Speaker
Description
Introduction
Intercropping is a management practice inspired by the interaction between the biodiversity of an ecosystem and its resilience under stress. Intercropping is of strategic interest in the face of climatic and environmental issues, having already shown the capacity to bring beneficial outcomes on production and ecosystem services (for example Gardarin, A. et al, 2022). However its ability to regulate pests and diseases, and thus reduce pesticide use with limited yield loss, is less well characterised and generally assessed separately from other services. An integrated approach would allow the design and adaptation of these systems considering all of the services they provide.
Such an approach can be found in the modelling of plant-microbe interactions within a soil-climate system. To our knowledge, modelling work conducted so far on diseases within intercropping systems has been limited mostly to cultivar mixtures and functional processes focused on epidemiological dynamics. We have found limited works dealing with interspecific diversification, crop development and larger ecosystem processes, alongside the main drivers of an epidemic.
Based on previous modelling work (Caubel, J et al, 2017) on the coupling of a generic disease model (MILA) with a crop model (STICS), our aim is to adapt MILA from a single crop context to a bi-specific intercrop system, using STICS intercropping options (Vezy et al., 2023).
Proposed method
Our work rests on the following steps: a comprehensive review of the literature to create an inventory of the processes for fungal disease regulation in diversified canopies, and the short-listing of two modelling approaches to develop and test within the MILA-STICS framework.
STICS is an integrated, deterministic, process-based model which runs at a daily time-step using input variables related to climate and soil as well as the cropping system including its management practices. MILA simulates the dynamics of disease severity and the development of fungal pathogens depending on crop phenology, developmental stages and canopy microclimate. Thanks to STICS daily calculations of canopy characteristics (LAI, microclimate, etc), MILA can provide feedback on the progress of an epidemic throughout the simulation period.
The two alternatives to be tested will be chosen in order to compare two contrasting approaches: one resting on hypotheses which simplify the system, the other on more realistic hypotheses. Some leads may be found in Levionnois, S. et al (2023) and Calonnec, A et al (2012).
Expected results
The main fungal disease regulation processes at play in diversified canopies stem from the dilution and barrier effects on spore dispersal and interception as well as the effect of the modification in canopy microclimate on infection success. STICS already covers calculations of mixed canopy microclimate so adapting MILA resides only in the modification of the dispersal and interception modules by inserting the dilution and barrier effects.
Our testing procedure will involve studying the model behaviour in terms of coherence for the simulation of spore dispersal, and its sensitivity to canopy parameter modifications (e.g. height of the two species, leaf and/or plant density, interrow distance, etc).
The new version of the MILA-STICS model will simulate the growth dynamics of the crop and the development of the epidemic as a function of practices, soil and climate, considering the close interactions within the system comprising the host crop, the pathogenic fungus and the non-host crop. Built in a generic manner, the model will be used to search for crop management that maximise the regulation of the pathogen while maintaining the provision of multiple services in a context of global change. Studying the intercropping system as a whole allows to work on trade-offs between disease control and other benefits of diversifying cultures for a given system, to test various crop arrangements and management practices to optimise outcomes and to run long term simulations to include climate change.
References
Levionnois, S. et al (2023) Phytopathology 113(10): 1876-1889
Calonnec, A et al (2012). European Journal of Plant Pathology 135(3): 479-497.
Caubel, J et al (2017) European Journal of Agronomy 90: 53-66.
Gardarin, A. et al (2022). Agronomy for sustainable development 42(3).
Vezy, R. et al (2023). Agronomy for Sustainable Development, 43(5), 61.
| Keywords | agronomy; epidemiology; modelling; intercropping; plant-microbe interaction |
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