International Institute of Tropical Agriculture and African Agricultural Technology Foundation report, January 2009
H. Bouwmeester, V.M. Manyong, K.D. Mutabazi, C. Maeda, G. Omanya, H.D. Mignouna, and M. Bokanga
“This report presents results from a spatial analysis of selected data generated through a livelihoods project in Striga infested areas of Malawi, Tanzania, and Uganda. In addition to mapping spatial patterns on livelihood indicators using Global Information Systems (GIS), the study also compared two interpolation techniques (ordinary Kriging and averaging) of measured values to surrounding locations. Livelihood indicators considered and spatially mapped in this report are related to natural capital, human capital, financial capital, maize growing Striga infestation and livelihood outcomes. Results show that many variables and indicators are clearly related to space. This is especially true in Malawi where many maps show a clear gradient from the “poor” south to the “rich” north. Many other maps in Tanzania and Uganda seem to suggest a similar correlation in space as nearby administrative units tend to have similar values on indicators. Although the survey that generated data used for this report was set up according to socioeconomic criteria and not so much on spatial criteria, the findings show that any economical study can profit from spatial analysis. The report also makes recommendations on how to improve on the collection and recording of geo-referenced data in the farmers’ fields.
“The livelihood project was designed to understand the effects of Striga on the livelihoods of the poor. Therefore, the sampled households were always located in areas known to be heavily infested with Striga. Expansion of areas of interest to areas not heavily infested to assess the effects on the researched indicators is recommended. This study indicates the power of GIS in exposing the socioeconomic consequences of a biological threat (Striga in this case) on smallholder farmers via a set of quantifiable indicators. Therefore, it can be said that databases designed for socioeconomic purposes can be very useful in spatial analysis. Two methods of interpolation were applied that allow socioeconomic properties to be predicted for unvisited sites. The results indicate that applying the two methods generate a spatial correlation in many of the economic indicators.”
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