In pruettm/cropsyst: CropSyst Crop Evapotranspiration Modeling Framework

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.align = "center",
  fig.asp = 0.618,
  fig.width = 5,
  out.width = "70%",
  fig.retina = 3
)

library(tidyverse)
library(cropsyst)

Model construction

The constructor function cs_setup() is used to initialize an empty cropsyst model object to which additional model parameters can be added using the following functions:

• cs_add_weather()
• cs_add_soil()
• cs_add_canopy_cover()
• cs_add_crop()
• cs_add_irrigation() (optional)

Input Data and parameters

Weather

Daily weather data is required and shouild be inlcuded as a data frame and follow the naming format below.

Input Data Set

Input weather data should be at a continuous daily time scale and contain the following columns

• date Date of observation should ba a Date object
• precip Total daily precipitation (mm)
• tmax Maximum daily temperature (°C)
• tmin Minimum daily temperature (°C)
• s_rad Total daily solar radiation (MJ/m2)
• rh_max Maximum relative humidity (%)
• rh_min Minimum relative humidity (%)
• wind_speed Average daily wind speed (m/s)

An example dataset cs_weather is provided with cropsyst and is shown below:

head(cs_weather)

Additional Parameters

• latitude location latitude (degrees)
• elevation location elevation (m)
• screening_height wind speed measurement height (m)

Soil

Input Data Set

Soil data should contain the following columns

• horizon_thickness Thickness of soil horizon (m)
• sand_pct Percent of horizon composed of sand (%)
• clay_pct Percent of horizon composed of clay (%)
• silt_pct Percent of horizon composed of silt (%)
• initial_wc Initial water content on first day of planting (m3/m3)

An example dataset cs_soil is provided with cropsyst and is shown below:

head(cs_soil)

Additional Parameters

• thickness_evaporative_layer Thickness of soil evaporative layer (m)

Canopy Cover

Input Data Set

• gcc Green canopy cover
• tcc Total canopy cover
• dap Day after planting

An example dataset cs_canopy_cover is provided with cropsyst and is shown below:

head(cs_canopy_cover)

Irrigation

Cropsyst simulations are able to use 3 irrigation scenarios

1. Scheduled irrigation
2. Auto irrigation
3. No irrigation

Input Data Set

• dap Day after planting
• irrigation Irrigation (mm)

An example dataset cs_irrigation is provided with cropsyst and is shown below:

head(cs_irrigation)

Additional Parameters

• irrigated_fraction fraction of land irrigated in simulated area
• max_allowable_depletion Max soil water dfepletion which triggers irrigation
• min_leaf_water_potential Min leaf water potential which triggers irrigation
• max_system_capacity Max irrigation daily capacity
• dae_first_irrigation Day after emergence of first irrigation

Crop Parameters

• planting_date Planting date
• harvest_date Harvest date
• midseason_et_crop_coef mid season ET crop coefficient
• tree_fruit logical TRUE if tree fruit crop
• fruit_harvest_date harvest date of tree fruit
• max_crop_height max crop height (m)
• max_root_depth max root depth (m)
• perrenial logical TRUE if perennial
• max_water_uptake max water uptake
• transpiration_use_eff transpiration use efficiency
• C3 C3 = TRUE C4 = FALSE

Model Setup

cs_input <- cs_setup() %>% 
  cs_add_weather(weather = cs_weather, 
                 latitude = 35.2, 
                 elevation = 1170,
                 screening_height = 2) %>% 
  cs_add_soil(soil = cs_soil, 
              thickness_evaporative_layer = 0.005) %>% 
  cs_add_canopy_cover(cs_canopy_cover) %>% 
  cs_add_irrigation(cs_irrigation, 
                    irrigated_fraction = 0.0001) %>% 
  cs_add_crop(planting_date = ydoy(2016, 131), 
              season_end_date = ydoy(2016, 131+156-1),
              midseason_et_crop_coef = 1.15,
              tree_fruit = FALSE,
              fruit_harvest_date = ydoy(2016, 131+153),
              max_crop_height = 2,
              max_root_depth = 2, 
              perrenial = FALSE, 
              max_water_uptake = 14, 
              transpiration_use_eff = 7, 
              C3 = FALSE)

Run Model

cs_out <- cs_input %>% cs_run()

Model Output

The model output maintains the form of the model input with weather, irr_soil, and non_irr_soil as data frames and params as a list of model input parameters.

names(cs_out)

Potential ET

cs_out$weather %>% 
  ggplot() +
  geom_line(aes(x = dap, y = potential_crop_et))

Crop ET Coeficcient

cs_out$weather %>% 
  ggplot() +
  geom_line(aes(x = dap, y = et_crop_coef)) 

Crop Growth

cs_out$weather %>% 
  ggplot() +
  geom_line(aes(x = dap, y = -root_depth, color = "Root Depth")) +
  geom_line(aes(x = dap, y = crop_height, color = "Crop Height")) +
  labs(x = "Days after planting", y = "Height (m)")

Biomass

cs_out$weather %>% 
  ggplot() +
  geom_line(aes(x = dap, y = potential_biomass_prod, color = "potential")) +
  geom_line(aes(x = dap, y = biomass_production, color = "actual"))

cs_out$weather %>% 
  ggplot() +
  geom_line(aes(x = dap, y = cum_biomass_production))

Transpiration

cs_out$weather %>% 
  ggplot() +
  geom_line(aes(x = dap, y = attainable_transpiration, color = "Attainable")) +
  geom_line(aes(x = dap, y = potential_transpiration, color = "Potential")) +
  geom_line(aes(x = dap, y = actual_transpiration, color = "Actual"))

Soil Water Evaporation

cs_out$weather %>% 
  ggplot() +
  geom_line(aes(x = dap, y = attainable_soil_water_evaporation, color = "Attainable")) +
  geom_line(aes(x = dap, y = irr_zone_soil_water_evap, color = "Irrigated")) +
  geom_line(aes(x = dap, y = non_irr_zone_soil_water_evap, color = "Non-Irrigated"))

Leaf Water Potential

cs_out$weather %>% 
  ggplot() +
  geom_line(aes(x = dap, y = leaf_water_potential)) 

Actual Evapotranspiration

cs_out$weather %>% 
  ggplot() +
  geom_line(aes(x = dap, y = actual_evapotranspiration)) 

s

pruettm/cropsyst documentation built on May 23, 2022, 11:57 a.m.