A Forensic Framework and Decision Support for Harmonized and Holistic Food System Resilience and Sustainability Analysis

1. A Forensic Framework and DSS for
Food System Resilience and
Sustainability Analysis
Prince Agyemang, Ebenezer Kwofie, Marie-Anne Dessureault, John M. Ulimwengu

2. #2023ReSAKSS #2023ATOR
Present State of the African Food System
Source:Welthungerhlife & World Economic Forum
20% of people in Africa face chronic hunger
DR Congo
15.6 to 21.8 million
Zimbabwe
3.6 to 6 million
Southern Africa
Burkina Faso, Niger
and Mali, 3.2 to 12.7
million
Source: Africa Center for Strategic Studies
Central Africa
Sudan
7 to 9.6 million
East Africa
West Africa
2019-2020
Increase
in
number
of
people
facing
food
crises

3. #2023ReSAKSS #2023ATOR
Key DRIVERS
Shocks and Stressors affecting Africa
food system
(2022)
( Each year
since 2015)
( Each year
since 2019
( 2008-2018 is Sub-Saharan and North Africa)

4. #2023ReSAKSS #2023ATOR
 How do we measure these
shocks and stressors?
 How do we assess the resilience
of food systems?
 How do we assess the
sustainability of food systems?
Climate change Globalization & Trade
Income growth and
distribution
Politics and
Governance
Food Supply Chain
 Food production
 Storage and
distribution
 Processing
Food Environments
 Availability
 Affordability
 Product
properties
Diets
Socioeconomic factors
Individual
Factors
 Economic
 Situation
 Cognitive
 Is there a causal relationship
between resilience and a
sustainable food system?

5. #2023ReSAKSS #2023ATOR
Casual relationship between resilience and sustainable food systems
neglected?
3
No commonly agreed-upon domains for measuring the food systems’
resilience and sustainability?
2
There is a proliferation in resilience and sustainability indicators
1
Key Challenges in the food system
sustainability and resilience
measurement

6. #2023ReSAKSS #2023ATOR
Methodological Framework
Step 1:
Resilience and
sustainability
framework identification
Step 2:
Domain and indicator
identification and
characterization
Step 3:
Forensic Framework
development
Step 4:
Case Study
application
Step 5:
Scenario construction
(Continental and
regional level)
Step 6:
Development of a novel
Decision Support
System (FS-ROAS)

7. #2023ReSAKSS #2023ATOR
Domains and Indicators translation
Household
Fuzzy entropy approach
Politics and
Governance
Food Security
and nutrition
Socioeconomic
Environment
District Regional
Global
Rural or
Urban
Indicators
Domains identified
from literature
Step 2

8. #2023ReSAKSS #2023ATOR
Forensic Framework
What happened?
Why did it happen?
What will happen?
What is the best that
could happen?
KEY QUESTIONS
Data-Driven Approach
Step 3

9. #2023ReSAKSS #2023ATOR
What and Why did it happen?
 A weak negative correlation of between
0.19 and 0.76 is observed between cereal
supply
 A negative correlation in the range of
0.33 to 0.62 exists between milk supply
and the selected indicators
 Establish the relationship between
product, supply, loss, and other relevant
indicators
 A strong positive correlation between
the selected indicators and driving
forces.

10. #2023ReSAKSS #2023ATOR
Production increase by 15% by 2030.
Reduce food loss by 25%
Increase agriculture credit by 9-12%
Stable Scenario 1: Increased agricultural production
Increase food supply by ~12% and reduce
loss by ~10-15%.
Increase in support from international
donors (19-20 %)
Increase agriculture credit by 15-18%
Stable Scenario 2: Increased agricultural credit
What could/will happen?
Step 4

11. #2023ReSAKSS #2023ATOR
326.1 million people become undernourished
(71.5 million more than 2020 levels).
103.2 million people become obese.
167.5 million people become anemic.
Snapshots of unfolding futures at the Continental level
Increased Ag. Production
11.7 million fewer people are undernourished (9.4%
decrease).
15.5 million more obese people than BAU
80.2% increase in the number who become anemic
Increased Ag. Credit
23 million fewer people are undernourished
(9.4% decrease).
7.3 million more obese people than BAU
7.5% reduction in the number who are anemic
BAU
BAU = business-as-usual;
S1 = scenario 1; S2 = scenario 2.
Step 5

12. #2023ReSAKSS #2023ATOR
Snapshots of unfolding futures at the Continental level
Rise in surface temperature to 1.44 ℃ (18%
above the 2020 levels).
59.6% increase in food price index
Increased Ag. Production
Rise in surface temperature to 1.62℃ (25%
above 2020 levels)
1.1% marginal decrease in food price inflation
below the 2020 levels.
BAU
BAU = business-as-usual;
S1 = scenario 1;
S2 = scenario 2.
Increased Ag. Credit
Rise in surface temperature to 1.68℃ (27.8%
above the 2020 levels)
~17% increase in food price inflation above the
2020 levels.

13. #2023ReSAKSS #2023ATOR
Snapshots of unfolding futures at the sub-regional level
Increased Ag. Production
7.61 million fewer people are undernourished
in Central Africa(~17% below 2020 levels).
6.05 million more obese people in Southern
Africa
11% increase in emissions from agrifood
systems in Central and East African regions.
Increased Ag. Credit
7.5 million fewer people are undernourished in
Central Africa (17.5% below 2020 levels).
8.9 million more obese people in West Africa
(35.8% above the 2020 levels)
11.4% increase in emissions from the agrifood in
East Africa
BAU = business-as-usual;
S1 = scenario 1; S2 = scenario 2.

14. #2023ReSAKSS #2023ATOR
The persistence of trade-offs
(a) Increased agricultural production (b) Increased agricultural credit

15. #2023ReSAKSS #2023ATOR
Translation of the framework into the Proposed FS-ROAS
Step 6

16. #2023ReSAKSS #2023ATOR
Key takeaways and implications for
Post-Malabo Agenda
Provide a tool for exploration and
trade-off for different outcomes at
the national and regional level
The tool relies on the accuracy of
the data.
Positioning and making the
tool useful post-Malabo
Agenda
The need to revise and design better
strategies to collect accurate data.
Potential to adapt the tool for
a national level assessment

17. #2023ReSAKSS #2023ATOR
Thank you to the team on the McGill
Campus

18. THANK YOU