Audun Korsæth
Director
Abstract
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 significantly. At first cut, the root mean square error (RMSE) of dry matter yield prediction was reduced by 20% to a mean value of 58 g m−2, corresponding to a relative value (rRMSE) of 0.12. For the second cut, the RMSE decreased by 13% to 66 g m−2 (rRMSE: 0.18). The model was also evaluated in terms of the predictions of amounts of nitrogen in the harvested crop. Here, the calibration reduced the RMSE of the first cut by 38%, obtaining a mean RMSE value of 2.1 g N m−2 (rRMSE: 0.28). For the second cut, the RMSE reduction for simulated harvested N was 16%, corresponding to a mean RMSE value of 2.3 g N m−2 (rRMSE: 0.33). The large improvements in model accuracy for simulated dry matter and nitrogen yields obtained through calibration by utilizing remotely sensed information, indicate the importance of considering spatial variability when applying models under Nordic conditions, both for yield predictions and for decision support for nitrogen application.
Abstract
Soil organic carbon (SOC) was studied at 0–45 cm depth after 28 years of cropping with arable and mixed dairy rotations on a soil with an initial SOC level of 2.6% at 0–30 cm. Measurements included both carbon concentration (SOC%) and soil bulk density (BD). Gross C input was calculated from yields. Averaged over all systems, topsoil SOC% declined significantly (−0.20% at 0–15 cm, p = 0.04, −0.39% at 15–30 cm, p = 0.05), but changed little at 30–45 cm (+0.11%, p = 0.15). Declines in topsoil SOC% tended to be greater in arable systems than in mixed dairy systems. Changes in BD were negatively related to those in SOC%, emphasizing the need to measure both when assessing SOC stocks. The overall SOC mass at 0–45 cm declined significantly from 98 to 89 Mg ha−1, representing a loss of 0.3% yr−1 of the initial SOC. Variability within systems was high, but arable cropping showed tendencies of high SOC losses, whilst SOC stocks appeared to be little changed in conventional mixed dairy with 50% ley and organic mixed dairy with 75% ley. The changes were related to the level of C input. Mean C input was 22% higher in mixed dairy than in arable systems.
Abstract
No abstract has been registered