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Purpose of the assessment framework

Numerous indicators have been used and recommended to assess sustainable agricultural intensification1. However, a limited number of studies2 have explicitly explored the gaps and needs scientists face in using sustainability indicators in research for development projects. 

The Sustainable Intensification Assessment Framework used in this toolkit provides a synthesized list of indicators and metrics. These indicators and metrics should not be seen as the only way to assess SI. Instead, the goal is to provide a common framework that can guide research on SI and facilitate cross-program learning and assessment on the factors that lead to successfully working towards SI.

The indicators and metrics are categorized into five domains (productivity, economic, environmental, social, and human condition) each having four scales (plot, farm, household, and landscape). The framework of indicators and metrics includes both “gold standard” approaches as well as simplified methods that may be more feasible to use depending on spatial, temporal, and cost limitations. From these indicators, researchers and stakeholders can select those most relevant to their programs.    

How does the framework help researchers assess interventions?

Compare data with the status quo

Analyze the sustainability of intensification interventions by collecting data for the most relevant indicators to compare an SI intervention with the status quo.

Visualization techniques such as radar charts allow you to compare performance of innovations or interventions.  Instead of combining indicators into an index (where important details become obscured), the results for each indicator can be presented. This allows communities, scientists, implementation partners, and policy makers to objectively evaluate the research results based on the importance they assign to each indicator. 

Different stakeholder groups may have different priorities regarding sustainability-related goals (e.g., biodiversity conservation, agricultural production, food security, gender equity). Where long-term data are available, the SI framework can help you quantify trajectories of sustainable intensification by comparing indicators from all domains across time.

Identify potential tradeoffs and synergies

By conducting a trade-offs and synergies exercise, researchers can consider how the various indicators listed under each domain might be affected positively or negatively by an intervention that they are investigating or planning to research. This exercise provides a structured means of considering the broader farming and livelihood systems.

This type of qualitative assessment should be informed by the scientific literature as well as by discussions with farmers, fellow researchers, NGOs, or other stakeholders about the potential direct and indirect effects of an SI intervention. Researchers can anticipate potential synergies and tradeoffs and minimize unintended negative consequences by mitigating them through the research design. The exercise can also be used to select indicators for the type of data collection and assessment described above.

Guide measurement and evaluation efforts

The SI Assessment Framework can also be used to guide monitoring and evaluation (M&E) efforts in development projects. All of the key concepts and methods for measuring or estimating the indicators are presented in the framework. Several factors must be considered to effectively scale up or aggregate plot- and household-level indicators to assess project-level effects (such as at the village, watershed, or sub-district level). Nevertheless, the same process for selecting the most relevant indicators and reflecting on synergies and tradeoffs could be applied to M&E for development projects.

Who can use the SI Toolkit?

The framework is primarily intended to guide agricultural scientists working on development projects, but is flexible and can be used by scientists interested in sustainable intensification more broadly.

Lopez-Ridaura et al. 2005; Speelman et al, 2007; ISPC, 2014; Smith et al., 2016

2 ​ Smith et al., 2016