Mapping rice in Africa and assessing the potential for development

Mapping rice in Africa and

assessing the potential for

development

Sander Zwart

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:

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

West Africa Rice

Africa Rice Center (AfricaRice)

Africa Rice Center - Introduction 4 pillars:

• Genetic Diversity and Improvement (rice

breeding)

• Sustainable Productivity Enhancement (rice agronomy)

• Policy, Innovation Systems and Impact

Assessment (economy, sociology & impact) • RiceTIME: Training, Information Management

and Extension linkages (extension)

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)

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,

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

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

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

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

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

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

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)

Spatial analysis – Mapping inland valleys Inland valley

Areas suitable for rice production due to favorable hydrological conditions

Spatial analysis – Mapping inland valleys

stream

Selected inland valley bottom

30m

Digital Elevation Model

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

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.)

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.

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.

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

Thank you! Merci!