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The agricultural sector is undergoing a triple crisis, not only social but also economical and ecological, which requires a change in the production model. For political institutions, the solution lies in a twin transition, using information and communication technologies (ICT) as a lever to make the sector more sustainable. However, there are many uncertainties regarding the consequences of such a strategy. In particular, from an environmental point of view, few studies assess the effects of large-scale deployment of digital agriculture technologies. Yet the harmful effects of ICT on the environment are well known, and an increase in digital equipment and infrastructure for agriculture could be a threat to the sector’s sustainability. This thesis introduces methods for assessing the environmental impacts of large-scale deployment of digital agricultural equipment and its dependence on infrastructure. The general framework adopts a prospective scenario-based approach and parametric and consequential modelling. A first contribution concerns the estimation of the impacts of digital technologies deployed on farms in a given agricultural context. This context is defined as a type of production and a distribution of farms of varying sizes. This method is applied to two case studies in mainland France: the identification and monitoring of oestrus in dairy cattle, and robots for mechanical weeding and automated sowing in large-scale cereal crops. The results reveal heterogeneous impacts, depending on the number of pieces of equipment deployed, their mass and their technological complexity. Scenarios involving the most advanced technologies and using a larger mass of equipment tend to have the highest impacts. A second contribution extends the case study dedicated to weeding robots and focuses on the impacts associated with their use of the mobile network. This method simulates the deployment of robots in a given area, based on a set of agricultural parcels and existing mobile network infrastructure, with each site supporting a maximum data volume. The scenarios considered vary according to the design of the robots, and the volumes of data sent by the robots differ greatly from one design to another. Based on a traffic simulation of data and its impact on the mobile network, we estimate the additional impact on existing infrastructure in terms of embodied carbon footprint of required network equipment, and that associated with their energy consumption. We also estimate the proportion of agricultural land that can be managed by the deployed robots. The results show that for intensive use of the network, the capacity of the current network greatly limits the number of robots that can be deployed, even when the capacity of existing sites is maximized. Furthermore, for the most intensive scenarios, the additional power consumption of the network is of the same order of magnitude as that of the robots themselves. Thus, for these scenarios, only a small fraction of the parcels can be managed without adding sites, and increasing this fraction would imply a significant increase in the overall footprint of the mobile network in question. This manuscript provides an initial overview of the consequential carbon footprint associated with the deployment of digital equipment on farms and their use of the mobile network.

Amphi LaBRI