DynACof: A process-based model to study growth, yield and ecosystem services of coffee agroforestry systems - INRAE - Institut national de recherche pour l’agriculture, l’alimentation et l’environnement Access content directly
Journal Articles Environmental Modelling and Software Year : 2020

DynACof: A process-based model to study growth, yield and ecosystem services of coffee agroforestry systems

Peter Lehner
  • Function : Author
Denis Loustau

Abstract

The DynACof model was designed to model coffee agroforestry systems and study the trade-offs to e.g. optimize the system facing climate changes. The model simulates net primary productivity (NPP), growth, yield, mortality, energy and water balance of coffee agroforestry systems according to shade tree species and management. Several plot-scale ecosystem services are simulated by the model, such as production, canopy cooling effect, or potential C sequestration. DynACof uses metamodels derived from a detailed 3D process-based model (MAESPA) to account for complex spatial effects, while running fast. It also includes a coffee flower bud and fruit cohort module to better distribute fruit carbon demand over the year, a key feature to obtain a realistic competition between sinks. The model was parameterized and evaluated using a highly comprehensive database on a coffee agroforestry experimental site in Costa Rica. The fluxes simulated by the model were close to the measurements over a 5-year period (nRMSE = 26.27 for gross primary productivity; 28.22 for actual evapo-transpiration, 53.91 for sensible heat flux and 15.26 for net radiation), and DynACof satisfactorily simulated the yield, NPP, mortality and carbon stock for each coffee organ type over a 35-year rotation.
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Dates and versions

hal-02488996 , version 1 (14-10-2021)

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Rémi Vezy, Guerric Le Maire, Mathias Christina, Selena Georgiou, Pablo Imbach, et al.. DynACof: A process-based model to study growth, yield and ecosystem services of coffee agroforestry systems. Environmental Modelling and Software, 2020, 124, pp.104609. ⟨10.1016/j.envsoft.2019.104609⟩. ⟨hal-02488996⟩
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