Effects of climatic data low-pass filtering on the ICBM temperature- and moisture-based soil biological activity factors in a cool and humid climate

The air temperature (T_air), total precipitation (TP) and potential evapotranspiration (PET) are standard input data for soil carbon dynamic models, i.e., for calculating temperature and moisture effects on soil biological activity. The resolution needed depends on objectives, the complexity of models and inbuilt pedotransfer functions. The Introductory Carbon Balance Model (ICBM) soil climate front end model calculates a multiplicative soil-temperature (r_{e_temp}) and -moisture (r_{e_wat}) factor with a daily time-step to estimate soil biological activity, i.e., r_{e_crop} = r_{e_temp} × r_{e_wat}. Our objective was to determine how much r_{e_temp}, r_{e_wat} and r_{e_crop} are affected by low-pass filtering of the climatic input data for a cool, humid temperate region. To achieve this we conducted spectral analysis on T_air, TP, PET and r_{e_crop} in the frequency domain. Thereafter we applied Fourier low-pass filters of 5, 15, 30, 60 and 180 days on T_air, TP, PET and tracked their effects through the soil climate model's state variables and outputs. This was done using a sandy and a heavy clay soil and an 89-year daily time-series from a meteorological station in Quebec (Canada). The Fourier spectra showed that the variance for T_air, PET and r_{e_crop} was dominated by an annual cycle, as could be expected. There was no yearly cycle for TP. The variation in r_{e_temp} explained most of the variance in r_{e_crop}. The soil climate module outputs were not sensitive to low-pass filtering of PET. A daily time-step was needed to avoid overestimating r_{e_crop} for the sandy soil. Using a weekly time-step for TP and T_air allowed us to explain about 80% of the variance in r_{e_crop} for the heavy clay soil. This study also indicates that the standard leaf (and green) area index functions for calculating transpiration should receive more attention, since they have significant effects on the state and output variables.     

Uncertainty analyses for calibrating a soil carbon balance model to agricultural field trial data in Sweden and Kenya

How do additional data of the same and/or different type contribute to reducing model parameter and predictive uncertainties? Most modeling applications of soil organic carbon (SOC) time series in agricultural field trial datasets have been conducted without accounting for model parameter uncertainty. There have been recent advances with Monte Carlo-based uncertainty analyses in the field of hydrological modeling that are applicable, relevant and potentially valuable in modeling the dynamics of SOC. Here we employed a Monte Carlo method with threshold screening known as Generalized Likelihood Uncertainty Estimation (GLUE) to calibrate the Introductory Carbon Balance Model (ICBM) to long-term field trail data from Ultuna, Sweden and Machang’a, Kenya. Calibration results are presented in terms of parameter distributions and credibility bands on time series simulations for a number of case studies. Using these methods, we demonstrate that widely uncertain model parameters, as well as strong covariance between inert pool size and rate constant parameters, exist when root mean square simulation errors were within uncertainties in input estimations and data observations. We show that even rough estimates of the inert pool (perhaps from chemical analysis) can be quite valuable to reduce uncertainties in model parameters. In fact, such estimates were more effective at reducing parameter and predictive uncertainty than an additional 16 years time series data at Ultuna. We also demonstrate an effective method to jointly, simultaneously and in principle more robustly calibrate model parameters to multiple datasets across different climatic regions within an uncertainty framework. These methods and approaches should have benefits for use with other SOC models and datasets as well.     

Crop residue removal and fertilizer N: Effects on soil organic carbon in a long-term crop rotation experiment on a Udic Boroll

Biofuels can be produced by converting cellulose in crop residues to ethanol. This has recently been viewed as a potential supplement to non-renewable energy sources, especially in the Americas. A 50-yr field experiment was analyzed to determine the influence of (i) removing approximately 22% of the above-ground wheat (Triticum aestivum L.) residue each crop year, and (ii) N and P fertilization on soil carbon (C) in the top 15 cm depth of a fallow–wheat–wheat (F–W–W) rotation. The study was conducted from 1958 to 2007 on a clay soil, at Indian Head in sub-humid southeast Saskatchewan, Canada. Soil C concentrations and bulk densities were measured in the 0–7.5 and 7.5–15 cm depths in 1987, 1996 and 2007 and soil C changes were related to C inputs estimated from straw and root yields calculated from regressions relating these to grain yields. Two soil organic matter models [the Campbell model and the Introductory Carbon Balance Model (ICBM)] were also used to simulate and predict the effects of the treatments on soil C change over time, and to estimate likely soil C change if 50% or 95% of above-ground residues were harvested each crop year. Crop residue removal reduced cumulative C inputs from straw and roots over the 50-yr experiment by only 13%, and this did not significantly (P > 0.05) reduce soil C throughout the experiment duration. However, after 50 yr of applying N fertilizer at recommended rates, soil C increased significantly by about 3 Mg ha⁻¹ compared to the non-fertilized treatment. The simulated effect of removing 50% and 95% of the above-ground residues suggested that removing 50% of the straw would likely have a detectable effect on the soil C, while removing 95% of the straw certainly would. Measurements and model simulations suggest that adoption of no-tillage without proper fertilization will not increase soil C. Although it appears that a modest amount of residue may be safely removed from these Udic Borolls (Black Chernozems) without a measurable effect on soil C, this would only be feasible if accompanied by appropriate fertility management.