Assessing soil carbon dioxide and methane fluxes from a Scots pine raised bog-edge-woodland.

Scots pine bog edge woodland is a type of habitat typical on raised bogs where trees cohabitate with bog vegetation to form a low-density stand. Even though nowadays this habitat does not cover large areas, in a future scenario it is possible that this environment will expand, either naturally (drier climate) or anthropogenically, as the result of the application of new restoration strategies that could increase net landscape carbon benefits from both peatland and woodland environments. This study is the first reported investigation in Scotland exploring carbon flux dynamics from sparse woodlands on raised bogs. We examined how Scots pine trees directly or indirectly affected soil temperature and moisture, ground vegetation, and consequently carbon dioxide (CO2) and methane (CH4) soil fluxes. Soil CO2 and CH4 were measured at different distance from the tree and thereafter assessed for both spatial and temporal variability. Our results showed that these low-density trees were able to modify the ground vegetation composition, had no effect on soil temperature, but did affect the soil moisture, with soils close to tree roots significantly drier (0.25 ± 0.01 m3 m-3) than those on open bog (0.39 ± 0.02 m3 m-3). Soil CO2 fluxes were significantly higher in the vicinity of trees (34.13 ± 3.97 µg CO2 m-2 s-1) compared to the open bog (24.34 ± 2.86 µg CO2 m-2 s-1). On the opposite, CH4 effluxes were significantly larger in the open bog (0.07 ± 0.01 µg CH4 m-2 s-1) than close to the tree (0.01 ± 0.00 µg CH4 m-2 s-1). This suggests that Scots pine trees on bog edge woodland may affect soil C fluxes in their proximity primarily due to the contribution of root respiration, but also as a result of their effects on soil moisture, enhancing soil CO2 emissions, while reducing the CH4 fluxes. There is, however, still uncertainty about the complete greenhouse gas assessment, and further research would be needed in order to include the quantification of soil nitrous oxide (N2O) dynamics together with the analysis of complete gas exchanges at the tree-atmosphere level.

A novel approach to baseline water quality assessment at local and catchment scale: a case study from Berambadi, India.

Optimal design and maintenance are necessary for the sustainability of wastewater treatment systems. In this study, we present the outcome of a novel approach to baseline assessment conducted prior to the design and deployment of a decentralized wastewater treatment system at a school in rural India. The baseline water quality monitoring protocol was deployed to assess (a) the quality and quantity of wastewater (greywater and blackwater) flows from the school and (b) the status of surface water and groundwater quality in the catchment. Hourly greywater flows and water quality trends were monitored across four seasons at the school. Average freshwater consumption at the school was 518 ± 322 L/day for hand washing and 287 ± 97 L/day for cooking meals. Greywater generation showed high hourly variations in COD levels. Greywater generated from hand wash and kitchen sources contributed to 110 g/day and 96 g/day of BOD5 respectively and 214 g/day and 141 g/day of COD respectively. Based on additional data from a self-reporting sanitation survey, the organic contaminant load generated from the toilet was estimated to be 1.5 ± 0.1 kg COD/day. At the catchment scale, both groundwater and surface water quality were monitored seasonally to assess the impact of raw sewage and stormwater inputs. Compared with borewells, high nitrate-N levels (> 10 mg/L) were observed in the village hand pump samples throughout the year. Maximum nitrate-N (16 mg/L) and fecal coliforms (3.9 log MPN/100 mL) levels were observed in surface waters during monsoons, indicating the impact of sewage and surface runoff on water quality. The proposed approach is useful to estimate data on freshwater use and wastewater generation at the school and hence to make the case for, and design of, a sustainable water management intervention.

Estimating greenhouse gases emissions from horticultural peat soils using a DNDC modelling approach.

Peat soils represent an important global carbon (C) sink, but can also provide a highly fertile medium for growing horticultural crops. Sustainable crop production on peat soils involves a trade-off between ensuring food security and mitigating typically high greenhouse gas (GHG) emissions and rates of soil C loss. An alternative approach to resource intensive field-based monitoring of GHG fluxes for all potential management scenarios is to use a process-based model driven by existing field data to estimate emissions. The aim of this study was to evaluate the suitability of the Denitrification-Decomposition (DNDC) model for estimating emissions of CO2, N2O and CH4 from horticultural peat soils. The model was parameterised using climatic, soil, and crop management data from two intensively cultivated sites on soils of contrasting soil organic matter (SOM) contents (∼35% and ∼70% SOM content). Simulated emissions of CO2, N2O and CH4, and simulated soil physical and crop output values, were compared to actual GHG, soil and crop measurements. Model performance was assessed using baseline parameterisation (i.e. model defaults), then calibrated using pre-simulation and sensitivity analysis processes. Under baseline parameterisation conditions, DNDC proved poor at predicting GHG emissions and soil/crop variables. Calibration and validation improved DNDC performance in estimating the annual magnitude of emissions, but model refinement is still required for reproducing seasonal GHG patterns in particular. Key constraints on model functioning appear to be its ability to reliably model soil moisture and some aspects of C and nitrogen dynamics, as well as the quality of input data relating to water table dynamics. In conclusion, our results suggest that the DNDC (v. 9.5) model cannot accurately reproduce or be used to replace actual field measurements for estimation of GHG emission factors under different management scenarios for horticultural peat soils, but may be able to do so with further modification.

CO2 fluxes from drained and rewetted peatlands using a new ECOSSE model water table simulation approach.

The ability of peatlands to remove and store atmospheric carbon (C) depends on the drainage characteristics, which can be challenging to accommodate in biogeochemical models. Many studies indicate that restoration (by rewetting) of damaged peatlands can re-establish their capacity as a natural C sink. The purpose of this research was to improve the biogeochemical modelling of peatlands using the ECOSSE process-based model, which will account for the effects of drainage and rewetting during simulation, and potentially contribute towards improved estimation of carbon dioxide (CO2) fluxes from peatlands, using the IPCC Tier 3 approach. In this study, we present a new drainage factor with seasonal variability Dfa (i) developed specifically for ECOSSE, using empirical data from two drained and rewetted Irish peatlands. Dfa(i) was developed from the Blackwater drained bare-peat site (BWdr), and its application was tested at the vegetated Moyarwood peatland site under drained (MOdr) and rewetted conditions (MOrw). Dfa(i) was applied to the rainfall model inputs for the periods of active drainage in conjunction with the measured water table (WT) inputs. The results indicate that Dfa(i) application can improve the model performance to predict model-estimated water level (WL) and CO2 fluxes under drained conditions [WL: r2 = 0.89 (BWdr) and 0.94 (Modr); CO2: r2 = 0.66 (BWdr) and 0.78 (MOdr)] along with model-ability to capture their seasonal trends. The prediction of WL for the rewetted period was less successful at the MOrw site, where the simulation was run for drained to rewetted, which would suggest that additional work on the water model component is still needed. Despite this, the application of Dfa(i) showed successful model simulation of CO2 fluxes at MOrw (r2 = 0.75) and model ability to capture seasonal trends. This work hopes to positively contribute towards potential future development of Tier 3 methodology for estimating emissions/sinks in peatlands.

Global Research Alliance N2 O chamber methodology guidelines: Summary of modeling approaches.

Measurements of nitrous oxide (N2 O) emissions from agriculture are essential for understanding the complex soil-crop-climate processes, but there are practical and economic limits to the spatial and temporal extent over which measurements can be made. Therefore, N2 O models have an important role to play. As models are comparatively cheap to run, they can be used to extrapolate field measurements to regional or national scales, to simulate emissions over long time periods, or to run scenarios to compare mitigation practices. Process-based models can also be used as an aid to understanding the underlying processes, as they can simulate feedbacks and interactions that can be difficult to distinguish in the field. However, when applying models, it is important to understand the conceptual process differences in models, how conceptual understanding changed over time in various models, and the model requirements and limitations to ensure that the model is well suited to the purpose of the investigation and the type of system being simulated. The aim of this paper is to give the reader a high-level overview of some of the important issues that should be considered when modeling. This includes conceptual understanding of widely used models, common modeling techniques such as calibration and validation, assessing model fit, sensitivity analysis, and uncertainty assessment. We also review examples of N2 O modeling for different purposes and describe three commonly used process-based N2 O models (APSIM, DayCent, and DNDC).

Simulation of Soil Organic Carbon Effects on Long-Term Winter Wheat (Triticum aestivum) Production Under Varying Fertilizer Inputs.

Soil organic carbon (SOC) has a vital role to enhance agricultural productivity and for mitigation of climate change. To quantify SOC effects on productivity, process models serve as a robust tool to keep track of multiple plant and soil factors and their interactions affecting SOC dynamics. We used soil-plant-atmospheric model viz. DAISY, to assess effects of SOC on nitrogen (N) supply and plant available water (PAW) under varying N fertilizer rates in winter wheat (Triticum aestivum) in Denmark. The study objective was assessment of SOC effects on winter wheat grain and aboveground biomass accumulation at three SOC levels (low: 0.7% SOC; reference: 1.3% SOC; and high: 2% SOC) with five nitrogen rates (0-200 kg N ha-1) and PAW at low, reference, and high SOC levels. The three SOC levels had significant effects on grain yields and aboveground biomass accumulation at only 0-100 kg N ha-1 and the SOC effects decreased with increasing N rates until no effects at 150-200 kg N ha-1. PAW had significant positive correlation with SOC content, with high SOC retaining higher PAW compared to low and reference SOC. The mean PAW and SOC correlation was given by PAW% = 1.0073 × SOC% + 15.641. For the 0.7-2% SOC range, the PAW increase was small with no significant effects on grain yields and aboveground biomass accumulation. The higher winter wheat grain and aboveground biomass was attributed to higher N supply in N deficient wheat production system. Our study suggested that building SOC enhances agronomic productivity at only 0-100 kg N ha-1. Maintenance of SOC stock will require regular replenishment of SOC, to compensate for the mineralization process degrading SOC over time. Hence, management can maximize realization of SOC benefits by building up SOC and maintaining N rates in the range 0-100 kg N ha-1, to reduce the off-farm N losses depending on the environmental zones, land use and the production system.

Modelling nitrous oxide emissions from mown-grass and grain-cropping systems: Testing and sensitivity analysis of DailyDayCent using high frequency measurements.

The DailyDayCent biogeochemical model was used to simulate nitrous oxide (N2O) emissions from two contrasting agro-ecosystems viz. a mown-grassland and a grain-cropping system in France. Model performance was tested using high frequency measurements over three years; additionally a local sensitivity analysis was performed. Annual N2O emissions of 1.97 and 1.24kgNha-1year-1 were simulated from mown-grassland and grain-cropland, respectively. Measured and simulated water filled pore space (r=0.86, ME=-2.5%) and soil temperature (r=0.96, ME=-0.63°C) at 10cm soil depth matched well in mown-grassland. The model predicted cumulative hay and crop production effectively. The model simulated soil mineral nitrogen (N) concentrations, particularly ammonium (NH4+), reasonably, but the model significantly underestimated soil nitrate (NO3-) concentration under both systems. In general, the model effectively simulated the dynamics and the magnitude of daily N2O flux over the whole experimental period in grain-cropland (r=0.16, ME=-0.81gNha-1day-1), with reasonable agreement between measured and modelled N2O fluxes for the mown-grassland (r=0.63, ME=-0.65gNha-1day-1). Our results indicate that DailyDayCent has potential for use as a tool for predicting overall N2O emissions in the study region. However, in-depth analysis shows some systematic discrepancies between measured and simulated N2O fluxes on a daily basis. The current exercise suggests that the DailyDayCent may need improvement, particularly the sub-module responsible for N transformations, for better simulating soil mineral N, especially soil NO3- concentration, and N2O flux on a daily basis. The sensitivity analysis shows that many factors such as climate change, N-fertilizer use, input uncertainty and parameter value could influence the simulation of N2O emissions. Sensitivity estimation also helped to identify critical parameters, which need careful estimation or site-specific calibration for successful modelling of N2O emissions in the study region.

Estimating agro-ecosystem carbon balance of northern Japan, and comparing the change in carbon stock by soil inventory and net biome productivity.

Soil C sequestration in croplands is deemed to be one of the most promising greenhouse gas mitigation options for agriculture. We have used crop-level yields, modeled heterotrophic respiration (Rh) and land use data to estimate spatio-temporal changes in regional scale net primary productivity (NPP), plant C inputs, and net biome productivity (NBP) in northern Japan's arable croplands and grasslands for the period of 1959-2011. We compared the changes in C stocks derived from estimated NBP and using repeated inventory datasets for each individual land use type from 2005 to 2011. For the entire study region of 2193 ha, overall annual plant C inputs to the soil constituted 37% of total region NPP. Plant C inputs in upland areas (excluding bush/fallow) could be predicted by climate variables. Overall NBP for all land use types increased from -1.26MgCha(-1)yr(-1) in 1959-0.26 Mg Cha(-1)yr(-1) in 2011. However, upland and paddy fields showed a decreased in NBP over the period of 1959-2011, under the current C input scenario. From 1988, an increase in agricultural abandonment (bush/fallow) and grassland cover caused a slow increase in the regional C pools. The comparison of carbon budgets using the NBP estimation method and the soil inventory method indicated no significant difference between the two methods. Our results showed C loss in upland crops, paddy fields and sites that underwent land use change from paddy field to upland sites. We also show C gain in grassland from 2005 to 2011. An underestimation of NBP or an overestimation of repeated C inventories cannot be excluded, but either method may be suitable for tracking absolute changes in soil C, considering the uncertainty associated with these methods.

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.

Systems approaches in global change and biogeochemistry research.

Systems approaches have great potential for application in predictive ecology. In this paper, we present a range of examples, where systems approaches are being developed and applied at a range of scales in the field of global change and biogeochemical cycling. Systems approaches range from Bayesian calibration techniques at plot scale, through data assimilation methods at regional to continental scales, to multi-disciplinary numerical model applications at country to global scales. We provide examples from a range of studies and show how these approaches are being used to address current topics in global change and biogeochemical research, such as the interaction between carbon and nitrogen cycles, terrestrial carbon feedbacks to climate change and the attribution of observed global changes to various drivers of change. We examine how transferable the methods and techniques might be to other areas of ecosystem science and ecology.