Monitoring peanut contamination in Mali (Africa) using the AVHRR satellite data and a crop simulation model

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Details

Author(s):
V.K. Boken; G. Hoogenboom; J.H. Williams; B.Diarra; S. Dione; G.L. Easson

Type of Document:
Scholarly Article

 

Publisher/Journal:
International Journal of Remote Sensing

Date of Publication:
2007

Place of Publication:
Not Available

Description

Abstract: Peanut is the main legume crop of Mali, West Africa. It can be contaminated by aflatoxin, a natural toxin that may develop due to drought conditions at pre-harvest stage and also due to temperature- and humidity-related factors that may occur during the post-harvest storage. Consumption of aflatoxin-contaminated peanut can cause liver diseases, such as jaundice, hepatitis, or cancer. In this paper, we present a case study for Mali, West Africa, and identify weather and satellite based variables that could be used to indicate aflatoxin presence in peanut. Based on such monitoring and predicting, a warning may be issued against eating contaminated peanuts to keep the public health from deteriorating. The Normalized Difference Vegetation Index (NDVI) composites, that were derived from the Advanced Very High Resolution Radiometer (AVHRR) satellite data, were averaged for the reproductive phase of peanut and were examined for their relationship with the annual peanut yield, an indicator of drought and aflatoxin. The relationship was found to be moderate (R2 = 0.56). The commencement and termination dates for the reproductive phase of peanut were determined by using a crop simulation model. Aflatoxin amounts were measured for peanut samples collected from various locations across Mali and were found to be linked to the NDVI, total precipitation, and maximum temperature averaged over the reproductive phase of peanut.

Additional Bibliographic Information

Boken, V. K., Hoogenboom, G., Williams, J.H., Diarra, B., Dione, S., and Easson, G.L., 2008. Monitoring peanut contamination in Mali (Africa) using the AVHRR satellite data and a crop simulation model. International Journal of Remote Sensing, 29(1):117-129.

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