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Conceptual GIS derivation and spatial modelling of abiotic covariates influencing maize‐ Striga dynamics 1
Author(s) -
SCHMIDT K.
Publication year - 1996
Publication title -
eppo bulletin
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.327
H-Index - 36
eISSN - 1365-2338
pISSN - 0250-8052
DOI - 10.1111/j.1365-2338.1996.tb01494.x
Subject(s) - striga , covariate , biological dispersal , abiotic component , variance (accounting) , scale (ratio) , computer science , negative binomial distribution , scope (computer science) , process (computing) , spatial ecology , geostatistics , spatial analysis , statistics , spatial variability , ecology , mathematics , geography , cartography , biology , population , demography , accounting , sociology , sorghum , business , poisson distribution , programming language , operating system
The spatial scale of a model for description of the maize‐ Striga system has been extended. The model uses abiotic covariates transformed according to the different physiological responses of maize and Striga. The driving covariates may be derived from geographically distributed data sets. A digitized area in Mauretania, used as an example, combines the relevant attributes, while local references result in different submodels for the environmental variables. This additional information allows different simulations, reflecting the underlying structure of the area, but these are characterized by a degree of uncertainty. A second process for the description of the post‐maturity behaviour of Striga seed is added (derived from a Negative Binomial Distribution), and the GIS formation is used as a platform for simulating regional dispersal. The generated pattern, over time and space, was plausible, reflecting certain views on spatial heterogeneity. The system offers scope for development of spatially related control strategies. The implementation of a point model at regionalized scale shows advantages and limitations: the system is strongly dependent on the quality and accuracy of the underlying point model; requirements for simplicity, precision and computer capacity have to be reconciled.