Modelling above-ground herbage mass for a wide range of grassland community types

Whereas it is recognized that management of plant diversity can be the key to reconciling production and environmental aims, most grassland models are tailored for high-value grass species. We proposed to adapt a mono-specific grass model to take into account specific features of species-rich permanent grasslands, especially over the reproductive phase. To this end, we used the concept of plant functional type (PFT), i.e. the grouping of plant species according to plant traits determined by the response of plant species to different management practices (land use and fertilization) and characterizing of agronomic properties of the corresponding species. In the model, weather and nutrient availability act upon rates of biophysical processes (radiation capture and use, plant senescence). These rates are modified over times due to PFT-specific parameters determined experimentally which represent the different strategies of plant species regarding growth. The integration of these parameters into the model made it possible to predict herbage biomass accumulation rate under different management practices for a wide range of plant communities differing in their PFT composition. The model was evaluated in two steps, first by analyzing separately the effects of PFT and an indicator of nutrient availability on herbage accumulation and then by conducting a sensitivity analysis. It was validated using two independent datasets; a cutting experiment running over the whole growing season to examine the consistency of the model outputs under different cutting regimes, and a monitoring of meadows and pastures in spring over a whole growth cycle to assess the model’s ability to reproduce growth curves. Although a good fit was observed between the simulated and observed data, the few discrepancies noticed between field data and predicted values were attributed mainly to the potential presence of non-grass species. More specifically, we noticed that nutrient (mainly nitrogen) availability is the main driver of plant growth rate, and that PFT determines the times at which this rate changes in relation to the phenological characteristics of species present. We concluded that integration of the PFT concept into the initial mono-specific growth model is especially suited to evaluating the consequences of management practices on species-rich permanent grasslands to meet feed production targets.     

Modelling concentrations of airborne primary and secondary PM10 and PM2.5 with the BelEUROS-model in Belgium

The Eulerian Chemistry-Transport Model BelEUROS was used to calculate the concentrations of airborne PM₁₀ and PM_2.5 over Europe. Both primary as well as secondary particulate matter in the respirable size-range was taken into account. Especially PM_2.5 aerosols are often formed in the atmosphere from gaseous precursor compounds. Comprehensive computer codes for the calculation of gas phase chemical reactions and thermodynamic equilibria between compounds in the gas-phase and the particulate phase had been implemented into the BelEUROS-model. Calculated concentrations of PM₁₀ and PM_2.5 are compared to observations, including both the spatial and daily, temporal distribution of particulate matter in Belgium for certain monitoring locations and periods. The concentrations of the secondary compounds ammonium, nitrate and sulfate have also been compared to observed values. BelEUROS was found to reproduce the observed concentrations rather well. The model was applied to assess the contribution of emissions derived from the sector agriculture in Flanders, the northern part of Belgium, to PM₁₀- and PM_2.5-concentrations. The results demonstrate the importance of ammonia emissions in the formation of secondary particulate matter. Hence, future European emission abatement policy should consider more the role of ammonia in the formation of secondary particles.     

Selecting pseudo-absence data for presence-only distribution modeling: How far should you stray from what you know?

An important decision in presence-only species distribution modeling is how to select background (or pseudo-absence) localities for model parameterization. The selection of such localities may influence model parameterization and thus, can influence the appropriateness and accuracy of the model prediction when extrapolating the species distribution across time and space. We used 12 species from the Australian Wet Tropics (AWT) to evaluate the relationship between the geographic extent from which pseudo-absences are taken and model performance, and shape and importance of predictor variables using the MAXENT modeling method. Model performance is lower when pseudo-absence points are taken from either a restricted or broad region with respect to species occurrence data than from an intermediate region. Furthermore, variable importance (i.e., contribution to the model) changed such that, models became increasingly simplified, dominated by just two variables, as the area from which pseudo-absence points were drawn increased. Our results suggest that it is important to consider the spatial extent from which pseudo-absence data are taken. We suggest species distribution modeling exercises should begin with exploratory analyses evaluating what extent might provide both the most accurate results and biologically meaningful fit between species occurrence and predictor variables. This is especially important when modeling across space or time—a growing application for species distributional modeling.     

Model correlation in stochastic forest simulators—A case of multilevel multivariate model for seedling establishment

Forest development can be predicted by the use of forest simulators based on various statistical models describing the forest and its dynamics. One potential approach to study the reliability of the simulators is to utilise Monte Carlo simulation techniques to generate a predictive distribution of a forest characteristic. One problem in examining the effect of model uncertainty in forestry decision making, however, is correlation between the models. If this is not taken into account, predictions of the model systems may become biased, and the effect of errors on decision making may be underestimated. In reality, the models often are interdependent, but the correlations usually are not known because the models have been estimated in separate studies. The aim of this paper is to study the impacts of between-model dependencies on the predictive distribution of forest characteristics by Monte Carlo simulation techniques. We utilise a case of predicting seedling establishment of planted Norway spruce (Picea abies (L.) Karst.) stands as an example with multivariate multilevel model structures. Regardless of low cross-correlations between the models, ignoring them led to significant underestimation of the amount of competing broadleaves to be removed in pre-commercial thinning. Therefore, we recommend that between-model dependencies are clarified and considered in stochastic simulations. In our case, between-model interdependencies can be reliably estimated with a limited dataset. In addition, estimating the models separately and using the model residuals to estimate interdependencies between models were also sufficient to take the between-model dependencies into account when producing stochastic predictions for silvicultural decision making.     

易燃易爆重大危险源标准化管理模式初探

以易燃易爆重大危险源的控制系统(包括易燃易爆重大危险源的辨识、评价、管理、监察、应急计划)为基础,探讨性地提出了进行标准化管理的思路与方法,旨在建立一种标准化管理的新模式.     

用灰色建模法预测矿井瓦斯涌出量

在分析了传统的矿井瓦斯涌出量预测方法的基础上 ,应用灰色系统理论 GM(1,1)模型 ,对矿井瓦斯涌出量进行预测 ,并在实际中得到了较好的验证。     

Assessing the effect of long-term crop cultivation on distribution of Cd in the root zone

Trace elements such as cadmium (Cd) may be inadvertently added to cropland soils through application of fertilizers, irrigation water, and other amendments. These toxic trace elements pose a potentially threat to soil quality and, through the food chain transfer, to human health. A generalized soil trace element mass balance model that accounts for the interactive processes governing the reactions of trace elements in soils, and consequently removed with crop harvest and leaching out of the soil profile with irrigation water was developed in this research. The model conceptually approximates the mechanisms and kinetics of a real field cropland system. The model was used to evaluate the long-term cultivation on distribution of Cd in California croplands. Under typical California cropping practices, Cd added into the soils accumulated primarily in the plow layer while the Cd content below the plow layer was barely affected. After 100 years of continuous cultivation, the soil Cd content of the plow layer increases from the background level 0.22 mg kg⁻¹ to 0.40 mg kg⁻¹. The accumulation of Cd in the plow layer is in proportion to the external inputs and is affected by the soil and plant characteristics, and management practices. The model can be used to evaluate the environmental fates of other toxic element in soils with case specific parameters.     

Comparing model averaging with other model selection strategies for benchmark dose estimation

Model averaging (MA) has been proposed as a method of accommodating model uncertainty when estimating risk. Although the use of MA is inherently appealing, little is known about its performance using general modeling conditions. We investigate the use of MA for estimating excess risk using a Monte Carlo simulation. Dichotomous response data are simulated under various assumed underlying dose–response curves, and nine dose–response models (from the USEPA Benchmark dose model suite) are fit to obtain both model specific and MA risk estimates. The benchmark dose estimates (BMDs) from the MA method, as well as estimates from other commonly selected models, e.g., best fitting model or the model resulting in the smallest BMD, are compared to the true benchmark dose value to better understand both bias and coverage behavior in the estimation procedure. The MA method has a small bias when estimating the BMD that is similar to the bias of BMD estimates derived from the assumed model. Further, when a broader range of models are included in the family of models considered in the MA process, the lower bound estimate provided coverage close to the nominal level, which is superior to the other strategies considered. This approach provides an alternative method for risk managers to estimate risk while incorporating model uncertainty.