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https://e-catalogs.taat-africa.org/org/technologies/ricemore-digital-rice-activity-monitoring-and-reporting-system
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RiceMoRe: Digital rice activity monitoring and reporting system

A digital solution for monitoring and reporting rice activities

RiceMoRe is a web- and mobile-based digital system for recording, managing, and monitoring rice production data across farmers, cooperatives, and production units. It uses standardized digital forms to capture production progress, farming practices, varieties, yields, and other field-level information, with georeferenced records consolidated across production networks. The system enables users to track and compare farming practices and production performance, visualize data through maps, charts, and tables, and generate structured reports. For development partners, these capabilities can support monitoring rice production activities and farmer-level practices across project areas, while providing structured, location-specific data that can inform program implementation, reporting, and learning. Farm-level activity data can also be connected through an API to the SECTOR GHG Calculator to support GHG emission estimation.

This technology is pre-validated.

8•9

Scaling readiness: idea maturity 8/9; level of use 9/9

Positive impacts: 8

Target  Group Positive Impacts
Women smallholder farmers with low digital literacy
  • Greater visibility of farming activities: Standardized records can make women farmers’ production activities and practices more visible within farm, cooperative, project, or institutional monitoring systems.
  • Accessible participation in data collection: User-friendly predefined forms can facilitate participation in farm-data reporting without requiring advanced digital skills.
Smallholder farmers with low digital literacy in areas with limited internet connectivity
  • More structured participation in reporting: Standardized, user-friendly forms simplify the recording of farm-level information such as cultivated area, varieties, production progress, practices, and yields.
  • Inclusion in wider production monitoring: Farm-level records can be aggregated through existing reporting structures, allowing participating smallholders to be represented in broader production monitoring.
Smallholder farmers with low digital literacy and weak access to collective support structures
  • Structured farm records: Standardized forms provide a simpler and consistent way to record production activities and practices, including water, fertilizer, and straw management.
  • Access to selected public functions: Some RiceMoRe functions, including GHG emission estimation, can be accessed by public users without requiring an account.
Smallholder farmers with limited internet connectivity and weak access to collective support structures
  • Spatial visibility of production areas: Georeferenced records allow participating farms and production areas to be represented in spatial agricultural monitoring.
  • Integration into wider monitoring: Farm-level production and practice data can be consolidated across geographic or administrative levels, increasing the representation of participating production areas in broader monitoring systems.

 

More...
Unintended impacts: 8
Mitigations: 8
Barriers: 11
Mitigations: 11

Climate adaptability: Highly adaptable

The system is online and flexible, so it can be used in any climate setting

Farmer climate change readiness: Significant improvement

Informs government, extension agencies, cooperatives to provide suitable climate advisories to farmers.

Biodiversity: No impact on biodiversity

Carbon footprint: Much less carbon released

Supports MRV for emission reduction.

Environmental health: Moderately improves environmental health

Supports extension to provide advisory related to environment and projects/enterprises to monitor farming practices that have environmental impacts such as pesticide or fertilizer management.

Soil quality: Not yet estimated

Problem

  • Limited farm-level data: Limited access to standardized, reliable, and traceable data can make it difficult to identify appropriate project locations, establish baselines, and monitor changes over time.
  • Resource-intensive monitoring: Manual data collection, compilation, and consolidation increase project teams’ workload and the resources required to monitor activities across large numbers of farmers or fields.
  • Limited practice benchmarking: Difficulty tracking and comparing farming practices against project-specific sustainability or agroecological criteria can make it harder to assess progress and identify areas for improvement.
  • Limited geographic visibility: Lack of georeferenced data makes it difficult to monitor activities across project areas and identify geographic patterns or priority locations.
  • Limited GHG quantification: Difficulty quantifying GHG emissions and emission reductions at field or project level can constrain the assessment of climate mitigation outcomes and the generation of data for MRV and climate-related reporting.

Solution

  • Structured farm-level data: A standardized digital system provides structured, traceable production data that can support project targeting, baseline development, implementation monitoring, and assessment of changes over time.
  • Digital field monitoring: A mobile application enables field-level data collection, while automatic data aggregation reduces manual compilation and the workload associated with monitoring farmers and project activities.
  • Georeferenced project monitoring: Georeferenced records and map-based visualization enable project teams to locate and monitor activities across implementation areas and identify geographic patterns or priority areas.
  • Practice tracking and benchmarking: Customizable benchmarks allow users to track and compare farming practices against project-specific sustainability, agroecological, or other implementation criteria.
  • GHG emissions estimation: Farm-level activity data can be connected through an API to the SECTOR GHG Calculator, supporting estimation of GHG emissions and emission reductions and contributing to project-level MRV and climate reporting.

Key points to design your program

RiceMoRe can be integrated into programmes focused on sustainable rice production, digital agriculture, climate-smart agriculture, agricultural monitoring, climate mitigation, and MRV. By providing standardized, georeferenced, and traceable production data, the system can help programme teams plan and target interventions, establish baselines, monitor implementation, compare farming practices, and generate evidence for reporting and learning. Its connection to the SECTOR GHG Calculator also enables farm-level activity data to be used for GHG emissions estimation, supporting climate-related monitoring and MRV. RiceMoRe can therefore contribute to programme objectives related to food security and sustainable agriculture (SDG 2), innovation and digital infrastructure (SDG 9), responsible production (SDG 12), climate action (SDG 13), and partnerships (SDG 17), depending on how it is deployed within the programme.

To successfully integrate RiceMoRe into a programme:

  • Build partnerships around the data and implementation needs: Engage relevant government agencies, IRRI, research institutions, extension services, farmer organizations, and private-sector actors to agree on roles for system deployment, data collection, training, management, and technical support.
  • Define data and reporting requirements from the start: During programme design, agree on the key indicators, data fields, reporting frequency, geographic coverage, benchmarks, and GHG-related information required for monitoring, evaluation, learning, and MRV.
  • Build users’ capacity to collect and use the data: Train project staff, government officers, cooperative staff, and farmers on digital data collection, data quality checks, validation, visualization, benchmarking, and interpretation of the information generated by the system.
  • Integrate GHG monitoring into the programme where relevant: Connect farm-level activity data with the SECTOR GHG Calculator to estimate GHG emissions and emission reductions and generate structured information for climate-related monitoring and MRV.
  • Use the data to monitor implementation and results: Track indicators such as the number of farmers and fields registered, geographic coverage, data completeness and quality, adoption of targeted practices, system use, estimated GHG emissions and emission reductions, and the use of RiceMoRe data in programme planning, reporting, and learning.

2018–2024 Timeline

Co-created, tested, and implemented in Vietnam

2020–2021 Timeline

Initial piloting in Can Tho province

IP

Open source / open access

Technical Characteristics of RiceMoRe

1. System Architecture & Platform
  • Open-Source Infrastructure: Built on open-source web and mobile technologies, incurring zero software licensing fees and offering a low-cost, highly replicable system.
  • Multi-Platform Access: Features both a web-based management portal and mobile applications for Android and iOS devices.
  • Flexible Database & System Integration: Allows data storage in local, cloud, or custom organizational databases. It incorporates APIs to connect farm-level activity data with independent greenhouse gas (GHG) calculation engines and enables data exchange with existing Ministry systems.
2. Spatial & Temporal Resolution
  • Spatial Granularity: Operates at the field and commune unit level, utilizing georeferenced administrative boundary maps (compatible with GIS tools such as QGIS for polygon management).
  • Temporal Frequency: Supports weekly, bi-weekly, seasonal, and annual data collection, monitoring, and reporting schedules.
3. Hierarchical Data Flow & Approval Workflows
  • 5-Tier Administrative Hierarchy: Links workflows structured directly across Commune, District, Province, Region/Zone, and National levels.
  • Automated Data Aggregation: Automatically consolidates records from commune level up to district, province, region, and national scales, eliminating manual compilation while preserving traceability back to original commune entries.
  • Multi-Stage Validation Protocol: Implements status-tracked review and approval workflows (e.g., Draft, Submitted, Approved, or Rejected) where upper-level officials validate entries before they are committed to the national database.
4. Core Data Modules & Functional Indicators
  • 7 Standardized Reporting Modules: Features structured modules for Production Planning, Planting Progress, Growth Stages, Harvesting Progress, Technical Practice Application (e.g., seed rates, fertilizer reduction, water-saving techniques), Mechanization, and Yield Loss/Damage (from natural disasters or pests).
  • Customizable Forms: Supports customizable survey forms, indicators, and criteria tailored to contracted farming, company standards, or specific project targets.
5. GIS Mapping & Visual Analytics
  • 3 Base Map Options: Integrates Google Terrain, Google Satellite, and Bing Maps.
  • 6 Thematic Production Layers: Generates spatial maps for planting progress, growth stages, harvesting progress, technical application, mechanization, and damage/yield loss tracking.
  • Interactive Spatial Tools: Provides administrative area filters, seasonal query tools, map legends, and interactive map navigation (zooming and panning).
6. Automated GHG MRV Engine
  • Activity-Based Emission Calculation: Translates field management data (such as fertilizer application, straw management, and water regimes) into automated GHG emission estimates to support national Measurement, Reporting, and Verification (MRV).
  • Public Accessibility: Features an open-access GHG calculation tool accessible to public users and farmers without requiring a user login.
7. User Management & Security
  • Role-Based Access Control (RBAC): Manages permissions based on administrative levels or organizational units.
  • Granular Action Permissions: Controls specific functional rights for viewing, adding, editing, deleting, and importing/exporting Excel data.
  • Audit Logging & Security: Tracks login history and user operations in real-time, supported by phone/email account management and OTP password recovery.

Scaling Readiness describes how complete a technology's development is and its ability to be scaled. It produces a score that measures a technology's readiness along two axes: the level of maturity of the idea itself, and the level to which the technology has been used so far.

Each axis goes from 0 to 9 where 9 is the “ready-to-scale” status. For each technology profile in the e-catalogs we have documented the scaling readiness status from evidence given by the technology providers. The e-catalogs only showcase technologies for which the scaling readiness score is at least 8 for maturity of the idea and 7 for the level of use.

The graph below represents visually the scaling readiness status for this technology, you can see the label of each level by hovering your mouse cursor on the number.

Read more about scaling readiness ›

Scaling readiness score of this technology

Maturity of the idea 8 out of 9

Uncontrolled environment: tested

Level of use 9 out of 9

Common use by intended users, in the real world

Maturity of the idea Level of use
9
8
7
6
5
4
3
2
1
1 2 3 4 5 6 7 8 9

Positive impact 8

Target  Group Positive Impacts
Women smallholder farmers with low digital literacy
  • Greater visibility of farming activities: Standardized records can make women farmers’ production activities and practices more visible within farm, cooperative, project, or institutional monitoring systems.
  • Accessible participation in data collection: User-friendly predefined forms can facilitate participation in farm-data reporting without requiring advanced digital skills.
Smallholder farmers with low digital literacy in areas with limited internet connectivity
  • More structured participation in reporting: Standardized, user-friendly forms simplify the recording of farm-level information such as cultivated area, varieties, production progress, practices, and yields.
  • Inclusion in wider production monitoring: Farm-level records can be aggregated through existing reporting structures, allowing participating smallholders to be represented in broader production monitoring.
Smallholder farmers with low digital literacy and weak access to collective support structures
  • Structured farm records: Standardized forms provide a simpler and consistent way to record production activities and practices, including water, fertilizer, and straw management.
  • Access to selected public functions: Some RiceMoRe functions, including GHG emission estimation, can be accessed by public users without requiring an account.
Smallholder farmers with limited internet connectivity and weak access to collective support structures
  • Spatial visibility of production areas: Georeferenced records allow participating farms and production areas to be represented in spatial agricultural monitoring.
  • Integration into wider monitoring: Farm-level production and practice data can be consolidated across geographic or administrative levels, increasing the representation of participating production areas in broader monitoring systems.

 

Unintended impact 8

Target group Potential unintended impacts Mitigation measures
Women smallholder farmers with low digital literacy
  • Reduced participation in data management: If data entry is handled mainly by more digitally skilled household members or field actors, women may have less direct involvement in recording and reviewing their farming activities.
  • Underrepresentation in monitoring: Lower participation in digital reporting could result in women's activities being less completely represented in aggregated data.
  • Ensure direct participation: Include women directly in training, data entry, review, and validation activities.
  • Monitor representation: Check participation and completeness of records by gender and address identified gaps.
Smallholder farmers with low digital literacy and limited internet connectivity
  • Incomplete or delayed records: Difficulties entering or transmitting digital data may result in incomplete or delayed production records.
  • Underrepresentation in monitoring: Farmers unable to report as regularly as better-connected users may become less visible in aggregated and spatial monitoring.
  • Use existing reporting lines: Where direct farmer reporting is difficult, use established local reporting structures to support data collection, review, and transmission.
  • Monitor reporting coverage: Identify persistent gaps in reporting and provide targeted support to poorly represented users or areas.
Smallholder farmers with low digital literacy and weak access to collective support structures
  • Dependence on intermediaries: Farmers requiring assistance may become dependent on other actors to enter or manage their production information.
  • Unequal data visibility: Farmers receiving less organizational or technical support may generate less complete records than farmers integrated into stronger reporting networks.
  • Maintain farmer involvement: Use assisted data entry where necessary while allowing farmers to participate in and review the information recorded about their farms.
  • Extend participatory implementation: During deployment, involve farmers outside strong collective structures in adapting reporting arrangements and identifying support needs.
Smallholder farmers with limited internet connectivity and weak access to collective support structures
  • Geographic data gaps: Less regular reporting from poorly connected and weakly supported production areas may create gaps in georeferenced datasets.
  • Bias in data-based decisions: Persistent geographic gaps could cause monitoring and planning to reflect better-represented production areas more strongly.
  • Address coverage gaps: Use existing local reporting structures or other appropriate collection arrangements to improve data coverage in poorly connected areas.
  • Check representativeness: Assess geographic coverage and data completeness before using aggregated information for planning and targeting.

Barriers 11

Target group Adoption barriers Mitigation measures
Women smallholder farmers with low digital literacy
  • Limited digital skills: Limited familiarity with web/mobile tools may constrain independent use despite RiceMoRe's user-friendly design.
  • Unequal access to devices or training: Limited access to digital equipment or technical training may reduce direct participation.
  • Provide targeted practical training: Complement RiceMoRe's simplified interface with hands-on onboarding and continued support adapted to users' skills.
  • Ensure inclusive access: Include women directly in training and deployment and provide shared or institution-supported equipment where needed.
Smallholder farmers with low digital literacy and limited internet connectivity
  • Limited digital skills: Even with standardized and user-friendly forms, some users may still require assistance with digital data entry.
  • Limited connectivity: Weak or unstable internet can constrain regular transmission of production data.
  • Limited access to digital equipment: Some smallholders may not have regular access to suitable smartphones or computers.
  • Provide practical user support: Use hands-on onboarding and assistance while taking advantage of RiceMoRe's predefined and customizable forms.
  • Use existing reporting structures: Where direct transmission is difficult, organize data collection and transmission through established local reporting lines.
  • Facilitate equipment access: Provide shared or institution-supported devices where individual access is limited.
Smallholder farmers with low digital literacy and weak access to collective support structures
  • Limited digital skills: Farmers may still require assistance to use the system despite its simplified data-entry design.
  • Limited access to technical support: Farmers weakly connected to cooperatives, extension services, or institutional networks may have less access to onboarding and troubleshooting.
  • Limited reach of organized deployment: Farmers outside established reporting networks may be harder to include when RiceMoRe is deployed through institutional structures.
  • Provide adapted training: Use RiceMoRe's flexible forms together with practical onboarding appropriate to users' digital skills.
  • Extend support beyond organized groups: Make extension or other field-level technical support accessible to farmers outside cooperative networks.
  • Use participatory deployment: Identify and involve weakly connected farmers when adapting RiceMoRe to a new implementation context.
Smallholder farmers with limited internet connectivity and weak access to collective support structures
  • Limited connectivity: Poor network coverage can constrain regular transmission and use of web/mobile functions.
  • Limited access to support: Weak links to cooperatives or institutional structures may reduce access to assistance when technical or reporting problems occur.
  • Implementation costs: RiceMoRe has no software licensing fees, but access to equipment, connectivity, training, hosting, and technical support may still require resources.
  • Adapt reporting arrangements: Use existing reporting lines or other locally appropriate arrangements where direct digital transmission is unreliable.
  • Establish accessible support: Ensure technical assistance reaches users outside strong cooperative or institutional networks.
  • Budget beyond licensing: Plan resources for equipment, connectivity, training, hosting, and technical support despite the absence of software licensing fees.

Tested & adopted
Adopted
Tested
Testing ongoing
Egypt Equatorial Guinea Ethiopia Algeria Angola Benin Botswana Burundi Burkina Faso Democratic Republic of the Congo Djibouti Côte d’Ivoire Eritrea Gabon Gambia Ghana Guinea Guinea-Bissau Cameroon Kenya Libya Liberia Madagascar Mali Malawi Morocco Mauritania Mozambique Namibia Niger Nigeria Republic of the Congo Rwanda Zambia Senegal Sierra Leone Zimbabwe Somalia South Sudan Sudan South Africa Eswatini Tanzania Togo Tunisia Chad Uganda Western Sahara Central African Republic Lesotho
Countries where the technology is being tested or has been tested and adopted
Country Testing ongoing Tested Adopted
This technology has not been tested or adopted in any country.

This technology can be used in the colored agro-ecological zones. Any zones shown in white are not suitable for this technology.

Agro-ecological zones where this technology can be used
AEZ Subtropic - warm Subtropic - cool Tropic - warm Tropic - cool
Arid – – – –
Semiarid – – – –
Subhumid – – – –
Humid – – – –

Source: HarvestChoice/IFPRI 2009

The United Nations Sustainable Development Goals that are applicable to this technology.

Sustainable Development Goal 2: zero hunger
Goal 2: zero hunger

Supports effective agricultural planning and monitoring through timely, standardized rice production data.
Sustainable Development Goal 9: industry, innovation and infrastructure
Goal 9: industry, innovation and infrastructure

Provides digital infrastructure for agricultural data collection, management, GIS-based monitoring, and reporting.
Sustainable Development Goal 12: responsible production and consumption
Goal 12: responsible production and consumption

Supports monitoring and benchmarking of sustainable, low-emission, and agroecological farming practices.
Sustainable Development Goal 13: climate action
Goal 13: climate action

Supports GHG emissions estimation and MRV for low-emission rice production.
Sustainable Development Goal 17: partnerships for the goals
Goal 17: partnerships for the goals

Supports data sharing and collaboration among government, research, development, farmer, and private-sector actors.

RiceMoRe is used to collect and manage rice production information throughout the cropping season, with data collected weekly, every two weeks, or at other intervals depending on monitoring needs.

  1. Set up: Define what information needs to be collected, when it should be collected, and how it will be reported.
  2. Collect data: Collect production information directly from the field, farms, cooperatives, projects, or other production units using digital forms.
  3. Store and manage: Store and manage the collected information securely in a centralized system, either on the organization’s own infrastructure or in a trusted cloud environment.
  4. Consolidate and analyze: Automatically bring data together across farms, locations, reporting periods, and administrative levels to identify production trends and performance.
  5. Visualize: View and explore the information through maps, charts, and tables, making it easier to understand where activities are taking place and how production is performing.
  6. Report and act: Export data and generate reports that provide evidence for planning, monitoring, decision-making, and follow-up actions.

Last updated on Sep 24, 2026

International Rice Research Institute (IRRI)
 Yen Bui
Commodities
Rice
Sustainable Development Goals Details ›
Sustainable Development Goal 2: zero hunger
Sustainable Development Goal 9: industry, innovation and infrastructure
Sustainable Development Goal 12: responsible production and consumption
Sustainable Development Goal 13: climate action
Sustainable Development Goal 17: partnerships for the goals
public.components.sdgs.motre
Subcategory
Crop management
Value chain step
Production
Tested/adopted in Details ›
Tested & adopted
Adopted
Tested
Testing ongoing
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RiceMoRe: Digital rice activity monitoring and reporting system

https://e-catalogs.taat-africa.org/org/technologies/ricemore-digital-rice-activity-monitoring-and-reporting-system

Last updated on 24 September 2026, printed on Sep 25, 2026

Enquiries e-catalogs@taat.africa