Scales

system mapping across scales

• Real world problems occur across multiple scales and levels: water,
  nutrients, climate

• Actors and interactions in complex systems operate across scales and
  levels

• Requires thorough understanding and mapping

• Real world problems (can) change over time

• Drivers, impacts, pressures, state and responses (DIPSR)

• Changes are dynamic, causal and sometimes erratic

what are the benefits of system mapping across scales?

• mitigate unintended consequences:

  • allows decision-makers to see the cascading effects of policy

  • practice changes across different scales

• identify bottlenecks:

  • helps identify where to target intervention (e.g. policy adjustments, technology adoption) for maximum impact.

• enhance collaboration:

  • facilitates partnerships between unconventional stakeholders (e.g. connecting rural farmers to urban consumers or local governments)

• improve data quality:

  • enables the synthesis of diverse data sources to produce more consistent, high-quality, and public-available datasets

Why is system mapping used?

systems mapping involves creating visual depictions such as diagrams, maps, or sketched models to represent the complex relationships between multiple actors and issues

What are the 2 types of system mapping?

1. actor/stakeholder mapping

2. factor/system mapping

what is an Actor/stakeholder mapping?

• a visual depiction that identifies and maps out the relationships, flows and interactions between the multiple human actors, organizations, and sectors involved in a system.

• people & institutions

what is a Factor/system mapping?

• also a visual representation (such as a CLD, flow chart, or sketched model) that charts the complex, interconnected relationships between biophysical, economic, and systemic drivers.

• variables & drivers

What are the 4 analytical methods for cross-scale linkages?

1. process-based models (e.g. STONE, INITIATOR, ANIMO)

2. empirical/tier-1 models

3. machine learning & empirical models

4. expert-driven & data-driven guidelines

describe how a policy decision taken at one scale can produce unintended effects at another scale, and explain why this complicates governance?

1. scaling gap:

   1. when a policy is designed at the ecosystem level, its translation to farm-level practices might not perfectly align with local conditions, leading to unexpected outcomes.

2. system dynamics & cascading effects:

   1. real-world problems are dynamic and operate across multiple levels

   2. a policy intervention aimed at improving one aspect at a broad scale might trigger a chain of reactions across different parts of the system, some of which are not immediately obvious

3. ignoring hidden costs

this complicates governance because:

• when unintended effects emerge at a different scale, it becomes challenging to directly attribute them to the original policy decision.

  • makes it hard to hold policymakers accountable

• need for holistic approaches

why is scale an essential consideration in system analysis?

it influences which processes are considered relevant.

a farmer reduces NH3 emissions while keeping total nitrogen inputs (e.g. fertiliser) and outputs (e.g. milk) constant. What is the most likely consequence for the farm’s nitrogen balance?

other forms of nitrogen loss increase.