epid_output {landsepi} | R Documentation |
Generation of epidemiological and economic model outputs
Description
Generates epidemiological and economic outputs from model simulations.
Usage
epid_output(
types = "all",
time_param,
Npatho,
area,
rotation,
croptypes,
cultivars_param,
eco_param,
treatment_param,
pathogen_param,
audpc100S = 0.76,
writeTXT = TRUE,
graphic = TRUE,
path = getwd()
)
Arguments
types |
a character string (or a vector of character strings if several outputs are to be computed) specifying the type of outputs to generate (see details):
|
time_param |
list of simulation parameters:
|
Npatho |
number of pathogen genotypes. |
area |
a vector containing polygon areas (must be in square meters). |
rotation |
a dataframe containing for each field (rows) and year (columns, named "year_1", "year_2", etc.), the index of the cultivated croptype. Importantly, the matrix must contain 1 more column than the real number of simulated years. |
croptypes |
a dataframe with three columns named 'croptypeID' for croptype index, 'cultivarID' for cultivar index and 'proportion' for the proportion of the cultivar within the croptype. |
cultivars_param |
list of parameters associated with each host genotype (i.e. cultivars):
|
eco_param |
a list of economic parameters for each host genotype as if cultivated in pure crop:
|
treatment_param |
list of parameters related to pesticide treatments:
|
pathogen_param |
a list of i. pathogen aggressiveness parameters on a susceptible host for a pathogen genotype not adapted to resistance and ii. sexual reproduction parameters:
|
audpc100S |
the audpc in a fully susceptible landscape (used as reference value for graphics). |
writeTXT |
a logical indicating if the output is written in a text file (TRUE) or not (FALSE). |
graphic |
a logical indicating if a tiff graphic of the output is generated (only if more than one year is simulated). |
path |
path of text file (if writeTXT = TRUE) and tiff graphic (if graphic = TRUE) to be generated. |
Details
Outputs are computed every year for every cultivar as well as for the whole landscape.
- Epidemiological outputs.
-
The epidemiological impact of pathogen spread can be evaluated by different measures:
Area Under Disease Progress Curve (AUDPC): average number of diseased host individuals (status I + R) per time step and square meter.
Relative Area Under Disease Progress Curve (AUDPCr): average proportion of diseased host individuals (status I + R) relative to the total number of existing hosts (H+L+I+R).
Green Leaf Area (GLA): average number of healthy host individuals (status H) per time step and per square meter.
Relative Green Leaf Area (GLAr): average proportion of healthy host individuals (status H) relative to the total number of existing hosts (H+L+I+R).
Contribution of pathogen genotypes: for every year and every host (as well as for the whole landscape and the whole simulation duration), fraction of cumulative LIR infections attributed to each pathogen genotype.
- Economic outputs.
-
The economic outcome of a simulation can be evaluated using:
Crop yield: yearly crop yield (e.g. grains, fruits, wine) in weight (or volume) units per hectare (depends on the number of productive hosts and associated theoretical yield).
Crop products: yearly product generated from sales, in monetary units per hectare (depends on crop yield and market value). Note that when disease = "mildew" a price reduction between 0% and 5% is applied to the market value depending on disease severity.
Operational crop costs: yearly costs associated with crop planting (depends on initial host density and planting cost) and pesticide treatments (depends on the number of applications and the cost of a single application) in monetary units per hectare.
Crop margin, i.e. products - operational costs, in monetary units per hectare.
Value
A list containing, for each required type of output, a matrix summarising the output for each year and cultivar (as well as the whole landscape). Each matrix can be written in a txt file (if writeTXT=TRUE), and illustrated in a graphic (if graphic=TRUE).
References
Rimbaud L., Papaïx J., Rey J.-F., Barrett L. G. and Thrall P. H. (2018). Assessing the durability and efficiency of landscape-based strategies to deploy plant resistance to pathogens. PLoS Computational Biology 14(4):e1006067.
See Also
Examples
## Not run:
demo_landsepi()
## End(Not run)