Environmental Engineering Reference
The Disease progress module simulates the epidemics of a generic air-borne
fungal pathogen, considering the following components of the infection process:
infection (Analytis 1977 ; Magarey et al. 2005) , incubation, latency, infectiousness
(Blaise and Gessler 1992 ; Wadia and Butler 1994) , sporulation (Analytis 1977) ,
and spores dispersal (Aylor 1982 ; Waggoner 1973 ; Waggoner and Horsfall 1969) .
These processes, which are driven by weather conditions and interactions with the
host plant (Zadoks and Schein 1979) , are modelled as a function of meteorological
variables, temperature, air relative humidity, vapour pressure deficit, leaf wetness
duration, rain, and wind speed - hourly values estimated/generated from the CLIMA
libraries (Fig. 4.2 ), and parameters specific for each host-pathogen combination.
As output, the Disease progress module returns the proportion of host tissue affected
compared to the total host tissue.
The initial conditions for infections are derived from a pool of models which use
information about the preceding crops, site-specific potential, and a random compo-
nent obtained by sampling from a distribution, either provided by default or fitted
from historical data. Impact on plants is currently implemented as a reduction of the
photosynthesizing host tissue according to the Bastiaans' model (1991) , but it will be
extended to a more direct interaction with plant simulation as some air-borne pathogens
such as rusts inhibit the conversion of solar radiation to dry matter. Finally, agro-
management is accounted for through the impact of fungicide applications and other
disease control actions on the fungal population. Prototype study applications have
been made for vineyard diseases and powdery mildew of wheat ( Blumeria graminis
f.sp. tritici ). The component also contains a model to simulate the pathogen rice blast
( Pyricularia orize ) and its impact on the rice crop, and a generic model for potential
infection (Magarey et al. 2005) , with parameters for more than 80 diseases.
Grasses: Grassland Growth and Quality
The grassland component was developed by INRA. It allows a wide diversity of
grassland types to be simulated: (i) sown grass species including tall fescue
( Festuca arundinacea ), perennial ryegrass ( Lolium perenne ) and cocksfoot
( Dactylis glomerata ), sown legumes such as alfalfa ( Medicago sativa ), permanent
grasslands ranging from plant communities growing under nutrient-poor to those
from nutrient-rich conditions, and mixtures of grasses and alfalfa or white clover
( Trifolium repens ). For species-rich permanent grasslands the approach is based on
plant functional traits (Lavorel and Garnier 2002 ; Duru et al. 2009) .
The grass growth module is similar to that of the crops component except that
additional functionalities are included:
The calculation of the herbage feeding value: (i) protein content using the standing
herbage mass and the crop nitrogen index; (ii) digestibility from herbage age,
nitrogen index and plant type (Duru 2008 ; Duru et al. 2008 ).
A detailed phenological sub-model (McCall and Bishop-Hurley
2003) , for
which parameters are specific to vegetation type in order to simulate a large
range of defoliation regimes (cutting, grazing, short and long regrowth periods),
over the vegetative or the reproductive phase.