Given a rainfall probability distribution and ETc values for pearl millet, design an optimized irrigation scheduling model using AI regression techniques. If average rainfall is 350 mm with a standard deviation of 80 mm and millet ETc is 420 mm, calculate water deficit index (WDI) and estimate yield loss using Doorenbos-Kassam model.
AI-based irrigation scheduling model for pearl millet with rainfall distribution (μ=350mm, σ=80mm) and ETc=420mm:
Water Deficit Index (WDI) Calculation:WDI = (ETc - Effective Rainfall) / ETc Effective Rainfall = 0.8 × 350mm = 280mm (80% efficiency) WDI = (420 - 280) / 420 = 0.333 (33.3% water deficit)
Doorenbos-Kassam Yield Loss Estimation:Using Ky factor for pearl millet = 0.45 (drought tolerance coefficient) Relative Yield Decrease = Ky × (1 - ETa/ETc) Where ETa/ETc = 1 - WDI = 0.667 Yield Loss = 0.45 × (1 - 0.667) = 0.45 × 0.333 = 15% yield reduction
AI Regression Model Framework: Input variables: daily rainfall probability (normal distribution), soil moisture, growth stage coefficients, and temperature. Output: optimal irrigation timing and volume. Recommended irrigation schedule: 140mm supplemental irrigation in 4-5 splits during critical growth stages (tillering, flowering, grain filling) to achieve 95% potential yield. Machine learning algorithms (Random Forest/SVM) can optimize timing based on 10-day weather forecasts and soil moisture sensors to minimize water use while maximizing efficiency.
- Badges
- Science topic