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Informing Strategic Investments in Enhancing Agricultural Technology Development and Use: The Role of Agricultural Statistics. Stanley Wood Senior Research Fellow, International Food Policy Research Institute (IFPRI) Co-Principal Investigator, Harvest Choice.

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slide1

Informing Strategic Investments in Enhancing Agricultural Technology Development and Use:

The Role of Agricultural Statistics

Stanley Wood

Senior Research Fellow,

International Food Policy Research Institute (IFPRI)

Co-Principal Investigator, HarvestChoice

Contribution of Partners in the Development of Agricultural Statistics in Africa

Twentieth Session, African Commission on Agricultural Statistics

Algiers, Algeria, 10-13th December 2007

slide2

Overview

  • Reinvigorated engagement in agricultural development in Africa
  • What is HarvestChoice?
  • Need for improved agricultural statistical data to support strategy/policy/ investment analysis
  • HarvestChoice/FAO initiatives related to agricultural statistics in Africa
re engagement in agriculture some examples
Re-engagement in Agriculture(some examples)
  • NEPAD’s explicit strategy on the role of agricultural growth in economic growth→ CAADP→ReSAKSS→National Strategic (planning, design, M&E) Information System & Analysis Capacity
  • World Bank:Multi-Country Agricultural Productivity Program (MAPP), Rural Infrastructure,

[Re-emphasis: World Bank Assistance to Agriculture in Sub-Saharan Africa: An IEG Review. 2007. World Development Report. 2007]

  • Bill and Melinda Gates Foundation:Agricultural Development Program
slide5

What is HarvestChoice?

  • A BMGF-sponsored ($4M, 39 month) effort co-managed by IFPRI and U. of Minnesota to compile, generate, harmonize, and disseminate public-goods information on the potential payoffs from improved crop production technologies and practices.
  • Focus on poor farm households in SSA and S. Asia, but embedded in a perspective of national (social) welfare, and international flows of knowledge, technology, and trade
  • Institutionally-neutral portal supported and accessible to a growing number of R&D partners: FAO (Statistics Division), CIMMYT, CIAT, IRRI, ICRISAT, & Universities (Pretoria, VT, Georgia, Davis), World Bank.
slide6

What is HarvestChoice?

  • Partner/user programs: HarvestPlus, Generation Challenge Program, USAID/IPM-CRSP, USAID/IEHA, (AGRA/PASS,WB/MAPP, Howard Buffet Foundation, Sainsbury Family Trust)
  • Regional partners and processes, e.g. CAADP (ReSAKSS), ASARECA, (SADC, CORAF)
slide7

Some Strategic Questions

  • Where are the poor and what is their welfare status?
  • On what cropping systems do the poor most depend?
  • What are the constraints to the productivity of those systems?
  • What existing or potential technologies might best
  • address those constraints? Under what scenarios?
  • What is the magnitude and distribution of potential payoffs to the
  • poor from different investment targeting strategies?
    • by, e.g., districts, AEZs, production systems, crops, constraints, technologies..
slide8

Aligning

Location

-specific

(geo-

referenced)

data

Analytical (largely economic) tools

Harmonizing (Spatial) Thematic Data

Thematic Layers

harvest choice activities

1. Macro Trends:

Human Welfare &

Crop Systems

2. Micro Linkages:

Human Welfare &

Crop Systems

3. Crop Systems Evaluation Platform (Physical)

a. Baseline distribution & performance of crop systems

b. Distribution & severity of key productivity constraints

c. Potential responses to change (tech., man., climate.)

4. Technology

Landscape

5. (Economic) Evaluation

Other data (e.g,

prices, investments,

market, technology

spillover insights)

Dialogue with Stake-holder/User Groups on Scenarios

6. Commer-

cialization

Prospects

Constraint-Scale

Evaluation

Technology-Scale

Evaluation

  • 7. Outreach
  • (e.g., country delivery
  • CountrySTAT)
HarvestChoice Activities
slide10

Fixed

Geographies of Analysis

Flexible

Geographies of Analysis

Market/Policy Analysis

Macro Scale, Usually aggregate,

Geo-political units

e.g., IMPACT/WATER,

GTAP derivatives

e.g., DREAM,

MM models

informs

Household

Characterization

Micro Scale

Region

Urban/Rural

Income tercile

Consumption

Production

Inputs

informs

Change

(e.g., climate,

technologies)

Change

(e.g., policy)

Infrastructure/Market Access

Production System

Production System

Analysis

Meso Scale,

Pixels as Units of Analysis

Aggregation

By Commodity

Ecosystem Services

slide11

Inequality

Infant

Mortality

Children

Underweight

Poverty

CBS et al. 2003

Hunger Task Force/CIESIN 2005

Alderman et al 2002

Prepared by CIAT from WHO data

Where are the Africa’s poor

and what is their welfare status?

Compiling and harmonizing available, sub-national datasets on

Expenditure, poverty, undernourishment, child mortality and undernourishment,

Micronutrient deficiency, selected DALY’s

Hunger Task Force/CIESIN 2005

slide12

Consumption: g. per cap. per day

RWANDA, 2000

On what cropping systems do

the poor most depend?

CONSUMPTION

g. per cap. per day

Rwanda, 2000

slide13

Crop Consumption

  • (1st Admin * U/R * Expend. Class * M/F Headed)
  • For 17 countries in SSA
  • Includes 73 % of SSA population
  • All but 2 AGRA/PASS countries
  • Testing extrapolation using country typology

HarvestPlus (CIAT & IFPRI), maps prepared by Glenn Hyman

overview of spatial allocation
Overview of Spatial Allocation

Initial

Representation

Final

Representation

slide15

Av. Maize Output (kg per hh)

Uganda 1999-2000

Maize

Area

(1st Level

Admin)

Maize

Area

(2nd Level

Admin)

Least Poor

Quintile

Poorest

Quintile

West North Central East

On what cropping systems do

the poor most depend?

PRODUCTION

For 20+ major crops at 10km resolution

“Plausible” assessment of the spatial distribution of production systems and performance of crops.

Complemented by available data on technology adoption, market participation, land holding structure, land tenure & new data on input use/costs (FAO)

new tools for distributing validating crop data
New Tools forDistributing & Validating Crop Data
  • SPAM Results Web Accessible through Google Earth
slide17

Evaluating the Payoffs to

Crop Improvement for the Poor

  • Economic benefits of technical change arising from; higher (on- and off-farm) productivity, lower unit costs, lower variance of output, quality price premiums, commercialization constraints and opportunities (using 2+ stage assessment)
  • Share of benefits to poor producers and poor consumers
  • - Spatial incidence of benefits
  • - Implications for nutrition and incomes
  • Potential sources of benefit – Local, spillins
  • Economic implications of time lags, (e.g. R&D, regulation,
  • commercialization, adoption)
what yield response to n application

Increase in Potential Maize Yield per Kg N

Kg maize /

Kg N

What if...?

Baseline

What yield response to N Application?
  • Maize Yield Response to Fertilizer
  • kg[Maize Yield] / kg[N Fertilizer]
  • Maize in Year 2000 (medium maturity)
  • 0.5-degree grid (about 50 km)
  • 0 and 50 kg[N]/ha N fertilization

?

slide20

Initial Ideas from Data Strategy

  • Brainstorming (Oct 2007)
  • Regional network of harmonized (national) panel datasets
  • Strengthening national agricultural statistical services (especially ag. census and expenditure/welfare indicators)
  • Use of “new” technologies/approaches to data collection (satellite, GPS, PDA,..)

BMGF: An (Unofficial) Guide to Selected Investments, and Strategy Ideas with Potential Linkages to Agricultural Statistics Capacity in Africa

slide21

HarvestChoice/FAO activities related to agricultural statistics in Africa

  • Compilation and harmonization of agricultural census data (including capture/digitization of older data when necessary to better understand past trends)
  • [e.g., holdings, production systems, land tenure, cropping patterns, technology and input use, labour use, productivity, access to services, market participation]
  • Standardized analysis of national consumption and expenditure data
  • [e.g., household characteristics, expenditure/income, consumption of agricultural goods, food security]
slide22

HarvestChoice/FAO activities related to agricultural statistics in Africa

  • Production system characterization
  • (e.g., orientation, output and input mixes, technologies, management practices, cropping patterns, rotations/fallow use, natural resource needs/impacts, productivity)
  • Cost of production database
  • (to support more detailed productivity and profitability analysis - particularly in the light of potential change, e.g., increased investment or policy change)
  • NB All processed data generated will be made available in digital format and, wherever feasible, made available for national CountrySTAT implementations
slide23

HarvestChoice/FAO activities

Learning/Partner Hopes from AFCAS

  • Gather and consolidate information on the status of on-going and planned nationally representative survey and census activities of participating countries
  • Identify opportunities for “data rescue” of past census/ survey data
  • Start to identify potential synergies between country statistical service development plans and potential funding options of relevance to the Gates Foundation portfolio
  • Find partner countries to help develop and test the Cost of Production survey instrument to be administered by FAO/ESSD
  • Communicate new opportunities for investment in statistical and monitoring systems at country level