Maike Krauss

Impact of reduced tillage on greenhouse gas emissions and soil carbon stocks in an organic grass-clover ley - winter wheat cropping sequence

Highlights • First study comparing climate impacts of tillage systems in organic arable farming.• No tillage system impact on N2O and CH4 emissions in grass-clover and wheat.• Higher N2O pulses after tillage operations with increasing soil organic carbon.• Higher soil organic carbon stocks with reduced tillage in slurry fertilised fields.

N use efficiencies and N2O emissions in two contrasting, biochar amended soils under winter wheat—cover crop—sorghum rotation

Biochar, a carbon-rich, porous pyrolysis product of organic residues, is evaluated as an option to tackle major problems of the global food system. Applied to soil, biochar can sequester carbon and have beneficial effects on nitrogen (N) cycling, thereby enhancing crop yields and reducing nitrous oxide (N2O) emissions. There is little understanding of the underlying mechanisms, but many experiments indicated increased yields and manifold changes in N transformation, suggesting an increase in N use efficiency. Biochars effects can be positive in extensively managed tropical agriculture, however less is known about its use in temperate soils with intensive fertilisation. We tested the effect of slow pyrolysis wood chip biochar on N use efficiency, crop yields and N2O emissions in a lysimeter system with two soil types (sandy loamy Cambisol and silty loamy Luvisol) in a winter wheat—cover crop—sorghum rotation. 15N-labelled ammonium nitrate fertiliser (170 kg N ha−1 in 3 doses, 10% 15N) was applied to the first crop to monitor its fate in three ecosystem components (plants, soil, leachate). Green rye was sown as cover crop to keep the first years fertiliser N for the second years sorghum crop (fertilised with 110 kg N ha−1 in two doses and natural abundance 15N). We observed no effects of biochar on N fertiliser use efficiency, yield or N uptake for any crop. Biochar reduced leaching by 43 ± 19% but only towards the end of the experiment with leaching losses being generally low. For both soils N2O emissions were reduced by 15 ± 4% with biochar compared to the control treatments. Our results indicate that application of the chosen biochar induces environmental benefits in terms of N2O emission and N leaching but does not substantially affect the overall N cycle and hence crop performance in the analyzed temperate crop rotation.

Restricting the nonlinearity parameter in soil greenhouse gas flux calculation for more reliable flux estimates

The static chamber approach is often used for greenhouse gas (GHG) flux measurements, whereby the flux is deduced from the increase of species concentration after closing the chamber. Since this increase changes diffusion gradients between chamber air and soil air, a nonlinear increase is expected. Lateral gas flow and leakages also contribute to non linearity. Several models have been suggested to account for this non linearity, the most recent being the Hutchinson–Mosier regression model (hmr). However, the practical application of these models is challenging because the researcher needs to decide for each flux whether a nonlinear fit is appropriate or exaggerates flux estimates due to measurement artifacts. In the latter case, a flux estimate from the linear model is a more robust solution and introduces less arbitrary uncertainty to the data. We present the new, dynamic and reproducible flux calculation scheme, kappa.max, for an improved trade-off between bias and uncertainty (i.e. accuracy and precision). We develop a tool to simulate, visualise and optimise the flux calculation scheme for any specific static N2O chamber measurement system. The decision procedure and visualisation tools are implemented in a package for the R software. Finally, we demonstrate with this approach the performance of the applied flux calculation scheme for a measured flux dataset to estimate the actual bias and uncertainty. The kappa.max method effectively improved the decision between linear and nonlinear flux estimates reducing the bias at a minimal cost of uncertainty.