Rémi Mahmoud is one of the #DigitAg co-funded PhDs
Modeling the performance of annual intercrops : an approach combining functional ecology and data science.
- Start Date: 15/11/2019
- University: MUSE University of Montpellier
- PhD School: Information, Structures, Systems
- Field(s): Biostatistics
- Doctoral Thesis Advisor: Nadine Hilgert, Inra MISTEA
- Co-supervisors : Pierre Casadebaig & Noémie Gaudio, Inra, AGIR
- Funding: #DigitAg – Inra
- #DigitAg: Axes 5,6, Challenge 1
Keywords: intercrops, statistics, agrocecology
Abstract: Increasing plant diversity in agriculture is suggested as one of the main mechanisms to move towards more sustainable production systems. Plant diversity enhances and stabilizes primary production through complementarity between plants. In modern agriculture, the current challenge is to determine which species mixtures improve the overall performance of the agroecosystem through better use of available environmental resources in the crop system considered. Although the agronomic interest of these mixed canopies has been experimentally highlighted in low input context, the conclusions of this work underline the variability of these results according to the environmental conditions. Our goal is to develop predictive approaches (statistical models) based on theories from community ecology to design intercrops. These theories suggest that two ecological processes are particularly involved in the performance of intercrops. Niche complementarity can be quantified by the distance between key functional traits (plant morphological and physiological characteristics) of the mixture plant species. Phenotypic plasticity can be quantified by the variance of these traits between different cropping conditions. We seek to apply these concepts to selected and mixed annual crop species by calculating complementarity and plasticity metrics to predict the performance of intercrops in a wide range of environments. For this, we have a database of traits measurements on a dozen crop species, each represented by several varieties and in different environments (sites, years). From these measurements and pedo-climatic variables, we will mobilize statistical analyzes considering traits independently (trait by trait) or multivariate (multi-trait).
Contact: remi.mahmoud [AT] inrae.fr