Early sexual intercourse: Prospective associations with adolescents physical activity and screen time

Objectives: To assess the prospective associations of physical activity behaviors and screen time with early sexual intercourse initiation (i.e., before 15 years) in a large sample of adolescents. Methods: We used two waves of data from the Rotterdam Youth Monitor, a longitudinal study conducted in the Netherlands. The analysis sample consisted of 2,141 adolescents aged 12 to 14 years (mean age at baseline = 12.2 years, SD = 0.43). Physical activity (e.g., sports outside school), screen time (e.g., computer use), and early sexual intercourse initiation were assessed by means of self-report questionnaires. Logistic regression models were tested to assess the associations of physical activity behaviors and screen time (separately and simultaneously) with early sexual intercourse initiation, controlling for confounders (i.e., socio-demographics and substance use). Interaction effects with gender were tested to assess whether these associations differed significantly between boys and girls. Results: The only physical activity behavior that was a significant predictor of early sexual intercourse initiation was sports club membership. Adolescent boys and girls who were members of a sports club) were more likely to have had early sex (OR = 2.17; 95% CI = 1.33, 3.56. Significant gender interaction effects indicated that boys who watched TV ≥2 hours/ day (OR = 2.00; 95% CI = 1.08, 3.68) and girls who used the computer ≥2 hours/day (OR = 3.92; 95% CI = 1.76, 8.69) were also significantly more likely to have engaged in early sex. Conclusion: These findings have implications for professionals in general pediatric healthcare, sexual health educators, policy makers, and parents, who should be aware of these possible prospective links between sports club membership, TV watching (for boys), and computer use (for girls), and early sexual intercourse initiation. However, continued research on determinants of adolescents' early sexual initiation is needed to further contribute to the strategies for improving adolescents' healthy sexual development and behaviors

Challenges and research gaps in the area of integrated climate change risk assessment for European agriculture and food security

Priorities in addressing research gaps and challenges should follow the order of im­por­tance, which in itself would be a matter of defining goals and metrics of importance, e.g. the extent, impact and likelihood of occurrence. For improving assessments of cli­mate change impacts on agriculture for achieving food security and other sustainable develop­ment goals across the European continent, the most important research gaps and challen­ges appear to be the agreement on goals with a wide range of stakeholders from policy, science, producers and society, better reflection of political and societal prefer­ences in the modelling process, and the reflection of economic decisions in farm manage­ment within models. These and other challenges could be approached in phase 3 of MACSUR

Modelling Adaptation to climate change in agricultural systems

Modelling agricultural adaptation to climate change presents a range of challenges for modellers, but is vital to enabling decision makers to understand the potential costs and benefits of applying adaptation measures on-farm (or not) including risks and uncertainties associated with different actions. Here, the first stages of collaborative work undertaken at a workshop held in Braunschweig, Germany in autumn 2015, and subsequent analysis of findings, are reported. Subsequently, a second report will detail the development of these actions into a coherent overview of the state-of-the-art in modelling adaptation. Modellers and experimental researchers from a variety of disciplines (including biophysical and economic modellers from livestock, crop and grassland systems backgrounds) were asked to consider major climate impacts and associated adaptation options, and the challenges to modelling adaptations. Key modelling challenges fell into four main categories: information availability, accessibility of model outputs for stakeholders, technical challenges, and knowledge gaps. Within these categories, lists of specific challenges were compiled. The workshop revealed the diversity of approaches to modelling adaptation, and highlighted the different challenges associated with biophysical versus economic modelling. Understanding the state-of-the-art and key priorities for the modelling of climate change adaptation in agriculture is shown to be a complex and multi-faceted challenge. However, such an overview would provide a road map for stakeholder-driven improvement in modelling, with the potential to inform increased uptake of adaptation measures on-farm in Europe.(The main text will be published in a peer-reviewed journal

Prediction of enteric methane production, yield and intensity in dairy cattle using an intercontinental database

Enteric methane (CH4) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH4 is complex, expensive and impractical at large scales; therefore, models are commonly used to predict CH4 production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH4 production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH4 production (g/d per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross-validate their performance; and (4) assess the trade-off between availability of on-farm inputs and CH4 prediction accuracy. The intercontinental database covered Europe (EU), the US (US), Chile (CL), Australia (AU), and New Zealand (NZ). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6, 14.4, and 19.8% for intercontinental, EU, and US regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH4 production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH4 emission conversion factors for specific regions are required to improve CH4 production estimates in national inventories. In conclusion, information on DMI is required for good prediction, and other factors such as dietary NDF concentration, improve the prediction. For enteric CH4 yield and intensity prediction, information on milk yield and composition is required for better estimation

Modelling climate change adaptation in European agriculture: Definitions and Current Modelling

Confidential content, in preparation for a peer-reviewed publication

Symposium review: uncertainties in enteric methane inventories,measurement techniques, and prediction models

Ruminant production systems are important contributors to anthropogenic methane (CH4) emissions, but there are large uncertainties in national and global livestock CH4 inventories. Sources of uncertainty in enteric CH4 emissions include animal inventories, feed dry matter intake (DMI), ingredient and chemical composition of the diets, and CH4 emission factors. There is also significant uncertainty associated with enteric CH4 measurements. The most widely used techniques are respiration chambers, the sulfur hexafluoride (SF6) tracer technique, and the automated head-chamber system (GreenFeed; C-Lock Inc., Rapid City, SD). All 3 methods have been successfully used in a large number of experiments with dairy or beef cattle in various environmental conditions, although studies that compare techniques have reported inconsistent results. Although different types of models have been developed to predict enteric CH4 emissions, relatively simple empirical (statistical) models have been commonly used for inventory purposes because of their broad applicability and ease of use compared with more detailed empirical and process-based mechanistic models. However, extant empirical models used to predict enteric CH4 emissions suffer from narrow spatial focus, limited observations, and limitations of the statistical technique used. Therefore, prediction models must be developed from robust data sets that can only be generated through collaboration of scientists across the world. To achieve high prediction accuracy, these data sets should encompass a wide range of diets and production systems within regions and globally. Overall, enteric CH4 prediction models are based on various animal or feed characteristic inputs but are dominated by DMI in one form or another. As a result, accurate prediction of DMI is essential for accurate prediction of livestock CH4 emissions. Analysis of a large data set of individual dairy cattle data showed that simplified enteric CH4 prediction models based on DMI alone or DMI and limited feed- or animal-related inputs can predict average CH4 emission with a similar accuracy to more complex empirical models. These simplified models can be reliably used for emission inventory purposes

Towards a Comprehensive Model of Negotiated Interaction in Computer-mediated Communication

In this paper we explore and identify emerging patterns of synchronous digital discourse trajectories between dyads of native (NS) and non-native speakers (NNS), with a particular focus on (absence of) negotiated interaction. We will present a new model of L2 learning interaction that is a schematic representation of two main types of hearer response that have been found after a trigger of non- understanding: Task-appropriate response (TAR) and face-appropriate response (FAR). In addition, we outline five different discourse trajectories. The model we propose is based on data derived from interactive task performances of groups of Dutch and Australian students in two telecollaboration projects. The discourse trajectories represented in our model provide us with useful insights into the complexities of digital interaction in an L2-learning environment and show that NNS-NS communication is more complex than traditional negotiation of meaning models suggest. We expect our model to contribute to a better understanding of L2 learning processes related to interaction in digital settings