Deciphering the mechanisms of adaptation of Vitis species to nutrient availability: coupling of experimentation and modelling approaches in the rhizosphere (UMR EFGV / UMR ISPA)
In agriculture, the management of the culture has a great impact on the evolution of the agro-ecosystem for example by the introduction of new species/genotypes, the application of various chemicals (such as herbicides, pesticides and fertilizers) and by the physical disruption of the soil. In European vineyards, American rootstock genotypes have been introduced to tolerate the aphid pest phylloxera and are chosen to adapt to the local environmental conditions. Because the global climate is predicted to change, growers need rootstock genotypes able to adapt to the new environmental conditions. For that, we need a good understanding of the processes by which different genotypes respond to changing environmental conditions and how these genotypes interact with their environment. In this project we will focus on the adaptation of different rootstock genotypes to nutrient availability (mainly N and P) and their interactions with the rhizosphere. It is well known that mineral nutrition of plants is accompanied by changes in the soil composition due to import of minerals and water, and export of organic compounds (such as organic acids, secondary metabolic compounds, protons, etc.). The extent of exchange between the plant and the soil depend on the capacity of the rootstocks to develop their root system and/or on the functioning of their root systems (nutrient uptake/utilization efficiency, root exudation, etc.). Therefore, we will perform multi-level phenotyping of plant responses to nutrient supply and their interactions with the soil environment from the level of gene expression in the roots to changes in the rhizosphere physiochemistry and its consequences on whole plant physiology. The relative importance of these adaptation strategies will be evaluated using a mechanistic model of plant-soil nutrient transfer and the mechanisms conferring the most resilience identified.