Nimai Senapati

Net carbon storage measured in a mowed and grazed temperate sown grassland shows potential for carbon sequestration under grazed system

Background: Managed temperate grassland has the potential to sequester carbon if management practices are improved. In this study, CO2 flux was measured by the eddy covariance technique in two identical temperate sown grasslands under different managements, viz. mowing and grazing, to estimate and compare net carbon storage under both the management systems. Results: In both mowing and grazing systems, the averaged annual gross plant productivity, ecosystem respiration and net ecosystem exchange were –1720 and –1741, 1244 and 1510, and –476 and –231 g C m–2 year–1, respectively. Although the management practices did not significantly influence gross plant productivity (p > 0.05), grazing system increased Reco significantly by 21% (p < 0.05) but reduced net ecosystem exchange by 52% (p < 0.05) compared to mowing system. However, averaged annual net carbon storage were 23 and 141 g C m–2 year–1 under mowing and grazing, respectively. Conclusion: The results indicate that temperate sown grassland has the potential to sequester carbon under grazing.

Modelling heat, water and carbon fluxes in mown grassland under multi-objective and multi-criteria constraints

A Monte Carlo-based calibration and uncertainty assessment was performed for heat, water and carbon (C) fluxes, simulated by a soil-plant-atmosphere system model (CoupModel), in mown grassland. Impact of different multi-objective and multi-criteria constraints was investigated on model performance and parameter behaviour. Good agreements between hourly modelled and measurement data were obtained for latent and sensible heat fluxes (R2?=?0.61, ME?=?0.48?MJ?m-2?day-1), soil water contents (R2?=?0.68, ME?=?0.34%) and carbon-dioxide flux (R2?=?0.60, ME?=?-0.18?g?C?m-2?day-1). Multi-objective and multi-criteria constraints were efficient in parameter conditioning, reducing simulation uncertainty and identifying critical parameters. Enforcing multi-constraints separately on heat, water and C processes resulted in the highest model improvement for that specific process, including some improvement too for other processes. Imposing multi-constraints on all groups of variables, associated with heat, water and C fluxes together, resulted in general effective parameters conditioning and model improvement. Uncertainty-based modelling was done for heat, water and carbon flux in grassland.Multi-objective and multi-criteria were enforced to constrain model simulations.Multi-objective and multi-criteria constraints were effective in model calibration.Different multi-constraints conditioned model parameters differently.Different multi-constraints improved the model performance in different way.

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.

A Basic Concept on Modelling Soil Organic Carbon

Carbon (C) stored in soils is the largest C pool in most terrestrial ecosystems (~ 1500 Pg C in the top meter), roughly twice the amount of C in the atmosphere and three times the amount in vegetation. Thus, the small changes in the soil organic carbon (SOC) pool could have a vast impact on the environment. Diffusive process plays an important role in mass and gaseous movement/transportation in SOC dynamics. The major factors controlling the size of the SOC pool and its movement are changes in land usage, climate, soil, management practice and technology. Understanding the response of the soil C reserve and its transportation to changes in different factors is of critical importance. The SOC turnover models are able to simulate SOC dynamics under different land usage, climatic conditions, and management practices. Thus, they could help in the investigation of change in the SOC dynamics under different scenarios. The information compiled in this chapter might provide a basic understanding of SOC modelling and support decision making in land management and climate adaptation strategies.