Evaluation of the LINGRA timothy model under Nordic conditions

Abstract

Simulation models are frequently applied to determine the production potential of forage grasses under various scenarios, including climate change. Thorough calibrations and evaluations of forage grass models can help improve their applicability. This study evaluated the ability of the Light Interception and Utilization Simulator-GRAss (LINGRA) model to predict biomass yield of timothy (Phleum pratense L. cv. Grindstad) in the Nordic countries. Variety trial data for the first and second year after establishment were obtained for seven locations: Jokioinen, Finland (60°48′ N; 23°29′ E), Maaninka, Finland (63°09′ N; 27°18′ E), Korpa, Iceland (64°09′ N; 21°45′ W), Særheim, Norway (58°41′ N; 5°39′ E), Lillerud, Sweden (59°24′ N; 13°16′ E), Östersund, Sweden (63°15′ N; 14°34′ E) and Umeå, Sweden (63°49′ N; 20°13′ E) from 1992 to 2012. Two calibrations of the LINGRA model were carried out using Bayesian techniques. In the first of these (Særheim calibration), data on biomass yield and underlying variables obtained from independent field trials at Særheim were used. In the second (Nordic calibration), biomass data from the other locations were used as well. The model was validated against the remaining set of biomass yields from all locations not included in the Nordic calibration. The observed total seasonal yield the first and second year after establishment was 913 and 991gDMm−2 respectively on average across the locations. The corresponding average simulated yield after the Særheim calibration was 1044 (root mean square error (RMSE) 258) and 1112gDMm−2 (RMSE 312), respectively. After the Nordic calibration, the simulated average total seasonal yield was 863 (RMSE 242) the first year and 927gDMm−2 (RMSE 271) the second year after establishment. The differences between the observed and simulated first cut yield followed the same patterns, whereas the prediction accuracy for second cut yield did not differ substantially between the calibration approaches. Using the parameter set from the Nordic region decreased the model predictability at Særheim compared with only using model parameters derived from this location. These results show that using biomass data from several locations, instead of only one specific location, in the calibration of the LINGRA model improved the overall prediction accuracy of first cut dry matter yield and total seasonal dry matter yield across an environmentally heterogeneous region. To further analyse the usefulness of including multi-site data in forage grass model calibrations, other forage grass models could be evaluated against the same dataset.