Estimation of soil classes and their relationship to grapevine vigor in a Bordeaux vineyard: advancing the practical joint use of electromagnetic induction (EMI) and NDVI datasets for precision viticulture
Abstract
Working within a vineyard in the Pessac Léognan Appellation of Bordeaux, France, this study documents the potential of using simple statistical methods with spatially-resolved and increasingly available electromagnetic induction (EMI) geophysical and normalized difference vegetation index (NDVI) datasets to accurately estimate Bordeaux vineyard soil classes and to quantitatively explore the relationship between vineyard soil types and grapevine vigor. First, co-located electrical tomographic tomography (ERT) and EMI datasets were compared to gain confidence about how the EMI method averaged soil properties over the grapevine rooting depth. Then, EMI data were used with core soil texture and soil-pit based interpretations of Bordeaux soil types (Brunisol, Redoxisol, Colluviosol and Calcosol) to estimate the spatial distribution of geophysically-identified Bordeaux soil classes. A strong relationship (r = 0.75, p < 0.01) was revealed between the geophysically-identified Bordeaux soil classes and NDVI (both 2 m resolution), showing that the highest grapevine vigor was associated with the Bordeaux soil classes having the largest clay fraction. The results suggest that within-block variability of grapevine vigor was largely controlled by variability in soil classes, and that carefully collected EMI and NDVI datasets can be exceedingly helpful for providing quantitative estimates of vineyard soil and vigor variability, as well asmore »
- Authors:
-
[1];
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Bordeaux INP - UBM, Talence (France)
- Bordeaux INP - UBM, Talence (France); SNCF Réseau, Bordeaux (France)
- Univ. Paris-Saclay, Orsay (France)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI Identifier:
- 1826561
- Grant/Contract Number:
- AC02-05CH11231
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Precision Agriculture
- Additional Journal Information:
- Journal Volume: 22; Journal Issue: 4; Journal ID: ISSN 1385-2256
- Publisher:
- Springer Nature
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; Geophysics; Precision viticulture; Electrical conductivity; Electromagnetic Induction (EMI); NDVI; Hyperspectral; Bordeaux; Vineyard soil; Grapevine vigor
Citation Formats
Hubbard, Susan S., Schmutz, Myriam, Balde, Abdoulaye, Falco, Nicola, Peruzzo, Luca, Dafflon, Baptiste, Léger, Emmanuel, and Wu, Yuxin. Estimation of soil classes and their relationship to grapevine vigor in a Bordeaux vineyard: advancing the practical joint use of electromagnetic induction (EMI) and NDVI datasets for precision viticulture. United States: N. p., 2021.
Web. doi:10.1007/s11119-021-09788-w.
Hubbard, Susan S., Schmutz, Myriam, Balde, Abdoulaye, Falco, Nicola, Peruzzo, Luca, Dafflon, Baptiste, Léger, Emmanuel, & Wu, Yuxin. Estimation of soil classes and their relationship to grapevine vigor in a Bordeaux vineyard: advancing the practical joint use of electromagnetic induction (EMI) and NDVI datasets for precision viticulture. United States. https://doi.org/10.1007/s11119-021-09788-w
Hubbard, Susan S., Schmutz, Myriam, Balde, Abdoulaye, Falco, Nicola, Peruzzo, Luca, Dafflon, Baptiste, Léger, Emmanuel, and Wu, Yuxin. Mon .
"Estimation of soil classes and their relationship to grapevine vigor in a Bordeaux vineyard: advancing the practical joint use of electromagnetic induction (EMI) and NDVI datasets for precision viticulture". United States. https://doi.org/10.1007/s11119-021-09788-w. https://www.osti.gov/servlets/purl/1826561.
@article{osti_1826561,
title = {Estimation of soil classes and their relationship to grapevine vigor in a Bordeaux vineyard: advancing the practical joint use of electromagnetic induction (EMI) and NDVI datasets for precision viticulture},
author = {Hubbard, Susan S. and Schmutz, Myriam and Balde, Abdoulaye and Falco, Nicola and Peruzzo, Luca and Dafflon, Baptiste and Léger, Emmanuel and Wu, Yuxin},
abstractNote = {Working within a vineyard in the Pessac Léognan Appellation of Bordeaux, France, this study documents the potential of using simple statistical methods with spatially-resolved and increasingly available electromagnetic induction (EMI) geophysical and normalized difference vegetation index (NDVI) datasets to accurately estimate Bordeaux vineyard soil classes and to quantitatively explore the relationship between vineyard soil types and grapevine vigor. First, co-located electrical tomographic tomography (ERT) and EMI datasets were compared to gain confidence about how the EMI method averaged soil properties over the grapevine rooting depth. Then, EMI data were used with core soil texture and soil-pit based interpretations of Bordeaux soil types (Brunisol, Redoxisol, Colluviosol and Calcosol) to estimate the spatial distribution of geophysically-identified Bordeaux soil classes. A strong relationship (r = 0.75, p < 0.01) was revealed between the geophysically-identified Bordeaux soil classes and NDVI (both 2 m resolution), showing that the highest grapevine vigor was associated with the Bordeaux soil classes having the largest clay fraction. The results suggest that within-block variability of grapevine vigor was largely controlled by variability in soil classes, and that carefully collected EMI and NDVI datasets can be exceedingly helpful for providing quantitative estimates of vineyard soil and vigor variability, as well as their covariation. The method is expected to be transferable to other viticultural regions, providing an approach to use easy-to-acquire, high resolution datasets to guide viticultural practices, including routine management and replanting.},
doi = {10.1007/s11119-021-09788-w},
journal = {Precision Agriculture},
number = 4,
volume = 22,
place = {United States},
year = {Mon Feb 01 00:00:00 EST 2021},
month = {Mon Feb 01 00:00:00 EST 2021}
}
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