Run wofost (multiple location)

Uncomment the following line to install the latest version of cropengine if needed.

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# !pip install -U cropengine

# !pip install -U cropengine

Import libraries¶

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import os
import pandas as pd
import seaborn as sns
from cropengine import WOFOSTCropSimulationBatchRunner
from cropengine.agromanagement import WOFOSTAgroEventBuilder

import os import pandas as pd import seaborn as sns from cropengine import WOFOSTCropSimulationBatchRunner from cropengine.agromanagement import WOFOSTAgroEventBuilder

Instantiate batch crop simulation engine for WOFOST¶

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# Define the model name
MODEL_NAME = "Wofost72_WLP_CWB"

# Define the csv path with 'id', 'latitude', and 'longitude'
locations_csv_path = "test_data/batch_run/location.csv"

# Initialize Engine
batch_runner = WOFOSTCropSimulationBatchRunner(
    model_name=MODEL_NAME,
    locations_csv_path=locations_csv_path,
    workspace_dir="test_output/batch_workspace",
)

# Define the model name MODEL_NAME = "Wofost72_WLP_CWB" # Define the csv path with 'id', 'latitude', and 'longitude' locations_csv_path = "test_data/batch_run/location.csv" # Initialize Engine batch_runner = WOFOSTCropSimulationBatchRunner( model_name=MODEL_NAME, locations_csv_path=locations_csv_path, workspace_dir="test_output/batch_workspace", )

User inputs¶

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# Crop Configuration
# Note: Use dashboard.get_..._options() to see valid values if unsure
models = batch_runner.get_model_options()

crops = batch_runner.get_crop_options(MODEL_NAME)
CROP_NAME = "sugarbeet"
varieties = batch_runner.get_variety_options(MODEL_NAME, CROP_NAME)
CROP_VARIETY = "Sugarbeet_601"

# Timing
crop_start_end = batch_runner.get_crop_start_end_options()
CAMPAIGN_START = "2006-01-01"
CROP_START = "2006-04-05"
CROP_START_TYPE = "emergence"
CROP_END_TYPE = "harvest"
CROP_END = "2006-10-20"
CAMPAIGN_END = "2007-01-01"
MAX_DURATION = 300

# Crop Configuration # Note: Use dashboard.get_..._options() to see valid values if unsure models = batch_runner.get_model_options() crops = batch_runner.get_crop_options(MODEL_NAME) CROP_NAME = "sugarbeet" varieties = batch_runner.get_variety_options(MODEL_NAME, CROP_NAME) CROP_VARIETY = "Sugarbeet_601" # Timing crop_start_end = batch_runner.get_crop_start_end_options() CAMPAIGN_START = "2006-01-01" CROP_START = "2006-04-05" CROP_START_TYPE = "emergence" CROP_END_TYPE = "harvest" CROP_END = "2006-10-20" CAMPAIGN_END = "2007-01-01" MAX_DURATION = 300

Create agromanagements with user inputs¶

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agro_event_builder = WOFOSTAgroEventBuilder()

# Note: Use agro_event_builder.get_..._events_info() to see valid values if unsure
timed_events_info = agro_event_builder.get_timed_events_info()
state_events_info = agro_event_builder.get_state_events_info()

# Build timed events (irrigation)
irrigation_schedule = [
    {"event_date": "2006-05-25", "amount": 3.0, "efficiency": 0.7},
    {"event_date": "2006-06-30", "amount": 2.5, "efficiency": 0.7},
]

irrigation_events = agro_event_builder.create_timed_events(
    signal_type="irrigate", events_list=irrigation_schedule
)

# Build state Events (fertilization based on DVS)
nitrogen_schedule = [
    {"threshold": 0.3, "N_amount": 40, "N_recovery": 0.7},
    {"threshold": 0.6, "N_amount": 60, "N_recovery": 0.7},
    {"threshold": 1.12, "N_amount": 40, "N_recovery": 0.7},
]

nitrogen_events = agro_event_builder.create_state_events(
    signal_type="apply_n",
    state_var="DVS",
    zero_condition="rising",
    events_list=nitrogen_schedule,
)

agro_event_builder = WOFOSTAgroEventBuilder() # Note: Use agro_event_builder.get_..._events_info() to see valid values if unsure timed_events_info = agro_event_builder.get_timed_events_info() state_events_info = agro_event_builder.get_state_events_info() # Build timed events (irrigation) irrigation_schedule = [ {"event_date": "2006-05-25", "amount": 3.0, "efficiency": 0.7}, {"event_date": "2006-06-30", "amount": 2.5, "efficiency": 0.7}, ] irrigation_events = agro_event_builder.create_timed_events( signal_type="irrigate", events_list=irrigation_schedule ) # Build state Events (fertilization based on DVS) nitrogen_schedule = [ {"threshold": 0.3, "N_amount": 40, "N_recovery": 0.7}, {"threshold": 0.6, "N_amount": 60, "N_recovery": 0.7}, {"threshold": 1.12, "N_amount": 40, "N_recovery": 0.7}, ] nitrogen_events = agro_event_builder.create_state_events( signal_type="apply_n", state_var="DVS", zero_condition="rising", events_list=nitrogen_schedule, )

Prepare batch system (must be implemented before running the batch simulation)¶

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batch_runner.prepare_batch_system(
    campaign_start=CAMPAIGN_START,
    campaign_end=CAMPAIGN_END,
    crop_start=CROP_START,
    crop_end=CROP_END,
    crop_name=CROP_NAME,
    variety_name=CROP_VARIETY,
    max_workers=5,
    crop_start_type=CROP_START_TYPE,
    crop_end_type=CROP_END_TYPE,
    max_duration=MAX_DURATION,
    timed_events=[irrigation_events],
    state_events=[nitrogen_events],
    force_update=False,
    force_param_update=True,
    crop_overrides=None,
    soil_overrides=None,
    site_overrides={"WAV": 10},  # Extra site params can be passed as overrides
)

batch_runner.prepare_batch_system( campaign_start=CAMPAIGN_START, campaign_end=CAMPAIGN_END, crop_start=CROP_START, crop_end=CROP_END, crop_name=CROP_NAME, variety_name=CROP_VARIETY, max_workers=5, crop_start_type=CROP_START_TYPE, crop_end_type=CROP_END_TYPE, max_duration=MAX_DURATION, timed_events=[irrigation_events], state_events=[nitrogen_events], force_update=False, force_param_update=True, crop_overrides=None, soil_overrides=None, site_overrides={"WAV": 10}, # Extra site params can be passed as overrides )

Run the simulation¶

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results = batch_runner.run_batch_simulation(max_workers=3)
print(results.shape)
results.head()

results = batch_runner.run_batch_simulation(max_workers=3) print(results.shape) results.head()

Plot the batch results¶

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import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import math

# Ensure 'day' is datetime
batch_results = results.copy()
batch_results["day"] = pd.to_datetime(batch_results["day"])

# Variables to plot (exclude metadata columns)
vars_to_plot = [
    col
    for col in batch_results.columns
    if col not in ["point_id", "latitude", "longitude", "day"]
]

# Layout
cols = 2
rows = math.ceil(len(vars_to_plot) / cols)

# Colors for point_id groups
unique_points = batch_results["point_id"].unique()
palette = sns.color_palette("tab10", len(unique_points))
color_map = {pid: palette[i] for i, pid in enumerate(unique_points)}

fig, axes = plt.subplots(rows, cols, figsize=(14, 3 * rows), sharex=True)
axes = axes.flatten()

for i, var in enumerate(vars_to_plot):
    ax = axes[i]

    for pid in unique_points:
        df_sub = batch_results[batch_results["point_id"] == pid]

        sns.lineplot(
            x=df_sub["day"],
            y=df_sub[var],
            ax=ax,
            label=f"Point {pid}",
            color=color_map[pid],
        )

    ax.set_title(var)
    ax.legend()

# Hide remaining empty subplots
for j in range(len(vars_to_plot), len(axes)):
    axes[j].axis("off")

plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt import seaborn as sns import numpy as np import math # Ensure 'day' is datetime batch_results = results.copy() batch_results["day"] = pd.to_datetime(batch_results["day"]) # Variables to plot (exclude metadata columns) vars_to_plot = [ col for col in batch_results.columns if col not in ["point_id", "latitude", "longitude", "day"] ] # Layout cols = 2 rows = math.ceil(len(vars_to_plot) / cols) # Colors for point_id groups unique_points = batch_results["point_id"].unique() palette = sns.color_palette("tab10", len(unique_points)) color_map = {pid: palette[i] for i, pid in enumerate(unique_points)} fig, axes = plt.subplots(rows, cols, figsize=(14, 3 * rows), sharex=True) axes = axes.flatten() for i, var in enumerate(vars_to_plot): ax = axes[i] for pid in unique_points: df_sub = batch_results[batch_results["point_id"] == pid] sns.lineplot( x=df_sub["day"], y=df_sub[var], ax=ax, label=f"Point {pid}", color=color_map[pid], ) ax.set_title(var) ax.legend() # Hide remaining empty subplots for j in range(len(vars_to_plot), len(axes)): axes[j].axis("off") plt.tight_layout() plt.show()