The Development and Utilization of Saline–Alkali Land in Western Jilin Province Promoted the Sequestration of Organic Carbon Fractions in Soil Aggregates

Soil samples from T (0~20 cm) and S (20~40 cm) layers of four saline–alkali rice fields (R5, R15, R20, and R35) with different reclamation years were selected to study the distribution of soil aggregates and the contents of readily oxidizable organic carbon (ROC), dissolved organic carbon (DOC), microbial biomass carbon (MBC), potentially mineralizable carbon (PMC), and soil organic carbon (SOC). The effects of large macroaggregate (>2 mm, LMA), small macroaggregate (0.25 to 2 mm, SMA), and microaggregate (<0.25 mm, MA) particle size, soil layer, and soil physicochemical properties on SOC fractions were also analyzed. The results showed that the LMA size in saline–alkali paddy fields were easily decomposed and was unstable due to the influence of the external environment. With the increase in reclamation years, the proportion of LMA in the S layer decreased gradually. The ROC, DOC, MBC and TOC contents of aggregates in the T and S layers gradually increased with the increase in reclamation years, and SOC fractions contents of aggregates in different grain sizes were SMA > LMA > MA. An effective way to increase carbon sink and improve the ecological environment in western Jilin Province is to change the soil environment by planting rice in saline–alkali land.

Impacts of Saline-Alkali Land Improvement on Regional Climate: Process, Mechanisms, and Implications

Studying land use change and its associated climate effects is important to understand the role of human activities in the regulation of climate systems. By coupling remote sensing measurements with a high-resolution regional climate model, this study evaluated the land surface changes and corresponding climate impact caused by planting rice on saline-alkali land in western Jilin (China). Our results showed that paddy field expansion became the dominant land use change in western Jilin from 2015 to 2019, 25% of which was converted from saline-alkali land; this percentage is expected to increase in the near future. We found that saline-alkali land reclamation to paddy fields significantly increased the leaf area index (LAI), particularly in July and August, whereas it decreased albedo, mainly in May and June. Our simulation results showed that planting rice on saline-alkali land can help decrease the air temperature and increase the relative humidity. The temperature and humidity effects showed different magnitudes during the growing season and were most significant in July and August, followed by September and June. The nonradiative process, rather than the radiative process, played a dominant role in regulating the regional climate in this case, and the biophysical competition between evapotranspiration (ET) and albedo determined the temperature and relative humidity response differences during the growing season.

Electrical Conductivity Inversion Method of Saline Soil based on Sentinel-2 MSI data

Electrical conductivity (EC) is not only an important index to evaluate the degree of soil salinization, but also an essential basis for judging whether saline soil can be improved and assess the effect of improvement efforts. Satellite remote sensing provides much information for large scale EC inversion of saline soil, which enables the possibility for evaluating the degree and distribution of soil salinization. Taking the salinized region of western Jilin Province as the study area, 328 salinized soil samples were collected, and the EC was measured in June 2019. The construction of the optimal spectral parameters was based on the correlation between the conductivity and the spectral reflectivity of Sentinel-2 MSI data; after satisfying the normal distribution for the Box-Cox transformation of EC, the inversion model of EC was established by using linear regression model, support vector machine (SVM), regression tree (RT), Gaussian process regression (GPR), and ensemble tree (ET). The verification results of the model on the validation set showed that the performance of GPR was optimal (R2 = 0.66, RMSE = 0.48 mS/cm, MAE=0.52 mS/cm), which increased R2 by 29.04% compared with the traditional linear regression model. Finally, according to the GPR model, the EC results of pixel-level resolution (10 m × 10 m) of saline soil in western Jilin Province were inversed, which provided a scientific basis for the study of the distribution characteristics and improvement scheme of saline soil.

Cu and Na contents regulate N uptake of Leymus chinensis growing in soda saline-alkali soil

Leymus chinensis (L. chinensis) is the dominant plant in the eastern margins of the Eurasian temperate grasslands. It is a very robust species, exhibiting good saline-alkali resistance and stabilizing soil. In this study, 67 soil samples and L. chinensis were collected in western Jilin province, China. The contents of N, P, K, S, Mn, Fe, Zn, Cu and Na were measured, revealing that the growth of L. chinensis was mainly restricted by N based on the stoichiometric N: P ratios of plant. Furthermore, path analysis indicated that N was significantly correlated with K, S, Cu, and Zn. Imbalances in the homeostasis of these four elements may thus constrain N. The homeostasis index of Cu (HCu) in sites with 100%-70% of vegetation cover was only 0.79, it was classified as a sensitive element. However, K, S and Zn, whose concentrations in L. chinensis were significantly related to those of N, exhibited no homeostatic characteristics. These results suggest that when seeking to treat saline-alkali stress, it is important to add fertilizers containing K, S, and Zn to avoid growth limitation. Na+, an ion associated with high soil alkalinity, exhibited weak homeostasis in L. chinensis even in sites with only 40%-10% of vegetation cover. When soil Na exceeded 16000 mg/kg, the homeostasis mechanism of L. chinensis appeared to be overwhelmed, resulting in rapid and probably harmful accumulation of Na. Proper control of N content can alleviate the toxicity of Na stress in L. chinensis and enhance its Na tolerance. Together, these results suggest that combined fertilization with N, K, S, Zn and Cu should be applied to improve grasslands growth. The results of this study can provide a reference basis for sustainable grassland management.

Prediction of the Structural Yield Strength of Saline Soil in Western Jilin Province, China: A Comparison of the Back-Propagation Neural Network and Support Vector Machine Models

With the increase in transportation emissions, road diseases in the saline soil area of Jilin Province have become a problem that requires serious attention. In order to improve the subgrade performance, the structural yield strength (SYS) of remolded soil and its factor sensitivity are investigated in this study. Saline soils in Western Jilin are structural in the sense that the bonding strength of soil skeleton is mainly provided by the solidification bond formed by a physicochemical interaction between particles. Its SYS is influenced by its cementation type, genetic characteristics, original rock structure, and environment. Because of the high clay content in Zhenlai saline soil, the specific surface area of soil particles is large, and the surface adsorption capacity of soil particles is strong. In addition, the main cation is Na+. The cementation strength of bound water film between soil particles is thus easily affected by water content and salt content, and compaction is also an important factor affecting the strength of soil. Therefore, in this study, the back-propagation neural network (BPNN) model and a support vector machine (SVM) are used to explore the relationship of saline soil’s SYS with its compactness, water content, and salt content. In total, 120 data points collected by a high-pressure consolidation experiment are applied to building BPNN and SVM model. For eliminate redundant features, Pearson correlation coefficient (rPCC) is used as an evaluation standard of feature selection. The K-fold cross-validation method was used to avoid over fitting. To compare the performance of the BPNN and SVM models, three statistical parameters were used: the determination coefficient (R2), root mean square error (RMSE), and mean absolute percentage deviation (MAPD). The result shows that the average values of R2, RMSE, and MAPD of the BPNN model are superior to the values of the SVM. We conclude that the BPNN model is slightly better than the SVM for predicting the SYS of saline soil. Thus, the BPNN model is used to analyze the factor sensitivity of SYS. The results indicate that the influence degrees of the three parameters are as follows: water content > compactness > salt content. This study can provide a basis for estimating the structural yield pressure of soil from its basic properties, and can provide a new way to obtain parameters for geotechnical engineering, ensuring safety while maintaining symmetry in engineering costs.