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

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  • Höglind, Mats
  • Cameron, David
  • Persson, Tomas
  • Huang, Xiao
  • van Oijen, Marcel

Abstract

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.

Suggested Citation

  • 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, Elsevier, vol. 417(C).
    • Handle: RePEc:eee:ecomod:v:417:y:2020:i:c:s0304380019304338
    DOI: 10.1016/j.ecolmodel.2019.108925
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    References listed on IDEAS

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    1. Höglind, Mats & Van Oijen, Marcel & Cameron, David & Persson, Tomas, 2016. "Process-based simulation of growth and overwintering of grassland using the BASGRA model," Ecological Modelling, Elsevier, vol. 335(C), pages 1-15.
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    6. Hjelkrem, Anne-Grete Roer & Höglind, Mats & van Oijen, Marcel & Schellberg, Jürgen & Gaiser, Thomas & Ewert, Frank, 2017. "Sensitivity analysis and Bayesian calibration for testing robustness of the BASGRA model in different environments," Ecological Modelling, Elsevier, vol. 359(C), pages 80-91.
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    1. Hjelkrem, Anne-Grete Roer & Geipel, Jakob & Bakken, Anne Kjersti & Korsaeth, Audun, 2023. "NORNE, a process-based grass growth model accounting for within-field soil variation using remote sensing for in-season corrections," Ecological Modelling, Elsevier, vol. 483(C).
    2. Daniel Forster & Jia Deng & Matthew Tom Harrison & Narasinha Shurpali, 2022. "Simulating Soil-Plant-Climate Interactions and Greenhouse Gas Exchange in Boreal Grasslands Using the DNDC Model," Land, MDPI, vol. 11(11), pages 1-13, November.

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