TITLE:
Spatial Modelling of Weather Variables for Plant Disease Applications in Mwea Region
AUTHORS:
Paul Onyango Ouma, Patroba Achola Odera, John Bosco Mukundi
KEYWORDS:
Climate Change, Rice Blast, GIS, Geostatistics, MarkSim GCM
JOURNAL NAME:
Journal of Geoscience and Environment Protection,
Vol.4 No.5,
May
20,
2016
ABSTRACT: Climate change is expected to affect the
agricultural systems, such as crop yield and plant disease occurrence and
spread. To be able to mitigate against the negative impacts of climate change,
there is a need to use early warning systems that account for expected changes
in weather variables such as temperature and rainfall. Moreover, providing such
information at high spatial and temporal resolutions can be useful in improving
the accuracy of an early warning system. This paper describes a methodology that
can be used to produce high spatial and temporal resolutions of minimum
temperature, maximum temperature and rainfall in an agricultural area. We
utilize MarkSim GCM, a weather file generator that incorporates IPCC based
climate change models to downscale the weather variables at monthly intervals.
An ensemble of 17 GCM models is used within the RCP 8.0 emission scenario
within the latest model based CMIP5. We first assess the usability of the
model, by comparing results produced to what has been recorded at weather
station level over a vast region. Then, we estimate the correction factors for
model results by implementing a linear regression that is used to assess the
relationship between the variables and the deviation of model outputs to the
weather station data. Finally, we use kriging geostatistical technique to
interpolate the weather data, for the year 2010. Results indicated that the
model overestimated the results of maximum temperature, while underestimating
the result of minimum temperature. Variability in the recorded weather
variables was also evident, indicating that the response variables such as
plant disease severity dependent on such weather information could vary in the
area. These datasets can be useful especially in predicting the occurrence of
plant diseases, which are affected by either rainfall or temperature.