Predicting Aflatoxin levels: An Spatial Autoregressive approach

Gissele Gajate-Garrido, IFPRI

International Food Policy Research Institute International Center for the Improvement of Maize

and Wheat International Crops Research Institute for the Semi-Arid Tropics University of Pittsburgh

Uniformed Services University of the Health Sciences ACDI/VOCA/Kenya Maize Development Program Kenya Agricultural Research Institute Institut d’Economie Rurale The Eastern Africa Grain Council

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.

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

This model gives more weight

to the information of my closest “neighbors” and less to the ones that are further away.

My “neighbors” information could help predict my own aflatoxin level since it could contain information that 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.

Aflatoxin level

Observable characteristics

Unobservable: - Attitudes - Risk aversion - Motivation

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.

In order to asses who is “closest” to me I use location variables:

Longitude

Latitude

Elevation

Slope

▪ (Only for the pre-harvest sample)

6% 2%

29%

6%

38%

9% 9%

27%

74% 63%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Treatedsoil (lime,manure,

etc.)

Improvedseed

Pesticide Fertilizer Insectdamage

Rodentdamage

Plasticbags forstorage

Storage:specialroominsidehouse

Frequentuse of

pestcidein

storage

Handsortingbefore

storage

Production

Storage

36%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

My neighbors' My characteristics Unobservable

The inside sample prediction captures 36% of the variation in prevalence values.

Yet, the information of my neighbors is not useful to predict my prevalence levels, only my characteristics are relevant.

64 %

Aflatoxin variation We use

data from Mali to test the model.

We start with pre-harvest data.

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

0

0.5

1

1.5

2

2.5

0 1 2 3

Measured prevalence (part per billion)

Predicted prevalence 45 degree line

1.04 ***

The relationship between predicted and real values is almost 1 to 1. It is significant at 1%.

0

.01

.02

.03

.04

Den

sity

0 100 200 300 400 50020prevalence (part per billion)

Kernel density measured prevalence

Kernel density predicted prevalence

kernel = epanechnikov, bandwidth = 3.8288

Kernel density estimate for Pre-harvest Aflatoxin levels

The model is not able to capture extremely high values of prevalence and in general overestimates lower values.

76%

43%

0

.00

2.0

04

.00

6.0

08

.01

Den

sity

0 50 100 150 200 25020

Kernel density predicted prevalence for Main HH

kernel = epanechnikov, bandwidth = 12.9933

Kernel density estimate for Main HH Pre-harvest Aflatoxin levels

Variable Obs Mean Std. Dev. Min Max Predicted prevalence for main HH survey 1169 58.4 59.3 0.0 223.1

37%

63%

Post-harvest data after 1 month in storage

During storage not only your characteristics but also your "neighbors" information help explain your aflatoxin level.

Unexplained variation = 62 %

Variation explained by personal characteristics

Variation explained by neighbors aflatoxin level

Total variation in aflatoxin levels

The inside sample prediction captures 38% of the variation in prevalence values.

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

The relationship between predicted and real values is almost 1 to 1. It is significant at 1%.

0

0.5

1

1.5

2

2.5

0 1 2 3

Measured prevalence (part per billion)

Predicted prevalence 45 degree line

0.95 ***

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.

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.

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