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

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…)

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 locationsPlot outline & area A = 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 access Dataset 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) mean7181,1273 range stdev

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

Methodological Experiment on Measuring Maize Productivity, Variety, and Soil Fertility (MAPS): Uganda Objectives Explore the implications of the size and the number of sub-plots selected for crop cutting on maize yield estimates, separately for monocropped vis-à-vis intercropped plots, while also collecting objective measurements on soil fertility and maize variety. Partnerships Uganda National Bureau of Statistics (UBOS) World Agroforestry Centre (ICRAF) FAO Standing Panel on Impact Assessment (SPIA) Stanford University/Skybox Imaging Status Fieldwork training currently ongoing (April 7 – 22, 2015) Household listing operation currently ongoing Post-planting fieldwork to commence April 25, 2015

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

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 450 intercropped plots to be measured 450 pure stand plots to be measured 3 passes of high- resolution satellite image acquisition

MAPS Sample 3 Strata in Eastern Uganda: Serere District (15 EAs) Sironko District (15 EAs) Span of Iganga and Mayuge districts for which remote sensing imagery will be collected (45 Eas, shown at right) Household Selection Stratified on maize cultivation status 6 pure stand households and 6 intercropping households selected from each EA Possible because of detailed household listing operation Plot Selection Survey Solutions program designed to randomly select one plot per household which matches the cultivation status for which it was selected

MAPS Supervision – GIS for Fieldwork Monitoring Survey Solutions’ Map Report: Provides at-a-glance updates on field work progress, including the status of questionnaires (approved by supervisor, completed, etc). Allows for quick troubleshooting of sampling issues – For example, in the case of MAPS, all EAs should have 12 HHs. A quick look at the Map Report revealed one team was only interviewing 6 HHs per EA. Ensures field teams are where they should be. solutions.worldbank.org

MAPS – GIS for Fieldwork Monitoring

Land And Soil Experimental Research (LASER): Ethiopia Objective – Test various measurement methodologies in order to: Validate the data quality associated with each method Determine the costs and benefits of each method Assess the feasibility and necessity of implementing each method in national household surveys Document best practices in data collection based on our findings from experiments in Ethiopia and beyond Partnerships Central Statistical Agency of Ethiopia World Agroforestry Centre (ICRAF) Status Fieldwork completed early March 2014 Soil testing complete, full data received March 2015

Methodologies tested: Land area Traversing (i.e., compass and rope) GPS measurement (Garmin) GPS measurement (Android tablet) Farmer self-reported area Clinometer Farmer self-reported incline Completed during the post-planting visit on up to two fields per household Soil fertility Spectral Soil Analysis Conventional Soil Analysis Farmer self-reported soil quality Samples collected during the post- planting visit, processed at regional labs and shipped to ICRAF Nairobi for analysis Maize production Crop-cutting using a 4m x 4m subplot and 2m x 2m subplot Farmer self-reported harvest Completed by field teams when alerted by household LASER Methodologies 1018 households interviewed 1799 fields selected for objective measurement and soil testing 3791 soil samples collected* 205 fields with crop- cutting *2 samples were collected from each field (different depths and sampling procedures), an additional sample was collected on fields with crop-cutting.

ElevationRainfallAEZ LASER Sample 85 EAs 12 HH Each

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

LASER Preliminary Results Soil Analysis is in early stages as data was received in March …and within enumeration areas

LASER Preliminary Results Soil Analysis is in early stages as data was received in March …and within enumeration areas Other variables available include: % nitrogen % clay, silt, and sand pH Elemental composition Exchangeable mineral concentration Many more

Malawi: Cassava Productivity and Variety Identification (CVIP) Objectives Validate methods for measuring cassava productivity and variety identification by evaluating the data quality and costs vs. benefits for each method Partnerships National Statistical Office Global Strategy Status Fieldwork to begin June 2015 Methodologies tested: Cassava production Crop-cutting with balance scales Crop diaries with enumerator visits twice a week Crop diaries with telephone calls twice a week Farmer self-reported harvest (12-month recall) Farmer self-reported harvest (6-month recall) Variety Identification DNA fingerprinting of leaf samples Farmer self-reported varieties and attributes with and without photo aids

Malawi CVIP 5 Districts 45 Enumeration Areas 1260 Households 315 Households per treatment arm 1 Crop cutting plot per household Timeline: TrainingMay 2015 Data collection using WB CAPI Survey Solutions June 2015 – May 2016 Crop cuttingStarting July month recall December month recall May 2016

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 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/lsms World Bank Living Standard Measurement