Modelling Tools Advance Sustainable Food Resources
Modelling tools advance sustainable food resources by simulating climate, crop, soil, and management scenarios to project superior outcomes.
Topics:
- Crop Modelling Tools for Sustainable Food Resources
- Integrating Crop Modelling Tools with Geospatial Products
- Machine Learning and Artificial Intelligence (AI)
- Mobile Apps
- Recent Advances in Crop Modelling Tools
- LandScale – Land Sustainability Modelling
- Blockchain & Hyperledger
Crop Modelling Tools for Sustainable Food Resources
Modelling plays an important role in projecting how climate change will impact future crop yields and profitability.
Crop modelling tools can simultaneously simulate several parameters. These include direct and indirect climate parameters, various soil parameters, and management techniques.
- The physiological responses of crops to climate and environmental stressors, such as temperature extremes, drought, downpours, pest outbreaks, and disease.
Crop modelling plays an important role in projecting how various crops will be impacted by climate change, how the various impacts may be offset by adaptation measures, and what the implications are for global food security.
Crop modelling tools help project crop yields and profitability under various climatic, economic, and management scenarios for the major crops.
The outputs project future crop yields for key climate scenarios. They project the implications for global food security. Crop models can evaluate the benefits of adaptation measures.
Crop modelling and related tools help to project crop yields and profitability under various climatic, economic, and management scenarios for the major crops.
For more information, please refer to Education.
Integrating Crop Modelling Tools with Geospatial Products
GIS Satellites: Sentinel 1 and 2, Landsat
Remote Sensing
Machine Learning and Artificial Intelligence (AI)
Machine Learning (Google Earth Engine – GEE)
Mobile Apps
Mobile App: iCrops
Recent Advances in Crop Modelling Tools
Crop modelling tools are advancing to improve the projected yield and food security outcomes by improving the parameterization or representation of:
- Advancing the physiological responses of crops to environmental stressors
- Improving influences of farm management practices on yields and profitability
- Advancing the impacts of pest outbreaks and disease on crop yields
- Advancing the impacts of ground-level ozone levels on crop yields…
The mission of the Agricultural Model Intercomparison and Improvement Project (AgMIP) is to improve crop models for assessing climate impacts and variability on food production, food security and related areas at local to global scales.
Examples of crop models include: DSSAT, APSIM, CropSyst, WOFOST, AquaCrop, among others…
Other initiatives include:
- the Consultative Group on International Research (CGIAR) – Crop Modelling Community of Practice and the CGIAR Platform for Big Data in Agriculture
- Modeling European Agriculture with Climate Change for food Security (MASCUR)
- The Coupled Model Intercomparison Project (CMIP5 and CMIP6) to improve our understanding of climate systems and projections for future climate scenarios.
- The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) offers a framework for consistently projecting the impacts of climate change across affected sectors and spatial scale. Examples of sectors include water, forests, lakes, and fisheries.
Continuous ongoing and upcoming improvements:
- Use Big Data, Multisource Data, and Data Analytics to improve model predictability
- Advance remote-sensing capabilities for field, regional, country, and global monitoring
- Interface crop models with large-scale models
- Include multiscale modelling for linking field modeling with regional or large-scale adaptation strategies
- Assess crop production considering environmental sustainability
- Incorporate more gene-to-cell and genetic trait capabilities (Genotype x Environment x Management)
- Enable multiscale gene-to-farm considering resilient, sustainable, changing climate food systems at global scales
- Encompass enhanced microbial benefits from carbon sequestration
- Encompass enhanced analysis of various seed cultivars to microbial seed treatments…
LandScale - Land Sustainability Modelling
LandScale includes three components to help with sustainability improvements at scale
- Assessment Framework – Measure landscape sustainability performance using a standard set of indicators
- Verification Mechanism – Evaluate the accuracy of LandScale assessments through a trusted verification process
- Reporting Platform – Conduct assessments and communicate impact with the user-friendly online platform
Blockchain & Hyperledger
Blockchain is a distributed set of data records or ledger that is not controlled by any single user – rather it is managed and time-stamped by a cluster of computers connected via the web. Once recorded, the record cannot be edited. Blockchain has 3 pillars: Decentralization, Transparency, and Immutability. Blockchain is also referred to as a Distributed Ledger Technology (DLT).
The global agri-food chain is complex, extending from growers and producers, to processors, transportation networks, distribution centres, grocers, restaurants, consumers, and ultimately to waste & recycling operations.
A component of the agri-food value chain is trust and transparency among the various stakeholders, and consumers recognize these features. However, problems may develop when a foodborne pathogen causing illness is discovered somewhere along the chain.
A blockchain enables an end-to-end, transparent food traceability system that can reduce the time required to identify a necessary recall.
Hyperledger, as part of the Linux Foundation. It is a multi-project, open-source collaborative created to advance blockchain technologies across various industries. Hyperledger application developments involve special interest groups (SIGs) for:
- Healthcare
- Climate Action and Accounting
- Social Impact
- Supply Chain
- Capital Markets
- Telecom.
- Last Modified: May 17, 2024