Georeferenced and Agricultural Productivity Data in Household Surveys

Geo-referenced and Agricultural Productivity Data in Household Surveys: LSMS Practices and Methodological Research Alberto Zezza Surveys and Methods Development Research Group The World Bank Integrating Biodiversity and Ecosystem Services into Foresight Models Bioversity, 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

Key features of LSMS surveys • LSMS: national poverty and socio-economic data collection since 1980 s • Integrated Surveys on Agriculture (-ISA) addon 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

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

GPS Measurements Global Positioning System (GPS) equipment: measuring of land area and geo-referencing of land holdings • HH locations • Plot outline & area A = 27992 m²

Release community location • Link survey data with any other geospatial data • Disseminate modified EA centerpoints • Prevent identification of communities & households

Geo-variables: confidentiality vs. data access Dataset Integration: generate geographic variables (rainfall, temp. , vegetation, soil, roads, ) to capture relevant sitespecific 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: ; CAPI Countries & 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

Measuring Maize Productivity, Variety, and Soil Fertility (MAPS): Uganda Methodologies tested: Maize production • • • Crop-cutting using a 4 m x 4 m subplot and 2 m x 2 m 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 • • DNA extraction from leaf samples collected from the 4 x 4 m crop-cutting subplot DNA extraction from grain samples collected from the 4 x 4 m crop-cutting harvest Subjective farmer assessment assisted by photo aid • Questionnaires administered on Survey Solutions • Maize variety identification • CAPI 900 households to be interviewed 450 intercropped plots to be measured 450 pure stand plots to be measured 3 passes of high -resolution satellite image acquisition

Ethiopia: LASER Preliminary Results Soil 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 Results Soil Analysis is in early stages as data was received in March 2015. Possible to observe variation of soil properties within zones…

LASER Preliminary Results Soil 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 p. H Elemental composition • Exchangeable mineral concentration • Many more

WATER MEASUREMENT

Rainfall Measurement Objective • 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

Challenges & Opportunities • 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; Postharvest losses; Labor inputs; Livestock income • Opportunities (subject to testing) – Soil fertility; Varietal identification; Rainfall

Web: ww. worldbank. org/lsms Email: lsms@worldbank. org World Bank Living Standard Measurement Study