Simulation of timothy nutritive value: A comparison of three process-based models

Abstract

Different forage grass models are used to simulate forage yield and nutritive attributes, but these models are seldom compared, particularly those for timothy (Phleum pratense L.), a widely grown forage grass species in agricultural regions with a cold temperate climate. We compared the models BASGRA, CATIMO and STICS for their predictions of timothy crude protein (CP) concentration, neutral detergent fibre (NDF) concentration and NDF digestibility (dNDF), three important forage nutritive attributes. Data on CP and NDF concentrations, and dNDF and the associated weather and soil data for seven cultivars, taken from eight field experiments in Canada, Finland, Norway, and Sweden, were divided into calibration and validation datasets. Model parameters were estimated for each cultivar separately (cultivarspecific calibration) and for all cultivars together (generic calibration), using different methods for the three models. Normalized root mean square error (RMSE) in prediction of CP concentration varied between 16 and 26 % for BASGRA, 45 and 101 % for CATIMO and 27 and 45 % for STICS across the two calibration methods and the calibration and validation datasets. Normalised RMSE in prediction of NDF concentration varied between 8 and 13 % for BASGRA, 14 and 21 % for CATIMO and 8 and 12 % for STICS, while for dNDF it varied between 7 and 22 % for BASGRA, 7 and 38 % for CATIMO and 5 and 6 % for STICS. Cultivar-specific calibration improved the performance of CATIMO and STICS, but not BASGRA, compared with generic calibration. The prediction accuracy for NDF concentration and dNDF with the three models was within the same range or better than that for forage dry matter (DM) yield of timothy. Overall, the three models performed well in predicting some nutritive attributes and yield in Northern Europe and Canada, but improvements are required, particularly to increase the prediction accuracy of CP concentration.

The Use of Thermal Time to Describe and Predict the Growth and Nutritive Value of Lolium perenne L. and Bromus valdivianus Phil

“…In general, the CP concentration was well represented by the linear regressions, which reported a variation in the RMSE of 1.08 for L. perenne in the summer and up to 0.53 and 0.54 for L. perenne and B. valdivianus, respectively, in the winter. These values were similar to those reported by other authors [58,59], who developed models using their own logarithms and adjusted these to a nitrogen consumption curve and integrated environmental variables such as precipitation and soil fertility. Regardless of the model used to explain the variations in CP concentration, all arrived at the same conclusion; the protein concentration decreased as the plant's phenological age increased [60].…”

Adapting the Grassland Model Basgra to Simulate Yield and Nutritive Value of Whole-Crop Barley

“…In Bayesian calibration, a prior distribution is updated based on observed data. In the present study, the prior distributions for parameters that were retained from the forage grass version of the BASGRA model were set as equal to the distributions used in a previous calibration against field trial data from northern Europe and Canada (Persson et al, 2019). The prior distributions (Appendix Table 1) of the plant parameters that were added in the BASGRA model version for whole-crop barley were set based on a combination of literature information (Slafer et al, 2002;Seog et al, 1993) (see also Section 2.1 for more information) and results from preliminary calibrations.…”

“…The latter model has been developed primarily for northern European conditions. Its ability to simulate forage grass, including both biomass (Korhonen et al, 2018) and nutritive value attributes such as fibre and protein content and digestibility (Persson et al, 2019), has also been extensively evaluated for this region. Moreover, the BASGRA model includes modules for winter stresses and their effect on winter survival (Höglind et al, 2016), which are processes that are also relevant for winter cereals.…”

Use, calibration and verification of agroecological models for boreal environments: A review

“…The BASGRA_N model is an advancement on the earlier BASGRA grasslands model (Höglind et al, 2013(Höglind et al, , 2016(Höglind et al, , 2020; both models are designed for cold climate growth of timothy in Scandinavia. The model is process-based and uses the source/sink concept for net exports and imports of photosynthetic assimilates between photosynthetically developed leaf tissue and source tissue, developing plant parts and roots (Persson et al, 2019) on a daily time-step with temperature and day length as driving variables. The model is also designed to account for winter conditions such as snow cover and soil freezing which are common to boreal regions and typically lead to high rates of tiller death and reduced spring season growth.…”