SUPPORTING STRATEGIC INVESTMENT CHOICES IN AGRICULTURAL TECHNOLOGY DEVELOPMENT AND ADOPTION

1. HarvestChoice & Technology Platform2. Household data3. Africa RISING M&E

B E L I Y O U H A I L E monitoring and evaluation

C A R L O A Z Z A R R I microeconomics, poverty

E L O D I E V A L E T T Eparticipatory GIS, spatial analysis

C I N D Y C O X technical writer, technology evaluation

C L E O R O B E R T S farming systems characterization

I V Y R O M E R O administrative coordinator

J A W O O K O O crop/technology modeling,

M A R I A C O M A N E S C U web development, programming

M E L A N I E B A C O U project management, microeconomics

Q U E E N I E G O N Gdata management, SPAM

S A R A S I G N O R E L L IMicroeconomics, impact evaluation

H O - Y O U N G K W O Ncrop and soil process modeling

U L R I K E W O O D - S I C H R A data management, SPAM, DREAM

Z H E G U O GIS coordinator, market accessibility, remote sensing

Har vestChoiceFIVE GUIDING QUESTIONS

1. Where are the poor, and what are their welfare status?

2. On what farming systems do the poor most depend?

3. What are the constraints affecting the productivity and market integration of those farming systems?

4. What present or prospective investments in technologies and practices might best address those constraints?

5. What will be the benefits of investment on productivity, income, and the reduction of poverty and hunger?

Production System& Market Access AnalysisMESO SCALEPixels as Units of Analysis

Production System

Ecosystem Services

Infrastructure/Market Access

Investment/Policy AnalysisMACRO SCALEAggregate, market-scale (geo-political) units

Fixed Geographies of Analysis

e.g., IMPACT/WATER,GTAP derivatives

Flexible Geographies/Units of Analysis

e.g., DREAM,MM models

AggregationBy Commodity

Urban/Rural Consumption InputsProductionIncome tercileRegionHousehold CharacterizationMICRO SCALE

Change(e.g., policy)

Change(e.g., climate,technologies)

Bio-physical land use, soil,

climate, aez (IIASA, CRU, USGS)

ProductionSPAM

(admin records, suitability)

Socio-eco pop. poverty,

factor productivity(LSMS, ag. census,

DHS, FAO)

Marketsinfrastructure, transportation, market access

Data harmonization

Up/down scaling

Calibration

HarvestChoice CELL5M (400+ 10 km spatial layers)

Data API

MAPPR TABLR 3rd-party tools

Web Map Service (WMS)

BMGF Project

Mapping Tool

Africa RISING FAO HarvestChoice

website

Try:harvestchoice.org/mapprharvestchoice.org/tablr

Flagship Datasets

SUB-NATIONAL POVERTY MAPPINGUpdated version using 24 nationally representative household surveys conducted in years circa-2008.

SPAM 2005Updated version including 42 crops to values centered around the year 2005, using more recent primary data from national statistics offices, ministries of agriculture, publications from other various organizations, and targeted internet searches. Underlying model is being developed as a customizable web application, in collaboration with GEOSHARE.

Ex-ante Modeling

TECHNOLOGY EVALUATIONPotential impact of agricultural technology adoption on productivity, globally simulated for maize, rice, and wheat.

MODELING CONSTRAINTSModel-estimated rainfall variability impacts on yield variability, under intensification scenarios

MAIZE RICE WHEAT

0% 20% 40%

Yield Impact

0% 20% 40%

Yield Impact

0% 20% 40%

Yield Impact

MIROC A1B

Drought Tolerance (DT)

Heat Tolerance (HT)

Integrated Soil Fertility Management (FM)

N Use Efficiency

No-Till (NT)

Precision Agriculture (PA)

Water Harvesting (WH)

Irrigation - Drip

Irrigation - Sprinkler

Organic Agriculture

Crop Protection (Diseases)

Crop Protection (Insects)

Crop Protection (Weeds)

32%

16%

28%

12%

5%

9%

8%

4%

1%

1%

0%

7%

9%

21%

34%

18%

2%

6%

0%

9%

7%

8%

20%

14%

11%

32%

26%

10%

6%

1%

7%

4%

0%

7%

7%

LOW INPUT

HIGHINPUT

Ex-ante Modeling

STRATEGIC ANALYSIS OF POTENTIAL INTERVENTIONSSpatially-explicit modeling of multiple management interventions in Gates focus countries

Data & Tools

TECHNOLOGY PLATFORM*Providing evidences for the New Alliance partners and national stakeholders to make informed investment decisions for scaling-up technology adoption

* HarvestChoice MINI in Country

Partner Supports

PARTNERING WITH AGRA:

SCALING SEEDS AND TECHNOLOGY PARTNERSHIP

1. Specifying and prioritizing and value-chains to focus; validating the selection of technologies to scale

2. Identifying the target areas to scale the technologies3. Estimating potential impacts of the technologies, in

terms of productivity and socio-economic aspects4. Assessing the need for complementary technology

investments to maximize the benefits from the technology

5. Help developing the M&E baseline through survey and/or existing databases

6. Evaluating the impact of subsidies on the attractiveness of identified technologies to the private sector

7. Monitoring and mapping the Partnership-invested activities of grantees and partners on the ground

8. Developing investment strategy to reduce the average distance from farmers to input agro-dealers

SPECIFIC RESEARCH AREAS WE AGREED TO SUPPORT:

HOUSEHOLD SURVEY DATA

Poverty -> flagship, the most downloadedConsumption• Subnational per-capita total consumption (in PPP$), extracted from 24 nationally

representative household consumption and expenditure surveys conducted in various years circa-2005 (+-2 years)

Nutrition• Information collected from 60 DHS (Demographic and Health Survey) Phase 4, 5,

and 6 (1999-2013) worldwide; 29 in Sub-Saharan Africa. Highly comparable across countries, wide spatial coverage, over time representation

1. Sub-national mapping (poverty, consumption, nutrition)

2. AgricultureFarming system characterization (crop mix)Use of inputs (land, fertilizers, seeds, irrigation, labor) and their combinationYield, productionLivelihoods

1. POVERTY, CONSUMPTION, NUTRITION Nutrition indicators

• child anthropometric indicators (stunting, wasting, underweight)

• BMI for women• hemoglobin for women• percentage of women and children with anemia• DDS for women and children• antenatal care• infant/young child breastfeeding practices• iron and vitamin A supplementation of women and <5 y.o.• infant and child under 5 mortality rate• percentage of children with diarrheaVarious• education• decision power• asset ownership (incl. livestock)• wealth index• dwelling conditions (drinking water, sanitation facilities,

wall and roof material, cooking fuel, bednets)

Poverty indicators• poverty headcount ratio at $1.25 and $2 PPP/day• poverty gap at $1.25 and $2 PPP/day• poverty severity at $1.25 and $2 PPP/day• std. dev. of poverty headcount ratio at $1.25 and

$2 PPP/day • number of poor at $1.25 and $2 PPP/day• poverty density at $1.25 and $2 PPP/dayConsumption indicators• per-capita total consumption expenditure• Gini index• per-capita food consumption expenditure (in

progress)

…by region, urban/rural, household headship

Farming System Characterization

Top 15 systems in Tanzania derived from AC 2007/08: HS analysis provides high granularity compared to Dixon farming system map.

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eans

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illet

mille

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wpeas

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rural Tanzania# of households growing

Highland temperate mixed

Root crop

Cereal-root crop mixed

Agro-pastoral millet/sorghum

Highland perennial

Artisanal fishing

Pastoral

Maize mixed

No data

Major Farming Systems in Tanzania, 2012

2. AGRICULTURE

…by AEZ

…by farming system

3. COMBINATION

consumption and child undernutrition

Maize yield production

Millet yield production

cattle stunting wasting

elevation wheat-> production yield

Low BMI LGP

Maize production yield

Rice production yield

Sorghum production yield

stunting poultry cattle

AFRICA RIS ING

How is Africa RISING linked to HarvestChoice? AR needs to be spatially-informed, requiring granular

information on a large suite of indicators

Both seek integration among different disciplines (agronomy, biophysical&crop modeling, economics)

Both are focused on farming system, ex-ante modeling, adoption study and scaling-up plan of agricultural technologies and innovation

HarvestChoice data need groundtruthing, indeed AR is an excellent opportunity for testing

Monitor ing & Evaluat ion Support

Africa RISING Feed the Future Compliance: Conform to the FtF

core indicators Multi-scale, multi-site reporting: Meet

stakeholder needs and support multi-scale/multi-site M&E

Monitoring and projection: Provide monitoring reports and short-term projections (targets) of key M&E indicators for intervention sites

Open-access data and analysis platform: Maintain a user-friendly, open-access M&E data management and analysis platform to serve the needs of SI stakeholders.

Quasi-RCTs design and implementation: Use randomization of control sites within the same stratum (identified by homogeneous agriculture potential) as intervention sites for Impact Evaluation

Eastern & Southern Africa Maize-based Systems

Sudano-Sahelian ZoneEthiopian Highlands

Systems

Sub-Systems

+

+ ++++

ActionSites

Three AR Mega-sites

Fostering multi-scalespillover by design

Monitor ing & Evaluat ion Support

Africa RISINGHIGHLIGHTS Site Stratification and

Selection/Re-Selection: Evidence-based, participatory approach to select intervention sites

Baseline Surveys: Design and development of survey toolkit using remote cloud-storing, tablets and intensive training

Project Mapping and Monitoring Tool: Web-based tool to provide spatially-disaggregated M&E analysis and support decisions and adjustments over project lifecycle, as well as data repository

Site selection

Survey design and training in Malawi

Monitoring of activities and indicators of sites in Ethiopia

IE Design

Beneficiary HHs

Non-beneficiary

HHs Control HHs

Action Sites

Control Sites

Spillover effects

Program impact BACK

ARBES Household summary contents

Location info, GPS Roster Education Labor Health Women and child anthropometrics• Agriculture -general-• Crop inputs (Conservation Agr.)• Crop production• Crop inputs (costs)• Crop inputs (labor time use)• Crop inputs (seeds)• Crop sales• Crop storage• Livestock ownership and income• Livestock feed

Problems and coping strategies• Agricultural extension and AR

program• Other income• Credit• Housing, utilities, assets,

distance to services• Subject welfare and food security• Food Expenditures/ Consumption• Non-Food Expenditures• Shocks• Re-contact info

FIRST VISIT

Head Individual Best Informed

SECOND VISIT

BACK

ARBES Qx Community summary contents

5 to 8 key community informants

Location info, GPS

Informants’ roster

Access to basic services

Agricultural labor, extension services, agricultural problems

Land use

Demographics, cooperatives, migration Water access, shocks, food consumption Market prices Conversion of non-standard units