State spaces for agriculture: A meta-systematic design automation framework

Area of Science:

• Agricultural Science
• Computer Science
• System Design

Background:

• Agriculture is a designed system with a significant global footprint.
• Current agricultural design approaches are fragmented and rely on intuition.
• Existing methods risk overlooking beneficial, non-obvious agricultural designs.

Purpose of the Study:

• To introduce a computational state space framework for agricultural system design.
• To address limitations in current, diverse, and fragmented approaches to agricultural design.
• To enable computational exploration and evaluation of a large agricultural design space.

Main Methods:

• Utilizing a state space framework from computer science.
• Developing general computational abstractions for exploring agricultural designs.
• Proposing a computational approach to systematically evaluate agricultural designs.

Main Results:

• The framework enables exploration of a vast agricultural design space.
• Computational methods can overcome limitations of intuition-based design.
• Facilitates identification of novel agricultural designs for sustainability.

Conclusions:

• A state space framework offers a systematic approach to agricultural system design.
• Computational methods can enhance the discovery of sustainable agricultural designs.
• This approach allows for empirical testing of computationally proposed designs.