Here’s a deeper review of the four studies with a focus on the **types of data used** and a detailed breakdown into **qualitative** and **quantitative data**: | **Study** | **Type of Data** | **Examples/Variables** | **Qualitative Data** | **Quantitative Data** | | ------------------------------ | ----------------------------------- | ------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------- | | **Salvia & Quaranta (2015)** | **Qualitative/Narrative Data** | - Historical analysis of rural systems<br> - Descriptive case studies of rural resilience patterns | - Descriptive case studies of rural communities<br> - Application of the adaptive cycle to rural systems | - Limited use of quantitative data<br> - Population changes over time<br> - Basic agricultural indicators | | | **Minimal Quantitative Data** | - Basic indicators for case study comparisons (e.g., population changes, land use) | | - Population statistics<br> - Agricultural land use changes<br> - Basic economic metrics | | | **Conceptual Framework** | - Adaptive cycle phases applied to rural development | - Theoretical application of adaptive cycles and resilience theory to rural systems | | | **Jiménez et al. (2020)** | **Quantitative Data** | - Historical adaptive cycles<br> - Population data<br> - Land-use changes<br> - Environmental indicators | - Historical narratives of Xochimilco’s transitions between adaptive cycle phases<br> - Social-ecological resilience framework | - GIS and remote sensing data<br> - Population changes over time<br> - Water quality and land-use data | | | **Survey Data** | - Social-ecological resilience assessments | - Stakeholder perceptions and traditional knowledge included in resilience assessment | - Survey results quantified for statistical analysis<br> - Social indicators (population, land use) | | | **Geospatial and Ecological Data** | - GIS data for land use changes over time | | - Geospatial data<br> - Time series of ecological variables (water flow, biodiversity) | | | **Temporal Analysis** | - Long-term trends in ecological and social variables | | - Time-series analysis of ecological and socio-economic indicators | | **Kuhmonen & Kuhmonen (2023)** | **Long-Term Quantitative Data** | - Agricultural production statistics<br> - Economic indicators over decades | - Long-term historical transitions in agrifood systems<br> - Policy analysis in context of adaptive cycles | - Time series of agrifood production<br> - Agricultural employment<br> - Economic performance of farms | | | **Socioeconomic Data** | - Economic performance of agrifood systems<br> - Employment and income statistics | - Descriptions of policy shifts and their impacts on local agrifood systems | - GDP and income data<br> - Economic output related to agrifood production | | | **Environmental Data** | - Environmental sustainability indicators<br> - Land use and biodiversity trends | - Historical analysis of land use and policy impacts on environmental sustainability | - Biodiversity indexes<br> - Environmental sustainability metrics<br> - Land use changes over time | | | **Historical and Time Series Data** | - Historical transitions in the Finnish agrifood system | - Case studies of agrifood system resilience | - Decades of agrifood production data<br> - Comparative analysis of historical vs. current outputs | | **Wentworth et al. (2022)** | **Cross-Scale Quantitative Data** | - Local, regional, and global food system indicators<br> - Trade and market data | - Case studies of local food systems and their interactions with global markets<br> - Cross-scale interactions between local and global processes | - Trade and export data<br> - Regional agricultural statistics<br> - Market data at global scales | | | **Qualitative/Narrative Data** | - Case studies of cross-scale dynamics in food systems<br> - Descriptive analysis of food system resilience across scales | - Descriptive analysis of local practices and global trade influences | | | | **Economic and Environmental Data** | - Agricultural production, trade data, environmental indicators | | - Agricultural production, trade, and export statistics<br> - Market dynamics across scales | | | **Survey Data** | - Survey results on food system stakeholders' perceptions of resilience | - Stakeholder interviews and surveys analyzing perceptions of resilience and sustainability | - Survey results quantified to measure stakeholder responses | ### **Deeper Analysis and Explanation**: #### **Salvia & Quaranta (2015)** @salvia.quaranta_2015 - **Qualitative Data:** The study heavily relies on narrative and descriptive case studies to explain rural development through the adaptive cycle framework. It examines rural systems' transitions between adaptive phases with minimal reliance on hard data, opting instead for historical and theoretical analysis. - **Quantitative Data:** The study uses very limited quantitative data, such as population changes or land use changes in rural areas, but the data is not central to the analysis. Instead, these numbers are used to complement the qualitative findings. #### **Jiménez et al. (2020)** @jimenez.etal_2020 - **Qualitative Data:** This study uses qualitative narrative descriptions to explain the historical adaptive cycles of the Xochimilco system. It applies the adaptive cycle model to illustrate how the system has transitioned between phases, relying on social-ecological resilience theory. Interviews and stakeholder surveys bring a qualitative perspective on local knowledge and resilience. - **Quantitative Data:** The study also incorporates substantial quantitative data, including GIS data to track land-use changes, population statistics, and environmental indicators like water quality and biodiversity. The authors use these metrics to conduct time-series analysis and spatial analysis, giving the study a robust quantitative component. #### **Kuhmonen & Kuhmonen (2023)** @kuhmonen.kuhmonen_2023 - **Qualitative Data:** The study adopts a historical long-term perspective on the Finnish agrifood system, examining how the system has transitioned through various phases of the adaptive cycle over decades. Qualitative insights are drawn from policy shifts and social responses to agricultural changes. - **Quantitative Data:** There is a strong emphasis on quantitative data in this study, with agricultural production statistics, employment data, and economic performance being analyzed over long periods. This allows the authors to draw insights into how the Finnish agrifood system has evolved and its current resilience. #### **Wentworth et al. (2022)** @wentworth.etal_2022 - **Qualitative Data:** The study uses case studies to describe how local food systems interact with regional and global markets. The focus is on cross-scale interactions, particularly how local practices are influenced by global food systems. Stakeholder surveys and interviews provide additional qualitative data. - **Quantitative Data:** The authors integrate agricultural production data, trade statistics, and global market data, comparing local food system outputs with regional and global trends. The quantitative data is used to illustrate the interactions between different scales and to quantify resilience at multiple levels. ### **Key Takeaways**: 1. **Salvia & Quaranta (2015)** heavily rely on a qualitative, narrative-driven analysis with minimal quantitative input, offering a more conceptual and theoretical approach. 2. **Jiménez et al. (2020)** successfully blend **narrative** and **quantitative data**, utilizing historical GIS data, surveys, and environmental indicators, making it both rich in narrative and supported by quantitative evidence. 3. **Kuhmonen & Kuhmonen (2023)** focus extensively on **quantitative, long-term data**, offering a historical lens on the adaptive cycle within the Finnish agrifood system, supplemented by qualitative descriptions of policy changes. 4. **Wentworth et al. (2022)** use a mix of **qualitative and quantitative data**, emphasizing the importance of **cross-scale dynamics**, comparing local agricultural systems with global trends through both narrative case studies and statistical analysis. This table and review provide a structured approach to how each study integrates qualitative and quantitative data to explore resilience and adaptive cycles, offering a roadmap for designing your cross-scale, mixed-method study on Hawaii's food system.