NORNE, a process-based grass growth model accounting for within-field soil variation using remote sensing for in-season corrections

• Published in: Ecological modelling 2023-09, Vol.483, p.110433, Article 110433
• Main Authors: Hjelkrem, Anne-Grete Roer, Geipel, Jakob, Bakken, Anne Kjersti, Korsaeth, Audun
• Format: Article
• Language: English
• Subjects: Calibration; Drone; Dynamic model; Precision nitrogen fertilization; Yield
• ISSN: 0304-3800; 1872-7026
• DOI: 10.1016/j.ecolmodel.2023.110433

•Development of the process-based model, the NORNE model, to predict grass growth and amount of nitrogen in yield n a daily basis for a node network within the field.•To further improve prediction accuracy, the system allows for in-season and node-wise model calibration, using predictions of dry matter biomass from drone-borne remote sensing.•The NORNE-model was shown to simulated crop dry matter yield and amount of nitrogen in yield with promising accuracy for two locations in Norway.•The inclusion of in-season nodewise calibration of the parameters soil nitrogen content (i.e. mineral and organic nitrogen) at initiation of growth and soil field capacity improved the model performance significantly. A process-based model was developed to predict dry matter yields and amounts of harvested nitrogen in conventionally cropped grassland fields, accounting for within-field variation by a node network design and utilizing remotely sensed information from a drone-borne system for increased accuracy. The model, named NORNE, was kept as simple as possible regarding required input variables, but with sufficient complexity to handle central processes and minimize prediction errors. The inputs comprised weather data, soil information, management data related to fertilization, and a visual estimate of clover proportion in the aboveground biomass. A sensitivity analysis was included to apportioning variation in dry matter yield outputs to variation in model parameter settings. Using default parameter values from the literature, the model was evaluated on data from a two-year study (2016–2017, 264 research plots in total each year) conducted at two locations in Norway (i.e. in South-East and in Central Norway) with contrasting climatic conditions and with internal variation in soil characteristics. The results showed that the model could estimate dry matter yields with a relatively high accuracy without any corrections based on remote sensing, compared with published results from comparable model studies. To further improve the results, the model was calibrated shortly before harvest, using predictions of above ground dry matter biomass obtained from a drone-borne remote sensing system. The only parameters which were hereby adjusted in the NORNE model were the starting values of nitrogen content in soil (first cut) and the plant available water capacity (second cut). The calibration based on the remotely sensed information improved the predictive performance of the model