Statistics for characterizing interactions between plant and its environment – Stat4Plant

Agriculture has currently to tackle new challenges, largely due to the need to increase global food supply under the declining availability of soil and water resources and increasing threats from climate change. It has to face main changes and to adapt to new conditions, in particular environmental ones. To better handle this adaptation, it is necessary to better understand several key notions such as genetic variability and interactions between the plant and its environment. In this context, predictive approaches relying on ecophysiology and genetic knowledge, as well as mathematical modeling are very promising.

The Stat4Plant project aims at developing new statistical methodologies and new algorithmic tools for modeling and analyzing genotypic variability and interaction between plant and its environment in a context of climate change. The project consortium gathers scientists in modeling and applied statistics with large experience in interdisciplinary collaborations in plant sciences and biologists with strong expertise in phenotype-genotype relations. The project is structured in four main research axes, supported by strong collaborations between statisticians and biologists and motivated by practical questions linked with biological dataset.

The first axis aims at developing new methods for identifying key biological processes driving plant development lying behind the observed genotypic variability. These works combine mechanistic ecophysiologic modeling highly nonlinear of plant development, statistical mixed effects modeling for genotypic variability and statistical testing procedures, in particular adapted to small data samples, to identify genotype-dependent parameters. These approaches will allow to better understand genotype by environment interactions and to identify new tools for varietal selection.

The second axis is dedicated to joint modeling of a time of interest such as flowering time or harvest time and of a phenotypic dynamical trait depending on time such as biomass or pest presence. The considered joint models combine survival models with random effects and covariates of high dimension and nonlinear mixed effects models for the dynamical trait. The objective is to identify the relevant covariates, to estimate the parameters and to predict the time of interest. These methods will allow for example to better predict flowering time or optimal harvest time.

The third axis aims at developing new methods for identifying among a high number of covariates those who are the most influent for a phenotypic trait of interest, solely or jointly with a time of interest. Nonlinear mixed effects models combining mechanistic models of plant development and genetic models integrating a high number of genetic covariates will be used to model genotypic variability of the trait of interest. New covariates selection methods adapted to the nonlinear context will be developed. These methods will allow to identify the main genetic factors influencing the trait.

Finally, the last axis aims at building new criteria for varietal selection, integrating randomness of environmental conditions and targeting simultaneously several objectives, such as maximizing yield and minimizing yield variability. New methodologies for optimizing these criteria will be developed. Such criteria will be new tools for decision support system in agriculture.