Research topics to scale up cover crop use: Reflections from innovative Iowa farmers

Cover crops as a conservation practice continue to receive attention from farmers, researchers, media, and policy makers, given their ability to effectively reduce water pollution and improve soil quality. Recent estimates of cover crop use across the midwestern Corn Belt, as well as the United States, demonstrate large acreage increases over the last number of years. The annual Sustainable Agriculture Research and Education–Conservation Technology Information Center (SARE– CTIC) survey found that nationally cover crop acreage doubled from 2011 to 2016, based on farmers self-reporting cover crop planting (CTIC 2016). However, the total cover crop acreage based on 2012 Census of Agriculture data only represents 3.2% of harvested cropland nationally and just 2.3% of the total cropland in the US Corn Belt (USDA NASS 2014a, 2014b)

Tea: A High-level Language and Runtime System for Automating Statistical Analysis

Though statistical analyses are centered on research questions and hypotheses, current statistical analysis tools are not. Users must first translate their hypotheses into specific statistical tests and then perform API calls with functions and parameters. To do so accurately requires that users have statistical expertise. To lower this barrier to valid, replicable statistical analysis, we introduce Tea, a high-level declarative language and runtime system. In Tea, users express their study design, any parametric assumptions, and their hypotheses. Tea compiles these high-level specifications into a constraint satisfaction problem that determines the set of valid statistical tests, and then executes them to test the hypothesis. We evaluate Tea using a suite of statistical analyses drawn from popular tutorials. We show that Tea generally matches the choices of experts while automatically switching to non-parametric tests when parametric assumptions are not met. We simulate the effect of mistakes made by non-expert users and show that Tea automatically avoids both false negatives and false positives that could be produced by the application of incorrect statistical tests.Comment: 11 page

Mixed-method study of a conceptual model of evidence-based intervention sustainment across multiple public-sector service settings.

BackgroundThis study examines sustainment of an EBI implemented in 11 United States service systems across two states, and delivered in 87 counties. The aims are to 1) determine the impact of state and county policies and contracting on EBI provision and sustainment; 2) investigate the role of public, private, and academic relationships and collaboration in long-term EBI sustainment; 3) assess organizational and provider factors that affect EBI reach/penetration, fidelity, and organizational sustainment climate; and 4) integrate findings through a collaborative process involving the investigative team, consultants, and system and community-based organization (CBO) stakeholders in order to further develop and refine a conceptual model of sustainment to guide future research and provide a resource for service systems to prepare for sustainment as the ultimate goal of the implementation process.MethodsA mixed-method prospective and retrospective design will be used. Semi-structured individual and group interviews will be used to collect information regarding influences on EBI sustainment including policies, attitudes, and practices; organizational factors and external policies affecting model implementation; involvement of or collaboration with other stakeholders; and outer- and inner-contextual supports that facilitate ongoing EBI sustainment. Document review (e.g., legislation, executive orders, regulations, monitoring data, annual reports, agendas and meeting minutes) will be used to examine the roles of state, county, and local policies in EBI sustainment. Quantitative measures will be collected via administrative data and web surveys to assess EBI reach/penetration, staff turnover, EBI model fidelity, organizational culture and climate, work attitudes, implementation leadership, sustainment climate, attitudes toward EBIs, program sustainment, and level of institutionalization. Hierarchical linear modeling will be used for quantitative analyses. Qualitative analyses will be tailored to each of the qualitative methods (e.g., document review, interviews). Qualitative and quantitative approaches will be integrated through an inclusive process that values stakeholder perspectives.DiscussionThe study of sustainment is critical to capitalizing on and benefiting from the time and fiscal investments in EBI implementation. Sustainment is also critical to realizing broad public health impact of EBI implementation. The present study takes a comprehensive mixed-method approach to understanding sustainment and refining a conceptual model of sustainment

FACSGen 2.0 animation software: Generating 3D FACS-valid facial expressions for emotion research

In this article, we present FACSGen 2.0, new animation software for creating static and dynamic three-dimensional facial expressions on the basis of the Facial Action Coding System (FACS). FACSGen permits total control over the action units (AUs), which can be animated at all levels of intensity and applied alone or in combination to an infinite number of faces. In two studies, we tested the validity of the software for the AU appearance defined in the FACS manual and the conveyed emotionality of FACSGen expressions. In Experiment 1, four FACS-certified coders evaluated the complete set of 35 single AUs and 54 AU combinations for AU presence or absence, appearance quality, intensity, and asymmetry. In Experiment 2, lay participants performed a recognition task on emotional expressions created with FACSGen software and rated the similarity of expressions displayed by human and FACSGen faces. Results showed good to excellent classification levels for all AUs by the four FACS coders, suggesting that the AUs are valid exemplars of FACS specifications. Lay participants' recognition rates for nine emotions were high, and comparisons of human and FACSGen expressions were very similar. The findings demonstrate the effectiveness of the software in producing reliable and emotionally valid expressions, and suggest its application in numerous scientific areas, including perception, emotion, and clinical and neuroscience research

Implementing a Reconciliation and Balancing Model in the U.s. Industry Accounts

As part of the U.S. Bureau of Economic Analysis’ integration initiative (Yuskavage, 2000; Moyer et al., 2004a, 2004b; Lawson et al., 2006), the Industry Accounts Directorate is drawing upon the Stone method (Stone et al., 1942) and Chen (2006) to reconcile the gross operating surplus component of value-added from the 2002 expenditure-based benchmark input-output accounts and the 2002 income-based gross domestic product-by-industry accounts. The objective of the reconciliation is to use information regarding the relative reliabilities of underlying data in both the benchmark input-output use table and the gross domestic product-by-industry accounts in a balanced input-output framework in order to improve intermediate input estimates and gross operating surplus estimates in both accounts. Given a balanced input-output framework, the Stone method also provides a tool for balancing the benchmark use table. This paper presents work by the Industry Accounts Directorate to develop and implement the reconciliation and balancing model. The paper provides overviews of the benchmark use table and gross domestic product-by-industry accounts, including features of external source data and adjustment methodologies that are relevant for the reconciliation. In addition, the paper presents the empirical model that the Industry Accounts Directorate is building and briefly describes the technology used to solve the model. Preliminary work during development of the model shows that reconciling and balancing a large system with disaggregated data is computationally feasible and efficient in pursuit of an economically accurate and reliable benchmark use table and gross domestic product-by-industry accounts.

Climate change challenges require collaborative research to drive agrifood system transformation

The recent Climate Science Special Report released as part of the Fourth National Climate Assessment confirms that we are living through the warmest period in modern civilization and that human activities are the primary driver of this warming (Wuebbles et al., 2017). These climatic changes have and will continue to impact global agricultural production, with food security and production consequences that will be felt unequally across the planet. Agricultural activities contribute to global warming emissions, while also offering opportunities for greenhouse gas mitigation. It is clear that the agrifood system will have to adapt to a changing climate. To better assess climate influences on agricultural systems in this themed issue of Renewable Agriculture and Food Systems, we challenged authors to submit interdisciplinary research that examines climate change adaptation and mitigation in agriculture and subsequent interconnected impacts to the food system. Indeed, agrifood systems provide a fertile context for examining climate change from multiple disciplines

Seeing is not always believing: Crop loss and climate change perceptions among farm advisors

As climate change is expected to significantly affect agricultural systems globally, agricultural farm advisors have been increasingly recognized as an important resource in helping farmers address these challenges. While there have been many studies exploring the climate change belief and risk perceptions as well as behaviors of both farmers and agricultural farm advisors, there are very few studies that have explored how these perceptions relate to actual climate impacts in agriculture. Here we couple survey data from United States Department of Agriculture farm service employees (n = 6, 514) with historical crop loss data across the United States to explore the relationship of actual climate-related crop losses on farm to farm advisor perceptions of climate change and future farmer needs. Using structural equation modelling we find that among farm advisors that work directly with farms on disaster and crop loss issues, there is a significant positive relationship between crop loss and perceived weather variability changes, while across all farm advisors crop loss is associated with reduced likelihood to believe in anthropogenic climate change. Further, we find that weather variability perceptions are the most consistently and highly correlated with farm advisors\u27 perceptions about the need for farm adaptation and future farmer needs. These results suggest that seeing crop loss may not lead to climate change belief, but may drive weather variability perceptions, which in turn affect farm adaptation perceptions. This lends further evidence to the debate over terminology in climate change communication and outreach, suggesting that weather variability may be the most salient among agricultural advisors

Association Between Sense of Coherence and Health Outcomes at 10 and 20 Years Follow-Up: A Population-Based Longitudinal Study in Germany

Background: The sense of coherence (SOC) is reported to influence health, but health may also have an impact on SOC. The objective of this study was to examine the longitudinal associations between SOC and selected self-reported and physician-assessed health outcomes over a period of 10 and 20 years and to determine the predominant direction of the associations. Methods: We conducted a population-based, longitudinal study, involving 392 participants (188 females and 204 males; mean age 43.01 years) who were followed for a median of 10 and 18 years. Analyses of variance were carried out to examine the longitudinal associations between SOC at baseline and health outcomes (i.e., self-rated health status, SHS; physical health status assessed by a physician, PHS; self-reported satisfaction with life, SWL) at follow-ups. The direction of associations was examined using a cross-lagged model on correlation coefficients. Results: There were significant group effects for SOC at baseline on SHS at 20-year follow-up (F = 4.09, p = 0.018, ηp(2) = 0.041), as well as on SWL at 10-year (F = 12.67, p < 0.01, ηp(2) = 0.072) and at 20-year follow-up (F = 8.09, p < 0.1, ηp(2) = 0.069). SHS (r = 0.238, p < 0.01), PHS (r = −0.140, p < 0.05) and SWL (r = 0.400, p < 0.01) predicted SOC at 10-year follow-up stronger than vice versa. The direction of associations between SOC and health parameters at 20-year follow-up was less consistent. Conclusions: The long-term associations between SOC and self-reported and physician-assessed health may be reciprocal in community-dwelling adults. More research is needed to examine the predictive power of health on SOC and whether interventions targeted at improving health parameters, may impact SOC