Julius-Kühn-Archiv (Mar 2012)

Understanding the present distribution of the parasitic weed Striga hermonthica and predicting its potential future geographic distribution in the light of climate change

  • Cotter, Marc,
  • de la Pena-Lavander, Renzoandre,
  • Sauerborn, Joachim

DOI
https://doi.org/10.5073/jka.2012.434.082
Journal volume & issue
no. 434
pp. 630 – 636

Abstract

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Parasitic weeds of the genus Striga (Orobanchaceae) are a major constraint to agricultural production in the semi-arid regions in Sub-Saharan Africa. Therefore, Striga hermonthica’s current and future distribution needs to be estimated urgently in order to better and more efficiently target available Striga management strategies. Using innovative GIS-based modeling complemented by greenhouse and field studies, our research aims to better understand the present geographic distribution of Striga species and to predict potential future expansion areas of these dangerous weeds. Parameters determining the presence or absence of Striga were analyzed and available data complemented by new studies on Striga ecology and seed bank dynamics gained at the University of Hohenheim and ICRISAT, Mali.In order to provide managers and decision maker with a useful tool to take precautionary and palliative actions against the menace of infestation by invasive or parasitic species, it is important to assess the possible future distribution of such species, especially in vulnerable areas where the parasite has not yet appeared. Based on the present geographic distribution and the factors affecting it, different climate projections have been applied to indicate areas that will become susceptible to Striga invasion in the future. Datasets on the impact of climate change from IPCC workgroups have been used as basis for this assessment, combined with information gained from field trips, herbaria assessments and literature. The results of this study show trends in the potential future distribution of Striga hermonthica, but also indicate areas where the methodology can be improved and refined to allow more precise and reliable predictions.

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