ECOSSE: Estimating Carbon in Organic Soils - Sequestration and Emissions: Final Report

Background Climate change, caused by greenhouse gas ( GHG) emissions, is one of the most serious threats facing our planet, and is of concern at both UK and devolved administration levels. Accurate predictions for the effects of changes in climate and land use on GHG emissions are vital for informing land use policy. Models which are currently used to predict differences in soil carbon (C) and nitrogen (N) caused by these changes, have been derived from those based on mineral soils or deep peat. None of these models is entirely satisfactory for describing what happens to organic soils following land-use change. Reports of Scottish GHG emissions have revealed that approximately 15% of Scotland's total emissions come from land use changes on Scotland's high carbon soils; the figure is much lower for Wales. It is therefore important to reduce the major uncertainty in assessing the carbon store and flux from land use change on organic soils, especially those which are too shallow to be deep peats but still contain a large reserve of C. In order to predict the response of organic soils to external change we need to develop a model that reflects more accurately the conditions of these soils. The development of a model for organic soils will help to provide more accurate values of net change to soil C and N in response to changes in land use and climate and may be used to inform reporting to UKGHG inventories. Whilst a few models have been developed to describe deep peat formation and turnover, none have so far been developed suitable for examining the impacts of land-use and climate change on the types of organic soils often subject to land-use change in Scotland and Wales. Organic soils subject to land-use change are often (but not exclusively) characterised by a shallower organic horizon than deep peats (e.g. organo-mineral soils such as peaty podzols and peaty gleys). The main aim of the model developed in this project was to simulate the impacts of land-use and climate change in these types of soils. The model is, a) be driven by commonly available meteorological data and soil descriptions, b) able to simulate and predict C and N turnover in organic soils, c) able to predict the impacts of land-use change and climate change on C and N stores in organic soils in Scotland and Wales. In addition to developing the model, we have undertaken a number of other modelling exercises, literature searches, desk studies, data base exercises, and experimentation to answer a range of other questions associated with the responses of organic soils in Scotland and Wales to climate and land-use change. Aims of the ECOSSE project The aims of the study were: To develop a new model of C and N dynamics that reflects conditions in organic soils in Scotland and Wales and predicts their likely responses to external factors To identify the extent of soils that can be considered organic in Scotland and Wales and provide an estimate of the carbon contained within them To predict the contribution of CO 2, nitrous oxide and methane emissions from organic soils in Scotland and Wales, and provide advice on how changes in land use and climate will affect the C and N balance In order to fulfil these aims, the project was broken down into modules based on these objectives and the report uses that structure. The first aim is covered by module 2, the second aim by module 1, and the third aim by modules 3 to 8. Many of the modules are inter-linked. Objectives of the ECOSSE project The main objectives of the project were to: Describe the distribution of organic soils in Scotland and Wales and provide an estimate of the C contained in them Develop a model to simulate C and N cycling in organic soils and provide predictions as to how they will respond to land-use, management and climate change using elements of existing peat, mineral and forest soil models Provide predictive statements on the effects of land-use and climate change on organic soils and the relationships to GHG emissions, including CO 2, nitrous oxide and methane. Provide predictions on the effects of land use change and climate change on the release of Dissolved Organic Matter from organic soils Provide estimates of C loss from scenarios of accelerated erosion of organic soils Suggest best options for mitigating C and N loss from organic soils Provide guidelines on the likely effects of changing land-use from grazing or semi-natural vegetation to forestry on C and N in organic soils Use the land-use change data derived from the Countryside Surveys of Scotland and Wales to provide predictive estimates for changes to C and N balance in organic soils over time

Modeling carbon biogeochemistry in agricultural soils

An existing model of C and N dynamics in soils was supplemented with a plant growth submodel and cropping practice routines (fertilization, irrigation, tillage, crop rotation, and manure amendments) to study the biogeochemistry of soil carbon in arable lands. The new model was validated against field results for short-term (1–9 years) decomposition experiments, the seasonal pattern of soil CO2 respiration, and long-term (100 years) soil carbon storage dynamics. A series of sensitivity runs investigated the impact of varying agricultural practices on soil organic carbon (SOC) sequestration. The tests were simulated for corn (maize) plots over a range of soil and climate conditions typical of the United States. The largest carbon sequestration occurred with manure additions; the results were very sensitive to soil texture (more clay led to greater sequestration). Increased N fertilization generally enhanced carbon sequestration, but the results were sensitive to soil texture, initial soil carbon content, and annual precipitation. Reduced tillage also generally (but not always) increased SOC content, though the results were very sensitive to soil texture, initial SOC content, and annual precipitation. A series of long-term simulations investigated the SOC equilibrium for various agricultural practices, soil and climate conditions, and crop rotations. Equilibrium SOC content increased with decreasing temperatures, increasing clay content, enhanced N fertilization, manure amendments, and crops with higher residue yield. Time to equilibrium appears to be one hundred to several hundred years. In all cases, equilibration time was longer for increasing SOC content than for decreasing SOC content. Efforts to enhance carbon sequestration in agricultural soils would do well to focus on those specific areas and agricultural practices with the greatest potential for increasing soil carbon content

Modeling soil organic carbon change in croplands of China

Using 1990 conditions, we modeled carbon (C) and nitrogen (N) biogeochemical cycles in croplands of China (and, for comparison, the United States) to estimate the annual soil organic-carbon (SOC) balance for all cropland. Overall, we estimate that China\u27s croplands lost 1.6% of their SOC (to a depth of 0.3 m) in 1990, and that U.S. cropland lost 0.1%. A key element in this difference was that ∼25% of aboveground crop residue in China was returned to the soil, compared to ∼90% in the United States. In China, SOC losses were greatest in the northeast (∼103 kg C·ha–1·yr–1), and were generally smaller (\u3c0.5 × 103 kg C·ha–1·yr–1) in regions with a longer cultivation history. Some regions showed SOC gains, generally \u3c103 kg C·ha–1·yr–1. Reduced organic-matter input to China\u27s cropland soils, and lower overall SOC levels in those soils, led to lower levels of N mineralization in the simulations, consistent with higher rates of synthetic-fertilizer application in China. C and N cycles are closely linked to soil fertility, crop yield, and non-point-source environmental pollution

Large scale spatial modelling of soil organic carbon dynamics

Under the Kyoto Protocol, participating nations are required to reduce National CO₂emissions according to their 'reduction commitment' or 'quantified emissions limitation', over the first commitment period, 2008-2012. One way in which nations could achieve this would be by increasing soil carbon storage through different management practices. Most former estimates of regional scale C sequestration potential have made use of either linear regressions based on long-term experimental data, whilst some have used dynamic soil organic matter (SOM) models linked to spatial databases. Few studies have compared these two methods, and none have compared regressions with two different SOM models. This thesis presents a case study investigation of the potential of different land management practices to sequester carbon in soil in arable land, and preliminary estimates of other potential C savings. Two dynamic SOM models were chosen for this study, RothC (a soil process model) and CENTURY (a general ecosystem model). RothC and CENTURY are the two most widely used and validated SOM models world-wide. Methods were developed to enhance use and comparability of the models in a predictive mode. These methods included a) estimation of the IOM pool for RothC, b) estimation of C inputs to soil, c) investigation of pool size distributions in CENTURY, and d) creation of a program to allow use of C inputs derived from CENTURY with the RothC model. This thesis has also investigated the importance of errors in C inputs to soil for predictive SOM modelling, and performed sensitivity analyses to investigate how errors in setting the size refractory SOM pools might affect predictions of SOC. RothC and CENTURY were compared at the site scale using datasets from seven European long-term experiments, in order to a) verify their ability to predict SOC changes under changes in land use and management relevant to studies of C sequestration potential, b) evaluate model performance under European climatic conditions, and c) compare the performance of the two models. Finally, a Geographic Information System (GIS) containing soil, land use and climate layers, was assembled for a case study region in Central Hungary. GIS interfaces were developed for the RothC and CENTURY models, thus linking them to spatial datasets at the regional level. This allowed a comparison of estimates of the C sequestration potential of different land management practices obtained using the two models and using regression-based estimates. Although estimates obtained by the different approaches were of the same order of magnitude, differences were observed. Encouragingly, some of the land management scenarios studied here showed sufficient C mitigation potential to meet Hungarian CO₂reduction commitments

Understanding Structure and Function in Semiarid Ecosystems: Implications for Terrestrial Carbon Dynamics in Drylands

This study advances understanding of how the changes in ecosystem structure and function associated with woody shrub encroachment in semi-arid grasslands alter ecosystem carbon (C) dynamics. In terms of both magnitude and dynamism, dryland ecosystems represent a major component of the global C cycle. Woody shrub encroachment is a widespread phenomenon globally, which is known to substantially alter ecosystem structure and function, with resultant impacts on C dynamics. A series of focal sites were studied at the Sevilleta National Wildlife Refuge in central New Mexico, USA. A space-for-time analogue was used to identify how landscape structure and function change at four stages over a grassland to shrubland transition. The research had three key threads: 1. Soil-associated carbon: Stocks of organic and inorganic C in the near-surface soil, and the redistribution of these C stocks by erosion during high-intensity rainfall events were quantified using hillslope-scale monitoring plots. Coarse (>2 mm) clasts were found to account for a substantial proportion of the organic and inorganic C in these calcareous soils, and the erosional effluxes of both inorganic and organic C increased substantially across the vegetation ecotone. Eroded sediment was found to be significantly enriched in organic C relative to the contributing soil with systematic changes in OC enrichment across the vegetation transition. The OC enrichment dynamics observed were inconsistent with existing understanding (derived largely from reductionist, laboratory-based experiments) that OC enrichment is largely insignificant in the erosional redistribution of C. 2. Plant biomass: Cutting-edge proximal remote sensing approaches, using a remotely piloted lightweight multirotor drone combined with structure-from-motion (SfM) photogrammetry were developed and used to quantify biomass carbon stocks at the focal field sites. In such spatially heterogeneous and temporally dynamic ecosystems existing measurement techniques (e.g. on-the-ground observations or satellite- or aircraft-based remote sensing) struggle to capture the complexity of fine-grained vegetation structure, which is crucial for accurately estimating biomass. The data products available from the novel SfM approach developed for this research quantified plants just 15 mm high, achieving a fidelity nearly two orders of magnitude finer than previous implementations of the method. The approach developed here will revolutionise the study of biomass dynamics in short-sward ecogeomorphic systems. 3. Ecohydrological modelling: Understanding the effects of water-mediated degradation processes on ecosystem carbon dynamics over greater than observable spatio-temporal scales is complicated by significant scale-dependencies and thus requires detailed mechanistic understanding. A process-based, spatially-explicit ecohydrological modelling approach (MAHLERAN - Model for Assessing Hillslope to Landscape Erosion, Runoff and Nutrients) was therefore comprehensively evaluated against a large assemblage of rainfall runoff events. This evaluation highlighted both areas of strength in the current model structure, and also areas of weakness for further development. The research has improved understanding of ecosystem degradation processes in semi-arid rangelands, and demonstrates that woody shrub encroachment may lead to a long-term reduction in ecosystem C storage, which is contrary to the widely promulgated view that woody shrub encroachment increases C storage in terrestrial ecosystems.NERC Doctoral Training Grant (NE/K500902/1)NSF Long Term Ecological Research Program at the Sevilleta National Wildlife Refuge (DEB-1232294

Quantifying Potential Long-term Changes in Erosion, Discharge, and Total Suspended Solids Resulting from Agricultural Land Use Change in South Dakota

South Dakota is a mosaic of grasslands, wetlands, and cropland. A continuing shift from grassland to cropland has occurred over the past decade and is expected for the next 50 years. Rate of future conversion may vary greatly in response to regulatory, economic, and social factors. Concern has risen over environmental consequences associated with land conversion, which include but are not limited to changes in rill and sheet erosion rates from cultivated soils, stream and river discharge, and water quality. Quantifying future changes for these three externalities is important to understand the possible long-term consequences of complex grassland conversion decisions such as soil loss, flooding or drought, and diminished water quality. Systems Thinking and System Dynamics (SD) methodology was used to model complex land use and soil-related factors over time. The SD model replicated historic annual erosion rates (metric-tons/ha), discharge [million cubic meters (MCM)], and average total suspended solids (TSS; mg/L) from 1947 to 2012 with relative accuracy and precision in four South Dakota watercatchments, which included the Big Sioux, James, Bad, and Belle Fourche rivers. The SD model was utilized to forecast future annual and cumulative erosion [million metric-tons (Mt)], discharge (MCM), and TSS (mg/L) change under different potential future grassland conversion rates and conservation and conventional tillage from 2012 to 2062. Forecasted environmental externalities increased for policy scenarios that promoted grassland conversion but decreased for scenarios that limited grassland conversion to cropland or promoted grassland restoration. Policy implementation is likely to have the same general impact toward the reduction or increase of erosion, discharge, and TSS as cumulative estimates were 70 ̶ 77%, \u3c 1 ̶ 10%, and 70 ̶ 76% greater for the worst-case scenario compared to the best-case scenario estimates, respectively. However, externality change was greater in western verses eastern water-catchments. Results may provide producers, policymakers, and other stakeholders more specific quantitative estimates to assess the future impact of grassland conversion decisions. Additionally, comparisons between these estimates provide support that addressing grassland conversion issues and cultivation practices are important in order to preserve and conserve soil and water resources

Comparison of long-term field-measured and RUSLE-based modelled soil loss in Switzerland

Long-term field measurements to asses model-based soil erosion predictions by water are rare. We have compared field measurements based on erosion assessment surveys from a 10-year monitoring process with spatial-explicit model predictions with the Revised Universal Soil Loss Equation (RUSLE). Robust input data were available for both the mapped and the modelled parameters for 203 arable fields covering an area of 258 ha in the Swiss Midlands. The 1639 mapped erosion forms were digitized and converted to raster format with a 2 m resolution. A digital terrain model using 2 m resolution and a multiple flow direction algorithm for the calculation of the topographic factors and the support practice factor was available for modelling with the RUSLE. The other input data for the RUSLE were determined for each field. The comparison of mapped and modelled soil loss values revealed a substantially higher estimation of soil loss values from modelling by a factor of 8, with a mean mapped soil loss of 0.77 t/ha/yr vs. modelled soil loss of 6.20 t/ha/yr. However, high mapped soil losses of >4 t/ha/yr were reproduced quite reliably by the model, while the model predicted drastically higher erosion values for mapped losses of <4 t/ha/yr. Our study shows the value of long-term field data based on erosion assessment surveys for model evaluation. RUSLE-type model results should be compared with erosion assessment surveys at the field to landscape scale in order to improve the calibration of the model. Further factors related to land management like headlands, traffic lanes and potato furrows need to be included before they may be used for policy advice

Soil erosion risk map for Swiss grasslands : a dynamic approach to model the spatio-temporal patterns of soil loss

Soil erosion by water on grassland does not attract the same attention like erosion on arable land as it is usually assumed that the closed vegetation cover prevents soil loss. However, the complex terrain and intensive pasture use of mountain grasslands can potentially induce high soil loss. With a share of 72% of the total agricultural area, grassland is one of the most dominant land use in Switzerland and therefore should not be neglected in topics concerning soil protection. Previous soil erosion studies revealed that soil erosion rates in Switzerland are not constant over time but rather are highly dynamic within a year. Such seasonal variability is mainly caused by rainfall patterns and plant growth cycles. Hence, modeling of soil loss based on a seasonal resolution enables improved insights in the erosion dynamics within a year. The present work aims to model soil erosion with a sub-annual resolution for Swiss grasslands. Thereby we will focus on the most dynamic soil erosion risk factors namely rainfall erosivity and land cover and management. The soil erosion model itself relies on the Revised Universal Soil Loss Equation (RUSLE). Each of the erosion factors of the RUSLE (rainfall erosivity R, soil erodibility K, cover and management C, slope length L, slope steepness S, and support practices P) is modified according to the specific environmental conditions of Swiss grasslands. The factors R and C are the most variable factors within a year as they are directly related to the parameters rainfall intensity and plant growth cycle. Therefore, both factors are modeled on a monthly scale to capture the temporal variations of soil loss within the year. For flexibility and transparency reasons, we derived each factor separately with the most state-of-the-art data and methodology as each of the factor transmit information about its effect on the overall model. Support practices (P-factor) are not considered in the model as the parametrization of grassland management practices and their effect for erosion control is difficult due to a lack of data and studies. Monthly estimates of the rainfall erosivity (R-factor) are based on 10-minutes rainfall data of 87 gauging stations distributed all over Switzerland. Subsequently, the monthly rainfall erosivity is interpolated with spatial covariates representing snow cover, precipitation, and topography. For the C-factor, the fraction of green vegetation cover (FGVC) was derived from the 0.25 m spatial resolution Swissimage orthophotos by a linear spectral unmixing technique. A temporal normalization of the spatial distribution of the FGVC combined with R-factor weighting results in spatial and temporal patterns of the C-factor. Soil erodibility (expressed as the K-factor of the RUSLE equation) was modeled with cubist regression and multilevel B-splines on a national scale based on a total of 199 Swiss and 1639 European Land Use/Cover Area frame statistical Survey (LUCAS) topsoil samples. The LS-factor was adopted to the steep alpine environment by limiting the slope length to 100 m and using a fitted S-factor of empirical slope steepness factors. The mean monthly modeled R-factor for Switzerland is 96.5 MJ mm ha-1 h-1 month-1. On average, rainfall erosivity is 25 times higher in August (263.5 MJ mm ha-1 h-1 month-1) then in January (10.5 MJ mm ha-1 h-1 month-1). In general, the winter has relatively low R-factor values (average of 14.7 MJ mm ha-1 h-1 month-1). The mean monthly C-factor on Swiss grasslands is 0.012 with a maximum from May until September. The national average K-factor of Switzerland is 0.0327 t ha h ha-1 MJ-1 mm-1. The LS-factor for Switzerland is relatively high (14.8) compared to other countries but is mainly driven by the complex topography of the Alps with its steep slopes. The soil erosion modeling reveals distinct seasonal variations. July and August are identified to be the months with the highest soil loss rates (1.25 t ha-1 month-1) by water on Swiss grasslands. Spatially, hotspots of soil erosion are in the Central Swiss Alps (parts of the cantons Fribourg, Bern, Obwalden, Nidwalden, St. Gallen, Appenzell Innerrhoden, and Appenzell Ausserrhoden) in summer. Winter is the season with the lowest risk of soil loss due to low rainfall erosivity on snow-covered ground. The average annual soil loss for Switzerland, expressed as the sum of all monthly erosion rates, is 4.55 t ha-1 yr-1. The spatial rainfall erosivity patterns are heterogeneous in all months, but spatial differences are less pronounced in winter due to the low rainfall erosivity. The small-scale variability of rainfall erosivity is less distinct in all months as homogenous rainfall patterns usually cover larger regions controlled mainly by topography. However, the Swiss Alps are not equally affected by rainfall erosivity with a very low variability within a year in the western and eastern Alps. In contrast, the small-scale variability of the cover and management factor is higher in most of the months due to the impact of grassland land use. The average C-factor for Swiss grassland of 0.012 matches the commonly applied C-factor for grasslands (0.01) proposed in the literature. The Swiss K-factor is low to medium with a clear reduction under consideration of the surface stone cover. We expected a high LS-factor for Switzerland as steep slopes are frequently in the Swiss Alps. The dominance of soil erosion risk on grasslands in summer is surprising as it is commonly assumed that the closed vegetation cover protects soils. Though, the individual consideration of all factors, especially of the R- and C-factor, reveal their strong effect and interaction within the erosion model. The average annual soil loss prediction for Swiss grassland exceeds the maximum tolerable soil loss of Switzerland (2 t ha-1 yr-1; Schaub and Prasuhn, 1998) by a factor of 2. That modeling result highlights that soil erosion on grasslands is of high concern for the Swiss agricultural productivity and environmental protection of a large proportion of the Swiss territory. Based on the increased temporal resolution of soil erosion predictions, spatial and temporal patterns of soil loss by water on Swiss grasslands can be captured. The simultaneous identification of spatial and temporal patterns of soil loss on Swiss grasslands makes a targeted soil erosion control feasible. The knowledge about where and when soil erosion occurs enables the implementation of selective erosion control measures specifically for time periods and regions with high susceptibility. Developing a comprehensive soil erosion assessment on Swiss grassland that is comparable and connectable with available risk assessments such as the erosion risk map 2 for Swiss arable lands (Prasuhn et al., 2013) and the European Union’s assessment RUSLE2015 (Panagos et al., 2015) provides a national and even continental valuation of soil erosion risk. The soil erosion risk map can be seen as a prototype for other erosion modeling on grassland in the Alpine region

The Hidden Costs of Land Degradation in US Maize Agriculture

The United States is a world leader in the production of maize and other crops and the agricultural success of the country is directly linked to the intensive use of fertilizers and irrigation. However, even in advanced agricultural systems, soils can become degraded over time due to factors such as soil organic matter (SOM) loss and erosion. Here, we use a series of scenario-based model analyses to show that about one-third of current annual US. N fertilizer use in maize agriculture is used to compensate for the long-term loss of soil fertility through erosion and organic matter loss. This leads to over a half billion dollars per year in extra fertilizer supply costs to US farmers. These results highlight the potential to reduce both the input costs and environmental impacts of agriculture through the restoration of SOM in agricultural soils

Model Estimation of Land-Use Effects on Water Levels of Northern Prairie Wetlands

Wetlands of the Prairie Pothole Region exist in a matrix of grassland dominated by intensive pastoral and cultivation agriculture. Recent conservation management has emphasized the conversion of cultivated farmland and degraded pastures to intact grassland to improve upland nesting habitat. The consequences of changes in land-use cover that alter watershed processes have not been evaluated relative to their effect on the water budgets and vegetation dynamics of associated wetlands. We simulated the effect of upland agricultural practices on the water budget and vegetation of a semipermanent prairie wetland by modifying a previously published mathematical model (WETSIM). Watershed cover/landuse practices were categorized as unmanaged grassland (native grass, smooth brome), managed grassland (moderately heavily grazed, prescribed burned), cultivated crops (row crop, small grain), and alfalfa hayland. Model simulations showed that differing rates of evapotranspiration and runoff associated with different upland plant-cover categories in the surrounding catchment produced differences in wetland water budgets and linked ecological dynamics. Wetland water levels were highest and vegetation the most dynamic under the managed-grassland simulations, while water levels were the lowest and vegetation the least dynamic under the unmanaged-grassland simulations. The modeling results suggest that unmanaged grassland, often planted for waterfowl nesting, may produce the least favorable wetland conditions for birds, especially in drier regions of the Prairie Pothole Region. These results stand as hypotheses that urgently need to be verified with empirical data