Agricultural Greenhouse Gas Fluxes Under Different Cover Crop Systems

Cultivated lands that support high productivity have the potential to produce a large amount of GHG emissions, including carbon dioxide (CO2), nitrous oxide (N2O), and methane (CH4). Intensive land management practices can stimulate CO2, N2O, and CH4 emissions from the soil. Cover crop establishment is considered as one of the sustainable land management strategies under warm and humid environmental conditions. To better understand how the incorporation of cover crops affect three major GHGs, we compared trace gas fluxes in a no-till maize field over the whole growing season in 2018 in a no cover crop (Tr) system and three cover crop systems: crimson clover (CC), cereal rye (CR), and living mulch (LM) using white clover. In 2019, we further explored potential differences in the three GHGs between in-row (IR) and between-row (BWR) of maize for LM and Tr systems during the early growing season. Measurements were taken using a cavity ring-down spectroscopy gas analyzer in Watkinsville, GA. In 2018, the highest CO2 flux (7.00 μmol m−2 s−1) was observed from BWR of maize for LM. The maximum N2O flux observed in LM on June 20th in 2018 was when soil N increase rate was the largest. Soils served as sinks for CH4 and Tr system served as the smallest CH4 sink compared to the other three cover crop systems. For N2O, the highest fluxes were observed from the TrIR plot (4.13 μmol m−2 hr−1) in 2019 with the greatest N inputs. In 2019, we observed a smaller CH4 sink in TrIR (−0.13 μmol m−2 hr−1) compared to TrBWR (−0.67 μmol m−2 hr−1) due potentially to greater NH4+ inhibition effects on CH4 consumption from greater N fertilizer inputs. The net carbon equivalent (CE) from May 23rd to Aug 16th in 2018, taking into account the three GHG fluxes, soil carbon content, and fertilizer, irrigation, and herbicide application, were 32–97, 35–101, 63–139, and 40–106 kg ha−1 yr−1 for CC, CR, LM, and Tr, respectively. LM had the lowest net CE after removing white clover respiration (−16–60 kg ha−1 yr−1). Our results show that implementing different types of cover crop systems and especially the LM system have some potential to mitigate climate change.

Global Climate Change by Wetland Greenhouse Gas Fluxes

Wetlands (WLs) in the landscapes are important for the GHGs production, ingesting, and exchange with the atmosphere. In this chapter, the authors illustrated how the WLs influence climate change, even though it is typical for determining the climatic role of WLs in the broader perspective. The conclusions might be wary based on the radiative balance as the radiative forcing since the 1750s or climatic roles are continuously changing in the wetlands. Degradation of WLs leads to reducing their functioning, and GHG fluxes might change and alter the climatic roles of the WLs. The chapter demonstrated that WL disturbances might cause global warming for a longer duration even though the WLs are restored or managed by replacing them with the mitigation WLs. Thus, activities that cause disturbance in the WLs leading to carbon oxidation in the soils should be avoided. Regulating the climate is an ecosystem service in the WLs; during the planning of the WLs, protection, restoration, and creation, environmental management should be considered.

A Deep Learning Method for Fully Automatic Stomatal Morphometry and Maximal Conductance Estimation

Stomata are integral to plant performance, enabling the exchange of gases between the atmosphere and the plant. The anatomy of stomata influences conductance properties with the maximal conductance rate, gsmax, calculated from density and size. However, current calculations of stomatal dimensions are performed manually, which are time-consuming and error prone. Here, we show how automated morphometry from leaf impressions can predict a functional property: the anatomical gsmax. A deep learning network was derived to preserve stomatal morphometry via semantic segmentation. This forms part of an automated pipeline to measure stomata traits for the estimation of anatomical gsmax. The proposed pipeline achieves accuracy of 100% for the distinction (wheat vs. poplar) and detection of stomata in both datasets. The automated deep learning-based method gave estimates for gsmax within 3.8 and 1.9% of those values manually calculated from an expert for a wheat and poplar dataset, respectively. Semantic segmentation provides a rapid and repeatable method for the estimation of anatomical gsmax from microscopic images of leaf impressions. This advanced method provides a step toward reducing the bottleneck associated with plant phenotyping approaches and will provide a rapid method to assess gas fluxes in plants based on stomata morphometry.

Importance of the Webb, Pearman, and Leuning (WPL) correction for the measurement of small CO₂ fluxes

The WPL (Webb, Pearman, and Leuning) correction is fully accepted to correct trace gas fluxes like CO2 for density fluctuations due to water vapour and temperature fluctuations for open-path gas analysers. It is known that this additive correction can be on the order of magnitude of the actual flux. However, this is hardly ever included in the analysis of data quality. An example from the Arctic shows the problems, because the size of the correction is a multiple of the actual flux. As a general result, we examined and tabulated the magnitude of the WPL correction for carbon dioxide flux as a function of sensible and latent heat flux. Furthermore, we propose a parameter to better estimate possible deficits in data quality and recommend integrating the quality flag derived with this parameter into the general study of small carbon dioxide fluxes.