Agriculture Reference
In-Depth Information
fungus in a heavy infection creates a signifi cant 'sink' for diffusion of metabolites to the
site of infection (Whipps & Lewis, 1981; Farrar, 1992; Walters
et al.
, 2008a).
The impact on yield of pathogen-induced modifi cations to radiation interception, RUE
and partitioning will depend on the timing of the disease epidemic and its relative effects
on 'source' and 'sink' tissues. Here the term 'source' refers to the supply of photosyn-
thates from mature actively photosynthesising leaves or temporary storage reserves.
'Sink' on the other hand refers to the demand for photosynthates by metabolically active
non-photosynthesising tissues or tissues that do not have the photosynthetic capacity to
meet their own demand. The 'sink' of primary interest in most determinate crops is the
developing seed or fruit. In cereals, early disease epidemics that coincide with canopy
growth, such as Rynchosporium leaf blotch and powdery mildew of barley, can reduce
canopy size and reserves of soluble carbohydrates in the stem at the start of the grain-
fi lling period. Thus, potential assimilate supply for grain fi lling is limited. However, since
the period of canopy growth is also the period during which tillers and spikelets develop,
early disease can reduce grain sink capacity by restricting the number of ears produced
and to a lesser extent the number of grains per ear (Brooks, 1972; Lim & Gaunt, 1986;
Conry & Dunne, 1993). Late epidemics mainly affect the average grain weight (Wright &
Hughes, 1987). In other pathosystems, such as the wheat-
Septoria tritci
system, disease
tends to develop late after canopy expansion has been completed and the stem carbohy-
drate reserves deposited. Its main effects are on the duration of the canopy post-fl owering
and thus photosynthate supply for grain fi lling rather than the development of grain sink
capacity (Paveley
et al.
, 2001).
7.4
Development of techniques for the reliable quantifi cation of tolerance in the fi eld is the
fi rst step towards identifying the traits and genes that confer tolerance (Parker
et al.
,
2004). There was a perception, until relatively recently, that tolerance can only be assessed
through a comparison of genotypes under identical levels of infection. This presented
major practical diffi culties in quantifying tolerance because equivalent infection is almost
impossible to achieve in fi eld experiments using natural epidemics. More recent studies
have adopted a reaction norm approach in which yield is measured over a range of infection
or damage severity. Tolerance is given by the slope of the relationship, thereby obviat-
ing the need for absolute parity of infection (Parker
et al.
, 2004; Inglese & Paul, 2006).
The most common measurement of infection severity is the area under disease progress
curve (AUDPC) which provides a measure of lesion area integrated over time (Kramer
et al.
, 1980; Inglese & Paul, 2006). Newton
et al.
(1998) used a variation of this approach
to determine the relative tolerance of powdery mildew amongst spring barley geno-
types. The yield loss of a genotype in response to disease was plotted against its AUDPC
and a common relationship for all genotypes fi tted by linear regression (Figure 7.2).
Tolerant and intolerant genotypes were identifi ed by their position relative to the regres-
sion line; those positioned one or more standard deviations away from the line were
designated either tolerant (if below the line) or intolerant (if above the line). Assessments
of AUDPC were made from whole canopy scores of infection severity. The approach
allows large numbers of genotypes to be evaluated in one experiment, but without
supporting measurements provides no information on the mechanisms responsible.
How can tolerance be quantifi ed?
