When degraded and unproductive farmland is restored, food security improves. This seems logical, but the reality of proving it isn’t as straightforward. Teasing out the reasons why a restoration activity led to better productivity, as opposed to other factors, can be complicated. For example, what if it was just because farmers enjoyed a more favorable pattern of rainfall or temperatures than in the past?
New research focused on science-based evidence and robust data analysis shows that food security can be directly linked to farmland restoration. A recent study, Tackling the challenges of assessing socioeconomic impacts of farmland restoration: The case of Malawi, examines the socioeconomic impacts that are attributable to farmland restoration in Malawi. It answers this question: How do we know that farmland restoration is what led to a specific socioeconomic outcome?
One critical result of this finding is that development agencies and institutional investors can be better informed about whether farmland restoration can really improve food security and alleviate poverty in Malawi. The potential result? Better financing and accelerated investment in restoration.
Landscape Restoration in Malawi
In 2017, Malawi became the first member country of the African Forest Landscape Restoration Initiative (AFR100) to develop and endorse a national strategy for forest landscape restoration. The strategy focused on implementing five priority restoration activities to support sustainable development goals like improved food security, poverty alleviation and increased climate resilience (Figure 1), and is consistent with the Malawi 2063 agenda.
Decades of scattered adoption of restoration activities throughout the country, like farmer-managed natural regeneration and other agroforestry technologies, had provided enough anecdotal evidence of the benefits of restoration to influence Malawi’s government to adopt a nationwide strategy. However, anecdotal evidence, while important, does not make for a solid, scientific case for Malawi to attract the $272 million (278 billion MWK) of needed public and private investments to scale up its restoration efforts and create a long-term restoration-based revenue stream for the country.
Household Food Security Linked to Increased Restoration
Our analysis identified two tiers of causal links between farmland restoration and improved food security at household level. Food security refers to a situation where “all people, at all times, have physical and economic access to sufficient, safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life.” Food security is a first-tier impact of restoration when it is achieved directly through increased agricultural output (i.e., yield gains) or forest foods on restored agricultural land or forests. It can be treated as a second-tier impact when it is not a direct result of farmland restoration activities, but rather when food security, including quantity and variety of food, is achieved through income improvement that are attributed to restoration activities.
Using World Bank’s Living Standards Measurement Study (LSMS) household survey data, we developed an econometric model to analyze whether, and to what extent, household-level food security in Malawi has improved since the implementation of Malawi’s National Forest Landscape Restoration Strategy in 2017.
Figure 2 shows that household-level restoration efforts, which are represented by the Household Restoration Intensity Score (HRIS), have significantly intensified across the country between 2016 (before endorsement of the national strategy) and 2019 (after endorsement of the national strategy). Restoration intensification (as indicated by a higher HRIS) has also led to an increased Food Consumption Score (FCS) for the same household in the same period, indicating that food security at the household level has significantly improved over time. FCS is a proxy for food security, reflecting both the quantity and quality of food consumed by households.
The Findings are Encouraging, But More Data is Needed…
While the modeled results are promising, their interpretation needs to be treated with care due to several major caveats. A key limiting factor in this study was the use of household survey data. Although robust nationwide household survey data broadly exists, most surveys are not specifically designed to measure and track socioeconomic impacts associated with restoration interventions. For instance, in Malawi, household surveys only ask about aggregated household income, but not the portion of income that is attributable to restoration activities.
The lack of data for establishing a baseline before the restoration activity took place and monitoring the quality and effectiveness of restoration over time is another limiting factor. Without a clear baseline, the direct attribution of restoration to socioeconomic improvements of the local rural communities may be obscured by other factors, such as macro-level policy interventions or microcredits that allow rural households to generate greater level of income, consumption and wealth through investment.
In addition to the data limitations, we must also acknowledge that monitoring and tracking restoration impacts are challenging due to the complexity of restoration practices and their associated suite of benefits. In fact, multiple land restoration interventions may overlap across space and time, with varying levels of intensity. This makes attribution between a specific intervention and outcome complicated. However, linking a set of restoration interventions to observed environmental and socioeconomic outcomes at landscape level is possible, if more fit-for-purpose data can be collected to support such analyses. To do this properly, the restoration sector could benefit from collaborations between multidisciplinary teams (e.ge.g., local communities, economists, restoration specialists, data scientists, anthropologists) to develop standardized guidelines for collecting data, selecting key indicators and operationalizing monitoring frameworks. Together, they can help systemize restoration impact assessments. Furthermore, these efforts will also benefit from innovative data collection approaches that are tailored to landscape restoration for evaluating such complex relationships.
…Especially Data Linked to Restoration Goals
Monitoring restoration activities and analyzing the resultant data is critically important for understanding the cause-effect relationship of an activity on the outcome. Restoration itself is never the goal being sought, but rather it is the means to achieving a goal that benefits people and the environment. Like with Malawi’s National Forest Landscape Restoration Strategy, it is important to first identify what those goals are, and then identify or produce relevant indicators and datasets to track progress. The Framework for Monitoring Progress on Malawi’s National Forest Landscape Restoration Strategy proposes such a structure for monitoring their goals, and our study represents a first attempt at harnessing available survey data to show progress toward achieving the strategy’s goals on food security and poverty alleviation via agricultural restoration interventions. As learned through our study, however, survey questions that are tailored to the cause-effect relationship between restoration interventions and outcomes would help to streamline attribution. For example, including questions about the extent and quality of the restoration activity on a particular plot, the date of implementation and the portion of crop yield associated with the restored plot would support a more detailed assessment of the relationship.
Restoration Commitments Require Investment in Monitoring
In March 2023, WRI and partners, including the Regional Centre for Mapping of Resources for Development (RCMRD), the African Union Development Agency (AUDA-NEPAD) and the Center for International Forestry Research and World Agroforestry Centre (CIFOR-ICRAF) convened the first-ever Landscape Monitoring Accelerator workshop in Africa to support development of robust monitoring frameworks and data collection for landscape restoration activities. Held in Nairobi, Kenya, participants from five countries — Malawi, Ethiopia, Kenya, Rwanda and Cameroon — assembled at a week-long workshop to review their progress on developing restoration monitoring systems and identify a way forward to address challenges that have hindered robust monitoring to date. A key component of the workshop was for countries to revisit their priority goals for restoration, associated indicators and datasets to support tracking progress toward those goals, and tools that can be used to measure those indicators.
While restoration implementation is gaining momentum across African countries, robust and consistent restoration monitoring systems at the national level — ones that connect restoration goals with indicators of progress and reporting mechanisms — are still in their infancy. We call for restoration stakeholders to invest in restoration monitoring by allocating adequate technical and financial resources for the development of well-defined and coherent monitoring systems that allow better tracking of restoration progress and linking specific restoration activities to outcomes. The need is high for establishing a feedback loop that assesses those relationships. Only better data and robust causal relationships between restoration activities and suite of outcomes can guide more cost-effective investment in restoration and maximize the benefits to communities at scale.
