The following research developed an approach and methodology to simultaneously gather and integrate social and natural science farm system data of developing countries into one data base. The overall approach was based on Weber's theory of abstraction, which requires the identification of the broadest number of variables as possible. The first step to understanding the farm system was to overview a number of the key variables which represented a number of key farm components; the second step was to juxtapose and blend together the various forms of data in linear forms against a test variable of Striga infestation levels; the third step was to evaluate if the amount of knowledge gained in predicting Striga infestation levels was statistically significant by cross correlating soil nutrient levels, crop management approaches, farmers' perceptions of Striga infestation and spatial distances; the fourth step was to use parametric and non-parameterc analytical tools in conjunction with data compression to locate the best combination of parameters to better manage Striga. The final part of the process was to identify and integrate the crop, field and social data into a profile of farmer's who have the highest and lowest likelihood of being infested by Striga by using a soil nutrient concentration baseline as the indicator. The results were that natural and social science data could be successfully combined, integrated and have statistically significant cross correlations. These correlations indicate that specific spatial parameters combined with specific soil components, farmer's management and crop placement could be used as predictors of Striga infestation levels. As well the farmers' perception could be validated using natural science data.

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