Agriculture Reference
In-Depth Information
CHAPTER 8
MODELLING AND INTERPRETING DISEASE PROGRESS
IN TIME
XIANGMING XU
8.1 INTRODUCTION
The primary objective of epidemiological research is to increase our understanding
of how diseases develop in host crop populations and how other factors influence
their development in order to develop sustainable and effective strategies for
managing diseases. One of main epidemiological activities in achieving this
objective is to understand the relationship of temporal disease dynamics with
external factors. With improved knowledge, more efficient, sustainable and effective
management strategies may be developed to reduce the impact of diseases on crop
yield. In order to measure the temporal disease development, the amount of disease
present in a population of plants is usually assessed several times. These data can
be presented collectively as a disease progress curve, essentially depicting the
dynamics of disease development with time. This simple temporal progress curve
represents outcomes of complex interactions between host, pathogen, environments
and crop husbandry. Depending on the nature of the pathosystem and how the
disease is assessed, the observed disease progress curve does not necessarily
describe monotonic increase.
Quantitative descriptions and analysis of temporal disease progress was
recognised and applied in many occasions prior to the 1960s. For example, Ware
et al.
(1932) and Ware and Young (1934) presented curves illustrating the
effects of cultivar resistance and fertilizer treatment on the dynamics of cotton
wilt. Large (1945, 1952) proposed the use of disease progress curves and rate of
disease progress as a means of demonstrating the benefits of fungicide
applications on development of potato late blight. He also used transformation
based on the normal distribution to linearise the observed disease progress curve
and noticed the value of using the half-decay point for comparing epidemics.
However, quantitative analysis of epidemiological data entered the mainstream of
plant pathology only after the contribution by Van der Plank (1963) and the
availability of modern computers. He not only introduced a new way of
conceptualising disease increase in populations but also introduced important models
into the mainstream of plant disease epidemiology. He introduced several quantities,
such as apparent infection rate, infectious and latent periods, demonstrated how
differential equations could form a basis for quantifying disease progress curves and
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