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.
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.
Introduction Objectives Methods Results Conclusions
2015-2016 Drought
ppt(mm)
Significant El Nino event starting in 2014
stoked fears of potential famine

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.
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.
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.
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.
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.
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.
Introduction Objectives Methods Results Conclusions
Variable Importance - Maize
Dates
NDVI
ppt
Other
Variables
Importance
Dates
NDVI

11.
Introduction Objectives Methods Results Conclusions
Marginal Effect – Plant Date
Change estimated plant date period
Observations
(N)
LossMoreLikely

12.
Introduction Objectives Methods Results Conclusions
Marginal Effect – Median NDVI
Change in median NDVI
Observations
(N)
LossMoreLikely

13.
Introduction Objectives Methods Results Conclusions
Estimation Results
Performance Metrics by Mid-Season (peak greenness)
MaizeWheat

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.
Introduction Objectives Methods Results Conclusions
THANK YOU
Questions?