Planning new vineyard needs accurate information about soil features and their spatial variability. The use of soil proximal sensors, coupled by few detailed soil observations and analysis allows to obtain high detailed maps of soil variability at affordable costs. The work showed the methodology to interpolate the proximal sensors data and to delineate homogeneous area by clustering, corresponding to likely soil units. The description and analysis of one profile for each homogeneous area allowed to describe the soil features of each soil typological units and to produce useful thematic maps for vineyard planning

Costantini, E.A.C.

Fantappiè, M.

L'Abate, G.

Priori, S.

Organic Eprints

EQA – Environmental quality / Qualité de l’Environnement / Qualità ambientale, 30 (2018) 9-15  DOI: 10.6092/issn.2281-4485/7890 9 MAPPING SOIL SPATIAL VARIABILITY AT HIGH DETAIL  BY PROXIMAL SENSORS FOR NEW VINEYARD PLANNING  Simone Priori*, Giovanni L’Abate, Maria Fantappiè, Edoardo A.C. Costantini  CREA-AA, Research Centre for Agriculture and Environment, Florence, Italy  *Corresponding author email: simone.priori@crea.gov.it    Abstract  Planning new vineyard needs accurate information about soil features and their spatial variability. The use of soil proximal sensors, coupled by few targeted soil observations and analysis, allows to obtain high detailed maps of soil variability at affordable costs. The work shows a methodology to interpolate the proximal sensors data and to delineate homogeneous areas by clustering, corresponding to likely soil units. The description and analysis of one soil profile for each homogeneous area allowed to describe the soil features of each soil typological units and to produce useful thematic maps for vineyard planning.         Keywords: Electromagnetic induction, gamma-ray spectroscopy, soil hydrology, carbonates, land preparation, viticulture  Introduction  Perennial crops, like grapevine, need a good land preparation, able to balance the mechanization adaptation and the soil quality preservation. An accurate and precise mapping of soil spatial variability is basic before vineyard planning, in order to plan a site-specific planning of ploughing depth, fertilizing, drainage, irrigation, and rootstocks selection.  Recently, the use of proximal sensors to map soil spatial variability at high detail has been increasing, even in professional consultations. These sensors allow to obtain a large number of data within a field related to the most important soil features, like texture (Morari et al., 2009; Priori et al., 2013), soil depth (Priori et al., 2013), stoniness (Priori et al., 2014), water availability (Ortuani et al., 2016), and salinity (Doolittle et al., 2001).  Moreover, cluster analysis of the maps obtained by the soil proximal sensing allows the delineation of soil homogeneous zones, presumably correspondent to the main soil typological units (STUs, Taylor et al. 2009; Bonfante et al., 2015).  This work wants to show a simple procedure to select soil profile localization for direct observations and analysis and to delineate the STUs limits, with the final aim to provide practical advice to the farmer about soil and water management during a new vineyard planning.            S.Priori, G. L’Abate, M. Fantappiè, E.A.C. Costantini / EQA, 30 (2018) 9-15  10 Materials and methods   Study area  The study area was located in Petra winery, near Suvereto village, in the coastal region of Tuscany (Italy). The field was about 4 ha in size, on a wide alluvial plain, characterized by fine sandy deposits with scarce content of calcareous materials, although several hills around the field are made by limestone. In the past, the corn was cultivated in the field in rotations with legumes.          Proximal sensing  The proximal sensors applied were: i) the EM38-Mk2 electromagnetic induction sensor (Geonics Ltd., Ontario, Canada) and ii) “The Mole”, gamma-ray spectroradiometer (Soil Company, The Nederlands). EM38-Mk2 measures the soil apparent electrical conductivity (ECa) across two depth ranges of 0-75 (ECa1) and 0-150 cm (ECa2), approximately (Mc Neill, 1990). “The Mole” spectroradiometer measures continuously the gamma-ray natural emission coming from the first 30-40 cm of the soil and rocks, through a Cesium Iodide scintillator crystal (van Egmond et al., 2008). The outputs of gamma-ray survey were the total counts (TC) of gamma-ray emitted from the soil, and the calculated amount of the three main radionuclides, namely potassium (40K), Thorium (232Th) and Uranium (238U).     For the survey, an ATV-quad were set up with a GPS, a gamma-ray spectroradiometer on the back, and a non-metallic chariot where EM38-Mk2 was inserted. Parallel survey line 8 meters far were made Eastward-Westward, and other 6 perpendicular survey lines were made Northward-Southward, to check eventual sensor drift or measurements errors.  Data were interpolated by ordinary kriging, after semivariogram modeling, using SAGA-Gis. Maps of ECa1, ECa2, and gamma-rays TC were grouped by k-means cluster analysis (SAGA-Gis), after standardization, using 4 groups (STUs). The choice of 4 clusters was a compromise between precision and costs for soil profile descriptions and analysis. The radionuclides maps were not used in k-means calculation, because of very high heterogeneity of and very high correlation between with TC.      Figure 1. The survey line of proximal sensors and the interpolated map of gamma-ray total count (on the left), and the cluster groups calculated using gamma-ray spectroscopy and ECa maps (on the right). The numbered points represent the soil profiles.    EQA – Environmental quality / Qualité de l’Environnement / Qualità ambientale, 30 (2018) 9-15  DOI: 10.6092/issn.2281-4485/7890 11 Profile description, analysis and new STU delineation  Near the center of each STUs (4), a soil profile was dug at a depth of about 120 cm, and described according Schoeneberger (2002). Particular attention was given to redoximorphic mottles and nodules, to understand the pedo-hydrological characteristics of the field. The soil horizons individuated during description were sampled for standard soil laboratory analysis: texture, pH, electrical conductivity, total and active carbonates, organic carbon, nitrogen, available phosphorus, cation exchange capacity (CEC) and exchangeable bases (Ca, K, Mg, and Na). Moreover, undisturbed soil samples were collected to measure the soil bulk density. Close to each soil profile, saturated hydraulic conductivity (Ksat) was measured by means of Guelph permeameter, following Reynolds and Elrick (1987), to determine the soil hydraulic permeability.  Each soil profile was classified according to IUSS Working Group WRB (2014) and local soil typological unit (STU) was attributed. A supervised classification of the STU was made by SAGA-Gis, using the grids of TC, ECa1, and ECa2 and the STU classified profiles, with a 5 m buffer, as training areas. Supervised classification is a method, often used in remote sensing (Stephens and Diesing, 2014), that use algorithms that “learn” patterns in grids to predict an associated discrete class. The learning is made in training areas, where the discrete class is already determined. The algorithms select the most probable class in the whole grid surface.        Results and discussion  The proximal sensing with EM38-Mk2 provided very similar maps of ECa1 (0-75 cm) and ECa2 (0-150 cm), showing lower ECa values (10-20 mS/m) in the northern part of the field (profiles 1 and 2) and higher values (25-40 mS/m) in the southern part (profiles 3 and 4) (Fig.2).  Gamma-ray total counts (TC) showed a general homogeneous pattern (260-280 Bq/kg), with the only exception of a small area in the southern part, represented of red area (220-230 Bq/kg) observable in Fig.2 and delimited by the area of profile 4. The maps of radionuclides (40K, 232Th, 238U) showed the same low values in the area of profile 4, but high noise in the rest of the field. For this reason, we decided to remove these layers from the clustering calculation.     The data and pictures of soil profiles (P) were reported in Tab.1 and Fig.3. P4 was strongly different from the others soil profiles for texture (clay: 41% over an average of 32.5%), bulk density and calcium carbonate. P3 showed a topsoil similar to the other soil profiles, but it was characterized by the presence of a deep clayey horizon (90-140 cm), which reduces the soil water drainage. P2 showed the coarsest texture (around 44% of sand and 28% of clay), with a good soil structure and porosity, confirmed by low bulk density. P1 was similar to P2, but it was also characterized by greater stoniness, higher clay content and slower drainage. Since the differences among the four profiles were important for grapevine water and chemical nutrition (Lanyon et al., 2004; Mackenzie and Christy, 2005; Costantini S.Priori, G. L’Abate, M. Fantappiè, E.A.C. Costantini / EQA, 30 (2018) 9-15  12 and Bucelli, 2008), we decided to map 4 STUs by supervised classification. Therefore, the map obtained by the supervised classification (Fig. 3) was very similar to that obtained by k-means clustering (Fig.1).      Figure 2 Maps of total count of gamma-ray (TC),  and apparent electrical conductivity at about 0-75 cm (ECa1) and 0-150 cm (ECa2).  ////  Table 1. Soil profiles description  P Horizon Depth Sand Clay Silt Coarse fragments CaCO3 tot. BD(1) Ksat(2) SOC(3) Ntot(4) CEC(5) Kexch(6)   cm % g∙cm-3 mm∙h-1 g∙kg-1 meq∙100g-1 mg∙kg-1 1 Ap1 25 35.6 34.6 29.8 18 2.3 1.40 6.0 10.3 1.18 14.9 125.1 Ap2 45 36.7 34.5 28.8 23 1.2 1.55 10.3 1.17 15.3 129.1 Bw1 90 31.8 41.3 26.9 30 2.3 n.d. 4.6 0.78 15.5 70.4 Bw2 110 38.7 32.5 28.8 10 3.2 n.d. 3.0 0.56 14.2 66.5 2 Ap1 30 40.6 29.7 29.7 10 0.6 1.34 17.0 12.0 1.22 14.5 109.5 Ap2/Bw1 90 48.5 27.4 24.1 10 2.6 1.43 8.3 0.88 14.5 70.4 Bw2 120 26.5 37.7 35.8 2 3.2 1.66 3.8 0.54 14.1 62.6 3 Ap1 30 36.2 35.7 28.1 8 0.7 1.50 0.6 10.3 1.12 15.5 113.4 Ap2/Bw 90 39.9 33.8 26.3 15 0.6 1.36 8.5 0.94 16.4 117.3 Bw/Btg 110 24.5 46.9 28.6 2 0.2 1.55 2.6 0.4 16.4 62.6 Btg 140 23.8 49.2 27.0 15 0.1 n.d. 1.3 0.4 16.0 50.8 4 Ap 15 19.5 41 39.5 10 38.5 1.62 <0.1 6.8 0.86 11.5 105.6 Bkg1 40 21.9 40.1 38.0 10 41.6 1.66 5.2 0.79 11.1 89.9 Bkg2 100 12.5 39.1 48.4 10 48.7 1.68 1.2 0.28 8.6 78.2 (1) Bulk density; (2) Saturated Hydraulic permeability, measured by Guelph permeameter; (3) Soil organic carbon; (4) Total nitrogen; (5) Cation Exchange Capacity; (6) Exchangeable potassium.   EQA – Environmental quality / Qualité de l’Environnement / Qualità ambientale, 30 (2018) 9-15  DOI: 10.6092/issn.2281-4485/7890 13 If STU1 and 2 were similar, and they could be grouped to simplify the vineyard management, STU3 and 4 had important peculiarities, which have to be taken in account during vineyard planning.    The peculiarities of STU4 was probably due to the proximity of the calcareous hill. Although the actual surface of the field was approximately flat, the STU4 represent an edge of the calcareous hill, which was levelled in the past. Indeed, the soil of STU showed all the feature of truncated soil, which were shallow A and B horizons (40 cm), very poor organic matter and very high calcium carbonate contents, starting from the surface (Fig.3, P4).   Figure 3. Pictures of the studied soil profiles.   This high content of calcium carbonate and the high frequency of waterlogging in the soil of STU4, classified as Calcaric Stagnosol (IUSS Working Group WRB, 2014), testifies sub-surface water fluxes from calcareous hill, which tend to transport a great amount of Ca+ ions in water solution, partly released as pedogenic calcium carbonates in this area. STU3 showed slow water drainage and waterlogging features probably due to poor structure and compaction in depth (90-100 cm).        Table 2. Summary of the mean values of proximal sensing data of each STU and the soil features of the associated soil profile.   STU ECa1 ECa2 TC Area Texture Classification mS∙m-1 Bq∙Kg-1 m2 WRB 2014 1 13.1 ±1.0 29.5 ±1.4 249.2 ±8.5 4865 Clay loam Calcaric Cambisol (Loamic) 2 11.4 ±0.7 26.8 ±1.3 254.7 ±5.3 9016 Sandy clay loam Calcaric Cambisol (Loamic) 3 14.7 ±1.1 32.1 ±1.9 255.6 ±7.0 9819 Clay loam Endostagnic Luvisol (Loamic, Cutanic) 4 18.6 ±2.0 38.1 ±3.0 230.8 ±6.2 860 Clay Calcaric Stagnosol (Clayic) ECa1 and ECa2: apparent electrical conductivity measured by EM38-MK2 at 0-75 and 0-150 cm, respectively; TC: gamma-ray total count measured by “The Mole”.  ///////// S.Priori, G. L’Abate, M. Fantappiè, E.A.C. Costantini / EQA, 30 (2018) 9-15  14  Figure 4  Final STUs, obtained after Supervised classification and simple filter to simplify the geometry.     The detailed mapping of STUs (Fig.4), allowed to base the vineyard planning on soil spatial variability. 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MAPPING SOIL SPATIAL VARIABILITY AT HIGH DETAIL BY PROXIMAL SENSORS FOR A VINEYARD PLANNING

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MAPPING SOIL SPATIAL VARIABILITY AT HIGH DETAIL BY PROXIMAL SENSORS FOR A VINEYARD PLANNING

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