Crop yield and water productivity modeling using nonlinear growth functions.

Area of Science:

• Agricultural Science
• Plant Physiology
• Biomathematics

Background:

• Precision agriculture relies on understanding plant growth dynamics for optimal productivity.
• Crop growth mechanisms are complex and require accurate modeling for effective management.

Purpose of the Study:

• To employ nonlinear Logistic and Gompertz models for predicting silage maize yield and water productivity.
• To assess model performance using growing degree days (GDD) under different irrigation regimes in arid/semi-arid regions.

Main Methods:

• Utilized Logistic, Gompertz, and sigmoid growth models to simulate silage maize growth.
• Applied deficit (60%, 80%) and full (100%) irrigation regimes (W2, W3, W1).
• Evaluated models using R², NRMSE, and MAPE, with GDD as a key predictor.

Main Results:

• Logistic and Gompertz models demonstrated high accuracy (R² > 99% pulse, > 80% continuous irrigation).
• Maximum biological yield rate identified at 1014°C GDD (50 days post-planting).
• Growth rate patterns differed: bell-shaped (Logistic) and right-skewed (Gompertz).

Conclusions:

• Logistic and Gompertz models effectively simulate silage maize growth under varied irrigation and temperature.
• These models offer quantitative predictions and decision support for irrigation and precision management.
• Integration into smart farming enhances resource optimization and sustainable agriculture in arid regions.