International Food Policy Research Institute (IFPRI). 2023. Statistics from Space: Next-Generation Agricultural Production Information for Enhanced Monitoring of Food Security in Mozambique. PowerPoint presentation given during the Project Kickoff Meeting (virtual), January 12, 2023
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Statistics from Space: Next-Generation Agricultural Production Information for Enhanced Monitoring of Food Security in Mozambique - Project Kickoff
1. STATISTICS FROM SPACE
Next-Generation Agricultural Production
Information for Enhanced Monitoring of
Food Security in Mozambique
Project Kickoff Meeting
12 January 2023
2. Project Information
• Objective: To produce and disseminate accurate agricultural
production statistics data for timely food security decisions in
Mozambique
• Timeline: November 2022 – December 2024
• Implementing agencies:
1. International Food Policy Research Institute (IFPRI)
2. University of Twente/ITC (ITC)
3. Centro de Estudos de Políticas e Programas
Agroalimentares (CEPPAG)
4. Seoul National University (SNU)
*Areas with
maize, rice,
and beans
cultivation in
four provinces
3. Agenda
1. Welcome message (IFPRI)
2. Opening remark (MAFRA)
3. Introduction of the project team members/institutions (All)
4. Presentation of the proposal content (IFPRI)
5. Discussion on the work plan and the timeline (All)
6. Planning of an inception workshop in Maputo in early 2023
7. Any other business items
4. BACKGROUND
Challenges and Missed Opportunities
CHALLENGES
1. The lack of reliable information on
agricultural production hinders efforts to
generate value from investments in the data.
2. Agricultural statistical services in developing
countries have not been able to make an
impact. They remain mainly logistical
operations. Hence, their ability to capitalize
on some new technologies has been limited.
3. The demand from the private sector is weak.
Value-chain actors, Central Banks, Ministries
of Finance, and others have relatively little
experience with accurate production
forecasts and estimates.
POTENTIAL OUTCOMES
Good agricultural data can:
• Informs better policymaking on agricultural
investments, subsidies, and initiatives.
• Enables relief and humanitarian organizations
to plan interventions, provide more effective
and cost-efficient services.
• Drives agricultural markets, with prices often
contingent on government statistics.
• Mitigates against seasonal price volatility, with
welfare implications for small farmers. Many
sell their crops at low prices post-harvest and
purchase back at high prices pre-harvest.
5. BACKGROUND
Mozambique’s agricultural production information system
CURRENT SITUATION
• Agricultural production data have
been analyzed as unreliable.
• Low agricultural investment and
development in recent decades.
• Statistical analysis is often
constrained by a lack of
sufficiently disaggregated data.
CAUSES
1. Lack of coordination and
coherence between agencies.
2. Misleading artifacts from missing
data.
3. Insufficient budget and technical
capacity.
4. Information designed to serve the
government, not farmers
5. Disincentivized government
6. BACKGROUND
Mozambique’s agricultural production information system
CURRENT SITUATION
• Agricultural production data have
been analyzed as unreliable.
• Low agricultural investment and
development in recent decades.
• Statistical analysis is often
constrained by a lack of sufficiently
disaggregated data.
CAUSES
1. Lack of coordination and
coherence between agencies.
2. Misleading artifacts from missing
data.
3. Insufficient budget and technical
capacity.
4. Information designed to serve the
government, not farmers
5. Disincentivized government
7. BACKGROUND
Mozambique’s agricultural production information system
REPORTING ON SDG2
• Mozambique’s 2022 Voluntary
National Review (VNR) only focuses
on nutrition, not production.
• VNR recognizes the challenge of
implementing systematic
approaches to develop the report.
MONITORING OF THE
OPERATIONALIZATION OF THE
NATIONAL AGRICULTURAL
INVESTMENT PLAN
• PNISA (Agricultural Investment
Plan) operationalizes PEDSA
(Agricultural Development
Strategy). PEDSA includes a Result
Area focusing on the annual
monitoring of crop production.
8. BACKGROUND
Republic of Korea Government’s ODA Priorities
The ROKG's ODA strategic goals are:
1) Pursuing humanitarian values
2) Supporting innovation and
development in developing
countries
3) Pursuing coexistence of the global
community and mutual prosperity
4) Upgrading partnerships.
▪ The ODA tasks include supporting
countries to overcome the food
crisis, supporting digital
transformation, creating a
foundation for economic and social
development, and promoting
international cooperation.
▪ Agricultural information system is
a synergetic enabling area relevant
to all these goals and key tasks.
9. BACKGROUND
ROK and Mozambique
▪ ROK Prime Minister Han Duck-soo
officially visited Mozambique in
November 2022 and met with
Mozambique President Philippe
Jacinto Nyusi to discuss ways to
strengthen infrastructure and
development cooperation.
▪ This project outcome can contribute
to the strengthening of practical and
technical cooperation between the
two countries.
10. Project Components
1. Stakeholder
engagement
• Lead: IFPRI
• Meetings,
workshops,
and other
stakeholder
engagement
activities
2. Enhanced
area frames
• Lead: ITC
• Baseline
review, data
acquisition,
stratification,
and ag stat
applications
3. Groundtruth
data
• Lead: CEPPAG
• Digitally
collecting
quality
groundtruth
data to
support 2 & 4.
4. Crop
analytics
• Lead: SNU
• AI/ML-based
crop mapping,
modeling, and
predictions for
timely
decisions.
11. COMPONENT 1
Stakeholder Engagement
Activities
• Website
• Baseline assessment
• Invitational workshop
• Engagement with the government
• Engagement with farmers’
organizations
Outputs
1. Baseline assessment report
2. Presentations to government
agencies in Mozambique
3. Workshop report
4. Meeting notes
5. Other communication materials
12. COMPONENT 2
Enhanced Area Frames
Activities
• Review of current sample frames
• Data acquisition and processing
• Stratifications
• Cluster and sampling square
selections
• Assessment of the statistical
estimation performance
Outputs
1. A new workflow to improve area
frames
2. User manual describing the
process
3. Datasets and metadata document
4. Technical report assessing the
improvement process and
performance
13. COMPONENT 3
Groundtruth Data
Activities
• Collection of groundtruth data
using the new sampling frames
• Data types: Crop type, area,
condition, and yield
• Methods: Traditional + sensor-
based digital technologies
• Schedule: Three times for each
season
Outputs
1. Protocol document: A technical
document describing the ground-
truthing data collection protocol.
2. Datasets: A collection of collected
groundtruthing data, annotated
in compliant of the STAC
standard, and quality checked.
14. COMPONENT 4
Crop Analytics
Activities
• Review of existing approaches
• Developing a new analytical
framework
• Generating a series of new maps of
crop type, area, and yields
Outputs
1. Technical report: A technical
document of reviews on crop
yield mapping approaches and a
new approach developed.
2. Dataset: A set of maps showing
crop harvest area, yield, and
production, developed using a
new hybrid modeling approach.