The document describes using a Spatial Autoregressive (SAR) model to predict aflatoxin levels in households in Mali by taking into account household characteristics and the aflatoxin levels of neighboring households; the model explains 36% of the variation in pre-harvest aflatoxin levels and 38% of the variation in post-harvest levels after 1 month of storage; applying this same methodology in Kenya could provide useful predictions of aflatoxin levels but may have different results due to differences in crops and production/storage practices between the two countries.

1. Predicting Aflatoxin levels: An Spatial
Autoregressive approach
Gissele Gajate-Garrido, IFPRI
International Food Policy Research Institute Uniformed Services University of the Health Sciences
International Center for the Improvement of Maize ACDI/VOCA/Kenya Maize Development Program
and Wheat Kenya Agricultural Research Institute
International Crops Research Institute for the Semi- Institut d’Economie Rurale
Arid Tropics The Eastern Africa Grain Council
University of Pittsburgh

2.  Collecting aflatoxin information is time
consuming and expensive.
 Sometimes we can have aflatoxin
information from a smaller sample of
households.
 These information could be useful to
predict the level of aflatoxins in other
households with similar characteristics.

3. A Spatial Autoregressive
Model (SAR) uses the
household characteristics
and the aflatoxin level of
people around it to predict
aflatoxin levels in each
household.

4.  This model gives more weight
Aflatoxin level to the information of my
closest “neighbors” and less to
the ones that are further away.
 My “neighbors” information
could help predict my own
Observable Unobservable: aflatoxin level since it could
characteristics - Attitudes
- Risk aversion
contain information that
- Motivation
usually is not captured by
surveys.
 When we estimate models
there is always an error term
present that represents the
variation that we are unable to
capture.

5.  There are variables such as a person’s
determination or innate ability that could help
predict how much time and effort they will invest
in preventing aflatoxins in their crops.
 These variables cannot be observed or recorded in
a survey.
 However, by capturing information about my
peers this could help provide additional
information about how I behave and how high is
my aflatoxin level.

6.  In order to asses who is “closest” to me I use
location variables:
 Longitude
 Latitude
 Elevation
 Slope
▪ (Only for the pre-harvest sample)

7. 90% Storage
80%
70% 63%
74%
60% Production
50%
38%
40%
29% 27%
30%
20%
6% 9% 9%
10% 6%
2%
0%
Treated Improved Pesticide Fertilizer Insect Rodent Plastic Storage: Frequent Hand
soil (lime, seed damage damage bags for special use of sorting
manure, storage room pestcide before
etc.) inside in storage
house storage

8.  We use 100%
Aflatoxin variation
data from 90%
80% 36%
Mali to
70% The inside sample prediction captures
test the 60% 36% of the variation in prevalence
model. 50%
values.
40% 64 % Yet, the information of my neighbors
is not useful to predict my prevalence
 We start 30%
levels, only my characteristics are
with pre- 20%
relevant.
10%
harvest
0%
data. My neighbors' My characteristics Unobservable

9. 2.5
2
The relationship
1.5
between
1 1.04 *** predicted and
0.5 real values is
0 almost 1 to 1.
0 1 2 3 It is significant at
Measured prevalence (part per billion) 1%.
Predicted prevalence 45 degree line
Variable Obs Mean Std. Dev. Min Max
Measured prevalence 247 27.2 64.0 0.05 492.0
Predicted prevalence 247 29.6 26.9 0.00 130.7

10. Kernel density estimate for Pre-harvest Aflatoxin levels
.04
76% The model is not
.03
able to capture
Density
extremely high
.02
values of
prevalence and in
general
43%
.01
overestimates
lower values.
0
0 20 100 200 300 400 500
prevalence (part per billion)
Kernel density measured prevalence
Kernel density predicted prevalence
kernel = epanechnikov, bandwidth = 3.8288

11. Kernel density estimate for Main HH Pre-harvest Aflatoxin levels
.01
.008
.006
Density
37%
.004
63%
.002
0
0 20 50 100 150 200 250
Kernel density predicted prevalence for Main HH
kernel = epanechnikov, bandwidth = 12.9933
Variable Obs Mean Std. Dev. Min Max
Predicted prevalence for main HH survey 1169 58.4 59.3 0.0 223.1

12.  Post-harvest data
after 1 month in Total variation in aflatoxin levels
storage
 During storage
Variation Variation
not only your explained by explained by
characteristics but personal neighbors
characteristics aflatoxin level
also your
"neighbors"
information help Unexplained variation = 62 %
explain your
aflatoxin level. The inside sample prediction captures 38% of
the variation in prevalence values.

13. 2.5
2 The
1.5 relationship
between
1
0.95 *** predicted and
0.5 real values is
almost 1 to 1.
0
0 1 2 3 It is significant
Measured prevalence (part per billion) at 1%.
Predicted prevalence 45 degree line
Variable Obs Mean Std. Dev. Min Max
Measured prevalence 243 121.9 256.9 0.0 1911.2
Predicted prevalence 243 129.0 130.5 0.0 778.0

14.  The same methodology applied to the data in Mali
will be applied to the data in Kenya.
 Hence will be able to predict prevalence levels for
the main household survey and use it for further
analysis.
 Should we expect similar results?
 Different crops
▪ Mali –groundnuts vs. Kenya – maize
 It also depends on production and storage practices in
Kenya.

16.  We have two models that can be used to
predict aflatoxin models:
 Maxent
 SAR model
 We need to compare the strengths and
weakness of both models.
 We can also consider introducing other
variables to improve the predictions.

17. Current Partners:
Donor: Bill and Melinda Gates Foundation
Center/ Universities
IFPRI: C. Narrod (Project lead), P. Trench(Project manager), M. Tiongco,
D. Roy, A. Saak, R. Scott, W. Collier, M. Elias.
CIMMYT: J. Hellin, H. DeGroote, G. Mahuku, S. Kimenju, B. Munyua
ICRISAT: F. Waliyar, J. Ndjeunga, A. Diallo, M. Diallo, V. Reddy
University of Pittsburgh: F. Wu, Y. Liu
US Uniformed Health Services: J. Chamberlin, P. Masuoka, J. Grieco
Country Partners
ACDI/VOCA: S. Collins, S. Guantai, S. Walker
Kenya Agricultural Research Institute: S. Nzioki, C. Bett
Institut d’Economie Rurale: B. Diarra, O. Kodio, L. Diakite