AquaCropUC DavisView code
AquaCrop at UC Davis — Crop–water science, scaled, illustrated with a maize plant, soil profile, roots, rain, and groundwater.

Crop–water science, scaled

From one field
to a changing world.

A flexible, open Python implementation of FAO AquaCrop—advanced at UC Davis to connect process-based crop science with field observations, remote sensing, and subnational yield data.

Explore the framework Get AquaCrop Python
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
Decision-ready outputsYield · crop water demand · evapotranspiration · soil-water balance

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
Schematic showing coordinated maize canopy and root development 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.

Schematic showing water uptake by maize roots, transport through the plant, and transpiration from leaves

Crop transpiration

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

Schematic showing aboveground maize biomass and its partitioning into harvestable grain

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.

Schematic showing rainfall, runoff, infiltration, evaporation, root uptake, transpiration, drainage, capillary rise, and a multilayer soil profile

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

The UC Davis approach

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

The people behind the framework

Built through
collaboration.

AquaCrop at UC Davis brings together model development, crop and climate science, calibration, remote sensing, and applications across scales.

Erwan MonierPrincipal investigator

Erwan Monier

Project leadership.

Shuaiqi WuFormer 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.

Heather ChildersGraduate researcher

Heather Childers

Multiscale calibration framework integrating field, remote-sensing, and subnational yield data.

Zach Van VechtenFormer undergraduate intern

Zach Van Vechten

Investigated geoengineering impacts on crop yield using AquaCrop. Now a Ph.D. student at Boston University.

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()

Knowledge base

Read the science.

From our team

Manuscript in preparation · Wu et al. (2026)Scaling AquaCrop for Continental Applications: A Python-Based, County-Calibrated Framework with a U.S. Maize Case StudyGlobal sensitivity analysis · county calibration · GGCMI evaluation Doctoral dissertation · Wu (2025)Advancing the Modeling of Climate Impacts on Annual and Perennial Crops to Inform Adaptation Pathways: Lessons from California Almonds and U.S. Maize

UC Davis · View on eScholarship

In developmentA flexible multiscale calibration framework

Field observations, remote sensing, county records, and global subnational yield data. Led by Heather and the UC Davis Global Change team.

Foundational AquaCrop

All FAO publications
2009
Foundational paperAquaCrop—The FAO Crop Model to Simulate Yield Response to Water: I. Concepts and Underlying Principles

Steduto, Hsiao, Raes & Fereres

2009
Foundational paperAquaCrop—The FAO Crop Model to Simulate Yield Response to Water: II. Main Algorithms and Software Description

Raes, Steduto, Hsiao & Fereres

2012
FAO referenceCrop Yield Response to Water — FAO Irrigation and Drainage Paper 66

Steduto, Hsiao, Fereres & Raes

2021
FAO reportThe AquaCrop Model: Enhancing Crop Water Productivity

Salman, Garcia-Vila, Fereres, Raes & Steduto