Microclimatic Variations

The climate changes within a few hundred metres. With the climate, the treatments needed change, and with the number of treatments, their crop-protection cost.

1. Leaf wetness · same plot 37%
   mean 37% peak 81%

   Average difference in leaf wetness between points of the same plot.

   Wet leaves let pathogens penetrate the tissues. A single sensor is not enough to represent the vineyard.

2. Rainfall · Barolo DOCG, 2026 season +55%
   Barolo Cannubi160 mm
   Serralunga d'Alba248 mm

   Difference in rainfall between two vineyards just a few km apart in a single growing season. 
   

3. Treatments · same vineyard +4passes
   Driest zone

   Wettest zone

   Difference in the number of passes indicated between different zones of the same vineyard.

   Between different vineyards, the recommended treatments can double.

4. COST · AVOIDABLE TREATMENTS 970€/ha
   €215

   €215

   €215

   €215

   €215

   4–5 extra treatments× €215/ha ≈ €970/ha

   Average cost of 4–5 avoidable treatments between two plots a few kilometres apart.

   In one season, the cost of climate variability can reach 50% of the total cost.

Two seasons of measurements in three wine-growing areas of Piedmont. Method and limitations are described in the methodological note.

Within the same plot, 50–200 metres apart, the leaf stays wet for different lengths of time: on average a 37% difference.

Four points in the same plot, 50 to 200 metres apart, record leaf wetness values that differ on average by 37%, up to a peak of 81%. This is not an instrument error or a wrong reading: it is a stable property of the microclimate, which recurs identically season after season.

For two years, in collaboration with the Politecnico di Torino and DISAFA of the University of Turin, we measured the microclimate of nine vineyards in three areas of Piedmont, using a distributed sensor network. This article presents the outcome of the research: how much the climate varies within a few hundred metres and how this variability translates into operations, treatments and euros per hectare.

Six wineries, three areas of Piedmont: measurements in real vineyards, managed by the people who farm them.

Wineries

6

Vineyards

17

Wine-growing areas

3

Parameters collected

8,027,580

The trial grew out of an applied research project that brings together the academic and winemaking worlds. Six wineries opened their vineyards to the instruments and followed the work season after season.

Measurement points were defined after careful morphological analysis with GIS technology and field inspections: for each plot unit, at least one weather station and several data collection points distributed densely within it.

Each point transmits data every twenty minutes, which are then harmonised into a single hourly series and checked by proximity against the certified stations of RAM and Arpa Piemonte. Every measurement originating from a network sensor was checked and validated, guaranteeing the rigour of the results.

1. 1 Placement GIS analysis and field inspections
2. 2 Measurement one reading every 20 minutes from each point
3. 3 Harmonisation a single hourly series
4. 4 Verification against certified RAM and Arpa Piemonte stations

LoraWine's trial also contributed to the creation of the Politecnico di Torino's experimental vineyard, at the heart of the campus, which monitors continuously.

In Barolo, between municipalities 1–7 km apart, seasonal rainfall varies by 55%.

The data show that rainfall is one of the most discontinuous meteorological factors within a single wine-growing area, to the point of making the amount that falls vary significantly from one vineyard to another. Within the Barolo DOCG the difference is marked. In the four municipalities of the experimental vineyards, 1 to 7 km apart, total rainfall for the 2026 season ranges from 160 to 248 millimetres: a 55% difference between the driest and the wettest zone, in an area you can cross in ten minutes by car.

The interesting point is that differences do not grow with distance. Barolo Cannubi and La Morra are 1.25 km apart and rainfall differs by 44%. La Morra and Serralunga are three times farther apart (3.8 km), yet rainfall differs by only 7.7%. In Monferrato variability is even greater: between zones 2–8 km apart the median difference is 66%, with almost a factor of two between the extremes.

This pattern is driven, without doubt, by the morphology of the terrain, which imposes strong climatic variability between neighbouring zones. But climate change is also making rainfall increasingly convective throughout the season, falling in blocks rather than gradients: a phenomenon typical of warmer periods.

Understanding and quantifying this phenomenon is essential for pathogen control. Rain, besides being one of the main drivers of fungal infections, also governs the wash-off of protectant products. There are two risks.

The deviation is systematic, not the effect of a single storm: the driest zones are the driest in each of the six months of the season.

Air temperature and humidity converge over the course of the season. Leaf wetness does not: it is the signature of the plot, and the reference station cannot see it.

Meteorological variables behave differently depending on the measurement point and the time of the season. For this reason, for each quantity two components were distinguished: the hourly excursion, the difference between two points in the same hour, and the persistent component, the part of that difference that does not fade over the season. The first indicates a sudden, momentary change; the second a systematic variation, sustained over time.

It emerged that “general” climate variables, such as air temperature and humidity, can show a large hourly excursion but tend to converge over the course of the day and the season. Agro-climatic variables such as leaf wetness, on the other hand, capture the effect of the microclimate, with systematic and persistent variations.

On average two points differ by 2 °C in the same hour, but 89% of that difference fades over the course of the season. For leaf wetness the opposite happens: three quarters of the difference between two points remains stable and recurs season after season. It is the microclimatic signature of the plot, which depends on exposure, altitude, cold-air drainage, canopy density and surrounding features.

In Monferrato the microclimatic signature is so stable that the order of the four plots, from wettest to driest, is identical in the 2025 and 2026 vintages. The amplitude changes between the two vintages, halving in the less rainy one, but the structure remains: the zones follow the same gradient from one vintage to the next.

A further practical confirmation, validated by the data, is that the area's reference station does not represent the vineyard. For example, the values of the reference station closest to the vineyards fall outside the ranges measured by the in-vineyard sensors on 32% of days at La Morra and 64% at Monforte d'Alba.

The same probes installed within a single vineyard give differing readings on 26–47% of days, confirming that climatic variability is significant even within a single vineyard, not only between different plots.

Between the wettest and the driest probe in the same plot there can be up to 5 hours of leaf wetness per day.

To assess differences within plots, several sensors were placed among zones with uniform morphological and agronomic characteristics. In this way, with a large number of sensors, an experimental design was followed that was conceived to assess both the differences between plots and those within them.

In plots with more than one wetness sensor, we recorded a median excursion between probes of the same plot of 3.1 hours of wetness per day. Comparing different plots of the same vineyard also gives values of the same order: 1.97 hours per day at La Morra across three plots, 4.15 hours in Monferrato across four.

The added value lies in positioning sensors well, not in multiplying the quantities measured at each point.

Where the leaf stays wet longer, the models suggest more treatments: up to 4 extra passes in the same vineyard.

Leaf wetness is a key parameter for pathogen control: it is the condition that lets fungi penetrate the tissues. Hours of leaf wetness, together with rainfall and air data, feed the epidemiological models (DSS) for downy and powdery mildew.

The collected data were therefore analysed with the classic DSS from the scientific literature, producing multiple scenarios of hypothetical crop-protection strategy. It emerged that climatic differences lead to a number of DSS-recommended treatments that can almost double from one vineyard to another. At Serralunga d'Alba, where numerous plots took part in the trial, the number of passes indicated matches the wetness signatures almost perfectly, with a correlation of +0.91. The plots the sensors show as wettest are the same ones the models show as most exposed.

Finally, the leaf wetness measurements from each point were analysed with the DSS holding the climate constant, so that the microclimate was the only factor varying. The result: between the zones of a single vineyard the number of passes indicated can differ by up to 4.

One pass costs on average €215/ha, but the most expensive plot costs two and a half times the cheapest.

From the maps of the trial plots and the measurements taken, cross-referenced with GIS terrain models, the cost of a pass was reconstructed vineyard by vineyard: average slope, row length and orientation, number of turns, tank capacity and distance from the farm centre.

The result is a full cost, covering everything the pass consumes: operator labour, materials, transfers, execution, turns, refills, machinery depreciation and agricultural diesel.

The cost of plant protection products is an estimate that can vary widely depending on the defence strategy, the formulation and the dealer's price list. The average value falls in an estimated range of €45 to €90/ha: an estimate that also accounts for treatments carried out with a mix of products.

Between the cheapest plot and the most expensive one the gap is two and a half times. It depends on plot geometry, slope and the route needed to reach it: characteristics that define the effective work capacity in hectares per hour, one of the largest cost items in difficult terrain such as Piedmont.

The trial results indicate that today's climate variability, if monitored carefully, can become an advantage. We measured that it can lead to a difference of up to 4–5 treatments between two plots a few kilometres apart: at the reconstructed average cost, €970/ha. In a season of 10 treatments, the cost of climate variability can reach 50% of the total cost.

Two indicative scenarios of 10 and 20 crop-protection treatments. 

The number of passes in a season varies widely: it depends on how the vintage unfolds, on the location and on the strategy adopted. We built two indicative scenarios to quantify the cost of crop-protection management for one hectare in two vintages of different character.