Designing NSA

Designing Nutrition-Sensitive Agriculture Interventions

Given that the agricultural sector provides 80% of occupations in rural populations, it is regarded as a key tool to be used in the fight against malnutrition. Previously however, agriculture policies focused heavily on food security primarily concentrating on improving the productivity of staple crops such as wheat, maize and rice while neglecting the production of fruit, vegetable, pulse and nut crops. Nutrition-sensitive agriculture (NSA) aims to rectify this.

In the fight to overcome malnutrition and micronutrient deficiencies around the world, NSA aims to sustainably produce a variety of affordable, nutritious, culturally appropriate and safe foods which meet the dietary requirements of populations. Ultimately, NSA projects seek to improve the nutritional status of vulnerable communities by addressing the underlying causes of malnutrition such as access to safe and nutritious food, income, nutrition knowledge and norms and women empowerment, to name but a few.

In order to design and implement a successful NSA intervention, one must first plan it. This requires identifying the best and most appropriate form of intervention as well as the best metrics and indicators for the specified intervention in the context of the target community. Below are the principles required to design a successful NSA intervention.

Besides assessing the context of a target community when designing an NSA project, collaboration with different sectors and programs is recommended as well as incorporating nutrition objectives and indicators in the design while also promoting nutrition and education.

To measure the impact both positive and/or negative of an NSA intervention, appropriate indicators are necessary and must be used. The selection of indicators depends on the pathway an intervention follows. To assess an indicator, certain data should be gathered with different metrics.

There are still gaps in the designing of NSA interventions which can bring about challenges in both the design and implementation phases of these interventions. In some cases, it is not very clear what interventions are the most suitable for certain projects and/or communities. Additionally, the most appropriate indicators will vary depending on the nature of the intervention and the pathway that it follows. Since there are too many indicators, it can be difficult to keep track of what each indicator reflects, potentially increasing the risk of misinterpreting the evaluation of the indicators and/or choosing ones that are not the most appropriate.

Currently, no tool exists to help NSA project designers and implementers from donor organisations and development agencies to rationally and carefully select the best route to design their NSA project maximising the potential outcome of the project. Our tool aims to fill this gap by providing step by step help in the design and implementation of any NSA project as demonstrated in the diagram.

Multi-criteria decision analysis (MCDA) provides stepping-stones and techniques that support the decision maker in finding solutions in their unique and personal decision process. MCDA methods place the decision maker at the centre of the process incorporating preference (subjective) information.

MCDA has a wide application encompassing mathematics, management, informatics, psychology, social science and economics. MCDA can either make use of aggregation, surpassing, geometric distance, interactive or verbal decision methods. Each MCDA method has its own limitations, particularities, hypotheses, premises, perspectives and thus, it is not usually possible to determine whether one method is more suitable when compared to another given a specific problem situation. There are, however, some ways of choosing appropriate MCDA methods to solve specific problems by for example, looking at the data and parameters of the method, modelling effort as well as looking at the outcomes and their granularity.

Within the field of agriculture, MCDA has been used to resolve complex problems such as erosion and degradation, the measurement of the sustainability level of agricultural system, and the tillage practices to mitigate environment impact to soils, to name a few.

The technique for Order Preference by Similarity to Ideal Solutions (TOPSIS) is an example of an MCDA which makes use geometric distance methods. TOPSIS, as recently as 2017, was applied to build a dynamic quantitative national-level food and nutrition security index to be used as a benchmark for the dimensions of food and nutrition while simultaneously prioritising the vulnerabilities of food systems in the delivery of food and nutrition security in Iran.

To our knowledge, MCDA has not been used to optimise the design of nutritional-sensitive agriculture interventions. However, it is fair to say that MCDA is a powerful tool with the potential to help design better NSA projects. Markedly, TOPSIS has an advantage as the amount of effort needed during input is relatively low while offering a complete ranking with closeness scores.

Our current project looks to build a technological tool, specifically a web application that helps the user to choose not only the most suitable types of intervention for a target community, but also provides the most appropriate metrics and indicators as they design their NSA project(s). This tool will also prove useful in the assessment of the agriculture-nutrition nexus which is affected by complex, dynamic and scale-dependent interlinkages among farms, markets, wild foods, diets, intra-household and gender dynamics. Thus, our technological too is expected to asses the baseline nutrition-nexus and evaluate it after the interventions(s).

To further understand the current status of NSA project design other than conducting a systematic review of NSA projects, we have launched an NSA survey targeted at project managers and designers of nutritional-sensitive agriculture projects. Through this survey, we hope to further understand the difficulties and challenges faced by NSA project designers, managers and implementers. This will help us develop a robust and all-inclusive stand-alone NSA evaluation system.