Article Figures & Data
Figures
- Figure 1.
The figure shows the map of Sidhi district (right) where the assessment was undertaken, in the context of India (left) and the state of Madhya Pradesh (middle).
- Figure 2.
The social landscape of Sidhi (source: Buckingham et al. 2018). The color of the bubbles indicates the type of actors like local community, NGOs, government. The size of the bubble indicates the connectivity of organizations and individuals in the network and influential actors. The color of the lines indicates how network organizations and individuals are connected in terms of exchanging information (blue), flows finance (green), authority relationships (purple), and conflict (red).
- Figure 3.
An influence and interest stakeholder map of Madhya Pradesh (source: Buckingham et al. 2018). The color of the bubbles indicates the type of actors like local community, NGOs, government. The size of the bubble indicates the connectivity of organizations and individuals in the network and influential actors. The color of the lines indicates how network organizations and individuals are connected in terms of exchanging information (blue), flows finance (green), authority relationships (purple), and conflict (red).
Tables
- Table 1.
The three components of ROAM where modifications were made, highlighting the key questions and methods used for data collection and analysis.
Component Key Question Methods: Data Collection and Analysis Tools Used During Consultations Ecosystem services analysis (New addition to ROAM) Q1. What ecosystem services and benefits can be derived from the identified restoration interventions in the targeted landscape? Estimations made and areas mapped for biodiversity conservation, erosion control, carbon sequestration, provisioning of fuelwood based on digital mapping and modeling, and stakeholder consultations for identified restoration interventions. Following the development of the spatial layers through digital mapping and modeling, the Ecosystem Services Diagnostic Tool developed by the team was used in the pre-validation workshop, enabling stakeholders to prioritize competing demands and identify restoration interventions best suited for delivering the essential ecosystem services. Social landscape analysis (New addition to ROAM) Q2. Who are the actors that can facilitate the implementation of landscape restoration in an area? Key actors and networks identified through social network analysis using Net-Map—a methodology developed by Schiffer (2007) and improved by Buckingham et al. (2018). Relationship maps made with Kumu, a powerful, free data visualization software. Additionally, the team created a checklist for collecting data from different departments and other sources. To gauge institutional functioning with stakeholders during sub-district consultations, the team developed an institutional checklist before consultations that stakeholders used to reflect the status of local institutions' through different color codes. Livelihood analysis (New addition to ROAM) Q3. What are the livelihood benefits if the identified restoration potential were implemented? Livelihood mapping and profiling at the “gram panchayat” level undertaken with a partner organization yielded a comprehensive list of interventions. These were matched with the identified restoration interventions and were shortlisted for further subsector analysis based on select parameters. Checklist of questions for focus group discussion and household survey questionnaires for assessing livelihood potential and value chain analysis were prepared in advance. - Table 2.
The details of stakeholder consultations we conducted to address our key questions, the number of participants, and the profile of participants.
Type of Consultations Number and Location of the Consultations Number of Participants Profile of Participants Stakeholder mapping (to map social landscape— actors and networks—for implementing landscape restoration) (Table 1, Q2) Two. Bhopal 14 (stakeholders mapping), 52 Representatives from State Departments, National Bank for Agriculture and Rural Development (NABARD), non-government organizations (NGO), “Zilla Panchayat” elected assembly at district level (Sidhi and Umaria districts), research institutions, private sector, retired forest officials, lawyers, media, a former member of the legislative assembly (MLA). Two. Sidhi district headquarters 12, 12 Representatives of: district officials (like Rural Development, Watershed); locally elected leaders of “gram panchayat” (village assembly), water-user associations; farmers; a former MLA; a local community-based organization (CBO). Representatives from the Forest Department. Sub-district consultations (to stock take existing interventions and key actors undertaking it; to map and identify the potential for restoration and type of interventions feasible; to understand opportunities and challenges for landscape restoration) (Table 1, Q1) Four. In different sub-district locations and Sidhi district headquarters 82 Representatives of: farmers, “gram panchayat” presidents, government departments (like the Forest, Rural Development, Panchayats, Agriculture Departments), NGO, CBO, farmer producer organization, “Lok Vaniki” (private forestry group), and entrepreneurs. Pre-validation workshop (to validate initial findings) (Table 1, Q1, Q2) One. Sidhi district headquarters 106 Representatives of: farmers, local communities, district administration, farmer producer organizations and local political leaders. Way forward workshop (to present the study’s results and discuss an implementation strategy) (Table 1, Q1, Q2, Q3) One. Sidhi district headquarters 214 Representatives of: state and district level government officials, political leaders, regional NABARD officials, farmers, representatives from farmer producer organizations, CBO, local user groups, community members, media, NGOs, retired forest officials, and private sector. - Table 3.
A representation of the relative power of actors in the network. Social network analysis uses centrality terminology—centrality measures of degree, closeness, betweenness, and eigenvector—to identify the network’s powerful and important actors by looking at how many connections the actor has and whether the actor is connected to other powerful actors. Degree centrality is one type of network centrality, measured as the number of connections held by each actor. Closeness centrality is another type of network centrality, measured as the distance actors are from other actors. Betweenness centrality is measured as the frequency with which the actors lie on the shortest path between other actors. Eigenvector centrality is measured as the extent to which actors are connected to other central actors.
Centrality Who? Connectors Degree Centrality Divisional Forest Officer, Zilla Panchayat, Farmer, Public, Collector (District Administration) Spreaders Closeness Centrality Divisional Forest Officer, Zilla Panchayat, Public, Farmer, Former Member of Legislative Assembly Gatekeepers Betweenness Centrality Divisional Forest Officer, Farmer, Public, Zilla Panchayat President, Zilla Panchayat Change Champions Eigenvector Centrality Divisional Forest Officer, Sub-Divisional Officer Forest, Range Officer, Zilla Panchayat, Farmer
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