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: [email protected]
World Bank Living Standard Measurement Study
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