ORCHIDEE‐STICS, a process‐based model of sugarcane biomass production: calibration of model parameters governing phenology. Issue 5 (9th May 2013)

• Record Type: Journal Article
• Authors: Valade, Aude; Vuichard, Nicolas; Ciais, Philippe; Ruget, Françoise; Viovy, Nicolas; Gabrielle, Benoît; Huth, Neil; Martiné, Jean‐François
• Is Part Of: Global change biology. Volume 6:Issue 5(2014)
• Page Start: 606
• Page End: 620
• Publication Date: 2013-05-09
• Subjects: Biomass energy -- Periodicals; Biomass energy -- Environmental aspects -- Periodicals; Energy crops -- Periodicals; 662.88
• DOI: 10.1111/gcbb.12074
• Languages: English
• ISSNs: 1757-1693

Abstract

<abstract abstract-type="main" id="gcbb12074-abs-0001"> <title>Abstract</title> <p>Agro‐Land Surface Models (agro‐LSM) combine detailed crop models and large‐scale vegetation models (DGVMs) to model the spatial and temporal distribution of energy, water, and carbon fluxes within the soil–vegetation–atmosphere continuum worldwide. In this study, we identify and optimize parameters controlling leaf area index (LAI) in the agro‐LSM ORCHIDEE‐STICS developed for sugarcane. Using the Morris method to identify the key parameters impacting LAI, at eight different sugarcane field trial sites, in Australia and La Reunion island, we determined that the three most important parameters for simulating LAI are (i) the maximum predefined rate of LAI increase during the early crop development phase, a parameter that defines a plant density threshold below which individual plants do not compete for growing their LAI, and a parameter defining a threshold for nitrogen stress on LAI. A multisite calibration of these three parameters is performed using three different scoring functions. The impact of the choice of a particular scoring function on the optimized parameter values is investigated by testing scoring functions defined from the model‐data RMSE, the figure of merit and a Bayesian quadratic model‐data misfit function. The robustness of the calibration is evaluated for each of the three scoring functions with a systematic cross‐validation method to find the most satisfactory one. Our results show that the figure of merit scoring function is the most robust metric for establishing the best parameter values controlling the LAI. The multisite average figure of merit scoring function is improved from 67% of agreement to 79%. The residual error in LAI simulation after the calibration is discussed.</p> </abstract>