ARMOSA model
ARMOSA (Analysis of Ropping systems for Management Optimization and Sustainable Agriculture) is a cropping-systems model that performs dynamic simulations of crops rotations and soil state variables in interaction with the environment and the agronomic management operations.
The model can simulate the evolution in time (using a daily time step) of the following main compartments:
- soil water dynamic
- soil C and N cycling
- crop phenology and growth (both above ground biomass and roots)
The model is also able to reproduce with accuracy the different management practice in term of crop residue management, fertilization (both organic and mineral) and tillage operations.
The daily simulation cycle involves the sequential execution of the following main modules:
1. Reference evapo-transpiration calculation module
Using the meteorological data, this module calculates the daily reference evapo-transpiration value (ET0)
2. Tillage operations module
Calculates the effects of the tillage management operations on the soil profile characteristics in terms of both texture and bulk density changes, and soil hydraulic and C-N parameters modification
3. Water balance module
Calculates the evolution of the water content of the whole soil profile taking into account the contributions of:
- meteorological data
- irrigations
- roots water uptake
- vertical water percolation
- water evaporation from the soil surface
4. Soil Carbon-Nitrogen module
Independently for each soil layer, the model simulates the following processes:
- mineralization
- nitrification
- denitrification
- volatilization
- leaching
Each organic matter (C and N) input is managed as an independent pool.
5. Crop module
Simulates the development of the crop on field managing in separate pools: leaves, stem, storage (grain), roots.
The phenological stage and biomass amount state are affected by both water and nitrogen availability. Crop growth is based on gross and net CO2 assimilation with hourly calculation of intercepted radiation in 5 layers of canopy.
ARMOSA exposes more than one hundred between state and rate variables at each daily time step. The output can be configured to in terms of the amount of files that have to be generated and the variables that the user want in each file. The data can be daily or aggregated per year and/or per crop on cycle.