COMPUTATIONAL AGRICULTURAL SCIENCE

The Virtual Field Trials Company.

Engineered to accelerate Agriculture.

We developed an Environmental Engine that runs Virtual Field Trials, and simulates how agricultural products perform across thousands of environments, so R&D and portfolio teams can make better decisions before committing to field trials.

SEE HOW IT WORKS CONTACT SALES

TRUSTED BY GLOBAL LEADERS:

Decades of Testing. Simulated in Seconds. Know where your product performs before you go to the field.

THE CHALLENGE

Field trials are the bottleneck of agricultural innovation.

Three structural forces are pushing physical experimentation past its limits. Scaling field trials cannot solve them.

1

Climate
variability

Environmental conditions are becoming less predictable, reducing the representativeness of historical trial data.
2

Product
Complexity

Biological inputs and advanced genetics introduce interactions that field trial networks cannot fully capture.
3

Faster Innovation
Cycles

Companies must evaluate larger candidate pools while compressing timelines. Physical trials cannot keep pace.

THE CALICE APPROACH

Most models treat the environment as a variable. We model it as a biological system.

Calice models how products, environment, and management interact, built from biological first principles rather than statistical correlations. The result is a probability map of performance in locations your product has never seen.

SAME LOCATION, DIFFERENT ENVIRONMENT EVERY SEASON.

Each year and each planting date creates a distinct environment from the plant's perspective. We model the signals that explain why products succeed or fail across them: climate event timing, soil dynamics, and crop cycle interactions.

We model the signals that explain why products succeed or fail:climatic event timing, soil dynamics, and crop cycle interactions.

PRODUCT
ENVIRONMENT
MANAGEMENT

WHAT WE DELIVER

PERFORMANCE PROBABILITY MAPS

Ranked product-environment recommendations showing where each candidate is most likely to perform, where it is not, and what environmental conditions drive the difference.
ENVIRONMENTAL FINGERPRINT ANALYSIS

A unique systems-level signature for each PxExM interaction, identifying optimal placement zones and stress scenarios at scale.
DECISION-READY OUTPUTS

Insights integrated into your existing R&D and portfolio pipelines.

UNIQUE ENVIRONMENTAL FINGERPRINT FOR EACH P×E×M INTERACTION

Traditional agronomy treats the environment as one variable. We model how the crop perceives it.

Traditional approaches model the environment as a simple variable, with limited predictive power when conditions change. We model how each crop perceives and responds to its environment over time, capturing the full complexity of its performance.

ENGINEERED FOR BIOLOGICAL COMPLEXITY:

SYSTEMS-LEVEL INTERACTIONS:

Systems Biology, AI, and Chronobiology working together to generate a unique Environmental Fingerprint for each P×E×M interaction.
ROBUST UNDER NOISY DATASETS:

Next-level accuracy even with incomplete or heterogeneous data sources.
TRANSFERABLE ACROSS CONTEXTS:

Models work across products, environments, geographies, and decision contexts.

HOW WE DELIVER

Built around your methodology.

Calice adapts to the way your R&D and portfolio teams already operate.
Your trial design, evaluation criteria, and decision frameworks remain in place.
We bring environmental intelligence into the methodology you already trust.

PLUG INTO EXISTING TOOLS

Compatible with leading agricultural software ecosystems. We integrate where your data already lives.
ADAPT TO WORKFLOW

Our models work the way your team works, not the other way around.
NO DISRUPTION

Implementation does not require restructuring R&D or product pipelines.

One engine, three ways to deliver:

Platform

Web Access Tool.
Reports

Decision-ready Reports
API

API Integration.

GLOBAL FOOTPRINT

Active projects across regions,
crops, and biological systems.

Argentina

Brasil

México

EE.UU

France

Belgium

Greece

Turkey

Australia

Barley Global Testing
- Challenge: Sourcing strategy under climate variability across global geographies
- Approach: Matching envirotypes across regions to identify equivalent environments
- Impact: Reduced import dependency, improved portfolio resilience
Corn Hybrids Placement
- Challenge: Anticipating which hybrids would succeed before planting decisions
- Approach: Simulated hybrid performance across environmental fingerprints
- Impact: Better placement, accelerated breeding decisions
Soybean Placement
- Challenge: Variety placement decisions under shifting climate and management conditions across geographies
- Approach: Virtual trials across thousands of location-environment combinations to identify optimal zones
- Impact: Smarter placement, reduced uncertainty, faster go-to-market
Rice Quality
- Challenge: Unexplained grain defects affecting quality and market value
- Approach: Identified hidden climate triggers via chronobiological modeling
- Impact: Adaptive management, stress cause detection
Biologicals Deployment
- Challenge: Determining right environments for biological product efficacy at scale
- Approach: Mapped soil and environmental conditions that maximize biological response
- Impact: Optimal market entry, targeted deployment

WHO WE WORK WITH

We know your problem.
We built the engine to solve it.

R&D and portfolio teams face the same questions every season: which products will perform where, how to allocate experimental resources, when to advance or drop candidates, how to scale to new geographies without losing rigor. We built our engine to answer them.

Corn Breeding

Anticipated which hybrids would succeed before planting,
streamlining breeding decisions and accelerating product advancement.

Rice Quality

Identified hidden climate triggers behind grain defects,
enabling adaptive management.

Barley Sourcing & Quality

Developed sourcing strategies under climate variability,
reducing dependency on imports and improving portfolio resilience.

Wheat Placement

Revealed environmental fingerprints driving yield and quality,
guiding variety deployment with greater precision.

Soybean Placement

Pinpointed regions and management strategies where varieties perform best, enabling smarter placement under shifting conditions.

Biological Inputs

Mapped environmental and soil conditions that maximize efficacy,
supporting targeted deployment and faster market entry.

The Virtual Field Trials Company.

Engineered to accelerate Agriculture.

We developed an Environmental Engine that runs Virtual Field Trials, and simulates how agricultural products perform across thousands of environments, so R&D and portfolio teams can make better decisions before committing to field trials.

Decades of Testing. Simulated in Seconds. Know where your product performs before you go to the field.

THE CHALLENGE

Field trials are the bottleneck of agricultural innovation.

Three structural forces are pushing physical experimentation past its limits. Scaling field trials cannot solve them.

1

Climate variability

2

Product Complexity

3

Faster Innovation Cycles

Most models treat the environment as a variable. We model it as a biological system.

Calice models how products, environment, and management interact, built from biological first principles rather than statistical correlations. The result is a probability map of performance in locations your product has never seen.

SAME LOCATION, DIFFERENT ENVIRONMENT EVERY SEASON.

PRODUCT

ENVIRONMENT

MANAGEMENT

WHAT WE DELIVER

PERFORMANCE PROBABILITY MAPS

ENVIRONMENTAL FINGERPRINT ANALYSIS

DECISION-READY OUTPUTS

UNIQUE ENVIRONMENTAL FINGERPRINT FOR EACH P×E×M INTERACTION

Traditional agronomy treats the environment as one variable. We model how the crop perceives it.

Traditional approaches model the environment as a simple variable, with limited predictive power when conditions change. We model how each crop perceives and responds to its environment over time, capturing the full complexity of its performance.

SYSTEMS-LEVEL INTERACTIONS:

ROBUST UNDER NOISY DATASETS:

TRANSFERABLE ACROSS CONTEXTS:

Built around your methodology.

Calice adapts to the way your R&D and portfolio teams already operate. Your trial design, evaluation criteria, and decision frameworks remain in place. We bring environmental intelligence into the methodology you already trust.

PLUG INTO EXISTING TOOLS

ADAPT TO WORKFLOW

NO DISRUPTION

One engine, three ways to deliver:

Platform

Reports

API

Active projects across regions, crops, and biological systems.

Barley Global Testing

Corn Hybrids Placement

Soybean Placement

Rice Quality

Biologicals Deployment

We know your problem. We built the engine to solve it.

R&D and portfolio teams face the same questions every season: which products will perform where, how to allocate experimental resources, when to advance or drop candidates, how to scale to new geographies without losing rigor. We built our engine to answer them.

Corn Breeding

Rice Quality

Barley Sourcing & Quality

Wheat Placement

Soybean Placement

Biological Inputs

News & updates

Together, empowering agricultural R&D teams.

Climate
variability

Product
Complexity

Faster Innovation
Cycles

Calice adapts to the way your R&D and portfolio teams already operate.
Your trial design, evaluation criteria, and decision frameworks remain in place.
We bring environmental intelligence into the methodology you already trust.

Active projects across regions,
crops, and biological systems.

We know your problem.
We built the engine to solve it.

Together, empowering
agricultural R&D teams.