Innovative strategies and genetic engineering solutions are needed in order to manage agroecosystems more efficiently, build improved varieties and reduce inputs. In this context, phenotyping has recently become a bottleneck for the selection of high-achieving stresstolerant genotypes. In France, the Phenome project is responding to these stakes with a network of various high throughput facilities distributed in relevant geographical locations for studies at different scales and conditions. Phenovia is a platform managed by Terres Inovia. It is incorporated into the INRA experimental unit (EU) of Epoisses, located in Bretenière (Côte-d’Or, Bourgogne-Franche- Comté, France) approximately 10 km southwest of Dijon. The study site is a 15-ha field divided in four subplots of approximately 4 ha each where a four-year rotation is performed. The aim of this work is to define an extractable soil water map for the platform. First, a soil depth map was estimated from exhaustive electrical resistivity measurements combined with a smaller soil thickness dataset. Second, available water content (AWC) values were computed from soil depth according to typological soil units. Soil apparent resistivity measurements were acquired by INRA, using a specific device developed by the Geocarta company (Panissot et al, 1997, Samouëlian et al, 2005). It is a resistivity device connected to spiked wheels acting as electrodes and towed by an all-terrain quad bike. An exhaustive set of 153 000 points was available. The soil thickness dataset was made by INRA using a quad bike especially equipped for soil sampling. The aim was to measure the depth of the alluvial coarse deposit. There were 541 boreholes made although a limit of this dataset is that the measurements did not exceed 0.90 m. A geostatistical approach was used for spatial interpolation of datasets. First, soil and resistivity maps (Figure 1a and 1.) were generated through ordinary kriging. Then Kriging with External Drift (KED) was performed (Figure 1c). This method is relevant for the spatial interpolation of a low sampled variable of interest when an auxiliary variable is exhaustively described on studied area. For this purpose, the relationship between the variable of interest (soil depth) and the auxiliary variable (soil resitivity) is modelled. The residuals (deviations from the model) are described as a random variable and kriged on the entire plot. The spatial structure of these residuals locally defines the respective importance of the variable of interest and the drift in the estimate (Bourennane et al, 2003, Chiles and Delfinger, 2012, Loiseau, 2015). Soil Typological Units (STU) were determined in the framework of the previous CAREX project (Seger et al, 2017). Each soil type is characterized by soil horizons, whose available water contents are known. The profile AWCs (Figure 1d) were computed toa depth of 1 m by adding AWC of soil horizons (from ground to estimated soil depth) with AWC of the alluvial coarse deposit (from estimated soil depth to 1m depth). 99 Figure 1 : Field maps of Phenovia with (a) Ordinary kriging of soil depth. (b) Soil apparent resistivity. (c) KED of soil depth with apparent resistivity as external drift. (d) Soil available water content map. This available water content map globally meet to the expectations of the platform managers. However, it is important to remember that soil depth measurements did not exceed 0.90 m. Therefore, soil depth and AWC content could be underestimated. Complementary measurements (with manual auger) are needed to improve the quality of this map. Further work is also needed to determine maximum or real time plant roots depths (arbitrarily set at 1 meter) and to transfer the method to other experimental sites. Bourennane, H., King, D., 2003. Using multiple external drifts to estimate a soil variable. Geoderma, 114, 1–18. Chilès, J.P., Delfiner, P., 2012. Geostatistics: Modeling Spatial Uncertainty, 2nd Ed. John Wiley & Sons, New York,726p. Loiseau T., 2015. Approches géostatistiques pour l’extraction de l’information pertinente dans des données géo-électriques en vue de la cartographie de propriétés des sols. Rapport de Master 2, Université François Rabelais, 39 p. Panissod, C., Dabas, M., Jolivet, A., Tabbagh, A., 1997. A novel mobile multipole system (MUCEP) for shallow (0-3 m) geoelectrical investigation: the ‘Vol-de-canards’ array. Geophysical Prospecting, 45, 983–1002. Samouëlian, A., Cousin, I., Tabbagh, A., Bruand, A., Richard, G., 2005. Electrical resistivity survey in soil science: a review. Soil and Tillage Research, 83, 173–193. Seger, M., Girot, G., Hugard, R., Ubertosi, M., Cousin, I., Perrier, C., Mistou, M. N., 2017. UE Epoisses cartographie des sols et de la réserve utile - Approche pédologique