Modelling long-term soil salinity dynamics using SaltMod in Hetao Irrigation District, China

Chang, Xiaomin; Gao, Zhanyi; Wang, Shaoli; Chen, Haorui

doi:10.1016/j.compag.2018.12.005

Cited by 50 publications

(15 citation statements)

References 41 publications

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“…Agriculture drainage in the studied area ranged from excessive (PU6) to very poor (PU8) (Figure 3c), and the dominant drainage status was the poor class under CS. Adequate drainage is essential to discharge leached salts from locations that have suffered from salinity problems [69,70]. In El-Fayoum, the main source of irrigation is Bahr Youssif (an old branch of the River Nile) with a water salinity value < 0.4 dSm −1 .…”

Section: Drainagementioning

confidence: 99%

Assessment of Soil Suitability for Improvement of Soil Factors and Agricultural Management

et al. 2019

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The dramatic growth of the world’s population is increasing the pressure on natural resources, particularly on soil systems. At the same time, inappropriate agricultural practices are causing widespread soil degradation. Improved management of soil resources and identification of the potential agricultural capability of soils is therefore needed to prevent further land degradation, particularly in dryland areas such as Egypt. Here, we present a case study in the El-Fayoum depression (Northern Egypt) to model and map soil suitability for 12 typical Mediterranean crops. Two management scenarios were analyzed: the current situation (CS) and an optimal scenario (OS) of soil variables. The Almagra model was applied to estimate soil suitability under CS and OS. Management options based on the CS assessment were proposed to reduce some limiting factors: a fixed value of 2 dSm−1 for soil salinity and 5% for sodium saturation; these defined the OS. Under optimal management, the OS scenario showed potential, where a notable increase of the area covered by a high suitability class (around 80%) for annual and semi-annual crops was observed. There was also a marked increase (about 70% for CS and 50% for OS) for perennial crops shifting from the marginal to moderate soil suitability class. The results reveal the importance of proper management to massively alter soil suitability into better states in order to achieve sustainable land use in this fertile agro-ecosystem.

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Section: Drainagementioning

confidence: 99%

Assessment of Soil Suitability for Improvement of Soil Factors and Agricultural Management

et al. 2019

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“…Due to the dry climate, high evaporation, and shallow depth of groundwater in arid and semi-arid areas, a large amount of salts in the soil gather on the surface, causing salinization; this, coupled with the mismatch between the irrigation and drainage systems of residents and unscientific farming methods, where a large amount of irrigation water is replenished to groundwater, causing secondary salinization, makes the already fragile soil ecosystem of arid areas even more unstable [3][4][5]. Excessive salt accumulation can affect the growth and development process of crops through infiltration and ionic stress, and can also affect the soil structure, eventually reducing the fertility of arable land into a wasteland and affecting food production [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Inversion of Soil Salinity Using Multisource Remote Sensing Data and Particle Swarm Machine Learning Models in Keriya Oasis, Northwestern China

et al. 2022

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Soil salinization is a global problem that damages soil ecology and affects agricultural development. Timely management and monitoring of soil salinity are essential to achieve the most sustainable development goals in arid and semi-arid regions. It has been demonstrated that Polarimetric Synthetic Aperture Radar (PolSAR) data have a high sensitivity to the soil dielectric constant and soil surface roughness, thus having great potential for the detection of soil salinity. However, studies combining PALSAR-2 data and Landsat 8 data to invert soil salinity information are less common. The particle swarm optimization (PSO) algorithm is characterized by simple operation, fast computation, and good adaptability, but there are relatively few studies applying it to soil salinity as well. This paper takes the Keriya Oasis as an example, proposing the PSO-SVR and PSO-BPNN models by combining PSO with support vector machine regression (SVR) and back-propagation neural network (BPNN) models. Then, PALSAR-2 data, Landsat 8 data, evapotranspiration data, groundwater burial depth data, and DEM data were combined to conduct the inversion study of soil salinity in the study area. The results showed that the introduction of PSO generated a satisfactory estimating performance. The SVR model accuracy (R2) improved by 0.07 (PALSAR-2 data), 0.20 (Landsat 8 data), and 0.19 (PALSAR+ Landsat data); the BP model accuracy (R2) improved by 0.03 (PALSAR-2 data), 0.24 (Landsat 8 data), and 0.12 (PALSAR+ Landsat data), and then combined with the model inversion plots, we found that PALSAR+ Landsat data combined with the PSO-SVR model could achieve better inversion results. The fine texture information of PALSAR-2 data can be used to better invert the soil salinity in the study area by combining it with the rich spectral information of Landsat 8 data. This study complements the research ideas and methods for soil salinization using multi-source remote sensing data to provide scientific support for salinity monitoring in the study area.

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“…Soil salinity and moisture are important factors affecting agricultural production in arid areas [52]. Understanding and mastering the spatial distribution of soil salinity and moisture can provide basic information for preventing and controlling soil salinization and lead to scientific agricultural management [53].…”

Section: Discussionmentioning

confidence: 99%

Spatial Distribution Characteristics of Soil Salinity and Moisture and Its Influence on Agricultural Irrigation in the Ili River Valley, China

Zhang

2019

Sustainability

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Soil salinization is a global problem, which threatens agricultural productivity and sustainability, especially in arid and semi-arid regions. Soil salinity and moisture are important factors affecting agricultural production in arid regions. However, few studies have considered the influence of topographic factors on the spatial distribution patterns of soil salinity and moisture. This research aims to explore the spatial distribution characteristics and its influencing factors of soil salinity and moisture in the oasis farmland of arid areas. In this paper, GIS and geostatistics methods were applied to analyze the spatial distribution characteristics and variability of soil salinity and moisture, and then the corresponding proxy variables were used to quantitatively study the influence factors by using the geographical detector model. The results showed the coefficients of the variation of soil salinity and moisture to be 71.25% and 31.89%, respectively. There was moderate spatial autocorrelation of soil salinity and moisture. Soil salinity in the southwest was higher than in the northeast, and soil moisture in the northwest and southeast were lower than in the center and the northeast edge. The main influencing factors were available phosphorus, roughness of terrain, alkaline nitrogen, available potassium, and elevation. Combined action of topographic factors and soil nutrients has a major influence on the spatial distribution of soil salinity and moisture. Therefore, developing a suitable fertilizer regime under different topographic conditions could be an effective way to promote the sustainability of oasis agriculture in arid areas.

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Modelling long-term soil salinity dynamics using SaltMod in Hetao Irrigation District, China

Cited by 50 publications

References 41 publications

Assessment of Soil Suitability for Improvement of Soil Factors and Agricultural Management

Assessment of Soil Suitability for Improvement of Soil Factors and Agricultural Management

Inversion of Soil Salinity Using Multisource Remote Sensing Data and Particle Swarm Machine Learning Models in Keriya Oasis, Northwestern China

Spatial Distribution Characteristics of Soil Salinity and Moisture and Its Influence on Agricultural Irrigation in the Ili River Valley, China

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