Abstract
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.
Highlights
Soil salinization in the coastal zone of the Yangtze River alluvial sediments in Eastern China is a constant threat to agriculture and ecology
The lowest average EM38 in the horizontal (EMh) and EMv measurements occurred on 8 June 2012, indicating the lowest soil salinity at the first electromagnetic induction (EMI) survey time
Significant correlation between apparent electrical conductivity (ECa) and soil electrical conductivity of saturated paste extracts (ECe) and crop yield allowed for rapid characterization of the spatio-temporal variation in soil salinity and crop annual output (CAO) using ECa survey data
Summary
Soil salinization in the coastal zone of the Yangtze River alluvial sediments in Eastern China is a constant threat to agriculture and ecology. The coastal region of Jiangsu Province has possessed a total of about 8 ×105 ha salinized soil resources including mud flats, accounting for over one quarter of total tidal flats in China [1] These soils are naturally saline due to marine immersion, the presence of a shallow, saline water table and coarse soil texture. The most widely used technique is proximal sensing electromagnetic induction (EMI) instruments including the EM31, EM38, EM38-DD, and EM38-MK2 meters, the DUALEM-1 and DUALEM-2 meters, and the Profiler EMP-400 [6] These EMI sensors gauge the apparent soil electrical conductivity (ECa) with the advantages such as high speed, ease of use, relatively low cost, and large volume of data collected over traditional methods [7]. The real challenge is that EMI techniques work best in areas where there are large changes in one soil property that influences soil electrical conductivity, and do not work as well when soil properties that influence electrical conductivity are largely homogenous [15]
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