Process-based simulation of growth and overwintering of grassland using the BASGRA model

• Published in: Ecological modelling 2016-09, Vol.335, p.1-15
• Main Authors: Höglind, Mats, Van Oijen, Marcel, Cameron, David, Persson, Tomas
• Format: Article
• Language: English
• Subjects: agroclimatology; Calibration; climate change; Cold hardening; Computer simulation; data collection; Dynamics; farmers; forage grasses; Frost injury; frost resistance; Grasses; Grasslands; ice; latitude; leaf area index; Mathematical models; overwintering; Phleum pratense; Phleum pratense L; plant breeders; plant damage; Process-based modelling; regrowth; risk; roads; snow; soil; Soil (material); spring; Summer; sward; temperature; temporal variation; Winter survival; Yield
• ISSN: 0304-3800; 1872-7026
• DOI: 10.1016/j.ecolmodel.2016.04.024

Process-based models (PBM) for simulation of weather dependent grass growth can assist farmers and plant breeders in addressing the challenges of climate change by simulating alternative roads of adaptation. They can also provide management decision support under current conditions. A drawback of existing grass models is that they do not take into account the effect of winter stresses, limiting their use for full-year simulations in areas where winter survival is a key factor for yield security. Here, we present a novel full-year PBM for grassland named BASGRA. It was developed by combining the LINGRA grassland model (Van Oijen et al., 2005a) with models for cold hardening and soil physical winter processes. We present the model and show how it was parameterized for timothy (Phleum pratense L.), the most important forage grass in Scandinavia and parts of North America and Asia. Uniquely, BASGRA simulates the processes taking place in the sward during the transition from summer to winter, including growth cessation and gradual cold hardening, and functions for simulating plant injury due to low temperatures, snow and ice affecting regrowth in spring. For the calibration, we used detailed data from five different locations in Norway, covering a wide range of agroclimatic regions, day lengths (latitudes from 59° to 70°N) and soil conditions. The total dataset included 11 variables, notably above-ground dry matter, leaf area index, tiller density, content of C reserves, and frost tolerance. All data were used in the calibration. When BASGRA was run with the maximum a-posteriori (MAP) parameter vector from the single, Bayesian calibration, nearly all measured variables were simulated to an overall normalized root mean squared error (NRMSE)