Spatial typology for targeted food and nutrition security interventions
Core approach and possible extensions
In this interactive we develop a typology to help design and improve spatial targeting of food and nutrition security (FNS) interventions. Based on the efficiency estimation within a four-indicator diagram which represent the core segments of a food system, the proposed approach allows for the broad identification and location of major FNS bottlenecks. After discussing the basic concepts, we illustrate the core typology using a real-world example and propose a series of extensions to further refine its approach for improved policymaking.
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Many developing countries are confronted with a sort of “Food System Paradox”, which is manifested in the combination of more-than-sufficient agricultural potential, on the one hand, and poor nutrition outcomes, on the other. This paradox can be explained through a wide range of bottlenecks, constraints and inefficiencies of different kind. Confronted with a multitude of possible explanations and based on only four core indicators, researchers at IFPRI developed a spatial FNS typology that helps locate and prioritize development strategies to overcome prevailing constraints. This data-light approach is especially useful in settings where development challenges appear ubiquitous while resources are limited.
2. Food System Paradox
1. Introduction
3. Conceptual framework
4. Efficiency Estimation
Agricultural potential and nutrition outcomes in Burundi
5. Example Core Approach
6. Possible Extensions
7. Conclusions and further Readings
The food produced needs to arrive at households, the amount and range which depend on prevailing access characteristics (such as road quality, market information services and people’s purchasing power), indicating their level of access efficiency. This access efficiency will determine household’s food access to a sufficiently diversified diet.
However not all farmers are able to fully tap into the potential of their land, which is due to a number of production issues (such as access to credit, inputs or extension), indicating their level of production efficiency. This production efficiency will determine the quantity and quality of food production.
Agricultural potential of land is determined by biophysical factors, such as soil, rain, slope.
Agricultural potential
Food production
Food access
Nutritional outcomes
The food arrived at the household should be correctly prepared and absorbed by all family members, which depends on various issues related to cooking practices, intra-household allocation and WASH and health care conditions, indicating their level of utilization efficiency. This utilization efficiency will determine to what extent food access leads to acceptable nutritional outcomes.
• Land rights • Credit systems • Input markets • Extension services
• Transport infrastructure • Transaction costs • Market information • Real income
• Food safety and cooking habits • Intra-household allocations • WASH characteristics • Health care services
To categorize individual constraints into broader types, the typology makes use of four FNS indicators to derive efficiency performances for each of three composing segments:
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Production efficiency
Access efficiency
Utilization efficiency
P
A
U
N
Average efficiency
Using this diagram as a four-dimensional scatterplot, we estimate relative efficiency levels of subnational areas for each constraint type as follows.
We construct and derive values of subnational areas for each of the FNS indicators. Dots in each quadrant represent different areas of a country and take on the values of two sequential indicators.
We use these lines of average efficiency as benchmarks. The efficiency level of each area is defined as the relative distance between the actual observation and what is expected based on the country’s mean performance. For example, if the same efficiency as the rest of the country were reached, the area represented by P-A-U would have a food production at P*, a food access at A*, and a nutritional status at U*.
We estimate lines of average efficiency using regression analysis. These lines indicate the mean efficiency level of a country in converting agricultural potential into food production (production efficiency); food production into food access (access efficiency); and food access into nutritional status (utilization efficiency).
We summarize total nutrition efficiency (NE) as the sum of production (PE), access (AE) and utilization efficiencies (UE) of corresponding length. Important for setting priorities, the underperformance of this area is not equal across all three dimensions, with access constraints being clearly most severe.
Utilization constraints
Production constraints
Access constraints
Nutrition constraints
NE
PE
AE
UE
N*
U*
A*
P*
To help prioritize among different interventions across locations, the typology derives efficiency levels for each constraint type by:
As such, the spatial FNS typology provides insight into a country’s food system paradox by “explaining” nutrition outcomes of different areas as a combination of inefficiencies of different types and degrees.
PE + AE + UE = NE
U - U* = UC
A - A* = AC
P - P* = PC
In this section we apply the spatial FNS typology to the 45 departments of Senegal (2016–2019).
Construction of four FNS indicators Based on available data, we construct four FNS indicators which respectively reflect the extent by which people in each department (i) are able to produce a sufficiently diversified diet, (ii) actually produce a sufficiently diversified diet, (iii) have access to a sufficiently diversified diet, and (iv) obtain acceptable nutritional outcomes.
Efficiency levels for each department by type We estimate and map the efficiency level for production, access and utilization for each department based on the relative distance between the actual and country performance.
Values by department and estimation of average efficiency We compute the corresponding values for each department, populate the four quadrants and estimate the average efficiency lines for each segment. Note: we also define a fork indicating 75–125% of average efficiency.
Summary of low efficiency combinations We map all low efficiency combinations defined by a score below 75% for each segment.
125%
75%
Close Map
To further improve policymaking, future research will focus on the following extensions.
Extension 1: Extend analysis to identify precise bottlenecks.
Extension 3: Run simulations.
Extension 2: Increase time sensitivity within and across typology applications.
The core typology approach only refers to the broad constraint types without detailing the precise issues at stake within those dimensions. Based on auxiliary data, an additional set of extensions could focus on each of the three constraint types separately, either by developing similar approaches on sub-segments, extending the number of key indicators, or performing multivariate analyses to model the variation in production, access and utilization efficiencies.
Increase time sensitivity
Simulate impact of interventions
Within each application, a better account of seasonality and delayed impacts could be pursued to improve the efficiency estimation throughout the agricultural year. Repeating the same typology over multiple periods could also yield a refined qualification of observed constraints into a structural or stochastic component, which may be indicative of the policy response needed and serve as a basis for resilience measurement.
An essential feature for policymakers is to be able to assess and compare the return of different investment options. For example, what is the effect on food production, food access and nutritional status following an increase of production efficiency by x%? Or, what is the minimum amount of resources needed as well as their allocation to bring stunting rates down to 5% in each area of a country? Running such simulations could be an important add-on to the policymakers’ decision toolkit.
Uncover precise constraints within segments
The core approach of this spatial typology is an operational tool that combines different dimensions or food system segments in a coherent way, which can help design food and nutrition policies in developing countries. With the current set of extensions in mind and depending on data availability, policymaking could be further improved to account for the pronounced diversity of development challenges throughout the Global South.
FURTHER READINGS Academic output Spatial typology for targeted food and nutrition security interventions – in: World Development, 120(2019), p.62-75. Mapping the nutrient adequacy of farm production and food consumption to target policy in Uganda. – in: Development Policy Review, 2022. Spatial food and nutrition security typologies for agriculture and food value chain interventions in Eastern DRC. IFPRI Discussion Paper 01971, Washington DC, IFPRI, 2020. Determinants of Resilience for Food and Nutrition Security in South Sudan. IFPRI Discussion Paper 02117, Washington DC, IFPRI, 2022. Spatial typology for food system analysis: Taking stock and setting a research agenda – in: World Development Perspectives, 35 (2024), 100623
Atlas work Understanding the Democratic Republic of the Congo’s agricultural paradox: Based on the eAtlas data platform, International Food Policy Research Institute, Addis Ababa, 2018. Policy atlas on food and nutrition security and resilience, International Food Policy Research Institute and SNV, Dakar, 2020. • Kenya • Burkina Faso • Rwanda • Ghana Project reports Typologies spatiales intégrées pour identifier l'insécurité alimentaire et les goulots d'étranglement de la pauvreté, FARM-TRAC Policy Note, 2021. • Cas du Mali • Cas du Burkina Faso • Cas du Sénégal
More information can be obtained on this page, or by directly reaching out to the authors: • Wim Marivoet, Research Fellow, Africa Region, IFPRI (w.marivoet@cgiar.org) • John Ulimwengu, Senior Research Fellow, Africa Region, IFPRI (j.ulimwengu@cgiar.org)
