Assessing Impacts in Agriculture at Ultra-low Costs

Assessing Impacts in Agriculture at Ultra-low Costs David B. Lobell, Marshall B. Burke, Meha Jain Department of Earth System Science Center on Food Security and the Environment (FSE), Stanford University

Photo: M. Burke

3 elements for ultra-low cost, accurate crop monitoring Satellite Data Computation (e.g., Google Earth Engine) Robust Algorithms

Algorithms are getting more robust and scalable

Four sites for Skybox 4 Good W Kenya Rwanda E Uganda NE India

Testing Skybox in smallholder systems Skybox Image Dates June 17 2014 July 5 2014

Single-date VI vs. yield All Maize Fields (n = 114) Fields > 0.25 ha (n = 84)

Four sites for Skybox 4 Good W Kenya Rwanda E Uganda NE India

Questions: -what are impacts of early sowing extension efforts? -what facilitates faster adoption? -what are main constraints to yields? -what are potential benefits of precision interventions? Photo: B. Singh

Skybox Landsat

80 crop cuts ~250 farmer surveys

Slides below might be useful to you

Four sites for Skybox 4 Good W Kenya Rwanda E Uganda NE India

540 crop cuts in 2015 Joint with LSMS team (Talip Kilic, Sydney Gourlay, Siobhan Murray) and Uganda Bureau of Statistics

Assessing Impacts in Agriculture at Ultra-Low Costs: An Application to Uganda & An Idea for Future TALIP KILIC Senior Economist Living Standards Measurement Study Team Development Research Group, The World Bank tkilic@worldbank.org CEGA-Global Resilience Partnership-BRAC Workshop Remote Sensing in Global Development: Technologies for Crisis Response & Community Resilience Berkeley, CA 06/22/2015

Methodological Experiment on Measuring Maize Productivity, Variety, and Soil Fertility (MAPS) Support LSMS Methodological Validation Program, funded by UK Aid Objectives Test subjective approaches to measurement vis-à-vis objective methods for maize production, soil fertility & variety identification Partnerships Uganda Bureau of Statistics, World Agroforestry Centre Soil Fertility Component, CGIAR Standing Panel on Impact Assessment Variety Identification Component, Stanford University/Skybox Remote Sensing Component Status Post-Planting Fieldwork: April 25 June 15 Crop Cutting Fieldwork: June 25 August 10 Post-Harvest Fieldwork: August 19 September 20 Analysis & Dissemination: October 15 September 16

MAPS Methodologies Methodologies tested: Maize production Crop-cutting using a 4m x 4m & a 2m x 2m subplot Yield estimation via high-resolution satellite imagery (First in testing the method in a smallholder production system against an objective ground truth) Farmer self-reported harvest Land area GPS measurement (Garmin etrex 30 handheld units) Farmer self-reported area Soil fertility Conventional Soil Analysis (subsample) Spectral Soil Analysis Farmer self-reported soil quality Maize variety identification DNA fingerprinting of leaf samples collected from the 4x4m crop-cutting subplot DNA fingerprinting of grain samples collected from the 4x4m crop-cutting harvest Subjective farmer assessment assisted by photo aid CAPI Questionnaires administered on Survey Solutions 900 households 515 intercropped plots 385 pure stand plots 3 passes of highresolution satellite image acquisition

3 Strata in Eastern Uganda MAPS Sample 15 Enumeration Areas in Each of Serere & Sironko Districts 45 Enumeration Areas from a 400 Km2 remote sensing tasking area across Iganga & Mayuge districts Household (HH) Selection Original Plan: 6 pure stand & 6 intercropping HHs selected following a detailed HH listing operation 450 in each universe Result: 385 vs. 515 split due to changes in cultivation status between listing & enumeration & resulting inadequate # of pure stand HHs 249 vs. 291 split in the remote sensing tasking area Plot Selection Survey Solutions CAPI application to randomly select one plot per HH (irrespective of location), matching the HH cultivation status

MAPS Area Measurement Figure 1: Mean Self-Reported-GPS-Based Plot Area Difference (Acres) by GPS-Based Plot Area Decile Mean SR-GPS Area Difference (Acres) -.1 0.1.2.3.4 1 2 3 4 5 6 7 8 9 10 Area Decile Source: MAPS 2015. Dotted Line = Mean GPS-Based Plot Area of 0.36 Acres

MAPS Area Measurement (2) Figure 2: Distribution of GPS-Based Plot Areas at Self-Reported Rounding Intervals (0.25, 0.5 & 1 Acres) Kernel Density 0 1 2 3 4 0.5 1 1.5 2 2.5 3 3.5 4 GPS-Based Plot Area(Acres) Distribution of GPS-Based Plot Areas w/ SR Value = 0.25 Distribution of GPS-Based Plot Areas w/ SR Value = 0.5 Distribution of GPS-Based Plot Areas w/ SR Value = 1 Source: MAPS 2015.

MAPS Remote Sensing Task Area

MAPS Remote Sensing Task Area (2)

MAPS Ground Truth: Crop Cutting

MAPS Ground Truth: Crop Cutting (2)

An Idea for Future: Collaborative Work Program on Sentinel Sites for Resilience Monitoring Formulation of a theory of resilience for development applications is lagging behind the pace at which resilience is targeted among policy objectives Focus should be on monitoring individual & collective capacity to avoid & escape from poverty in the face of stressors & shocks (Barrett & Constas, 2014) Measurement & evaluation remain a challenge even with coherent theory Sentinel sites proposed as platforms to operationalize continuous resilience monitoring (Barrett & Headey, 2014) High-frequency surveys of households & communities at strategically selected locations that are broadly representative of livelihood zones within countries Scale at start-up need to be small - demonstrate value prior to scaling up Focus on scalable approaches to measurement, data processing & dissemination Aspire for integration into national statistical systems (Context-specific) key decision areas: time horizon, frequency, cross-section vs. panel vs. hybrid, scope of data collection (thick vs. thin rounds)