Soil Salinity Risk Assessment

Regional land salinization assessment and simulation through cellular automaton-Markov modeling and spatial pattern analysis. Land salinization and desalinization are complex processes affected by both biophysical and human-induced driving factors. Conventional approaches of land salinization assessment and simulation are either too time consuming or focus only on biophysical factors. The cellular automaton (CA)-Markov model, when coupled with spatial pattern analysis, is well suited for regional assessments and simulations of salt-affected landscapes since both biophysical and socioeconomic data can be efficiently incorporated into a geographic information system framework. Our hypothesis set forth that the CA-Markov model can serve as an alternative tool for regional assessment and simulation of land salinization or desalinization. Our results suggest that the CA-Markov model, when incorporating biophysical and human-induced factors, performs better than the model which did not account for these factors when simulating the salt-affected landscape of the Yinchuan Plain (China) in 2009. In general, the CA-Markov model is best suited for short-term simulations and the performance of the CA-Markov model is largely determined by the availability of high-quality, high-resolution socioeconomic data. The coupling of the CA-Markov model with spatial pattern analysis provides an improved understanding of spatial and temporal variations of salt-affected landscape changes and an option to test different soil management scenarios for salinity management.

Assessing secondary soil salinization risk based on the PSR sustainability framework. Risk assessment of secondary soil salinization, which is caused in part by the way people manage the land, is an essential challenge to agricultural sustainability. The objective of our study was to develop a soil salinity risk assessment methodology by selecting a consistent set of risk factors based on the conceptual Pressure-State-Response (PSR) sustainability framework and incorporating the grey relational analysis and the Analytic Hierarchy Process methods. The proposed salinity risk assessment methodology was demonstrated through a case study of developing composite risk index maps for the Yinchuan Plain, a major irrigation agriculture district in northwest China. Fourteen risk factors were selected in terms of the three PSR criteria: pressure, state, and response. The results showed that the salinity risk in the Yinchuan Plain was strongly influenced by the subsoil and groundwater salinity, land use, distance to irrigation canals, and depth to groundwater. To maintain agricultural sustainability in the Yinchuan Plain, a suite of remedial and preventative actions were proposed to manage soil salinity risk in the regions that are affected by salinity at different levels and by different salinization processes. The weight sensitivity analysis results also showed that the overall salinity risk of the Yinchuan Plain would increase or decrease as the weights for pressure or response risk factors increased, signifying the importance of human activities on secondary soil salinization. Ideally, the proposed methodology will help us develop more consistent management tools for risk assessment and management and for control of secondary soil salinization.