This presentation was delivered by Dr. Greenwell Matchaya, ReSAKSS-SA Coordinator

1. Effects of Covid 19 on Agricultural
Production, Food Markets, Growth
and Poverty in Malawi
Presented at the Malawi Policy Learning Event by
Greenwell Matchaya, PhD
g.matchaya@cgiar.org
Virtual - April 28, 2021

2. PRESENTATION STRUCTURE
• Introduction
• COVID-19-related market disruptions and maize price behavior-
Malawi
• Spatial community vulnerability to Covid-19 impacts in Malawi
• Food production systems disruptions under Covid-19
• Macroeconomic effects of global trade disruptions
• Conclusions and recommendations

3. PRICE EFFECTS OF COVID-19 IN FOOD SURPLUS AND
DEFICIT AREAS OF MALAWI.
• COVID-19 introduced in Southern Africa- end Feb 2020
• Control measures to curb the spread of the pandemic : restrictions
of the movement of people, Market bans, border crossing
restrictions, curfews, etc.
• Inadvertently led to market disruptions
• We focus on maize markets in Malawi, and the SADC

4. Key Messages
• We focus on local staples and community-level markets which
are often overlooked in international data collection and
analysis efforts.
• In most of the countries examined, price effects of the Covid
crisis propagated very quickly across space.
• The magnitude of price deviations also increased very quickly.
• In some countries, the shock caused prices to rise in deficit
areas and decline in surplus areas as transport restrictions
took hold.
• But in countries that export staples regionally, trade restrictions
overrode those effects and prices fell both in surplus and deficit
areas.
4

5. Key Messages (continued)
• In responses to future shocks, care should be taken to
minimize disruptions in local market operations.
• Effects of local market disruptions on incomes, access to food,
etc. can be devastating.
• Measures should be coordinated across countries to respond
to crises while minimizing interference in cross-border trade.
5

6. METHODOLOGY
• To understand the price impacts of COVID-19
• We use data collected by WFP in Malawi
• Employ Seasonal Auto Regressive Integrated Moving Average (SARIMA)
modeling
• to calculate model parameters with 2015-2019 sample
• Then use the model parameters to predict prices for the first half of 2020
• We also cross-check results with Difference-in-Differences methodology

7. • Prices showed a general downward trend from end of February in both deficit and surplus
areas in Malawi
• Although the trends are not homogenous across cities, there was a general decline after
February
COVID-19-related market disruptions and maize price
behavior-Malawi
0
50
100
150
200
250
300
350
400
Jan-19 Fév-19 Mar-19 Avr-19 Mai-19 Juin-19 Juil-19 Aou-19 Sep-19 Oct-19 Nov-19 Déc-19 Jan-20 Fév-20 Mar-20 Avr-20 Mai-20
Malawi
Kwacha/kg
Maize prices
Blantyre Lilongwe Mzimba

8. RAPID INCREASE IN THE SHARE OF MARKETS WITH LOWER-THAN-PREDICTED PRICES FOR
MALAWI
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Jan Feb Mar Apr May
Rural primary markets
Rising prices Declining prices
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Jan Feb Mar Apr May
Urban markets
Rising prices Declining prices
COVID-19-related market disruptions and maize price behavior-Malawi

9. SUMMARY AND RECOMMENDATIONS
 COVID-19 pandemic has led to governments measures to control the
spread of the COVID-19 pandemic
 Some of these measures have impeded arbitrage between markets and
further led to rising supplies relative to demand
 The reduced demand, together with an increase in supplies from the
harvest season have led to rapidly falling prices in local markets

10. SUMMARY AND RECOMMENDATIONS (CONT’D)
 The potential negative impact from the observed decline in prices shows
the critical importance of trans-border trade for smallholder farmers and
small businesses.
• To avoid enormous negative effects on demand, future restrictions and
interventions should be sequenced such that the impact on domestic
markets and cross-border operations are minimized

12. • In the world over, disease and disaster mitigation have gained traction because
of the COVID-19 Pandemic.
• This has heightened the urgency for public policy intervention.
• COVID-19 pandemic has spread worldwide and affected individuals,
communities, & national and global economy
• Effective and efficient response to this pandemic requires resources be directed
to the most vulnerable sections and areas
• To be effective and efficient such response must be informed by data and
evidence
Assessing Community Vulnerability to Covid-19 in Malawi

13. Assessing CommunityVulnerability
 Due to limited resources - prioritize the most vulnerable communities where
the effects of the pandemic are likely to be proportionately more devastating
 Vulnerability is defined as the propensity of an area to be exposed to the spread of Covid-19
combined with limited capacity to control the it and care for infected people, as well as high
exposure to negative food security impacts
 Vulnerability is not geographically uniform
 This study focused on the differentiated spatial vulnerability using an overlay of
indicators: -
• Food and nutrition security,
• Disease burden,
• Health infrastructure and outcomes,
• Population density

14. Key Messages
• The impacts of crises are especially severe in areas where
chronic vulnerability is high.
• Understanding patterns of vulnerability is thus of major
importance to guide strategies to prepare for and respond to
crises.
• Our goal is to identify areas which, if affected by Covid, would
have the least capacity to absorb shocks and would face the
most serious consequences.
14

15. Key Messages (continued)
• We construct an indicator to measure the level of vulnerability
to serious food security consequences of Covid-19.
• Three components: Susceptibility to the spread of the
disease, inability to care for infected people, and existing
food and nutrition insecurity.
• We identify areas of particular concern where governments
should focus crisis monitoring and mitigation efforts.
15

16. • We use data from various sources in Malawi including DHS and
LSMS
• We construct 9 sub-indices of vulnerability using data on
prevalence of blood pressure, diabetes, stunting, access to
hospitals, food expenditure, and population density
• We then combine those sub-indices to construct an overall
community level vulnerability index.
Methodology

17. • An overwhelming 35 % of districts are much more vulnerable (Dedza,
Mchinji, Nkhotakota, Ntcheu, Salima, Neno, Zomba, Blantyre, Chikwawa,
Thyolo and Nsanje).
• Further, another overwhelming 39% of districts are also much vulnerable
(Mwanza, Mulanje, Chiradzulu, Phalombe, Machinga, Balaka, Mangochi,
Lilongwe, Dowa, Ntchisi, Mzimba and Nkhatabay) Nkhatabay)..
• The factors contributing to spatial variation include, density, diets,
food security, health access services.
Key Findings

18. • The vulnerability indices revealed widespread inequalities across the three
administrative regions of Malawi
• The most vulnerable locations are likely to be found in Chitipa, Thyolo,
Blantyre Rural, Chikwawa, Dedza, Mchinji, Nsanje, Neno, Nkhota Kota,
Ntcheu, Salima, Zomba Rural, Balaka, Lilongwe Rural, Mangochi and
Mulanje.
• These areas need to be prioritised in the design of intervention efforts to
prevent and mitigate the effects of the pandemic and future crises.
Recommendations

19. • Malawi Government and her other stakeholders;
• Information useful to guide and inform their interventions spatially in
the short term
• There is a need to reduce inequalities in the longer term, beyond the
current COVID-19 pandemic
• Preparation for future pandemics and other health shocks that are
inevitable.
• Implement policies that reduce vulnerability to such shocks in the future.
Recommendations

21. Production Systems Disruption
• The challenge of COVID-19 on food production systems is not only the likely
extent and complexity of the disruptions but also the difficulty to identify and
track them in real time.
• The propagation of the disease can be tracked through testing and tracing,
while it is impossible, even in normal times, to have accurate information on
cropping activities.
• The lack of information about growing conditions can be overcome by using
today’s digital technologies e.g., remote sensing data and machine learning
techniques.
• The many weaknesses hampering the access to good quality agricultural
statistics can be overcome using the same digital technologies.
21

22. 2. Remotely Sensed Data
• Remotely sensed data through sat. images provide a wealth of information
about features on earth.
• Several advantages of using multispectral satellite images
• Vegetation, including crops, have a specific way to respond to light
Figure 1. (left) False RGB color scene of the North of Senegal with agricultural lands, bare soil, and water.
(Right) The same scene after an unsupervised classification with seven clusters using K-means and
Landsat 8 spectral bands.
Key messages
1. Features on earth react
differently to the electromagnetic
spectrum.
2. Features on earth can be
identified from satellite images
based on their reflectance
signature.
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23. Key Messages
• Access to adequate data for development planning and crisis
response is always a challenge, even more so during crises.
• It is important to invest in ways to access data faster and more
efficiently to guide crisis interventions.
• Remote sensing data and machine learning techniques offer
novel ways to access and learn from data to improve the
quality of interventions.
• We track crop production systems as they evolve during
growing seasons and forecast harvests and yields.
• Our methodology allows us to track developments in near-real
time to inform crisis monitoring and management.
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24. Methodology
• We utilize remote sensing and Machine Learning techniques to understand
production changes over time.
• We use these techniques to extract satellite data and train computer
machines to explore the complex relationship between inputs and their
corresponding outputs.
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25. Results (Cont’d)
• The map shows the ratio between
the 2020 (predicted) and 2017
maize production quantities in
Malawi.
• When the ratio is below unity, the
2020 production is expected to be
less than the 2017 production.
• The central and southern areas are
expected to have more areas with a
decline in production compared to
the north.
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26. 6. Conclusions
• The COVID-19 suggests the need to build a more resilient food system and to
increase countries’ level of preparedness and capacity to respond to shocks.
• Such requires the availability of quality data and analytics to support
policymaking and more efficient interventions.
• Emerging technologies – remote sensing and machine learning – can help to
bring those efficiencies in decision-making processes.
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27. 6. Conclusions
• Capacity Building in emerging technologies must be institutionalized; The use
of such technologies into the agricultural sector needs to be incentivized.
• A robust and efficient ICT infrastructure must be built and maintained to
facilitate data gathering on the ground and analytics.
> Internet connectivity in rural areas, cloud storage and computing.
• To fully take advantage of emerging technologies for analytics, metadata are
as important as primary data for contextualization.
> Collecting crop type data, farm GPS coordinates, seeds and fertilizers types, among others.
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29. Effects of Global PRIMARY Commodity Market DISRUPTION on Growth and
Poverty in MALAWI
Why assessing the effects of global primary commodity market disruption?
• The COVD-19 pandemic affects African economies
through several channels including,
 Global market disruption and the change in the
international price of primary commodities.
• The COVID-19 pandemic has driven most primary
commodity prices down in 2020.
• Heavy contribution of primary commodities in the
export basket of Malawi. 80% total exports

30. Key Messages
• With African countries closely linked to global markets,
disruptions in global trade have important effects on African
economies.
• Exposure to global trade shocks is a function of a country’s
import and export basket as well as the capacity of the
economy to respond.
• The same disruption could have positive or negative impacts
depending on how well the economy absorbs new costs or
pursues new opportunities.
• Responses to Covid-19 or other crises should not undermine
the economy’s ability to respond to changes in the global
economy.
30

31. • Use Computable General Equilibrium
Models
• Micro-simulations.
Methodology

32. IV. Findings
Slight Decline in GDP Growth in Malawi
Percentage point change in GDP growth, COVID vs. BaU, Price and Trade Shocks
-11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4
Angola
Zimbabwe
Zambia
Mozambique
South Africa
Malawi
Namibia
Lesotho
Eswatini
Botswana
A
A
A
A
B
B
B
B
C
C

33. Change in Poverty Headcount Ratio, COVID vs. Baseline, Price and Trade Shocks (pp)
IV. Findings
Slight Increase in Income Poverty in Malawi
8,0
0,3
3,1
-0,3
0,7
-0,5
0,0
0,2
Angola
Zambia
Mozambique
South Africa
Malawi
Namibia
Lesotho
Eswatini

34. IV. Findings
Increased Urban Poverty More Than Rural Poverty in Malawi
Changes in urban and rural headcount poverty rates, COVID vs. Baseline, Price and Trade Shock (pp)
-2 0 2 4 6 8 10 12
Angola
Zambia
Mozambique
Malawi
Namibia
Lesotho
Eswatini
Rural Urbain

35. • Decline in the international price of
primary commodities traded by Malawi.
• Negative effects on economic growth
and (income) poverty in Malawi.
• Increase in urban poverty more than
rural poverty in Malawi.
IV. Conclusion: Key findings

36. • Protect vulnerable populations
• Diversify the external trade
Increase the contribution of agricultural and food commodities
and other high value commodities in the export basket
IV. Conclusion: Policy Recommendations

37. FURTHER INFORMATION
https://www.akademiya2063.org/events.php?lang=en
www.resakss.org
https://www.iwmi.cgiar.org/

38. Thank
You