Labile pools of soil organic carbon (SOC) and nitrogen (N) are affecting the carbon (C) and N fluxes in the terrestrial soils, whereas long-term C and N storage is determined by the long-lived recalcitrant fractions. Tillage and subsurface drainage influences these pools; however, the effect of these systems on poorly drained soils may be different. Therefore, the present study was conducted on a field experiment, established at the Waterman Farm of the Ohio State University in 1994. Specific objectives of the study are to assess the influence of no-tillage (NT), chisel tillage (CT) with drainage (TD) and non-drainage (ND) management under a continuous corn (Zea mays L.) system on SOC, C fractions (heavy and light), and water stable aggregates (WSA). Data from this study showed that the SOC stock for the NT was 25, 37 and 32% higher for the 0-10, 10-20 and 40-60 cm depths, respectively, as compared to that under CT system. Tillage significantly influenced the light fraction (LF) and heavy fraction (HF) of carbon. The NT system increased LF and HF by 10 and 12%, respectively, compared to CT for the 0-10 cm depth. Eighteen years of NT management decreased soil bulk density and improved macroaggregates and mean weight diameter compared to that under CT system. Drainage impacts on soil parameters were negligible, and may be partially due to the reason that the corn-corn cropping system may not have sufficiently improved the soil properties between the two tillage systems. In general, drainage improved porosity and the corn yield. It can be concluded that NT with subsurface management improves SOC dynamics and promotes aggregation and corn yield as compared to that with CT system.
National scale projections of bioenergy crop yields and their environmental impacts are essential to identify appropriate locations to place bioenergy crops and ensure sustainable land use strategies. In this study, we used the process‐based Daily Century (DAYCENT) model with site‐specific environmental data to simulate sorghum (Sorghum bicolor L. Moench) biomass yield, soil organic carbon (SOC) change, and nitrous oxide emissions across cultivated lands in the continental United States. The simulated rainfed dry biomass productivity ranged from 0.8 to 19.2 Mg ha−1 year−1, with a spatiotemporal average of 9.7‐2.4+2.1 Mg ha−1 year−1, and a coefficient of variation of 35%. The average SOC sequestration and direct nitrous oxide emission rates were simulated as 0.79‐0.45+0.38 Mg CO2e ha−1 year−1 and 0.38‐0.06+0.04 Mg CO2e ha−1 year−1, respectively. Compared to field‐observed biomass yield data at multiple locations, model predictions of biomass productivity showed a root mean square error (RMSE) of 5.6 Mg ha−1 year−1. In comparison to the multi State (n = 21) NASS database, our results showed RMSE of 5.5 Mg ha−1 year−1. Model projections of baseline SOC showed RMSE of 1.9 kg/m2 in comparison to a recently available continental SOC stock dataset. The model‐predicted N2O emissions are close to 1.25% of N input. Our results suggest 10.2 million ha of cultivated lands in the Southern and Lower Midwestern United States will produce >10 Mg ha−1 year−1 with net carbon sequestration under rainfed conditions. Cultivated lands in Upper Midwestern states including Iowa, Minnesota, Montana, Michigan, and North Dakota showed lower sorghum biomass productivity (average: 6.9 Mg ha−1 year−1) with net sequestration (average: 0.13 Mg CO2e ha−1 year−1). Our national‐scale spatially explicit results are critical inputs for robust life cycle assessment of bioenergy production systems and land use‐based climate change mitigation strategies.
Various approaches of differing mathematical complexities are being applied for spatial prediction of soil properties. Regression kriging is a widely used hybrid approach of spatial variation that combines correlation between soil properties and environmental factors with spatial autocorrelation between soil observations. In this study, we compared four machine learning approaches (gradient boosting machine, multinarrative adaptive regression spline, random forest, and support vector machine) with regression kriging to predict the spatial variation of surface (0-30 cm) soil organic carbon (SOC) stocks at 250-m spatial resolution across the northern circumpolar permafrost region. We combined 2,374 soil profile observations (calibration datasets) with georeferenced datasets of environmental factors (climate, topography, land cover, bedrock geology, and soil types) to predict the spatial variation of surface SOC stocks. We evaluated the prediction accuracy at randomly selected sites (validation datasets) across the study area. We found that different techniques inferred different numbers of environmental factors and their relative importance for prediction of SOC stocks. Regression kriging produced lower prediction errors in comparison to multinarrative adaptive regression spline and support vector machine, and comparable prediction accuracy to gradient boosting machine and random forest. However, the ensemble median prediction of SOC stocks obtained from all four machine learning techniques showed highest prediction accuracy. Although the use of different approaches in spatial prediction of soil properties will depend on the availability of soil and environmental datasets and computational resources, we conclude that the ensemble median prediction obtained from multiple machine learning approaches provides greater spatial details and produces the highest prediction accuracy. Thus an ensemble prediction approach can be a better choice than any single prediction technique for predicting the spatial variation of SOC stocks.
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