Adequacy of nitrogen-based indicators for assessment of cropping system performance: A modelling study of Danish scenarios.

The EU nitrogen expert panel (EUNEP) has proposed nitrogen-based indicators for farm productivity (N output), efficiency (NUE) and environmental emissions (N surplus). This model-based study (using the Daisy model) was carried out, i) to study the effects of soil type, soil organic matter (SOM), cropping pre-histories varying in C input, 3-to-4 manure-to-mineral N proportions and ten crop rotations on the N-based indicators, and ii) to evaluate the adequacy of these indicators by establishing quantitative relationships between N surplus, N loss and soil organic N (SON) stock change. The results, averaged over 24-year simulation period, indicated that grass-clover dominant rotations had highest N output and showed a tendency to increase SON stocks when compared with spring-cereal monocultures. For most rotations, the NUE ranged between 70 and 75 %. The SON stocks were mainly influenced by initial SOM and cropping prehistory, and stocks increased only under low initial SOM and low C input cropping pre-history (spring barley). Overall, SON stocks tended to increase under low C input pre-history, coarse sand, low initial SOM and high manure N, however, this combination did not result in highest productivity, NUE, and lowest N losses. The relations between N surplus, N loss and SON stock change were strongly affected by crop rotations, emphasizing that using N surplus as an indicator for N leaching/losses while ignoring changes in SON stocks may result in biased conclusions, e.g. estimated average error for N losses ranged from -45 % (underestimation) for maize monoculture to +50 % (overestimation) for continuous grass-clover ley. The results also imply that the environmental assessment of cropping systems must be improved by combining above indicators with estimation of N loss and SON stock changes. This study provides a detailed account of N balance components/N indicators for diverse crop rotations and their use according to the recommendations of the EUNEP.

Scenario analysis using the Daisy model to assess and mitigate nitrate leaching from complex agro-environmental settings in Denmark.

Nitrate (N) leaching from intensively managed cropping systems is of environmental concern and it varies at local scale. To evaluate the performance of agricultural practices at this scale, there is a need to develop comprehensive assessments of N leaching and the N leaching reduction potential of mitigation measures. A model-based analysis was performed to (i) estimate N leaching from Danish cropping systems, representing 20 crop rotations, 3 soil types, 2 climates and 3-4 levels of manure (slurry)-to-fertilizer ratios, but with same available N (according to regulatory N fertilization norms), and (ii) appraise mitigation potential of on-farm measures (i.e. catch crops, early sowing of winter cereals) to reduce N leaching. The analysis was performed using a process-based agro-environmental model (Daisy). Simulated average N leaching over 24 years ranged from 16 to 85 kg N/ha/y for different crop rotations. Rotations with a higher proportion of spring crops were more prone to leaching than rotations having a higher proportion of winter cereals and semi-perennial grass-clover leys. N leaching decreased with increasing soil clay content under all conditions. The effect of two climates (different regions, mainly differing in precipitation) on N leaching was generally similar, with slight variation across rotations. Supplying a part of the available N as manure-N resulted in similar N leaching as mineral fertilizer N alone during the simulation period. Among the mitigation measures, both undersown and autumn sown catch crops were effective. Effectiveness of measures also depended on their place and frequency of occurrence in a rotation. Adopting catch crops during the most leaching-prone years and with higher frequency were effective choices. This analysis provided essential data-driven knowledge on N leaching risk, and potential of leaching reduction options. These results can serve as a supplementary guiding-tool for farmers to plan management practices, and for legislators to design farm-specific regulatory measures.

Modeling Soil Carbon Dynamics in Northern Forests: Effects of Spatial and Temporal Aggregation of Climatic Input Data.

Boreal forests contain 30% of the global forest carbon with the majority residing in soils. While challenging to quantify, soil carbon changes comprise a significant, and potentially increasing, part of the terrestrial carbon cycle. Thus, their estimation is important when designing forest-based climate change mitigation strategies and soil carbon change estimates are required for the reporting of greenhouse gas emissions. Organic matter decomposition varies with climate in complex nonlinear ways, rendering data aggregation nontrivial. Here, we explored the effects of temporal and spatial aggregation of climatic and litter input data on regional estimates of soil organic carbon stocks and changes for upland forests. We used the soil carbon and decomposition model Yasso07 with input from the Norwegian National Forest Inventory (11275 plots, 1960-2012). Estimates were produced at three spatial and three temporal scales. Results showed that a national level average soil carbon stock estimate varied by 10% depending on the applied spatial and temporal scale of aggregation. Higher stocks were found when applying plot-level input compared to country-level input and when long-term climate was used as compared to annual or 5-year mean values. A national level estimate for soil carbon change was similar across spatial scales, but was considerably (60-70%) lower when applying annual or 5-year mean climate compared to long-term mean climate reflecting the recent climatic changes in Norway. This was particularly evident for the forest-dominated districts in the southeastern and central parts of Norway and in the far north. We concluded that the sensitivity of model estimates to spatial aggregation will depend on the region of interest. Further, that using long-term climate averages during periods with strong climatic trends results in large differences in soil carbon estimates. The largest differences in this study were observed in central and northern regions with strongly increasing temperatures.