滨海重度盐碱地改良土壤盐渍化动态特征及预测
作者:
作者单位:

1.浙江师范大学地理与环境科学学院;2.南京大学地理与海洋科学学院

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基金项目:

国家自然科学基金项目(42101068,41871083,41230751)和浙江省自然科学基金项目(LQ21D010007)资助


Dynamics and Prediction of Soil Salinization Parameters under the Amelioration of Heavy Coastal Saline- alkali Land
Author:
Affiliation:

1.College of Geography and Environmental Sciences, Zhejiang Normal University;2.School of Geography and Ocean Science, Nanjing University

Fund Project:

Supported by the National Natural Science Foundation of China (Nos. 42101068, 41871083, 41230751) and Natural Science Foundation of Zhejiang Province (No. LQ21D010007)

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    摘要:

    试验设置了对照处理(CK)、有机肥(OM)、聚丙烯酰胺+有机肥(PAM+OM)、秸秆覆盖+有机肥(SM+OM)、秸秆深埋+有机肥(BS+OM)和生物菌肥+有机肥(BM+OM)6个处理方式来探讨滨海盐碱地不同改良方式对土壤含盐量、pH、钠吸附比(SAR)和碱化度(ESP)的影响,进而识别影响土壤盐渍化程度的主要因子,并构建多元线性回归模型(Multi-linear Regression, MLR)、BP神经网络模型(BP Artificial Neural Network, BP-ANN)和随机森林模型(Random Forest, RF)对滨海重度盐碱地改良背景下的土壤盐渍化参数进行模拟预测。研究结果表明:各改良措施均能有效的降低表层土壤盐渍化水平,其中SM+OM处理对于土壤含盐量的抑制效果最好,而BM+OM处理则对于土壤碱分的抑制效果最好。改良过程中气象条件和土壤性质均对表层土壤盐渍化水平产生了显著影响。在模型预测中,随机森林模型对土壤含盐量、pH、SAR和ESP的综合预测精度明显优于BP神经网络模型和多元线性回归模型,体现在随机森林模型具有较高决定系数(Coefficient of determination, R2)和纳什系数(Nash-sutcliffe efficiency, NSE)和较低的均方根误差(Root mean square error, RMSE)。

    Abstract:

    【Objective】Soil salinization is one of the main types of land degradation, which seriously inhibits the improvement of soil quality and the growth and grain yield of crops. Reclamation of coastal land is increasingly being used as a means of raising agricultural productivity and improving food security in China. Determining the importance of potential influencing factors of soil salinization parameters and thus predicting their concentrations are important for formulating targeted control measures to improve soil quality and crop yield in tidal flat reclamation areas.【Method】In this study, six treatments including control (CK), organic manure (OM), polyacrylamide plus organic manure (PAM+OM), straw mulching plus organic manure (SM+OM), buried straw plus organic manure (BS+OM), and bio-organic manure plus organic manure (BM+OM) were applied to explore the effect of different reclamation treatments on different soil parameters. The effect of all treatments on soil salt content (SSC), pH, sodium adsorption ratio (SAR), and exchange sodium percentage (ESP) was analyzed and the main factors affecting the degree of soil salinization were identified. Thereafter, the multi-linear regression model (MLR), BP artificial neural network model (BP-ANN), and random forest model (RF) were conducted to predict the soil salinization parameters (SSC, pH, SAR, and ESP) using covariates, such as air temperature, precipitation, evaporation, wind speed, soil water content, soil temperature, and soil bulk density.【Result】The results indicated that the concentration of SSC, SAR, and ESP gradually increased, while the pH gradually decreased during the oat growing stage. All reclamation treatments effectively reduced the level of surface soil salinization. Among them, SM+OM treatment had the best inhibition effect on SSC, whereas BM+OM treatment had the best inhibition effect on soil pH, SAR and ESP. Besides, both meteorological parameters and soil properties had a significant impact on the level of surface soil salinization during the amelioration of coastal saline-alkali land. Additionally, the RF model performed much better than BP-ANN and MLR as it revealed a much higher coefficient of determination (R2) and Nash-Sutcliffe efficiency (NSE), and lower root mean square error (RMSE) than BP-ANN and MLR model.【Conclusion】The above results indicate that the reclamation treatments can effectively inhibit soil evaporation, improve soil structure, increase soil water holding capacity, and thus reduce the salinization level of surface soil. Our results also suggest that the RF model is a more powerful modeling approach in predicting soil salinization dynamics of coastal saline-alkali land due to its advantages in handling the nonlinear and hierarchical relationships between soil salinization parameters and covariates, and insensitivity to overfitting and the presence of noise in the data. Thus, our findings could provide a reference for predicting the soil salinization parameters in areas with similar environmental conditions.

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解雪峰,濮励杰,沈洪运,吴 涛,朱 明,黄思华.滨海重度盐碱地改良土壤盐渍化动态特征及预测[J].土壤学报,DOI:10.11766/trxb202101240043,[待发表]
XIE Xuefeng, PU Lijie, SHEN Hongyun, WU Tao, ZHU Ming, HUANG Sihua. Dynamics and Prediction of Soil Salinization Parameters under the Amelioration of Heavy Coastal Saline- alkali Land[J]. Acta Pedologica Sinica, DOI:10.11766/trxb202101240043,[In Press]

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