# Role of exposure in adoption and intensity of tree planting practices among smallholder households in Rwanda

Hilda Kegode, Damien Jourdain, Tesfaye Woldeyohanes, Karl Hughes, Selma Tuemumunu Karuaihe

► To cite this version:

Hilda Kegode, Damien Jourdain, Tesfaye Woldeyohanes, Karl Hughes, Selma Tuemumunu Karuaihe. Role of exposure in adoption and intensity of tree planting practices among smallholder households in Rwanda. Agroforestry Systems, 2025, 99 (5), pp.104. 10.1007/s10457-025-01197-6. hal-05193257

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# Role of exposure in adoption and intensity of tree planting practices among smallholder households in Rwanda

Hilda Kegode Ⓔ · Damien Jourdain Ⓔ · Tesfaye Woldeyohanes Ⓔ · Karl Hughes Ⓔ · Selma Tuemumunu Karuaihe Ⓔ

Received: 13 August 2024 / Accepted: 7 April 2025 / Published online: 13 May 2025
© The Author(s) 2025

**Abstract** Agroforestry, widely promoted across Sub-Saharan Africa to restore degraded landscapes and improve livelihoods, has the potential to positively impact Sustainable Development Goals (SDG), specifically SDG15 (life on land) and contribute towards the achievement of SDG2 (food security) and other SDGs. However, despite substantial investment in agroforestry programs, evidence of program effectiveness in enhancing adoption is inadequate. This paper employed the augmented inverse probability weighting method to analyze the impact of exposure to agroforestry practices on the adoption and intensity of tree planting using panel household data from Eastern Rwanda. The findings show that exposure increased the probability of adoption

by 7% (p=0.03). A higher probability of adoption (15%, p=0.01) was observed in households that were exposed both before and after the baseline period, suggesting cumulative effects of exposure over time. Exposure modestly enhanced the diversity of trees and the number of trees in cropping fields, but farmers tended to adopt more exotic than indigenous species. A higher probability of adoption and higher tree numbers were observed when male household members were exposed, and seedling provision enhanced tree numbers and species among adopters. Our findings underscore the need for continuous engagement of farmers and targeted gender-sensitive interventions. We also recommend a more structured approach to training and information dissemination, and a focus seedling availability and a suitable policy environment.

**Keywords** Agroforestry programs · Tree-planting adoption · Augmented inverse probability weighting · Rwanda

## Introduction

Many communities in Low- and Middle-Income Countries (LMICs), such as Rwanda, depend on natural resources to produce food and sustain their livelihoods. However, environmental degradation poses a major challenge especially to rural smallholder households. Approximately 1.5 billion people are affected by

H. Kegode (✉) · T. Woldeyohanes · K. Hughes
Impact Assessment and Acceleration Unit, World
Agroforestry (ICRAF), CIFOR-ICRAF, UN Avenue,
Gigiri, Nairobi, Kenya
e-mail: h.kegode@cifor-icraf.org; u20487623@tuks.co.za

H. Kegode · D. Jourdain · S. T. Karuaihe
Department of Agricultural Economics, Extension
and Rural Development, Center for Environmental
Economics and Policy in Africa (CEEPA), University
of Pretoria, Private Bag X20, Hatfield 0028, South Africa

D. Jourdain
CIRAD, UMR G-EAU, Hatfield 0028, South Africa

D. Jourdain
G-EAU, Univ Montpellier, AgroParisTech, BRGM,
CIRAD, INRAE, Institut Agro, IRD, Montpellier, France
land degradation globally (UNCCD 2014), with the highest percentage in Sub-Saharan Africa (SSA) (Nkonya et al. 2016; Wawire et al. 2021). Hillbrand et al. (2017) estimated that 65% of agricultural land, 30% of pastureland and 20% of forests in Africa are degraded. This causes low agricultural productivity, threatens food security and leads to poverty especially among rural smallholder farmers (Coulibaly et al. 2017; Hillbrand et al. 2017; Pratiwi and Suzuki 2020; Cyamweshi et al. 2023; Ngango et al. 2023). These negative effects are aggravated by climate variability, unsustainable agricultural practices and population pressure (Asrat and Simane 2017; Ngango et al. 2023; Veste et al. 2024; Zaca et al. 2023). To date, environmental degradation continues to elicit discussion globally, with increased focus and investment particularly in LMICs. Various solutions have been proposed to address such degradation, some of which include low tillage agriculture, conservation farming, using physical technologies to control soil erosion and agroforestry.

Agroforestry, the integration of trees or woody perennials in farming systems, has been widely promoted and considered an effective land restoration strategy (Mwase et al. 2015; Coulibaly et al. 2017; Hillbrand et al. 2017; Cornelius and Miccolis 2018; Pantera et al. 2021; Wawire et al. 2021; Muthee et al. 2022; Ntawuruhunga et al. 2023; Veste et al. 2024). By providing ecosystem services, such as climate regulation, soil erosion control, and soil moisture and fertility retention, agroforestry is one of the ways through which landscapes can be restored (Hillbrand et al. 2017; Buyinza et al. 2020; Wawire et al. 2021; Ngango et al. 2023). Additionally, it supplies products such as fuelwood, fruits, livestock fodder and herbs for household consumption and sale (Hillbrand et al. 2017). Agroforestry technologies, practices and activities, including tree planting, grafting, farmer-managed natural regeneration (FMNR), nursery establishment and management and tree care have been promoted across Africa, directly supporting SDG15 by reducing natural habitat depletion and SDG2, by fostering food security to eliminate hunger.

Despite the potential of agroforestry to improve landscapes and livelihoods, its effectiveness depends on household adoption and sustained practice of promoted technologies and activities (Kulindwa and Ahlgren 2021; Ngango et al. 2023). Various studies exist on agroforestry adoption and practice

in SSA. Kpoviwanou et al. (2024) found that 54% of agroforestry adoption literature in Africa focused on factors affecting adoption, and 27% assessed trends and challenges in agroforestry adoption. About 12% investigated environmental and socio-economic benefits of adoption while the remainder examined motivation for adoption and intensification of agroforestry. In Eastern Africa, Buyinza et al. (2020) analyzed how psychological factors affect farmer agroforestry adoption decisions in Uganda, Syano et al. (2022) studied factors influencing agroforestry adoption in Kenya, and Jha et al. (2021) investigated factors affecting adoption of agroforestry by smallholders in Tanzania. However, these studies mainly focus on factors affecting the adoption decision and not necessarily the intensity of agroforestry practices. The closest examples are Ngango et al. (2023) who analyzed adoption decisions, intensity of adoption and challenges of agroforestry adoption in Rwanda and Kulindwa (2016) who specifically analyzed household tree planting behaviour in Tanzania. However, both Ngango et al. (2023) and Kulindwa (2016) used cross-sectional data, hence ignored the time aspect in adoption. The former study analyzed adopters and non-adopters of agroforestry without controlling for selection bias. Both studies also used different variables as indicators of intensity, with the former using the area under agroforestry practice and the latter using only the number of trees planted.

While previous studies contribute significantly to agroforestry literature, very few rigorously assessed the effectiveness of agroforestry programs in encouraging adoption of practices in SSA (Baylis et al. 2016; Cameron et al. 2016; Hughes et al. 2020; Miller et al. 2020; Castle et al. 2021; Malan et al. 2024). A systematic review by Castle et al. (2021) found that most studies on agroforestry program impact relied on non-experimental data and overlooked methodological risks, like selection bias, thus neglecting the non-random participation of farmers or households in the programs. This could impact outcomes and affect conclusions (Leßmeister et al. 2018; Castle et al. 2021; Malan et al. 2024), for example, benefits may be overestimated if selection bias and confounders are not accounted for. Another research gap relates to inadequate research on information barriers which have been identified as potential impediments to adoption among farmers (Kpoviwanou et al. 2024; Mugizi 2025;
Kiyani et al. 2017). Many agroforestry programs aim to increase human capital, by providing information, knowledge and skills to farmers through trainings and extension programs. However, this factor has not received much attention in the literature (Danquah and Amankwah-Amoah 2017; Wossen et al. 2017; Rudolf et al. 2020), resulting in the dearth of credible empirical evidence required to design or improve programs. Additionally, agroforestry adoption studies such as Zaca et al. (2023) only analyzed access to extension service as a binary factor affecting adoption but did not explore how heterogenous parameters of exposure such as the gender of the persons exposed, the topics of exposure and the sources of exposure influenced adoption. This obscures critical pathways that may be important for optimizing programs.

This study attempts to address the gaps highlighted above in the following ways: first, using a quasi-experimental approach—the augmented inverse probability weighting (AIPW) method. AIPW controls potential selection bias and provides more credible results than regression models (Kurz 2022). Second, using multiple indicators besides adoption to evaluate the intensity of tree planting provides richer information that can inform future agroforestry program decisions and actions. The intensity of agroforestry practice is estimated by six indicators including tree species diversity and number of trees found around homes and in cropping fields. Furthermore, we unpack the treatment variable to estimate the impact of specific aspects including the gender of household members exposed, the various sources that provided exposure and the topics covered on adoption and agroforestry practice using AIPW. This approach acknowledges the heterogeneity in exposure and allows more specific recommendations to be made. The use of panel data also captures the dynamism in the adoption indicators. Finally, the paper provides important insights about impact of agroforestry programs and contributes to the agroforestry impact evaluation literature.

The rest of this paper proceeds as follows: Sect. "Study area and context, sampling, and data" describes the study area, sample, data, and descriptive statistics. Sect. "Theoretical and empirical approaches" discusses the theoretical and empirical models employed. Results are presented in Sect. "Results" and discussed in Sect. "Discussion". Sect. "Conclusion and recommendations" concludes and makes some recommendations.

# Study area and context, sampling, and data

## Study area

Rwanda is a small country in Eastern Africa. It has different physical features including rivers, lakes, forests and a hilly terrain, which makes it susceptible to soil erosion (Ngango et al. 2023). With 503 persons/km², the country is densely populated (NISR 2023), placing a huge demand on natural resources. Farm sizes are small and households engage in subsistence rain-fed agriculture as the main economic activity (NISR 2023); cultivating crops throughout the year. This makes land fallowing impossible and leads to degradation (Mugizi 2025; Veste et al. 2024). This study was conducted in four out of seven districts in the Eastern province: Bugesera, Gatsibo, Kayonza and Nyagatare (Fig. 1).

These districts were specifically selected because they had been targeted by a land restoration program, Regreening Africa (RA), which was implemented from September 2017 to February 2023, by World Vision Rwanda in areas that had experienced high levels of land degradation. The program was part of a larger program implemented in eight African countries.<fnref n="1" />

Although the four districts have similar environmental conditions like climate and soils, Gatsibo and Kayonza districts are more forested than other districts in the province (MinEnv 2019). The villages in this study, however, are not within the highly forested zones, hence their selection for land restoration interventions. Some parts of Gatsibo district are high altitude, supporting the cultivation of crops such as coffee, while parts of Kayonza district border the Akagera National Park and exhibit drier conditions compared to other parts of the district. Land degradation, population pressure and climate variability in the province underscore the need for land restoration and strategic resource management. While Rwanda experiences tropical climate with annual rainfall averaging over 1000 mm, the Eastern province

<footnote n="1">¹ The program was implemented in Ethiopia, Ghana, Kenya, Mali, Niger, Rwanda, Senegal, and Somalia by five international development organizations including: World Vision International, Catholic Relief Services, CARE International, Oxfam International and SAHEL-Eco. Information on the program can be found on the website: https://regreeningafrica.org/</footnote>
<figure><img src="image_2.png" /><figcaption>**Fig. 1** Map showing the study areas. Source: World Agroforestry Spacial Laboratory</figcaption></figure>

frequently experiences dry spells due to mismanagement of forests (Jonah et al. 2021; Ngango et al. 2023). The province is characterized by shrubland and wooded savannah covering over 200,000 hectares (MinEnv 2019). Natural forests cover less than 2000 hectares. Despite extensive national reforestation and conservation efforts, the province reported high deforestation rates, declining forest cover and low density in existing forests (MinEnv 2019). The province is notably the only rural area where the deforestation rate exceeded afforestation efforts, with significant forest losses recorded particularly in Kayonza (14%) and Nyagatare (16%) over a 10-year period (MinEnv 2019). This study area presents a suitable case for investigating agroforestry adoption because of the contextual issues highlighted above and previous investment in agroforestry interventions in the province (Ihli et al. 2022). This is crucial for informing future interventions and policy in similar agroecological zones.

## Context, sampling and data collection

Data for this study was collected in sites where World Vision Rwanda implemented the RA program. RA was originally set up as a randomized control trial, and the sampling strategy and data collection were designed to align with that framework. However, non-compliance with the experimental protocol during program implementation rendered the experimental evaluation of the program unfeasible. This study therefore uses non-experimental approaches to investigate the impact of agroforestry interventions in the study area during the period coinciding with the implementation of RA, without necessarily attributing the impacts solely to RA.

Data were obtained from two surveys conducted by World Agroforestry (ICRAF) in the RA program sites in June 2018 and February 2022. Both surveys were conducted through face-to-face interviews. A team of trained

<figure><img src="image_3.png" /></figure>
enumerators interviewed respondents and entered data into the SurveyCTO® data collection tool on tablets and smartphones. Interviews were mainly done with either heads of households or their spouses. Prior to data collection, the survey tool was pretested and translated into the local language to ensure clarity and cultural relevance.

Stratified random sampling was used. From each of the four districts, eight cells<fnref n="2" /> were randomly chosen, and from each cell, four villages were randomly selected. A random sample of 40 households was selected from the selected villages. The resultant sample was 1280 households, but only 1268 were interviewed at baseline, and 1132 re-interviewed at end-line. This represents an 11% attrition rate. Attrition can be problematic if attritors are significantly different from non-attritors, in which case the sample would no longer represent the population from which it was drawn. To control any attrition bias, we apply the method suggested by Baulch and Quisumbing (2011). This entails conducting two probit regressions – one runs a full model with factors that might be associated with attrition (for example household demographic and socio-economic characteristics). The second model omits the variables that are found to be significantly associated with attrition in the first model. The ratio of predicted probabilities from the two regressions is then used to reweight the observations (Baulch and Quisumbing 2011). The resultant attrition weight is then incorporated in further analysis.

## Data and description of treatment and outcome variables

The data collected includes household demographics and livelihood activities, social capital, land, livestock and asset ownership, farming practices, tree species, tree tenure and management, tree product use and earnings. The treatment variable, referred to as ‘exposure’ in this study is based on respondents’ reports of receipt of any form of training, extension or advisory services related to the following topics in agroforestry: tree care and management, tree planting, FMNR, tree grafting, and nursery establishment and management. A household is considered to be exposed if an adult

household member received any such training, extension services, or advisory sessions from any source during the study period. We decompose ‘exposure’ to further investigate heterogenous impacts by evaluating specific elements like the sources of exposure, the gender of the household members exposed and the topics of exposure. This approach is similar to that taken by Ragasa and Mazunda (2018), where the impact of specific measures of agricultural extension service delivery including the source, type and form of extension services delivery were examined to estimate their impact on farm productivity and food security in Malawi. In particular, we test the following: whether households that receive exposure from more than one source are more likely to adopt and practice agroforestry more intensively; whether the gender of household members exposed matters in tree planting adoption and practice, and whether the topics covered affect the outcomes. The impact of receiving tree seedlings among adopters is also analyzed to give a general picture of its effect on the intensity of practice.

## Outcome variables

The primary objective of most agroforestry programs is to increase tree cover through tree planting. In this study, adoption is considered as binary, where a household is classified as having adopted if they planted at least one tree during the study period. Besides adoption, the intensity of practice is also important. Indicators of intensity include the number of trees, where they are planted and their diversity (Rudolf et al. 2020).

The intensity of agroforestry practice is estimated using the following indicators:

1. The change in the number of:

* Tree species

* Indigenous tree species

* Exotic tree species

2. The change in the number of trees:

* At the homestead

* In the main cropping field

<footnote n="2">² Cell: lowest administrative unit comprising several neighbouring villages where citizens can access basic government services.</footnote>
<table><caption>**Table 1** Comparison of baseline characteristics and outcome variables</caption><thead><tr><th></th><th>All HHs means</th><th>Exposed HHs means</th><th>Non-exposed HHs means</th><th>Difference</th></tr></thead><tbody><tr><td colspan="5"><em>Household characteristics</em></td></tr><tr><td>Gender of HH head, 1 = Female</td><td>0.25</td><td>0.20</td><td>0.27</td><td>-0.22*** (0.08)</td></tr><tr><td>Off-farm activities, 1 = Yes</td><td>0.34</td><td>0.36</td><td>0.32</td><td>0.12 (0.11)</td></tr><tr><td>Group membership, 1 = Yes</td><td>0.58</td><td>0.62</td><td>0.56</td><td>0.14 (0.10)</td></tr><tr><td>Age of household head</td><td>44.75</td><td>44.84</td><td>44.69</td><td>0.15 (0.89)</td></tr><tr><td>Household head level of education</td><td>3.98</td><td>4.34</td><td>3.76</td><td>0.58** (0.25)</td></tr><tr><td>Household size</td><td>4.66</td><td>4.78</td><td>4.59</td><td>0.19* (0.11)</td></tr><tr><td>Household had prior exposure, 1 = Yes</td><td>0.11</td><td>0.11</td><td>0.11</td><td>0.05 (0.09)</td></tr><tr><td>HH planted trees before baseline, 1 = Yes</td><td>0.18</td><td>0.22</td><td>0.16</td><td>0.19** (0.09)</td></tr><tr><td colspan="5"><em>Farm characteristics</em></td></tr><tr><td>Approximate number of trees</td><td>22.25</td><td>24.02</td><td>21.20</td><td>2.82 (2.01)</td></tr><tr><td>Land size</td><td>0.43</td><td>0.48</td><td>0.40</td><td>0.08** (0.03)</td></tr><tr><td>Distance from administrative office</td><td>1.85</td><td>1.75</td><td>1.91</td><td>-0.17 (0.14)</td></tr><tr><td>Distance to nearest urban centre</td><td>6.80</td><td>6.56</td><td>6.95</td><td>-0.39 (0.52)</td></tr><tr><td colspan="5"><em>Outcome variables</em></td></tr><tr><td>Household adopted tree planting</td><td>0.63</td><td>0.67</td><td>0.61</td><td>0.06** (0.03)</td></tr><tr><td>Change in number of trees—homestead</td><td>3.32</td><td>5.52</td><td>2.02</td><td>3.51*** (1.11)</td></tr><tr><td>Change in number of trees—main fields</td><td>6.70</td><td>11.23</td><td>4.03</td><td>7.20*** (2.02)</td></tr><tr><td>Change in number of tree species</td><td>1.09</td><td>1.29</td><td>0.97</td><td>0.32** (0.16)</td></tr><tr><td>Change in number of native species</td><td>-0.04</td><td>-0.04</td><td>-0.05</td><td>0.01 (0.07)</td></tr><tr><td>Change in number of exotic species</td><td>1.29</td><td>1.51</td><td>1.17</td><td>0.35** (0.14)</td></tr><tr><td>Observations</td><td>1132</td><td>420</td><td>712</td><td>1132</td></tr></tbody></table>

Standard errors in parentheses. *p* < 0.1, **p* < 0.05, ***p* < 0.01

### 3. Number of trees planted over the study period

To reduce the high variation inherent in the tree numbers, the two variables in No. 2 were generated by following several steps. First, for each species, respondents self-reported the number of trees at the homestead and the main field by selecting one option from the ranges: 1 to 2; 3 to 5; 6 to 10; 11 to 20; 21 to 50; 51 to 100; 101 to 200; 201 to 500; over 500. For each species, the approximate number of trees is the midpoint of the range. The total number of trees either at the homestead or the main cropping field is obtained by adding up all the species estimates in each respective site (Hughes et al. 2022). For outcomes 1 and 2, the outcome variable is the difference between the baseline and endline values.

### Descriptive statistics

We first examined if there were similarities in baseline characteristics between households exposed and those

not exposed during the study period. Table 1 shows some imbalance in the mean values of some covariates, implying potential selection bias between exposed and non-exposed households. The average age of the household head was 44.75 years. In terms of the gender, 25% of household heads were female and 75% were male. The level of education was generally low with the mean overall number of years of education for household heads being almost 4 years. About 30% of household heads had no formal education. The average household size was 4.66 persons, but the number of people within households ranged from one to 14 members. More than half of the households – 58% had members who belonged to diverse social groups in the community, and 34% of the households engaged in at least one off-farm activity.

Key informant interviews revealed that several projects were implemented in the study area before 2018 both by government and non-governmental organizations. Most of these projects sought to address informational barriers to adoption and encourage tree
planting among farmers. The effect of these other programs is estimated using two variables: prior exposure and prior practice. From the data, 11% of households were exposed to agroforestry practices before the baseline study. The percentage rose to 37% at the endline. Households had also practiced agro-forestry before the baseline survey and 18% indicated that they had planted trees in the 12-month period prior to the baseline survey. This variable showed some significant difference between exposed and non-exposed households with the former having a significantly higher mean. This suggests that more exposed households were already engaged in tree planting activities beforehand. The percentage of households that adopted tree planting during the study period was 63%. Significant differences are evident in all outcomes except the change in the number of indigenous (native) tree species, indicating a potential positive association with exposure. However, due to potential selection bias we cannot draw any causal effect conclusions from this table.

## Theoretical and empirical approaches

Lack of knowledge and skills have been identified as a major bottleneck to agroforestry adoption, which can be resolved through various means, including training and extension programs (Kpoviwanou et al. 2024). The human capital (HC) theory suggests that the productive capacity of people can be increased through education and skills training. In this study, exposure to agroforestry practices is an investment in human capital which enhances farmers’ knowledge, technical capacities and decision-making abilities in agroforestry enabling them to better undertake agroforestry practices (Casey 2004). Through this, farmers can better understand benefits and optimize the integration of trees in farming systems, leading to better agroforestry adoption and intensified practice. However, noting that exposure in this study was not random, estimating the impact for exposed households in the absence of exposure poses a counterfactual challenge. Exposure may suffer from selection bias, with exposed farmers being systematically different from non-exposed ones. To address this counterfactual challenge, this study applies the HC theory within the potential outcomes framework.

A potential outcome is the possible outcome of a unit under a particular treatment state. There are two potential outcomes: $Y_i(1)$—if treated and $Y_i(0)$—if not treated. The treatment effect is the difference in potential outcomes if the unit received or did not receive treatment. The average treatment effect (ATE) which is the population mean of (Y1-Y0) and the average treatment effect on the treated (ATT), which is the mean of (Y1-Y0) among treated subjects can be estimated.

$$ \text{Treatment effect} = Y_i(1) - Y_i(0) \quad (1) $$

But, for the same unit, both outcomes cannot be observed at the same time, meaning individual level effects in Eq. 1 cannot be estimated. We therefore estimate ATE as in Eq. 2.

$$ \text{ATE} = E[Y_i(1)|D=1] - E[Y_i(0)|D=0] \quad (2) $$

where D is the treatment dummy.

However, simply comparing treated to untreated units in Eq. 2 may be biased because the two groups may differ in observable or unobservable ways. Furthermore, we want to know the effect of a specific treatment by estimating the causal impact on the treated subpopulation as shown in Eq. 3.

$$ \text{ATT} = E[Y_i(1)|D=1] - E[Y_i(0)|D=1] \quad (3) $$

Since the second term in the equation, which is the potential outcome for treated households had they not been treated (the counterfactual) is not observed, a substitute for it must be found. The mean outcomes of non-treated units cannot be used in estimation due to potential selection biases. Selection bias implies that certain covariates that influence treatment selection may also correlate with the outcomes. Consequently, in the absence of treatment, the mean outcomes for the two groups could still exhibit differences. Experimental methods overcome this challenge by randomly assigning units to different treatment conditions. Randomization ensures similarity in both observational and unobservable characteristics between the two groups, except for treatment (Ferraro and Miranda 2014; Hughes et al. 2020). Although experimental impact evaluations are preferred, when impractical, non-experimental methods are employed (Wiik et al. 2019). To minimize confounding in non-experimental studies,
differences between treatment groups that might influence the treatment status, or the outcome should be controlled. In the case of observable differences, various methods can be used, for example, propensity score matching (PSM), distance matching or inverse probability weighting (IPW). We adopt the IPW rather than PSM because it reduces bias by weighting the data using the estimated propensity scores.

IPW has two parts: (i) the selection model which uses a probit or logistic regression to estimate the probability of assignment to a treatment group (Kurz 2022). This is shown in Eq. 4 which generates propensity scores.

$$P(D = 1 | X) = \frac{1}{1 + e^{-(\beta_0 + \beta_1 X_1 + \beta_2 X_2 + \dots + \beta_k X_k)}} \quad (4)$$

The inverse of the propensity scores is then calculated to obtain the inverse probability weights (Eqs. 5a and 5b) for both the exposed and non-exposed households.

$$\omega_{i1} = \frac{1}{P(D_i | X_i)} \quad \text{for exposed households} \quad (5a)$$

$$\omega_{i0} = \frac{1}{1 - P(D_i | X_i)} \quad \text{for non-exposed households} \quad (5b)$$

(ii) The weights account for the probability of treatment, therefore adjust for selection bias when analyzing the outcome.

The outcome model takes the regression form below:

$$Y = \alpha + \beta_0 D + \beta_1 X_1 + \beta_2 X_2 + \dots + \beta_k X_k + \varepsilon_i \quad (6)$$

where Y is the outcome, α is the intercept, β₀ is the treatment effect and β₁ are effects of the covariates in the model and εᵢ is the error term.

The ATE is then estimated using the weighted estimates in Eqs. 7a and b.

$$Y_{i1}^* = \omega_{i1} * Y_i, \quad \text{for the treated group (D = 1)} \quad (7a)$$

$$Y_{i0}^* = \omega_{i0} * (1 - D_i) * Y_i, \quad \text{for the control group (D = 0)} \quad (7b)$$

$Y_{i1}^*$ and $Y_{i0}^*$ represent the weighted outcomes for the treated and non-treated groups, derived by

multiplying the outcome, Y, by the corresponding weighted estimates. The ATE is then calculated as the difference between the expected values of weighted outcomes in (7a) and (7b).

IPW relies on various assumptions: (i) exchangeability – exposed and non-exposed households have similar covariate distributions. This assumption, however, does not apply in non-experimental cases (Shiba and Kawahara 2021); so, instead we seek to establish that conditional exchangeability holds. Conditional exchangeability assumes that exposed and non-exposed households share the distribution of a given vector of covariates – therefore we use the same covariates in the matching process and ensure balance between them before further analysis; (ii) positivity – there is a positive probability of households either being exposed or not being exposed, providing a basis for comparison and (iii) consistency which requires that the treatment is well defined (Shiba and Kawahara 2021; Chesnaye et al. 2022). Given the non-homogenous nature of the main treatment variable, this last assumption is addressed by separately analyzing various elements related to the treatment – which are more consistent, for example gender of exposed members and the topic of exposure.

This study uses a variation of IPW called augmented inverse probability weighting (AIPW), which adds an augmentation term to the outcome model to reduce variability and improve the estimation (Kurz 2022). When either the treatment or outcome models are correctly specified, AIPW produces efficient estimates. This property, known as ‘doubly-robustness’ makes AIPW more advantageous than IPW or regression methods (Kurz 2022). Furthermore, AIPW utilizes all observations in a dataset, effectively maintaining the sample size and making it preferable to PSM which sometimes eliminates some observations (Chesnaye et al. 2022).

To weight exposed and non-exposed households, we include factors like age, sex, and level of education of the household head and household size. Household participation in community level groups and participation in off-farm activities were also included. The size of land was included because it may influence the feasibility, scalability, and effectiveness of tree planting adoption. Another characteristic was the number of trees found in different land use areas before the baseline, which may affect household decision to adopt depending on whether they consider the trees to be sufficient
<table><caption>**Table 2** Impact of exposure on adoption of tree planting</caption><thead><tr><th rowspan="2">Comparison of households</th><th rowspan="2">Probability of adopting tree planting</th><th colspan="6">Indicators of intensity</th></tr><tr><th>Change in # of tree species</th><th>Change in # of native trees</th><th>Change in # of exotic species</th><th>Change in # of trees homestead</th><th>Change in # of trees main field</th><th># Of trees planted</th></tr></thead><tbody><tr><td>Exposed after the baseline only vs never exposed</td><td>0.07** (0.03)</td><td>0.43*** (0.16)</td><td>0.05 (0.07)</td><td>0.40*** (0.14)</td><td>2.86** (1.13)</td><td>7.77*** (1.92)</td><td>4.45 (2.85)</td></tr><tr><td>Exposed before the baseline only vs never exposed</td><td>0.09* (0.05)</td><td>-0.48* (0.25)</td><td>-0.13 (0.12)</td><td>-0.28 (0.24)</td><td>-3.44** (1.55)</td><td>4.30* (2.56)</td><td>3.39 (4.20)</td></tr><tr><td>Exposed before and after the baseline vs never exposed</td><td>0.15** (0.06)</td><td>-0.15 (0.41)</td><td>-0.22 (0.14)</td><td>0.08 (0.39)</td><td>3.83 (2.44)</td><td>12.06** (4.77)</td><td>5.81 (5.95)</td></tr><tr><td>Exposed before the baseline vs exposed after baseline</td><td>-0.02 (0.06)</td><td>0.91*** (0.27)</td><td>0.18 (0.13)</td><td>0.68*** (0.25)</td><td>6.29*** (1.70)</td><td>3.47 (2.85)</td><td>1.06 (4.46)</td></tr></tbody></table>

Standard errors in parentheses, *p* < 0.1, **p* < 0.05, ***p* < 0.01

or not. Prior exposure and prior adoption of tree planting (before baseline) were included because they may determine household participation in current exposure programs. The distance to the cell office was included as a measure of access to administrative services and nearness to urban/peri-urban centres indicates proximity to information, markets and extension services. Baseline covariates were used in the weighting under the assumption that the variables were unaffected by the interventions that occurred later. Data analysis was done in Stata-17 and R.

## Results

### Treatment effects of exposure

Table 2 shows the ATE of exposure on tree planting adoption and on the intensity of tree planting practice. Comparisons were made between no exposure at all and (i) exposure before baseline, (ii) after baseline and (iii) both before and after baseline, as well as (iv) exposure after baseline versus exposure before baseline.

The results in Table 2 show a significant increase in the probability of adoption of 7% (p = 0.033) among households that were exposed after the baseline compared to those entirely unexposed.

Households that were only exposed before baseline had a 9% (p = 0.100) increase in probability of adoption compared to those never exposed. The largest effect on adoption was 15%, (p = 0.011), which was observed when households were exposed in both periods compared to those never exposed. The results also show that households exposed after baseline only reported a significant increase in tree species diversity (average of 0.43 species) compared to households that were never exposed. However, households exposed before the baseline only reported a significant decline of 0.48 in the number of species, compared to those never exposed. From the results, it is evident that the overall change in number of species was primarily driven by a change in the number of exotic species, rather than indigenous species. Exposure after the baseline also resulted in a significant increase in the number of trees at the homestead (2.86, p = 0.011) and in the main cropping field (7.77, p = 0.001). The last column shows the number of trees specifically planted in 2021. For this outcome, the impact of exposure was not statistically significant.

### Treatment effects of exposure-related factors

Table 3 shows the impact of individual elements of exposure on the intensity of tree planting. Each
<table><caption>**Table 3** Impact of exposure-related factors on the adoption and practice of tree planting</caption><thead><tr><th rowspan="2">Comparisons</th><th rowspan="2">Probability of adopting tree planting</th><th colspan="6">Indicators of intensity</th></tr><tr><th>Change in # of tree species</th><th>Change in # of native trees</th><th>Change in # of exotic species</th><th>Change in # of trees homestead</th><th>Change in # of trees main field</th><th># Of trees planted</th></tr></thead><tbody><tr><td colspan="8"><strong>Topics of exposure</strong></td></tr><tr><td>Tree planting only vs none</td><td>0.10** (0.05)</td><td>0.61** (0.27)</td><td>0.03<br>(0.13)</td><td>0.53** (0.21)</td><td>0.28<br>(1.97)</td><td>7.64** (3.27)</td><td>1.56<br>(3.95)</td></tr><tr><td>Other topics vs none</td><td>0.07<br>(0.07)</td><td>0.44 (0.27)</td><td>0.13<br>(0.12)</td><td>0.32<br>(0.24)</td><td>3.63* (2.13)</td><td>5.87<br>(4.10)</td><td>1.63<br>(4.37)</td></tr><tr><td>Tree-planting vs other topics only</td><td>0.03<br>(0.08)</td><td>0.17 (0.37)</td><td>-0.10<br>(0.17)</td><td>0.21<br>(0.30)</td><td>-3.36 (2.81)</td><td>1.77<br>(5.09)</td><td>-0.07<br>(5.55)</td></tr><tr><td colspan="8"><strong>Gender of household member exposed</strong></td></tr><tr><td>Male only vs none</td><td>0.20*** (0.04)</td><td>0.49** (0.24)</td><td>0.06<br>(0.11)</td><td>0.42** (0.20)</td><td>5.81*** (1.82)</td><td>10.11*** (2.67)</td><td>10.07***<br>(3.83)</td></tr><tr><td>Female only vs none</td><td>0.06<br>(0.05)</td><td>0.46 (0.30)</td><td>-0.02<br>(0.10)</td><td>0.46* (0.25)</td><td>3.17* (1.70)</td><td>-0.35<br>(3.09)</td><td>0.63<br>(4.48)</td></tr><tr><td>Both male and female vs none</td><td>-0.05 (0.04)</td><td>0.37 (0.23)</td><td>0.01<br>(0.11)</td><td>0.42** (0.19)</td><td>3.11* (1.59)</td><td>5.15*<br>(2.66)</td><td>5.18<br>(3.88)</td></tr><tr><td>Male only vs female only</td><td>-0.13** (0.06)</td><td>-0.02 (0.36)</td><td>-0.08<br>(0.14)</td><td>0.04<br>(0.30)</td><td>-2.64 (2.33)</td><td>-10.46***<br>(3.85)</td><td>-9.44*<br>(5.44)</td></tr><tr><td colspan="8"><strong>Number of sources of exposure</strong></td></tr><tr><td>One source vs none</td><td>0.09** (0.04)</td><td>0.35* (0.20)</td><td>-0.00<br>(0.09)</td><td>0.36** (0.17)</td><td>3.50** (1.47)</td><td>7.01*** (2.21)</td><td>4.73<br>(3.42)</td></tr><tr><td>Many sources vs none</td><td>0.04<br>(0.04)</td><td>0.41** (0.19)</td><td>0.04<br>(0.09)</td><td>0.42** (0.16)</td><td>3.51** (1.37)</td><td>6.97*** (2.54)</td><td>3.87<br>(3.30)</td></tr><tr><td>One vs many sources</td><td>-0.05 (0.05)</td><td>0.06 (0.25)</td><td>0.04<br>(0.11)</td><td>0.05<br>(0.21)</td><td>0.01<br>(1.83)</td><td>-0.04<br>(3.07)</td><td>-0.86<br>(4.18)</td></tr><tr><td colspan="8"><strong>Free seedling provision among adopters</strong></td></tr><tr><td>Received vs did not receive</td><td>0.53*** (0.17)</td><td>0.11<br>(0.08)</td><td></td><td>0.44*** (0.15)</td><td>3.31** (1.30)</td><td>7.62*** (2.10)</td><td>22.68***<br>(3.11)</td></tr></tbody></table>

Standard errors in parentheses. * *p* < 0.1, ** *p* < 0.05, *** *p* < 0.01. # = number, (native species = indigenous species)

element was analyzed separately. Households that were specifically exposed to tree planting as a topic had a significant 10% increase in probability of adopting tree planting. Significant positive impacts were also observed on tree species diversity and number of exotic tree species which increased by 0.6 and 0.53, respectively. The number of trees on the main field significantly increased by 7.6, when farmers were specifically exposed to tree planting. Besides tree planting, other topics that farmers were exposed to included nursery management, FMNR, tree care and management, and grafting. These are collectively referred to as 'other topics'. Exposure to other topics only significantly affected the number of trees at the homestead by 3.6.

The impact of gender-related factors varied across outcomes. Exposing only male members was

positive and significant for all outcomes, except the number of indigenous species. Exposing only male household members significantly increased the probability of adopting tree planting by 20%, while exposing only female household members did not significantly affect the probability of adopting tree planting. Exposing only female household members had significant positive impacts on the number of exotic species (0.46) and the number of trees at the homestead (3). Exposure of both male and female household members increased the number of trees at the homestead and the main field by 3 and 5, respectively and the number of exotic species by 0.42. Exposing male members only, as opposed to female members only, had a significant effect on the probability of adoption and led to a significant increase in
the number of trees on the main field by 10 and the number of trees planted in 2021 by 9.

The findings also indicate that households exposed by single sources are more likely to adopt tree planting while there was no significant difference in other outcomes whether a household was exposed by one or multiple sources. The treatment effects in both cases were positive and significant for the overall diversity of tree species, diversity of exotic species, and the number of trees around homesteads and in the main fields.

Providing adopters with tree seedlings significantly increased species diversity by 0.53 (p=0.002), with exotic species significantly increasing by 0.44 (p=0.002). Seedling provision also significantly increased the number of trees planted during the study period by 23 and the number of trees around the homestead by 3.3 and in main fields by 7.6.

## Robustness checks

The robustness of the AIPW model and the estimates were checked as follows: firstly, covariate balance was assessed by estimating standardized differences before and after weighting. After applying the AIPW, there was an improvement in the balance of covariates between exposed and non-exposed households. This is illustrated in Appendix 1, where the standardized differences of the variables tend towards a mean of zero and a standard deviation of one, indicating a more equitable distribution of covariates after weighting. Secondly, we confirmed that the overlap condition was satisfied using kernel density plots shown in Appendix 2. The illustration shows a significant area of overlap between the exposed and non-exposed households. The range of propensity scores (from just below 0.2 to 0.7) shows that there were no households close to 0 or 1, making the AIPW ideal. Finally, we compared the AIPW estimates with those from the traditional IPW analysis. The results are shown in Appendix 3. In many instances, the coefficients and standard errors of the AIPW estimates were quite close to the IPW estimates, providing reassurance that the results are robust.

## Discussion

First, we find that exposure received in only one period – either before or after the baseline had modest effects on the probability of adopting tree planting.

This suggests that, while important, simply exposing farmers to agroforestry practices may not be sufficient to boost tree planting. The probability of adopting was higher for households that received exposure both before and after the baseline. This implies that ongoing engagement with farmers may be required, as agroforestry demands extensive technical knowledge that goes beyond basic awareness creation (Pratiwi and Suzuki 2019; Pérez-Marulanda et al. 2025). Previous studies, for instance Wafula et al. (2016) found that enhancing the technical capacity of smallholders, though essential, was insufficient to spur adoption of conservation practices. Likewise, Rudolf et al. (2020) found that information provision alone led to only a few farmers planting indigenous trees in oil palm plantations in Indonesia. Providing new information to farmers can be beneficial; however, in some instances, it may have no impact (or have a negative effect) due to factors unrelated to the message itself (Mugizi 2025). For example, tree planting may be hindered by lack of planting materials, small land sizes or other factors. Therefore, other than exposure, programs should identify factors that may hinder adoption of agroforestry practices within their contexts and seek to address them. These factors could be environmental or socio-economic (Mwase et al. 2015; Wafula et al. 2016; Kiyani et al. 2017; Mgendi et al. 2021); intrinsic (Meijer et al. 2015b) or incentive-related (Ruseva et al. 2015).

Second, with respect to the intensity of tree planting practices, the study finds that exposure positively impacted the number and diversity of trees, with exposed households having more trees and tree species on farms and around homesteads. The diversity, however, was mostly due to an increase in exotic, rather than indigenous species. This could be attributed to various factors, for example, lack of indigenous tree seedlings or preference for exotic trees (Elias and Fabien 2024). It could also result from prior promotional efforts which emphasized the potential benefits of exotic trees at the expense of indigenous species. Deliberate efforts should be made to increase the diversity of indigenous species, through education and improved access to planting materials.

Third, gender analysis reveals that training male household members had a more significant impact on adoption and tree planting outcomes, for example the numbers of trees established in different land use areas, compared to training female members only.
The difference may be due to gender roles or complex socio-cultural structures generally existing in SSA (Niemann et al. 2024). The roles of men and women in agricultural activities differ across cultures (Catacutan and Naz 2015; Kiptot 2015). Traditional division of labour often confines women to more subsistence household roles limiting their participation in agroforestry and other climate change related decision-making (Niemann et al. 2024). Since men are traditionally the landowners, they also have tree ownership rights and typically make most decisions regarding land and trees (Kiptot 2015; Wafula et al. 2016; Niemann et al. 2024). Existing land and tree tenure systems deny women ownership rights (Kosoe and Darko 2022), particularly to high value timber trees like *Eucalyptus spp.* and *Grevillea robusta*. Consequently, the contribution of women may be undervalued despite their knowledge of conservation practices. In Malawi, although decision-making for most agricultural activities were made either by the husband or by the husband and wife together, decisions concerning agroforestry in the communities were mostly done by men (Meijer et al. 2015a). Since the community in our study area is patrilineal, this may explain the higher rates of adoption and more significant changes in tree numbers when male household members are exposed compared to when female members are exposed to agroforestry practices. And although not quite definitive from our results, the higher impact on outcomes that are observed when male household members are exposed, might suggest that men have more influence over land-use decisions in Rwanda, despite efforts to equalize ownership and rights through law (Bayisenge 2018). Besides, women are sometimes excluded from training and extension programs due to involvement in other household chores and nurturing children. Lack of access to such programs limits women's participation in agroforestry activities (Kosoe and Darko 2022), and may explain the lack of significance in the outcomes in this study. Agroforestry interventions should recognize and value the gender roles of both men and women, as women play a critical role in the early stages of tree establishment and management (Catacutan and Naz 2015; Kiptot 2015). Therefore, participation of both genders in agroforestry initiatives should be encouraged (Ajayi et al. 2016).

Another important factor was the number of sources of information, which included government extension officers, local administrative staff, lead farmers

or non-governmental organization staff. Our findings reveal that exposure by either one or multiple agents was generally beneficial. In both cases, there were significant increases in the number and diversity of trees on farms. However, the marginal gains from an additional source beyond a single source were not significant. This suggests that a more focused approach using a single, well-designed information source may be sufficient and effective in reaching more people, rather than using multiple agents to reach a limited audience with the same information. This result agrees with findings from other studies, for example, Mwase et al. (2015) found that a pluralistic approach to extension provision might not really be beneficial as sometimes messages conflict and could confuse farmers. In the same vein, focusing on tree planting rather than other practices like grafting and FMNR is important in ensuring higher adoption of tree planting. Programs should therefore harmonize information provided to farmers and be very specific on the content (Mwase et al. 2015; Ragasa and Mazunda 2018).

Lastly, we investigated the impact of provision of tree seedlings on the intensity of tree planting among adopters. Previous studies have identified provision of seedlings as a major factor in agroforestry practice, with some linking low adoption rates to limited access to planting materials (Mwase et al. 2015; Ruseva et al. 2015; Jha et al. 2021; Tumuhe et al. 2021; Cyamweshi et al. 2023). Our findings corroborate these earlier studies showing that seedling provision significantly enhances number of trees and species diversity, although it does not notably impact the diversity of indigenous species. While the study context is different, Rudolf et al. (2020) found that when coupled with information provision, seedling provision led to more farmers planting indigenous trees in oil palm plantations in Indonesia. This implies that programs with higher capacity to provide tree seedlings will most likely attain higher adoption amongst farmers. However, such interventions should be well researched to establish how they can be promoted given cost implications and need for sustainability. Key informant interviews and field observations in the study area revealed that most nurseries only propagated exotic species, limiting access to indigenous species. Farmers were inclined to plant seedlings provided for free and did not take the initiative to plant other species. To increase species diversity, indigenous and multipurpose agroforestry trees should be made

<figure><img src="image_4.png" /></figure>
accessible and issues such as propagation of unsuit-
able or invasive tree species addressed (Ajayi et al.
2016; Veste et al. 2024). Attention should be paid to
trees preferred by farmers and awareness created to
improve understanding and acceptance of important
but less-preferred trees.

As earlier indicated, diverse factors may hinder
agroforestry practice. Previous studies highlighted
lack of knowledge and skills, weak institutional sup-
port, access to land or secure land and tree tenure,
high initial costs of adoption and access to plant-
ing materials as key barriers to agroforestry adop-
tion (Kiyani et al. 2017; Kpoviwanou et al. 2024).
Various recommendations can be drawn from the
broader literature. To enhance adoption, policies to
expand agroforestry programs to improve farmers’
knowledge about sustainable practices, potential
benefits and environmental impacts should be pur-
sued. This would make farmers more independent in
adopting technology and increasing long-term prac-
tice (Kpoviwanou et al. 2024; Kiyani et al. 2017).
Strengthening land and tree tenure is required to
encourage long-term investment in agroforestry prac-
tices (Veste et al. 2024; Tafere and Nigussie 2018).
Historically, Rwanda’s tree-planting initiatives have
been driven by government policy, emphasizing the
importance of aligning interventions with existing
regulatory frameworks and ensuring better collabora-
tion between formal and informal institutions (Binam
et al. 2017; Veste et al. 2024). For instance, estab-
lishing tree nurseries and extension support through
existing institutions like farmer cooperatives or
groups may provide an avenue through which expo-
sure to agroforestry practices and access to planting
materials can be enhanced.

Conclusion and recommendations

Agroforestry programs are critical in enhancing adop-
tion of land restoration practices by exposing farm-
ers to various technologies through training, advisory
services or technical support. This study examined
whether exposure increased the likelihood of adopting
tree planting and enhancing the diversity and number
of trees in different land use areas among smallholder
farmers in Eastern Rwanda. The study used panel data
and applied the AIPW method to estimate impact while
addressing selection bias. Beyond the often-analyzed

binary access to training/extension services variable,
this study decomposed ‘exposure’ to investigate three
factors: gender of exposed household members, specific
topic of exposure and sources of exposure enabling a
more nuanced analysis of how they influence outcomes
and providing insights for designing future agroforestry
interventions. We conclude that although exposure is
important, it is not sufficient to boost adoption of agro-
forestry practices. We suggest continuous engagement
with farmers to sustain adoption. Challenges that hinder
adoption and intensive practice in particular contexts
should be identified and addressed. Exposure to men
in households had more significant effects compared to
exposure to women only. Given women’s critical role
in rural production systems, both genders should be
equitably involved in agroforestry initiatives. For more
conclusive insights on gender dynamics, future research
should consider the nature of the treatment in more
detail, for example, if interventions to men and women
provided similar content and targeted similar technolo-
gies. Another finding was that it was not necessary to
have multiple sources of information as the marginal
gains from an additional source of information were not
significant, and that one source of information may just
be effective if well-structured and targeted.

This study acknowledges some limitations. First,
while the proportion of households exposed in our
sample accurately reflects exposure levels in the
population, the overall low exposure rates may
have limited our ability to detect significant effects.
Future research could benefit from interventions with
higher saturation or more intensive exposure to more
robustly assess their impacts. Second, while our find-
ings reveal a significant correlation between seedlings
provision and intensity of tree planting, the question
about the source of tree seedlings, whether acquired
freely or purchased, was only posed to adopting
households. This oversight potentially introduces
bias, by excluding households that may have received
seedlings but chose not to plant them. Therefore, the
implications drawn from seedling provision must be
cautiously interpreted. Future research should address
this gap to provide a more comprehensive under-
standing of the relationship between free tree seedling
distribution and subsequent tree planting practices.
Nonetheless, this result shows the effect of seedling
provision on important outcomes among adopters, for
instance, farmers tended to grow only the tree spe-
cies available in nurseries or offered to them, hence
attention should be paid to the kind of tree species raised in nurseries. Future research should also refine the methodologies used, for example use difference-in-difference analysis for panel data and explore the complexities of agroforestry interventions by investigating different elements that might be critical including: the period covered by the intervention, the form of exposure, the content and delivery method of messages and the frequency of exposure. The long-term effects of agroforestry exposure programs should also be evaluated in future studies to gauge program efficacy and to better understand if behavioural changes have been sustained over time.

**Author contribution** Author contribution: HK: conceptualization, methodology, analysis, writing main draft, paper revision. DJ: methodology, analysis, writing main draft. TW: conceptualization, methodology, writing main draft, paper revision. KH: conceptualization, methodology, writing main draft. STK: writing and reviewing draft. All authors reviewed the manuscript.

**Funding** Open access funding provided by University of Pretoria.

**Data availability** Survey data used in this study will be made available upon reasonable request to the corresponding author.

**Declarations**

**Conflict of interest** The authors have no relevant financial or non-financial interests to disclose. The authors have no conflicts of interest to declare that are relevant to the content of this article. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. The authors have no financial or proprietary interests in any material discussed in this article.

# Appendixes

## Appendix 1: Standardized mean differences for the treatment variable

<figure><img src="image_5.png" /></figure>

## Appendix 2: K-density distributions of propensity scores

<figure><img src="image_6.png" /></figure>

## Appendix 3: Treatment effects of exposure-related factors on the adoption and practice of tree planting using IPW
<table><thead><tr><th rowspan="2">Comparisons</th><th rowspan="2">Probability of adopting tree planting</th><th colspan="6">Indicators of intensity</th></tr><tr><th>Change in # of tree species</th><th>Change in # of native trees</th><th>Change in # of exotic species</th><th>Change in # of trees homestead</th><th>Change in # of trees main field</th><th>#Of trees planted</th></tr></thead><tbody><tr><td colspan="8"><em>Topics of exposure</em></td></tr><tr><td>Tree planting only vs none</td><td>0.10** (0.05)</td><td>0.60** (0.27)</td><td>0.04 (0.13)</td><td>0.50** (0.21)</td><td>0.21 (1.94)</td><td>7.88** (3.17)</td><td>1.53 (3.86)</td></tr><tr><td>Other topics vs none</td><td>0.07 (0.06)</td><td>0.32 (0.28)</td><td>0.10 (0.12)</td><td>0.23 (0.25)</td><td>4.43** (2.17)</td><td>4.16 (3.73)</td><td>3.63 (5.25)</td></tr><tr><td>Tree-planting vs other topics only</td><td>0.03 (0.07)</td><td>0.28 (0.38)</td><td>-0.06 (0.17)</td><td>0.27 (0.32)</td><td>-4.22 (2.83)</td><td>3.72 (4.74)</td><td>-2.09 (6.21)</td></tr><tr><td>Male only vs none</td><td>0.19*** (0.036)</td><td>0.44* (0.24)</td><td>0.05 (0.11)</td><td>0.39* (0.20)</td><td>5.95*** (1.85)</td><td>9.70*** (2.70)</td><td>9.70*** (3.76)</td></tr><tr><td colspan="8"><em>Gender of household member exposed</em></td></tr><tr><td>Female only vs none</td><td>0.06 (0.05)</td><td>0.51* (0.30)</td><td>-0.01 (0.11)</td><td>0.50* (0.26)</td><td>3.34** (1.66)</td><td>0.46 (3.39)</td><td>0.46 (4.52)</td></tr><tr><td>Both male and female vs none</td><td>-0.05 (0.04)</td><td>0.39* (0.24)</td><td>0.02 (0.11)</td><td>0.44** (0.19)</td><td>2.93* (1.56)</td><td>5.72** (2.69)</td><td>4.87 (4.23)</td></tr><tr><td>Male only vs female only</td><td>-0.13** (0.06)</td><td>0.07 (0.37)</td><td>-0.05 (0.14)</td><td>0.11 (0.31)</td><td>-2.61* (2.33)</td><td>-9.24** (4.11)</td><td>-9.24* (5.42)</td></tr><tr><td colspan="8"><em>Number of sources of exposure</em></td></tr><tr><td>One source vs none</td><td>0.09** (0.04)</td><td>0.36* (0.20)</td><td>-0.00 (0.09)</td><td>0.37** (0.17)</td><td>3.71** (1.47)</td><td>7.45*** (2.19)</td><td>4.65 (3.41)</td></tr><tr><td>Many sources vs none</td><td>0.04 (0.04)</td><td>0.42** (0.19)</td><td>0.03 (0.09)</td><td>0.44*** (0.16)</td><td>3.62*** (1.39)</td><td>6.21** (2.52)</td><td>5.13 (3.35)</td></tr><tr><td>One vs many sources</td><td>-0.04 (0.05)</td><td>0.06 (0.25)</td><td>0.03 (0.12)</td><td>0.07 (0.21)</td><td>-0.09 (1.83)</td><td>-1.24 (3.04)</td><td>0.49 (4.21)</td></tr><tr><td colspan="8"><em>Free seedling provision among adopters</em></td></tr><tr><td>Received vs did not receive free seedlings</td><td></td><td>0.53*** (0.17)</td><td>0.11 (0.08)</td><td>0.44*** (0.15)</td><td>3.40** (1.27)</td><td>7.94*** (2.08)</td><td>23.07*** (3.10)</td></tr></tbody></table>

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