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Algorithm based on latent Gaussian fields to allocate two different crop cultivars across the simulated landscapes.
multiN(d, area, aggreg, prop)a matrix of the pairwise distances between the centroids of the fields of the landscape.
vector containing the areas of the fields.
level of spatial aggregation (<0 for fragmented landscapes, >0 for aggregated landscapes, =0 for random allocation of cultivars).
relative proportion of the second crop.
A dataframe containing the index of each field (column 1) and the index (0 or 1) of the cultivar grown on these fields (column 2).
This algorithm allows the control of the proportions of each cultivar in terms of surface coverage, and their level of spatial aggregation. A random vector of values is drawn from a multivariate normal distribution with expectation 0 and a variance-covariance matrix which depends on the pairwise distances between the centroids of the fields. The variance-covariance matrix is computed from a periodic function for highly fragmented or highly aggregated landscapes, an exponential function for moderately aggregated landscapes, and from a normal distribution for a random allocation of cultivars. Next, the crop cultivars are allocated to different fields depending on whether the each value drawn from the multivariate normal distribution is above or below a threshold. The proportion of each cultivar in the landscape is controlled by the value of this threshold (parameter prop).
# NOT RUN {
d <- matrix(rpois(100,100), nrow=10)
area <- data.frame(num=1:10, area=10)
## Fragmented landscape
multiN(d, area, aggreg=-2, prop=0.5)
# }
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