Climate Change&IoT in Viticulture history
Subjects: Food Science & Technology |
Submitted by: Veronica Chedea
View times: 92 | Release time: 2021-09-07


The evolving overall wine-growing environment represents three facets of the wine world: production, distribution, and wine consumption.

The first element of the wine’s evolving environment is its biological and chemical origins.

The countries with the highest grape harvested area are: Spain, France, China mainland, Italy, Turkey, Unites States of America, Argentina, Chile, Portugal and Romania.

In terms of wine quality the term ' teroir is a very important one. Terroir is a term with French origins that means for a wine produced from grapes grown in a specific region, under certain circumstances, its fragrance will have particular aromas, tastes, appearances, and textures.

The 'teroir' is related to the  physical environment including the slope, the soil composition, the depth, the parent materials, mineral quality, texture, humidity content, and the water retention, astronomical, climate, and weather aspects (sun angles and emplacement during the growing period, dawn-day visibility, humidity range and timing, rain, temperature, heating grades, cooling at night, wind speed and direction. These environmental elements contribute to the seasonal pattern in the atmosphere, timing and intensity of severe weather, such as hail, freezing, and snowfall during the most biologically active seasons for grapevine.

Climate change is undermining the fundamentals of viticulture and, consequently, of winemaking because grapevine growing depends on local weather and hydrological conditions being stable.

In these conditions of climate change, precision agriculture, together with data analytics, has the potential to reshape the entire viticulture environment.

Precision agriculture technologies and equipment are a combination of Geographic Positioning Systems (GPS) and Geographic Information Systems (GIS) for geo-mapping, automatics steering systems, acquisition units, and sensing devices, which can be mounted on farm machines or fields to work in an unattended manner, variable rate technologies and communication modules. Moreover, UAVs (Unmanned Aerial Vehicles) can overtake agricultural tasks as image acquisition, spraying, crop damage identification, weed detection, and infestation mapping. Increasing UAVs tasks, though, determine higher power consumption and, consequently, might render the precision agriculture systems less sustainable or viable.
Climate-Smart Agriculture (CSA) is a concept that encourages the adoption of smart agriculture practices for water and nutrient management, the cultivation of stress-resistant crops, precision fertigation, green manures, and UAVs, thus rendering precision agriculture as an instrument to decrease the environmental impact.
Climate Change-based Precision Viticulture (CCPV) represents a particularization of the CSA concept for vineyards and viticulture. This concept enriches the previously defined CSA by adopting sustainable low-environmental impact viticultural procedures and practices. CCPV further improves these technologies through the exploitation of the current climate modifications and Internet of Things technologies.

According to the FAO’s statistics, in 2019, Romania ranked 10th harvested 176.340 ha and 18th with 973,990 tonnes. The total tonnage of wine produced in Romania in 2018 was 125,743 tonnes.

Moreover, the OIV (Organisation Internationale Vitivinicole) statistics in 2020 revealed that Romania kept its position in the grapevine cultivated area ranking with a surface of 190.000 ha.

More than half of Romania, more precisely 62%, is covered by arable land, with viticulture mainly concentrated in hilly and plateau areas. Due to the adequate climate for grape production, in Romania, grapevine growing is a traditional practice that has arisen and developed throughout history.

Located at the intersection of the geographical coordinates of 46°–47° Northern latitude and 23°–24° Eastern longitude. They are situated on the Transylvanian Plateau. The most of the vineyards are located on the slopes that delineate the valleys of the rivers Târnava Mare and Târnava Mica, is known and appreciated for its quality wines with a specific flavor and a good sugar/acidity balance.

In 2020, 25 wine-grape cultivars: 11 cultivars, including 8 white cultivars and 3 red cultivars; 13 clones for white wine, homologated at SCDVV Blaj. Some of the cultivars and clones homologated at SCDVV Blaj have tolerance to cryptogamic diseases and cold, which are valuable characteristics for sustainable grapevine growing.

The climate of the vineyards in the Transylvanian Plateau is characterized by low values of the thermal balance and by a relatively short vegetation period. The average daily temperature above 10 °C is recorded in spring, during the second day of April. In autumn, it falls below this limit, starting from the second day of October.

Plantations in the Târnave vineyard are frequently affected by low winter temperatures, temperatures that often fall below the grapevine’s resistance limit.

Period 2000–2020

The main climatic data (minimum absolute temperature, maximum absolute temperature, medium temperature, rainfall, and thermal balance) for the period 2000–2020, having as a reference the multi-annual average for the years 1975–2010, are presented in Figure 2, Figure 3 and Figure 4 The annual sum of temperature degrees, the global thermal balance, is between 3043.7 (2004) and 3682.5 °C (2015) with an average value of 3289.7 °C. In the vegetation period, the active thermal balance has values between 2950 (2010) and 3896.5 °C (2004). The useful thermal balance registers on average 1451.5 °C, with oscillations between 1246.5 (2004) and 1733 °C (2012), as we depicted in Figure 3.

The average value (period 2000–2020) for the average annual temperature is 10.6 °C, with oscillations between 8.6 (2016) and 11.9 °C (2019). The average annual temperature, against which the data analysis was performed as a reference value, is 10.1 °C (monthly) and, respectively, 17.2 °C (during vegetation), calculated for the period 1975–2012 (Figure 2).

The average annual rainfall value for the period 2000–2020 was 618.3 mm, with a maximum recorded in 2016 of 892 mm and a minimum of 303.9 mm registered for the year 2011. The precipitations during the vegetation period (April–September) had the average value of 427.6 mm with a maximum of 647 mm recorded in 2016 and a minimum of 201.8 mm for the year 2011. We presented the evolution of the rainfall in Figure 4

Comparing the Two Periods (1975–2007 and 2000–2020)

After examining the two periods (1975–2007 and 2000–2020), we can remark that, for the Târnave vineyard, the climate changed as follows:

  • The period of active vegetation increased by approx. 15–20;
  • The average annual temperature increased by 1–1.5 °;
  • The amount of useful temperatures increased (useful thermal balance for the vine);
  • Frost periods have reduced.

Morphological Characteristics

Amurg grapevine cultivar has a white-green, fluffy rosette (vegetative shoots), and its flower is a normal hermaphrodite.

Agrobiological and Technological Characteristics

Amurg is a cultivar of medium to great vigor, with the maturation of the grapes in the 5th epoch(Figure 5). It shows tolerance to low winter temperatures, up to minus 22 °C and is resistant to both downy mildew and Botrytis.

New Climate Change-Based Precision Viticulture (CCPV) Technology

We propose a new approach based on deploying an IoT-based platform that monitors the cultivated area and the productive cycle of grapes.

The sensing infrastructure is based on Wireless Sensor Networks (WSN) and remote terminal units deployed in the field. Further, a central unit collects the data and forwards it to the component that performs inferences to recommend on-time actions that one must take to ensure the grapes’ quality.

The design will follow an efficient CCPV-driven approach as a pathway to:

(a) Increase grapevine productivity and income,

(b) Adapt the Amurg cultivar to climate extremes in the Transylvania region,

(c) Support new technologies in acquiring data.

System Architecture and Workflow

In Figure 6, we depicted the overall CCPV-driven architecture for monitoring and tracing the Amurg cultivar.

The architecture consists of four main layers: Vineyard, Network, Cloud, and Application layers.

Vineyard Layer

The Vineyard layer includes the static fertigation system and the sensing devices. The innovative component of some of these sensors is combined with other necessary technologies, such that a self-learning platform is developed. The platform is further provided with pseudo-real-time and real-time analytical capacity and with predictive capabilities.

Data collection is done by deploying intelligent devices that collect viticulture-specific data that is turned into valuable information.

The employed devices monitor the parameters that are used to predict ripening stages and the production quality.

This layer also embeds the fertigation trigger, which receives the fertigation command from the superior layers based on analyzed data.

Before reaching the superior layers, the data must be validated by a specific module that eliminates the observations, which significantly differ from the expected range of values due to, for example, intrinsic sensor errors.

Network Layer

This layer comprises the network devices and data buses. In what concerns the communication technology, 4G, Wi-Fi, and LoRa technologies are the candidates, each suitable for a certain scenario. Thus, for image transmission, 4G technology is employed.

Four scenarios arise: 

Short-range communication scenario (I). The sensors are close to the gateway/router, and fast or/and frequent data retrieval is desired. Consequently, Wi-Fi technology is used.

Short-range communication scenario (II). The sensors are close to the gateway/router, and no fast and frequent data retrieval is required. Thus, LoRa technology is used.

Long-range communication scenario (I). The sensors are placed far from the gateway (outside the Wi-Fi module’s range), and fast retrieval is desired. Thus, drones act as a Wi-Fi flying ad-hoc network, forwarding the data from the sensors to the Wi-Fi router (as we emphasized in Figure 7).

Long-range communication scenario (II). The sensors are placed far from the gateway (outside the Wi-Fi module’s range), and no fast or frequent retrieval is required. Thus, LoRa communication will ensure data transmission.

Cloud Layer

The data are stored in Hadoop Distributed File System (HDFS) repositories, successfully proven to outperform other approaches as those using a local file system .

HDSF will ensure the scalability of the system, allowing the application of distributed analysis techniques in large volumes of data, such as those supported by Spark or Map Reduce.

This analysis layer will allow the computer processing implementation through machine learning libraries such as those of Spark, which is also useful in determining cybersecurity issues.

Application Layer

The application layer comprises the application server where the applications are hosted. Furthermore, at this layer, cybersecurity measures are implemented.


This paper proposes a novel technology, Climate Change-based Precision Viticulture, which fusions viticultural practices and experience, climate change influence, and IoT-based systems for precision viticulture.

The novel technology proposed in the framework of the viticultural practice in the Târnave vineyard, once the challenges are overcome, will open new perspectives for grapevine cultivation in this area. Enriching the wine assortment with a new, red, autochthonous, disease tolerant grape cultivar has been an aim of SCDVV Blaj since the Amurg cultivar was homologated in 1989.

Open challenge: the extension of the system in Transylvania and beyond.

Finally, if the international specialists find our idea applicable, we would be more than happy to collaborate for it.

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