PPMaP

PPMaP predicts and maps plant phenological phases by upscaling multi-location point observations into landscape-level spatial predictions to inform crop management and phenology research.

Key Features:

• Model derivation from field experiments: Derives phenology models from multi-location field experiment data for specific crop varieties.
• Model types supported: Implements both process-oriented and temperature-driven plant phenology models.
• Spatial upscaling: Upscales point-location observations to wide-area landscape-level spatial predictions.
• Spatial mapping and LGP: Generates spatial maps of plant phenological phases and spatially explicit length of growing period (LGP).
• Crop applicability: Applies derived models to any crop variety within a spatial framework.
• Empirical demonstration: Demonstrated using maize (Zea mays L.).
• Computational environment: Implemented in R.
• Computational performance: Provides high computing efficiency for model fitting and spatial prediction.

Scientific Applications:

• Irrigation, fertilization and protection scheduling: Optimize timing of irrigation, fertilization, and crop protection interventions.
• Yield and quality optimization: Support optimization of grain yield and quality through phenology-informed management.
• Upscaling experiments: Enable upscaling of field-scale experiment results to landscape-level applications.
• Length of growing period (LGP) assessment: Determine spatially explicit length of growing period (LGP) for crop varieties.
• Regional phenology mapping: Produce spatial phenology maps for regions of interest to inform agronomic decision-making.

Methodology:

Derives models from multi-location field experiment data; implements process-oriented and temperature-driven plant phenology models; applies derived models within a spatial framework to produce landscape-level phenology predictions and spatial maps; implemented in R.