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Resiliants: WATER aims to create a predictive dashboard for avocado...
Resiliants: WATER aims to create a predictive dashboard for avocado farming in La Palma, integrating AI-based climate projections to guide agricultural investments. By forecasting water footprints and market prices, this tool helps investors and farm managers make informed decisions, ensuring resilience against climate change.
- Predictive insights for climate-resilient farming
- Bridges research with practical farm management
- Enhances data precision with sector-specific systems
See more information about this level and the TRL and SRL levels.
This project focuses on adapting the Canary Islands’ agricultural sector to climate change through advanced data integration and decision-making tools. It leverages historical meteorological and market data, combined with real-time sensor data from producers, to create highly granular insights. By developing and applying these tools, the project aims to improve agricultural resilience, optimize water use, and support sustainable practices. The results and methodologies are intended to be scalable and applicable to similar case studies in other regions, such as water management in Lake Ohrid and Lake Prespa or efficiency in the Main River. This approach fosters a data-driven strategy for climate adaptation, enhancing both environmental and economic outcomes.
The project presented to develop a predictive model of investment costs in agriculture, based on water use and climate projections, is
a spin-off of a research project we are carrying out on the generation of water footprint and carbon balance indicators for carbon
farming. It is a line of R+D+i of the company that has gone from the first prototypes of irrigation recommendations by whatsapp to the
design and implementation of digital crop twins, in which we integrate the aforementioned developments on water footprint and
agricultural carbon balance. In the development of the various projects that have enabled us to develop the predictive cost model
based on climate projections and the use of water resources, we have received co-financing from the Canary Islands Government
through the Agencia Canaria de Investigación, Innovación y Sociedad de la Información (ACIISI).
Data volume: As with any innovation process involving data models, a limiting factor for the effectiveness of the model to be developed is the amount of data required to learn it.
Predictive algorithms: It is also a critical factor in the project to have a precise theoretical and methodological framework that allows the creation of an effective and, in the best case, efficient algorithm for determining the costs associated with the value of water in future scenarios.
Cost of processing: An essential aspect of effectiveness is also related to the current limitations of cloud computing
Resiliants: WATER aims to create a predictive dashboard for avocado farming in La Palma, integrating AI-based climate projections to guide agricultural investments. By forecasting water footprints and market prices, this tool helps investors and farm managers make informed decisions, ensuring resilience against climate change.
- Predictive insights for climate-resilient farming
- Bridges research with practical farm management
- Enhances data precision with sector-specific systems
The main components of the system have been tested separately, and an initial integration exercise has been conducted.
This project focuses on adapting the Canary Islands’ agricultural sector to climate change through advanced data integration and decision-making tools. It leverages historical meteorological and market data, combined with real-time sensor data from producers, to create highly granular insights. By developing and applying these tools, the project aims to improve agricultural resilience, optimize water use, and support sustainable practices. The results and methodologies are intended to be scalable and applicable to similar case studies in other regions, such as water management in Lake Ohrid and Lake Prespa or efficiency in the Main River. This approach fosters a data-driven strategy for climate adaptation, enhancing both environmental and economic outcomes.
The project presented to develop a predictive model of investment costs in agriculture, based on water use and climate projections, is
a spin-off of a research project we are carrying out on the generation of water footprint and carbon balance indicators for carbon
farming. It is a line of R+D+i of the company that has gone from the first prototypes of irrigation recommendations by whatsapp to the
design and implementation of digital crop twins, in which we integrate the aforementioned developments on water footprint and
agricultural carbon balance. In the development of the various projects that have enabled us to develop the predictive cost model
based on climate projections and the use of water resources, we have received co-financing from the Canary Islands Government
through the Agencia Canaria de Investigación, Innovación y Sociedad de la Información (ACIISI).
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