The weather in your field is not necessarily the weather in the nearest city

When we look for a forecast, we usually search by city. Córdoba. Río Cuarto. Pergamino. Venado Tuerto. It is practical, fast, and sufficient in many situations. But a field is not always located where a city name appears in a weather application. It may be several kilometers away, at a different elevation, on different terrain, or simply at coordinates that a general forecast does not represent directly. That is why location matters when a decision depends on a specific condition.

A city is a reference

Weather applications need a simple way to organize information. Cities serve that purpose very well. You search for a name and receive the forecast associated with a location. The problem begins when we interpret that result as though it precisely described every point in the surrounding area. That is not necessarily the case. The distance may look small on a map, yet there can still be differences in temperature, wind, rain, and other variables.

In the field, a different point matters

If you are monitoring frost, a storm, or a spraying window, the question is not:

What is the forecast for the nearest city?

The real question is:

What conditions are expected where the field is located?

That change may seem minor, but it determines how the information is queried. In Vienor, the field is the unit of work. Not the city.

How a field is defined in Vienor

When creating a field in Vienor, the user can specify its location directly on the map. The platform supports geometries such as points and polygons. This makes it possible to represent the place you want to monitor without relying exclusively on the name of a city. Once created, rules and forecasts are linked to that field. The frost alert you configured belongs to that location. The same applies to a wind, rain, heat, or storm rule.

Why we use geometry instead of only a city search

When we designed this part of Vienor, the goal was to prevent a field from being just a label within an account. We needed to know where it was. Geometry makes it possible to associate each field with a specific location and maintain that reference across the platform's different features. This also makes sense for other tools, such as displaying satellite information. If we want to observe NDVI over a field, we need to know which surface corresponds to that field.

A polygon provides more context than a name

Calling something “North Field” identifies a property for someone who knows it. Saying it is near a particular city adds a little more information. But a polygon on a map defines the area much more concretely. That allows different features to use a shared geographic reference. Weather monitoring and satellite information can both begin with the same user-defined field.

This does not mean Vienor has sensors inside the field

Working with the field's precise location does not turn the forecast into a local measurement. Vienor uses weather data produced by models. There is no weather station automatically installed inside each field and no direct reading of temperature or wind on the ground. The difference is geographic: the system queries and organizes information around the field's location instead of asking the user to rely on a nearby city.

It does not eliminate forecast uncertainty either

More precise coordinates do not turn a weather prediction into certainty. Models still have resolution limits, other limitations, and uncertainty. Actual conditions can also vary within a field. It is therefore important to separate two ideas:

Using a more representative location

and

Having an exact measurement of what is happening on the ground

Vienor does the first. It does not claim to provide the second.

The advantage becomes clear when you have several fields

With a single location, checking the forecast manually may be manageable. The situation changes when you have several fields. Each one may be in a different area. Each one may have different rules. And each forecast may evolve differently. In Vienor, fields are monitored as independent units within the same organization. For example, this lets you configure one frost rule for one field and a different rule for another location. There is no need to assume that relatively close fields require exactly the same criteria.

Location and rules work together

An automatic alert has two fundamental parts:

Where I want to monitor

Which condition I want to detect

The field answers the first question. The rule answers the second. For example, you could define a field on the map and configure an alert for that location when forecast wind gusts exceed a certain value. Vienor updates the forecast for that field and compares the data with the rule. This gives the automation a clear geographic context.

From searching for a city to organizing monitoring by field

General weather applications are designed to answer quick queries. That is useful. Vienor has a different goal. It organizes weather monitoring around the places where decisions are actually made. That is why the structure begins with fields and then adds rules, forecasts, alerts, and other tools on top of them. We did not need another list of cities and temperatures. We needed to know which field had to be monitored.

Add your fields to Vienor

In Vienor, you can define your fields directly on the map and configure specific weather rules for each one. Using that location, the platform updates the forecast and automatically evaluates the conditions you chose to monitor.

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