PROPOSED PROJECT
Crop Trial Comparison
Give seed and agronomy teams a visual comparison of field trials by field section, crop variety, and growing method. Extract observations from field notes, align dated photos with recorded measurements, and chart outcomes alongside growing conditions and missing data for the agronomist's interpretation.
Keep the missing plot in the picture.
Equal 10 m² plots
Same recorded growing method
Upper field / freely draining
Middle field / freely draining
Lower field / wetter ground
The project in practice.
A fictional trial grows varieties A and B in equal 10 m² plots within three field blocks under the same recorded method. Harvest weights are 18 and 20 kg in block 1, 22 and 23 kg in block 2, and 16 kg for A with B missing in block 3. Across the two complete pairs, B averages 1.5 kg more per plot. Block 3 stays visible but is excluded from that paired calculation; a note records drainage differences between blocks.
WHAT IS USUALLY DONE
Field observations arrive in notebooks, photos, and spreadsheets. A single overall average can hide differences between field sections or quietly exclude a missing harvest record.
WHAT SOVRUN CAN DO FOR YOU
Sovrun would build a trial-comparison workspace that connects measurements and dated photos to the correct field section, variety, and growing method. It compares like-for-like plots, keeps missing records visible, and presents the surrounding conditions for an agronomist to interpret.
Compare the pairs
you actually measured.
B exceeds A by 2 kg in block 1 and 1 kg in block 2. The mean of those paired differences is 1.5 kg per 10 m² plot. Block 3 cannot contribute a paired difference while B’s harvest record is missing.
Common scale: 0–25 kg. A missing record is not zero.
Observed difference
for B versus A.
The drainage note helps explain the field context. This small example does not establish whether variety, field conditions, or other differences caused the result.
Look for the original B harvest entry and verify its plot ID, area, date, and units. Keep the record missing until it is recovered. Do not substitute a neighboring plot, an estimated yield, or a zero.
A trial summary
an agronomist can inspect.
Measurements, comparable pairs, and growing conditions stay together. The expected result is a clearer basis for review and a follow-up trial, not a guaranteed crop improvement.
Common scale: 0–25 kg. A missing record is not zero.
Two differences. One mean.
- Block 1
- 20 − 18 = +2 kg
- Block 2
- 23 − 22 = +1 kg
- Mean difference
- (2 + 1) ÷ 2 = +1.5 kg
- Block 3
- Excluded / B missing
The context stays attached.
- Plot area
- 10 m² each
- Growing method
- Same recorded method
- Lower field note
- Wetter ground
- Next check
- Recover missing B entry
How we’d make it happen.
Align the field records
Match every observation to its plot, variety, date, area, and growing method. Check units and keep missing measurements visible.
Compare equivalent plots
Use the trial’s comparison plan. Show paired field sections, conditions, and source records instead of mixing unmatched results.
Review the interpretation
An agronomist checks the design and confounding conditions before approving conclusions or planning a follow-up trial.
Where AI helps
Document and language models extract proposed observations from notes and match explicit plot references. Code checks units, areas, dates, and comparable pairs before drawing charts. Image models can suggest visible observations, but do not replace measured yield or establish a cause.
What people approve
An agronomist confirms plot identity, trial design, measurements, and growing conditions. They decide which comparisons are meaningful and what additional trial work is needed. The workspace does not rank a variety as universally better from a small example.
What we’d need to start
- Plot map, variety assignments, growing methods, and measured plot areas
- Dated field notes, photos, harvest measurements, and weather records
- Trial comparison rules and agronomist review owners
How we’d test it
Test mixed units, unequal plot areas, duplicated measurements, missing paired records, and date mismatches. Independently recompute the comparisons and ensure absent harvest data is not treated as zero. Observed differences must not become causal or commercial performance claims.
Prepared for Agriculture & food systems. We would agree on the work after reviewing your brief, materials, and requirements.
This is a proposed project, not completed client work. The visuals and numbers show example outputs, not measured results.
