[PhD’s corner]Daniel Pasquel : Metrics to assess the spatialisation of crop models for Precision Agriculture, application to a vine water status model

Daniel is one of the #DigitAg cofunded PhDs


Metrics to assess the spatialisation of crop models for Precision Agriculture: application to a vine water status model

  • Start Date: 1st November 2020
  • University: L’Institut Agro – Montpellier SupAgro
  • PhD School: GAIA
  • Field(s): Agronomy, viticulture, modelization, precision agriculture
  • Doctoral Thesis Advisor: James Taylor, ITAP, Inrae
  • Co-supervisors : Sébastien Roux, Mistea, Inrae/Bruno Tisseyre, ITAP, L’Institut Agro – Montpellier SupAgro
  • Funding: #DigitAg- Inrae
  • #DigitAg: Axis 6 : Multiscale modelling and simulation , Axis 5: Data Mining, Data Analysis and Knowledge Discovery – Challenge : cross-cutting subject

Keywords: Sensitivity analysis, Geostatistics, Spatialized crop models

Abstract: There exists enormous knowledge gaps in how to best manipulate crop models to accept and to be updated with spatial (and temporal) high-resolution ancillary data, the implications that changing the model has on predictive power (and subsequent management options), and how to properly assess model performance at varying scales. Agricultural scientists need help achieve this and the crux of this thesis will be method development to generate metrics to assess the effect of incorporating multi-temporal spatial crop and environmental observations into existing crop models. The intent will be to incorporate aspects of spatial variance decomposition into a Sobol-based sensitivity analysis. The intent is to improve understanding of how to spatialize model predictions for enhanced spatial management. It will not and cannot address all issues, but will start to provide tools to achieve this. The intent is not to arrive at the best spatialize model, but to develop tools that will help all models arrive at this point. Spatial crop (agri-environmental) models will generate outputs with a change in extend, coverage and/or support from traditional crop model applications. The need for correct methods of sensitivity analysis has been previously discussed and proposed, but only for large scale, regional applications. High resolution, sub-field, agronomic applications are an area of sensitivity analysis that requires further work.

Contact: daniel.pasquel [AT] inrae.fr – Phone:

Papers in international journals

Daniel Pasquel, Sébastien Roux, Jonathan Richetti, Davide Cammarano, Bruno Tisseyre & James A. Taylor (2022) A review of methods to evaluate crop model performance at multiple and changing spatial scales , Precision Agriculture