Trans-disciplinary approach integrating farm system data to better manage and predict Striga infestations

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dc.contributor.authorMacLean, R.R.
dc.date.accessioned2007-11-13T05:47:13Z
dc.date.available2006-01-18en
dc.date.available2007-11-13T05:47:13Z
dc.date.issued2002
dc.description.abstractThe 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.en
dc.description.degreeDoctor of Philosophy
dc.formatTexten
dc.format.extent1 digital file (152 p. : ill.)en
dc.format.mimetypeApplication/pdf
dc.identifier.urihttp://hdl.handle.net/10625/32398
dc.identifier.urihttp://digitool.Library.McGill.CA:80/R/-?func=dbin-jump-full&object_id=38228&silo_library=GEN01
dc.language.isoen
dc.publisherMcGill University, Montreal, QC, CAen
dc.subjectWEED CONTROLen
dc.subjectFARMING SYSTEMSen
dc.subjectON-FARM RESEARCHen
dc.subjectFARMERSen
dc.subjectMALIen
dc.subjectRESEARCH METHODSen
dc.subjectSOCIAL ASPECTSen
dc.subjectSOIL ANALYSISen
dc.subjectSTRIGAen
dc.titleTrans-disciplinary approach integrating farm system data to better manage and predict Striga infestationsen
dc.typeThesisen
idrc.dspace.accessIDRC Onlyen
idrc.project.number914101
idrc.project.titleBiological Control for Striga (McGill/West Africa)en
idrc.rims.adhocgroupIDRC SUPPORTEDen

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IDRC Research Results / Résultats de recherches du CRDI
2000-2009 / Années 2000-2009
Agriculture, Food and Nutrition Sciences Division / Division des sciences de l'agriculture, de l'alimentation et de la nutrition