Dataset Information

Mapping spatial variability of soil salinity in a coastal paddy field based on electromagnetic sensors.

ABSTRACT: In coastal China, there is an urgent need to increase land area for agricultural production and urban development, where there is a rapid growing population. One solution is land reclamation from coastal tidelands, but soil salinization is problematic. As such, it is very important to characterize and map the within-field variability of soil salinity in space and time. Conventional methods are often time-consuming, expensive, labor-intensive, and unpractical. Fortunately, proximal sensing has become an important technology in characterizing within-field spatial variability. In this study, we employed the EM38 to study spatial variability of soil salinity in a coastal paddy field. Significant correlation relationship between ECa and EC1:5 (i.e. r >0.9) allowed us to use EM38 data to characterize the spatial variability of soil salinity. Geostatistical methods were used to determine the horizontal spatio-temporal variability of soil salinity over three consecutive years. The study found that the distribution of salinity was heterogeneous and the leaching of salts was more significant in the edges of the study field. By inverting the EM38 data using a Quasi-3D inversion algorithm, the vertical spatio-temporal variability of soil salinity was determined and the leaching of salts over time was easily identified. The methodology of this study can be used as guidance for researchers interested in understanding soil salinity development as well as land managers aiming for effective soil salinity monitoring and management practices. In order to better characterize the variations in soil salinity to a deeper soil profile, the deeper mode of EM38 (i.e., EM38v) as well as other EMI instruments (e.g. DUALEM-421) can be incorporated to conduct Quasi-3D inversions for deeper soil profiles.

SUBMITTER: Guo Y

PROVIDER: S-EPMC4447356 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Mapping spatial variability of soil salinity in a coastal paddy field based on electromagnetic sensors.

Guo Yan Y Huang Jingyi J Shi Zhou Z Li Hongyi H

PloS one 20150528 5

In coastal China, there is an urgent need to increase land area for agricultural production and urban development, where there is a rapid growing population. One solution is land reclamation from coastal tidelands, but soil salinization is problematic. As such, it is very important to characterize and map the within-field variability of soil salinity in space and time. Conventional methods are often time-consuming, expensive, labor-intensive, and unpractical. Fortunately, proximal sensing has be ...[more]

PMID: 26020969

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Project description: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 (ECe) and crop annual output (CAO) using ancillary data, and spatial distribution of soil ECe 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 EMh as a predictor was satisfactory for root zone ECe calibration, whereas a full model with both EMh and EMv as predictors met the requirement of CAO calibration. The obtained distribution maps of ECe 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 ECe and CAO exhibited reliability and high accuracy. A general increasing trend of ECe was observed and moderately saline and very saline soils were predominant during the survey period. The temporal dynamics of root zone ECe 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.

Project description:Fungi underpin almost all terrestrial ecosystem functions, yet our understanding of their community ecology lags far behind that of other organisms. Here, red paddy soils in subtropical China were collected across a soil depth profile, comprising 0-to-10-cm- (0-10cm-), 10-20cm-, and 20-40cm-deep layers. Using Illumina MiSeq amplicon sequencing of the internal transcribed spacer (ITS) region, distance-decay relationships (DDRs), and ecological models, fungal assemblages and their spatial patterns were investigated from each soil depth. We observed significant spatial variation in fungal communities and found that environmental heterogeneity decreased with soil depth, while spatial variation in fungal communities showed the opposite trend. DDRs occurred only in 0-10cm- and 10-20cm-deep soil layers, not in the 20-40cm layer. Our analyses revealed that the fungal community assembly in the 0-10cm layer was primarily governed by environmental filtering and a high dispersal rate, while in the deeper layer (20-40cm), it was primarily governed by dispersal limitation with minimal environmental filtering. Both environmental filtering and dispersal limitation controlled fungal community assembly in the 10-20cm layer, with dispersal limitation playing the major role. Results demonstrate the decreasing importance of environmental filtering and an increase in the importance of dispersal limitation in structuring fungal communities from shallower to deeper soils. Effectively, "everything is everywhere, but the environment selects," although only in shallower soils that are easily accessible to dispersive fungal propagules. This work highlights that perceived drivers of fungal community assembly are dependent on sampling depth, suggesting that caution is required when interpreting diversity patterns from samples that integrate across depths.IMPORTANCE In this work, Illumina MiSeq amplicon sequencing of the ITS region was used to investigate the spatial variation and assembly mechanisms of fungal communities from different soil layers across paddy fields in subtropical China, and the results demonstrate the decreasing importance of environmental filtering and an increase in the importance of dispersal limitation in structuring fungal communities from shallower to deeper soils. Therefore, the results of this study highlight that perceived drivers of fungal community assembly are dependent on sampling depth and suggest that caution is required when interpreting diversity patterns from samples that integrate across depths. This is the first study focusing on assemblages of fungal communities in different soil layers on a relatively large scale, and we thus believe that this study is of great importance to researchers and readers in microbial ecology, especially in microbial biogeography, because the results can provide sampling guidance in future studies of microbial biogeography.

Dataset Information

Mapping spatial variability of soil salinity in a coastal paddy field based on electromagnetic sensors.

Publications

Mapping spatial variability of soil salinity in a coastal paddy field based on electromagnetic sensors.

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