Crop–water science, scaled

From one field
to a changing world.

A flexible, open Python implementation of FAO AquaCrop, developed and maintained by the UC Davis Global Environmental Change Lab to connect process-based crop science with field observations, remote sensing, and subnational yield data.

Get AquaCrop Python

Conceptual illustration connecting process-based crop and soil-water modeling from a field to regional agricultural landscapes and continental-scale simulations.

Process-based modeling across scales

The model

A clear view of the
soil–water–plant–atmosphere system.

AquaCrop is FAO's process-based crop model for simulating crop growth and yield response to water. It balances biological realism with a relatively small set of parameters—making rigorous analysis accessible where data are limited.

Visit FAO AquaCrop

AquaCrop model schematic showing how weather, temperature stress, water stress, canopy cover, evapotranspiration, biomass, harvest index, and rooting depth interact to determine crop yield.

AquaCrop process schematic

OpenPython workflow

DailyProcess dynamics

3 scalesField to global

Biometeorological processesCrop phenology · canopy and root development · transpiration · temperature and water stress

Parsimonious by designBiological realism with a relatively small, interpretable parameter set

Management-responsive scenariosRainfed and irrigated systems · deficit irrigation · planting and soil management

Decision-ready outputsCrop yield · crop water demand · crop evapotranspiration · soil-water balance

Maize canopy and root development simulated by AquaCrop from emergence to maturity

Canopy cover

Tracks canopy development from emergence through senescence, including how temperature, weather stresses, and soil-water availability accelerate or constrain green canopy expansion.

Crop transpiration

Calculates crop water use from reference evapotranspiration and canopy cover, while adjusting root uptake and stomatal response as soil water becomes limiting.

Biomass and harvestable yield

Converts transpiration into aboveground biomass through normalized water productivity and applies the dynamic harvest index to translate biomass into crop yield.

Hydrology

Represents the full hydrological cycle across multiple soil layers, including rainfall, runoff, infiltration, evaporation, root uptake, redistribution, deep drainage, and groundwater interactions.

From process to decisions

One model.
Many questions.

AquaCrop turns daily weather, soil, crop, and management data into water-balance and crop-growth information for planning across fields, regions, and climates.

Water resources

Crop water demand

Estimate daily evapotranspiration, irrigation requirements, soil-water depletion, and seasonal crop demand under changing weather.

Daily and seasonal demand
Rainfed and irrigated systems
Basin and regional planning

Food production

Yield and water productivity

Assess attainable and water-limited yield while tracing how transpiration becomes biomass and harvestable production.

Yield gaps
Water productivity
Crop comparisons

Climate risk

Weather and climate impacts

Test crop response to heat, drought, rainfall variability, elevated CO₂, and future climate scenarios.

Extreme events
Climate projections
Adaptation pathways

Management

Irrigation strategies

Compare full, deficit, and supplemental irrigation schedules to balance yield outcomes with limited water supplies.

Deficit irrigation
Scheduling and timing
Water-saving scenarios

One flexible framework

Calibration at the scale your question demands.

Bring the best available observations into one transparent workflow—from detailed field experiments and farm records to remote-sensing products, regional statistics, and long subnational yield records. The framework calibrates AquaCrop at the spatial scale required by the research question while maintaining a consistent, reproducible treatment of parameters, observations, and uncertainty.

Local

Field-scale data

Calibrate phenology, canopy, biomass, soil moisture, and yield against observations from trials and farms.

In situ observations
Management records
Multiple target variables

Regional

Remote sensing + counties

Merge satellite observations with county yield records to constrain crop dynamics across heterogeneous landscapes.

Canopy and soil-moisture signals
County-level yield
Spatially explicit calibration

Continental to global

Subnational yield data

Use long yield records and representative locations to build robust crop parameterizations across climate zones.

Global sensitivity analysis
Parallel calibration
Gridded simulation

Our framework

Screen. Calibrate.
Evaluate.

A transparent three-stage workflow selects climate-representative rainfed and irrigated locations, uses global sensitivity analysis to identify the most influential parameters, and applies Shuffled Complex Evolution to calibrate them efficiently. Evaluation beyond the calibration locations shows that this approach improves the model accuracy across climates and management systems.

Global sensitivity analysis

Calibrate what matters.

Sobol analysis screens the full model and retains parameters with meaningful total-order influence across climate, soils, and management.

Canopy decline

Water productivity

Stomatal control

Root depth

Calibration engine

Explore a difficult parameter space.

Shuffled Complex Evolution evolves candidate solutions in parallel and regularly shares information—combining broad exploration with focused refinement while resisting local optima.

Sample→Evolve→Shuffle→Converge

U.S. maize evaluation

Seventeen counties. Continental reach.

Twenty years of observations from nine rainfed and eight irrigated counties improved performance across U.S. maize-producing regions.

Mean absolute bias28% → 16%

Normalized RMSE47% → 26%

Temporal correlation0.32 → 0.42

Wu et al. (2026), manuscript in preparation

Open science

Research code,
ready to extend.

The UC Davis implementation reproduces the official FAO model while opening the workflow to Python's data, visualization, calibration, and machine-learning ecosystem.

AquaCrop Python Website repository

aquacrop · python

from aquacrop import AquaCropModel

model = AquaCropModel(
  weather=climate,
  soil=soil_profile,
  crop="Maize",
  management=scenario
)

results = model.run()

The people behind the framework

Built through
collaboration.

The UC Davis Global Environmental Change Lab, led by Professor Erwan Monier, brings together model development, crop and climate science, calibration, remote sensing, and applications across scales.

Principal investigator

Erwan Monier

Project leadership.

Former Ph.D. researcher

Shuaiqi Wu

Python implementation, sensitivity analysis, and continental calibration and evaluation for U.S. maize. Now a postdoctoral researcher at Emory University, calibrating AquaCrop for peanut and soybean in Georgia.