Predicting Aflatoxin levels: An Spatial                            Autoregressive approach                                ...
   Collecting aflatoxin information is time    consuming and expensive.   Sometimes we can have aflatoxin    information...
A Spatial AutoregressiveModel (SAR) uses thehousehold characteristicsand the aflatoxin level ofpeople around it to predict...
   This model gives more weight       Aflatoxin level                  to the information of my                          ...
   There are variables such as a person’s    determination or innate ability that could help    predict how much time and...
   In order to asses who is “closest” to me I use    location variables:     Longitude     Latitude     Elevation    ...
90%                                                            Storage80%70%                                              ...
   We use      100%                                        Aflatoxin variation    data from   90%                80%    3...
2.5 2                                                         The relationship1.5                                         ...
Kernel density estimate for Pre-harvest Aflatoxin levels          .04                                   76%               ...
Kernel density estimate for Main HH Pre-harvest Aflatoxin levels                   .01                .008                ...
   Post-harvest data    after 1 month in                Total variation in aflatoxin levels    storage   During storage ...
2.5     2                                                       The    1.5                                                ...
   The same methodology applied to the data in Mali    will be applied to the data in Kenya.   Hence will be able to pre...
   We have two models that can be used to    predict aflatoxin models:     Maxent     SAR model   We need to compare t...
Current Partners:Donor: Bill and Melinda Gates FoundationCenter/ Universities         IFPRI: C. Narrod (Project lead), P. ...
Predicting aflatoxin levels a spatial autoregressive approach
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Predicting aflatoxin levels a spatial autoregressive approach

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Predicting aflatoxin levels a spatial autoregressive approach

  1. 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 Programand 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. 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. 3. A Spatial AutoregressiveModel (SAR) uses thehousehold characteristicsand the aflatoxin level ofpeople around it to predictaflatoxin levels in eachhousehold.
  4. 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 ownObservable Unobservable: aflatoxin level since it couldcharacteristics - 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. 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. 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. 7. 90% Storage80%70% 63% 74%60% Production50% 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. 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. 9. 2.5 2 The relationship1.5 between 1 1.04 *** predicted and0.5 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 lineVariable Obs Mean Std. Dev. Min MaxMeasured prevalence 247 27.2 64.0 0.05 492.0Predicted prevalence 247 29.6 26.9 0.00 130.7
  10. 10. Kernel density estimate for Pre-harvest Aflatoxin levels .04 76% The model is not .03 able to captureDensity 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. 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 = 12.9933Variable Obs Mean Std. Dev. Min MaxPredicted prevalence for main HH survey 1169 58.4 59.3 0.0 223.1
  12. 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. 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 lineVariable Obs Mean Std. Dev. Min MaxMeasured prevalence 243 121.9 256.9 0.0 1911.2Predicted prevalence 243 129.0 130.5 0.0 778.0
  14. 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.
  15. 15.  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.
  16. 16. Current Partners:Donor: Bill and Melinda Gates FoundationCenter/ 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. GriecoCountry 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

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