Choosing precision agriculture software comes down to three things: whether the data and the advice are independent, whether you can see how every rate was made, and whether the prescription actually reaches your machine. Ask every vendor the same ten questions below, then test the two or three that answer best on one of your own fields. A scorecard at the end keeps the comparison honest.
Start with the decision, not the feature list
Every platform demo shows a colourful NDVI map. That map is not the product. The product is the decision it leads to: a seeding rate, a nitrogen plan, a lime map, a sampling plan, a trial you can read after harvest.
So before you book demos, write down the three decisions you want the software to improve this year. For most operations that is some mix of:
- variable rate seeding or nitrogen on the fields with the most variation
- zone-based soil sampling instead of a fixed grid
- a yield map you trust enough to judge last season’s inputs
Which data layers matter, and what each one cannot tell you
No single layer explains a field. Each one records something real and misses something else, and good software lets you see which layer is doing the work.
| Layer | What it tells you | Watch out for | Best used for |
|---|---|---|---|
| Multi-year satellite productivity | What it tells youWhere the crop has done consistently better or worse over many seasons | Watch out forMixed crops, years lost to hail, flood or replant, cloud and shadow; NDVI flattens out in dense canopies | Best used forBase zones for seeding, sampling and variable rate |
| In-season imagery (NDVI, NDRE) | What it tells youHow the crop looks right now | Watch out forOne moment, not potential; growth stage changes the reading; NDRE holds up better than NDVI in dense canopies | Best used forIn-season nitrogen and scouting |
| Yield maps | What it tells youWhat the field actually produced | Watch out forNeeds cleaning first; one season mostly records its weather; combines calibrate differently | Best used forChecking zones, profit maps, judging last season’s inputs |
| Soil lab results | What it tells youMeasured pH, P, K and organic matter at the sampled points | Watch out forOnly as good as the sampling density; values between points are estimates | Best used forLime, phosphorus and potassium rates |
| Soil EC scan | What it tells youTexture and water holding, at high density | Watch out forReadings shift with moisture and temperature on the day; relative, not absolute | Best used forZones where soil drives yield; seeding |
| Topography | What it tells youElevation, slope and where water collects | Watch out forCoarse elevation data hides small depressions | Best used forExplaining wet and dry patterns; drainage |
| As-applied maps | What it tells youWhat actually went on, and where | Watch out forRate lag and section switching errors in the log | Best used forProving the plan was applied; post-season checks |
A few habits separate zones you can act on from zones that only look convincing:
- Trust agreement. Where two or three independent layers show the same pattern, the zone is real. Where they disagree, scout before you prescribe.
- Choose the seasons. Use years of the same crop that represent normal, dry and wet conditions. Drop years ruined by hail, flood or replant, or they will draw zones around the damage.
- Match zones to the machine. Three to five zones per field is typical. A zone smaller than the area your planter or spreader can change rate across is noise.
- Ground-truth. Sample soil by zone and walk the best and worst areas before the first prescription goes out.
- Leave a check strip. A strip at your usual flat rate is the only way the yield map can tell you whether the variable rate paid.
Black box or clear box: can you see how the rate was made?
Every prescription is built from three things: the inputs, the method that turns them into zones, and the rule that turns zones into rates. You should be able to see all three.
- Inputs. Which layers, which years, and which images were dropped for cloud or shadow.
- Method. How many zones, how much each layer counted, and the minimum zone size. You do not need the maths. You need to change a setting and see the map change.
- Rates. The formula or table behind each rate, and where its numbers come from: a university extension guideline, your own trials, or the vendor’s model.
"AI-driven" is not an answer on its own. A model can be useful, but if the vendor cannot say which inputs drive the rate, your agronomist cannot check it, and you cannot explain it to a landlord, a lender or an auditor.
A quick test in any demo: remove one layer, or change one weight, and see whether the map changes in a way you can explain. If nothing you change makes sense, you are being asked to trust, not to decide.
Who benefits from the recommendation?
Precision ag software is often owned by, sold by, or bundled with companies that also sell seed, fertiliser or crop protection. That is not automatically a problem, and the agronomy behind it may be sound. But a prescription is a purchase order, and you should know whose model set it.
Done well, variable rate mostly moves product: less where the field cannot use it, more where it pays. Depending on the field, the total can go up or down. Watch for these signals:
- prescriptions that only ever add product, never take it away
- rate ranges that sit above your local extension guidelines
- product or brand names built into the prescription
- recommendations you cannot export and take to another agronomist
- data terms that allow your field data to be used for marketing to you
A ten-minute check: compare the total product in the prescription with your flat-rate plan and with the public guideline for your region. For corn nitrogen in the US, the university Corn Nitrogen Rate Calculator is a good reference. If the prescription keeps coming out higher, ask why and ask to see the trial data behind it.
Ten questions to ask any precision agriculture software vendor
1. Who owns the data, and who else sees it?
Ask for the policy in writing. A good answer is short: your data stays yours, it is never resold, and you can export all of it, raw and processed, whenever you want. The Ag Data Transparent core principles are a useful checklist for this conversation.
How GeoPard answers: your field data stays yours, is never resold, and can be exported at any time.
2. Who earns money from the rate?
Ask how the company makes money. If any of it comes from inputs, ask how the rate logic is kept separate from sales, and whether you can replace their rates with your own or your agronomist’s.
How GeoPard answers: GeoPard is independent and sells no seed, fertiliser or crop protection. Rates come from your formula or your agronomist’s, not from a product catalogue.
3. How many layers can it combine, and can you weight them?
Ask whether you can combine satellite history, yield, soil lab results, soil scans and topography in one zone map, and set how much each layer counts.
How GeoPard answers: zones combine as many layers as you have, with a weight per layer, so a field where topography drives yield can be zoned differently from one where soil texture does.
4. How far back does the satellite history go?
Productivity zones need years, so that a dry season and a wet season both get a vote. Ask how many years of imagery go into a long-term productivity map, and how cloud and cloud shadow are handled. A shadow mistaken for a weak zone ends up in your prescription.
How GeoPard answers: productivity maps draw on more than 35 years of satellite history, with cloud and shadow screened automatically before an image is used.
5. Can you trace every rate back to its inputs?
Ask to see the layers, weights and formula behind one rate in one zone. A transparent formula is something your agronomist can check against extension guidelines and adjust.
How GeoPard answers: equation-based maps let you write the rate formula yourself, or start from a published one, and apply it across any combination of layers. Every input and weight stays visible. Validate the formula and the rates with local agronomic advice before you apply them.
6. Does it clean yield data before using it?
Raw yield files carry header-up passes, flow delay, overlapping swaths and calibration drift between combines. Zones and profit maps built on uncleaned data inherit every one of those errors. Ask what cleaning happens automatically and whether you can see what was removed. Our yield data cleaning workflow shows what a good result looks like.
How GeoPard answers: yield files are cleaned and calibrated across combines before analysis, and fields with no usable yield history can get a synthetic yield map from imagery and soil data.
7. Does the prescription reach your machine without a USB relay?
A prescription that cannot get to the display is a report. Ask which file formats the platform exports (ISO-XML under the ISO 11783 standard, shapefile, and the native formats of your displays) and whether it sends files directly to your machinery account.
How GeoPard answers: prescriptions export as ISO-XML, SHP, KML, PDF and the John Deere, CNH and AGCO formats on every plan. With John Deere Operations Center connected, boundaries, yield and as-applied data come in automatically, and prescriptions go back as work plans, several application maps in one if needed.
8. Can you check the result after harvest?
Ask whether you can overlay the as-applied map on the yield map, compare zones, and read a strip trial without exporting everything to a spreadsheet. Here is how to read variable rate results after harvest.
How GeoPard answers: as-applied, yield and trial layers sit on the same field, so the post-season comparison is a few clicks, not a project.
9. Can it connect to the rest of your systems?
Ask about the API (is it documented, does it reach raw data and prescriptions, not just reports), white-label options if you serve growers under your own brand, and whether an AI assistant can work with your field data.
How GeoPard answers: GraphQL and REST APIs cover raw data, analytics and prescriptions. White-label and enterprise deployments are available, and the AI Assistant works on every plan.
10. How does the price scale?
Ask how the price is counted (per area, per field, per user, per feature) and what happens when you double your hectares or add a second agronomist. Watch for features sold as add-ons that you will need on day one, such as machine export.
How GeoPard answers: priced per area, with pay-as-you-go per operation or an annual plan with unlimited use. Machine export is included on every plan. The pricing page has the full breakdown, and the variable rate ROI calculator estimates payback for your own fields.
Score your shortlist
Rate each platform on the ten questions as you go through demos. Set the weight of each question to match your three decisions: an ag retailer serving growers will weight the API and white label higher than a single farm will.
How to run a fair trial of precision agriculture software
A demo field is chosen to look good. Your field is the only honest test. Pick one field you know well, with at least three years of yield data and some real variation, and give each finalist the same job:
- Import the boundary, yield files and any soil data. Note how long it takes and what breaks.
- Build management zones, then ask which layer drove each zone and compare that with what you know about the field.
- Build one prescription, seeding or nitrogen, and trace one rate back to its inputs.
- Upload a revised boundary and check that the older data follows it without re-uploading.
- Send the file to your display and check in the cab that rates and zones read correctly before anything moves.
Time each step. The platform you will actually use every season is usually the one that needed the fewest workarounds, not the one with the most features on the slide.
Red flags in a demo
- The vendor only shows their own demo field and avoids loading yours.
- Data ownership answers are verbal, not written.
- "Why this rate?" gets "the model says so".
- Rates arrive with a product name attached.
- Zones cannot be explained layer by layer.
- Machine export is "coming soon" or costs extra.
- No way to compare as-applied with yield after harvest.
Frequently asked questions
Do I need yield data to start?
No. Satellite history and soil data are enough for zones and a first prescription. Yield data makes zones more reliable and is what lets you check results, so start collecting clean yield files now.
Is advice from an input supplier's platform always biased?
No. Many supplier agronomists give sound advice. The point is to know whose model set the rate, to check it against public guidelines, and to keep the option of applying your own rates on your own data.
How many platforms should I trial?
Two or three. Use the ten questions to cut the list before you trial anything.
Is precision agriculture software worth it on a small farm?
It depends on variation, not size. A small field with large differences in soil and yield returns more from variable rate than a large uniform one. Run the numbers on your own fields first; the GeoPard browser tools, such as the ROI calculator and the seeding rate calculator, are a quick place to start.
Precision Farming
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