The impacts of agroforestry interventions on agricultural productivity, ecosystem services, and human well-being in low- and middle-income countries: A systematic review

Abstract

Background: Agroforestry, the intentional integration of trees or other woody perennials with crops or livestock in production systems, is being widely promoted as a conservation and development tool to help meet the 2030 UN Sustainable Development Goals. Donors, governments, and nongovernmental organizations have invested significant time and resources into developing and promoting agroforestry policies and programs in low- and middle-income countries (LMICs) worldwide. While a large body of literature on the impacts of agroforestry practices in LMICs is available, the social-ecological impacts of agroforestry interventions is less well-studied. This knowledge gap on the effectiveness of agroforestry interventions constrains possibilities for evidence-based policy and investment decisions to advance sustainable development objectives.

Objectives: The primary objective of this Campbell systematic review was to synthesize the available evidence on the impacts of agroforestry interventions in LMICs on agricultural productivity, ecosystem services, and human well-being. The secondary objectives were to identify key pathways through which agroforestry interventions lead to various outcomes and how the interventions affect different sub-groups of the population.

Search methods: This review is based on a previously created evidence and gap map (EGM) of studies evaluating the impacts of agroforestry practices and interventions on agricultural productivity, ecosystem services, and human well-being. We included published and unpublished literature in the English language covering the period between 2000 and October 20, 2017. We searched six academic databases and 19 organization websites to identify potentially relevant studies. The search was conducted for our EGM in mid-2017, and we did not conduct an additional search for this systematic review.

Selection criteria: We included randomized control trials (RCTs) and quasi-experimental studies assessing the effect of an agroforestry intervention on at least one outcome measure of agricultural productivity, ecosystem services, or human well-being for farmers and their farmland in LMICs. Agroforestry interventions include any program or policy designed to promote and support the adoption or maintenance of agroforestry practices, which include trees on farms, silvopasture, shade-grown crops, and homegardens with trees, among others. Moreover, the studies needed to include a nonagroforestry comparator, such as conventional agriculture or forestry systems or a before-after comparison.

Data collection and analysis: We used a standardized data extraction spreadsheet to extract details about each included study. We also used a standardized form to assess risk of bias for each of the included studies in this SR. Meta-analysis techniques were used to combine and synthesize effect size estimates for the outcomes measures that had sufficient data. We used a random effects models for the meta-analyses and use Hedge's g (difference in means divided by the pooled standard deviation) to report effect size estimates. The outcomes without enough evidence for meta-analysis were discussed narratively.

Main results: We identified 11 studies across nine countries, all of which used quasi-experimental methods. Overall, the quality of the evidence base was assessed as being low. Studies were rated as having high or critical risk of bias if they failed to convincingly address more than one of the main potential sources of bias, namely selection bias, group equivalence, and spillover effects. Given the low number of studies and the high risk of bias of the evidence base, the results of this SR are limited and should be considered a baseline for future work. The results of the meta-analysis for impacts on yields indicated that agroforestry interventions overall may lead to a large, positive impact on yield (Hedge's g = 1.16 [-0.35, 2.67] (p = .13)), though there was high heterogeneity in the results (I ² = 98.99%, ${\tau }^2$ = 2.94, Q(df = 4) = 370.7). There were positive yield impacts for soil fertility replenishment practices, including incorporating trees in agricultural fields and improved fallow practices in fields where there are severe soil fertility issues. In other cases, incorporating trees into the production system reduced productivity and took land out of production for conservation benefits. These systems generally used an incentive provision scheme to economically offset the reductions in yields. The result of the meta-analysis on income suggests that agroforestry interventions overall may lead to a small, positive impact on income (Hedge's g = 0.12 [-0.06, 0.30] (p = .20)), with moderately high heterogeneity in the results (I ² = 75.29%, ${\tau }^2$ = 0.04, Q(df = 6) = 19.16). In cases where improvement yields were reported, there were generally attendant improvements in income. In the cases where payments were provided to offset the potential loss in yields, incomes also generally improved, though there were mixed results for the certification programs and the tenure security permitting scheme. One program, which study authors suggested may have been poorly targeted, had negative yield impacts. There was not enough comparable evidence to quantitatively synthesize the impacts of agroforestry interventions on nutrition and food security outcomes, though the results indicted positive or neutral impacts on dietary diversity and food intake were likely. Surprisingly, there was little evidence on the impacts of agroforestry interventions on environmental outcomes, and there was no consistency of environmental indicator variables used. However, what has been studied indicates that the environmental benefits are being achieved to at least some extent, consistent with the broader literature on agroforestry practices. The evidence base was insufficient to evaluate the interaction between environmental and social impacts. Several studies explicitly considered variable impacts across different population sub-groups, including differential impacts on small-holders versus large-holders, on woman-headed households versus male-headed households, and on richer groups versus poorer groups. Small-holder farmers typically experienced the most positive effect sizes due to the agroforestry interventions. Women and poorer groups had mixed outcomes relative to men and richer households, highlighting the importance of considering these groups in intervention design.

Authors' conclusions: There is limited evidence of the impacts of agroforestry interventions, restricting our ability to draw conclusions on the effect sizes of different intervention types. The existing evidence forms a baseline for future research and highlights the importance of considering equity and socio-economic factors in determining suitable intervention design. Some key implications for practice and policy include investing in programs that include pilot programs, funding for project evaluation, and that address key equity issues, such as targeting to smallholders, women, poor, and marginalized groups. Funding should also be given to implementing RCTs and more rigorous quasi-experimental impact evaluations of agroforestry interventions over longer time-periods to collect robust evidence of the effectiveness of various schemes promoting agroforestry practices.