# A two-year dataset of georeferenced unproductive vines in a southern France Mediterranean vineyard

## TL;DR

This paper presents a high-precision dataset of unproductive vines in a French vineyard, including geolocation and soil data for two years.

## Contribution

The study provides a unique, high-accuracy spatial dataset of unproductive vines and soil resistivity data for vineyard analysis.

## Key findings

- A dataset of 14,287 unproductive vines was collected over two years with <5 cm geolocation accuracy.
- Soil resistivity data at three depths was gathered using MuCep for each of the 50 vineyard blocks.
- The dataset supports research on vineyard sampling methods and remote sensing feasibility.

## Abstract

This study describes a dataset of georeferenced unproductive vines (dead and missing) and associated observations on fields and soil. The dataset results from a survey of 14 287 unproductive vines performed in a French Mediterranean vineyard made of 50 blocks with a total area of 28 hectares. The survey was carried out over two successive years, 2022 and 2023. Geolocation-accurate data (<5 cm) was collected via a Real Time Kinematic (RTK) Global Navigation Satellite System (GNSS) and includes detailed information of the 50 vineyard blocks, such as boundaries, area, grape variety, plantation density and location of surveyed unproductive vines. In order to provide information on the soil situation, apparent soil resistivity data at three depths (0-50cm, 0-1m, 0-1.5m) acquired with Multi-depth continuous electrical profiling (MuCep) is also provided. This dataset has already been used to investigate optimised sampling methods aiming at assessing the proportion of unproductive vines at the field level. To our knowledge, a spatialized dataset of this precision and scale concerning unproductive vines is currently unavailable. This dataset is valuable for driving any experimentation aimed to study the spatial distribution of unproductive vines, potential correlations with soil characteristics, variety, and other factors. Furthermore, this data is relevant to consider the feasibility of estimating the proportion of unproductive vines, for example, via remote sensing techniques.