DOI: 10.5061/dryad.zs7h44j5b
title: Why we fail: stakeholders' perceptions of the social and ecological barriers to reforestation in southern Malawi

description: Reversing deforestation is a pressing challenge for planetary health and
 global development, yet the path to restoring forests on a globally
 significant scale is ill-defined, and government implementation of
 afforestation projects often generates conflict with user groups.
 Understanding the needs and values of forest-reliant stakeholders in
 developing countries and effectively engaging them in forest landscape
 restoration planning and implementation are critical steps toward meeting
 afforestation goals, and yet few examples of inclusive participatory
 processes applied in this context exist. Inspired by an approach used to
 mediate other natural resource conflicts, I structured a series of
 stakeholder focus groups in the Zomba-Machinga region of southern Malawi
 around Systems Thinking and Bayesian Belief Networks. Through these focus
 groups, I engaged forest-reliant households, academics, and government
 staff in a participatory process to explore the conditions of past
 restoration failure; identify the barriers to future reforestation
 success; and develop proposed solutions. Out of eight focus groups held,
 seven viewed poverty as the single greatest obstacle to afforestation
 success, and all seven perceived small business capacity building as one
 of the three most important factors in poverty alleviation, followed in
 order of frequency by improved agricultural practices (six of seven
 groups), and non-forest employment (three of seven groups). If each focus
 group could successfully implement the three factors they considered most
 important, the groups’ perceptions that poverty could be alleviated ranged
 from a likelihood of 15.8% to 62.3%, with differences among focus groups
 that underscored inequities in social agency and vocational opportunities
 of the participants based on age and gender. This study illustrates where
 these diverse groups of stakeholders from a single region converge and
 diverge in their thinking, underscoring the importance of establishing
 durable participatory processes to work through conflicts, nurture trust,
 and collaboratively develop solutions that are relevant to local
 conditions in order for global forest landscape restoration to succeed.
description: Please refer to the manuscript for a complete description of the
 methods for participant selection, data collection, and
 analysis. In brief, the data were collected in eight
 focus groups. Each group developed a Bayesian Belief Network, and then
 individual participants in the focus groups completed conditional
 probability tables for four of the nodes in the network. Networks were
 constrained to one primary node, three secondary nodes, and three tertiary
 nodes for each secondary node, totaling 13 nodes. Nodes were constrained
 to two states; one described a positive or desirable outcome, and one a
 negative or undesirable outcome. In each group, a conditional probability
 table was developed for the primary node based on the states of the
 secondary nodes, and for each of the secondary nodes based on the states
 of the adjoining tertiary nodes. The conditional probability table
 presented eight possible scenarios (in other words, combinations of
 secondary or tertiary node states that were read as a hypothetical
 scenario to the participants) in each case, so each focus group
 participant assigned probabilities of positive outcomes for the subject
 node to 32 scenarios for their group's Bayesian Belief Network. The
 data provided here were taken from those conditional probability tables,
 so that other researchers can reconstruct and analyze the Bayesian Belief
 Network for each group. In the data, individual participants are
 identified by a numeric code to protect their identities, and each row in
 the data represents one scenario, so there are 32 rows of data (four
 eight-scenario conditional probability tables) for each
 participant.
description: In order to analyze the data in Netica, a blank row will need to
 be added at the top of each data sheet, with the following script pasted
 in the first cell (A1): //
 ~-&gt;[CASE-1]-&gt;~