Generates epidemiological and economic outputs from model simulations.
epid_output(
types = "all",
time_param,
Npatho,
area,
rotation,
croptypes,
cultivars_param,
eco_param,
GLAnoDis = cultivars_param$max_density[1],
ylim_param = list(audpc = c(0, 0.76), audpc_rel = c(0, 1), gla = c(0, 1.48), gla_rel
= c(0, 1), eco_cost = c(0, NA), eco_yield = c(0, NA), eco_product = c(0, NA),
eco_margin = c(NA, NA), contrib = c(0, 1)),
writeTXT = TRUE,
graphic = TRUE,
path = getwd()
)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).
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):
"audpc": Area Under Disease Progress Curve
"audpc_rel": Relative Area Under Disease Progress Curve
"gla": Green Leaf Area
"gla_rel": Relative Green Leaf Area
"eco_yield": Total crop yield
"eco_cost": Operational crop costs
"eco_product": Crop products
"eco_margin": Margin (products - operational costs)
"contrib": contribution of pathogen genotypes to LIR dynamics
"HLIR_dynamics", "H_dynamics", "L_dynamics", "IR_dynamics", "HLI_dynamics", etc.: Epidemic dynamics related to the specified sanitary status (H, L, I or R and all their combinations). Graphics only, works only if graphic=TRUE.
"all": compute all these outputs (default).
list of simulation parameters:
Nyears = number cropping seasons,
nTSpY = number of time-steps per cropping season.
number of pathogen genotypes.
a vector containing polygon areas (must be in square meters).
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.
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.
list of parameters associated with each host genotype (i.e. cultivars):
name = vector of cultivar names,
initial_density = vector of host densities (per square meter) at the beginning of the cropping season as if cultivated in pure crop,
max_density = vector of maximum host densities (per square meter) at the end of the cropping season as if cultivated in pure crop,
cultivars_genes_list = a list containing, for each host genotype, the indices of carried resistance genes.
a list of economic parameters for each host genotype as if cultivated in pure crop:
yield_perHa = a dataframe of 4 columns for the theoretical yield associated with hosts in sanitary status H, L, I and R, as if cultivated in pure crops, and one row per host genotype (yields are expressed in weight or volume units / ha / cropping season),
planting_cost_perHa = a vector of planting costs (in monetary units / ha / cropping season),
market_value = a vector of market values of the production (in monetary units / weight or volume unit).
the value of absolute GLA in absence of disease (required to compute economic outputs).
a list of graphical parameters for each required output: bounds for y-axes for audpc, gla, gla_rel, eco_cost, eco_yield, eco_product, eco_margin, contrib.
a logical indicating if the output is written in a text file (TRUE) or not (FALSE).
a logical indicating if a tiff graphic of the output is generated (only if more than one year is simulated).
path of text file (if writeTXT = TRUE) and tiff graphic (if graphic = TRUE) to be generated.
Outputs are computed every year for every cultivar as well as for the whole landscape.
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.
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).
Operational crop costs: yearly costs associated with crop planting in monetary units per hectare (depends on initial host density and planting cost).
Crop margin, i.e. products - operational costs, in monetary units per hectare.
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.
evol_output
if (FALSE) {
demo_landsepi()
}
Run the code above in your browser using DataLab