GITNUXSOFTWARE ADVICE
Agriculture FarmingTop 10 Best Rice Software of 2026
Top 10 Rice Software tools ranked for farm operations, comparing farmOS, Taranis, Cropwise, and other options by features and fit.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
Gitnux may earn a commission through links on this page — this does not influence rankings. Editorial policy
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
farmOS
Rules-based automation triggers that create and update farm records through a documented entity model and API.
Built for fits when farms need API-driven integrations and governed workflows with audit-friendly record trails..
Taranis
Editor pickSchema-driven automation runs with API provisioning and RBAC governance over rule and configuration changes.
Built for fits when security or operations teams need governed automation with API provisioning and schema consistency..
Cropwise
Editor pickField-centric agronomic recordkeeping that ties tasks, treatments, and scouting outcomes to consistent field entities.
Built for fits when rice teams need schema-driven workflow automation with governance and audit visibility..
Related reading
Comparison Table
This comparison table evaluates Rice Software tools by integration depth, including API surface, automation hooks, and how each product maps farm telemetry and agronomic records into a defined data model. It also compares extensibility, provisioning paths, and the governance layer, with specific attention to RBAC, audit log coverage, and admin controls for configuration and change management. The goal is to clarify tradeoffs in automation and configuration workflows as well as how each platform supports higher-throughput data ingestion.
farmOS
open source farm opsOpen-source farm management system with a schema-driven data model for crops, inputs, tasks, and events plus REST endpoints for automation, reporting, and integration workflows.
Rules-based automation triggers that create and update farm records through a documented entity model and API.
farmOS organizes operational data around entities such as logs, assets, events, and contacts, so records stay linkable across inventory and production workflows. The REST API supports CRUD access to these entities and related fields, which supports integration depth for external systems that need throughput and deterministic schemas. Automation is handled with configurable rules that can create, update, or move data after specific events. Extensibility comes from Drupal module architecture and custom fields that add columns to the underlying schema without replacing the core data model.
A key tradeoff is that farmOS is configuration-heavy because modules, data fields, and rules must be mapped to the farm’s process model. farmOS fits best when an organization needs API-first integrations and repeatable automations rather than ad-hoc spreadsheets. A common usage situation is connecting a crop and inventory workflow to a mobile or warehouse system through the REST API while enforcing permissions for handlers and approvers.
- +REST API exposes core entities for deterministic integrations
- +Configurable rules trigger record updates across modules
- +Drupal-based extensibility supports custom fields and modules
- –Configuration and schema mapping take time to set correctly
- –Rules can become complex without clear workflow boundaries
- –Automation depends on event design and consistent data capture
Ops managers
Coordinate inventory with field work orders
Fewer manual adjustments
Systems integrators
Sync assets and logs into external apps
Consistent cross-system state
Show 2 more scenarios
Farm administrators
Control access across roles and modules
Tighter governance
RBAC permissions restrict who can edit records and perform actions.
Field teams
Capture structured events from mobile workflows
Faster reporting
Logged activity records stay searchable and link to inventory and assets.
Best for: Fits when farms need API-driven integrations and governed workflows with audit-friendly record trails.
Taranis
crop monitoring analyticsField imagery analytics platform with API-accessible workflows for crop monitoring, issue tracking, and operational recordkeeping that supports agriculture-specific data flows.
Schema-driven automation runs with API provisioning and RBAC governance over rule and configuration changes.
Teams use Taranis when they need repeatable automation across multiple systems with consistent schema and validated inputs. Integrations are managed through a structured configuration model that supports mapping data, defining execution rules, and controlling targets. The API surface supports automation and provisioning so environments can be created and updated without manual UI steps.
A tradeoff appears when deployments require significant upfront schema work and integration mapping for each data source. Taranis fits scenarios where configuration changes must be governed with RBAC and audit log visibility, such as shared security operations runbooks. It also fits high-volume scheduling where rule evaluation and task execution must stay controlled and observable.
- +Schema-driven configuration keeps integration inputs consistent across runs
- +API supports provisioning and automation without UI dependence
- +RBAC and audit logging improve governance for shared operations
- +Extensibility supports custom automation tied to the same data model
- –Integration mapping work can be heavy for new data sources
- –Rule design needs careful schema alignment to avoid failures
Security operations teams
Automate asset risk workflows from multiple feeds
Lower manual handling of findings
Platform engineering teams
Provision automation environments via API
Faster repeatable environment rollout
Show 2 more scenarios
Governance and compliance teams
Track controlled changes to automation
Stronger evidence for controls
Apply RBAC and review audit logs for configuration updates tied to automation outcomes.
Operations analysts
Run scheduled workflows with consistent inputs
More predictable automation throughput
Use the data model and schema to standardize throughput and reduce run-to-run variation.
Best for: Fits when security or operations teams need governed automation with API provisioning and schema consistency.
Cropwise
agronomy operationsDigital agronomy workflow software for crop planning and operations management with data capture structures and integration points for farm activity records.
Field-centric agronomic recordkeeping that ties tasks, treatments, and scouting outcomes to consistent field entities.
Cropwise organizes agronomic inputs, field boundaries, tasks, and outcomes into a schema used to connect seasonal plans to in-field execution logs. For rice operations, that data model typically maps planting windows, scouting events, treatments, and yield or performance reporting to specific fields and time periods. Automation runs through configurable workflows that reduce manual re-entry when moving from plan to scouting to application records.
A key tradeoff is that deeper governance and schema consistency can require disciplined setup of field structures, user roles, and reference data before scale. Cropwise fits best when teams need controlled change tracking across multiple users and locations, and when external systems must exchange records through documented interfaces for higher throughput during peak operations.
- +Agronomic data model links plans to scouting and treatment history
- +Automation and configuration reduce manual record copying across seasons
- +Governance supports role-based editing of field and activity data
- +Audit-oriented change tracking supports traceability for agronomic decisions
- –Setup discipline is required to maintain consistent field and schema definitions
- –External integrations depend on defined data exchange patterns and mapping
Agronomy operations teams
Manage scouting and treatment execution
Faster field history lookup
Farm managers
Coordinate multi-user field tasking
Reduced unauthorized changes
Show 2 more scenarios
Integrations and data teams
Sync rice records to external systems
Lower manual data transfer
Exchanges field and agronomic event data through an automation and API surface.
Compliance and QA leads
Maintain agronomic audit trails
Stronger decision documentation
Tracks updates across plans and activities to support traceability of decisions.
Best for: Fits when rice teams need schema-driven workflow automation with governance and audit visibility.
Climate FieldView
farm recordsAgriculture operations platform for field records, scouting, recommendations, and machine integration with an automation-friendly data model for farm-level workflows.
FieldView data model ties inputs, activities, and outcomes to specific fields for controlled reporting and automation.
Climate FieldView connects farm operations data into a field-level data model and keeps agronomic context attached to each asset. It integrates with ag hardware and software used on the machine and farm sides, then normalizes outputs into consistent records for planning and analysis.
Automation centers on workflow configuration and repeatable actions around scouting, prescriptions, and yield reporting. Extensibility is driven by documented integration points and an API surface for moving data between FieldView and external systems with controlled governance.
- +Field-level data model keeps agronomic context linked to measurements and prescriptions.
- +Integration supports machine and farm data ingestion with normalized record structures.
- +Workflow configuration enables repeatable scouting and agronomy processes.
- +API and integration points support external data movement and automation.
- –Admin governance depends on careful role design and workspace structure.
- –Complex multi-system schemas can require significant mapping effort.
- –Automation depth varies by workflow type and available integration events.
Best for: Fits when farm operations need field-scoped integration and workflow automation with a documented API surface.
Agworld
farm collaborationFarm management and collaboration platform that captures crop operations and agronomy notes in structured objects with APIs and admin controls for multi-entity governance.
Agworld audit log and change tracking across field records and agronomy activities.
Agworld provisions farm data, agronomy activities, and field records in a structured workflow used for rice operations. Its value as a Rice Software solution comes from integration depth around agricultural master data, auditability, and automation over agronomic tasks.
Agworld supports configuration of permissions through RBAC style access control and provides operational logs that track changes across records. Its extensibility focuses on connecting agronomy data and operational events to external systems via an API and automation surface.
- +Field, variety, and activity data model supports traceable agronomy workflows
- +API and integration surface connects farm records to external systems
- +RBAC-style access control reduces cross-team data exposure
- +Audit log coverage helps trace edits across field and activity objects
- +Automation supports repeatable task lifecycles tied to agronomic states
- –Automation rules depend on the platform’s existing workflow schema
- –Complex cross-object integrations can require careful data mapping
- –Admin governance features are harder to model for highly custom org structures
- –Throughput limits for bulk agronomic imports can impact high-volume onboarding
- –Schema changes can increase integration maintenance effort for custom clients
Best for: Fits when rice teams need structured field workflows, RBAC governance, and API-driven integration for agronomy operations.
Agrian
field operationsAgronomic recordkeeping and farm planning software with structured fields for operations and analytics inputs that supports integration for decision workflows.
Agrian data model for agronomy and product records supports integration-ready schema mapping for workflow automation.
Agrian fits rice and ag input organizations that need structured crop, field, and product data aligned to downstream operational workflows. Agrian centers on integration depth through a defined product and agronomy data model plus configuration-driven catalog and record maintenance.
Automation and extensibility appear through workflow triggers and interoperability features that support provisioning, data exchange, and system-to-system connectivity. Governance depends on role-based access patterns and traceability via audit-ready record changes.
- +Structured agronomy and product data model supports consistent downstream records.
- +Integration-oriented schema reduces mapping work across connected systems.
- +Configuration controls catalog and reference data without custom code.
- +Workflow automation can trigger actions off crop and field record changes.
- +Interoperability supports system-to-system data exchange for operations.
- –Automation depends on correct schema setup for each agronomy workflow.
- –API surface details can be difficult to validate without sandbox tooling.
- –Governance relies on role design that must be enforced in connected apps.
- –Data model complexity can raise onboarding time for new teams.
Best for: Fits when rice teams need structured crop and product data plus automation and integrations across operations systems.
Farmbrite
farm documentationField and farm documentation system that organizes tasks, inputs, and field notes into consistent records with configuration and integration capabilities for operations.
Field operation workflows tied to statuses and responsibility, so automation stays grounded in operational records.
Farmbrite is distinct for its farm-first schema and workflow automation around field operations, orders, and task execution. Farmbrite connects operational records to real-world scheduling so teams can track work status, ownership, and outcomes.
Core capabilities include provisioning of users and roles, configurable operational templates, and reporting based on structured work and inventory events. Integration options center on an API and export paths that fit data synchronization and system-to-system workflows.
- +Farm-first data model ties tasks, inventory, and operations into one schema
- +Role-based access control supports separation between worker and manager actions
- +Workflow configuration enables repeatable processes across fields and seasons
- +API and exports support data synchronization with external systems
- +Audit-oriented history improves traceability for operational changes
- –Automation depth depends on template coverage for each farm operation type
- –Advanced orchestration needs external tooling when workflows span multiple systems
- –API surface can feel narrower for custom reporting requirements
- –Configuration management can be heavy when many sites need differing rules
Best for: Fits when farm operations teams need controlled workflow automation with an integration-ready data model.
Trimble Agriculture
connected agriculture dataAgriculture technology software suite focused on field data management with structured asset and operation records and integration surfaces across connected devices.
Field operation execution tied to structured agronomic and machinery event data, supporting automation via integration and provisioning workflows.
In precision farm software comparisons, Trimble Agriculture focuses on field and farm execution connected to enterprise systems, with deep integration options for machinery, yield, and operations workflows. Core capabilities center on managing agronomic data, tracking work events, and coordinating field operations with configuration that supports repeatable processes.
The product’s value for rice teams comes from its data model alignment across agronomy and equipment inputs, plus an automation surface designed for integration and provisioning workflows. Admin governance centers on role-based access, auditability of changes, and controlled configuration across sites and users.
- +Integration depth across field execution and farm systems
- +Consistent agronomic data model for work and yields
- +Automation hooks and API surface for workflow integration
- +RBAC controls and audit trail support governance needs
- –Automation depth depends on external system integration quality
- –Schema mapping can require admin time for multi-site setups
- –Complex operations configuration increases change-management overhead
- –Throughput for bulk imports varies by dataset normalization
Best for: Fits when rice operations need equipment and agronomy data connected to enterprise workflows with controlled access.
Strider
farm workflowAgriculture management software that supports field and farm workflows with configurable data capture and automation-oriented integration options for ops teams.
Run-level configuration and results capture, tied to pipeline steps, supports repeatable execution and audit review.
Strider provisions and automates software workflows using an API-driven control plane. It models work as pipelines with configurable steps and environment-bound variables for repeatable execution.
Strider supports integration with external services through webhooks and API calls, then records results so operators can audit runs. Governance centers on team and role boundaries plus run visibility across projects.
- +API-driven workflow orchestration with versioned configuration
- +Pipeline data model supports step reuse across environments
- +Webhook and API integration enables automated handoffs
- –Schema changes can require careful migration of pipeline configs
- –Audit and run history depth can be limited for long retention windows
- –Throughput under heavy concurrency needs explicit tuning and sizing
Best for: Fits when engineering teams need API-driven pipeline automation and RBAC-scoped governance across projects.
FarmLogs
crop recordkeepingCrop and field record platform that structures planting, tasks, and scouting notes with integration paths for farm data workflows.
Field scouting and activity record model that ties observations to planned operations for consistent reporting.
FarmLogs fits rice operations that need field-to-record traceability with agronomic decision support driven by farm data. The core capabilities center on crop planning, scouting notes, and yield or input record management tied to spatial farm context.
Integration depth relies on a defined data model for fields, activities, and observations that can be carried into reporting workflows. Automation and extensibility are mostly configuration and exports driven, with an API surface intended for data retrieval and provisioning of operational records.
- +Field-level agronomic recordkeeping keeps plans, scouting, and harvest data aligned
- +Data model supports consistent schemas for fields, tasks, and observations
- +Exports and integrations support downstream reporting and record ingestion
- +Configuration-driven workflows reduce repeated manual entry across seasons
- –API automation scope centers on data retrieval and record actions, not full workflow orchestration
- –Schema changes can require process coordination when mapping field attributes
- –Admin governance features like granular RBAC and audit log controls need tighter documentation
- –Throughput for bulk updates may require batching to avoid long import sessions
Best for: Fits when rice teams need structured field records, exports, and light automation to keep agronomy decisions auditable and repeatable across farms.
How to Choose the Right Rice Software
This buyer’s guide covers rice-focused software workflows across farmOS, Taranis, Cropwise, Climate FieldView, Agworld, Agrian, Farmbrite, Trimble Agriculture, Strider, and FarmLogs. It maps how each tool handles integration depth, its data model, and the automation and API surface used to move records between systems.
The guide also compares admin and governance controls like RBAC, audit-friendly change tracking, and configuration governance that affect long-running rice operations across fields, assets, and seasons. Readers can use the criteria and decision steps to select the tool that matches required integration breadth and control depth.
Rice operations systems that model fields, tasks, and agronomy events for controlled integration
Rice Software typically captures farm and field entities like crops, varieties, assets, work orders, scouting notes, treatments, and outcomes in a structured data model. It solves issues caused by manual record copying, inconsistent field definitions, and uncontrolled data exchange between planners, agronomists, equipment systems, and reporting tools.
For example, farmOS uses a schema-driven entity model with REST endpoints and rules-based triggers that update farm records across modules. Climate FieldView centers on a field-level data model that ties inputs, activities, and outcomes to specific fields, then normalizes machine and farm inputs into consistent records for planning and analysis.
Evaluation criteria for rice tools built for automation, API integration, and governance
Rice teams succeed when the tool’s data model and API contract make record mapping predictable across fields, tasks, and observations. Integration depth matters most when provisioning and automation run without relying on manual UI steps.
Governance controls determine whether edits can be scoped by role and audited in a way that supports agronomic traceability. farmOS, Taranis, Agworld, and Cropwise each tie automation and change tracking to governed workflows, which reduces cross-team risk during schema and configuration updates.
Schema-driven entity model for fields, tasks, and agronomy records
farmOS models crops, inputs, tasks, assets, and events through a shared schema-driven data model that supports deterministic integrations. Cropwise and Climate FieldView also keep agronomic context attached to consistent field entities so scouting notes, treatments, and outcomes stay traceable.
REST and API surface for provisioning and deterministic integrations
farmOS exposes a REST API that serves core entities and custom fields for integration and provisioning workflows. Taranis adds API-accessible provisioning and controlled execution flows so automation can provision rule inputs and run without UI dependence.
Rules and workflow automation that updates records across modules
farmOS uses rules-based automation triggers that create and update farm records across modules when event design and capture stay consistent. Farmbrite supports workflow configuration for operational templates and status-driven task execution tied to inventory and orders, which keeps automation grounded in operational records.
RBAC and audit-friendly change tracking across agronomy objects
Agworld provides RBAC-style access control plus operational logs that track changes across field records and agronomy activities. farmOS also relies on role-based permissions and audit-friendly change tracking across logged events, which helps maintain decision traceability.
Extensibility hooks aligned to the same data model and automation logic
farmOS uses Drupal-based extensibility for custom fields and modules so extensions remain consistent with the existing entity model. Taranis supports extensibility for custom automation tied to the same schema-driven configuration and governed rule execution.
Field-scoped integration and normalized outputs from machine and farm sources
Climate FieldView connects machine and farm data and normalizes outputs into consistent field-scoped records for planning and analysis. Trimble Agriculture similarly aligns agronomic and equipment event data and ties governance to RBAC plus auditability for controlled access.
Decision framework for selecting rice software by integration depth and control depth
Start with the data model and API contract required for record exchange between planning, field execution, and reporting systems. Tools like farmOS, Taranis, and Climate FieldView provide documented entity or field-level models that reduce ambiguity during mapping.
Then validate the automation and governance controls needed for multi-user edits and auditability. Agworld, Cropwise, and Farmbrite fit teams that require RBAC-scoped permissions and traceable change history across agronomy workflows and operational templates.
Map required entities to a schema the tool can model end to end
Define the rice workflow entities that must be consistent across systems, including fields, crop plans, tasks, treatments, scouting notes, and outcomes. farmOS supports a shared data model across modules, while Cropwise and Climate FieldView emphasize field-centric agronomic recordkeeping tied to consistent field entities.
Confirm provisioning and automation can run through API, not just UI
If automation needs to provision records and configuration inputs, prioritize tools with API-driven provisioning such as farmOS and Taranis. Strider also uses an API-driven control plane with run-level configuration and results capture, which fits engineering-led orchestration.
Evaluate how automation writes back, not only how it reads
Choose automation that creates or updates records in the same data model when workflows need closed-loop operations. farmOS triggers update farm records across modules, while Farmbrite ties automation to statuses and responsibility inside operational templates so task execution writes back to operational records.
Check governance features for RBAC scope and audit log coverage
For shared operations, require RBAC-style access control and audit-friendly change tracking across agronomy objects. Agworld tracks changes across field records and agronomy activities with operational logs, while farmOS and Cropwise support audit-oriented traceability for logged events and edits.
Stress-test schema mapping effort for each connected data source
Assume schema mapping work will be required for new data sources and plan time for alignment before high-volume onboarding. Taranis and Climate FieldView both require careful schema alignment for rule evaluation and normalized outputs, while Agworld highlights that complex cross-object integrations can need careful mapping.
Pick the deployment fit for equipment-connected events and multi-site operations
If machinery and enterprise systems must feed field execution, prioritize Trimble Agriculture or Climate FieldView due to structured agronomic and equipment event data plus controlled access. If rice planning and decisions depend on tying plans to scouting and treatments, Cropwise and Agrian provide schema-driven workflow automation focused on agronomic history and product records.
Rice software audience matches based on API depth, governed automation, and field-level traceability
Different rice teams need different control points across fields, records, and integrations. The right tool matches required integration breadth and whether automation must be governed with auditable configuration and rule changes.
The segments below map directly to the best-fit scenarios where each tool’s data model, API surface, and governance controls align with the stated use case.
Farm operations teams needing REST integrations and governed record trails
farmOS fits teams that need deterministic API integrations plus rules-based automation that creates and updates farm records through a documented entity model. farmOS also provides role-based permissions and audit-friendly change tracking across logged events for controlled multi-user workflows.
Security or operations teams that require schema-consistent automation with RBAC and auditability
Taranis fits security and operations groups that want schema-driven automation runs with API provisioning and RBAC governance over rule and configuration changes. Taranis also ties extensibility to the same data model so automation changes remain auditable.
Rice agronomy teams that need field-centric planning, scouting, treatments, and traceable outcomes
Cropwise fits rice teams that need a field-centric agronomic recordkeeping model tying tasks, treatments, and scouting outcomes to consistent field entities. Climate FieldView fits teams that also need field-level data normalization from machine and farm sources so agronomic context stays attached to each field.
Organizations running structured collaboration with audit logs across agronomy activities
Agworld fits rice operations that need RBAC-style access control plus operational logs tracking changes across field records and agronomy activities. It also supports API and integration surface for connecting farm records to external systems via an automation and integration workflow.
Engineering-led automation that runs repeatable pipelines with versioned configuration
Strider fits engineering teams that want an API-driven pipeline orchestration model with run-level configuration and results capture. It records results so operators can audit runs and keep repeatable execution across environments.
Common selection pitfalls that break integration and governance in rice operations
Rice tools often fail in the field when schema mapping work is underestimated or when automation rules do not match the team’s event capture quality. Governance issues also appear when RBAC scope and audit history expectations are not validated early.
The pitfalls below show where teams typically lose time with the reviewed tools and how to avoid the same failure patterns.
Underestimating schema mapping time for new data sources
Taranis and Climate FieldView both require careful schema alignment so rule evaluation and normalized outputs do not fail when inputs diverge. Plan for mapping effort similar to how Taranis notes heavy integration mapping work for new data sources and how Climate FieldView flags multi-system schema complexity.
Designing automation rules without clear workflow boundaries
farmOS rules can become complex without clear workflow boundaries when event design and capture are inconsistent. Reduce complexity by aligning each ruleset to a specific record lifecycle so record updates stay predictable across farm modules.
Assuming API-driven automation covers full orchestration without governance
FarmLogs focuses on exports and configuration-driven workflows where API automation scope centers on data retrieval and record actions, not full orchestration. If full orchestration with governed execution is required, tools like farmOS and Taranis provide deeper rules-based automation and schema-driven runs.
Ignoring audit log and RBAC scope during requirements definition
Agworld highlights that operational logs cover change tracking across field records and agronomy activities, so RBAC and audit expectations must be defined up front. farmOS also relies on role-based permissions and audit-friendly change tracking, so governance requirements should be captured before integration build-out.
Choosing equipment-connected workflows without validating integration depth for event data
Trimble Agriculture and Climate FieldView depend on structured agronomic and equipment event data plus integration quality from connected systems. If equipment data quality and mapping are not ready, automation depth can be constrained, so event normalization requirements must be confirmed early.
How We Selected and Ranked These Tools
We evaluated each rice software tool on features coverage, ease of use, and value, then produced an overall score as a weighted average where features carried the most weight. Ease of use and value each received the same secondary weight so adoption friction and practical returns affected final ordering.
This editorial research used the provided tool capabilities and constraints, including each product’s data model, API surface, automation mechanics, and admin and governance controls, and it did not rely on any private benchmark experiments. farmOS separated itself from the lower-ranked tools by combining a documented entity model with REST endpoints and rules-based automation triggers that create and update farm records across modules, which directly strengthened the features factor.
Frequently Asked Questions About Rice Software
Which Rice Software options provide the strongest API for integration and provisioning?
How do these tools handle SSO and security governance for user access?
What tools are better for schema-driven workflows where configuration must stay consistent?
Which Rice Software options make data migration smoother by enforcing a shared data model?
How do admin controls differ when teams need audit logs and change traceability?
Which tools are designed for field-centric automation tied to specific assets?
What integration patterns work best for connecting farm operations with external enterprise systems?
Which option fits teams that want governed automation runs with predictable execution throughput?
What common setup issue causes errors, and how do these tools prevent it?
Conclusion
After evaluating 10 agriculture farming, farmOS stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
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