models → false-precision
Modern agronomy has access to more data than ever before.
The danger is not lack of information — it is false precision: treating uncertain numbers as exact truths.
False precision drives over-optimisation and fragile systems.
False precision occurs when: - Measurements appear exact - Uncertainty is ignored - Variability is hidden - Decisions are made on narrow margins
A value with one decimal place is not necessarily accurate.
False precision arises from: - Single sensors - Averaged data - Model outputs without context - Clean graphs masking variability - Software interfaces that imply certainty
Presentation often exceeds reliability.
Over-optimised systems: - Operate close to limits - Have no buffer - Fail rapidly when conditions change
Biological systems require margin to absorb variability.
Common examples include: - Feeding to exact EC targets - Running VPD at theoretical optima - Tight irrigation thresholds - Minimal safety margins - Aggressive growth pushing
These systems perform well until they don’t.
Variability is: - Real - Structural - Informative
Ignoring variability removes early warning signals.
Chasing precise targets often leads to: - Frequent intervention - Stress stacking - Root damage - Increased disease pressure - Reduced yield ceiling
Stability usually outperforms precision.
Safer approaches include: - Working within ranges - Allowing buffers - Watching trends - Linking data to observation - Accepting uncertainty explicitly
Key mistake: - Assuming more data means more control
Precision without context creates fragility.