Optimizing the MoSt GG model a sensitivity-driven calibration for better grass growth forecasting

Abstract

Grasslands offer an efficient and eco-friendly way to produce high-quality feed for ruminants, benefiting both livestock production and human nutrition. However, its high sensitivity to its environment makes its management challenging for farmers. Predicting week ahead grass growth results in better-informed decision making on farms. The Moorepark St Gilles Grass Growth Model (MoSt GG) has been used since 2018 to predict weekly grass growth on grassland farms across Ireland with 84 farms involved in 2023. The repeated use of the model on these farms has identified a need to improve its accuracy, which has been addressed in this study. First, a sensitivity analysis using the Morris method was conducted to identify the parameters that have the most influence on the model’s grass growth output, both on an annual and monthly time step. From that analysis, ten parameters were selected, all of which related either to temperatures, day length or nitrogen demand and availability for the grass. These ten parameters were calibrated using a semi-automatic iterative method of calibration on a dataset of 14 commercial farms containing four years of grass measurements. Nine iterations were necessary to calibrate the model resulting in a reduction of MAPE from 30.0% to 19.8% in its final calibrated version, and notably increasing the final R² from 0.58 to 0.71. Finally, the model was evaluated over a new dataset of ten commercial farms for four years. The evaluation confirmed the improvement of the model with a final MAPE of 19.1% and a R² of 0.67 compared to 30.1% and 0.57 respectively before the calibration. The calibration process of the MoSt GG model has significantly improved the model accuracy to predict on farm grass growth. This improvement is expected to be particularly valuable for farmers in their decision making process, providing them with more reliable on farm grass growth predictions.