yield_resp_barley.R

library(tidyverse)

dat <- tibble::tribble(
  ~precio_cereal, ~gasto_comer, ~ingreso_neto, ~`kg/$`, ~producto, ~costo_dosis, ~mosquito, ~costo_apli, ~rinde_obj, ~`pisada_%`, ~pisada_kg, ~costo_trat_kg, ~costo_tot_kg,
  310L,          20L,           248,    4.03,      "MD",          22L,       8.5,        30.5,      2500L,         2.5,       62.5,           123L,          185L,
  310L,          20L,           248,    4.03,      "MD",          22L,       8.5,        30.5,      3000L,         2.5,         75,           123L,          198L,
  310L,          20L,           248,    4.03,      "MD",          22L,       8.5,        30.5,      3500L,         2.5,       87.5,           123L,          210L,
  310L,          20L,           248,    4.03,      "MD",          22L,       8.5,        30.5,      4000L,         2.5,        100,           123L,          223L,
  320L,          20L,           256,    3.91,      "MD",          22L,       8.5,        30.5,      2500L,         2.5,       62.5,           119L,          182L,
  320L,          20L,           256,    3.91,      "MD",          22L,       8.5,        30.5,      3000L,         2.5,         75,           119L,          194L,
  320L,          20L,           256,    3.91,      "MD",          22L,       8.5,        30.5,      3500L,         2.5,       87.5,           119L,          207L,
  320L,          20L,           256,    3.91,      "MD",          22L,       8.5,        30.5,      4000L,         2.5,        100,           119L,          219L,
  330L,          20L,           264,    3.79,      "MD",          22L,       8.5,        30.5,      2500L,         2.5,       62.5,           116L,          178L,
  330L,          20L,           264,    3.79,      "MD",          22L,       8.5,        30.5,      3000L,         2.5,         75,           116L,          191L,
  330L,          20L,           264,    3.79,      "MD",          22L,       8.5,        30.5,      3500L,         2.5,       87.5,           116L,          203L,
  330L,          20L,           264,    3.79,      "MD",          22L,       8.5,        30.5,      4000L,         2.5,        100,           116L,          216L,
  310L,          20L,           248,    4.03,      "MT",          37L,       8.5,        45.5,      2500L,         2.5,       62.5,           183L,          246L,
  310L,          20L,           248,    4.03,      "MT",          37L,       8.5,        45.5,      3000L,         2.5,         75,           183L,          258L,
  310L,          20L,           248,    4.03,      "MT",          37L,       8.5,        45.5,      3500L,         2.5,       87.5,           183L,          271L,
  310L,          20L,           248,    4.03,      "MT",          37L,       8.5,        45.5,      4000L,         2.5,        100,           183L,          283L,
  320L,          20L,           256,    3.91,      "MT",          37L,       8.5,        45.5,      2500L,         2.5,       62.5,           178L,          240L,
  320L,          20L,           256,    3.91,      "MT",          37L,       8.5,        45.5,      3000L,         2.5,         75,           178L,          253L,
  320L,          20L,           256,    3.91,      "MT",          37L,       8.5,        45.5,      3500L,         2.5,       87.5,           178L,          265L,
  320L,          20L,           256,    3.91,      "MT",          37L,       8.5,        45.5,      4000L,         2.5,        100,           178L,          278L,
  330L,          20L,           264,    3.79,      "MT",          37L,       8.5,        45.5,      2500L,         2.5,       62.5,           172L,          235L,
  330L,          20L,           264,    3.79,      "MT",          37L,       8.5,        45.5,      3000L,         2.5,         75,           172L,          247L,
  330L,          20L,           264,    3.79,      "MT",          37L,       8.5,        45.5,      3500L,         2.5,       87.5,           172L,          260L,
  330L,          20L,           264,    3.79,      "MT",          37L,       8.5,        45.5,      4000L,         2.5,        100,           172L,          272L
) %>% 
  janitor::clean_names()

p <- dat %>% #names
  ggplot()+
  aes(x=precio_cereal, y=costo_tot_kg, col=factor(rinde_obj))+
  geom_point()+
  geom_line()+
  facet_wrap("producto")+
  labs(col="Rinde objetivo (kg/ha)", 
       x = "Precio cereal - enero '23 (US$/Ton)", 
       y = "Costo tratamiento (kg/ha)", 
       caption = 
         "Pisada: 2.5% 
       Gastos de comercialización: 20% 
       MD-Mecla doble triazol + estrobi: 22 US$/dosis
       MT-Mecla triple triazol + estrobi + carboxa: 37 US$/dosis") +
  theme(legend.position = "top", legend.direction = "horizontal")

p       

dat %>% 
  pivot_longer(cols = c(pisada_kg, costo_trat_kg)) %>% 
  ggplot()+
  aes(x = precio_cereal, y = value, fill = name, label = value) +
  geom_bar(stat = "identity") +
  geom_text(size = 3, position = position_stack(vjust = 0.5))+
  facet_grid(producto ~ rinde_obj)+
  labs(fill="Componentes costo", 
       x = "Precio cereal - enero '23 (US$/Ton)", 
       y = "Costo total tratamiento (kg/ha)", 
       caption = 
         "Pisada: 2.5% 
       Gastos de comercialización: 20% 
       MD-Mecla doble triazol + estrobi: 22 US$/dosis
       MT-Mecla triple triazol + estrobi + carboxa: 37 US$/dosis") +
  theme(legend.position = "top", legend.direction = "horizontal")

dat_resp <- tibble::tribble(
  ~`resp%`, ~rinde_obj, ~resp_abs,
        2L,      2500L,       50L,
        2L,      3000L,       60L,
        2L,      3500L,       70L,
        2L,      4000L,       80L,
        4L,      2500L,      100L,
        4L,      3000L,      120L,
        4L,      3500L,      140L,
        4L,      4000L,      160L,
        6L,      2500L,      150L,
        6L,      3000L,      180L,
        6L,      3500L,      210L,
        6L,      4000L,      240L,
        8L,      2500L,      200L,
        8L,      3000L,      240L,
        8L,      3500L,      280L,
        8L,      4000L,      320L
  )%>% 
  janitor::clean_names()

dat_resp %>% 
  ggplot()+
  aes(rinde_obj, resp_abs, col= factor(resp_percent))+
  geom_point()+
  geom_line()+
  labs(
       col="Cebada - Respuesta rinde (%)", 
       x = "Rinde objetivo (kg/ha)", 
       y = "Respuesta absoluta (kg/ha)", 
       caption = "Datos de ensayos no publicados. 
       Respuesta a fungicidas con baja presion de enfermedades en HB-1 y HB-2
       Campañas 2018 a 2021") +
  theme(legend.position = "top", legend.direction = "horizontal")