. This model was calibrated against soil water content measurements at different depths and finally, as validation, computed transpiration rates were compared with the sap flow measurements. Modeling results, and calibrated rooting depths, suggest that petrocalcic horizons induce a limited hydraulic connection between the shallow soil portions and the phreatic aquifer. Thus, water storage is enhanced above the petrocalcic horizon in the natural grassland plot particularly during the periods of low atmospheric demand, whereas it is immediately consumed by forest to soil wilting point below the afforested grassland (hydric stress). The numerical methodology followed here appears as an alternative to estimate the forest groundwater usage in afforested grasslands, a key issue in soil salinization research in great plain areas. [ABSTRACT FROM AUTHOR]

Soil salinization is a serious land degradation issue in Northwest China and significantly threatens food security; thus, a comprehensive understanding of plant response to salinity stress is desirable. The objective was to detect the effect of salinity stress on alfalfa transpiration rate and plant growth under four salinity levels. The thermal infrared remote (TIR) technique was used to acquire the canopy temperature of plants exposed to four salinities (no salinity, S 0 ; slight salinity, S 3 ; moderate salinity, S 5 ; and severe salinity, S 7), while transpiration was evaluated based on the three temperature (3 T) model. Results indicated that the 3 T model and TIR technique successfully simulated transpiration with a high coefficient determination of R2 above 0.8. The general pattern is that canopy temperature values correspond well with salinity values. With the increase of soil salinity for the treatment of S 3 , S 5 , and S 7 , the canopy temperature increased by 0.88 °C, 0.98 °C, and 1.19 °C, respectively. Moreover, the growth of alfalfa was inhibited, resulting in a corresponding decrease in leaf area index by 19.3%, 31.2%, and 54.2%, respectively. Transpiration reduced by 1.2%, 6.7%, and 20.0%, correspondingly, because of stunted growth and less canopy cover. Salinity stress on agricultural crops was extremely negative; the higher the soil salinity was, the lower the canopy cover, so salinity reduced the growth rate and resulted in reduced plants growing due to osmotic effects. Where salt accumulation occurs in a horizontally nonuniform way, transpiration may be presented by spatial heterogeneity; however, there remains limited research on this issue. This research will provide a scientific basis for ecological construction programs. • We assessed the usefulness of thermal infrared remote technique in detecting transpiration. • Spatial heterogeneity of transpiration was addressed in this research. • The effect of salinity stress on alfalfa transpiration rate and plant growth were quantitatively evaluated. [ABSTRACT FROM AUTHOR]

Soil salinization is one of the most common land degradation processes that impacts agricultural production, environmental health, and economic welfare. The first task in managing soil salinity is to understand its spatiotemporal characteristics, causal factors and influences, both over large regions and within individual fields. In this study, a typical irrigated agro-ecosystem located in the upper Yellow River basin, suffering from long-term soil salinization problems, was selected as the case study area. Field sampling experiments and investigations related to soil layer salt content, groundwater, irrigation and ground elevation were carried out at three scales (field scale, canal scale and regional scale) covering both croplands and natural vegetation patches during 2016 and 2017. Spatiotemporal characteristics of soil salinity were analyzed for the three scales based on sampling data, remote sensing, geostatistical analysis and model simulation. Results showed that, at the regional scale, the soil salinity distribution was dominated by topography and groundwater depth; at the canal scale, it was dominated by irrigation and cropping pattern; at the field scale, it was dominated by micro-topography and the uniformity of field irrigation. Scale-dependent soil salinity management strategies should be adopted to maintain agricultural production and the sustainability of natural ecosystems. It can be concluded that using integrated approaches including multi scale soil sampling, model simulation and other modern techniques is an effective way for thorough understanding and management of soil salinity. • Soil sampling, GIS/RS and dynamic model were integrated for salinity analysis. • Salinity characteristics and their causal factors were scale dependent. • The proportion of salt-affected soil increased by 14% during the growing season. • Multiple modern techniques are needed for salt prediction and management. [ABSTRACT FROM AUTHOR]