DOI: 10.5061/dryad.tqjq2bw8m
title: ‘Sowing and harvesting water’: revisiting forest restoration in the Peruvian Andes through a multi-stakeholder analysis

description: Efforts to restore Peru’s megadiverse Andean Forests are rapidly growing.
 While ecological determinants for restoration success are well known,
 knowledge on the socio-economic and governance conditions that allow for
 success of ecological restoration using native species are scarce. Using a
 multi-stakeholder approach this paper analyses the motivations,
 preferences, success factors and governance models for effective
 ecological restoration of Andean Forests, through 75 semi-structured
 interviews with local community members, NGOs, and government actors in 11
 restoration sites in Peru. We find that across sites and stakeholder
 groups, the primary motivations for Andean Forest restoration were tied to
 restoring and improving hydrological resources. Stakeholders valued Andean
 Forests mostly for their provisioning ecosystem services - with water
 provision valued by all stakeholders and firewood provision predominantly
 by communities - followed by regulating services (water retention and
 climate regulation). Restoration success – the degree of perceived
 achievement of projects objectives - was high at all sites and scored
 between 2.4-3 out of 3. Enabling factors for the restoration success were
 mostly social and institutional. There was no ‘silver bullet’ to
 successful restoration; rather, enabling factors included high resource
 dependence of communities, support from NGOs, participatory management and
 governance, and creation of communal conservation agreements. Communities
 emphasized primarily social and institutional limiting factors, while
 government stakeholders emphasized technical challenges. We further
 identified three typologies of how projects engage and compensate
 communities: a ‘payment model’, a ‘capacity model’ and a ‘mixed model’
 which differ in their rentability, longevity, and socio-economic benefits
 provided. All stakeholder groups favoured active forest restoration and
 community members identified desirable native plant species with local use
 and hydrological value. Interviewees also highlighted that restoration
 needs to go beyond forests, and combine native tree planting,
 agroforestry, restoration of mountain grasslands and peatlands to
 holistically improve water resources and long-term economic benefits at a
 landscape scale. Synthesis and applications: Andean Forest restoration
 projects need to consider hydrological ecosystem services in all key
 restoration stages. Communities need to be involved through participatory
 processes and receive long-lasting benefits – both ecosystem services and
 livelihood incentives - to guarantee long-term project success.
description: The attached data shows the end results of a thematic analysis of
 75 semi-structured interviews with various stakeholders.
 The interviews were conducted in August-September 2022 in the
 Peruvian Andes in 11 restoration project locations. Interviews followed a
 semi-structured question template covering various restoration-related
 topics and lasted between 10-90mins. they were conducted in Spanish, and
 sometimes in English or Quechua. Interviews were recorded and transcribed
 manually and a thematic analysis was conducted in NViVo. Themes and codes
 were created inductively and deductively.  The attached
 excel table has different Tabs for different topics (Motivations, Forest
 values, Perceived objectives, Community impacts, Success ratings, Species
 preference, Enabling factors, Limiting Factors, Restoration Intervention).
 In each tab it gives a quantitaive breakdown of either occurrence of codes
 or number of interviewees. When it states 'number coding
 references' this means the number of times a code has been mentioned
 (used for quantification of Enabling and Limiting factor categories). When
 it states 'number of interviewees' or '% of
 interviewees' this relates to how many (or % of) participants have
 mentioned a code (used for analysis of all other themes).
 Please refer to the published article for detailed information on
 data collection.
description: # ‘Sowing and harvesting water’: revisiting forest restoration in the
 Peruvian Andes through a multi-stakeholder analysis
 [https://doi.org/10.5061/dryad.tqjq2bw8m](https://doi.org/10.5061/dryad.tqjq2bw8m) ## Description of the data and file structure this dataset shows the results of a thematic coding of semi-structured interviews with 75 participants who took part in forest restoration in the Peruvian Andes. Interviews were recorded with the participant's consent and transcribed manually in the original language. The codes were extracted from a thematic analysis in NVivo and show frequency information for interviewees and coding references. The attached dataset shows for different topics the themes, codes and sub-codes and counts and breaks these down by various predictor variables (stakeholder groups, sites etc). Please refer to the paper for the full methodology on data collection including project locations, sampling, data analysis. ### Files and variables #### File: Results\_DRYAD.xlsx **Description:** Contains all the quantitative information used for analysis and the basic charts, ##### Variables The different tabs in the provided excel sheet denote different research topics and align with the research questions answered in the paper: 1. (1)   What are motivations for forest restoration with native species in the Peruvian Andes across different stakeholder groups? --&gt; Tabs 'Motivations' and 'Forest Values' and 'Perceived Objectives' 2. (2)   What restoration interventions are preferred by stakeholders (e.g. species, ecosystem types, restoration methods)? --&gt; Tabs 'Species preference' and 'Restoration interventions' 3. (3)   Which social, economic, biophysical, institutional/legal, and technical factors affect restoration success according to different stakeholder groups? --&gt; Tabs 'Enabling factors' and 'Limiting factors'. 4. (4)   How do restoration projects impact community livelihoods? --&gt; Tabs 'Community impacts' and 'Success rating' 5. (5)   Through what governance models do restoration projects achieve community benefits, project sustainability, and restoration success? --&gt; Tab 'Community impacts' Response variables (and thematic codes and sub-codes) are: * Restoration motivations classified as: Intrinsic, Instrumental, Relational, Other * Forest values classified into ecosystem services as: Cultural services, Provisioning services, Regulating services, Supporting services * Perceived restoration objectives classified into: Ecological, Economic, Social * Community impacts of the restoration classified into: Negative impacts, Positive impacts (Direct economic, Indirect economic and Non-economic) * Success rating of the sites on a scale of 1,2,3 * Species preference classified into: Preference for native species only, Preference for a mix of native and exotics * Attitude to exotic species as: Positive, Negative * Enabling and limiting factors classified into five categories: Technical, Economic, Social, Institutional, Biophysical Predictor variables are: * stakeholder group: Community member, Non Governmental Organisation (NGO), Government actor, Academic actor, Private actor * Restoration sites: 11 project locations (Macchu Picchu, Independencia, Ancash, Challabamba, Vilcanota, Mariño, Aquia, Kiuñalla, Huascarán and Ampay). See the exact description of these sites in Table 1 of the manuscript. Quantitative numbers are given in number of interviewees (i.e. number of cases, n), percentage of interviewees (%), and in some cases as number of coding references (i.e. how often a code was mentioned overall with multiple-mentions possible per interviewee). It is always stated on top of the column which of these is used. Whenever quantitative numbers are given in % of a stakeholder group, this refers to how many % of interviewees within that stakeholders have mentioned a certain code. NAs are used to indicate missing values ## Code/software The thematic analysis was conducted in NViVo, where the coding took place. Matrices were extracted from NViVo and transferred into excel for data analysis and charting. ## Access information Other publicly accessible locations of the data: * NA Data was derived from the following sources: * NA