Crop Water Requirement using ETo Prediction ...

 

Extending ETo Prediction to Estimate Crop Water Requirements

The estimation of crop water requirements (CWR) is a vital application of ETo predictions. CWR helps in determining the amount of water needed by crops for optimal growth, aiding in efficient irrigation planning and water management.

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Steps to Extend ETo Prediction for Crop Water Requirements

1. Understand the Crop Water Requirement Formula: The standard formula for CWR is:

   $$CWR = ETo \times K_c$$
   • ETo (Reference Evapotranspiration): Calculated using the models we developed earlier.
   • Kc (Crop Coefficient): A dimensionless coefficient that varies with crop type, growth stage, and local conditions.

   Example Kc Values for Common Crops (Approximate, vary by growth stage):

   • Rice: 1.0–1.2
   • Wheat: 0.8–1.15
   • Maize: 0.95–1.2
   • Vegetables: 0.7–1.05

2. Incorporate Local Climate Data: ETo alone doesn’t capture the variability in crop needs across regions. Integrate data such as:

   • Temperature
   • Humidity
   • Wind speed
   • Solar radiation

3. Add Soil Moisture Data:

   • Use soil sensors or simulation models to monitor current moisture levels.
   • Combine with CWR to determine irrigation schedules.

4. Develop an Irrigation Schedule:

   • Based on the estimated CWR and water availability.
   • Prioritize crops based on their growth stage and water needs.

5. Integrate with AI for Dynamic Predictions:

   • Machine Learning: Build models to predict CWR dynamically using historical data and weather forecasts.
   • IoT Sensors: Use field sensors for real-time adjustments in irrigation.

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Python Code for CWR Calculation Using Predicted ETo

The code below extends ETo predictions to calculate the crop water requirement.

import numpy as np
#step 0 read from predicted value
# From previous blog ET0 Prediction sv1.py output 'svm1predictedET0.txt' minus First Line
file_path = "svm1predictedET0.txt"  
data = np.loadtxt(file_path, delimiter=",")  # Specify the delimiter (default is comma)
print("NumPy Array:")
print(data)


# Step 1: Predicted ETo (from the ML model)
predicted_ETo = np.array([3.5, 4.0, 4.5, 5.0])  # Example ETo values (mm/day)
# Step 2: Define Crop Coefficients (Kc)
kc_values = {
    "rice": 1.2,
    "wheat": 0.85,
    "maize": 1.0,
    "vegetables": 0.95
}
# Step 3: Calculate Crop Water Requirements
def calculate_cwr(eto_values, kc):
    return eto_values * kc
# Example for multiple crops
for crop, kc in kc_values.items():
    cwr = calculate_cwr(predicted_ETo, kc)
    print(f"Crop: {crop}, Kc: {kc}, CWR (mm/day): {cwr}")

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Results  (Subject to validation and verification)

Crop: rice, Kc: 1.2, CWR (mm/day): [1.2        5.28778618]

Crop: wheat, Kc: 0.85, CWR (mm/day): [0.85       3.74551521]

Crop: maize, Kc: 1.0, CWR (mm/day): [1.         4.40648848]

Crop: vegetables, Kc: 0.95, CWR (mm/day): [0.95       4.18616406]

Applications in Irrigation Management

1. Efficient Irrigation Systems:

   • Use CWR estimates to design drip or sprinkler irrigation systems.
   • Avoid over- or under-watering, improving crop yields.

2. Water Resource Allocation:

   • Optimize water distribution among fields during water scarcity.
   • Predict peak water demands during critical growth stages.

3. Cost Reduction:

   • Reduce water pumping costs by scheduling irrigation when necessary.

4. Integration with Mobile Apps:

   • Provide farmers with real-time updates on CWR based on ETo predictions.
   • Suggest irrigation times and amounts.

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Future Extensions

1. Crop-Specific AI Models:

   • Train ML models for predicting both ETo and Kc dynamically.
   • Incorporate satellite imagery for large-scale monitoring.

2. Climate Change Scenarios:

   • Use climate models to assess long-term water requirements.

3. Automated Systems:

   • Integrate with IoT-based irrigation systems for automated water delivery.

Let me know if you’d like assistance in building advanced CWR prediction models or integrating them into a practical irrigation system!