Dataset Information

Exploring agricultural livelihood transitions with an agent-based virtual laboratory: global forces to local decision-making.

ABSTRACT: Rural populations are undergoing rapid changes in both their livelihoods and land uses, with associated impacts on ecosystems, global biogeochemistry, and climate change. A primary challenge is, thus, to explain these shifts in terms of the actors and processes operating within a variety of land systems in order to understand how land users might respond locally to future changes in broader-scale environmental and economic conditions. Using 'induced intensification' theory as a benchmark, we develop a generalized agent-based model to investigate mechanistic explanations of relationships between agricultural intensity and population density, environmental suitability, and market influence. Land-use and livelihood decisions modeled from basic micro-economic theories generated spatial and temporal patterns of agricultural intensification consistent with predictions of induced intensification theory. Further, agent actions in response to conditions beyond those described by induced intensification theory were explored, revealing that interactions among environmental constraints, population pressure, and market influence may produce transitions to multiple livelihood regimes of varying market integration. The result is new hypotheses that could modify and enrich understanding of the classic relationship between agricultural intensity and population density. The strength of this agent-based model and the experimental results is the generalized form of the decision-making processes underlying land-use and livelihood transitions, creating the prospect of a virtual laboratory for systematically generating hypotheses of how agent decisions and interactions relate to observed land-use and livelihood patterns across diverse land systems.

SUBMITTER: Magliocca NR

PROVIDER: S-EPMC3764159 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Exploring agricultural livelihood transitions with an agent-based virtual laboratory: global forces to local decision-making.

Magliocca Nicholas R NR Brown Daniel G DG Ellis Erle C EC

PloS one 20130905 9

Rural populations are undergoing rapid changes in both their livelihoods and land uses, with associated impacts on ecosystems, global biogeochemistry, and climate change. A primary challenge is, thus, to explain these shifts in terms of the actors and processes operating within a variety of land systems in order to understand how land users might respond locally to future changes in broader-scale environmental and economic conditions. Using 'induced intensification' theory as a benchmark, we dev ...[more]

PMID: 24039892

Similar Datasets

Project description:ObjectivesMedical error is a significant cause of patient harms in clinical practice, but education and training are recognised as having a key role in minimising their incidence. The use of virtual patient (VP) activities targeting training in medical error allows learners to practice patient management in a safe environment. The inclusion of branched decision-making elements in the activities has the potential to drive additional generative cognitive processing and improved learning outcomes, but the increased cognitive load on learning risks negatively affecting learner motivation. The aim of this study is to better understand the impact that the inclusion of decision-making and inducing errors within the VP activities has on learner motivation.MethodsUsing a repeated study design, over a period of six weeks we provided undergraduate medical students at six institutions in three countries with a series of six VPs written around errors in paediatric practice. Participants were divided into two groups and received either linearly structured VPs or ones that incorporated branched decision-making elements. Having completed all the VPs, each participant was asked to complete a survey designed to assess their motivation and learning strategies.ResultsOur analysis showed that in general, there was no significant difference in learner motivation between those receiving the linear VPs and those who received branched decision-making VPs. The same results were generally reflected across all six institutions.ConclusionsThe findings demonstrated that the inclusion of decision-making elements did not make a significant difference to undergraduate medical students' motivation, perceived self-efficacy or adopted learning strategies. The length of the intervention was sufficient for learners to overcome any increased cognitive load associated with branched decision-making elements being included in VPs. Further work is required to establish any immediate impact within periods shorter than the length of our study or upon achieved learning outcomes.

Project description:Women comprise a significant portion of the agricultural workforce in developing countries but are often less likely to attend government sponsored training events. The objective of this study was to assess the feasibility of using machine-supported decision-making to increase overall training turnout while enhancing gender inclusivity. Using data obtained from 1,067 agricultural extension training events in Bangladesh (130,690 farmers), models were created to assess gender-based training patterns (e.g., preferences and availability for training). Using these models, simulations were performed to predict the top (most attended) training events for increasing total attendance (male and female combined) and female attendance, based on gender of the trainer, and when and where training took place. By selecting a mixture of the top training events for total attendance and female attendance, simulations indicate that total and female attendance can be concurrently increased. However, strongly emphasizing female participation can have negative consequences by reducing overall turnout, thus creating an ethical dilemma for policy makers. In addition to balancing the need for increasing overall training turnout with increased female representation, a balance between model performance and machine learning is needed. Model performance can be enhanced by reducing training variety to a few of the top training events. But given that models are early in development, more training variety is recommended to provide a larger solution space to find more optimal solutions that will lead to better future performance. Simulations show that selecting the top 25 training events for total attendance and the top 25 training events for female attendance can increase female participation by over 82% while at the same time increasing total turnout by 14%. In conclusion, this study supports the use of machine-supported decision-making when developing gender inclusivity policies in agriculture extension services and lays the foundation for future applications of machine learning in this area.

Project description:The Centers for Disease Control and Prevention (CDC) Select Agent Program establishes a list of biological agents and toxins that potentially threaten public health and safety, the procedures governing the possession, utilization, and transfer of those agents, and training requirements for entities working with them. Every 2 years the Program reviews the select agent list, utilizing subject matter expert (SME) assessments to rank the agents. In this study, we explore the applicability of multi-criteria decision analysis (MCDA) techniques and logic tree analysis to support the CDC Select Agent Program biennial review process, applying the approach broadly to include non-select agents to evaluate its generality. We conducted a literature search for over 70 pathogens against 15 criteria for assessing public health and bioterrorism risk and documented the findings for archiving. The most prominent data gaps were found for aerosol stability and human infectious dose by inhalation and ingestion routes. Technical review of published data and associated scoring recommendations by pathogen-specific SMEs was found to be critical for accuracy, particularly for pathogens with very few known cases, or where proxy data (e.g., from animal models or similar organisms) were used to address data gaps. Analysis of results obtained from a two-dimensional plot of weighted scores for difficulty of attack (i.e., exposure and production criteria) vs. consequences of an attack (i.e., consequence and mitigation criteria) provided greater fidelity for understanding agent placement compared to a 1-to-n ranking and was used to define a region in the upper right-hand quadrant for identifying pathogens for consideration as select agents. A sensitivity analysis varied the numerical weights attributed to various properties of the pathogens to identify potential quantitative (x and y) thresholds for classifying select agents. The results indicate while there is some clustering of agent scores to suggest thresholds, there are still pathogens that score close to any threshold, suggesting that thresholding "by eye" may not be sufficient. The sensitivity analysis indicates quantitative thresholds are plausible, and there is good agreement of the analytical results with select agent designations. A second analytical approach that applied the data using a logic tree format to rule out pathogens for consideration as select agents arrived at similar conclusions.

Dataset Information

Exploring agricultural livelihood transitions with an agent-based virtual laboratory: global forces to local decision-making.

Publications

Exploring agricultural livelihood transitions with an agent-based virtual laboratory: global forces to local decision-making.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets