Application of multivariate geostatistics in delineating management zones within a gravelly vineyard using geo-electrical sensors

In gravelly soils, surveys are generally time-consuming, labour-intensive and costly. This limits the possibility of adopting an appropriate sampling to determine within-field spatial variability. The potential use of electro-magnetic induction scans (EMI) to measure apparent electrical conductivity (EC) and improve the estimate accuracy of sparsely sampled primary variables was assessed in a 5-ha gravelly soil vineyard in Valpolicella, north-eastern Italy. EC was measured using a Geonics EM38DD operating in both horizontal and vertical mode. Geo-electrical investigations were also done in 18 positions with the electrical resistivity tomography (ERT) method to obtain high-resolution images of the soil profile. The spatial variability of soil properties and their relationships with EC in horizontal and vertical mode was estimated using multivariate geostatistical techniques. Spatial dependence between EC and physical soil properties (particle-size distribution) was explored with factorial kriging analysis (FKA) that could isolate and display sources of variation acting at different spatial scales, expressed as regionalised factors, which was followed by fuzzy c-means classification for zoning the vineyard. There was a generally close relationship between EC and the measured physical properties: EC was negatively correlated with the coarser texture components (gravel and sand) and positively with the finer ones (clay and silt). EC measurements were also consistent with ERT profiles, evidencing the presence of gravelly parent material, with low electrical conductivity, variably distributed in the 3 dimensions and affecting vine rooting depth. FKA isolated two significant regionalised factors which, with an acceptable loss of information, give a concise description of the soil physical variability at the different selected spatial scales. These factors, used in fuzzy c-means classification, allowed the delineation of zones to be managed separately. The results prove that EM38DD could be advantageously used to map soil spatial variability in gravelly soils, even if ground-truth soil samples are obligatory to understand and interpret the EC measurements.

[1]  Zhou Shi,et al.  Delineation of site-specific management zones using fuzzy clustering analysis in a coastal saline land , 2007 .

[2]  Amos Gilat,et al.  Matlab, An Introduction With Applications , 2003 .

[3]  D. Mulla,et al.  Geostatistics, remote sensing and precision farming. , 1997, Ciba Foundation symposium.

[4]  Alex B. McBratney,et al.  An overview of pedometric techniques for use in soil survey , 2000 .

[5]  P. Goovaerts.  Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall , 2000 .

[6]  R. Reese.  Geostatistics for Environmental Scientists , 2001 .

[7]  Louise Pellerin,et al.  Multi-dimensional electromagnetic modeling and inversion with application to near-surface earth investigations , 2005 .

[8]  Dennis L. Corwin,et al.  Site-specific management in salt-affected sugar beet fields using electromagnetic induction , 2005 .

[9]  Josse De Baerdemaeker,et al.  Key soil and topographic properties to delineate potential management classes for precision agriculture in the European loess area , 2008 .

[10]  Emanuele Lugato,et al.  Long‐term effects of recommended management practices on soil carbon changes and sequestration in north‐eastern Italy , 2006 .

[11]  Clyde W. Fraisse,et al.  Management Zone Analyst (MZA) , 2004, Agronomy Journal.

[12]  M. Goulard,et al.  Linear coregionalization model: Tools for estimation and choice of cross-variogram matrix , 1992 .

[13]  R. Murray Lark,et al.  Using Yield Maps to Regionalize Fields into Potential Management Units , 1999 .

[14]  D. Corwin,et al.  Apparent soil electrical conductivity measurements in agriculture , 2005 .

[15]  Jamie Goode,et al.  Precision agriculture: spatial and temporal variability of environmental quality. Symposium proceedings. Wageningen, The Netherlands, 21-23 January 1997. , 1997, Ciba Foundation symposium.

[16]  R. Bramley,et al.  Understanding variability in winegrape production systems 2. Within vineyard variation in quality over several vintages , 2005 .

[17]  J. Triantafilis,et al.  Mapping clay content variation using electromagnetic induction techniques , 2005 .

[18]  A. Castrignanò,et al.  Study of spatial relationships among some soil physico-chemical properties of a field in central Italy using multivariate geostatistics , 2000 .

[19]  G. Heuvelink,et al.  A generic framework for spatial prediction of soil variables based on regression-kriging , 2004 .

[20]  Pierre Goovaerts,et al.  Scale-dependent correlation between topsoil copper and cobalt concentrations in Scotland , 1994 .

[21]  Z. Bing,et al.  Finite element three-dimensional direct current resistivity modelling: accuracy and efficiency considerations , 2001 .

[22]  Z. L. Frogbrook,et al.  Comparing the spatial predictions of soil organic matter determined by two laboratory methods , 2001 .

[23]  George E. Meyer,et al.  Remote Sensing to Detect Nitrogen Deficiency in Corn , 1995 .

[24]  J. Kjeldahl,et al.  Neue Methode zur Bestimmung des Stickstoffs in organischen Körpern , 1883 .

[25]  E. Ziegel,et al.  geoENV VII: Geostatistics for Environmental Applications , 1997 .

[26]  J. Deckers,et al.  World Reference Base for Soil Resources , 1998 .

[27]  Jacques Rivoirard,et al.  Which Models for Collocated Cokriging? , 2001 .

[28]  G. Gee,et al.  Particle-size Analysis , 2018, SSSA Book Series.

[29]  A. Walkley,et al.  AN EXAMINATION OF THE DEGTJAREFF METHOD FOR DETERMINING SOIL ORGANIC MATTER, AND A PROPOSED MODIFICATION OF THE CHROMIC ACID TITRATION METHOD , 1934 .

[30]  R. M. Lark,et al.  Forming Spatially Coherent Regions by Classification of Multi-Variate Data: An Example from the Analysis of Maps of Crop Yield , 1998, Int. J. Geogr. Inf. Sci..

[31]  J. Andrew Royle,et al.  A hierarchical approach to multivariate spatial modeling and prediction , 1999 .

[32]  Alex B. McBratney,et al.  Soil pattern recognition with fuzzy-c-means : application to classification and soil-landform interrelationships , 1992 .

[33]  Brian R. Spies,et al.  Three-Dimensional Electromagnetics , 1999 .

[34]  J. D. Rhoades,et al.  Soil salinity assessment :methods and interpretation of electrical conductivity measurements , 1999 .

[35]  Vincenzo Lapenna,et al.  High-resolution images of the fault-controlled High Agri Valley basin (Southern Italy) with deep and shallow electrical resistivity tomographies , 2004 .

[36]  R. Casa,et al.  Analysis of spatial relationships between soil and crop variables in a durum wheat field using a multivariate geostatistical approach , 2008 .

[37]  Hannes Flühler,et al.  Sample size for determination of coarse fragment content in a stony soil , 1994 .

[38]  D. J. Brus,et al.  The use of electromagnetic measurements of apparent soil electrical conductivity to predict the boulder clay depth , 1992 .

[39]  Stefano Bocchi,et al.  Application of factorial kriging for mapping soil variation at field scale. , 2000 .