Long-term fertilization of a boreal Norway spruce forest increases the temperature sensitivity of soil organic carbon mineralization

Boreal ecosystems store one-third of global soil organic carbon (SOC) and are particularly sensitive to climate warming and higher nutrient inputs. Thus, a better description of how forest managements such as nutrient fertilization impact soil carbon (C) and its temperature sensitivity is needed to better predict feedbacks between C cycling and climate. The temperature sensitivity of in situ soil C respiration was investigated in a boreal forest, which has received long-term nutrient fertilization (22 years), and compared with the temperature sensitivity of C mineralization measured in the laboratory. We found that the fertilization treatment increased both the response of soil in situ CO2 effluxes to a warming treatment and the temperature sensitivity of C mineralization measured in the laboratory (Q10). These results suggested that soil C may be more sensitive to an increase in temperature in long-term fertilized in comparison with nutrient poor boreal ecosystems. Furthermore, the fertilization treatment modified the SOC content and the microbial community composition, but we found no direct relationship between either SOC or microbial changes and the temperature sensitivity of C mineralization. However, the relation between the soil C:N ratio and the fungal/bacterial ratio was changed in the combined warmed and fertilized treatment compared with the other treatments, which suggest that strong interaction mechanisms may occur between nutrient input and warming in boreal soils. Further research is needed to unravel into more details in how far soil organic matter and microbial community composition changes are responsible for the change in the temperature sensitivity of soil C under increasing mineral N inputs. Such research would help to take into account the effect of fertilization managements on soil C storage in C cycling numerical models

Impact of spatial soil and climate input data aggregation on regional yield simulations

We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations

Isothermal microcalorimetry provides new insight into terrestrial carbon cycling

Energy is continuously transformed in environmental systems through the metabolic activities of living organisms, but little is known about the relationship between the two. In this study, we tested the hypothesis that microbial energetics are controlled by microbial community composition in terrestrial ecosystems. We determined the functional diversity profiles of the soil biota (i.e., multiple substrate-induced respiration and microbial energetics) in soils from an arable ecosystem with contrasting long-term management regimes (54 years). These two functional profiling methods were then related to the soils' microbial community composition. Using isothermal microcalorimetry, we show that direct measures of energetics provide a functional link between energy flows and the composition of below-ground microbial communities at a high taxonomic level (Mantel R = 0.4602, P = 0.006). In contrast, this link was not apparent when carbon dioxide (CO2) was used as an aggregate measure of microbial metabolism (Mantel R = 0.2291, P = 0.11). Our work advocates that the microbial energetics approach provides complementary information to soil respiration for investigating the involvement of microbial communities in below-ground carbon dynamics. Empirical data of our proposed microbial energetics approach can feed into carbon-climate based ecosystem feedback modeling with the suggested conceptual ecological model as a base

On the relationships between the size of agricultural machinery, soil quality and net revenues for farmers and society

Mechanization in agriculture has greatly improved the efficiency of field operations, but also resulted in heavier agricultural vehicles, which has led to increased risks of soil compaction. Hence, farmers benefit from machinery with higher capacity but may suffer from decreased yields caused by compaction. Compaction may result in further environmental costs to society. We present a framework that relates the machinery capacity to soil compaction and its impacts on crop yields and environmental disservices, and associated revenues and costs for farmers and society. We combined simulations using a soil compaction model and a soil-crop model with simple economic analyses. We applied the framework to a case study of cereal production in Sweden, to derive the optimal combine harvester size that maximizes the farmer’s private profit and the societal net benefit, respectively. Increased machinery size decreased harvesting costs, but also reduced simulated crop yields and thus crop revenue as a result of soil compaction. Furthermore, in the model simulations, compaction also increased surface run-off, nitrogen leaching and greenhouse gas emissions. Intermediate machinery size maximized the farmer’s net revenue. Net benefits for society were highest for the lowest possible compaction level, due to the considerable external costs from soil compaction. We show that the optimal machinery size and thus compaction level for maximum farmer revenue would decrease if either producer prices were higher, harvesting costs savings from larger machinery were smaller, or if farmers were charged for (part of the) environmental costs

Modelling dynamic interactions between soil structure and the storage and turnover of soil organic matter

Models of soil organic carbon (SOC) storage and turnover can be useful tools to analyse the effects of soil and crop management practices and climate change on soil organic carbon stocks. The aggregated structure of soil is known to protect SOC from decomposition and, thus, influence the potential for long-term sequestration. In turn, the turnover and storage of SOC affects soil aggregation, physical and hydraulic properties and the productive capacity of soil. These two-way interactions have not yet been explicitly considered in modelling approaches. In this study, we present and describe a new model of the dynamic feedbacks between soil organic matter (SOM) storage and soil physical properties (porosity, pore size distribution, bulk density and layer thickness). A sensitivity analysis was first performed to understand the behaviour of the model. The identifiability of model parameters was then investigated by calibrating the model against a synthetic data set. This analysis revealed that it would not be possible to unequivocally estimate all of the model parameters from the kind of data usually available in field trials. Based on this information, the model was tested against measurements of bulk density, SOC concentration and limited data on soil water retention and soil surface elevation made during 63 years in a field trial located near Uppsala (Sweden) in three treatments with different organic matter (OM) inputs (bare fallow, animal and green manure). The model was able to accurately reproduce the changes in SOC, soil bulk density and surface elevation observed in the field as well as soil water retention curves measured at the end of the experimental period in 2019 in two of the treatments. Treatment-specific variations in SOC dynamics caused by differences in OM input quality could be simulated very well by modifying the value for the OM retention coefficient epsilon (0.37 for animal manure and 0.14 for green manure). The model approach presented here may prove useful for management purposes, for example, in an analysis of carbon sequestration or soil degradation under land use and climate change

Oxalate-extractable aluminum alongside carbon inputs may be a major determinant for organic carbon content in agricultural topsoils in humid continental climate

The relative importance of various soil mineral constituents (e.g. clay-sized particles, aluminum- and iron-bearing mineral reactive phases) in protecting soil organic carbon (SOC) from decomposition is not yet fully understood in arable soils formed from quaternary deposits in humid continental climates. In this study, we investigated the relationships between soil physico-chemical properties (i.e. contents of oxalate-extractable aluminum (Alox) and iron (Feox) and clay size particle < 2 mu m), grain yield (as a proxy for carbon input) and total SOC as well as SOC in different soil fractions for samples taken from the topsoil of an arable field at Bjertorp in south-west Sweden. We found a positive correlation between Alox and total SOC content, where Alox explained ca. 48% of the spatial variation in SOC. We also found that ca. 80% of SOC was stored in silt- and claysized (SC) fractions, where Al-bearing reactive mineral phases (estimated by Alox) may be important for organicmineral associations and clay aggregation. Our results were supported by data collated from the literature for arable topsoil in similar climates, which also showed positive correlations between SOC and Alox contents (R-2 = 23.1 - 74.5%). Multiple linear regression showed that including spatially-variable crop yields as a proxy for carbon inputs improved the prediction of SOC variation across the Bjertorp field. Other unquantified soil properties such as exchangeable calcium may account for the remaining unexplained variation in topsoil SOC. We conclude that Al-bearing reactive mineral phases are more important than clay content and Fe-bearing reactive mineral phases for SOC stabilization in arable topsoil in humid continental climates

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

A framework for modelling soil structure dynamics induced by biological activity

Acknowledgments: This work was funded by the Swedish Research Council for Sustainable Development (FORMAS) in the project “Soil structure and soil degradation: improved model tools to meet sustainable development goals under climate and land use change” (grant no. 2018-02319). We would also like to thank Mikael Sasha Dooha for carrying out the measurements for the water retention curves shown in figure 4.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