Application of Multivariate Geostatistics in Delineating Management Zones within a gravelly vineyard using geo-electrical electrical sensors

Transcription

1 Application of Multivariate Geostatistics in Delineating Management Zones within a gravelly vineyard using geo-electrical electrical sensors Francesco Morari, DAAPV, Università di Padova Annamaria Castrignanò, CRA-ISA, Bari Chiara Pagliarin, DAAPV, Università di Padova

2 Skeletal soils wide spread in Northern Italy Characterization of their spatial variability and properties is important: to predict the effects of land use alternatives on groundwater quality to apply site-specific techniques (e.g. fertilisation, irrigation)

3 Valpolicella

4 Due to the geological origins many soils in Valpolicella are skeletic soils, with a high content of gravel (> 30%) Gravel content affect the soil fertility and consequently the wine quality The peculiar interactions soilclimate-vine give origin to the terroir effect The analysis of skeletal soil is particularly time-consuming, labour-intensive and costly

5 Some problems encountered in the analysis of skeletic soils: 1) difficulties to collect undisturbed samples for lab analysis 2) difficulties to measure in situ some physical properties 3) large samples to collect Minimal sample weight (Violante, 2000) Diameter of material contributing > 10% (mm) Minimal sample weight (kg)

6 Data recorded intensively by electro-magnetic induction scans (EMI) are relatively easy and inexpensive to collect If the sparse and the more intensive data are spatially correlated, then the additional information from the ancillary data can be used to improve the estimate precision of the sparsely sampled primary variable. Even if the value of spatial measurements of EMI to precision agriculture is now widely acknowledged, nevertheless EMI is sometimes misunderstood and misinterpreted. Objectives The objective of this work was to propose a procedure, based on multivariate geostatistics, to build maps of soil attributes and classify gravelly vineyards in zones to be differently managed.

7 The vineyard Site: San Pietro in Cariano, Tenuta Pule Size: 5 ha Variety: Corvinone Training system: Guyot (1 x 2.5 m) Soil: Calcareous Skeletic Fluvisols, clay loam, gravel 44%

8 Soil sampling (1) Period: July 2005 Scheme: 28 samples taken at the nodes of a 40-m grid + 12 additional 1 m-apart samples located at random Analysis: -particle size distribution (<0.002 mm, mm, mm, 2-20 mm, mm, > 100 mm) -bulk density (fine component + total mass) -ph, -EC 1:2 -CEC -SOM -N tot

9 Electro-magnetic Induction (EMI) Scans Period: Novemeber 2005 EM38DD (GEONICS) -vertical orientation (to 1.5 m) -horizontal orientation (to 0.5 m) -EM38DD towed by a tractor with associated DGPS antenna -Observations made along parallel transects (5 m apart) -Both types of data were simultaneously recorded at 1 s (5782 values)

10 Intensity of sampling

11 Soil sampling (2) Period: July 2006 Electrical Resistivity Tomography (ERT): 18 profiles 5.75 m long, N-S oriented along the vine rows Iris-Syscal Pro resistivity meter. Each profile was done by means of dipole-dipole electrode arrays using 24 electrodes with 0.25 m Analysis 12 pits: -particle size distribution (<0.002 mm, mm, mm, 2-20 mm, mm, > 100 mm)

12 Electrical resistivity tomography

13 Geostatistical analysis to identify MZs Explorative data analysis and data normalisation /standardisation Structural data analysis (Modelling the coregionalization with a Linear Model of Coregionalization (LMC) ) Map creation by Multi-Collocated Cokriging Identification of homogeneous zones by Factorial Kriging Analysis +fuzzy c-means classification procedure

14 Matrix correlation of the parameters in the first layer

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16 Nested function: nugget effect + cubic model (short-range comp. 70 m) + spherical model (long- range comp. 120 m)

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20 a b c d e a b c d e a b c d e

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