Quantitative risk assessments of pollution and data related to the effectiveness of mitigating best management practices (BMPs) are important aspects of nonpoint source pollution control efforts, particularly those driven by specific water quality objectives and by measurable improvement goals, such as the total maximum daily load (TMDL) requirements. Targeting critical source areas (CSAs) that generate disproportionately high pollutant loads within a watershed is a crucial step in successfully controlling nonpoint source pollution. The importance of watershed simulation models in assisting with the quantitative assessments of CSAs of pollution (relative to their magnitudes and extents) and of the effectiveness of associated BMPs has been well recognized. However, due to the distinct disconnect between the hydrological scale in which these models conduct their evaluation and the farm scale at which feasible BMPs are actually selected and implemented, and due to the difficulty and uncertainty involved in transferring watershed model data to farm fields, there are limited practical applications of these tools in the current nonpoint source pollution control efforts by conservation specialists for delineating CSAs and planning targeting measures. There are also limited approaches developed that can assess impacts of CSA-targeted BMPs on farm productivity and profitability together with the assessment of water quality improvements expected from applying these measures. This study developed a modeling framework that integrates farm economics and environmental aspects (such as identification and mitigation of CSAs) through joint use of watershed- and farm-scale models in a closed feedback loop. The integration of models in a closed feedback loop provides a way for environmental changes to be evaluated with regard to the impact on the practical aspects of farm management and economics, adjusted or reformulated as necessary, and revaluated with respect to effectiveness of environmental mitigation at the farm- and watershed-levels. This paper also outlines steps needed to extract important CSA-related information from a watershed model to help inform targeting decisions at the farm scale. The modeling framework is demonstrated with two unique case studies in the northeastern United States (New York and Vermont), with supporting data from numerous published, location-specific studies at both the watershed and farm scales. Using the integrated modeling framework, it can be possible to compare the costs (in terms of changes required in farm system components or financial compensations for retiring crop lands) and benefits (in terms of measurable water quality improvement goals) of implementing targeted BMPs. This multi-scale modeling approach can be used in the multi-objective task of mitigating CSAs of pollution to meet water quality goals while maintaining farm-level economic viability.
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http://dx.doi.org/10.1016/j.jenvman.2012.10.034 | DOI Listing |
BMC Health Serv Res
January 2025
Department of Clinical Pharmacology and Evidence-Based Medicine, NCJSC "Karaganda Medical University", 40, Gogolya St, Karaganda, 100000, Kazakhstan.
Background: Kazakhstan inherited the Semashko health system model, known for the centralized adoption of rules at the Ministry of Health (MoH) level that regulate the healthcare system. In 2019 MoH established a national framework with indicators aimed at collecting qualitative and quantitative data from healthcare organizations as part of their annual self-evaluation, and biannual external evaluation by the National Research Center for Health Development (NRCHD). The purpose of this study was to pilot the MoH framework on rational use of medicines and evaluate its effects on medicine use practices in health care organizations and at the national level.
View Article and Find Full Text PDFPlant Methods
January 2025
School of Electronic and Information Engineering, Liaoning Technical University, Huludao, 125105, China.
Apricot trees, serving as critical agricultural resources, hold a significant role within the agricultural domain. Conventional methods for detecting pests and diseases in these trees are notably labor-intensive. Many conditions affecting apricot trees manifest distinct visual symptoms that are ideally suited for precise identification and classification via deep learning techniques.
View Article and Find Full Text PDFBMC Health Serv Res
January 2025
Early Detection, Prevention & Infections Branch, International Agency for Research on Cancer, 25 Avenue Tony Garnier, Lyon, 69366 Cedex 07, France.
Background: Barriers to the cancer continuum organization and interventions to approach them have been identified; however, there is a lack of a tool matching them. Our aim was to develop a web-based tool to identify the main barriers to the process of the cancer continuum organization, and propose matched evidence-based interventions (EBI) to overcome them.
Methods: A questionnaire on barriers at six steps of the process of the cancer continuum organization was answered by collaborators.
BMC Health Serv Res
January 2025
Kirby Institute, University of New South Wales, Sydney, Australia.
Background: Indonesia has implemented a series of healthcare reforms including its national health insurance scheme (Jaminan Kesehatan Nasional, JKN) to achieve universal health coverage. However, there is evidence of inequitable healthcare utilization in Indonesia, raising concerns that the poor might not be benefiting fully from government subsidies. This study aims to identify factors affecting healthcare utilization in Indonesia.
View Article and Find Full Text PDFImplement Sci
January 2025
Research group: Implementation Research, Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany.
Background: The COVID-19 pandemic has highlighted the need for more effective immunization programs, including in limited resource settings. This paper presents outcomes and lessons learnt from a COVID-19 vaccination campaign (VC), which used a tailored adaptive strategy to optimise vaccine uptake in the Boeny region of Madagascar.
Methods: Guided by the Dynamic Sustainability Framework (DSF), the VC implementation was regularly reviewed through multi-sectoral stakeholder feedback, key informant interviews, problem-solving meetings, and weekly monitoring of outcome indicators to identify and apply key adaptations.
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