Journal Paper Digests 2023 #6
- Distribution of soil bacteria involved in C cycling across extensive environmental and pedogenic gradients
- Identifying criteria for greenhouse gas flux estimation with automatic and manual chambers: A case study for N2O
- Comparing satellites and vegetation indices for cover crop biomass estimation
Comparing satellites and vegetation indices for cover crop biomass estimation
Cost-share programs based on measures of participation rather than performance are available to farmers who plant cover crops. However, cover crops only provide significant ecological benefits like reduced nutrient loss when adequate biomass is established. The purpose of this study was to determine whether satellite imagery can effectively estimate cover crop biomass in fields with diverse species composition, and whether increased spatial resolution and satellite imaging frequency can increase biomass estimation accuracy. Aboveground biomass samples of 1 m(2) were collected for 86 sites within 26 agricultural fields containing unique cover crop species composition to assess biomass production. In-field sensors were used to measure normalized difference vege-tation index (NDVI) and groundcover percentage. Three satellites (Landsat-8 [30 m resolution], Sentinel-2 [10 m resolution], and PlanetScope [3 m resolution]) were used to calculate eight vegetation indices (VIs) for com-parison with cover crop biomass. Multiple linear regression, correlation coefficients, and root mean square error (RMSE) were used to perform hierarchical clustering to rank VIs calculated from each satellite for biomass estimation accuracy. Satellites predicted cover crop biomass at the field level very accurately (r(2) up to 0.79), demonstrating the potential of large-scale biomass estimation at relatively low cost compared to in-field sam-pling. All satellite-VI pairs estimated biomass more accurately than the in-field sensors. Performance of VIs varied by satellite, but each satellite had at least one VI that performed very well for both site-level and field-averaged data. When using PlanetScope or Landsat-8 imagery, the perpendicular vegetation index provided the most accurate cover crop biomass estimation on a per-site basis and ratio vegetation index performed best using Sentinel-2 imagery. PlanetScope was the only satellite to provide useable imagery for every site due to increased revisit period; however, its increased spatial resolution did not increase estimation accuracy overall compared to Landsat-8 or Sentinel-2.
Identifying criteria for greenhouse gas flux estimation with automatic and manual chambers: A case study for N2O
Fluxes of the powerful greenhouse gas nitrous oxide (N2O) are mainly quantified using manually operated or automatic non-steady-state chambers. With both systems, fluxes are calculated as the change in N2O concentration over time using linear or non-linear regression, but the type of regression selected can have a strong influence on the N2O flux magnitude. The HMR package, a regression-based implementation of the Hutchinson & Mosier Regression, is a widely used software for trace gas flux calculation that provides a recommendation on the most appropriate regression method based on several criteria. New parameters were recently introduced which allow for pre-filtering based on sample variance (pfvar and pfalpha), and for constraining the curvilinearity of concentration-time series(SatPct and SatTimeMin). Currently, there are no guidelines on how to choose the best parameters for specific user conditions. Here we address this knowledge gap using datasets from manual and automatic chambers, and sensitivity analyses. We found that the effect of parameter settings on the cumulative fluxes was greater for manual chamber data compared to the automatic chamber data, with ranges of up to 67.3% and 1.5%, respectively. The parameter pfvar was identified as highly sensitive for both manual and automatic chambers; it is, therefore, critical to select a threshold for when to allow for non-linear flux calculation that accurately represents the given measurement precision. This can be estimated from the variance of N2O measurements at ambient concentration levels. The parameter SatTimeMin was critical for manual chamber data where the curvature is much less constrained due to the lower number of observations. The parameter pfalpha was the least sensitive and can be set at a p-value equal to 0.05 following common statistical practice. Parameter values depend on the expected flux characteristics with a given chamber design and are currently best selected based on visual inspection of the data. This study identifies where and how specific care should be taken for the selection of parameters in the HMR package, which may contribute to standardizing the methodologies used worldwide for N2O flux calculations, supporting initiatives in which data from many studies need to be combined. Highlights The HMR package, an implementation of the Hutchinson & Mosier Regression, is a widely used software for trace gas flux calculation that provides a recommendation on the most appropriate regression method based on several criteria. New parameters in the updated HMR package constrains the use of nonlinear flux calculation and inclusion of outliers. The new parameters affect the cumulative emissions of N2O obtained with manual and automated chambers differently. We found that the choice of parameter values led to differences in cumulative N2O emissions of up to 67.3% This study identifies where and how specific care should be taken for the selection of parameters.
Distribution of soil bacteria involved in C cycling across extensive environmental and pedogenic gradients
Microorganisms play pivotal roles in soil processes. Metabolically related microorganisms constitute functional groups, and diverse microbial functional groups control nutrient cycling in soils. This study explored environmental (i.e., rainfall, temperature) and soil factors driving the distribution of bacterial functional groups involved in soil carbon (C) cycling in paired natural and agricultural ecosystems. Soil samples were collected at a regional scale covering gradients of temperature and rainfall across two orthogonal transects (similar to 1000 km) in New South Wales, Australia. Putative functions of bacteria were linked to two soil C fractions: particulate organic carbon (POC) and mineral-associated organic carbon (MAOC). We found: (i) temperature and rainfall were important drivers of bacterial functional groups, while soil properties, such as pH, soil C and nitrogen (N), also presented significant contributions; (ii) community structure of bacteria involved in C cycling was mainly related to POC content but not to MAOC; (iii) paired sampling showed that agricultural practices had significant impacts on the composition and responses of soil bacterial functional groups. This study demonstrated the environmental regulation (e.g., temperature and rainfall) of soil microbial functional groups at large scales, which was altered by agricultural practices.HighlightsSoil bacteria involved in C cycling were investigated across two similar to 1000 km transects.Temperature and rainfall were important drivers of bacterial functional groups at large scale.Paired sampling showed that agriculture led to a significant shift in bacterial functional groups.Community structure of bacterial functional groups were correlated with soil POC but not MAOC.