{
"cells": [
{
"cell_type": "markdown",
"id": "5b5a9730132ec93e",
"metadata": {},
"source": [
"# Demo Notebook\n",
"\n",
"This notebook demonstrates how to use the available irrigation model pipelines for the agricultural domain to predict the moisture content of the soil in the future, according to weather forecast data and current status of the field we are analysing, and simulate irrigation strategies.
\n",
"\n",
"The currently available models are two:\n",
"- **XGBoost**, one of the most widespread forecasting models available, it is a tree-based algorithm which excels in adaptability and requires fewer datapoints then deep learning models. We use XGBoost to predict soil moisture levels for the next few days.\n",
"- **Aquacrop** this model has been originally developed by the Food and Agriculture Organization (FAO) of the United Nations for simulating irrigation strategies based on different input scenarios.
\n",
"The OpenIot Unit has adapted the model to compute the irrigation suggestions in a forecasted mode for the next 7 days based on the input weather data and parameters.\n",
"\n",
"The models have been made public in this implementation by the OpenIoT unit of Fondazione Bruno Kessler (FBK) in Trento, Italy."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "initial_id",
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-22T10:07:45.893128500Z",
"start_time": "2025-12-22T10:07:45.883625900Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"import os\n",
"if '\\\\' in os.getcwd():\n",
" main_dir = '//'.join(os.getcwd().split('\\\\')[:-1]) + '\\\\'\n",
"else:\n",
" main_dir = '/'.join(os.getcwd().split('/')[:-1]) + '/'\n",
"os.chdir(main_dir)"
]
},
{
"cell_type": "markdown",
"id": "7f37802e",
"metadata": {},
"source": [
"## Introduction"
]
},
{
"cell_type": "markdown",
"id": "2f9e7338",
"metadata": {},
"source": [
"In this demo, we will use pre-processed input data and parameters for a specific consortium-sensor combination.
\n",
"This is meant to be a guide for future uses. The user can modify the input data and parameters to run the model on their own data, as explained below.
\n",
"\n",
"In this notebook, we work on a consortium-based level. A **consortium** is a set of agricultural fields which are managed together and which share some properties like crop type. We work with data coming from tensiometers, which have been plated on grapevine (`consortium0`) or apple (`consortium1`) fields. A tensiometer is a device which measures how tightly water is held by the soil matrix and it is a crucial quantity for understanding water availability for plants. A higher measurement of a tensiometer (usually, in our case, above $400$) means that then soil is dry, while low value (in the range $0$ to $200$) indicate high levels of water availability; the values in the range $200$ to $400$ are usually - depending on crop types - the optimal ones."
]
},
{
"cell_type": "markdown",
"id": "d614bf5e",
"metadata": {},
"source": [
"For this demo, we will use the following example combination of consortium-sensor:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "9e684b223a00a801",
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-22T10:07:45.905937500Z",
"start_time": "2025-12-22T10:07:45.893128500Z"
}
},
"outputs": [],
"source": [
"consortium_name = 'consortium0'\n",
"sensor_forecasted = 'consortium0_TN_sector2_10354791_management1'"
]
},
{
"cell_type": "markdown",
"id": "f23f5893e4887aea",
"metadata": {},
"source": [
"### Full List of Available Sensors\n",
"**Note:** You can select any of the following consortium–sensor combinations to run the XGCast analysis.
\n",
"There are 2 available consortia - `consortium0` and `consortium1` - providing respectively **16** and **20** sensors you can choose from.\n",
"\n",
"| Consortium | Sensor |\n",
"|------------|--------|\n",
"| consortium0| consortium0_TN_sector1_9961572 |\n",
"| | consortium0_TN_sector1_10354791 |\n",
"| | consortium0_TN_sector0_9961572 |\n",
"| | consortium0_TN_sector0_10354791 |\n",
"| | consortium0_TN_sector2_9961572_management2 |\n",
"| | consortium0_TN_sector2_9961572_management1 |\n",
"| | consortium0_TN_sector2_10354791_management2 |\n",
"| | consortium0_TN_sector2_10354791_management1 |\n",
"| | consortium0_TN_sector3_9961572_management2 |\n",
"| | consortium0_TN_sector3_9961572_management1 |\n",
"| | consortium0_TN_sector3_10354791_management2 |\n",
"| | consortium0_TN_sector3_10354791_management1 |\n",
"| | consortium0_TN_sector5_9961572 |\n",
"| | consortium0_TN_sector5_10354791 |\n",
"| | consortium0_TN_sector4_9961572 |\n",
"| | consortium0_TN_sector4_10354791 |\n",
"| consortium1 | consortium1_TN_9699426_9961572 |\n",
"| | consortium1_TN_9764963_9961572 |\n",
"| | consortium1_TN_9830500_9961572 |\n",
"| | consortium1_TN_9896037_9961572 |\n",
"| | consortium1_TN_9961574_9961572 |\n",
"| | consortium1_TN_10027111_9961572 |\n",
"| | consortium1_TN_10092648_9961572 |\n",
"| | consortium1_TN_10158185_9961572 |\n",
"| | consortium1_TN_10223722_9961572 |\n",
"| | consortium1_TN_10289259_9961572 |\n",
"| | consortium1_TN_3276850_9961572 |\n",
"| | consortium1_TN_9830499_9961572 |\n",
"| | consortium1_TN_3342387_9961572 |\n",
"| | consortium1_TN_3407924_9961572 |\n",
"| | consortium1_TN_3473461_9961572 |\n",
"| | consortium1_TN_3538998_9961572 |\n",
"| | consortium1_TN_3604535_9961572 |\n",
"| | consortium1_TN_3670072_9961572 |\n",
"| | consortium1_TN_3735609_9961572 |\n",
"| | consortium1_TN_3801146_9961572 |"
]
},
{
"cell_type": "markdown",
"id": "81f79714c973f081",
"metadata": {},
"source": [
"### Input data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "8ee9b5f7dcabdd6f",
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-22T10:07:52.050409700Z",
"start_time": "2025-12-22T10:07:45.907945700Z"
}
},
"outputs": [],
"source": [
"from pipelines.demo_run import *"
]
},
{
"cell_type": "markdown",
"id": "11afdd9de04531ad",
"metadata": {},
"source": [
"The dataset and parameters we are working with are imported here below. We call the table of data `sensor_data`: it contains sensor measurements, corredated by envorinmental information and information about the sensors themselves. We extract the data relative to sensor `sensor_forecasted` using the function `get_sensor_data`:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "43f42d0b2fecde3a",
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-22T10:07:52.191264900Z",
"start_time": "2025-12-22T10:07:52.116550500Z"
}
},
"outputs": [],
"source": [
"sensor_data = get_sensor_data(consortium_name, sensor_forecasted)"
]
},
{
"cell_type": "markdown",
"id": "e29eb80cca592b72",
"metadata": {},
"source": [
"Below is a snapshot of the DataFrame:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "0d22797f",
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-22T10:07:52.224236800Z",
"start_time": "2025-12-22T10:07:52.191264900Z"
}
},
"outputs": [
{
"data": {
"application/vnd.microsoft.datawrangler.viewer.v0+json": {
"columns": [
{
"name": "result_time",
"rawType": "datetime64[ns]",
"type": "datetime"
},
{
"name": "datastream_name",
"rawType": "object",
"type": "string"
},
{
"name": "result",
"rawType": "float64",
"type": "float"
},
{
"name": "ground_offset",
"rawType": "float64",
"type": "float"
},
{
"name": "irrigation",
"rawType": "float64",
"type": "float"
},
{
"name": "forecasted_relative_humidity_2m_next_1period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_relative_humidity_2m_next_2period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_relative_humidity_2m_next_3period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_relative_humidity_2m_next_4period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_relative_humidity_2m_next_5period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_relative_humidity_2m_next_6period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_relative_humidity_2m_next_7period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_temperature_2m_min_next_1period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_temperature_2m_min_next_2period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_temperature_2m_min_next_3period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_temperature_2m_min_next_4period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_temperature_2m_min_next_5period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_temperature_2m_min_next_6period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_temperature_2m_min_next_7period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_soil_temperature_0_to_7cm_max_next_1period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_soil_temperature_0_to_7cm_max_next_2period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_soil_temperature_0_to_7cm_max_next_3period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_soil_temperature_0_to_7cm_max_next_4period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_soil_temperature_0_to_7cm_max_next_5period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_soil_temperature_0_to_7cm_max_next_6period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_soil_temperature_0_to_7cm_max_next_7period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_soil_temperature_0_to_7cm_min_next_1period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_soil_temperature_0_to_7cm_min_next_2period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_soil_temperature_0_to_7cm_min_next_3period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_soil_temperature_0_to_7cm_min_next_4period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_soil_temperature_0_to_7cm_min_next_5period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_soil_temperature_0_to_7cm_min_next_6period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_soil_temperature_0_to_7cm_min_next_7period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_soil_moisture_0_to_7cm_next_1period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_soil_moisture_0_to_7cm_next_2period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_soil_moisture_0_to_7cm_next_3period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_soil_moisture_0_to_7cm_next_4period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_soil_moisture_0_to_7cm_next_5period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_soil_moisture_0_to_7cm_next_6period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_soil_moisture_0_to_7cm_next_7period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_temperature_2m_mean_next_1period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_temperature_2m_mean_next_2period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_temperature_2m_mean_next_3period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_temperature_2m_mean_next_4period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_temperature_2m_mean_next_5period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_temperature_2m_mean_next_6period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_temperature_2m_mean_next_7period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_et0_fao_evapotranspiration_next_1period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_et0_fao_evapotranspiration_next_2period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_et0_fao_evapotranspiration_next_3period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_et0_fao_evapotranspiration_next_4period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_et0_fao_evapotranspiration_next_5period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_et0_fao_evapotranspiration_next_6period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_et0_fao_evapotranspiration_next_7period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_soil_temperature_0_to_7cm_mean_next_1period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_soil_temperature_0_to_7cm_mean_next_2period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_soil_temperature_0_to_7cm_mean_next_3period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_soil_temperature_0_to_7cm_mean_next_4period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_soil_temperature_0_to_7cm_mean_next_5period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_soil_temperature_0_to_7cm_mean_next_6period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_soil_temperature_0_to_7cm_mean_next_7period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_direct_radiation_next_1period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_direct_radiation_next_2period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_direct_radiation_next_3period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_direct_radiation_next_4period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_direct_radiation_next_5period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_direct_radiation_next_6period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_direct_radiation_next_7period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_temperature_2m_max_next_1period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_temperature_2m_max_next_2period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_temperature_2m_max_next_3period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_temperature_2m_max_next_4period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_temperature_2m_max_next_5period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_temperature_2m_max_next_6period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_temperature_2m_max_next_7period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_precipitation_next_1period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_precipitation_next_2period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_precipitation_next_3period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_precipitation_next_4period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_precipitation_next_5period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_precipitation_next_6period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_precipitation_next_7period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_wind_speed_10m_next_1period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_wind_speed_10m_next_2period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_wind_speed_10m_next_3period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_wind_speed_10m_next_4period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_wind_speed_10m_next_5period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_wind_speed_10m_next_6period",
"rawType": "float32",
"type": "float"
},
{
"name": "forecasted_wind_speed_10m_next_7period",
"rawType": "float32",
"type": "float"
},
{
"name": "temperature_2m_mean",
"rawType": "float32",
"type": "float"
},
{
"name": "temperature_2m_min",
"rawType": "float32",
"type": "float"
},
{
"name": "temperature_2m_max",
"rawType": "float32",
"type": "float"
},
{
"name": "relative_humidity_2m",
"rawType": "float32",
"type": "float"
},
{
"name": "precipitation",
"rawType": "float32",
"type": "float"
},
{
"name": "et0_fao_evapotranspiration",
"rawType": "float32",
"type": "float"
},
{
"name": "wind_speed_10m",
"rawType": "float32",
"type": "float"
},
{
"name": "soil_temperature_0_to_7cm_mean",
"rawType": "float32",
"type": "float"
},
{
"name": "soil_temperature_0_to_7cm_min",
"rawType": "float32",
"type": "float"
},
{
"name": "soil_temperature_0_to_7cm_max",
"rawType": "float32",
"type": "float"
},
{
"name": "soil_moisture_0_to_7cm",
"rawType": "float32",
"type": "float"
},
{
"name": "direct_radiation",
"rawType": "float32",
"type": "float"
},
{
"name": "precipitation_local_meteo",
"rawType": "float64",
"type": "float"
},
{
"name": "relative_humidity_local_meteo",
"rawType": "float64",
"type": "float"
},
{
"name": "solar_radiation_local_meteo",
"rawType": "float64",
"type": "float"
},
{
"name": "air_temperature_mean_local_meteo",
"rawType": "float64",
"type": "float"
},
{
"name": "air_temperature_min_local_meteo",
"rawType": "float64",
"type": "float"
},
{
"name": "air_temperature_max_local_meteo",
"rawType": "float64",
"type": "float"
},
{
"name": "wind_speed_local_meteo",
"rawType": "float64",
"type": "float"
},
{
"name": "ndvi",
"rawType": "float64",
"type": "float"
},
{
"name": "grvi",
"rawType": "float64",
"type": "float"
},
{
"name": "rvi",
"rawType": "float64",
"type": "float"
},
{
"name": "rgi",
"rawType": "float64",
"type": "float"
},
{
"name": "aci",
"rawType": "float64",
"type": "float"
},
{
"name": "maci",
"rawType": "float64",
"type": "float"
},
{
"name": "gndvi",
"rawType": "float64",
"type": "float"
},
{
"name": "ngrdi",
"rawType": "float64",
"type": "float"
},
{
"name": "ngbdi",
"rawType": "float64",
"type": "float"
},
{
"name": "bgvi",
"rawType": "float64",
"type": "float"
},
{
"name": "brvi",
"rawType": "float64",
"type": "float"
},
{
"name": "wi",
"rawType": "float64",
"type": "float"
},
{
"name": "varig",
"rawType": "float64",
"type": "float"
},
{
"name": "gli",
"rawType": "float64",
"type": "float"
},
{
"name": "g_perc",
"rawType": "float64",
"type": "float"
},
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"### How to Run on Your Own Data (for XGBoost)\n",
"\n",
"The model we developed is public and free to use. We make it possible to retrain the model on user-defined custom datasets, as long as the context remains the task of soil moisture forecasting and as long as the input dataset contains on-field soil moisture sensor measurements.
\n",
"\n",
"We here detail all the properties we require for the model to train and run correctly.\n",
"- first, the folder `03_primary` under the `data` folder should at least contain the tables `field_sensor_data_{consortium_name}`, `irrigation_data_{consortium_name}` and `locations_ids_{consortium_name}`; the additional tables `crop_type_data_{consortium_name}` (for crop type information), `soil_type_data_{consortium_name}` (for soil type information) and `weather_data_{consortium_name}` (for on-field sensor-based weather measurements) are supported.\n",
"- An association map of irrigators to on-field sensors needs to be provided in the form of a YAML file in the `config` folder.\n",
"\n",
"You can then call the `xgcast_run` function for model training. Once every step is completed, you can get predictions as above."
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"metadata": {},
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]
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"metadata": {},
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from pipelines.xgcast_run import xgcast_run\n",
"xgcast_run('consortium1')"
]
},
{
"cell_type": "markdown",
"id": "89b055bf501c9b2e",
"metadata": {},
"source": [
"## AquaCrop"
]
},
{
"cell_type": "markdown",
"id": "fe7d6c2ef868d7fe",
"metadata": {},
"source": [
"The AquaCrop model is a crop simulation tool that can be used to simulate the soil water balance, crop growth, and yield under different irrigation strategies.
\n",
"Traditionally, AquaCrop is applied to simulate an entire growing season in a descriptive manner. In this work, the model has been adapted to operate in a short-term forecasting mode, simulating crop and soil dynamics over the next 7 days using forecasted weather data and relevant agronomic parameters.
\n",
"\n",
"Three case studies are considered:\n",
"1. AquaCrop Suggestions Only\n",
"2. Real Irrigation\n",
"3. Past Real Irrigation + AquaCrop Suggestions (next days)\n",
"\n",
"In all three cases, the model computes soil water balance, crop growth, and yield for the following days.
\n",
"The scenarios differ only in the irrigation strategy applied, allowing a direct comparison between real irrigation practices and model-based irrigation suggestions."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "a1345d7e42aef739",
"metadata": {},
"outputs": [],
"source": [
"from pipelines.aquacrop_pipeline import *"
]
},
{
"cell_type": "markdown",
"id": "c3f47474ce02599a",
"metadata": {},
"source": [
"For this part of the demo, we will use the following example combination of consortium-sensor:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "50f59c7f03cd1cbf",
"metadata": {},
"outputs": [],
"source": [
"sensor_forecasted = 'consortium0_TN_sector3_10354791_management2'"
]
},
{
"cell_type": "markdown",
"id": "a9bb294690e7005d",
"metadata": {},
"source": [
"**Note:** AquaCrop doesn't require sensor data; a sensor is only used as a reference for a specific location in the consortium."
]
},
{
"cell_type": "markdown",
"id": "7e19eea8d9b38d0c",
"metadata": {},
"source": [
"The reference input data and parameters are the following:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "5e9401a8f32e058d",
"metadata": {},
"outputs": [
{
"data": {
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"15:15:54.887 | INFO | Task run 'read_input_data_consortium0_consortium0_TN_sector3_10354791_management2' - Finished in state Completed()\n",
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]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reference Date: 2024-07-15\n",
"Reference Soil Type: SandyLoam\n",
"Reference Crop Type: Grapevine\n",
"Planting Date: 04/05\n",
"Max Harvest Date: 10/10\n",
"Simulation Start Date: 2024/04/02\n",
"Simulation End Date (excluded): 2024/07/23\n",
"Soil Moisture Targets (SMT) (%TAW): [80, 80, 80, 80]\n",
"Maximum Irrigation (mm/day): 50\n"
]
}
],
"source": [
"weather_aquacrop, irrigation_aquacrop, soil_type, crop_type, planting_date, harvest_date, reference_date, sim_start_date, sim_end_date, SMT, MaxIrr = read_input_data(consortium_name, sensor_forecasted)\n",
"\n",
"print(f'Reference Date:', reference_date)\n",
"print(f'Reference Soil Type:', soil_type)\n",
"print(f'Reference Crop Type:', crop_type)\n",
"print(f'Planting Date:', planting_date)\n",
"print(f'Max Harvest Date:', harvest_date)\n",
"print(f'Simulation Start Date:', sim_start_date)\n",
"print(f'Simulation End Date (excluded):', sim_end_date)\n",
"print(f'Soil Moisture Targets (SMT) (%TAW):', SMT)\n",
"print(f'Maximum Irrigation (mm/day):', MaxIrr)"
]
},
{
"cell_type": "markdown",
"id": "90442fbd4d8e8e50",
"metadata": {},
"source": [
"**Note:** To have updated parameters based on the `aquacrop_params.yml` file, you need to re-run the `aquacrop_preparation` pipeline after modifying the yml file."
]
},
{
"cell_type": "markdown",
"id": "bb55bde6c4548b38",
"metadata": {},
"source": [
"### Interpretation of Input Parameters\n",
"\n",
"| Input | Meaning |\n",
"|------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------|\n",
"| **Reference Date** | Calendar day (YYYY-MM-DD) taken as reference for suggesting next 7 days of irrigation |\n",
"| **Reference Soil Type** | Soil Type of the study area |\n",
"| **Reference Crop Type** | Crop Type of the study area |\n",
"| **Planting Date** | Calendar day (MM/DD) related to the crop planting |\n",
"| **Max Harvest Date** | Calendar day (MM/DD) related to the latest crop harvesting |\n",
"| **Simulation Start Date** | Calendar day (YYYY/MM/DD) related to the beginning of the simulation, corresponded to the day which the Initial Water Content is set |\n",
"| **Simulation End Date (excluded)** | Calendar day (YYYY/MM/DD) related to the end of the simulation, corresponded to the day after the last day of the irrigation suggestion |\n",
"| **Soil Moisture Targets (SMT)** | Soil Moisture Target expressed as percentage of Total Available Water (TAW) to be maintained in the root zone |\n",
"| **Maximum Irrigation (MaxIrr)** | Maximum daily irrigation amount (mm/day) that can be applied to the field |"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "3d0a4897e9b6b950",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.microsoft.datawrangler.viewer.v0+json": {
"columns": [
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"name": "index",
"rawType": "int64",
"type": "integer"
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"name": "Date",
"rawType": "datetime64[ns]",
"type": "datetime"
},
{
"name": "Depth",
"rawType": "float64",
"type": "float"
}
],
"ref": "37ed5555-eb77-476f-9b25-4914b50f79b5",
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"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Date | \n",
" Depth | \n",
"
\n",
" \n",
" \n",
" \n",
"
\n",
"
\n",
"The system generates these suggestions based on AquaCrop outputs for the selected location, taking into account both current conditions and previously computed recommendations from AquaCrop for that area."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "30d7ab30f63aa317",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"15:15:56.826 | INFO | Task run 'aquacrop_run' - Finished in state Completed()\n",
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"water_flux, water_storage, crop_growth, summary_season, summary_table = aquacrop_run(weather_aquacrop, irrigation_aquacrop, soil_type, crop_type, planting_date, harvest_date, reference_date, sim_start_date, sim_end_date, SMT, MaxIrr, strategy='aquacrop')"
]
},
{
"cell_type": "markdown",
"id": "43cd5a0dcc4d405c",
"metadata": {},
"source": [
"The outputs of the AquaCrop model include the following tables:\n",
"- `water_flux`: Daily water fluxes in the soil-plant-atmosphere system\n",
"- `water_storage`: Daily water storage in the soil profile\n",
"- `crop_growth`: Daily crop growth parameters\n",
"- `summary_season`: Summary of the crop season with key performance indicators\n",
"- `summary_table`: Overall summary table with key metrics for the entire simulation period"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "c173df4e5b292f0a",
"metadata": {},
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" Percentage Depletion | \n",
" Irrigation | \n",
" Precipitation | \n",
" CanopyCover | \n",
" CanopyCoverPot | \n",
" Biomass | \n",
" BiomassPot | \n",
" DryYield | \n",
" YieldPot | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 2024-04-02 | \n",
" 2.450000 | \n",
" 36.0 | \n",
" 6.805556 | \n",
" 0.000000 | \n",
" 0.2 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" | 1 | \n",
" 2024-04-03 | \n",
" 3.640000 | \n",
" 36.0 | \n",
" 10.111111 | \n",
" 0.000000 | \n",
" 0.0 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" | 2 | \n",
" 2024-04-04 | \n",
" 5.370000 | \n",
" 36.0 | \n",
" 14.916667 | \n",
" 0.000000 | \n",
" 0.0 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" | 3 | \n",
" 2024-04-05 | \n",
" 3.609397 | \n",
" 36.0 | \n",
" 10.026102 | \n",
" 0.000000 | \n",
" 0.0 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" | 4 | \n",
" 2024-04-06 | \n",
" 6.584613 | \n",
" 36.0 | \n",
" 18.290593 | \n",
" 0.000000 | \n",
" 0.0 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" | ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
"
\n",
" \n",
" | 107 | \n",
" 2024-07-18 | \n",
" 27.424047 | \n",
" 144.0 | \n",
" 19.044477 | \n",
" 0.000000 | \n",
" 0.0 | \n",
" 0.680373 | \n",
" 0.731307 | \n",
" 1001.476209 | \n",
" 1035.307983 | \n",
" 1.227681 | \n",
" 1.265204 | \n",
"
\n",
" \n",
" | 108 | \n",
" 2024-07-19 | \n",
" 30.366784 | \n",
" 144.0 | \n",
" 21.088044 | \n",
" 30.366784 | \n",
" 0.0 | \n",
" 0.680373 | \n",
" 0.731307 | \n",
" 1018.376173 | \n",
" 1052.997573 | \n",
" 1.316682 | \n",
" 1.357207 | \n",
"
\n",
" \n",
" | 109 | \n",
" 2024-07-20 | \n",
" 2.326094 | \n",
" 144.0 | \n",
" 1.615343 | \n",
" 0.000000 | \n",
" 0.3 | \n",
" 0.680373 | \n",
" 0.731307 | \n",
" 1035.246893 | \n",
" 1070.654220 | \n",
" 1.407910 | \n",
" 1.451530 | \n",
"
\n",
" \n",
" | 110 | \n",
" 2024-07-21 | \n",
" 6.346899 | \n",
" 144.0 | \n",
" 4.407569 | \n",
" 0.000000 | \n",
" 0.4 | \n",
" 0.680373 | \n",
" 0.731307 | \n",
" 1052.088371 | \n",
" 1088.277922 | \n",
" 1.501358 | \n",
" 1.548167 | \n",
"
\n",
" \n",
" | 111 | \n",
" 2024-07-22 | \n",
" 9.128571 | \n",
" 144.0 | \n",
" 6.339285 | \n",
" 0.000000 | \n",
" 0.0 | \n",
" 0.680373 | \n",
" 0.731307 | \n",
" 1068.900605 | \n",
" 1105.868682 | \n",
" 1.597021 | \n",
" 1.647110 | \n",
"
\n",
" \n",
"
\n",
"
112 rows × 12 columns
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Season | \n",
" crop Type | \n",
" Harvest Date (YYYY/MM/DD) | \n",
" Harvest Date (Step) | \n",
" Dry yield (tonne/ha) | \n",
" Fresh yield (tonne/ha) | \n",
" Yield potential (tonne/ha) | \n",
" Seasonal irrigation (mm) | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 0 | \n",
" Grapevine | \n",
" None | \n",
" None | \n",
" 19.421253 | \n",
" 0.0 | \n",
" 19.981765 | \n",
" 111.694433 | \n",
"
\n",
" \n",
"
\n",
"
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"source": [
"planting_date_full = pd.to_datetime(f\"{pd.to_datetime(reference_date).year}-{planting_date}\").strftime(\"%Y-%m-%d\")\n",
"show_soil_depletion(summary_season, 'Date', 'Percentage Depletion', 'Irrigation', 'Precipitation', threshold=20, planting_date=planting_date_full, reference_date=reference_date)"
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"From the soil depletion plot, we can observe how the soil moisture levels change over time in relation to irrigation and precipitation events. The threshold line indicates the critical depletion level below which the crop may experience water stress.
\n",
"In this case, irrigation events are scheduled by AquaCrop to maintain soil moisture below the threshold.\n",
"\n",
"**Note:** In this case, the AquaCrop model suggests irrigation events homogeneously during the growing season, with water amounts that vary from 8.77 to 30.37 mm/day depending on the soil moisture conditions and crop water needs."
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")"
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\n",
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"show_real_benchmark(\n",
" df=summary_season,\n",
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},
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"id": "75ca6364aab3396a",
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"source": [
"# Case Study 2: Real Irrigation\n",
"\n",
"In this scenario, the user can simulate the irrigation balance and the derived quantities based solely on actual irrigation events or user-provided irrigation inputs.
\n",
"The system calculates water availability and related metrics for the selected location using only the real or manually entered irrigation data."
]
},
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"id": "767dc99964150b2a",
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"15:16:00.733 | INFO | Task run 'aquacrop_run' - Finished in state Completed()\n",
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"water_flux, water_storage, crop_growth, summary_season, summary_table = aquacrop_run(weather_aquacrop, irrigation_aquacrop, soil_type, crop_type, planting_date, harvest_date, reference_date, sim_start_date, sim_end_date, SMT, MaxIrr, strategy='real')"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "5f7b7b61fd69eb7d",
"metadata": {},
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"id": "bb3f72a0abe04380",
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"# Case 3: Past Real Irrigation + AquaCrop Suggestions (next days)\n",
"\n",
"In this scenario, the user can simulate irrigation by combining past real or user-provided irrigation events with AquaCrop-generated recommendations for the next 7 days.
\n",
"The system takes into account historical irrigation data for the location and then applies AquaCrop suggestions to forecast irrigation needs in the upcoming week."
]
},
{
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"execution_count": 27,
"id": "1986dae69dae42fc",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"15:16:04.176 | INFO | Task run 'aquacrop_run' - Finished in state Completed()\n",
"\n"
],
"text/plain": [
"15:16:04.176 | \u001b[36mINFO\u001b[0m | Task run 'aquacrop_run' - Finished in state \u001b[32mCompleted\u001b[0m()\n"
]
},
"metadata": {},
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"source": [
"water_flux, water_storage, crop_growth, summary_season, summary_table = aquacrop_run(weather_aquacrop, irrigation_aquacrop, soil_type, crop_type, planting_date, harvest_date, reference_date, sim_start_date, sim_end_date, SMT, MaxIrr, strategy='hybrid')"
]
},
{
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"id": "cf41a5c79a719e55",
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"**Note:** In this case, the irrigation events during the growing season are based on real data, which are absent in this case.
\n",
"For the next 7 days after the reference date, the AquaCrop model suggests an irrigation event of around 30 mm to maintain soil moisture below the threshold."
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"### How to Run on Your Own Data (AquaCrop)\n",
"\n",
"To run the AquaCrop model on your own data, you need to prepare the input data and parameters in the same format as shown above.
\n",
"In particular, you need to provide a directory containing:\n",
"1. `aquacrop_settings.json` file with the input parameters\n",
"2. `weather_aquacrop.parquet` file with the weather data\n",
"3. `irrigation_aquacrop.parquet` file with the irrigation data (optional)
\n",
"\n",
"You can then call the `read_input_data` and the `aquacrop_run` function with your prepared data and parameters. Make sure to specify the desired irrigation strategy (`aquacrop`, `real`, or `hybrid`) when calling the function.
\n",
"Alternately, you can prepare the data and run the model using the `aquacrop_preparation` and `aquacrop_run` pipelines respectively. After that you can import the tables generated in the `aquacrop_output` folder and visualize the results using the provided visualization functions."
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