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Regional assessments of soil organic carbon (SOC) trends and the carbon sequestration potential of alternative management practices (AMP) are highly relevant for developing climate change mitigation strategies for the agricultural sector. Such studies could benefit from simplified SOC modeling approaches on the scale of administrative units as this often corresponds to the level of policy-making and data availability. However, there is a risk of systematic errors in such scaling operations. To overcome this problem, we performed a scaling experiment where we simulated the SOC dynamics of the arable soils of the State of Saxony (Germany) across a series of scales using the CANDY Carbon Balance (CCB) model. Specifically, we developed model set-ups on four different administrative levels (NUTS1, NUTS2, NUTS3, and LAU) and evaluated the simulation results of the upscaled models against a 500 m grid-based reference model. Furthermore, we quantified the carbon sequestration potential of selected AMP scenarios (addressing field grass, cover crops, and conservation tillage) across all scales. The upscaled model set-ups adequately simulated the SOC trends of Saxon arable land compared to the grid-based reference simulation (scaling error: 0.8–3.8%), while providing significant benefits for model application, data availability and runtime. The carbon sequestration potential of the AMP scenarios (1.33 Mt C until 2050) was slightly overestimated (+0.07–0.09 Mt C) by the upscaled model set-ups. Regardless of the scale of model set-up, we showed that the use of aggregated statistical input data could lead to a systematic underestimation of SOC trends. LAU and NUTS3 levels were shown to be a suitable compromise for effectively quantifying SOC dynamics and allowed for an acceptable spatial prioritization of AMPs. Such simplified, scale-adapted assessments are valuable for cross-regional comparisons and for communication to and among decision-makers, and might provide a quantitative

Phosphorus is a nonrenewable resource, which is required for crop growth and to maintain high yields. The soil P cycle is very complex, and new model approaches can lead to a better understanding of those processes and further guide to research gaps. The objective of this study was to present a P-submodel, which has been integrated in the existing Carbon Candy Balance (CCB) model that already comprises a C and N module. The P-module is linked to the C mineralization and the associated C-pools via the C/P ratio of fresh organic material. Besides the organic P cycling, the module implies a plant-available P-pool (Pav), which is in a dynamic equilibrium with the nonavailable P-pool (Pna) that comprises the strongly sorbed and occluded P fraction. The model performance was tested and evaluated on four long-term field experiments with mineral P fertilization, farmyard manure as organic fertilizer and control plots without fertilization. The C dynamics and the Pav dynamics were modelled with overall good results. The relative RMSE for the C was below 10% for all treatments, while the relative RMSE for Pav was below 15% for most treatments. To accommodate for the rather small variety of available P-models, the presented CNP-model is designed for agricultural field sites with a relatively low data input, namely air temperature, precipitation, soil properties, yields and management practices. The CNP-model offers a low entry threshold model approach to predict the C-N and now the P dynamics of agricultural soils., Fachagentur Nachwachsende Rohstoffe http://dx.doi.org/10.13039/501100010812, Peer Reviewed

Phosphorus is a non-renewable resource which is required for crop growth and to maintain high yields. The soil P cycle is very complex, and new model approaches can lead to a better understanding of those processes and further guide to research gaps. The objective of this study is to present a P-submodel which has been integrated in the existing Carbon Candy Balance (CCB) model that, already comprises a C and N module. The P module is linked to the C mineralization and the associated C-pools via the C/P ratio of fresh organic material. Besides the organic P cycling the module implies a plant available P-pool (Pav), which is in a dynamic equilibrium with the non available P-pool (Pna) that comprises the strongly sorbed and occluded P fraction. The model performance was tested and evaluated on four long-term field experiments with mineral P fertilization, farm yard manure as organic fertilizer and control plots without fertilization. The C dynamics as well as the Pav dynamics were modelled with overall good results. The relative RMSE for the C was below 10% for all treatments while the relative RMSE for Pav was below 15% for most treatments. To accommodate for the rather small variety of available P models, the presented CNP-model is designed for agricultural field sites with a relatively low data input, namely air temperature, precipitation, soil properties, yields and management practices. The CNP-model offers a low entry threshold model approach to predict the C-N and now the P dynamics of agricultural soils.

The fertilization with organic amendments and digestates from biogas plants is increasingly used to increase carbon stock and to improve the soil quality, but little is still known about their long-term effects. A common method to analyse organic amendments and their mineralization is incubation experiments, where amendments get incubated with soil while CO2 release is measured over time. In a previous study, carbon models have been applied to model the carbon dynamics of incubation experiments. The derived parameters describing the carbon turnover of the CCB model (CANDY Carbon Balance) are used to simulate the SOC and SON dynamics of a long-term field trial. The trial was conducted in Berge (Germany) where organic amendments like slurry, farmyard manure or digestates were systematically applied. To grant a higher model flexibility, the amounts of crop residues were calculated for roots and stubble separately. Furthermore, the mineralization dynamics of roots and stubble are considered by the model parameters for each crop. The model performance is compared when using the dry matter and carbon content received from the field trial and the incubation experiments, to evaluate the transferability. The results show that the incubation parameters are transferable to the field site, with rRMSE < 10% for the modelled SOC and rRMSE between 10% and 15% for the SON dynamics. This approach can help to analyse long-term effects of unexplored and unusual organic fertilizers under field conditions, whereat the model is used to upscale the C dynamics from incubation experiments, considering environmental conditions., Fachagentur Nachwachsende Rohstoffe http://dx.doi.org/10.13039/501100010812, HELMHOLTZ‐ZENTRUM FUR UMWELTFORSCHUNG UFZ., Peer Reviewed

To investigate the effects of management practices on the dynamics of soil temperature, during 2014–2017, a field experiment was carried out in Bad Lauchstaedt, Germany. In this study, four management systems are compared for determining management-induced changes in soil temperature at different depths: (i) conventional tillage (TC) with the standard rate of N fertilizer (P1N1), (ii) conventional tillage with the half-standard rate of N fertilizer (P1N0), (iii) reduced tillage (TR) with the standard rate of N fertilizer (P0N1), and iv) reduced tillage with the half-standard rate of N fertilizer (P0N0). Temporal analysis of soil temperature is assessed to examine data observed at a specific time to achieve a better understanding of the soil temperature dynamic that occurs at different time scales. The results showed that the soil temperature has decreasing amplitudes and increasing phase shifts with increasing soil depth, i.e., the deeper the measurement depth, the smoother the soil temperature changes cycle and the smaller the variability. Results showed that the diurnal temperature variation is found up to 45 cm depth of soil whereas annual temperature variation is up to a depth of 180 cm. The results, moreover, revealed that soil temperature dynamic was affected by tillage systems and fertilization and a time lag is observed between the temperature fluctuations at the surface and deeper layers, due to induced management effects on plant cover, residues, and soil properties. Although higher soil temperature at the sowing stage under TR is contributed to higher amounts of surface crop residues in crop rotations, the effect of residues on soil temperature variation reduces with an increase in percent plant cover and shading of soil, which happens in the last stage of plant growth. At the last stage of crop development regardless of tillage systems, applying more N fertilization increased crop yield, resulting in cooling soil temperature.

There is currently a significant focus on oil organic carbon, as interest in mitigating climate change by increasing soil carbon stocks is leading to efforts to include this within carbon farming and the trade of CO2 certificates. In addition, soil organic carbon controls many other soil functions, such as soil productivity. However, results from long-term field experiments suggest that an ever-increasing carbon content in soil, at some point, will no longer increase productivity, but will cause environmental risks, especially from excess nitrogen. In Germany, the most widely recognized soil organic matter (SOM) balance method, VDLUFA (Association of German Agricultural Investigation and Research Institutions), addresses soil management only, without a relation to the soil carbon stock. To close this gap, a methodology is developed based on results from European long-term field experiments that allows for an assessment of agricultural management both in terms of the carbon input to soil and the amount of carbon stored in soil. Due to the transformation of carbon stock into carbon flux, it is possible to apply the classification scheme of the VDLUFA balance to the carbon content of topsoils. This provides information to qualify further decisions about fostering carbon accumulation. This was demonstrated on experimental results from Bad Lauchstädt, as well as on data from the German Agricultural Soil Inventory (BZE-LW) for arable soils on a regional scale.

The fertilisation with organic amendments and digestates from biogas plants are increasingly used to increase carbon stock and to improve the soil quality, but little is still known about their long term effects. A common method to analyse organic amendments and their mineralisation are incubation experiments, where amendments get incubated with soil while CO2 release is measured over time. In a previous study carbon models have been applied to model the carbon dynamics of incubation experiments. The derived parameters describing the carbon turnover of the CCB model (CANDY Carbon Balance) are used to simulate the SOC and SON dynamics of a long term field trial. The trial was conducted in Berge (Germany) where organic amendments like slurry, farmyard manure or digestates where systematically applied. To grant a higher model flexibility, the amounts of crop residues were calculated for roots and stubble separately. Furthermore the mineralisation dynamics of roots and stubble are considered by the model parameters for each crop. The model performance is compared when using the dry matter and carbon content received from the field trial and the incubation experiments, to evaluate the transferability. The results show that the incubation parameters are transferable to the field site, with rRMSE < 10% for the modelled SOC and rRMSE between 10% and 15% for the SON dynamics. This approach can help to analyse long term effects of unexplored and unusual organic fertilisers under field conditions, whereat the model is used to upscale the C dynamics from incubation experiments, considering environmental conditions.

Organic amendments are important to sustain soil organic matter (SOM) and soil functions in agricultural soils. Information about the contribution of organic amendments to SOM can be derived from incubation experiments. In this study, data from 72 incubated organic amendments including plant residues, digestates and manure were analysed. The incubation data was compiled from three experimental setups with varying incubation times, soils and incubation temperatures, in which CO2 release was measured continuously. The analysis of the incubation data was performed with an approach relying on conceptual parts of C‐TOOL, CCB, Century, ICBM, RothC and Yasso which are all well approved first order carbon models that differ in structure and abstraction level. All models are an approximation of reality, whereby each model differs in understanding of the processes involved in soil carbon dynamics. To accumulate the advantages from each model a model ensemble was performed for each substrate. With the ability of each carbon model to compute the distribution of carbon into specific SOM pools a new approach for evaluating organic amendments in terms of humus building efficiency is presented that, depends on the weighted model fit of each ensemble member. Depending on the organic substrate added to the soil, the time course of CO2 release in the incubation studies was predicted with different accuracy by the individual model concepts. Averaging the output of the individual models leads to more robust prediction of SOM dynamics. The EHUM value is easy to interpret and the results are in accordance with the literature.

Until 2019, the Brazilian federal government employed a number of policy measures to fulfill the pledge of reducing greenhouse gas emissions from land use change and agriculture. While its forest law enforcement strategy was partially successful in combating illegal deforestation, the effectiveness of positive incentive measures in agriculture has been less clear. The reason is that emissions reduction from market-based incentives such as the Brazilian Low-Carbon Agriculture Plan cannot be easily verified with current remote sensing monitoring approaches. Farmers have adopted a large variety of integrated land-use systems of crop, livestock and forestry with highly diverse per-hectare carbon balances. Their responses to policy incentives were largely driven by cost and benefit considerations at the farm level and not necessarily aligned with federal environmental objectives. This article analyzes climate-related land-use policies in the state of Mato Grosso, where highly mechanized soybean–cotton and soybean–maize cropping systems prevail. We employ agent-based bioeconomic simulation together with life-cycle assessment to explicitly capture the heterogeneity of farm-level costs, benefits of adoption, and greenhouse gas emissions. Our analysis confirms previous assessments but suggests a smaller farmer policy response when measured as increase in area of integrated systems. In terms of net carbon balances, our simulation results indicate that mitigation effects at the farm level depended heavily on the exact type of livestock and grazing system. The available data were insufficient to rule out even adverse effects. The Brazilian experience thus offers lessons for other land-rich countries that build their climate mitigation policies on economic incentives in agriculture.

Long-term field experiments (LTEs) are an invaluable source to improve understanding of soil organic matter (SOM) turnover, as some of the involved processes are proceeding over centuries. Prediction of such slow carbon fluxes depends especially on the initialisation of slow reacting model pools and requires monitoring for a very long time for evaluation. This study reports soil organic matter (SOM) modelling covering more than 100 years for a dataset of 36 treatments of the “Extended Static Experiment” that is based on the classical “Static Experiment Bad Lauchstädt”. The experimental scheme includes different fertilisation treatments where the plots are arranged in two field stripes. A special feature of this experiment is the management change for each stripe after seven decades of continuous cultivation that includes a considerable alteration of organic amendments on different SOM levels. The CCB model was calibrated for SOC dynamics regarding initial SOC content and one parameter describing the physical protection of SOM. Calibration was performed for each plot individually, for each stripe, and for the whole experiment resulting in a relative root mean square error (rRMSE, [-]) of 0.050, 0.058 and 0.064 respectively. Model predictions for SON dynamics were satisfactory using the calibration results for SOC and the average C/N ratio from all observations within this dataset (11.8) for model initialisation. The model error for SON prediction reached from rRMSE=0.09 (whole experiment) over rRMSE=0.085 (each stripe) to rRMSE=0.084 (each plot). Calibration results suggest some heterogeneity between the two field stripes. Restarting the model after the management change in 1978 using the plot specific calculated SOC value from previous simulations for the whole time period provided different results of predicted SOC trends and values suggesting that the precondition of a SOM in steady state was still not fulfilled after seven decades. &nbsp

Partitioning soil organic carbon (SOC) into two kinetically different fractions that are stable or active on a century scale is key for an improved monitoring of soil health and for more accurate models of the carbon cycle. However, all existing SOC fractionation methods isolate SOC fractions that are mixtures of centennially stable and active SOC. If the stable SOC fraction cannot be isolated, it has specific chemical and thermal characteristics that are quickly (ca. 1 h per sample) measurable using Rock-Eval® thermal analysis. An alternative would thus be to (1) train a machine-learning model on the Rock-Eval® thermal analysis data for soil samples from long-term experiments for which the size of the centennially stable and active SOC fractions can be estimated and (2) apply this model to the Rock-Eval® data for unknown soils to partition SOC into its centennially stable and active fractions. Here, we significantly extend the validity range of a previously published machine-learning model (Cécillon et al., 2018) that is built upon this strategy. The second version of this model, which we propose to name PARTYSOC, uses six European long-term agricultural sites including a bare fallow treatment and one South American vegetation change (C4 to C3 plants) site as reference sites. The European version of the model (PARTYSOCv2.0EU) predicts the proportion of the centennially stable SOC fraction with a root mean square error of 0.15 (relative root mean square error of 0.27) at six independent validation sites. More specifically, our results show that PARTYSOCv2.0EU reliably partitions SOC kinetic fractions at its northwestern European validation sites on Cambisols and Luvisols, which are the two dominant soil groups in this region. We plan future developments of the PARTYSOC global model using additional reference soils developed under diverse pedoclimates and ecosystems to further expand its domain of application while reducing its prediction error.