BASGRA_N: a model for grassland productivity, quality and greenhouse gas balance

Höglind, Mats; Cameron, David; Persson, Tomas; Huang, Xiao; Van Oijen, Marcel. 2020 BASGRA_N: a model for grassland productivity, quality and greenhouse gas balance. Ecological Modelling, 417, 108925. 13, pp. https://doi.org/10.1016/j.ecolmodel.2019.108925

Before downloading, please read NORA policies.

Preview

Text
N526810PP.pdf - Accepted Version
Download (590kB) | Preview

Official URL: http://dx.doi.org/10.1016/j.ecolmodel.2019.108925

Abstract/Summary

The main objective of this paper is to present the new model BASGRA_N, to show how it was parameterized for grass swards in Scandinavia, and to evaluate its performance in predicting above-ground biomass, crude protein, cell wall content and dry matter digestibility. The model was developed to allow simulation of: (1) the impact of N-supply on the plants and their environment, (2) the dynamics of greenhouse gas emissions from grasslands, (3) the dynamics of cell-wall content and digestibility of leaves and stems, which could not be simulated with its predecessor, the BASGRA-model. To calibrate and test the model, we used field experimental data. One dataset included observations of biomass (DM) and crude protein content (CP) under different N fertilizer regimes from five sites in central and southern Sweden. The other dataset included observations of DM, and sward components as well as CP, cell wall content (NDF) and DM digestibility as affected by harvesting regime from one site in southwestern Norway. The total number of experiments was nine, of which three were used for model testing. When BASGRA_N was run with the maximum a-posteriori (MAP) parameter vector from the Bayesian calibration for the Swedish test sites, DM and CP were both simulated to an overall Pearson correlation coefficient (Rb) of minimum 0.58, Willmott's index of agreement (d) of minimum 0.69 and normalized root mean squared error (NRMSE) of maximum 0.30. Corresponding metrics for Norwegian test sites were 0.93, 0.96 and 0.27 for DM and >0.73, >0.61, <0.18 for DM digestibility, NDF and CP content, respectively. We conclude that BASGRA_N can be used to simulate yield and CP responses to N with satisfactory precision, while maintaining key features from its predecessor. The results also suggest that DM digestibility and NDF can be simulated satisfactorily, which is supported by results from a recent model comparison study. Further testing of the model is needed for a few variables for which we currently do not have enough data, notably leaching and emission of N-containing compounds. Further work will include application of the model to investigate greenhouse gas mitigation options, and evaluation against independent data for the conditions for which it will be applied.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1016/j.ecolmodel.2019.108925
UKCEH and CEH Sections/Science Areas:	Atmospheric Chemistry and Effects (Science Area 2017-)
ISSN:	0304-3800
Additional Keywords:	crude protein, digestibility, NDF, N2O, Phleum pratense L., process-based modelling
NORA Subject Terms:	Ecology and Environment
Date made live:	11 Feb 2020 11:54 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/526810

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1016/j.ecolmodel.2019.108925

UKCEH and CEH Sections/Science Areas:

Atmospheric Chemistry and Effects (Science Area 2017-)

ISSN:

0304-3800

Additional Keywords:

crude protein, digestibility, NDF, N2O, Phleum pratense L., process-based modelling

NORA Subject Terms:

Ecology and Environment