🎒 Weather-Based Crop Simulation

This lesson builds core elective concepts in BSc Agriculture with practical applications and exam-oriented clarity.

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Weather-Based Crop Simulation

Weather is the single most important driver of crop growth variability. Crop simulation models use daily meteorological data to calculate phenological development, biomass accumulation, and water balance throughout the growing season.

Meteorological Data Integration

Crop models require four core daily weather variables:

Variable	Unit	Source
Maximum temperature (Tmax)	degrees C	IMD, AWS, POWER-NASA
Minimum temperature (Tmin)	degrees C	IMD, AWS, POWER-NASA
Rainfall	mm/day	IMD, district-level records
Solar radiation	MJ/m²/day	Measured or estimated from sunshine hours

• IMD (India Meteorological Department) provides district-level historical weather data
• NASA POWER database offers free gridded data (0.5 degree resolution) globally
• Automatic Weather Stations (AWS) provide real-time, location-specific measurements
• Missing data can be estimated using weather generators like WGEN or LARS-WG

Growing Degree Days (GDD)

Growing Degree Days (also called thermal time or heat units) quantify the accumulated temperature that drives crop development:

• Formula: GDD = [(Tmax + Tmin) / 2] - Tbase
• Tbase varies by crop — rice (10 degrees C), wheat (5 degrees C), maize (10 degrees C)
• Phenological stages (emergence, flowering, maturity) are triggered when GDD thresholds are reached
• GDD is more reliable than calendar days for predicting crop stages across locations and seasons

GDD Requirements for Key Crops

Crop	Sowing to Maturity (GDD)	Tbase
Rice	1800 - 2200	10 degrees C
Wheat	1500 - 1800	5 degrees C
Maize	1400 - 1800	10 degrees C
Soybean	1600 - 2000	10 degrees C

Water Balance Modeling

Crop models simulate the soil water balance on a daily basis:

• Inputs — rainfall, irrigation
• Outputs — evapotranspiration (ET), runoff, deep drainage
• Storage — soil water content across multiple layers

Key processes modelled:

• Potential evapotranspiration (PET) — calculated using Penman-Monteith or Priestley-Taylor equations
• Actual ET — adjusted for soil water availability and crop canopy cover
• Runoff — estimated using SCS Curve Number method based on rainfall intensity and soil type
• Root water uptake — distributed across the root zone depth, limited by available water

Weather-Yield Relationships

• Temperature stress — high temperatures during flowering cause pollen sterility (rice above 35 degrees C, wheat above 32 degrees C)
• Drought stress — water deficit during grain filling reduces final yield significantly
• Excess rainfall — waterlogging reduces root oxygen supply and increases disease risk
• Frost — temperatures below 0 degrees C damage crop tissues, especially at anthesis

Weather-based simulation enables real-time crop monitoring, pre-harvest yield estimation, and early warning systems for weather-related crop losses.

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Summary Cheat Sheet

Topic	Key takeaway
Main focus	Weather-Based Crop Simulation — meteorological data integration, growing degree days, water balance modeling, and weather-yield relationships.
Section context	Revise this lesson with the rest of System Simulation and Agro-Advisory for stronger conceptual continuity.