In Silico Farming
zapp2photo @ stock.adobe.com
A digital simulation of a real farming ecosystem based on a complex mathematical model to simulate how yields are expected to perform under any given circumstance. Sensors in the field and satellite aerial imaging provide data from real crops and use machine learning algorithms to make real-time projections. Planting patterns, tillage systems, sunlight and shading, water availability, and microbial interactions are some of the variables that could be digitally tested. This computer simulation reflects the real world as closely as possible, enabling crop optimization analysis and supporting climate change adaptation measures for farmers.
These programs need to be designed for women without ICT skills to avoid excluding people (especially women) from understanding the educational resources available through computerized farming. Otherwise, this could potentially increase the feminization of poverty in agricultural settings.
Enables those without access to educational opportunities to achieve independence and liberation from the monopolization of agricultural methods.
This could create opportunities for communities to share their knowledge on a global scale, including facilitates cooperation, as it enables the creation of open-source social networks.