This presentation was delivered by Dr. Racine Ly, Director of Data Management, Digital Products and Technology at AKADEMIYA2063

1. Malawi Policy Learning Event
Food Production System Disruption
Racine Ly (*), Khadim Dia (*), Mariam Diallo (*)
(*) AKADEMIYA2063

2. Outline
1. Introduction & Context
2. Remotely Sensed Data
3. Machine Learning Framework
4. Food Crop Production Model
5. Results & Recommendations
Notice:
The shown boundaries and names, and the designations used on maps do not imply official endorsement or acceptance by AKADEMIYA2063.
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3. 1. Introduction & Context
• Measures taken to mitigate the COVID-19 propagation put a heavy strain onto the
agricultural sector.
• Inadequate growing conditions can also push African countries at the blink of a
food crisis.
• From the production side, the interrelationship between food crop production and
the COVID-19 is not well established.
• In periods of uncertainties, forecasts can play a major role to reduce the cost of
inadequate decisions and allow to plan for the recovery process.
• We combined remotely sensed data and machine learning techniques to provide
maps of food crop production forecasts for several countries in Africa.
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4. 1. Introduction & Context (Cont’d)
Better agricultural statistics through remote sensing and artificial intelligence
• 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. 4

5. 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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6. 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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7. 2. Remotely Sensed Data (Cont’d)
Application to the Food Crop Production Model
Figure 2. Reflectance of healthy and stressed plants across the visible and infrared spectrum
filter wavelengths. (McVeagh et al., 2012)
• Vegetation (crops) only absorb
specific wavelengths as energy for
photosynthesis.
• What is not absorbed is
considered as reflected by the
leaves.
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8. Figure 4. Senegal Millet
Production (left) 2005;
Middle 2010; (Right) 2017).
Data Source: IFPRI, 2020,
Map Source: Ly et al., 2020.
3. Machine Learning Framework
• Machine Learning techniques are gaining attention from the research community.
• Two main ways of training a machine learning: (Supervised) Building a relationship
between inputs and their corresponding examples; (Unsupervised) Identify
similarities within the dataset (without examples).
• In our case, we use artificial neural networks which are supervised.
Production values as examples
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9. 4. Food Crop Production Model
Training Scheme
NDVI
LST
RAIN
2005
2010
2017
2005
2010
2017
2005
2010
2017
Crop Masks
2005
2010
2017
2005
2010
2017
2005
2010
2017
Neural Net.
Raw sat. Images Masked images Labels
(Examples)
Learning Process
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10. 5. Results
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11. 5. 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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12. 5. Results (Cont’d)
• The NDVI anomaly measures the
dispersion of the 2020 mean NDVI to
the 20 years historical mean.
• The highest the anomaly value, the
greener (healthy) a vegetation is
expected to be.
Policymaking Use Cases using NDVI time-
series.
• (Lein, 2012) showed how a tax-free
agricultural ordinance in 2006
impacted multiple cropping practices
adoption in China.
• (Arvor et al., 2011) Relationship
between agricultural dynamics in
Amazonia during the period 2000-
2007 and the region’s existing public
policies. 12

13. 5. Results (Cont’d)
• The northern area of the country, on
average, received more rainfall than
the other parts of the country.
• The sharpest decline in rainfall occur
at the center and southeast areas.
Policymaking Use Cases
• The knowledge of drying areas at the
pixel level can support the design and
implementation of irrigation policies
for the agricultural sector.
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14. 5. Results (Cont’d)
• Areas with temperature spikes, on
average, are scattered across the
country.
• Most of the country experienced, on
average, an increase between +0.1 and
+2.0 degree Celsius.
Policymaking Use Cases
• The knowledge of where temperature
are expected to increase and decrease,
facilitate the monitoring of climate
change and its impacts on
communities.
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15. 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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16. 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.
• Appeal to emerging technologies into decision-making processes.
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18. THANK YOU
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