FAQs

ARMOSA runs at rotation level and treats crop residues and cover crops explicitly:

– Each crop has its own phenology, biomass allocation, rooting depth, and residue management.

– Cover crops are modelled as explicit crops in the rotation (not as a generic multiplier).

– Perennial crops are simulated as explicit crops, with multi-year dynamics.

ARMOSA is readily available for Europe and is calibrated for the following IPCC climatic zones:
Warm Temperate (Dry and Moist) and Cool Temperate (Dry and Moist).

– For many European countries, coverage depends on where farms fall within these calibrated climate zones.

– For non-European regions (e.g., Argentina, parts of the US), additional calibration/validation is typically required before crediting-grade deployment.

– Calibration for relevant agricultural areas around the world is currently underway.

Our calibration framework is organized by IPCC climatic regions / climate zones. Extending calibration and validation to new zones/countries typically takes ~12–16 weeks, depending mainly on the availability and quality of regional scientific data (e.g., long-term trials, SOC datasets, management information).

ARMOSA simulates soil carbon and nitrogen in multiple layers down the profile, limited by the available soil/pedological data. For crediting and standard reporting, we currently quantify SOC stock changes over 0–30 cm.

We support multiple baseline approaches depending on the project design:

1. Field-specific baselines

A baseline is built per field, using that field’s own historical data (management/rotation where available). Best when you have strong, field-level history and want maximum specificity.

2. Rotational baseline

The baseline is defined from the historical rotation and management pattern (e.g., typical crop sequence + inputs over a reference period). Best when practice history is consistent and you want a baseline that reflects “business as usual” rotation.

3. Blended baseline

It is built by replaying each historical cultivation cycle as its own “thread” (one per cycle). Each thread is simulated, and the results are then averaged to produce the final baseline emissions.

For crediting-grade simulations you need three groups of inputs:

1. Location + weather:

field geographic coordinates, plus time-series of temperature, rainfall, wind speed, evapotranspiration, and solar radiation (typically sourced from Open-Meteo, with Agri4Cast as backup for europe).

2. Soil (0–30 cm):

measured soil organic carbon, texture (sand/clay/silt %), and bulk density are mandatory; soil pH is optional. If customer measurements are missing, SoilGrid or LucasSoil can be used as a secondary source.

3. Crops & management:

full crop rotation (including cover crops) and key operations/dates: sowing and harvest dates, tillage type/date/depth, fertilisation type/date/amount, whether the system is rainfed or irrigated, and residue type. Yield type and amount are optional.

ARMOSA is available via an API in our infrastructure. Your platform sends structured inputs, we run the simulations on our side, and the API returns crediting-relevant outputs. Turnaround time depends on scenario complexity and batch size, but the process is fully programmatic and scalable.

Yes. We maintain documentation describing:

– Required input formats (schemas, units, accepted values)

– Output formats (variables, units, aggregation rules)

– API endpoints and usage patterns

We use a spin-up procedure where the model is run over a reconstructed management history (baseline). The spin-up duration is 5 years, designed to minimize sensitivity to initial conditions and stabilize the partitioning of carbon and nitrogen pools.

At model level, we can represent management that affects crops, residues, soil cover, organic/mineral inputs, irrigation, and related practices (including undersowing and agroforestry where data is available). Certified practices currently available via the API include:

– Tillage changes: conventional, reduced, no-till

– Crop rotations and cover crops (explicitly modelled)

– Organic inputs: manure, compost, digestate, crop residues…

– Fertilizer rates changes