Lesson 2: Optimization Through Controlled Adjustment

What is the most impactful inefficiency in our system?
What single variable can we safely adjust?
How do we isolate one change at a time?
How do we determine whether an adjustment truly improved performance?
How do unintended consequences reveal deeper system complexity?

Optimization
Iteration
Independent variable
Dependent variable
Controlled variable
Photoperiod
Density
Flow rate
Turnover
Trade-off
Feedback loop

HS-ETS1-3
Evaluate a solution to a complex real-world problem based on prioritized criteria and trade-offs.

HS-LS2-4
Use mathematical representations to support claims for cycling of matter.

Science and Engineering Practice – Planning and Carrying Out Investigations
Science and Engineering Practice – Analyzing and Interpreting Data
Science and Engineering Practice – Constructing Explanations

Crosscutting Concept – Cause and Effect
Crosscutting Concept – Stability and Change

Students evaluate competing solutions using evidence.
Students analyze variable manipulation and outcome relationships.
Students justify conclusions using multi-day data trends.

Day 1 – Identifying the Optimization Target

Students revisit their efficiency analysis from Segment 1.

Each group identifies:

One measurable inefficiency
One suspected cause
One potential variable they can adjust

Examples:

Excess nitrate accumulation
Uneven plant growth
Flow too fast for absorption
Insufficient plant density

Students must propose exactly one change.

Teacher enforces the rule:

One variable only.

Purpose of Day 1
Introduce disciplined engineering iteration.

Day 2 – Controlled Adjustment Plan

Students design an optimization plan including:

What will change
Why this change should improve efficiency
How they will measure improvement
What variable remains constant

Students predict:

What parameter should shift
How quickly change might be visible
What unintended consequence could appear

Students submit plan for approval before implementation.

Purpose of Day 2
Shift from reaction to hypothesis-driven refinement.

Day 3 – Implementation

Students implement their single adjustment.

Examples:

Increase plant density
Adjust flow rate
Modify light duration
Redistribute plant placement
Alter feeding frequency

Students document:

Exact change made
Time of change
Initial observation

Purpose of Day 3
Make iteration explicit and measurable.

Day 4 – Monitoring and Data Collection

Students measure parameters before and after change.

They graph:

Nitrate trend
Ammonia stability
Plant growth progression

Students ask:

Did nitrate reduction improve?
Did any new instability appear?
Did flow adjustment create pooling or stress?

Purpose of Day 4
Train students to look for secondary effects.

Day 5 – Evaluation and Trade-Off Analysis

Students evaluate:

Did this change improve efficiency?
Did it introduce new trade-offs?
Is the improvement sustainable?

Students write a short evaluation including:

Claim
Evidence
Explanation

Optional Extension
Students compare across groups to identify most effective optimizations.

Purpose
Reinforce that engineering refinement requires trade-off reasoning.

DOK Level

DOK 2
Describe variable changes and record parameter shifts.

DOK 3
Evaluate the effectiveness of a controlled change using multi-day evidence.

Approaches DOK 4
When students analyze unintended consequences and propose next-level refinements.

Agricultural engineers adjust irrigation and density to maximize yield.
Aquaculture managers adjust feeding rates based on nutrient trends.
Urban farms rely on controlled iteration to improve production without expanding footprint.

Students see optimization as essential for sustainable food systems.

More changes improve faster.
Immediate improvement equals long-term success.
Optimization means maximizing growth only.
If numbers improve slightly, the system is fully optimized.

Provide optimization planning template.
Allow visual modeling before written proposal.
Provide structured CER framework for evaluation.
Challenge advanced students to estimate percent change in nitrate reduction.

Optimization Report

Students submit:

Description of variable adjusted
Before-and-after data comparison
Graph of at least one parameter
Trade-off analysis
Evaluation of sustainability