The objective of the present study was to evaluate a strategy for three-dimensional (3-D) digital soil mapping on two farms in southwest Sweden. Apparent electrical conductivity (ECa) and gamma radiation data from proximal sensors and laser-scanned elevation data were used as predictors. Depth-integrated ECa measurements from a non-invasive sensor were used directly, but also calibrated against probe sensor ECa measurements to obtain layer-specific values. This allowed the predictive powers of depth-integrated and layer-specific ECa to be compared. Clay and sand fractions, and organic matter content (OM) were modelled for three depth layers by multivariate adaptive regression splines (MARSplines). Clay and sand were consistently better predicted in the topsoil than in the subsoil. MARSplines models based on layer-specific ECa data rather than on depth-integrated ECa data yielded more successful estimations of these soil properties in both subsoil layers (0.4–0.6 and 0.6–0.8 m) on both the farms but this was not always the case in the topsoil. Topsoil OM was better predicted by spatial interpolation of the calibration data than by using MARSplines models with ancillary predictors. In the two subsoil layers, the mapping procedure could not be appropriately tested, because the OM was low and homogeneous. We concluded that a 3-D soil texture map of an agricultural field could be prepared using MARSplines models based on layer-specific ECa values, gamma radiation data and a digital elevation model.
Precision Agriculture – Springer Journals
Published: Oct 7, 2014
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera