In agroecosystems, crop yield is reduced by epidemics. At the field scale, epidemiology succeeded knowledge percolation across theory, empirical studies, and recommendations to actors. Achieving similar success at the landscape level requires understanding of ecosystems. The aim of this manuscript is to formalize cyclic epidemics, in which development depends on interaction in space and in time between host plants, pathogens, environment, and human actions. In agroecosystems, human actions exacerbate homogeneities alternating with sharp discontinuities on scales of time and space. The dynamics of cyclic epidemics takes discontinuities into account. This allows decomposing control at the field and at the landscape scales into goals to reach, corresponding to the components of the pluriannual dynamics of epidemics. Articulating disciplinary concepts open the prospect of optimization by identification of one’s potential contributions. Finally, we propose that cyclic epidemics could be controlled by looking for a local solution, in a decentralized manner. 1. Introduction: The Need to Formalize Epidemics in order to Control Them An ecosystem is defined as “a dynamic complex of plant, animal, and microorganism communities and their non-living environment interacting as a functional unit” [1]. Thus in the broadest sense, an “agroecosystem” includes all managed and unmanaged environments, domesticated and wild communities, including human communities [2]. In agroecosystems, plant populations are organised as crops, managed towards the goal of production. Epidemics reduce crop yield, and current disease control is questioned [3, 4]. This implies reconsidering disease control by making explicit the ecology of agroecosystems. Under the light of epidemiological knowledge, articulating the contributions of scientific disciplines is needed because the development of epidemics depends on the interaction in space and in time between host plants, pathogens, environment, and human actions [5]. At the field scale, epidemiology succeeded knowledge percolation across theory, empirical studies, and recommendations to actors. This success in rooted on a clear theoretical formalization, disease foci, and their growth in time and in space [6], that allowed modelling [7] prompted empirical studies [8, 9] and allowed links to other disciplines. The results of epidemiological studies on the modulation of quantitative growth of foci depending on the climate [10] on the differentiation of pathogens into pathotypes [11] and on quantitative resistance [12] have been translated into
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