Mapping rice in Africa and
assessing the potential for
development
Sander Zwart
Researcher Remote Sensing & GIS

Remote Se...
Short CV – Sander Zwart
Born in 1976 in the Netherlands
Wageningen:
• 1994-2000 MSc Irrigation and Water Engineering
• 200...
Africa Rice Center - Introduction
• Started as 40 years ago as the West-African
Rice Development Association
(WARDA/ADRAO)...
West Africa Rice
Development Association
(WARDA)
Remote Sensing – Beyond Images
14-15 December 2013, Mexico City
Africa Rice Center
(AfricaRice)
Remote Sensing – Beyond Images
14-15 December 2013, Mexico City
Future

Remote Sensing – Beyond Images
14-15 December 2013, Mexico City
Africa Rice Center - Introduction
4 pillars:
• Genetic Diversity and Improvement (rice
breeding) – major achievement: NERI...
Africa Rice Center – Modus Operandi
1. Projects are always in collaboration with
National Agricultural Research Systems
(N...
Africa Rice Center – Introduction
Rice Sector Development Hubs:
• Regions where research and development are
concentrated ...
Africa Rice Center – Spatial analysis activities
Unit is operational again since 4 years
• Researcher
• Postdoctoral Fello...
Africa Rice Center – Spatial analysis activities
1. Mapping rice and rice ecologies
(upland/lowland/mangrove/deep water)
2...
Spatial analysis – Mapping rice
Justification
Rice statistics are very unreliable in Africa
Rice is spatially highly dynam...
Spatial analysis – Mapping rice
AfricaRice and IRRI co-organized an expert
meeting in Cotonou (June 2012)
Goal: discuss th...
Spatial analysis – Mapping rice
Differences between Asian and African rice
environemnts
Asia

Africa

Irrigated rice (80%)...
Spatial analysis – Mapping rice
Recommendations/findings:
- Radar remote sensing is best bet
- Alternative method needs to...
Spatial analysis – Mapping rice
Pilot testing of radar remote sensing in two hubs:
Cosmo-SkyMed imagery is acquired every ...
Spatial analysis – Mapping rice

Preliminary results December 2013
Remote Sensing – Beyond Images
14-15 December 2013, Mex...
Spatial analysis – Mapping inland valleys
Inland valley
Areas suitable for rice production due to favorable
hydrological c...
Spatial analysis – Mapping inland valleys

Remote Sensing – Beyond Images
14-15 December 2013, Mexico City
Spatial analysis – Mapping inland valleys

Remote Sensing – Beyond Images
14-15 December 2013, Mexico City
Spatial analysis – Mapping inland valleys

stream
Digital Elevation Model
(2-dimensional)
30m

25

24

24

25m

23

23

21...
Spatial analysis – Mapping inland valleys

Remote Sensing – Beyond Images
14-15 December 2013, Mexico City
Spatial analysis – Mapping inland valleys
Benin: IMPETUS project (Germany): +/- 100
digitized inland valleys from Benin (a...
Spatial analysis – Mapping inland valleys

Remote Sensing – Beyond Images
14-15 December 2013, Mexico City
Spatial analysis – Mapping inland valleys

Remote Sensing – Beyond Images
14-15 December 2013, Mexico City
Spatial analysis – Mapping potential
Question what is the potential for development?
Currently only 10% cultivated
Goal: p...
Spatial analysis – Mapping potential
Suitability mapping is usually done with a selection
of indicators that are given a v...
Spatial analysis – Mapping potential
Methodology has been applied to map the
potential for irrigated rice development in L...
Spatial analysis – Mapping potential
• On-going activity in two pilot sites in Benin.
• Collection of data on inland valle...
Thank you! Merci!
Center of Excellence for Rice Research
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Mapping rice in Africa and assessing the potential for development

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Remote sensing –Beyond images
Mexico 14-15 December 2013

The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)

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Mapping rice in Africa and assessing the potential for development

  1. 1. Mapping rice in Africa and assessing the potential for development Sander Zwart Researcher Remote Sensing & GIS Remote Sensing – Beyond Images 14-15 December 2013, Mexico City
  2. 2. Short CV – Sander Zwart Born in 1976 in the Netherlands Wageningen: • 1994-2000 MSc Irrigation and Water Engineering • 2000-2002 MSc Geoinformation Science • 2002-2010 WaterWatch company (water resources / remote sensing, ET mapping) (Delft:) • 2003-2010 PhD Mapping and modelling of water productivity Cotonou: • 2010-present Africa Rice Center Remote Sensing – Beyond Images 14-15 December 2013, Mexico City
  3. 3. Africa Rice Center - Introduction • Started as 40 years ago as the West-African Rice Development Association (WARDA/ADRAO) • Pan-African organization with member states • Goals: reduce poverty and reduce imports through increasing rice production in Africa • Member of the CGIAR group of international agricultural research organizations Remote Sensing – Beyond Images 14-15 December 2013, Mexico City
  4. 4. West Africa Rice Development Association (WARDA) Remote Sensing – Beyond Images 14-15 December 2013, Mexico City
  5. 5. Africa Rice Center (AfricaRice) Remote Sensing – Beyond Images 14-15 December 2013, Mexico City
  6. 6. Future Remote Sensing – Beyond Images 14-15 December 2013, Mexico City
  7. 7. Africa Rice Center - Introduction 4 pillars: • Genetic Diversity and Improvement (rice breeding) – major achievement: NERICA • Sustainable Productivity Enhancement (rice agronomy) • Policy, Innovation Systems and Impact Assessment (economy, sociology & impact) • RiceTIME: Training, Information Management and Extension linkages (extension) Remote Sensing – Beyond Images 14-15 December 2013, Mexico City
  8. 8. Africa Rice Center – Modus Operandi 1. Projects are always in collaboration with National Agricultural Research Systems (NARS) + capacity building 2. Taskforces (Gender, Rice Breeding, Policy, Agronomy) 3. Rice Sector Development Hubs Remote Sensing – Beyond Images 14-15 December 2013, Mexico City
  9. 9. Africa Rice Center – Introduction Rice Sector Development Hubs: • Regions where research and development are concentrated along the entire rice value chain • Participatory on-farm / real-life research • Hubs are operated by NARS; locations are appointed by NARS • Efficient impact pathway: research answers to demands and is tested in real conditions, adopted by development sector for scaling out Remote Sensing – Beyond Images 14-15 December 2013, Mexico City
  10. 10. Africa Rice Center – Spatial analysis activities Unit is operational again since 4 years • Researcher • Postdoctoral Fellow • Three research assistant • Two PhD students Strong collaboration between IRRI and AfricaRice through CRP GRiSP – exchange of data and development of approaches Remote Sensing – Beyond Images 14-15 December 2013, Mexico City
  11. 11. Africa Rice Center – Spatial analysis activities 1. Mapping rice and rice ecologies (upland/lowland/mangrove/deep water) 2. Mapping the potential for rice development 3. Mapping biotic and abiotic stresses in rice production systems Remote Sensing – Beyond Images 14-15 December 2013, Mexico City
  12. 12. Spatial analysis – Mapping rice Justification Rice statistics are very unreliable in Africa Rice is spatially highly dynamic compared to Asia Rice is booming in Africa Impact assessment AfricaRice Figure Remote Sensing – Beyond Images 14-15 December 2013, Mexico City
  13. 13. Spatial analysis – Mapping rice AfricaRice and IRRI co-organized an expert meeting in Cotonou (June 2012) Goal: discuss the options for mapping rice using remote sensing (optical/radar) and develop a strategy for operational monitoring Question: what methodologies exist and can they they be applied for African rice environments? Remote Sensing – Beyond Images 14-15 December 2013, Mexico City
  14. 14. Spatial analysis – Mapping rice Differences between Asian and African rice environemnts Asia Africa Irrigated rice (80%) upland rainfed lowland rainfed lowland irrigated (~10%) Stable area Dynamic & expanding 30% of arable land 4% of arable land Contiguous rice areas Fragmented Paddy land preparation Dry land preparation High fertilizer inputs Low fertilizer inputs Remote Sensing – Beyond Images 14-15 December 2013, Mexico City
  15. 15. Spatial analysis – Mapping rice Recommendations/findings: - Radar remote sensing is best bet - Alternative method needs to be adopted - Sentinel program will likely provide high spatial and temporal resolution imagery - Focus on monitoring rice area in Rice Sector Development Hubs - Mapping of inland valleys and lowland to distinguish upland from lowland Remote Sensing – Beyond Images 14-15 December 2013, Mexico City
  16. 16. Spatial analysis – Mapping rice Pilot testing of radar remote sensing in two hubs: Cosmo-SkyMed imagery is acquired every 16 days during rice season Spatial resolution of 3m Senegal: irrigated rice conditions (July-December) Benin: upland and lowland rice (June-december) Goals: mapping rice and assessing crop phenology dates (SoS and harvest) Field validation collected (500 points) Remote Sensing – Beyond Images 14-15 December 2013, Mexico City
  17. 17. Spatial analysis – Mapping rice Preliminary results December 2013 Remote Sensing – Beyond Images 14-15 December 2013, Mexico City
  18. 18. Spatial analysis – Mapping inland valleys Inland valley Areas suitable for rice production due to favorable hydrological conditions Important for current and future rice production Remote Sensing – Beyond Images 14-15 December 2013, Mexico City
  19. 19. Spatial analysis – Mapping inland valleys Remote Sensing – Beyond Images 14-15 December 2013, Mexico City
  20. 20. Spatial analysis – Mapping inland valleys Remote Sensing – Beyond Images 14-15 December 2013, Mexico City
  21. 21. Spatial analysis – Mapping inland valleys stream Digital Elevation Model (2-dimensional) 30m 25 24 24 25m 23 23 21 20 20 21 Selected inland valley bottom Remote Sensing – Beyond Images 14-15 December 2013, Mexico City altitude (m)
  22. 22. Spatial analysis – Mapping inland valleys Remote Sensing – Beyond Images 14-15 December 2013, Mexico City
  23. 23. Spatial analysis – Mapping inland valleys Benin: IMPETUS project (Germany): +/- 100 digitized inland valleys from Benin (accomplished) Togo: SMART-IV project: student collecting field data with GPS, 50 in Benin and 50 in Togo Burkina Faso: existing data set from Min of Agriculture Mali: RAP-IV project, 40 inland valleys Sierra Leone & Liberia: RAP-IV project (planned) GOAL: entire West-Africa mapped and validated Remote Sensing – Beyond Images 14-15 December 2013, Mexico City
  24. 24. Spatial analysis – Mapping inland valleys Remote Sensing – Beyond Images 14-15 December 2013, Mexico City
  25. 25. Spatial analysis – Mapping inland valleys Remote Sensing – Beyond Images 14-15 December 2013, Mexico City
  26. 26. Spatial analysis – Mapping potential Question what is the potential for development? Currently only 10% cultivated Goal: provide maps that indicate the potential for development of rice-based systems in an IV. Users: NGO’s, government bodies (inland valley development cell, national IV development projects, etc.) Remote Sensing – Beyond Images 14-15 December 2013, Mexico City
  27. 27. Spatial analysis – Mapping potential Suitability mapping is usually done with a selection of indicators that are given a value of importance based on expert knowledge Disadvantage: not objective, biased Random Forest is a statistical analysis tool that allows explaining the presence or non-presence without prior knowledge. Remote Sensing – Beyond Images 14-15 December 2013, Mexico City
  28. 28. Spatial analysis – Mapping potential Methodology has been applied to map the potential for irrigated rice development in Laos (IRRI / Laborte et al., 2012) Use of data sets on roads, travel distance, villages, markets, population density, soil suitability, water availability, rainfall, precipitation, etc., etc. Remote Sensing – Beyond Images 14-15 December 2013, Mexico City
  29. 29. Spatial analysis – Mapping potential • On-going activity in two pilot sites in Benin. • Collection of data on inland valleys and presence or non-presence of rice or agriculture • Building a spatial data base containing roads, markets, travel distance, population density, villages, inland valleys, soil types, water availability, rainfall (remote sensing), etc. Outlook: application at national level for westAfrican states. Implementation and validation with national partners and users. Remote Sensing – Beyond Images 14-15 December 2013, Mexico City
  30. 30. Thank you! Merci! Center of Excellence for Rice Research

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