Geospatial Measurements of Soil Electrical Conductivity, Soil Salinity, and Soil Saturation Percentage in Irrigated Farmland

<p>These data are from soil salinity surveys conducted on California irrigated farmland between 1991 and 2017. The data consist of: (i.) geospatial field survey measurements of bulk soil electrical conductivity (ECa) and (ii.) laboratory determinations of soil salinity (ECe) and saturation percentage (SP) made on soil core sections extracted from the surveyed fields. The data consist of 277,624 ECa measurements and 8,575 ECe and SP determinations.</p>
<p>Soil bulk electrical conductivity (ECa) is relatively easy to measure in agricultural fields using electromagnetic induction (EMI) instrumentation. EMI instruments are readily mobilized and thus can be used to characterize in detail the spatial variability of ECa within fields (Corwin, 2005; 2008). ECa is a useful property because it often correlates with difficult-to-measure soil physical and chemical properties that affect crop production, including soil water content, clay percentage, bulk density, PH, and especially soil salinity. The standard quantitative measure of soil salinity is defined to be the electrical conductivity of the soil saturation paste extract, or ECe (U.S. Salinity Laboratory Staff, 1954). Saturation percentage (SP) is the dry-weight moisture percentage of the saturation paste.</p>
<p>The data can be used to test and explore model relationships between ECe, SP, and ECa (EMv and EMh), as well as their spatial variability. In particular, the data may be useful for comparing and testing modeling approaches that account for both deterministic and random components of soil spatial variability at single-field and multi-field scales, and to support high-resolution digital soil mapping studies across irrigated lands.</p>
Data Files
<p>Data are stored column-wise in two comma-delimited text files, ECe_USDA_ARS_USSL_v01.csv and ECa_USDA_ARS_USSL_v01.csv. Joining the files on the 'ID' column returns data for geolocations at which field measurements of ECa and laboratory determinations of ECe and SP both exist.  For example:</p>
<pre><code>ECe <- read.csv('ECe_USDA_ARS_USSL_v01.csv')
ECa <- read.csv('ECa_USDA_ARS_USSL_v01.csv')
dat <- plyr::join(ECe, ECa, 'ID')
plot3D::scatter3D(dat$ECe, dat$EMv_grd, dat$EMh_grd, 
       zlab='EMh (dS/m)', xlab='ECe (dS/m)',
       ylab='EMv (dS/m)', clab = c("dS/m"), bty = "b2")</code></pre>
Salinity Survey Identifiers (DATASET)
<p>The DATASET label in each file indicates the survey or field campaign from which the data are taken. </p>
<p><strong>DATASET_1</strong>. Survey of the Broadview Water District in California performed by Corwin and co-workers in 1991 (Corwin et al, 1999). Data include: (i.) ECe and SP determinations on 1,889 soil samples (depths) from 315 soil cores (locations) and (ii.) 2613 ECa (EMv and EMh) field measurements. Data from this survey have been used previously for interpreting the spatial variability of soil salinity at the regional scale (Corwin, 2005).</p>
<p><strong>DATASET_2</strong>. Survey of Coachella Valley, California farmland conducted between 2005 and 2008 and led by the Coachella Water District. Data consist of: (i.) ECe and SP determinations on 2,088 samples from 476 soil cores and (ii.) 133,037 ECa (EMv and EMh) measurements across the Coachella Valley. This dataset has been used in previous work for validating linear approaches to regional-scale ECa and ECe calibration (Corwin and Lesch, 2014).</p>
<p><strong>DATASET_3</strong>. Survey led by Singh and colleagues across four fields in western San Joaquin Valley for the purpose of assessing environmental risk associated with saline drainage (Singh et al,. 2020). Data include: (i.) ECe and SP determinations on 1,080 samples from 273 soil cores and (ii.) 36,236 ECa (EMv and EMh) field measurements.</p>
<p><strong>DATASET_4</strong>. Soil salinity survey led by USDA-ARS U.S. Salinity Laboratory between 2012 and 2013. The survey covered 21 fields in San Joaquin Valley, California. Data consist of: (i.) ECe and SP determinations on 1,634 samples from 180 soil cores and (ii.) 63,225 ECa (EMv and EMh) field measurements. These data were used previously for large scale soil salinity assessments and is described in detail by Scudiero et al. (2014).</p>
<p><strong>DATASET_5</strong>. Data from surveys of 6 miscellaneous fields in California led by the USDA-ARS U.S. Salinity Laboratory. Data consist of: (i.) 244 determinations of ECe and SP on samples taken from 62 soil cores and (ii.) 62 corresponding ECa (EMv and EMh) field measurements.</p>
<p><strong>DATASET_6</strong>. Soil salinity surveys led by the USDA-ARS U.S. Salinity Laboratory between 1999 and 2012. One field in southern San Joaquin Valley was assessed several times over many years. Data consist of: (i.) ECe and SP determinations on 1,640 samples from 239 soil cores and (ii.) 42,458 ECa (EMv and EMh) field measurements. These data have been used in previous works focusing on long-term and short-term monitoring and mapping of the spatial and temporal variability of soil salinity (Corwin, 2008, Corwin, 2012, Scudiero et al., 2017).</p>
<p>Majority funding provided by USDA-ARS Office of National Programs.  Additional funding provided by Office of Naval Research (No. 3200001344), Coachella Valley Resource Conservation District (No. 09FG340003), and California Department of Water Resources (No. 4600011273).</p>
References
<p>Corwin, D.L. (2005). Geospatial Measurement of Apparent Soil Electrical Conductivity for Characterizing Soil Spatial Variability. doi: 10.1201/9781420032086 (Chapter 18)</p>
<p>Corwin, D.L. (2008). Past, present, and future trends of soil electrical conductivity measurement using geophysical methods. Handbook of Agricultural Geophysics, CRC Press.</p>
<p>Corwin, D.L. (2012). Field-scale monitoring of the long-term impact and sustainability of drainage water reuse on the west side of California's San Joaquin Valley. Journal of Environmental Monitoring 14(6), 1576-1596. doi: 10.1039/c2em10796a.</p>
<p>Corwin, D.L., Carrillo, M.L.K., Vaughan, P.J., Rhoades, J.D., Cone, D.G. (1999). Evaluation of a GIS-linked model of salt loading to groundwater. Journal of Environmental Quality 28(2), 471-480. doi: 10.2134/jeq1999.00472425002800020012x.</p>
<p>Corwin, D.L., Lesch, S. (2014). A simplified regional-scale electromagnetic induction: Salinity calibration model using ANOCOVA modeling techniques. Geoderma. s 230-231. 288-295. 10.1016/j.geoderma.2014.03.019.</p>
<p>Scudiero, E., Skaggs, T., Corwin, D.L. (2014). Regional Scale Soil Salinity Evaluation Using Landsat 7, Western San Joaquin Valley, California, USA. Geoderma Regional. 2-3. 82-90. 10.1016/j.geodrs.2014.10.004.</p>
<p>Scudiero, E., Skaggs, T. H., Corwin, D. L. (2017). Simplifying field-scale assessment of spatiotemporal changes of soil salinity. Sci. Total Environ., 587–588:273–281. doi:10.1016/j.scitotenv.2017.02.136.</p>
<p>Singh, A., Quinn, N.W.T., Benes, S.E., Cassel, F. (2020). Policy-Driven Sustainable Saline Drainage Disposal and Forage Production in the Western San Joaquin Valley of California. Sustainability 12(16), 6362.</p>
<p>U.S. Salinity Laboratory Staff. 1954. Diagnosis and improvement of saline and alkali soils. USDA Agric. Handbook. 60. U.S. Gov. Print. Office, Washington, DC.</p>

Tags: ars, data-gov, earth-science-agriculture-soils-soil-salinity-soil-sodicity, geospatial-data, np211, np212, np216
Organization: Department of Agriculture