Effects of input data aggregation on simulated crop yields in temperate and Mediterranean climates

The modelling exercise for this study was highly supported by partner universities and research institutes in the framework of the MACSUR project and financially supported by the German Federal Ministry of Education and Research BMBF (FKZ 2815ERA01J) in the framework of the funding measure “Soil as a Sustainable Resource for the Bioeconomy – BonaRes”, project “BonaRes (Module B): BonaRes Centre for Soil Research (FKZ BOMA03037514, 031B0026A and 031A608A) and by the Ministry of Agriculture and Food (BMEL) in the framework of the MACSUR project (FKZ 2815ERA01J). In addition, the relevant co-authors from the partner institutes are separately financed by their respective projects. AV, EC, and EL were supported by The Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (220-2007-1218) and by the strategic funding ‘Soil-Water-Landscape’ from the faculty of Natural Resources and Agricultural Sciences (Swedish University of Agricultural Sciences). JC thank the INRA ACCAF metaprogramm for funding. KCK, CN, XS and TS were supported by MACSUR2 (FKZ 031B0039C). MK thanks for the funding by the UK BBSRC (BB/N004922/1) and the MAXWELL HPC team of the University of Aberdeen for providing equipment and support for the DailyDayCent simulations. FE acknowledges support by the German Science Foundation (project EW 119/5-1). GRM, TG, and FE thank Andreas Enders and Gunther Krauss (INRES, University of Bonn) for support. The authors also would like to acknowledge the support provided by the BMBF and the valuable comments of the scientists of the Institut für Nutzpflanzenwissenschaften und Ressourcenschutz (INRES), University of Bonn, Germany.Peer reviewedPostprin

Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations

This work was financially supported by the German Federal Ministry of Food and Agriculture (BMEL) through the Federal Office for Agriculture and Food (BLE), (2851ERA01J). FT and RPR were supported by FACCE MACSUR (3200009600) through the Finnish Ministry of Agriculture and Forestry (MMM). EC, HE and EL were supported by The Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (220-2007-1218) and by the strategic funding ‘Soil-Water-Landscape’ from the faculty of Natural Resources and Agricultural Sciences (Swedish University of Agricultural Sciences) and thank professor P-E Jansson (Royal Institute of Technology, Stockholm) for support. JC, HR and DW thank the INRA ACCAF metaprogramm for funding and Eric Casellas from UR MIAT INRA for support. CB was funded by the Helmholtz project “REKLIM—Regional Climate Change”. CK was funded by the HGF Alliance “Remote Sensing and Earth System Dynamics” (EDA). FH was funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) under the Grant FOR1695. FE and SS acknowledge support by the German Science Foundation (project EW 119/5-1). HH, GZ, SS, TG and FE thank Andreas Enders and Gunther Krauss (INRES, University of Bonn) for support. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

Impact analysis of climate data aggregation at different spatial scales on simulated net primary productivity for croplands

For spatial crop and agro-systems modelling, there is often a discrepancy between the scale of measured driving data and the target resolution. Spatial data aggregation is often necessary, which can introduce additional uncertainty into the simulation results. Previous studies have shown that climate data aggregation has little effect on simulation of phenological stages, but effects on net primary production (NPP) might still be expected through changing the length of the growing season and the period of grain filling. This study investigates the impact of spatial climate data aggregation on NPP simulation results, applying eleven different models for the same study region (∼34,000 km2), situated in Western Germany. To isolate effects of climate, soil data and management were assumed to be constant over the entire study area and over the entire study period of 29 years. Two crops, winter wheat and silage maize, were tested as monocultures. Compared to the impact of climate data aggregation on yield, the effect on NPP is in a similar range, but is slightly lower, with only small impacts on averages over the entire simulation period and study region. Maximum differences between the five scales in the range of 1–100 km grid cells show changes of 0.4–7.8% and 0.0–4.8% for wheat and maize, respectively, whereas the simulated potential NPP averages of the models show a wide range (1.9–4.2 g C m−2 d−1 and 2.7–6.1 g C m−2 d−1 for wheat and maize, respectively). The impact of the spatial aggregation was also tested for shorter time periods, to see if impacts over shorter periods attenuate over longer periods. The results show larger impacts for single years (up to 9.4% for wheat and up to 13.6% for maize). An analysis of extreme weather conditions shows an aggregation effect in vulnerability up to 12.8% and 15.5% between the different resolutions for wheat and maize, respectively. Simulations of NPP averages over larger areas (e.g. regional scale) and longer time periods (several years) are relatively insensitive to climate data aggregation. However, the scale of climate data is more relevant for impacts on annual averages of NPP or if the period is strongly affected or dominated by drought stress. There should be an awareness of the greater uncertainty for the NPP values in these situations if data are not available at high resolution. On the other hand, the results suggest that there is no need to simulate at high resolution for long term regional NPP averages based on the simplified assumptions (soil and management constant in time and space) used in this study

The legacy of one hundred years of climate change for organic carbon stocks in global agricultural topsoils

Abstract Soil organic carbon (SOC) of agricultural soils is observed to decline in many parts of the world. Understanding the reasons behind such losses is important for SOC accounting and formulating climate mitigation strategies. Disentangling the impact of last century’s climate change from effects of preceding land use, management changes and erosion is difficult and most likely impossible to address in observations outside of warming experiments. However, the record of last century’s climate change is available for every part of the globe, so the potential effect of climate change on SOC stocks can be modelled. In this study, an established and validated FAO framework was used to model global agricultural topsoil (0–30 cm) SOC stock dynamics from 1919 to 2018 as attributable to climate change. On average, global agricultural topsoils could have lost 2.5 ± 2.3 Mg C ha−1 (3.9 ± 5.4%) with constant net primary production (NPP) or 1.6 ± 3.4 Mg C ha−1 (2.5 ± 5.5%) when NPP was considered to be modified by temperature and precipitation. Regional variability could be explained by the complex patterns of changes in temperature and moisture, as well as initial SOC stocks. However, small average SOC losses have been an intrinsic and persistent feature of climate change in all climatic zones. This needs to be taken into consideration in reporting or accounting frameworks and halted in order to mitigate climate change and secure soil health

Comparison of measured and modelled soil organic carbon for a northern European long-term experiment site

Soil organic carbon is a key variable with regard to soil fertility influencing yield and yield security of agricultural crop production by regulating water budget and nutrient cycling. Those services might become even more relevant with respect to climate change. The sensitivity of crop yields on soil organic carbon content is influenced by site-specific conditions. To assess future vulnerability of yield security with respect to soil organic carbon contents in European croplands soil-crop models must consider the interaction of SOC and crop growth. Long term experiments that include treatments which lead to variable soil organic carbon contents can provide information on those relationships. Because the effect of soil fertility functions supported by SOC depends on a range of natural and anthropogenic factors we used various long term experiments in Sweden and Germany to evaluate the model CENTURY4.6. Thereafter we examined the impact of SOC on crop yields on site level by scenario runs modifying initial SOC levels and weather conditions. Preliminary results show differences in the modeled and observed soil organic carbon values for a range of observed long term experiments. The difference between modelled and measured of SOC stocks is up to 30% after 56 years. Overall, The use of the default values and setting were not appropriate to derive acceptable results, so the adjustment of some model parameter are required

Evaluation of denitrification and decomposition from three biogeochemical models using laboratory measurements of N-2, N2O and CO2

Biogeochemical models are essential for the prediction and management of nitrogen (N) cycling in agroecosystems, but the accuracy of the denitrification and decomposition sub-modules is critical. Current models were developed before suitable soil N-2 flux data were available, which may have led to inaccuracies in how denitrification was described. New measurement techniques, using gas chromatography and isotope-ratio mass spectrometry (IRMS), have enabled the collection of more robust N-2, N2O and CO2 data. We incubated two arable soils - a silt-loam and a sand soil - for 34 and 58 d, respectively, with small field-relevant changes made to control factors during this period. For the silt-loam soil, seven treatments varying in moisture, bulk density and NO3- contents were included, with temperature changing during the incubation. The sandy soil was incubated with and without incorporation of litter (ryegrass), with temperature, water content and NO3- content changing during the incubation. The denitrification and decomposition sub-modules of DeNi, Coup and DNDC were tested using the data. No systematic calibration of the model parameters was conducted since our intention was to evaluate the general model structure or "default" model runs. Measured fluxes generally responded as expected to control factors. We assessed the direction of modeled responses to control factors using three categories: no response, a response in the same direction as measurements or a response in the opposite direction to measurements. DNDC responses were 14 %, 52% and 34 %, respectively. Coup responses were 47 %, 19% and 34 %, respectively. DeNi responses were 0 %, 67% and 33 %, respectively. The magnitudes of the modeled fluxes were underestimated by Coup and DNDC and overestimated by DeNi for the sandy soil, while there was no general trend for the silt-loam soil. None of the models was able to determine litter-induced decomposition correctly. To conclude, the currently used sub-modules are not able to consistently simulate the denitrification and decomposition processes. For better model evaluation and development, we need to design better experiments, take more frequent measurements, use new or updated measurement techniques, address model complexity, add missing processes to the models, calibrate denitrifier microbial dynamics, and evaluate the anaerobic soil volume concept

Estimation of N2O Fluxes at the Regional Scale: Data, Models, Challenges

Empirical and process-based models simulating N2O fluxes from agricultural soils have the advantage that they can be applied at the scale at which mitigation measures can be designed and implemented. We compared bottom-up results from studies providing N2O fluxes at a regional/country or continental scale with estimates from the process-based model DNDC-EUROPE and from the TM5-4DVAR inverse modeling system. While the agreement between different bottom-up models is generally satisfying, only in a few cases a thorough validation of the result was done. Complex empirical or process-based models do not appear to have a better agreement with inverse model results in estimating N2O emissions from agricultural soils for countries or countrygroups than simple ones. Both bottom-up and inverse models are limited by the density and quality of observations. Research needs to focus on developing tools that inherit the advantages of both methods.JRC.H.4-Monitoring Agricultural Resource

Analysing data aggregation effects on large-scale yield simulations

Analysing data aggregation effects on large-scale yield simulations. iCROPM 2016 International Crop Modelling Symposium "Crop Modelling for Agriculture and Food Security under Global Change

Increased microbial anabolism contributes to soil carbon sequestration by mineral fertilization in temperate grasslands

Ecosystem responses to nitrogen (N) additions are manifold and complex, and also affect the carbon (C) cycle. It has been suggested that increased microbial carbon use efficiency (CUE), i.e. growth per C uptake, due to higher N availability potentially increases the stabilization rates of organic inputs to the soil. However, evidence for a direct link between altered microbial anabolism and soil organic C (SOC) stocks is lacking. In this study, unfertilized (control) and NPK-fertilized (NPK) treatments of seven temperate grassland experiments were used to test the hypothesis that fertilizer-induced differences in SOC stocks (ΔSOC) cannot be explained by differences in C input alone, but that microbial anabolism plays an important role in C sequestration. At two experimental sites, microbial CUE and related metabolic parameters was determined using an 18O labeling approach at two different incubation temperatures (10 °C and 20 °C). Fertilization effects on the abundance of Bacteria, Archaea and Fungi were also determined using quantitative PCR targeting the respective rRNA genes. Due to the availability of yield and belowground biomass data, the introductory carbon balance model (ICBM) could be used for all seven sites to estimate the contribution of C input to ΔSOC. A significantly higher microbial growth (+102 ± 6%), lower specific respiration (−16 ± 7%) and thus significantly higher CUE (+53 ± 21%) was found for the NPK treatments, which was consistent across experiments and incubation temperatures and correlated with measured root C:N ratios. Growth (+49 ± 5%) and respiration (+70 ± 9%) were increased by a higher incubation temperature, but this was not the case for CUE. The fungi to bacteria ratio changed significantly from 0.18 ± 0.02 (control) to 0.09 ± 0.02 (NPK). On average, only 77% (51% when excluding one extreme site) of observed ΔSOC was explained by C inputs. The optimized humification coefficient h of the model used to fit the observed ΔSOC was strongly correlated to differences in the root C:N ratio between the control and NPK treatments (R2 = 0.71), thus confirming a link between microbial anabolism and substrate C:N ratio. Furthermore, varying h directly by observed differences in CUE improved the model fit at the two sites investigated. This study provides direct evidence that CUE of soil microbial communities is relevant for SOC sequestration, and its dependency on soil N availability or substrate C:N ratio might allow for its inclusion in models without explicit microbial C pools.</p

Managing Agricultural Greenhouse Gases Network (MAGGnet): Exploring Greenhouse Gas Mitigation Potential of Cropland Management Practices

Global Research Alliance on Agricultural Greenhouse Gases - ESTABLISHED: December 2009, United Nations Climate Change Conference, Copenhagen, Denmark; PURPOSE: Facilitate research, development and extension of technologies and practices that will help deliver ways to grow more food (and more climate-resilient food systems) without growing greenhouse gas emissions; CURRENT MEMBERSHIP: 46 countries (Europe, Americas, Asia Pacific, Africa