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Predicting High-Magnitiude, Low-Frequency Crop Losses Using Machine Learning: An Application to Cereal Crops in Ethiopia

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James Warner
WEBINAR
Using Satellite Imagery for Early Warning of Productivity Constraints
Organized by the Food Security Portal (FSP)
OCT 31, 2019 - 11:00 AM TO 12:30 PM EDT

Published in: Government & Nonprofit
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Predicting High-Magnitiude, Low-Frequency Crop Losses Using Machine Learning: An Application to Cereal Crops in Ethiopia

  1. 1. Introduction Objectives Methods Results Conclusions PREDICTING HIGH-MAGNITUDE, LOW-FREQUENCY CROP LOSSES USING MACHINE LEARNING: AN APPLICATION TO CEREAL CROPS IN ETHIOPIA Prof. Michael Mann & Arun Malik The George Washington University http://michaelmann.i234.me James Warner International Food Policy Research Institute Addis Ababa Ethiopia j.warner@cigar.org
  2. 2. Introduction Objectives Methods Results Conclusions Ethiopian Agriculture • Smallholder subsistence farms constitutes 46% of GDP & employs 80% of population • Erratic rain-fed systems • Heterogeneous smallholder plots • Shifts in rainfall • vulnerability to changes in climate
  3. 3. Introduction Objectives Methods Results Conclusions 2015-2016 Drought ppt(mm) Significant El Nino event starting in 2014 stoked fears of potential famine
  4. 4. Introduction Objectives Methods Results ConclusionsIntroduction Objectives Methods Results Conclusions Localized effects of drought—2015-2016 [4] Sample 56 sub-kebeles, Mean loss 45.7% Solid and hollow circles represent above and below the mean, respectively. Area of circle is relative distance from the mean. Very few “small circles” (ie. close to mean). Medians typically significantly lower than mean Farmer Reported Crop Loss— South Tigray and Wag Humera Zones
  5. 5. Introduction Objectives Methods Results ConclusionsIntroduction Objectives Methods Results Conclusions Talk Objectives – 2010-2015 1. Use remote sensing to observe substantial crop losses early in season • Use precipitation, greenness and water moisture balance estimates to predict substantial losses for wheat, maize, sorghum, teff, & barley
  6. 6. Introduction Objectives Methods Results Conclusions PREDICT LOSSES > 25% Longitudinal Data 2010-2015 For wheat, maize, sorghum, & teff By Mid-Season Using Remote Sensing Only At sub-kebele level—approx. 24 sq. kms.)
  7. 7. Introduction Objectives Methods Results Conclusions Input Data Dependent Variable • Farmer reported crop damage (>25%) • 1,750 sub-kebeles * 6 yrs. ≈ 10,500 obs. Independent Variables Geographic Information • Elevation Weather/Climate (mid-season) • CHIRPS Rainfall • MODIS – Vegetation Indexes 250m 8-day NDVI • Climatic Water Deficit • Potential Evapotranspiration • Actual Evapotranspiration deviation 250m MODIS 2x16day NDVI
  8. 8. Introduction Objectives Methods Results Conclusions Random Forests • Creates hierarchical trees based on binary decisions • NDVI & Precip < 0.2mm • Trains many trees on subsets of variables and observations • Final decision based on majority vote of “weak learners”
  9. 9. Introduction Objectives Methods Results Conclusions Estimation Method Random Forests with Longitudinal Groupings • Control for cross-sectional and temporal properties of any site • Good out-of-sample performance • Data driven non-linear estimator • Robust to noisy training data
  10. 10. Introduction Objectives Methods Results Conclusions Variable Importance - Maize Dates NDVI ppt Other Variables Importance Dates NDVI
  11. 11. Introduction Objectives Methods Results Conclusions Marginal Effect – Plant Date Change estimated plant date period Observations (N) LossMoreLikely
  12. 12. Introduction Objectives Methods Results Conclusions Marginal Effect – Median NDVI Change in median NDVI Observations (N) LossMoreLikely
  13. 13. Introduction Objectives Methods Results Conclusions Estimation Results Performance Metrics by Mid-Season (peak greenness) MaizeWheat
  14. 14. Introduction Objectives Methods Results Conclusions Conclusions/further work 1. Even coarse remotely sensed data can be used successfully to identify losses > 25% in near real time (working toward higher resolution) 2. Understanding persistent high damage neighborhoods (e.g. rain shadows, etc. for Bayesian approach?) 3. How variable is local damage? Non-parametric dist. (median < mean) 4. Improve nexus between models and policy makers (e.g. dashboards, “trust,” access)
  15. 15. Introduction Objectives Methods Results Conclusions THANK YOU Questions?

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