Impacts on Plant Pathogens - Climate Resilient Agriculture for Ensuring Food Security - page 171

• Better understanding of gene expression in
plants and pathogens in response to climatic
factors
• Integrated omic studies of host and pathogen
responses, as well as communities of soil- and
plant-associated microbes
• Multifactor studies of climate change effects
• Better models of adaptation rates
• Better data and models related to dispersal,
current levels of intraspecifi c diversity,
strength of selection under different climate
change scenarios, and heritability of traits
• Long-term large-scale records of pathogen
and host distributions
• Models of regional processes that incorporate
disease
• Data and models describing dispersal of prop-
agules and vectors
• Integrated multidisciplinary international net-
works for data collection and synthesis

change operates at a global scale, a lack of under-
standing of epidemic processes at relevant envi-
ronmental and spatial scales has hampered
progress. The uncertainties associated with cli-
mate change projections and the diffi culty in
extracting epidemiologically meaningful envi-
ronmental variables such as surface wetness from
GCMs have contributed to this.
From a disease management viewpoint, infor-
mation is generally required for a specifi c disease
at a fi eld scale; hence, data on potential impacts
of climate change need to be assessed and evalu-
ated at a detailed level to capture important
mechanisms and dynamics that drive epidemics.
In the absence of climate change considerations,
existing site-specifi c knowledge of individual
pathosystems, often incorporating environmental
variables at the microclimate level, serves well to
understand and manage disease. When climate
change considerations are included, defi ciencies
arise because of a lack of detailed knowledge of
epidemiology and the relevant meteorological
variables needed to predict epidemics at this spa-
tial scale. Ideally, the necessary epidemiological
data would be gathered from long-term fi eld
studies in facilities where more than one climate
change variable can be examined (Norby et al.
1997 ). As for meteorological data, statistical
downscaling of GCM output offers interesting
opportunities for developing climate change pre-
dictions for small-scale spatial units such as a
farm (Seem et al. 1999).
Information is also required by planners and
policymakers at a much broader spatial scale
such as a region, state, or country. Climate match-
ing and similar models are not based on mecha-
nisms or dynamics that drive epidemics;
nevertheless, these approaches are useful as fi rst
pass analyses and to develop integrated assess-
ment models that incorporate socioeconomic
aspects (Sutherst et al. 1996 ). If measures of
uncertainty are included (Scherm 2000 ), output
from GCMs is well suited for impact assessment
at these coarse levels of resolution. Data on
pathosystems would have to be acquired and syn-
thesized at this scale to include both on- and off-
farm effects of disease and other production
constraints for a realistic appraisal of crop loss.

8.13 Conclusions

Climate change can have positive, negative, or
neutral impact on individual pathosystems
because of the specifi c nature of the interactions
of host and pathogen. As a result, it has been dif-
fi cult to decipher rules of thumb that may be used
for specifi c impact assessment. Three factors are
largely responsible for this apparent lack of gen-
eral principles. First is a serious lack of knowl-
edge of the effects of some important factors
such as CO 2 . The role of pathogens in the
response of plants to increased CO 2 has not been
well studied; hence, its effect on disease is not
currently considered in crop simulation models.
Second, there is only rudimentary information on
the interactions of individual factors that collec-
tively infl uence plant disease in a changing cli-
mate. For example, recent studies showed that
the impacts of ozone in the fi eld cannot be esti-
mated without considering the predisposing
effects of fungal infections and the compensating
effects derived from elevated CO 2 (von
Tiedemann and Firsching 2000 ). Third, impacts
on plant disease have largely been considered in
small-scale experiments. Given that climate