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Malawi Policy Learning Event
Food Production System Disruption
Racine Ly (*), Khadim Dia (*), Mariam Diallo (*)
(*) AKADEM...
Outline
1. Introduction & Context
2. Remotely Sensed Data
3. Machine Learning Framework
4. Food Crop Production Model
5. R...
1. Introduction & Context
• Measures taken to mitigate the COVID-19 propagation put a heavy strain onto the
agricultural s...
1. Introduction & Context (Cont’d)
Better agricultural statistics through remote sensing and artificial intelligence
• The...
Key Messages
• Access to adequate data for development planning and crisis response is always a
challenge, even more so du...
2. Remotely Sensed Data
• Remotely sensed data through sat. images provide a wealth of information about
features on earth...
2. Remotely Sensed Data (Cont’d)
Application to the Food Crop Production Model
Figure 2. Reflectance of healthy and stress...
Figure 4. Senegal Millet
Production (left) 2005;
Middle 2010; (Right) 2017).
Data Source: IFPRI, 2020,
Map Source: Ly et a...
4. Food Crop Production Model
Training Scheme
NDVI
LST
RAIN
2005
2010
2017
2005
2010
2017
2005
2010
2017
Crop Masks
2005
2...
5. Results
10
5. Results (Cont’d)
• The map shows the ratio between the
2020 (predicted) and 2017 maize
production quantities in Malawi....
5. Results (Cont’d)
• The NDVI anomaly measures the
dispersion of the 2020 mean NDVI to
the 20 years historical mean.
• Th...
5. Results (Cont’d)
• The northern area of the country, on
average, received more rainfall than
the other parts of the cou...
5. Results (Cont’d)
• Areas with temperature spikes, on
average, are scattered across the
country.
• Most of the country e...
6. Conclusions
• The COVID-19 suggests the need to build a more resilient food system and to
increase countries’ level of ...
6. Conclusions
• Capacity Building in emerging technologies must be institutionalized; The use of such
technologies into t...
17
THANK YOU
AKADEMIYA2063 – Kicukiro / Niboye KK 360 St 8 I P.O. Box 4729 Kigali-Rwanda
FIND MORE COVID-19 RELATED WORK at
h...
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Malawi Policy Learning Event - Food Production Systems Disruption - April 28, 2021

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

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Malawi Policy Learning Event - Food Production Systems Disruption - April 28, 2021

  1. 1. Malawi Policy Learning Event Food Production System Disruption Racine Ly (*), Khadim Dia (*), Mariam Diallo (*) (*) AKADEMIYA2063
  2. 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. 2
  3. 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. 3
  4. 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. 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. 5
  6. 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. 6
  7. 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. 7
  8. 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 8
  9. 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 9
  10. 10. 5. Results 10
  11. 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. 11
  12. 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. 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. 13
  14. 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. 14
  15. 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. 15
  16. 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. 16
  17. 17. 17
  18. 18. THANK YOU AKADEMIYA2063 – Kicukiro / Niboye KK 360 St 8 I P.O. Box 4729 Kigali-Rwanda FIND MORE COVID-19 RELATED WORK at https://akademiya2063.org/covid-19.php

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