Résumé
The development of effective crop irrigation strategies is a key element of water management in agriculture, seeking a necessary parsimonious use of resources. Years of practice and accumulation of knowledge have led to numerous irrigation policies, including Threshold-Based Irrigation Strategies (TBIS). This work involves the Controlled Crop Irrigation (CCI) model, a simple dynamical system describing the order-1 biophysical processes at play, for which optimal irrigation policies may be proven and written as TBIS. We aim at studying both the optimal TBIS and their sub-optimal declensions, to identify the most influential parameters (which dictate the structure of optimal irrigation scenarios) but also the effects of errors made on their determination. Much attention is therefore drawn to the crucial "decision time" at which the irrigator decides to increase soil water content from an initial "stress tolerance level" to a "maximal biomass production" setpoint. In this study, we couple known results from optimal control theory and numerical methods for sensitivity analysis and global model exploration. The most striking results are that the optimal decision time is mostly driven by plant growth dynamics and the correct evaluation of stress effects on biomass production, as slightly too low soil moisture levels during the productive stage induce significant losses in final biomass. The genericity of the CCI model and the modularity of the methodology offer promising perspectives for various operational contexts.