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Data and Tools A Framework for Integrating multi-scale Biophysical and Socio-economic Data and Tools A Framework for Integrating multi-scale Biophysical and Socio-economic

Data and Tools A Framework for Integrating multi-scale Biophysical and Socio-economic - PowerPoint Presentation

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Data and Tools A Framework for Integrating multi-scale Biophysical and Socio-economic - PPT Presentation

Integrating Biodiversity and Ecosystem Services into Foresight Models BIOVERSITY Rome May 78 2015 What We Do Research program coled by the International Food Policy Research Institute IFPRI CGIAR and University of Minnesota ID: 687840

harvestchoice data production market data harvestchoice market production road indicators ifpri investments scale country modeling consumption lsms poverty layers

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Slide1

Data and Tools

A Framework for Integrating multi-scale Biophysical and Socio-economic Datasets into Foresight Models

Integrating Biodiversity and Ecosystem Services into Foresight

Models

BIOVERSITY, Rome,

May

7-8, 2015Slide2

What We Do…

Research program co-led by the International Food Policy Research Institute (IFPRI, CGIAR) and University of Minnesota.

We generate data products to help guide strategic investments for more

productive and profitable farming in sub-Saharan Africa

We do this through:Harmonizing and geo-referencing datasets at administrative units down to 10-km grid across agricultural domainsAnalytics, modeling and data visualization toolsFlagship studies, interactive atlases

2Slide3

Questions

we are passionate about…

Where are the poor and what is their welfare status?

Which

farming systems do the poor most depend?How best to tune productivity targets to different sub-national conditions?What are the constraints affecting on-farm productivity, technology adoption, and market

integration?What investments in technologies and practices might best address those constraints?What is the aggregate landscape of investments across the region?

What are the benefits of investments on productivity, food security and farm income and ultimately, the reduction of poverty and hunger?3Slide4

4

Production system, market

a

ccess

a

nalysis

MESO SCALEPixels as units of

analysis

Production System

Ecosystem Services

Infrastructure/Market Access

Investment/policy

a

nalysis

MACRO SCALE

Aggregate, market-scale (geo-political)

units

Fixed

g

eographies

of

analysis

e.g

.

IMPACT/WATER,

GTAP derivatives

Flexible

units

of Analysis

e.g

.

DREAM,

MM models

Change

e.g. policy, investments

Change

e.g. climate, pests, technologies

Household

Characterization

MICRO SCALE

Farms and plots as

units

of

analysis

Urban/Rural

Consumption

Inputs

Production

Income

RegionSlide5

Meso

Scale…

Crop Modeling

at global and regional-scale on

grids

Seasonality

Soil Fertility

Harvested Area

Average Rainfall

Yield Potential

5Slide6

Meso

Scale… Market Access Modeling

6

Accessibility is determined using a cost-distance function

measuring

hours to nearest market cen­ter for each location. Estimated based on combination of global spatial

layers, incl. road and river networks, assessed in terms of their “friction” or km per hour travel time. The latter is adjusted based on a number of input variables, incl. road location and type, elevation, slope, country boundaries, bodies of water, coastline, and land cover.

Key input layers:Road network’s improvement and characterization (low/high speed)Off-road travel characteristics: terrain (elevation, slope), land cover

Markets and human

settlementsSlide7

Meso

Scale… Market Access Modeling

2010 updates

7Improving the road network affects market accessibility.

Countries for which we are able to update the road network:Country

Data Source(s)

MaliIITA

Kenya

MSU,

Google

Earth

Malawi

African Development Bank

Senegal

IITA

Uganda

World Bank

Nigeria

IITA

Tanzania

World Bank

Burundi

African Development Bank

Ethiopia

Michigan State University/IFPRI

Ghana

African Development Bank

Road network after HC

update (2010)

Original road network from Joint Research Center (2000)Slide8

Augmenting -and validating- HarvestChoice data layers with sub-national farm household characteristics, farm management practices, production, consumption and nutrition estimates. 5 harmonized

Country Snapshots released (Malawi, Uganda, Tanzania, Ghana, and Ethiopia),

2 more in pipeline.

Micro Scale

Mapping Agricultural Census, Demographic and Health Surveys, LSMS-ISA Surveys

DHS Child Stunting Prevalence

TZA AC 2007 – Tomato Yield

8Slide9

Production, Consumption, Nutrition

Production indicators (SPAM, Crop Models)

yield (kg/ha)

quantity (and value) of production

area harvestedyield variabilityNutrition indicators (DHS)child anthropometric indicators (stunting, wasting, underweight) (also LSMS)BMI for womenpercentage of women with anemia

hemoglobin of womeninfant/young child breastfeeding practicesiron and vitamin supplementation of women and <5 y.o.infant and child under 5 mortality ratepercentage of children with diarrheawealth index (also LSMS)

Consumption indicatorsper-capita total consumption expenditureGini indexper-capita food consumption expenditure (in progress)Poverty indicatorspoverty headcount ratio (std. dev.), density, gap, and severity at $1.25 and $2 PPP/day

number of poor at $1.25 and $2 PPP/day9Slide10

Open-Data Architecture

Bio-physical

land use, soils, climate, pests (IIASA, CRU, USGS, UMN,

AgMIP

)

Production

SPAM(land cover, admin records, IIASA GAEZ suitability)

Socio-Economic

pop. poverty, factor productivity

(LSMS,

ag

. census, DHS, FAO)

Markets

,

Infrastructure

road networks, transportation

Data h

armonization

Up/down scaling

C

alibration

HarvestChoice

CELL5M

catalog of 750+ 10 km resolution spatial

indicators

MAPPR

TABLR

LSMS-ISA

Map/Tile Service

(WMS)

CGIAR and BMGF Project Mapping Tools

Africa RISING

FAOSTAT

HarvestChoice

Website

Other Models, Calculators

Time-series

spatial indicators

Data API

10Slide11

Applications

Country, farmers, and value-chain targeting (e.g. BMGF strategy refresh)

G-8 New Alliance priority-setting tools

Fertilizer use profitability calculatorsAfrica RISING site selection and SI technology evaluation (Malawi - nitrogen fixation, legumes, nutrition; Ghana - GS3SLS approach looking at land-use changes over time and income effects)

Spatial analysis of investment effectiveness11Slide12

G8 New Alliance

Value-chain priority-setting calculator tool

Ghana

:

value-chain prioritization.Which CAADP value chains to focus on? Help identify country-specific priority commodities in terms of their economic impact, importance to the poor, nutritional values, impacts on natural resources, and attractiveness to private sector.

Prototype for each country populated by IFPRI/

HarvestChoice using available secondary data sources

National teams adapt as needed and validate or replace data sources

Prioritization criteria

and weighting

Baseline

p

roduction/adoption

Yield targets

Outcome

indicators

12Slide13

Where to Target?

Geospatial targeting t

ools

By overlaying grid-based geospatial

layers, this tool helps users select target areas in country/region matching user-defined criteria and thus have more potential for scalable impact – e.g., climate, elevation, market accessibility, and linkage to other

socio-economic factors.13

http://harvestchoice.org/products/tool?product_keyword=geospatial+targeting

. Example for irrigated rice expansion in SAGCOT http://goo.gl/fIvtXqSlide14

Africa RISING

Malawi

14

Currently in

Dedza

and

Ntcheu

districts. Employs “mother

and baby

trials” approach

(

Snapp

etal

., 2012, Kerr et al., 2007

). Innovations

being promoted

include e.g.:

Maize/pigeon pea intercrop with FULL NPK at planting

and top

dressing with FULL

urea

Maize/bean intercrop fertilized with FULL NPK at planting and top dressing with FULL urea

Pigeon pea/groundnut intercrop fertilized with HALF NPKPigeon pea/soybean intercrop fertilized with HALF NPK3 / 18

http://dev.harvestchoice.org/africarising/ Slide15

Use MAPPR to browse and download indicator maps

http://harvestchoice.org/mappr/

HarvestChoice

MAPPR

Map, summarize, tabulate, download…

#1 Select indicators

#2 Toggle and re-arrange layers

#3 Generate zonal statistics

#4 Save reports

15Slide16

HarvestChoice

data API documented at http://harvestchoice.org/tools/harvestchoice-data-services

HarvestChoice Data API

16

Call data API methods and return tables and maps in

GAMS

,

STATA

,

R

, JavaScript, etc..Slide17

COMING NEXT…

Harmonized layers on

farm management practices

and

input uses (LSMS-ISA, micro scale)Sub-national poverty updated to circa 2008 and 2010Additional

nutritional indicators (200 DHS surveys for 69 countries beyond SSA)Derived climate and soil quality indicators relating changes over time (CRU, AfSIS)Footprint of agricultural technologies in sub-Saharan Africa

Current and future landscape of public and private agricultural investments in the region17Slide18

About

IFPRI

ifpri.org

The International Food Policy Research Institute (IFPRI) seeks sustainable solutions for ending hunger and poverty. IFPRI is one of 15 centers supported by the Consultative Group on International Agricultural Research (CGIAR), an alliance of 64 governments, private foundations, and international and regional organizations. HarvestChoice

harvestchoice.orgHarvestChoice generates knowledge products to help guide strategic investments to improve the well-being of poor people in sub-Saharan Africa through more productive and profitable farming. To do this, a novel and spatially explicit evaluation framework is being developed and deployed. By design, primary knowledge products are currently targeted to the needs of investors, policymakers and program managers, as well as the analysts and technical specialists who support them.HarvestChoice Team at IFPRIJAWOO KOO

crop/technology modeling, biophysical constraintsCARLO AZZARRI micro-economics, sub-national poverty, nutritionBELIYOU HAILE M&E, micro-economics

APURBA SHEE

M&E

, data modeling

ELODIE VALETTE

diffusion

of innovation,

peri

-urban agriculture

CINDY COX

technical

writer, technology evaluationCLEO ROBERTS farming systems characterization

MARIA COMANESCU web development, programmingMELANIE BACOU

microeconomics, CRP mappingQUEENIE GONG crop

production statistics data managementHO-YOUNG KWON crop and soil process modeling

ULRIKE WOOD-SICHRA data management, SPAM, DREAMZHE GUO

GIS coordinator, market accessIVY ROMERO administrative coordinator

18