Agriculture Simulations makes use of Earth observation (EO) data to solve challenges for agricultural planning, harvesting, logistics and long-term food systems resilience in a changing climate. It gives dynamic insight into complex human and natural systems by combining computational simulations and EO data. It enables both short-term operational and long-term strategic decision-making by actors in the food chain – from independent farmers wanting to know where to harvest, to large traders needing to know who to buy sustainable produce from and governments and NGOs looking to optimise strategic infrastructure investments.
- Makes use of EO data to solve challenges for agricultural planning, harvesting, logistics and long-term food systems resilience in a changing climate.
- It enables both short-term operational and long-term strategic decision-making by actors
in the food chain – from independent farmers wanting to know where to harvest, to large traders needing to know who to buy sustainable produce from and governments and NGOs looking to optimise strategic infrastructure investments.
- It can capture the economics of the food system and be tuned to clients’ agricultural business models to show financial impact of their agronomic and buying decisions.
- It provides daily insights into unintended consequences on agricultural system as a whole, and creates insight into what consequences an agronomic, economic or social policy decision can lead to.
- It captures how decisions influence both humans and nature through thousands of simulations in a digitised version of the clients’ agricultural landscape.
Key technical features
- It is a rules-based computational simulation approach for analysing input from multiple EO data sources, in a changing environment.
- All simulations are carried out in a digitised version of reality, in which EO data layers makes up the components of the ‘simulated world’.
- Our solution is delivered via a web-based Software-as-a-Service.
- It can be integrated with other systems through an application programming interface (API).
- It consumes both open and commercial EO data – with optical EO being the main data source.
- The data is refreshed from daily to every month, dependent on data source.
- The resolution of imagery used is between
10-15 metre for systems understanding of large geographies, and down to 50 centimetre for individual farms where object identification may be necessary.
- Some input generation such as yield, and harvest timing requires local inputs through handheld devices to ground-truth estimates derived from EO.
Sensonomic is assisting the International Fund for Agricultural Development (IFAD) and the countries of Senegal, Mali and Cameroon to optimise their investment in agricultural infrastructure in rural areas. The product’s simulations show which roads to upgrade and where to invest in storage facilities to support agricultural intensification under different scenarios of climate change and population growth. In the case of rice, we have considered how potential investments will influence where agricultural areas will be expanded, and therefore how the potential new facilities will cope under the future agricultural landscape that they will enable.
Our UK operation is headquartered in London’s Canary Wharf, at Level39. Our diverse team is comprised of geographers, technologists, biologists, product developers, and economists. We have a strong research collaboration with leading academics from the University of Oxford.