Scenario Development as a Basis for Formulating a Research Program on Future Agriculture: A Methodological Approach

To increase the awareness of society to the challenges of global food security, we developed five contrasting global and European scenarios for 2050 and used these to identify important issues for future agricultural research. Using a scenario development method known as morphological analysis, scenarios were constructed that took economic, political, technical, and environmental factors into account. With the scenarios as a starting point future challenges were discussed and research issues and questions were identified in an interactive process with stakeholders and researchers. Based on the outcome of this process, six socioeconomic and biophysical overarching challenges for future agricultural were formulated and related research issues identified. The outcome was compared with research priorities generated in five other research programs. In comparison, our research questions focus more on societal values and the role of consumers in influencing agricultural production, as well as on policy formulation and resolving conflicting goals, areas that are presently under-represented in agricultural research. The partly new and more interdisciplinary research priorities identified in Future Agriculture compared to other programs analyzed are likely a result of the methodological approach used, combining scenarios and interaction between stakeholders and researchers.     

Parameter Evaluation and Model Validation of Ozone Exposure Assessment Using Harvard Southern California Chronic Ozone Exposure Study Data

Abstract

To examine factors influencing long‐term ozone (O₃) exposures by children living in urban communities, the authors analyzed longitudinal data on personal, indoor, and outdoor O₃ concentrations, as well as related housing and other questionnaire information collected in the one‐year‐long Harvard Southern California Chronic Ozone Exposure Study. Of 224 children contained in the original data set, 160 children were found to have longitudinal measurements of O₃ concentrations in at least six months of 12 months of the study period. Data for these children were randomly split into two equal sets: one for model development and the other for model validation. Mixed models with various variance‐covariance structures were developed to evaluate statistically important predictors for chronic personal ozone exposures. Model predictions were then validated against the field measurements using an empirical best‐linear unbiased prediction technique.The results of model fitting showed that the most important predictors for personal ozone exposure include indoor O₃ concentration, central ambient O₃ concentration, outdoor O₃ concentration, season, gender, outdoor time, house fan usage, and the presence of a gas range in the house. Hierarchical models of personal O₃ concentrations indicate the following levels of explanatory power for each of the predictive models: indoor and outdoor O₃ concentrations plus questionnaire variables, central and indoor O₃ concentrations plus questionnaire variables, indoor O₃ concentrations plus questionnaire variables, central O₃ concentrations plus questionnaire variables, and questionnaire data alone on time activity and housing characteristics. These results provide important information on key predictors of chronic human exposures to ambient O₃ for children and offer insights into how to reliably and cost‐effectively predict personal O₃ exposures in the future. Furthermore, the techniques and findings derived from this study also have strong implications for selecting the most reliable and cost‐effective exposure study design and modeling approaches for other ambient pollutants, such as fine particulate matter and selected urban air toxics.     

Estimating tree bole volume using artificial neural network models for four species in Turkey

Tree bole volumes of 89 Scots pine (Pinus sylvestris L.), 96 Brutian pine (Pinus brutia Ten.), 107 Cilicica fir (Abies cilicica Carr.) and 67 Cedar of Lebanon (Cedrus libani A. Rich.) trees were estimated using Artificial Neural Network (ANN) models. Neural networks offer a number of advantages including the ability to implicitly detect complex nonlinear relationships between input and output variables, which is very helpful in tree volume modeling. Two different neural network architectures were used and produced the Back propagation (BPANN) and the Cascade Correlation (CCANN) Artificial Neural Network models. In addition, tree bole volume estimates were compared to other established tree bole volume estimation techniques including the centroid method, taper equations, and existing standard volume tables. An overview of the features of ANNs and traditional methods is presented and the advantages and limitations of each one of them are discussed. For validation purposes, actual volumes were determined by aggregating the volumes of measured short sections (average 1 meter) of the tree bole using Smalian's formula. The results reported in this research suggest that the selected cascade correlation artificial neural network (CCANN) models are reliable for estimating the tree bole volume of the four examined tree species since they gave unbiased results and were superior to almost all methods in terms of error (%) expressed as the mean of the percentage errors.     

Development of Models for Predicting the Distribution of Indoor Nitrogen Dioxide Concentrations

Extensive data on residential indoor and outdoor NO₂ levels have been collected in a limited number of U.S. locations. To date, researchers have analyzed these data sets individually, but have not analyzed them in the aggregate. Results have not, therefore, been suitable for application in a nationwide exposure assessment. This paper presents an analysis of indoor and outdoor NO₂ field measurements from five U.S. metropolitan areas for homes with gas-fueled ranges and discusses potential applications of the results. Using linear regression analysis, the relationship between indoor NO₂ and various predictor variables was explored. Results indicated that ambient NO2 levels alone explain an estimated 37 percent of the variability in indoor NO₂ levels, that the relationship between indoor and outdoor NO₂ concentrations differs significantly from summer to winter months, and that homes with range pilot lights have indoor levels approximately 7 ppb greater than homes without pilot lights. A logistic regression model which predicts the distribution of indoor NO₂ levels based on ambient NO₂ concentrations was developed. Estimation and testing of the logistic model indicated good model performance. The model is particularly useful for addressing policy-oriented questions that involve the concept of "acceptable" threshold levels for human exposure to NO₂.     

Multispecies and Multiscale Conservation Planning: Setting Quantitative Targets for Red‐Listed Lichens on Ancient Oaks

Abstract: Species occurrence in a habitat patch depends on local habitat and the amount of that habitat in the wider landscape. We used predictions from empirical landscape studies to set quantitative conservation criteria and targets in a multispecies and multiscale conservation planning effort. We used regression analyses to compare species richness and occurrence of five red‐listed lichens on 50 ancient oaks (Quercus robur; 120–140 cm in diameter) with the density of ancient oaks in circles of varying radius from each individual oak. Species richness and the occurrence of three of the five species were best explained by increasing density of oaks within 0.5 km; one species was best explained by the density of oaks within 2 km, and another was best predicted by the density of oaks within 5 km. The minimum numbers of ancient oaks required for “successful conservation” was defined as the number of oaks required to obtain a predicted local occurrence of 50% for all species included or a predicted local occurrence of 80% for all species included. These numbers of oaks were calculated for two relevant landscape scales (1 km² and 13 km²) that corresponded to various species responses, in such a way that calculations also accounted for local number of oaks. Ten and seven of the 50 ancient oaks surveyed were situated in landscapes that already fulfilled criteria for successful conservation when the 50% and 80% criteria, respectively, were used to define the level of successful conservation. For cost‐efficient conservation, oak stands in the landscapes most suitable for successful conservation should be prioritized for conservation and management (e.g., grazing and planting of new oaks) at the expense of oak stands situated elsewhere.     

Influence of the Anterior and Middle Cranial Fossae on Brain Kinematics During Sagittal Plane Head Rotation

A 2D physical model of the human head was used to investigate how the irregular skull base structure affects brain kinematics during sagittal plane head dynamics. The model consisted of a rigid skull vessel with interchangeable skull base structures. One version of the model used a skull base mimicking the irregular geometry of the human. A second version used a skull base structure approximating the anterior and middle fossae as a flat surface. Silicone gel simulated the brain and was separated from the vessel by a paraffin layer which provided a slip condition at the interface between the gel and vessel. The model was exposed to 7600 rad/s² peak rotational acceleration with 6 ms pulse duration and 5° forced rotation. After 90° free rotation, the model was decelerated during 30 ms. Five repeated tests were conducted with each version. Rigid body displacement, shear strain and principal strains were determined from high-speed video recorded trajectories of grid markers located at different positions in the surrogate brain. The humanlike skull base reduced peak displacements of the inferior surfaces of the temporal and frontal lobes up to 87% and 48%, respectively. Up to 48% and 36% higher peak strains were obtained in the frontal and superior regions of the surrogate brain in the version containing the humanlike skull base. In contrast, the humanlike skull base decreased peak strain up to 28% in the central region of the surrogate brain. The results indicate that the irregular skull base offers natural protection of nerves and vessels passing through fissures and foramina in the cranial floor but also that it affects kinematics in different regions throughout the cerebrum. Implications of these results are discussed with respect to brain injury and modeling of head impact.     

Bee pollens as biological indicators: An ecological assessment of pollution in Northern Turkey via ICP-MS and XPS analyses

Environmental Science and Pollution Research - In this study, pollens were collected from 25 different locations of Northern Turkey to investigate pollution monitoring. Surface chemistry of pollen...