Atrazine is not readily mineralised in 24 temperate soils regardless of pre-exposure to triazine herbicides

Mineralisation of atrazine in soil has been shown to depend on previous exposure of the herbicide. In this study, 24 Danish soils were collected and screened for potential to mineralise atrazine. Six soils were chosen, because they had never been exposed to atrazine, whereas 18 soils were chosen because of their history of application of atrazine or the related compound terbuthylazine. None of the 24 soils revealed a mineralisation potential of more than 4% of the added atrazine within a 60 day timeframe. In an atrazine adapted French soil, we found 60% mineralisation of atrazine in 30 days. Cattle manure was applied in order to boost the microbial activity, and a 2-3% increase in the atrazine mineralisation was found in some of the temperate soils, while in the highly adapted French soil it caused a 5% reduction.     

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.