The Effects of Conjoint Behavioral Consultation: Results of a 4-Year Investigation

Conjoint behavioral consultation (CBC) is a structured indirect form of service delivery in which parents, teachers, and other support staff are joined to work together to address the academic, social, or behavioral needs of an individual for whom all parties bear some responsibility. In this article, outcome data from 4 years of federally funded projects in the area of CBC are presented. Thirty graduate students were trained in CBC and were responsible for providing consultation services to parents and teachers of students with disabilities or at risk for academic failure. Consultation clients included 52 students with disabilities such as behavior disorders, attention-deficit hyperactivity disorder, anxiety, and learning disabilities. The primary research objective concerned assessing the efficacy of CBC across home and school settings. Secondarily, a prediction model was investigated based on client age, case complexity, and severity of symptoms. Perception of effectiveness, process acceptability, and consultee satisfaction with consultants was also investigated. Meaningful effect sizes were yielded across home and school settings. A model fitting client age and symptom severity was found to predict school effect size relatively well. Consultees’ perceptions of effectiveness, acceptability of CBC, and satisfaction with consultants were also favorable. Implications of these findings and directions for future research are explored

Designing tools to predict and mitigate impacts on water quality following the Australian 2019/2020 wildfires: Insights from Sydney's largest water supply catchment

The 2019/2020 Australian bushfires (or wildfires) burned the largest forested area in Australia's recorded history, with major socio-economic and environmental consequences. Among the largest fires was the 280 000 ha Green Wattle Creek Fire, which burned large forested areas of the Warragamba catchment. This protected catchment provides critical ecosystem services for Lake Burragorang, one of Australia's largest urban supply reservoirs delivering ~85% of the water used in Greater Sydney. Water New South Wales (WaterNSW) is the utility responsible for managing water quality in Lake Burragorang. Its postfire risk assessment, done in collaboration with researchers in Australia, the UK, and United States, involved (i) identifying pyrogenic contaminants in ash and soil; (ii) quantifying ash loads and contaminant concentrations across the burned area; and (iii) estimating the probability and quantity of soil, ash, and associated contaminant entrainment for different rainfall scenarios. The work included refining the capabilities of the new WEPPcloud-WATAR-AU model (Water Erosion Prediction Project cloud-Wildfire Ash Transport And Risk-Australia) for predicting sediment, ash, and contaminant transport, aided by outcomes from previous collaborative postfire research in the catchment. Approximately two weeks after the Green Wattle Creek Fire was contained, an extreme rainfall event (~276 mm in 72 h) caused extensive ash and sediment delivery into the reservoir. The risk assessment informed on-ground monitoring and operational mitigation measures (deployment of debris-catching booms and adjustment of the water supply system configuration), ensuring the continuity of safe water supply to Sydney. WEPPcloud-WATAR-AU outputs can prioritize recovery interventions for managing water quality risks by quantifying contaminants on the hillslopes, anticipating water contamination risk, and identifying areas with high susceptibility to ash and sediment transport. This collaborative interaction among scientists and water managers, aimed also at refining model capabilities and outputs to meet managers' needs, exemplifies the successful outcomes that can be achieved at the interface of industry and science. Integr Environ Assess Manag 2021;17:1151–1161. © 2021 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).During manuscript preparation J. Neris, C. Santin, R. Lew, and S.H. Doerr were supported by a Natural Environment Research Council grant (NE/R011125/1)

1974

History of Golf (1) The Nine Toughest Holes in the World (2) Stockie Madness (3) Bartender, One More Round for Pythium (3) Panel: 1973 Turf Problems in Review - 1974 Possible Remedies (A1-A12) Movement of Water to a Holding Pond (A13) Maintenance of Low Budget, Short Season Golf Courses (A16) Turfgrass Fertilization (A18) Determining Turfgrass Fertilizer Needs (A25) Shortage of Plant Food and How to Adjust to Supply and Cost (A29) Panel: Tricalcium Arsenate - Use and Abuse (A33-A46) Operating and Maintaining Municipal Golf Courses (A48) Maintenance of a High Budget Golf Course (A51) Trends in Agricultural Education and Where Are the Emphases (A58) Maintenance of Municipal Parks and Recreation Areas (A60) Maintenance of Grass Tennis Courts (A63) Transition from Natural to Artificial Turf (A67) Plant materials for Outlying Areas (A71) Care of University Grounds (A76) Maintenance of Industrial Sites (A79) Turfgrass Diseases and Systemic Fungicides (A81) A Look at the Future (A 84) Watering of Golf Course Turf (A92

Understanding and using quantitative genetic variation

Quantitative genetics, or the genetics of complex traits, is the study of those characters which are not affected by the action of just a few major genes. Its basis is in statistical models and methodology, albeit based on many strong assumptions. While these are formally unrealistic, methods work. Analyses using dense molecular markers are greatly increasing information about the architecture of these traits, but while some genes of large effect are found, even many dozens of genes do not explain all the variation. Hence, new methods of prediction of merit in breeding programmes are again based on essentially numerical methods, but incorporating genomic information. Long-term selection responses are revealed in laboratory selection experiments, and prospects for continued genetic improvement are high. There is extensive genetic variation in natural populations, but better estimates of covariances among multiple traits and their relation to fitness are needed. Methods based on summary statistics and predictions rather than at the individual gene level seem likely to prevail for some time yet

Unmanned Aerial Vehicles for High-Throughput Phenotyping and Agronomic Research

Advances in automation and data science have led agriculturists to seek real-time, high-quality, high-volume crop data to accelerate crop improvement through breeding and to optimize agronomic practices. Breeders have recently gained massive data-collection capability in genome sequencing of plants. Faster phenotypic trait data collection and analysis relative to genetic data leads to faster and better selections in crop improvement. Furthermore, faster and higher-resolution crop data collection leads to greater capability for scientists and growers to improve precision-agriculture practices on increasingly larger farms; e.g., site-specific application of water and nutrients. Unmanned aerial vehicles (UAVs) have recently gained traction as agricultural data collection systems. Using UAVs for agricultural remote sensing is an innovative technology that differs from traditional remote sensing in more ways than strictly higher-resolution images; it provides many new and unique possibilities, as well as new and unique challenges. Herein we report on processes and lessons learned from year 1-the summer 2015 and winter 2016 growing seasons-of a large multidisciplinary project evaluating UAV images across a range of breeding and agronomic research trials on a large research farm. Included are team and project planning, UAV and sensor selection and integration, and data collection and analysis workflow. The study involved many crops and both breeding plots and agronomic fields. The project's goal was to develop methods for UAVs to collect high-quality, high-volume crop data with fast turnaround time to field scientists. The project included five teams: Administration, Flight Operations, Sensors, Data Management, and Field Research. Four case studies involving multiple crops in breeding and agronomic applications add practical descriptive detail. Lessons learned include critical information on sensors, air vehicles, and configuration parameters for both. As the first and most comprehensive project of its kind to date, these lessons are particularly salient to researchers embarking on agricultural research with UAVs