data-raw/yields/code_proc-yields.R

In vanichols/maRsden: Provides easy access to data collected from the Marsden Farm at Iowa State University

#########################
# created: sept 17 2019
#
# purpose: process corn yields
#
# NOTES: final data is in DRY Mg/ha
#
# last updated: 10/6/2020 (adding 2020 data, cleaning things up)
#
#########################


##### Clear environment and load packages #####
rm(list=ls())
library(dplyr)
library(tidyr)
library(readr)
library(lubridate)
library(stringr)
library(janitor) #--used to clean data




pk <- read_csv("data-raw/plotkey/plotkey.csv")


#--2008 onwards plots were managed the same.
#--2003-2007 the C2 was conventional herb and C3/C4 were low input herb (ie tillage)
#--currently plot key only goes back to 2012, so yields are only presesnted for 2012-2017
dhist <- read_csv("data-raw/yields/raw/rd_cornyld-2012-2017.csv")

#--make 2002-2020 dataset
d02 <-
  readxl::read_excel("data-raw/yields/raw/raw-from-ML/Corn yield 2002-2020.xlsx") %>%
  janitor::clean_names() %>%
  mutate(plot_id = paste(year, plot, sep = "_")) %>%
  select(year, plot_id, yield_mg_ha_15_5_percent_moisture) %>%
  group_by(plot_id) %>%
  summarise(yield_Mgha = (1-0.155) * mean(yield_mg_ha_15_5_percent_moisture, na.rm = T))  #--bc of the split herbicide plots

d02

# pk
#
#
# d18 <-
#   read_csv("data-raw/yields/raw/rd_cornyld-2018.csv", skip = 4) %>%
#   mutate(trt = gsub("[[:punct:]]+", "", trt)) %>%
#   rename(harv_crop = rot) %>%
#   group_by(plot, harv_crop) %>%
#   summarise(yield_Mgha = (1-0.155) * mean(yield_Mgha155, na.rm = T)) %>%
#   mutate(year = 2018)
#
#
# d19 <-
#   read_csv("data-raw/yields/raw/rd_cornyld-2019.csv", skip = 4) %>%
#   fill(plot, rot) %>%
#   mutate(trt = gsub("[[:punct:]]+", "", trt)) %>%
#   rename(harv_crop = rot) %>%
#   group_by(plot, harv_crop) %>%
#   summarise(yield_Mgha = (1-0.155) * mean(yield_Mgha155, na.rm = T)) %>%
#   mutate(year = 2019)
#
#
# d20 <-
#   read_csv("data-raw/yields/raw/rd_cornyld-2020.csv", skip = 5) %>%
#   mutate(trt = str_to_lower(trt)) %>%
#   rename(harv_crop = rot) %>%
#   group_by(plot, harv_crop) %>%
#   summarise(yield_Mgha = (1-0.155) * mean(yield_Mgha155, na.rm = T)) %>%
#   mutate(year = 2020)
#
#
#

mrs_cornylds <-
  d02 %>%
  left_join(pk) %>%
  arrange(plot_id)


#--quick look
library(ggplot2)
mrs_cornylds %>%
  left_join(pk) %>%
  ggplot(aes(year, yield_Mgha, color = rot_trt)) +
  geom_point()


mrs_cornylds %>%
  left_join(pk) %>%
  group_by(rot_trt, year) %>%
  summarise(yield_Mgha = mean(yield_Mgha, na.rm = T)) %>%
  ggplot(aes(year, yield_Mgha, color = rot_trt)) +
  geom_line()


ggsave("data-raw/yields/fig_corn-yields.png")


# commit to it! -----------------------------------------------------------


mrs_cornylds %>%  write_csv("data-raw/yields/mrs_cornylds.csv")

usethis::use_data(mrs_cornylds, overwrite = T)