DOI: 10.5061/dryad.000000008
title: Tree planting incentives in the Albertine Rift Region, Uganda

description: The study assessed the influence of incentives in tree planting by farmers
 in Kiryanga Sub county, Kagadi district, Albertine rift region. Key
 informant interviews, focus group discussions and household interviews
 were conducted to generate data on the influence of incentives on tree
 planting. The X2  test established associations between
 incentives and tree planting while a t-test was conducted to test for
 differences in characteristics of non-tree farmers and tree farmers.
 Results indicate that incentives are important in tree planting although
 some did not match farmers’ interests (X2 = 35.13, p
 &lt;0.05). The incentives that mattered most were: provision of tree
 seedlings (84.6%) and cash payments (69.4%) while farm tours as an
 incentive did not match farmers’ interests. The success attributed to
 incentives mainly depended on land size, and tree species’ preferences.
 The study concludes that, provision of tree seedlings and cash payments
 should be the incentives to be promoted. It is therefore important that
 different stakeholders adopt incentive-based tree planting.
description: Methodology Study area Kiryanga SC is located Kagadi district, Buyaga East County in the Albertine region between 1° 5' 40" North, and 31° 3' 32" East (Figure 1).  Modified Equatorial vegetation type mainly covers Kiryanga SC (Langdale, Brown, Osmaston, H, & Wilson, J, 1964). This Modified Equatorial type of vegetation used to be equatorial in nature but has been modified as a result of human activity. Rainfall ranges from 1000 mm to 1500 mm, soils are Ferrallistic and temperature ranges from 15<sup>o</sup>C to 30<sup>o</sup>C (Kakuru, Doreen, & Wilson, 2014). It has 24,622 people and 5,483 households (HH) mainly comprised of <i>Banyoro</i> ethnic group and depend on farming for their livelihood. Crops grown in Kiryanga SC include maize, cassava, potatoes, bananas, tobacco, rice and beans (Uganda Bureau of Stastics, 2016). Trees commonly planted include; <i>Melia azedarach</i> L., <i>Markhamia lutea </i>(Benth.) K. Schum., <i>Vitellaria paradoxa</i> C.F. Gaertn., <i>Senna spectabilis</i> (DC.) H.S. Irwin & Barneby, <i>Tamarindus indica</i> L., <i>Eucalyptus</i> spp., <i>Ficus</i> spp., <i>Euphorbia</i> spp., <i>Cascabela thevetia</i> (L.) Lippold and <i>Combretum</i> spp. These trees are important because they provide food, medicine, fuelwood, building poles, shade, windbreaks, and source of income - through the sale of firewood and/charcoal, and boundary markers (Kakuru et al., 2014).         Kiryanga SC borders with four forest reserves; Rwengeye, Ruzaire  Kasato, and Kyamurangi in Kagadi sector (Nyakana & Nyakana, n.d.).  Due to its adjacent location to the above forest reserves, Kiryanga’s rate of forest cover loss is 10 percent per year and about five times higher than the (1.8 percent) national forest cover loss rate (MWE, 2016).         The stakeholders which have implemented incentive-based tree planning projects in Kiryanga include; National Environmental Management Authority, Uganda Rural Development, and Training program (URDT), World Vision Uganda (WVU) and Chimpanzee Sanctuary and Wildlife Conservation Trust (CSWCT) (Gross-camp, Martin, Munyarukaza, McGuire, & Kebede, 2012).         Kiryanga SC was thus selected for this study because it is one of the eight Sub Counties in Hoima, Kagadi and Kakumiro districts of the Albertine rift region, the Murchison‐Semliki REDD+ Project has been involved in incentive-based tree planting mainly using free tree seedlings and cash incentives (Wieland, 2012). Furthermore, Kiryanga is located in the Albertine Rift whose forests are under threat due to various factors leading to loss of biodiversity. There is an increasing threat from rural communities whose high levels of poverty make them dependent on forest resources. Conservation International listed the Albertine Rift as one of the world's most endangered areas, based on levels of species endemism and rates of habitat destruction (NEMA, 2009).   Figure 1. About here <i>Research Design</i> This study adopted a cross-sectional descriptive design which involved collection of data at one time (March – May 2017). The study involved both quantitative and qualitative methods. Data collection took a mixed methods approach (Ward, Stringer, & Holmes, 2018) comprising of Key Informant Interviews (KII), Focus Group Discussions (FGD) (for qualitative data) and household survey (for quantitative data). <b><i>Data Collection techniques and instruments</i></b> <i>Key Informant Interviews</i> Eleven KIIs were held at participants’ respective homes and work places using the pre-set questions in the KII guides. The KII participants within the study area were selected purposively with guidance from SC staff depending on the positions and experiences they had in tree planting. The key informants selected were; five SC political and technical staff, three district staff from the natural resources and production departments, one National Forestry Authority (NFA) field staff and two tree farmer association leaders. Key informants were asked about important tree planting incentives preferred by farmers in the study area. <i>Focus Group Discussions</i> FGDs were organized at SC and Parish levels. The FGD participants were selected purposively by SC Agricultural Officer to have a representation of both tree and non-tree farmers. Each FGD had 6-12 people totalling to sixty people (Table 1). Participants were categorized into tree farmers and non-tree farmers to ensure homogeneity. These were interviewed using predetermined questions. There were mixed FGDs (male and female), only male and only female FGDs as well. FGD participants were asked what incentives they would prefer being given to plant trees. We talked to FGD participants until no newer information was arising which ensured saturation. Table 1. About here <i>Household surveys</i> Multistage sampling procedure was used where by Kiryanga SC was purposively selected from the eight sub-counties were Murchison‐Semliki REDD+ Project has been involved in incentive-based tree planting. This SC was selected because of the reasons presented in the ‘<i>study area’</i> section of this paper. All four parishes of Kiryanga were selected. One village per parish was randomly selected from the village list obtained from SC administration. The formula for determining sample size for research activities (Krejcie & Morgan, 1970) was used to get the appropriate total sample size of 237 households. A sample of 218 households was however selected and 19 households in Kijagi village (non-tree farmers) could not be accessed due to logistical constraints and heavy rains that had made roads impassable during the study period. The required sample size for each village was established basing on total number of households in each village (Table 2). In each village, households were put in two categories; tree farmers and non-tree farmers and samples randomly drawn from non-tree farmers but all tree farmers were considered owing to their limited number. Tree farmers, for purposes of this study were those with more than 50 trees (approximately) on their farm by the time of the study. The researcher moved with a field guide to administer the survey questionnaires. The field guide introduced the researcher to respondents and built rapport. The questions covered demographic characteristics, and incentives distributed. The purpose of the study was explained to respondents, and anonymity of responses, confidentiality and data protection were all emphasized. The main language used in the survey was Runyoro. <b><i>Data analysis</i></b> All qualitative data from KII and FGDs was transcribed and translated from the vernacular (local languages) to English. It was analysed inductively and manually using thematic analysis. It was condensed into meaning units and then coded. Themes were identified and organized into meaningful categories. Other responses were presented as verbatim quotes. Chi square helped to establish association between incentives and tree planting Descriptive statistics were used to get median scores, frequencies and percentages of preferred tree species.