Regionalization and Agroecosystem Indicators of Well-Being and Vulnerability
Describing indicators of rural well-being and variability and monitoring them through time can help identify their variation within a landscape and through time, while also allowing researchers to evaluate factors that are associated with this variation.
- LTAR Human Dimensions Indicators
- PI: Alycia Bean; co-PIs: J.D. Wulfhorst, Amanda Bentley Brymer, Gwendŵr Meredith, Zach Hurst, Claire Friedrichsen, and Alisa Coffin
- PHASE 1: IDENTIFYING THE SOCIO-ECOLOGICAL TEMPLATE FOR LTAR REGIONALIZATION
What individuals or communities need in order to thrive depends on a variety of variables such as geography and demography. In order to describe the human condition niche using socioeconomics as a template, this project will prioritize dimensions of individual well-being common across diverse rural settings including: basic material needs (e.g., safe, affordable housing), health (e.g., food quality / nutrition, exposure to toxins), security (e.g., reliable income, food security), social relations (e.g., family, neighbors, employer, markets), and adaptive capacity (e.g., flexibility, opportunities to plan, learn, reorganize). While demographic data are useful at different spatial and temporal scales, this project will allow for a more nuanced understanding of rural well-being by using enriched indices (e.g., household access to healthcare or health coverage, debt-to-income ratios). A regionalization approach derived from prioritized socioeconomic variables will provide the basic spatial template for analyses of rural prosperity, help identify factors correlated with willingness to adopt aspirational practices and support cross-site collaborations on the long-term effects of alternative agroecosystem management practices.
- PHASE 2: LTAR SPECIFIC COMPOSITE INDEX TOOL
This project will identify the specific indicators prioritized within Phase 1 and create a weighted statistical index for indicators most relevant to the three domains of LTAR: production, environment and rural well-being. The project will include the selecting, aggregating and visualizing of indicators of specific value to the LTAR network.
- PHASE 3: NETWORK DASHBOARDS
Once a composite index has been created of prioritized human dimensions of rural well-being, those data will be used to create an online visualization for cross-site comparison with an ESRI operations dashboard. The dashboards may populate in real-time, so depending on the dataset (I.e. AgCros vs. NASS) the LTAR inference region data will be either static or dynamic. This will be a collaborative effort with LTAR GIS specialists.
- Regionalization Implementation
- PIs: Alisa Coffin and Marguerite Madden; co-PIs: Alycia Bean, Marley Holder, Joe Powell, Austin Stone, Chandra Holifield Collins, Lynne Seymour
- This Southeast Agro-Ecosystem Services (SE AgES) project goal infers LTAR “common experiment” results and then extends those to a region. In order to address questions such as: ‘how far away can certain treatments be relevant?’ and ‘what are main gaps in the network to identify where LTAR should be funneling limited resources?’. The project uses spatial statistics and appropriate models for working at multiple scales provide for regional groupings. Social sciences will be integrated with remote sensing through methodologies that demonstrate the network salience and relevance. The project will analyze clustering of social ecological systems and map these dimensions nationally to identify and monitor changes and trends regarding climate and demographics (e.g. support scenario modeling of drought or disease). These results will allow for analysis of local and regional implications with value added impacts from correlating how this research integrates with crop modeling efforts at larger scales.
- Visualizing Adaptive Capacity to Weather Extremes
- PIs: Alycia Bean and Vivienne Fischer; co-PIs: Alisa Coffin and J.D. Wulfhorst
- This research project utilizes LTAR-specific indicators, prioritized for regionalization, to examine the adaptive capacity of LTAR inference and production regions given a suite of climate projection scenarios. Modeling efforts would focus on natural hazards most detrimental to integrated systems of croplands and grazing lands such as severe droughts and precipitation extremes to identify ‘hot spots’ of most vulnerable regions to focus limited resources. These results could be most impactful when combined with other impact models to support quantitative vulnerability assessments.