Accuracy and uncertainty assessment on geostatistical simulation of soil salinity in a coastal farmland using auxiliary variable

Environmental salinization processes: Detection, implications & solutions

A great portion of Earth's freshwater and land resources are salt-affected and thus have restricted use or may become unsuitable for most human activities. Some of the recent scenarios warn that environmental salinization processes will continue to be exacerbated due to global climate change. The most relevant implications and side-effects in ecosystems under excessive salinity are destructive and long lasting (e.g. soil dispersion, water/soil hypersalinity, desertification, ruined biodiversity), often with non-feasible on site remediation, especially at larger scales. Agro-ecosystems are very sensitive to salinization; after a certain threshold is reached, yields and food quality start to deteriorate sharply. Additionally, salinity often coincides with numerous other environmental constrains (drought, waterlogging, pollution, acidity, nutrient deficiency, etc.) that progressively aggravate the threat to food security and general ecosystem resilience. Some well-proven, widely-used and cost-effective traditional ameliorative strategies (e.g. conservation agriculture, application of natural conditioners) help against salinity and other constraints, especially in developing countries. Remotely-sensed and integrated data of salt-affected areas combined with in situ and lab-based observations have never been so easy and rapid to acquire, precise and applicable on huge scales, representing a valuable tool for policy-makers and other stakeholders in implementing targeted measures to control and prevent ecosystem degradation (top-to-bottom approach). Continued progress in biotechnology and ecoengineering offers some of the most advanced and effective solutions against salinity (e.g. nanomaterials, marker-assisted breeding, genome editing, plant-microbial associations), albeit many knowledge gaps and ethical frontiers remain to be overcome before a successful transfer of these potential solutions to the industrial-scale food production can be effective.

Digital Mapping of Soil Salinity and Crop Yield across a Coastal Agricultural Landscape Using Repeated Electromagnetic Induction (EMI) Surveys

Reliable and real-time information on soil and crop properties is important for the development of management practices in accordance with the requirements of a specific soil and crop within individual field units. This is particularly the case in salt-affected agricultural landscape where managing the spatial variability of soil salinity is essential to minimize salinization and maximize crop output. The primary objectives were to use linear mixed-effects model for soil salinity and crop yield calibration with horizontal and vertical electromagnetic induction (EMI) measurements as ancillary data, to characterize the spatial distribution of soil salinity and crop yield and to verify the accuracy of spatial estimation. Horizontal and vertical EMI (type EM38) measurements at 252 locations were made during each survey, and root zone soil samples and crop samples at 64 sampling sites were collected. This work was periodically conducted on eight dates from June 2012 to May 2013 in a coastal salt-affected mud farmland. Multiple linear regression (MLR) and restricted maximum likelihood (REML) were applied to calibrate root zone soil salinity (EC_e) and crop annual output (CAO) using ancillary data, and spatial distribution of soil EC_e and CAO was generated using digital soil mapping (DSM) and the precision of spatial estimation was examined using the collected meteorological and groundwater data. Results indicated that a reduced model with EM_h as a predictor was satisfactory for root zone EC_e calibration, whereas a full model with both EM_h and EM_v as predictors met the requirement of CAO calibration. The obtained distribution maps of EC_e showed consistency with those of EMI measurements at the corresponding time, and the spatial distribution of CAO generated from ancillary data showed agreement with that derived from raw crop data. Statistics of jackknifing procedure confirmed that the spatial estimation of EC_e and CAO exhibited reliability and high accuracy. A general increasing trend of EC_e was observed and moderately saline and very saline soils were predominant during the survey period. The temporal dynamics of root zone EC_e coincided with those of daily rainfall, water table and groundwater data. Long-range EMI surveys and data collection are needed to capture the spatial and temporal variability of soil and crop parameters. Such results allowed us to conclude that, cost-effective and efficient EMI surveys, as one part of multi-source data for DSM, could be successfully used to characterize the spatial variability of soil salinity, to monitor the spatial and temporal dynamics of soil salinity, and to spatially estimate potential crop yield.

Selection of optimal auxiliary soil nutrient variables for Cokriging interpolation

In order to explore the selection of the best auxiliary variables (BAVs) when using the Cokriging method for soil attribute interpolation, this paper investigated the selection of BAVs from terrain parameters, soil trace elements, and soil nutrient attributes when applying Cokriging interpolation to soil nutrients (organic matter, total N, available P, and available K). In total, 670 soil samples were collected in Fuyang, and the nutrient and trace element attributes of the soil samples were determined. Based on the spatial autocorrelation of soil attributes, the Digital Elevation Model (DEM) data for Fuyang was combined to explore the coordinate relationship among terrain parameters, trace elements, and soil nutrient attributes. Variables with a high correlation to soil nutrient attributes were selected as BAVs for Cokriging interpolation of soil nutrients, and variables with poor correlation were selected as poor auxiliary variables (PAVs). The results of Cokriging interpolations using BAVs and PAVs were then compared. The results indicated that Cokriging interpolation with BAVs yielded more accurate results than Cokriging interpolation with PAVs (the mean absolute error of BAV interpolation results for organic matter, total N, available P, and available K were 0.020, 0.002, 7.616, and 12.4702, respectively, and the mean absolute error of PAV interpolation results were 0.052, 0.037, 15.619, and 0.037, respectively). The results indicated that Cokriging interpolation with BAVs can significantly improve the accuracy of Cokriging interpolation for soil nutrient attributes. This study provides meaningful guidance and reference for the selection of auxiliary parameters for the application of Cokriging interpolation to soil nutrient attributes.

Multivariate-statistical assessment of heavy metals for agricultural soils in northern China

The study evaluated eight heavy metals content and soil pollution from agricultural soils in northern China. Multivariate and geostatistical analysis approaches were used to determine the anthropogenic and natural contribution of soil heavy metal concentrations. Single pollution index and integrated pollution index could be used to evaluate soil heavy metal risk. The results show that the first factor explains 27.3% of the eight soil heavy metals with strong positive loadings on Cu, Zn, and Cd, which indicates that Cu, Zn, and Cd are associated with and controlled by anthropic activities. The average value of heavy metal is lower than the second grade standard values of soil environmental quality standards in China. Single pollution index is lower than 1, and the Nemerow integrated pollution index is 0.305, which means that study area has not been polluted. The semivariograms of soil heavy metal single pollution index fitted spherical and exponential models. The variable ratio of single pollution index showed moderately spatial dependence. Heavy metal contents showed relative safety in the study area.