Geo-referenced and Agricultural Productivity Data in Household Surveys: LSMS Practices and

Methodological Research

Alberto ZezzaSurveys and MethodsDevelopment Research GroupThe World Bank

Integrating Biodiversity and Ecosystem Services into Foresight ModelsBioversity, 7 May 2015

Outline

• What is the Living Standard Measurement Study (LSMS)?• LSMS-ISA• Key features

• Examples of relevant work• Geo-referencing• Ag productivity

– Output, Soil quality, Varietal identification, Rainfall

• Challenges & Opportunities

• LSMS: national poverty and socio-economic data collection since 1980s

• Integrated Surveys on Agriculture (-ISA) add-on with specific ag focus (2008- )

• Country-owned, nationally representative• Monitor, but more importantly understand,

analyze• Multi-topic, household-level and community

data• Typically every 3-5 years

Key features of LSMS surveys

LSMS – Integrated Surveys on Agriculture (LSMS-ISA)

• Panel (longitudinal)• Geo-referenced (households, plots)• Gender disaggregated• Open access• Focus on methods

development, use of technology (GPS, tablets, data entry in the field, soil testing,…)

• Partnerships (CGIAR, ICRAF, ILRI, FAO, CIFOR…)

• http://www.worldbank.org/lsms

LSMS-ISA: Overview of Survey Instruments

Household & Ind.• Expenditures – Food

& Nonfood• Education• Health• Labour• Nonfarm Enterprises• Durable Assets• Anthropometry• Food Security• Shocks, Coping

Agriculture• Plot Details• Trees on farm• Inputs – Use• Crops – Cultivation &

Production• Livestock• Fisheries• Farm Implements &

Machinery• Forestry?• NRM practices

Community• Demographics• Services• Facilities• Infrastructure• Governance• Organizations &

Groups• Use of communal NR• Prices

GEO-REFERENCING

Geo-referencing• Recording longitude and latitude of

households and other POI (plots, markets, schools, health centers)

• GPS data collection not new: but getting cheaper, more accurate, expanding possibility for integration

• Multiple uses of GPS data:– Survey Management and Supervision– Data Validation (distances)– Data integration and analytical applications

• HH locations • Plot outline & area

A = 27992 m²

GPS Measurements

Global Positioning System (GPS) equipment: measuring of land area and geo-referencing of land holdings

• Link survey data with any other geospatial data

• Disseminate modified EA center-points

• Prevent identification of communities & households

Release community location

Geo-variables: confidentiality vs. data accessDataset Integration: generate geographic variables

(rainfall, temp., vegetation, soil, roads,) to capture relevant site-specific or landscape characteristics

elevation (m)

annual rainfall (mm)

travel time to city (hrs)

mean 718 1,127 3

range 1 - 2387 462 - 2377 0 - 20

stdev 615 324 4

Challenges for geo-referencing

• Set of variables:– Re-assess the current list– HWSD for soil (0.5 deg)

• Resolution and confidentiality– Cross-country comparability– Higher resolution may increase risk of

identifying hh and communities (data user agreement enough?)

OUTPUT, LAND AREA & SOIL QUALITY

Methods for measuring crop productivity

Domains

• Land Area; Soil Fertility; Extended-Harvest Crops; Labor; Skills; Rainfall:;CAPICountries & Components

• Uganda (MAPS): Output (maize); land area, soil fertility, varietal identification

• Ethiopia (LASER): Output (maize); land area, soil fertility• Malawi: Output (Cassava); varietal identification

Partners• NSO’s• FAO; Global Strategy for Ag Stats; SPIA; ICRAF; …• Stanford University/Skybox Imaging

Status• Uganda: Fieldwork training currently ongoing• Ethiopia: Fieldwork completed, full data received March 2015• Malawi: Fieldwork May 2015-June 2016

Methodologies tested:

Maize production

• Crop-cutting using a 4m x 4m subplot and 2m x 2m subplot

• Stratified plot selection over intercropped and pure stand plots

• Yield estimation via high-resolution satellite imagery

• Farmer self-reported harvest

Land area • GPS measurement (Garmin)• Farmer self-reported area

Soil fertility • Spectral Soil Analysis • Conventional Soil Analysis • Farmer self-reported soil quality

Maize variety identification

• DNA extraction from leaf samples collected from the 4x4m crop-cutting subplot

• DNA extraction from grain samples collected from the 4x4m crop-cutting harvest

• Subjective farmer assessment assisted by photo aid

CAPI • Questionnaires administered on Survey Solutions

Measuring Maize Productivity, Variety, and Soil Fertility (MAPS): Uganda

900 households

to be interviewed

450intercropped plots to be measured

450pure stand plots to be measured

3passes of

high-resolution satellite image

acquisition

Ethiopia: LASER Preliminary ResultsSoil Analysis is in early stages as data was received in March 2015.

Distribution of soil organic carbon by

administrative zone.

Analysis of subjective measures of soil quality against laboratory testing underway.

LASER Preliminary ResultsSoil Analysis is in early stages as data was received in March 2015.

Possible to observe variation of soil properties within zones…

02

46

8S

oil

Org

an

ic C

arb

on

(%)

excludes outside values

West Arsi Zone

Enumeration Area, West Arsi Zone

LASER Preliminary ResultsSoil Analysis is in early stages as data was received in March 2015.

…and within enumeration areas

Other variables available include:

• % nitrogen• % clay, silt, and

sand• pH• Elemental

composition• Exchangeable

mineral concentration

• Many more

WATER MEASUREMENT

Rainfall MeasurementObjective• Analyzing the trade-offs involved with different alternative methods

of obtaining rainfall information relevant for agricultural production: local rainfall gauges, weather stations, satellite data, and self-reported weather shocks

Partnership• Paris School of

Economics (Karen Macours) impact evaluation in Democratic Republic of Congo

Status• Data collection and

data entry completed• Paper comparing

different methods drafted by late 2015

• Geo-referencing– Variables, confidentiality, dissemination

• “Quick wins”– Non-standard units; Information on crop state;

Use of GPS for land area measurement; Work on data integration (satellite imagery, …)

• Tougher “nuts to crack”– Continuous and root crops; Intercropping; Post-

harvest losses; Labor inputs; Livestock income

• Opportunities (subject to testing)– Soil fertility; Varietal identification; Rainfall

Challenges & Opportunities

Web: ww.worldbank.org/lsmsEmail: lsms@worldbank.org

World Bank Living Standard Measurement Study