Aggregation of activity data on crop management can induce large uncertainties in estimates of regional nitrogen budgets

AbstractA complete understanding of the nexus between productivity and sustainability of agricultural production systems calls for a comprehensive assessment of the nitrogen budget (NB). In our study, data from the well-monitored Danish Agricultural Watershed Monitoring Program (LOOP-program; 2013–2019) is used for a quantitative inter-comparison of three different approaches to drive the process-based model LandscapeDNDC on the regional scale. The aim is to assess how assumptions and simplifications about farm management activities at a regional scale induce previously unquantified uncertainties in the simulation of yields and the NB of cropping systems. Our findings reveal that the approach based on detailed field-level management data (A) performs the best in simulation of yield (r2 = 0.93). In contrast, the other two different data aggregation approaches (B: Sequential mono-cropping of six major crops with simulation results averaged according to proportional area, and C: simulation of 20 most frequent crop rotations) have lower correlations to the observed yields (r2 = 0.92 and 0.77, respectively) but are still statistically significant at p < 0.05 level. Notable differences arise between detailed and more aggregated crop system simulations concerning the NB, particularly concerning N losses to the environment. Compared to the detailed approach (A) (gaseous N fluxes: 24.3 kg-N ha−1 year−1; nitrate leaching: 14.7 kg-N ha−1 year−1), the aggregation approach B leads to a 31.4% over-estimation in total gaseous N fluxes (+7.6 kg-N ha−1 year−1), while nitrate leaching shows a similar average with a distinct pattern. Conversely, employing aggregation approach C leads to a 17.6% over-estimation in total gaseous fluxes (+4.3 kg-N ha−1 year−1) and a 204.9% over-estimation in nitrate leaching (+30.2 kg-N ha−1 year−1). These findings suggest that management representation should be chosen carefully because it can induce large uncertainties, especially when simulating large-scale NBs or assessing the environmental impact of cropping management. This may compromise the accuracy of national and international nutrient budgets, and preclude comparisons among different sources when the approaches for management representation differ.