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Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

(Objective) Soil salinity is an abiotic challenge facing agricultural production in Xinjiang. One solution to ameliorate the impact of soil salinity is to leach the salt out of the root zone in winter-spring period after harvest but before planting. The purpose of this paper is to experimentally study the suitable irrigation scheduling to achieve that goal. (Method) The experiment was conducted in a field where irrigation amount in the winter varied from 0 to 3 600 m³/hm², combined with a spring irrigation with the amount 300 m³/hm². In each treatment, we measured the change in water and salt contents at different soil depths. (Result) 1 The winter irrigation affected water storage in the soil, especially in the early stage and in the top 0~30 cm soil layer where the irrigation significantly increased the soil moisture content, regardless of the irrigation amount. The increase was followed by a decline until commencement of the spring irrigation. The soil moisture content in 0~100 cm soil layer irrigated by 600 m³/hm² of water in the winter was lower than that before the irrigation; a supplementary spring irrigation boosted soil moisture content in the 0~30 cm soil layer by more than 15.66% compared to that with only one winter irrigation at 1 800 m³/hm². 2 In the early stage after the winter irrigation, salt leaching depth increased with the increase in irrigation amount; when the irrigation amount was 2 400 m³/hm² or above, the leaching depth was as deep as 100 cm. However, the prolonged drying season prior to the spring irrigation drove salt ascending, regardless of winter irrigation amount. The supplementary spring irrigation leached salt, especially from the 0~30 cm soil layer where the desalinization was most significant. 3 Combining a 2 400 m³/hm² winter irrigation and a 300 m³/hm² spring irrigation desalinated the 0~30 cm and 0~100 cm soil layers most efficiently. 4 After the spring irrigation, the average electric conductivity of the 0~30 cm and 0~10 cm soil layer was 1.40 mS/cm and 1.30 mS/cm, respectively, and the associated total salt content were 3.65 and 3.32 g/kg, respectively, lower than the critical salt content for cotton emergence (3.7 g/kg). (Conclusion) Considering water saving, desalination and soil water in the 0~30 cm soil layer after the spring irrigation, irrigating 2 400 m³/hm² in winter combined with a 300 m³/hm² irrigation in spring is optimal for cotton growth in the studied region. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A continuous long-term field experiment (2008-2018) was conducted in Xinjiang, north-western China, to assess the impact of farmyard manure (FYM) and inorganic fertilisers on the sustainable biomass yield of sweet sorghum cultivar (Xingaoliang No. 3) and soil chemical properties. Seven treatments, associated with nitrogen (N), phosphorus (P), potassium (K), FYM, and their different combination, were compared with the control plot (CK). As a result, the treatments NP, PK, NK, NPK and NPKM significantly increased the average biomass yields by 30-48% over CK. The 12 t/ha FYM per year with NPK (NPKM) increased both the yield and total soluble solids (TSS) by 48% and 7.9%, respectively, while the 18 t/ha/year application rate of FYM had an adverse effect on yield. Stem TSS, soil available N and K for all treatments decreased while soil organic carbon, soil total salt and the available P for FYM applied treatments increased over the years. The soil pH stabilised at 7.8-8.2 at the end. In conclusion, the 12 t/ha/year of FYM is the most efficient rate for a single application or incorporation with inorganic fertilisers. A more reasonable application rate of N and K fertiliser to increase the yield and irrigation rate to reduce soil salt needs for further investigation. [ABSTRACT FROM AUTHOR]

The film mulched drip irrigation (FMDI) in severe drought and salinity areas makes the transport of water, heat, and salt in the soil more complicated. In this paper, two-year field experiments were carried out in southern Xinjiang from 2020 to 2021 (Salt levels: 0.2%, 0.4%, and 0.6%; Irrigation levels: 30 mm and 45 mm (2020). 22.5 mm, 30 mm, 37.5 mm, and 45 mm (2021)). Using the measured data of soil moisture, heat, and salinity, the HYDRUS-2D model was calibrated and validated, and the effects of soil adsorption on the simulation results were compared. Meanwhile, six irrigation scenarios were designed (The three levels of irrigation times were 8 times, 10 times, and 12 times. The total irrigation amounts were 360 mm and 450 mm). The results indicate that the simulated results of salt transport under the condition of kinetic adsorption matched accurately with the observed data (the maximum of RMSE was 0.293 ds m−1, and the minimum values of R 2 and d were 0.923 and 0.934, respectively); similarly, the simulation results of soil water and heat transport were acceptable (the maximum values of RMSE were 0.033 cm cm−1 and 1.897 °C, and the minimum values of R 2 and d were 0.877, 0.99 and 0.915, 0.807, respectively). The scenario simulation indicate that salt leaching mainly occurred under plastic film, the desalination in the 0–40 cm soil layer was obvious (The rate range of salt leaching is 29.8∼58.8%), and salt accumulation appeared in the 40–100 cm layer under plastic film in the specific scenarios. Considering the optimal desalination in the cotton root zone, the irrigation mode with 8 irrigation times (The irrigation interval of 12 d during the budding period, 2 times; the irrigation interval of 8 d, 6 times) with a total irrigation amount of 450 mm was recommended, which provides a reference for the regulation of water, heat, and salt in severe drought and salinity areas. • HYDRUS accurately simulated the soil water, salinity, and heat transport with FMDI. • Under the condition of adsorption, the simulation effect of salt was more accurate. • The mode (8 irrigation times and 450 mm total irrigation amounts) was recommended. [ABSTRACT FROM AUTHOR]

Aiming at assessing the problems of the high land salinity, high spatial variability of soil nutrients, sloppy management, and low efficiency in the Xinjiang region, research on the precise delineation method of field management zones is being conducted to facilitate differentiated fertilizer management for farmers and increase the level of saline soil improvement. Taking the plots in the experimental area as the research object, traditional statistics, principal component analysis, and fuzzy c-mean classification were applied to divide the management zoning in the study area, and the suitability and effectiveness of the management zoning were evaluated. The study area was at a mild salinization level; the soil organic matter and total nitrogen content were at a low level; total phosphorus was at a medium level; and total potassium and pH reached a high level. pH had a coefficient of variation <0.1, which was weak, and the other soil attributes were of medium variability intensity. The spatial distribution of the soil attributes was highly variable. The results of the principal component analysis showed that the six soil attributes grouped into statistical factors could be divided into three principal components. The results of the fuzzy c-means classification showed that the study area could be divided into four management zones, and there were significant differences in the soil salinity, organic matter, soil nutrients, and other attributes in the different management zones. The different soil attributes could be utilized to determine the management zoning of farmland in the study area with the help of fuzzy c-mean classification. [ABSTRACT FROM AUTHOR]

The lowering of salt content in the field, especially in arid areas, after consecutive application of mulched drip irrigation (MDI) is of vital importance for sustainable cotton plantation. To elucidate the effects of long-term MDI on soil properties and cotton growth, this paper systematically monitored the soil salinity, ion concentrations and the yield of cotton in the field using MDI consecutively for six years in a typical oasis in Xinjiang, China. The results showed that MDI could significantly change salt distribution in the cotton field. During the six years tested, the soil salt content using MDI declined fast at first, and then the decline rate gradually decreased. In the 1st and 2nd year, the average salt content within 0–100 cm soil layer was larger than 20 g kg−1, which belonging to the saline soil. Then the salt content decreased to 10–20 g kg−1 in the 3rd and 4th year, and the cotton field declined to heavily saline soil. After 5 years of MDI, the soil turned to non-salinized. The Cl− and SO42− equivalence ratio (CSER) also decreased with the increase of application years of MDI. Saline-alkaline land developed from chloride-sulphate solonchak (0.2 < CSER < 1) into sulphate solonchak (CSER < 0.2) after 6 years of MDI. The survival rate of the cotton increased from 1.48% (1 year of MDI) to 76.3% (6 years of MDI), and the yield increased from 72.43 kg ha−1 to 4515.48 kg ha−1. When the average CSER, SAR and the soil salinity in 0–140 cm soil layer decreased to 0.60, 0.98 (mol kg−1)0.5 and 6.25 g kg−1, farmers can achieve a balance between income and expenditure. Moreover, when CSER, SAR, and the soil salinity continuously decreased to 0.44, 0.69 (mol kg−1)0.5 and 0.77 g kg−1, the cotton yield will exceed the average production level of cotton in Xinjiang. Under the current irrigation schedule in the oasis irrigation area, the soil salinity and groundwater level after applying MDI could be conducive to cotton growth. However, this situation had also caused a waste of nearly 200 mm of water resources. Therefore, authors suggested that further research on water-saving irrigation systems suitable for different soil conditions should be carried out, and also the differential quota management in production practice should be adopted. [ABSTRACT FROM AUTHOR]

Low-cost and efficient dynamic monitoring of surface salinization information is critical in arid and semi-arid regions, we conducted a remote sensing inversion exercise for soil salinity in the Bosten Lake watershed in Xinjiang, Northwest China, with a total area of about 43,930 km2, a typical watershed in an arid area. Sentinel MSI and Landsat OLI data were combined with measured soil salinity data in July 2020, and optimal combination bands were selected based on characteristic bands to create a grid search-support vector machine (GS-SVM) inversion model of soil salt content. The maximum value of soil salt content in the Bosten Lake watershed was 11.8 g/kg. The minimum value was 0.41 g/kg, and the average value was 4.77 g/kg, soil salinization is serious. The results of previous studies were applied to the estimation of salt content in Bosten Lake watershed and could not meet the monitoring requirements of the study area, R2 < 0.3. The GS-SVM soil salinity monitoring model was established based on the optimal DI, RI, and NDI remote sensing indexes for the Bosten Lake watershed. After model verification, it was found that the optimal model of image data was the Landsat OLI first-derivative model with R2 of 0.64, RMSE of 3.12, and RPD of 1.64, indicating that the prediction ability of the model was high. We used the first-order derivative model of Landsat OLI data to map the soil salt content in the Bosten Lake watershed in arid area, and found that soil salt content in most of the study area was between 10 and 20 g/kg, indicating severe salinization. This study not only reveals the distribution characteristics of salinization in Bosten Lake watershed, but also provides a scientific basis for soil salinization monitoring in Central Asia to lay a foundation for further soil salinization monitoring in arid areas. [ABSTRACT FROM AUTHOR]

The mulch film residues in cotton fields in south Xinjiang have caused serious harm to the soil environment and ecological security in the oasis areas. Non-mulched planting provides an alternative approach to this problem. In this experiment, irrigation was provided on the basis of the reference crop evapotranspiration (ET0). Two layouts of drip tapes (1T4R—one tape for four rows; 2T4R—two tapes for four rows) were applied to the non-mulched, drip-irrigated cotton fields in south Xinjiang, and their impacts on soil water–heat–salt dynamic changes and the water consumption and yield of cotton were compared and analyzed. The experiment shows that the 2T4R layout provided an excellent soil water–salt environment for cotton growth and yield formation. Soil temperature decreased by 0.8 °C and drip irrigation belt input increased by CNY1200·hm−2. However, a higher profit derived from the 2T4R layout could compensate for the increased expenditure. The results show that cotton cultivation using non-mulched drip irrigation instead of mulched drip irrigation can potentially alleviate soil environmental and ecological security problems caused by plastic mulch residues in oasis areas. Although cotton yield was reduced by about 15%, water and nitrogen strategies and other field management could be adjusted to compensate for the disadvantages. [ABSTRACT FROM AUTHOR]

Soil salinization is prominent environmental issue in arid and semi-arid regions, such as Xinjiang in Northwest China. Salinization severely restricts economic and agricultural development and would lead to ecosystem degradation. Finding a method of rapidly and accurately determining soil salinity (SS) is one of the main challenges in salinity evaluation, saline soil development, and utilization. In situ visible and near infrared (Vis-NIR) spectroscopy has proven to be a promising technique for detecting soil properties since it can realize real-time, rapid detection of SS. However, it still remains challenging whether Vis-NIR in situ spectroscopy can invert SS with high accuracy due to the interference of environmental factors (e.g., light, water vapor, solar altitude angle, etc.) on the spectral in the field. To fill this knowledge gap, we collected Vis-NIR in situ spectral and lab-measured SS data from 135 surface soil samples in the Kongterik Pasture Nature Reserve (KPNR) in the desert oasis ecotone of southern Xinjiang, China. We used genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA) algorithms to select the feature bands of SS. Subsequently, we combined extreme learning machines (ELM), back-propagation neural networks (BPNN), and convolutional neural networks (CNN) to build inversion models of SS. The results showed that different feature bands selection methods could improve the Vis-NIR in situ spectral prediction model accuracy. Either SS inversion models were built using full-band spectral data or feature-band spectral data. Compared with the full-band (401–2400 nm) spectral modeling, the validation set R2 of ELM, BPNN, and CNN models built selected feature bands selected by PSO, GA, and SA, respectively, were improved by more than 0.06. The accuracy of predicting SS varied widely among modeling methods. The accuracy of CNN model was obviously higher than that of BPNN and ELM models. The optimal hybrid model for predicting SS constructed in this study is SA-CNN model (R2 = 0.79, RMSE = 9.41 g kg−1, RPD = 1.81, RPIQ = 2.37). This study showed that the spectral feature bands selection methods can reduce the influence of environmental factors on in situ spectroscopy and significantly enhance the inversion accuracy of SS. The present study provided that estimating SS using in situ Vis-NIR spectral is feasible. [ABSTRACT FROM AUTHOR]

Accurate assessment of the spatial distribution and severity of soil salinization has long plagued local governments and researchers in the arid parts of Xinjiang Uygur Autonomous Region (XJUAR). The emergence of machine learning has brought hope to this research field, such as Random Forest (RF) and Stochastic Gradient Treeboost (SGT),however, which are few applications to the quantitative assessment of soil salinization. Therefore, in order to evaluate the accuracy level of the two algorithms for predicting soil salinity, twenty-seven environmental subsets were designed. Each data set is calculated using both RF and SGT to produce an optimal set of variables. The simulation results from 70.37% (19/27) of the subsets showed that the predicted value of soil salinity from SGT is closer to the observed value than is that from RF. The statistics of all datasets showed that the average values of R2 value for RF and SGT were 0.38 and 0.40, the average Root Mean Squared Error (RMSE) value were 28.59 and 27.46, and the Ratio of Prediction to Deviation (RPD) averages were 1.20 and 1.24, respectively. The important dominant factor were topographic variables with coarse resolution, temperature and vegetation indices, land use and landform. [ABSTRACT FROM AUTHOR]

Background: The layout of drip tapes under mulch has changed in Xinjiang, China, with the development of machine-harvest cotton (Gossypium hirsutum L.) planting technology. This study aims to demonstrate the effects of drip tape modes on soil hydrothermal conditions, cotton yield, and water use efficiency (WUE) of machine-harvest cotton under mulch in Xinjiang. Methods: A field experiment was conducted to set up two machine-harvest cotton planting patterns (T1: the cotton planting model with one film, two drip tapes and six rows; T2: the cotton planting model with one film, three drip tapes and six rows), and a conventional planting mode (T3: the cotton planting model with one film, two drip tapes and four rows) as a control. Results: Our results showed that the heat preservation and warming effects of the cotton planting model with one film, two drip tapes and six rows and the cotton planting model with one film, three drip tapes and six rows were better than that of the conventional planting mode. Soil temperature under the mulching film quickly increased and slowly decreased, which was beneficial to the early growth and development of cotton. The mean soil moisture content of the 0–60 cm soil layer in the cotton planting model with one film, three drip tapes and six rows was significantly higher than the other two treatments at the middle and late stage of cotton growth (90 days after sowing (DAS) and 135 DAS). Moreover, the water holding capacity of the middle and upper part of the tillage layer in the cotton planting model with one film, three drip tapes and six rows was the best. At the medium cotton growth stage, the main root layer in the cotton planting model with one film, three drip tapes and six rows formed a desalination zone. At the late cotton growth stage, the soil salinity content of the 0–60 cm soil layer showed that the cotton planting model with one film, three drip tapes and six rows was the lowest, the cotton planting model with one film, two drip tapes and six rows was the highest, and the conventional planting pattern was in the middle. Among these three modes, the cotton planting model with one film, three drip tapes and six rows was more efficient in controlling soil salt accumulation. The agronomic traits and cotton quality in the cotton planting model with one film, three drip tapes and six rows were better than that for the other two treatments. Compared with the other treatments, the cotton yield in the cotton planting model with one film, three drip tapes and six rows increased by 6.15% and 11.0% and 8.1% and 12.3%, in 2017 and 2018, respectively, and WUE increased by 17.4% and 22.7% and 20.9% and 22.8%, in 2017 and 2018 respectively. In conclusion, the cotton planting model with one film, three drip tapes and six rows can be recommended for machine-harvest cotton planting for arid areas in Xinjiang, considering water conservation and improving cotton yield. [ABSTRACT FROM AUTHOR]

Biomass allocation affects the ability of plants to acquire resources and nutrients; a limited allocation of nutrients, such as nitrogen and phosphorus, affects ecological processes. However, little research has been conducted on how plant allocation patterns change and on the trade-offs involved in allocation strategies when microhabitat gradients exist. We selected a 3.6 km transect in the Ebinur Lake Wetland Natural Reserve of Xinjiang, China, to investigate the relationships between plant traits (biomass and N and P concentrations) of herbaceous plants and environmental factors (soil moisture, salinity and nutrient content), and to determine the allometric scaling of biomass and stoichiometric traits between the above- and below-ground plant parts. The results show that the biomass and stoichiometric traits of plants reflected both the change of micro-environment and the natural characteristics of plants. With a decrease of the soil water availability and salinity, above- and below-ground N and P concentrations decrease gradually; scaling relationships exist between above- and below-ground plant parts, for biomass and N and P concentrations. Biomass allocation is influenced by soil nutrient ratios, and the allocation strategy tended to be conserved for N and variable for P. Second, the scaling relationships also show interspecific differences; all scaling exponents of Suaeda prostrata are larger than for other species and indicate a 'tolerance' strategy, while other species tend to increase the below-ground biomass and N and P concentrations, i.e. a 'capture' strategy. [ABSTRACT FROM AUTHOR]

In arid and semi-arid areas, timely and effective monitoring and mapping of salt-affected areas is essential to prevent land degradation and to achieve sustainable soil management. The main objective of this study is to make full use of synthetic aperture radar (SAR) polarization technology to improve soil salinity mapping in the Keriya Oasis, Xinjiang, China. In this study, 25 polarization features are extracted from ALOS PALSAR-2 images, of which four features are selected. In addition, three soil salinity inversion models, named the RSDI1, RSDI2, and RSDI3, are proposed. The analysis and comparison results of inversion accuracy show that the overall correlation values of the RSDI1, RSDI2, and RSDI3 models are 0.63, 0.61, and 0.62, respectively. This result indicates that the radar feature space models have the potential to extract information on soil salinization in the Keriya Oasis. [ABSTRACT FROM AUTHOR]

Soil salinization is a global ecological and environmental problem in arid and semi-arid areas that can be ameliorated via soil management, visible-near infrared-shortwave infrared (VNIR-SWIR) spectroscopy can be adapted to rapidly monitor soil salinity content. This study explored the potential of Grünwald–Letnikov fractional-order derivative (FOD), feature band selection methods, nonlinear partial least squares regression (PLSR), and four machine learning models to estimate the soil salinity content using VNIR-SWIR spectra. Ninety sample points were field scanned with VNIR-SWR and soil samples (0–20 cm) were obtained at the time of scanning. The samples points come from three zones representing different intensities of human interference (I, II, and III Zones) in Fukang, Xinjiang, China. Each zone contained thirty sample points. For modeling, we firstly adopted FOD (with intervals of 0.1 and range of 0–2) as a preprocessing method to analyze soil hyperspectral data. Then, four sets of spectral bands (R-FOD-FULL indicates full band range, R-FOD-CC5 bands that met a 0.05 significance test, R-FOD-CC1 bands that met a 0.01 significance test, and R-FOD-CC1-CARS represents CC1 combined with competitive adaptive reweighted sampling) were selected as spectral input variables to develop the estimation model. Finally, four machine learning models, namely, generalized regression neural network (GRNN), extreme learning machine (ELM), random forest (RF), and PLSR, to estimate soil salinity. Study results showed that (1) the heat map of correlation coefficient matrix between hyperspectral data and salinity indicated that FOD significantly improved the correlation. (2) The characteristic band variables extracted and used by R-FOD-CC1 were fewer in number, and redundancy between bands smaller than R-FOD-FULL and R-FOD-CC5, thus estimation accuracy of R-FOD-CC1 was higher than R-FOD-CC5 or R-FOD-FULL. A high prediction accuracy was achieved with a less complex calculation. (3) The GRNN model yielded the best salinity estimation in all three zones compared to ELM, BPNN, RF, and PLSR on the whole, whereas, the RF model had the worst estimation effect. The R-FOD-CC1-CARS-GRNN model yielded the best salinity estimation in I Zone with R2, RMSE and RPD of 0.7784, 1.8762, and 2.0568, respectively. The fractional order was 1.5 and estimation performance was great. The optimal model for predicting soil salinity in II and III Zone was, also, R-FOD-CC1-CARS-GRNN (R2 = 0.7912, RMSE = 3.4001, and RPD = 1.8985 in II Zone; R2 = 0.8192, RMSE = 6.6260, and RPD = 1.8190 in III Zone), with the fractional order of 1.7- and 1.6-, respectively, and the estimation performance were all fine. (4) The characteristic bands selected by the best model in I, II, and III Zones were 8, 9, and 11, respectively, which account for 0.45%, 0.51%, and 0.63%% of the full bands. This approach reduces the number of modeled band variables and simplifies the model structure. [ABSTRACT FROM AUTHOR]

Simple Summary: Soil microorganisms are the core of maintaining soil ecological functions. Recognition of microbial community diversity in saline soil contributes to nutrient management and crop production. Meanwhile, microbial activity is easily affected by changes in soil properties. This study addressed how the composition and diversity of bacterial and fungal communities changed under different saline conditions to identify the sensitivity of bacteria or fungi to salinity. The primary objective was to evaluate the relationship between soil's microbial community diversity and soil's physicochemical factors, and to explore the vital microbial predictors in salinized soil. The results showed that Firmicutes and Bacteroidetes are pivotal in salinized soil, and this finding can provide guidance for the demand for plant rhizosphere growth-promoting bacteria as a bioindicator in saline soil. To investigate the diversity and structure of soil bacterial and fungal communities in saline soils, soil samples with three increasing salinity levels (S1, S2 and S3) were collected from a maize field in Yanqi, Xinjiang Province, China. The results showed that the K+, Na+, Ca2+ and Mg2+ values in the bulk soil were higher than those in the rhizosphere soil, with significant differences in S2 and S3 (p < 0.05). The enzyme activities of alkaline phosphatase (ALP), invertase, urease and catalase (CAT) were lower in the bulk soil than those in the rhizosphere. Principal coordinate analysis (PCoA) demonstrated that the soil microbial community structure exhibited significant differences between different salinized soils (p < 0.001). Data implied that the fungi were more susceptible to salinity stress than the bacteria based on the Shannon and Chao1 indexes. Mantel tests identified Ca2+, available phosphorus (AP), saturated electrical conductivity (ECe) and available kalium (AK) as the dominant environmental factors correlated with bacterial community structures (p < 0.001); and AP, urease, Ca2+ and ECe as the dominant factors correlated with fungal community structures (p < 0.001). The relative abundances of Firmicutes and Bacteroidetes showed positive correlations with the salinity gradient. Our findings regarding the bacteria having positive correlations with the level of salinization might be a useful biological indicator of microorganisms in saline soils. [ABSTRACT FROM AUTHOR]

Soil salinization is an ecological challenge across the world. Particularly in arid and semi-arid regions where evaporation is rapid and rainfall is scarce, both primary soil salinization and secondary salinization due to human activity pose serious concerns. Soil is subject to various human disturbances in Xinjiang in this area. Samples with a depth of 0–10 cm from 90 soils were taken from three areas: a slightly disturbed area (Area A), a moderately disturbed area (Area B), and a severely disturbed area (Area C). In this study, we first calculated the hyperspectral reflectance of five spectra (R, R , 1/R, lgR, 1/lgR, or original, root mean square, reciprocal, logarithm, and reciprocal logarithm, respectively) using different fractional-order differential (FOD) models, then extracted the bands that passed the 0.01 significance level between spectra and total salt content, and finally proposed a partial least squares regression (PLSR) model based on the FOD of the significance level band (SLB). This proposed model (FOD-SLB-PLSR) is compared with the other three PLSR models to predict with precision the total salt content. The other three models are All-PLSR, FOD-All-PLSR, and IOD-SLB-PLSR, which respectively represent PLSR models based on all bands, all fractional-order differential bands, and significance level bands of the integral differential. The simulations show that: (1) The optimal model for predicting total salt content in Area A was the FOD-SLB-PLSR based on a 1.6 order 1/lgR, which provided good predictability of total salt content with a RPD (ratio of the performance to deviation) between 1.8 and 2.0. The optimal model for predicting total salt content in Area B was a FOD-SLB-PLSR based on a 1.7 order 1/R, which showed good predictability for total salt content with RPDs between 2.0 and 2.5. The optimal model for predicting total salt content in Area C was a FOD-SLB-PLSR based on a 1.8 order lgR, which also showed good predictability for total salt content with RPDs between 2.0 and 2.5. (2) Soils subject to various disturbance levels had optimal FOD-SLB-PLSR models located in the higher fractional order between 1.6 and 1.8. This indicates that higher-order FODs have a stronger ability to extract feature data from complex information. (3) The optimal FOD-SLB-PLSR model for each area was superior to the corresponding All-PSLR, FOD-All-PLSR, and IOD-SLB-PLSR models in predicting total salt content. The RPD value for the optimal FOD-SLB-PLSR model in each area compared to the best integral differential model showed an improvement of 9%, 45%, and 22% for Areas A, B, and C, respectively. It further showed that the fractional-order differential model provides superior prediction over the integral differential. (4) The RPD values that provided an optimal FOD-SLB-PLSR model for each area were: Area A (1.9061) < Area B (2.0761) < Area C (2.2892). This indicates that the prediction effect of data processed by fractional-order differential increases with human disturbance increases and results in a higher-precision model. [ABSTRACT FROM AUTHOR]

In arid and semi-arid regions, soil moisture and salinity are important elements to control regional ecology and climate, vegetation growth and land function. Soil moisture and salt content are more important in arid wetlands. The Ebinur Lake wetland is an important part of the ecological barrier of Junggar Basin in Xinjiang, China. The Ebinur Lake Basin is a representative area of the arid climate and ecological degradation in central Asia. It is of great significance to study the spatial distribution of soil moisture and salinity and its causes for land and wetland ecological restoration in the Ebinur Lake Basin. Based on the field measurement and Landsat 8 satellite data, a variety of remote sensing indexes related to soil moisture and salinity were tested and compared, and the prediction models of soil moisture and salinity were established, and the accuracy of the models was assessed. Among them, the salinity indexes D1 and D2 were the latest ones that we proposed according to the research area and data. The distribution maps of soil moisture and salinity in the Ebinur Lake Basin were retrieved from remote sensing data, and the correlation analysis between soil moisture and salinity was performed. Among several soil moisture and salinity prediction indexes, the normalized moisture index NDWI had the highest correlation with soil moisture, and the salinity index D2 had the highest correlation with soil salinity, reaching 0.600 and 0.637, respectively. The accuracy of the BP neural network model for estimating soil salinity was higher than the one of other models; R2 = 0.624, RMSE = 0.083 S/m. The effect of the cubic function prediction model for estimating soil moisture was also higher than that of the BP neural network, support vector machine and other models; R2 = 0.538, RMSE = 0.230. The regularity of soil moisture and salinity changes seemed to be consistent, the correlation degree was 0.817, and the synchronous change degree was higher. The soil salinity in the Ebinur Lake Basin was generally low in the surrounding area, high in the middle area, high in the lake area and low in the vegetation coverage area. The soil moisture in the Ebinur Lake Basin slightly decreased outward with the Ebinur Lake as the center and was higher in the west and lower in the east. However, the spatial distribution of soil moisture had a higher mutation rate and stronger heterogeneity than that of soil salinity. [ABSTRACT FROM AUTHOR]

Soil salinization process is a complex non-linear dynamic evolution. To classify a system with this type of non-linear characteristic, this study proposed a mixed master/slave chaotic system based on Chua's circuit and a fractional-order Chen-Lee chaotic system to classify soil salinization level. The subject is the soil in Xinjiang with different levels of human interference. A fractional-order Chen-Lee chaotic system was constructed, and the spectral signal processed by the Chua's non-linear circuit was substituted into the master/slave chaotic system. The chaotic dynamic errors with different fractional orders were calculated. The comparative analysis showed that 0.1-order has the largest chaotic dynamic error change, which produced two distinct and divergent results. Thus, this study converted the chaotic dynamic errors of fractional 0.1-order into chaotic attractors to build an extension matter-element model. Finally, we compared the soil salt contents (SSC) from the laboratory chemical analysis with the results of the extension theory classification. The comparison showed that the combination of fractional order mixed master/slave chaotic system and extension theory has high classification accuracy for soil salinization level. The results of this system match the result of the chemical analysis. The classification accuracy of the calibration set data was 100%, and the classification accuracy of the validation set data was 90%. This method is the first use of the mixed master/slave chaotic system in this field and can satisfy certain soil salinization monitoring needs as well as promote the application of the chaotic system in soil salinization monitoring. [ABSTRACT FROM AUTHOR]

Winter irrigation is an effective means of salt leaching, but the long-term effect on salinity is unclear. In 2008–2019, three different soil types of farmlands were selected as the study area by drip irrigation under film mulch combined with periodic winter irrigation in the non-growth period. The salinity of 0–150 cm as well as the survival rate and yield of cotton in the non-growth and growth periods were monitored, respectively. The mass fraction of soil salt decreased rapidly under winter irrigation, and then, the salt content in each observation layer increased with years of cultivation. After 10 years of application, the soil salt content basically stabilized at a low level. In 2008, the salinity of the 0–150 cm observation layer of loamy clay, loam, and sandy loam varied within 6–60, 10–65, and 4–22 g·kg−1; after four winter irrigations in 2019, corresponding values dropped below 5.74, 3, and 4.76 g·kg−1, respectively. The salinity returns rate of the different observation layers all exceeded 40%. The desalination rate of the different soils after four winter irrigations all exceeded 63.52%. Cotton survival rate and yield in different soils were directly proportional to each other. After the second winter irrigation, the survival rates on the different soils all exceeded 60%. The results of this study can provide technical support for the sustainable development of different types of soil, farmers' income increase, and salinization land improvement. [ABSTRACT FROM AUTHOR]

Soil salinization is one of the most serious issues of land degradation, especially in inland drylands, such as the Kashgar region in the Xinjiang province, western China. The investigation of the spatiotemporal variations of soil salinization and its causes is critical for regional ecological restoration and social development. In this study, salinization severity was firstly interpreted in Kashgar region for the years 2000, 2010, and 2017 using multitemporal Landsat images, and the spatiotemporal variations of salinized soil area, salinization severity index, and important index of salinization change were then analyzed using transition matrix method. Finally, the relationship between salinization and eco-hydrological parameters at the regional scale was investigated using correlation analysis and multivariate linear regression. The results show that salinized soil is mainly concentrated in irrigated oasis areas. Although the decrease rate of total salinized soil area is decreasing, the decrease rate of average salinization severity is increasing gradually. There is an increasing trend for the improved area of salinized soil, whereas an opposite trend was observed for the deteriorated area of salinized soil. The conversion from extremely severe salinized soil to the severe ones was the dominant transforming type from 2000 to 2017; meanwhile, the transformation from non-salinized soil to salinized soil for the newly reclaimed farmland was observed, indicating that some necessary irrigation control measures must be taken to avoid further soil salinizing. A significant negative correlation between salinization severity and evapotranspiration, normalized difference vegetation index (NDVI) was observed, implying that soil structure change induced by vegetation, associated with high evapotranspiration (ET) and low land surface temperature (LST), played a positive role in alleviating soil salinization in this region. It is concluded that the soil salinization had been alleviated from 2000 to 2017, mainly due to the combined effects of the farmland expansion and the reasonable irrigation system. [ABSTRACT FROM AUTHOR]

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