Aggregation of soil and climate input data can underestimate simulated biomass loss and nitrate leaching under climate change

Predicting areas of severe biomass loss and increased N leaching risk under climate change is critical for applying appropriate adaptation measures to support more sustainable agricultural systems. The frequency of annual severe biomass loss for winter wheat and its coincidence with an increase in N leaching in a temperate region in Germany was estimated including the error from using soil and climate input data at coarser spatial scales, using the soil-crop model CoupModel. We ran the model for a reference period (1980-2010) and used climate data predicted by four climate model(s) for the Representative Concentration Pathways (RCP) 2.6, 4.5 and 8.5. The annual median biomass estimations showed that for the period 2070-2100, under the RCP8.5 scenario, the entire region would suffer from severe biomass loss almost every year. Annual incidence of severe biomass loss and increased N leaching was predicted to increase from RCP4.5 to the 8.5 scenario. During 2070-2100 for RCP8.5, in more than half of the years an area of 95% of the region was projected to suffer from both severe biomass loss and increased N leaching. The SPEI3 predicted a range of 32 (P3 RCP4.5) to 55% (P3 RCP8.5) of the severe biomass loss episodes simulated in the climate change scenarios. The simulations predicted more severe biomass losses than by the SPEI index which indicates that soil water deficits are important in determining crop losses in future climate scenarios. There was a risk of overestimating the area where "no severe biomass loss + increased N leaching" occurred when using coarser aggregated input data. In contrast, underestimation of situations where "severe biomass loss + increased N leaching" occurred when using coarser aggregated input data. Larger annual differences in biomass estimations compared to the finest resolution of input data occurred when aggregating climate input data rather than soil data. The differences were even larger when aggregating both soil and climate input data. In half of the region, biomass could be erroneously estimated in a single year by more than 40% if using soil and climate coarser input data. The results suggest that a higher spatial resolution of especially climate input data would be needed to predict reliably annual estimates of severe biomass loss and N leaching under climate change scenarios

Aggregation of soil and climate input data can underestimate simulated biomass loss and nitrate leaching under climate change

Predicting areas of severe biomass loss and increased N leaching risk under climate change is critical for applying appropriate adaptation measures to support more sustainable agricultural systems. The frequency of annual severe biomass loss for winter wheat and its coincidence with an increase in N leaching in a temperate region in Germany was estimated including the error from using soil and climate input data at coarser spatial scales, using the soil-crop model CoupModel. We ran the model for a reference period (1980–2010) and used climate data predicted by four climate model(s) for the Representative Concentration Pathways (RCP) 2.6, 4.5 and 8.5. The annual median biomass estimations showed that for the period 2070–2100, under the RCP8.5 scenario, the entire region would suffer from severe biomass loss almost every year. Annual incidence of severe biomass loss and increased N leaching was predicted to increase from RCP4.5 to the 8.5 scenario. During 2070–2100 for RCP8.5, in more than half of the years an area of 95% of the region was projected to suffer from both severe biomass loss and increased N leaching. The SPEI3 predicted a range of 32 (P3 RCP4.5) to 55% (P3 RCP8.5) of the severe biomass loss episodes simulated in the climate change scenarios. The simulations predicted more severe biomass losses than by the SPEI index which indicates that soil water deficits are important in determining crop losses in future climate scenarios. There was a risk of overestimating the area where “no severe biomass loss + increased N leaching” occurred when using coarser aggregated input data. In contrast, underestimation of situations where “severe biomass loss + increased N leaching” occurred when using coarser aggregated input data. Larger annual differences in biomass estimations compared to the finest resolution of input data occurred when aggregating climate input data rather than soil data. The differences were even larger when aggregating both soil and climate input data. In half of the region, biomass could be erroneously estimated in a single year by more than 40% if using soil and climate coarser input data. The results suggest that a higher spatial resolution of especially climate input data would be needed to predict reliably annual estimates of severe biomass loss and N leaching under climate change scenarios.Peer reviewe

Comparison of wheat simulation models under climate change. I. Model calibration and sensitivity analyses.

A comparison of the performance of 5 wheat models was carried out for 2 sites in Europe with considerably different agroclimatic conditions Rothamsted, UK, and Seville, Spain. The models were calibrated against field data sets from both sites. For Rothamsted the measured time courses of crop growth, evapo-transpiration and nitrogen uptake were reproduced reasonably well by the different models, except for leaf area index. For Seville, the experimental data set was insufficient for such a detailed comparison and mainly simulated results were compared. The sensitivity of the model results to stepwise changes in individual weather variables was then determined. In the different model runs a temperature rise generally resulted in lower yields, an increase in precipitation and atmospheric CO2 concentration resulted in higher yields, and increased variability of weather variables often resulted in lower yields with increased yield variability

Comparison of wheat simulation models under climate change. II. Application of climate change scenarios.

A comparison of the performance of 5 wheat models (AFRCWHEAT2, CERES, NWHEAT, SIRIUS and SOILN) was carried out for 2 sites in Europe Rothamsted, UK, and Seville, Spain. The aims of this study were (1) to compare predictions of wheat models for climate change scenarios, and (2) to investigate the effects of changes in climatic variability in climate change scenarios on model predic-tions. Simulations were run for climate change scenarios derived from a number of 2 × CO2 equilibrium and transient GCM (global circulation model) experiments. For most climate change scenarios the model results were broadly similar. Where results differed, much of the difference could be explained by model sensitivity to climate and differences in initial conditions. Transient scenarios without changes in climatic variability usually resulted in large yield increases for Rothamsted and in nil to large yield increases for Seville. Incorporation of changed climatic variability in the transient scenario had a more profound effect on grain yield and resulted in a substantial decrease in mean yield with a strong increase in yield variation at Seville. This was associated with the changes in the duration of dry spells and a redistribution of precipitation over the vegetation period. The results show that future studies of the effect of climate change on crop yields must consider changes in climatic variability as well as changes in mean climate

Model inter-comparison on crop rotation effects ? an intermediate report

Data of diverse crop rotations from five locations across Europe were distributed to modelers to investigate the capability of models to handle complex crop rotations and management interactions

Nitrogen cycling in a Norway spruce plantation in Denmark - A SOILN model application including organic N uptake

A dynamic carbon (C) and nitrogen (N) circulation model, SOILN, was applied and tested on 7�years of control data and 3 years of manipulation data from an experiment involving monthly N addition in a Norway spruce (Picea abies, L. Karst) forest in Denmark. The model includes two pathways for N uptake: (1) as mineral N after mineralisation of organic N, or (2) directly from soil organic matter as amino acids proposed to mimic N uptake by mycorrhiza. The model was parameterised and applied to the data from the control plot both with and without the organic N uptake included. After calibration, the model�s performance was tested against data from the N-addition experiment by comparing model output with measurements. The model reproduced well the overall trends in C and N pools and the N concentrations in soil solutions in the top soil layers whereas discrepancies in soil-solution concentrations in the deeper soil layers are seen. In the control data, the needle-N concentration was well reproduced except for small underestimations in some years because of drought effects not included in the model. In the N-addition experiment, SOILN reproduces the observed changes; in particular, the changes in needle-N concentrations and the overall distribution within the ecosystem of the extra added 3.5 g N m�2 year�1 parallel the observations. When organic N uptake is included, the simulations indicate that in the control plot receiving c. 1.9 g N m�2 year�1, the organic N uptake in average supplies 35% of the total plant N uptake. By addition of an extra 35 kg N ha�1 year�1, the organic N uptake is reduced to 16% of the total N uptake. Generally, inclusion of the pathway for organic N uptake improves model performance compared with observations for both C and N. This is because mineral N uptake alone implies a larger mineralisation rate, leading to bigger concentrations of N in the soil and soil water, bigger N losses, and net loss of c. 100 kg C ha�1 year�1, thereby causing depletion of the organic soil layer