ANR Peerless(2013-2016) : Viabilité d’une gestion écologique renforcée de la santé des plantes dans les paysages agricoles - Predictive Ecological Engineering for Landscape Ecosystem Services and Sustainability (coordination : Pierre Franck, INRA PSH)
Partenaires du projet : Plantes et Systèmes de culture Horticoles (PSH), UR1115, Avignon ;
Agronomie, UMR211, Grignon ; Institut de Génétique Environnement et Protection des Plantes (IGEPP), UMR1349, Rennes & Angers ; AgroEcologie (AE), UMR1347, Dijon ; Biostatistique & Processus Spatiaux (BioSP), UR 546, Avignon ; Economie Public (EP), UMR INRA-AgroParisTech, Grignon
With the shift towards a reduced reliance on external inputs in agriculture, identifying management options that enhance the provision of ecosystem services has become a critical issue. Pest control resulting from the activity of naturally present predators and parasitoids is frequently cited as an important service that could reduce pesticide use as targeted by the French 2018 Ecophyto governmental action. However, the link between management options, pest control level and ultimately crop yield is poorly understood. The PEERLESS project aims to identify alternative management strategies that enhance the crop protection service provided by functional biodiversity and ultimately to optimize agricultural systems, at local and landscape scales, for economic viability and sustainability. PEERLESS brings together six partners organisations with extensive expertise in agronomy, spatial ecology, ecology of interactions and public economy. The project combines: (i) an empirical assessment of naturally occurring crop protection from weed and insects pests in annual (Wheat/OilSeed Rape [W/OSR] rotations, ) and perennial (apple orchards) systems across a broad range of landscape and agronomic situations; (ii) ecological engineering with an assessment of alternative plant protection system to improve crop protection at the local scale; (iii) an in-depth study of the structure of trophic networks; and, (iv) population dynamics of key pests and their regulators in case study areas. These components will support the parametrisation of spatially-explicit, predictive models to (v) test the effect of landscape patterns of alternative local and landscape management strategies on pesticide use, pest control, crop yield and farmer income and (vi) identify landscape scale viable management strategies to control insect and weed pests.