Publications by Beni Stocker
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LM3-PPA usage Koen Hufkens The rsofun package and framework includes two main models. The pmodel and lm3-ppa (which in part relies on pmodel component). Here we give a short example on how to run the lm3ppa model on the included demo datasets to familiarize yourself with both the data structure and the outputs. Demo data The package includes two ...
2109 sym R (6181 sym/10 pcs) 4 img
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Read data We’re reading data from Kevin’s database, selecting only data where only CO2 was manipulated, from experiments that lasted more than 2 years, and include OTC and FACE experiments. This is a demo and we’re using just ANPP data (any of c("aboveground_production", "annual_aboveground_biomass_production", "ANPP")). df <- read_csv("~/d...
1986 sym R (5384 sym/15 pcs) 2 img 2 tbl
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This was done with rsofun, branch photocold at commit ad1b734fec1e5371212ce767b611cbaaede2b02a. Load rsofun Get rsofun, photocold branch, and install it. # devtools::install_github("computationales/rsofun@v4.2") library(rsofun) Description This is to run the same evaluation of GPP simulated by the P-model as done for Stocker et al. (2020), usi...
1892 sym R (14178 sym/37 pcs) 6 img 1 tbl
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Read data We’re reading data from Kevin’s database, selecting only data where only CO2 was manipulated, from experiments that lasted more than 2 years, and include OTC and FACE experiments. This is a demo and we’re using just ANPP data (any of c("aboveground_production", "annual_aboveground_biomass_production", "ANPP")). df <- read_csv("~/d...
1712 sym R (4075 sym/9 pcs) 2 img 2 tbl
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The data is downloaded from the google spreadsheet [RSIP Working Copy], tab ‘Analysis sheet’, into a CSV (19.9.2019) and saved as data/RSIP_Analysis_sheet.csv. Use gsheet::gsheet2tbl Read the data. df <- read_csv("~/data/rootingdepth/rsip/RSIP_Analysis_sheet_210409.csv") %>% # previously done with "data/RSIP_Analysis_sheet.csv" # df <- r...
1097 sym R (9459 sym/50 pcs) 12 img
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Obtain outputs from Euler, generated with feature_elimination_leafnp.R. Download CSV files into data/ Load data aggregated to sites, done in randomforest_leafnp.Rmd. dfs <- readRDS("data/dfs_leafnp_20210729.rds") Leaf N Get FE results target <- "leafN" df_fe_summary <- read_csv(paste0("data/df_fe_summary_", target, ".csv")) ## Rows: 45 Columns:...
4550 sym R (18691 sym/111 pcs) 12 img
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R Markdown # detach("package:rpmodel", unload = TRUE) source("R/rpmodel.R") source("R/subroutines.R") out_pmodel <- rpmodel( tc = 20, # temperature, deg C vpd = 1000, # Pa, co2 = 400, # ppm, fapar = 1, # fraction , ppfd = 30, # mol/m2...
28 sym R (4292 sym/10 pcs)
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# age by age-bins age <- c(5, 10, 20, 50, 100, 200) # fractional area by age-bin farea <- rep(1/6, 6) # some function of similar form as in your paper (negative coefficienty, log of age) calc_bp <- function(age){10 - 1 * log(age)} # calculate BP by age bin and plot bp <- calc_bp(age) df <- tibble(age, bp) df %>% ggplot(aes(age, bp)) + ge...
467 sym R (438 sym/5 pcs) 1 img
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Linear model Assume a 60-days dry down event with the following setup: The initial water available to plants across the rooting zone, \(S_0\) is 100 mm. ET (termed \(T\), since it’s only transpiration here) is a linear function of the remaining water stored \(S_t\) and is independent of VPD. \[ T(t) = \alpha S(t)/S_0 \] The change in plant-ava...
2000 sym R (5405 sym/20 pcs) 20 img