# Life cycle assessment of active spring frost protection methods in viticulture: A framework to compare different technologies

Vincent Baillet, Ronan Symoneaux, Christel Renaud-Gentié

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Vincent Baillet, Ronan Symoneaux, Christel Renaud-Gentié. Life cycle assessment of active spring frost protection methods in viticulture: A framework to compare different technologies. Cleaner Environmental Systems, 2024, 14, pp.100209. 10.1016/j.cesys.2024.100209. hal-05101777

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# Life cycle assessment of active spring frost protection methods in viticulture: A framework to compare different technologies

Vincent Baillet*, Ronan Symoneaux, Christel Renaud-Gentié

GRAPPE, ESA, USC n°1422, INRAE, 49007, Angers, France

## ARTICLE INFO

### Keywords:

- Environmental assessment

- Context-specific scenarios

- Anti-frost protection method

- Wine-growing

## ABSTRACT

In viticulture, the risk of spring frost is mainly due to earlier budbreak, increasing the vulnerability of buds and green organs to freezing temperatures. Active Spring Frost Protection Methods (ASFPMs) aim to mitigate this risk by increasing the temperature in the bud area. ASFPMs are often seen as highly labour-intensive and resource consuming practices. ASFPM technologies are diverse and influenced by different external drivers, affecting differently their application strategies and the required equipment for efficiency. This study proposes a framework for analysing and comparing ASFPMs' potential environmental impacts using Life Cycle Assessment (LCA) methodology. We illustrate this proposal with an example: Winter cover, wind machine, sprinkler and anti-frost candles comparison in Loire Valley conditions. We modeled the attributional LCAs with Impact world + characterisation method using Abribalyse 3.1 and Ecoinvent 3.8 databases. Application and climatic scenarios were elaborated to set conditions of ASFPMs use. The overall combination of attributional LCAs and external scenarios designs a context-specific LCA. Required time of application for each ASFPM to protect 1 ha during frost hours was determined using linear regression of ASFPM application time in function of total seasonal frost hours based on a recent decade (2013–2023). Sensitivity analysis consisted in varying frost hours theoretically with a step of 1 unit, using the lowest and highest frost hour numbers from 2013 to 2023 as boundaries. Overall, the ranking between ASFPM environmental scores changes based on the theoretical frost duration. The implementation of context-specific elements allowed for the development of system boundaries in attributional LCA, enabling the analysis and comparison of different types of technologies. The framework of this study showed its relevance in the context of ASFPM technologies through a concrete example in Loire Valley viticulture. Future research may consider other contextual elements and ASFPM technologies. This framework could be used in different fields of study to analyse and compare contrasted technologies in terms of environmental impacts.

## 1. Introduction

In the climate change context, an increase in extreme event frequency, covering abnormal or violent meteorological events, has been observed in the last decades and is predicted to continue (AghaKouchak et al., 2020). As a result, it is estimated that crop production in Europe has decreased by 60% over the last three decades. Between 1980 and 2019, France has experienced the highest crop losses related to extreme climatic events, resulting in 50% of insured losses (COM 2021; EEA 2022). Among extreme events, frost significantly contributes to this drastic drop as its frequency has not decreased despite global warming, especially in several wine-growing regions (Schultze et al., 2016, De

Rosa et al., 2021; Dinu et al., 2021). Indeed, the risk of frost is mainly due to earlier budbreak, which increases the vulnerability of wine crops to freezing temperatures (Kartschall et al., 2015). An increase of one degree in average annual temperature is projected to advance the bud-break date by seven to ten days, consequently increasing the risk of frost damage (Fraga et al., 2012; Webb et al., 2012; van Leeuwen et al., 2019; Santos et al., 2020; Bernáth et al., 2021; Droulia and Charalampopoulos 2021). Moreover, the frequency of late frost events is projected to increase in France, Germany and UK in the 21st century (Kartschall et al., 2015, Mosedale et al., 2015; Sgubin et al., 2018).

To mitigate the impact of spring frost on crop production, a range of methods has been reviewed in the literature. Two categories are

Abbreviations: LCA, Life Cycle Assessment; ASFPM, Active Spring Frost Protection Method; PSFPM, Passive Spring Frost Protection Method; LCI, Life Cycle Inventory.

<footnote>* Corresponding author. 55 Rue Rabelais, 49000, Angers, France.</footnote>

<footnote>E-mail address: v.baillet@groupe-esa.com (V. Baillet).</footnote>
highlighted: the Active Spring Frost Protection Methods (ASFPM) and the passive ones (PSFPM), also known as indirect methods (Rochard et al., 2019, Poni, Sabbatini et al. 2022). Among the ASFPMs, three classes have been distinguished by Rochard et al. (2019): air mixing, heating, and warm air dynamic transfer. Additionally, a new class of emerging ASFPMs has recently entered the market, which consists in isolating vines from the cold using different types of cover. ASFPMs aim to alter the local climate by increasing the temperature in a specific area to mitigate spring frost damage. However, these solutions are activated by wine-growers based on specific weather conditions, such as temperature or wind speed, which vary between different ASFPMs, making them challenging to compare in terms of cost and environmental impacts. PSFPMs include all preventive practices aimed at increasing frost resistance of plants and delaying budbreak. These practices encompass breeding and selecting cold-hardy and/or late-blooming varieties, choosing suitable plantation sites, delaying pruning, adopting less stressful soil management techniques, and employing mowing strategies, etc. (Liu and Sherif 2019, Rochard et al. 2019; Poni et al., 2022).

Compared to PSFPMs, ASFPMs are often viewed as more labour-intensive, less cost-effective, and less environmentally sustainable (Liu and Sherif 2019). Nevertheless, they are essential in several regions due to the consequences of climate change, where PSFPMs are inapplicable or insufficient to ensure profitable productivity. Additionally, wine-growers tend to prioritise immediate and practical solutions at the farm level without considering the long-term sustainability and broader implications. To develop optimal adaptation strategies, it is crucial to adopt sustainable practices that minimise the environmental, resource, economic, and societal impacts in the long term, while aligning with future climatic scenarios, market demands, cultural values, and societal norms (Bertrand et al., 2020). It is necessary to measure and compare the environmental impacts of the different solutions to identify sustainable practices.

Life Cycle Assessment (LCA) is considered worldwide as a relevant and acknowledged environmental assessment methodology, as its international application, diffusion, and normalisation have been settled since 2006 (Jolliet et al., 2010). LCA methodology has been applied in viticulture more than a decade. Most wine LCAs have been developed in Italy, Spain, and France, representing more than 50% of the world's wine production and about 33% of the global vineyard area (Ferrara and De Feo 2018). Viticulture is one of the highest-impactful phases of the wine life cycle assessment, where the main environmental burdens are the following: the fossil fuel consumption of agricultural machinery during field operations (Renaud-Gentié et al., 2019), and the use of fertilisers and pesticides (Bessou et al., 2013; Ferrara and De Feo 2018). Framework for detailed LCA of viticulture practices are available in the literature (Bellon-Maurel et al., 2015, Rouault et al., 2016). However, only two studies in the literature are dealing with the environmental impacts of ASFPMs: an LCA of an innovative method in laboratory conditions (Frota de Albuquerque Landi, Di Giuseppe et al., 2021) and one qualitative indicator called “Ifrost” from the INDIGO model based on expert opinions and technical data for the energy consumption of ASFPMs (Thiollet-Scholtus and Bockstaller 2015). However, no environmental assessment studies comparing the different ASFPMs used in agriculture were found in the literature. Nevertheless, several ASFPMs consume a large quantity of energy or require a lot of labour and equipment; therefore, they would probably contribute greatly to the environmental impacts of the viticulture phase from LCA assessments.

LCA methodology and interpretation remain in development in all disciplines. Comparing different technologies can be challenging in LCA if their applications, drivers and efficiencies differ. This challenge is clear in the case of different ASFPMs. They are influenced by various external drivers that determine their application strategies and the required equipment for efficiency, which can vary between ASFPMs. Furthermore, their use strongly depends on local climatic conditions. However, there is a need for references or decision support tools to promote more sustainable practices in viticulture. One of the main

difficulties is thus to standardise the LCA of ASFPMs for comparison. In the literature, no LCA methodology has been identified to allow comparison of ASFPMs accounting for multiple drivers of impact variation. Indeed, scenario analyses coupled with LCA allow to build different Life Cycle Inventories (LCI) influenced by stakeholder's decisions, such as wastewater treatment, transportation systems, aluminium and wood production (Suh and Rousseaux 2002; Spielmann et al., 2005, Cluzel et al., 2014, Steubing et al., 2016). However, in the agricultural context, these approaches don't align with the stated challenges for comparative LCAs of ASFPMs, as they do not account for drivers beyond the scope of LCA. Yi, et al. (2023) analysed the implementation of system dynamics into LCA through several scientific papers in their review, highlighting its advantages for decision-making with stakeholders. However, system dynamics are designed to simulate large-scale system interactions such as “supply chains, financial markets, and energy systems” by modelling complex dynamics with causal loop and stock and flow diagrams. Aissani (2023), proposed the concept of contextualisation of LCA, accounting for the drivers between the system under study and its context. It was not yet applied as such, especially to agricultural context. In the scientific literature on LCA, few papers use this term in allocation methods for wood waste (Werner et al., 2007), in a characterisation method in social LCA (Siebert et al., 2018), in sustainability criteria (Radhakrishnan et al., 2019), and in LCA tools in ecological buildings (Kanafani et al., 2021). All of these papers underscore the necessity of broadening the LCA system boundaries by incorporating external drivers to model and calculate the LCI. In addition, Aissani (2023) emphasises the relevance of contextualisation for approaching “reality”, assessing the environmental performance of specific case studies, improving working conditions, and representing economic and/or technical risks for the decision-makers.

In this context, the aim of this study is to propose a conceptual framework to enable the analysis of ASFPMs and their comparison in terms of potential environmental impacts using the LCA methodology. The first part of the paper describes the methodology of the conceptual framework, followed by an application to illustrate the framework's use in analysing and comparing four different ASFPM technologies. Then, the results of the case studies are discussed, and future research perspectives are presented.

## 2. Material and method

The proposed framework integrates attributional LCAs with external scenarios, incorporating external drivers that influence the different environmental impacts modeled within the attributional LCA framework. This comprehensive approach, so called context-specific LCA, is presented in Fig. 1.

The context-specific LCA framework consists in incorporating the effects of various external drivers on the final environmental impacts. This enhancement enables a more relevant comparison between the identified ASFPMs (Fig. 2). The contextualisation of LCA occurs at two different levels of external drivers: application of the solutions and climatic conditions.

The primary function of the ASFPMs is to protect the bud area during frost events to prevent bud damage from freezing. Spring frost events vary greatly in length and frequency from one season to another, typically occurring at night when temperatures drop below 0 °C. Additionally, the different technologies of ASFPMs require different application protocols. Therefore, specificities such as the required equipment for specific field characteristics and optimal application temperatures have to be considered. These specificities constitute the first level of contextualisation, referred to as the “application scenario”, which gathers information on what is applied and how. Table 1 summarises the application strategies for each type of ASFPMs. These strategies, applied by wine-growers in the Loire Valley region, are considered efficient enough for frost protection.

Climatic conditions play a crucial role in determining ASFPM
<figure><img src="image_2.png" /><figcaption>Fig. 1. From an attributional LCA to a context-specific LCA through two levels of contextualisation using application scenarios of ASFPM and climatic scenarios. The inputs and outputs are presented in blue and orange, respectively, for each sub-system. The outputs of sub-systems used as inputs in other sub-systems are represented in yellow. The dark and light grey rectangles represent the background and foreground systems, respectively. The “T” circle items represent the transportation processes included in the assessment. The light blue rectangle includes the context specific elements to transform the reference flow unit of the attributional LCA to a context-specific functional unit. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>Fig. 2. System boundaries of the attributional LCA of ASFPMs. The inputs and outputs are presented in blue and orange for each sub-system, respectively. The outputs of sub-systems used as inputs in other sub-systems are represented in yellow. The dark and light grey rectangles represent the background and foreground systems, respectively. The “T” circle items represent the transportation processes included in the assessment. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

<caption>**Table 1**</caption>

Scenarios of application to use efficiently ASFPM for the first level of contextualisation.

<table><thead><tr><th>Type of ASFPMs</th><th>External drivers</th><th>Activation climatic conditions</th><th>Deactivation climatic conditions</th></tr></thead><tbody><tr><td>Wind machine</td><td>Dry temperature</td><td>Under 0,5 °C</td><td>Above 2,5 °C</td></tr><tr><td>Anti-frost candle</td><td>Dry temperature</td><td>Under 0,5 °C</td><td>Above 2 °C</td></tr><tr><td>Sprinkler</td><td>Wet temperature</td><td>Under 2,5 °C</td><td>Above 2,5 °C</td></tr><tr><td>Winter cover</td><td>Maximal wind speed</td><td>Under 12,5 m/s</td><td>Above 12,5 m/s</td></tr></tbody></table>

application strategies. These climatic drivers influence various aspects of ASFPM application, including frequency of use, installation and removal processes, labour transportation, and more. All these factors

constitute the second level of contextualisation, referred to as the “climatic scenario”. In the present case study, it gathers hourly climatic variables such as dry temperature, maximal wind speed, and relative humidity. Ten years of data have been collected between 2013 and 2023 during the spring season at risk for vine plots; i.e., April and May. Wet Temperature (WT) was subsequently calculated using the formula developed by Heinrich Gustav Magnus (Eq (1)) (Barenbrug 1974):

$$WT = 0,2831 * (U * 100)^{0,2735} * T + 0,0003018 * (U * 100)^2 + 0,01289 * (U * 100) - 4,0962$$

Eq 1

with U the relative humidity in %, and T the dry temperature in °C.

Both scenarios were combined to incorporate contextual drivers into the system boundaries of the attributional LCA, forming a context-specific LCA of ASFPM technologies (Fig. 2). The first type of scenarios set the conditions of use for a type of ASFPM, specifying how long it needs to be used to protect 1 ha under specific climatic conditions
(Table 1). The climatic scenarios provide a real context for applying the previously stated application strategies. Thus, the duration of ASFPM use is related to specific frost periods identified from historical data spanning from 2013 to 2023, accounting for their lengths and frequencies, as well as the climatic conditions before and after the frost events (Table 1). The functional unit of the context-specific LCA of ASFPM technologies is as follows: “To protect 1 ha of vines during spring under specific climatic conditions for a given year”.

The technical characteristics obtained from interviews with technical advisers, wine-growers and ASFPM manufacturing managers, supplemented with manufacturer's open data, were used to develop the LCI of ASFPMs. Wine-grower strategies and data from a climatic station were combined to contextualise the attributional LCAs at primary and secondary levels, respectively. One interview per ASFPM type has been conducted to collect data. An additional interview has been realised with the manufacturing manager of the winter cover as it is an innovative solution without open data from manufacturers. The missing ASFPM's information was issued from technical manuals or commercial documents.

The LCIs have been modeled using Agribalyse® 3.2 (Koch and Salou 2016) and Ecoinvent 3.9 databases (Wernet et al., 2016) with the SimaPro software. The characterisation method “IMPACT World + Midpoint” is employed for the impact assessment step (Verones et al., 2019). The functional unit of the ASFPM’s attributional LCA is: “To protect 1 ha of vines for 1 h”. The system boundaries of the attributional LCA are presented in Fig. 2. The sub-system “application of frost protection system” is modeled in five assembled categories, including the ASFPM’s application, equipment, installation, removal and transport from the distributor to the user. In modeling the sub-systems, the following assumptions were stated.

1. The recycling of the ASFPM equipment is optimized

2. The dismantling of ASFPM infrastructure was not taken into account

3. The manufacturing process of ASFPMs only considered input resources and had no direct emissions

4. Labour transport is 5 km for 2 persons for half a day of work (4 h)

5. When accessible, European market processes were taken into account from the Ecoinvent database. Otherwise, global market processes were used to account for the transportation of raw materials.

Uncertainties of the context-specific LCA results have been calculated using the Monte Carlo method with 1000 iterations and a 95% confidence interval. The Monte Carlo analysis was performed using the SimaPro software on the data uncertainties of the ASFPMs with the functional unit “to protect 1 ha of vines during 1 h of frost”, except for the winter cover, which has the functional unit “to protect 1 ha of vines with 1 fold and unfold operation”.

To test and illustrate the framework, the following four contrasting ASFPMs were compared and analysed through the context-specific LCA framework.

- Winter cover: consists in several plastic veils covering one or two rows of vine plants, tied together with elastics, leaving space without cover between vine rows. The cover retains the accumulated warmth of the day during clear spring nights by reflecting radiative waves emitted from the soil back to the vine plants. 600 kg of polypropylene, 30 steel anchorages, 900 plastic caps, 2000 elastics and 12500 rubber and plastic fixations are considered for covering 1 ha of a vine plot.

- Wind machine: mixes the hot air layer with the lower cold layer during a radiative frost event, i.e., when a thermal inversion occurs. Wind machines are often accompanied by anti-frost candles or containers filled with combustibles spread in the protected area. Wind machines can also be coupled with a heater placed in front of the propellers to warm the air layer before mixing it with the lower air layer. In this study, the wind machine is fixed and high around 10 m.

It is coupled with 12 containers filled with wood and a bit of paraffin to protect the buds against each frost event. Moreover, one wind machine was considered protecting 4.5 ha, and consuming 27.5 kg of diesel and around 60 kg of wood per hour of activation.

- Sprinklers: initially irrigation equipment, sprinklers can be used to water the plants in specific conditions before the temperature drops below 0 °C. Water droplets remain on the plants, including the buds, when they freeze, releasing heat and forming an ice layer that acts as insulation. The ice layer thus keeps the temperature acceptable around the buds during a frost event. In this study, a density of 37.5 sprinklers consuming 35 m³ of water per hour was considered to protect one ha of a vine plot.

- Anti-frost candles: mostly made of fossil resources, these candles are positioned and lit at the last moment to warm the air layer at the level of the vine buds. Many candles are required to effectively protect a full plot, approximately 350 candles weighing 5 kg each, per hectare. In this study, the candles were composed of 4.3 kg of paraffin each and were considered lasting 8 h during their application in a frost event.

The lifespan of each equipment composing the ASFPMs is taken into account in the LCA modeling. For instance, the wind machine encompasses the following components: a lead acid battery with a lifespan of 5 years, 4 carbon blades with a lifespan of 10 years, 0.325 m² of solar panel with a lifespan of 10 years, and a low-alloyed steel pipe of 11m with a lifespan of 30 years, among others. These details are provided by the dataset “Life cycle inventories and database processes of four spring frost protection methods in viticulture of the Loire Valley Region” by Baillet and Renaud-Gentié (2024). Additionally, this dataset presents the Unit Processes (UPR) and the LCI of each ASFPM. The dataset highlights the UPR influenced by climatic drivers, such as the consumption of resources for the direct application of ASFPM during frost events, and provides detailed calculations, showing their influences on the LCI for each ASFPM (Baillet and Renaud-Gentié 2024).

The application strategies of the four ASFPMs differ due to their different modes of operation, which can be permanent, semi-permanent and mobile installation. Therefore, their external drivers (Table 1) may also differ. For example, anti-frost candles are installed when freezing temperature occurs, providing immediate solutions compared to other ASFPMs which have to be installed in advance. However, this solution requires more human labour and a complex organisation during its application. As a contrasting example, while the winter cover is installed during the full spring season, it may need to be tied and secured to the vineyard trellis when strong winds are forecasted, making the wind speed a key driver of its application. By combining the application scenario from Table 1 with the climatic scenario from 10 years historical data (2013–2023), the application period per spring and per type of

<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Linear regression between yearly application hours of each ASFPM type in function of the yearly spring frost hours in the period 2013–2023. Fig A, Fig B and Fig C present the three following ASFPM types: wind machine, anti-frost candles and sprinkler, respectively.</figcaption></figure>
ASFPMs is determined (Fig. 3).

A linear regression model was applied to determine the ASFPM's application time per frost hours for the wind machine, anti-frost candles, and sprinkler ASFPM. Identifying the ASFPM's unit processes affected by the application time was an important step to shift from an attribu- tional LCA to a context-specific LCA. The linear regression was then applied to the identified ASFPM's unit processes, based on a specific number of frost hours. Using data from the decade 2013–2023, the highest and lowest total number of frost hours per spring were identified to establish the boundaries for sensitivity analysis of frost variation (Fig. 5). With a step of 1 unit between the boundaries of frost variation, a theoretical variation was simulated, and a context-specific LCA was analysed for each of its values. The highest and lowest total number of frost hours recorded per spring are 11 and 0 h, respectively.

## 3. Results

Fig. 4 compares the ASFPM technologies using the following seven mid-point indicators from the “IMPACT world +” characterisation method: Climate change short term (kg CO₂), Mineral resource use (kg deprived), Fossil and nuclear energy use (MJ deprived), Human toxicity cancer (CTUh), Terrestrial acidification (kg SO₂ eq), Land occupation biodiversity (m²yr arable) and Water scarcity (m³ world eq). The four ASFPMs are compared while the number of frost hour varies between 0 and 11 h. For each indicator pattern of Fig. 4, the winter cover results presented are averages of its overall variation during the decade's data. Winter cover is varying in function of the maximal wind speed while it is

installed; i.e., passing over 12 m/s during spring season (April and May).

The uncertainties of the ASFPM results for each of the seven indicators can be retrieved in the provided supplementary material. These uncertainties are based on the Ecoinvent and Agribalayse databases' uncertainties. The ASFPM unit processes have no uncertainty inputs, as the data is directly sourced from interviews conducted in the Loire Valley region. Among the assessed indicators, water scarcity has the highest relative magnitude of uncertainty. This is attributed to its highest coefficient of variation across all ASFPMs, with the exception of the winter cover, where human toxicity cancer has the highest coefficient of variation instead. On the other hand, the climate change indicator shows the lowest uncertainty across all ASFPMs. Overall, the absolute uncertainties are quite low for all environmental indicators, except for the water scarcity indicator of all ASFPM results and the human toxicity cancer indicator of the winter cover.

A pattern figure is drawn to easily identify which ASFPM has the highest and lowest score for each environmental indicator and for each number of frost hours per spring. Green, red and yellow colours respectively indicate low, high and medium scores based on the theoretical variation in frost hours. The wind machine has the lowest impacts on all indicators regardless of the number of frost hours except for Land occupation biodiversity. The anti-frost candle shows the highest scores for Climate change, Land occupation biodiversity, Fossil and nuclear energy use, and Terrestrial acidification indicators after different numbers of frost hours according to the impact category, i.e., 1, 5, 2, and 2 h of frost events, respectively. The winter cover shows a medium impact on all indicators when the occurrence of frost is high, and the

<figure><img src="image_5.png" /><figcaption>**Fig. 4.** Comparison of the four ASFPM technologies through seven LCA indicators with a theoretical frost hour variation from 0 to 11 h for the wind machine, anti-frost candle and sprinkler. The averages of the winter cover environmental impacts are compared to the other ASFPMs. A: Climate change short term. B: Mineral resources use. C: Fossil and nuclear energy use. D: Human toxicity cancer. E: Terrestrial acidification. F: Land occupation biodiversity. G: Water scarcity. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Fig. 5.** Comparison of the sub-system impact's variation for the four ASFPM technology through the selected LCA indicators. CC: Climate change short term in kg of CO₂ eq. FNEU: Fossil and nuclear energy use. MR: Mineral resources use. HTC: Human toxicity cancer in CTUh. TA: Terrestrial acidification in kg of SO₂ eq. LO: Land occupation biodiversity in m²yr arable. WS: Water scarcity in m³ world eq. The y-axis represents the indicator score in log (1 + specific indicator value) per type of ASFPM technology. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

highest impact on Land occupation, Climate change, Fossil and nuclear energy use, Human toxicity and Terrestrial acidification when the occurrence of frost is less than 5, 1, 3, 1, and 2 h, respectively. The sprinkler has the highest impact on Water scarcity even with no recorded frost hours. It shows the lowest impact for the Land occupation biodiversity indicator after 1 h of recorded frost. Overall, it demonstrates a low score for all the other indicators.

In Fig. 5, the boxplots illustrate the variability of the seven mid-point LCA indicators based on the theoretical frost hour variation from the period 2013–2023. For each theoretical frost hour value, the seven indicators of each ASFPM are calculated and presented in the boxplots. The environmental scores are transformed with the log (1 + specific indicator value) to enhance data visualisation as it allows to deal with several orders of magnitude by compressing scales, and to reduce the impact of outliers with the variation of frost hours. These plots provide detailed insights into the environmental impacts of each ASFPM, including its sub-systems such as application, removal, implementation, transport and equipment manufacturing. The aim is to identify the environmental hot-spots, and the main sub-systems responsible of the variation in the environmental scores for each ASFPM. Among all the indicators, the impacts resulting from the application of wind machines and sprinklers show the greatest variation in relation to the occurrence of frost hours, primarily due to fuel and water consumption,

respectively. Similarly, the impacts of anti-frost candle manufacturing show the highest variability with the occurrence of frost hours, mainly due to the paraffin production affecting the Mineral resources use (MR), Human Toxicity Cancer (HTC), Terrestrial acidification (TA), Land occupation (LO) and Water scarcity (WS) indicators. The equipment for anti-frost candles primarily consists of paraffin, which needs to be resupplied every 8 h of application. Approximately 350 anti-frost candles are necessary to protect 1 ha of vine plot. The significant consumption of paraffin leads to high emissions. Emissions from anti-frost candle application contribute the most to the Climate change (CC) indicator due to the release of fossil greenhouse gases during paraffin combustion. Wind machine application is the largest contributor to the Climate Change (CC) and Terrestrial Acidification (TA) indicators, primarily due to diesel combustion. Additionally, it contributes significantly to the Fossil and nuclear energy use (FNEU), and Land Occupation (LO) indicators due to diesel consumption. Equipment manufacturing of sprinklers is the largest contributor to all environmental indicators due to the plastic production for the various pipes and their waste treatment. However, for Water scarcity (WS) and Terrestrial Acidification (TA), the application of sprinklers remains the largest contributor due to water and diesel consumption during use, respectively. The environmental impacts of the winter cover do not vary significantly with the number of frost hours, and plastic production
remains the main environmental issue for all indicators. Plastic pro-
duction for winter covers is particularly impactful due to the high
amount of plastic in the equipment, its relatively short life and its waste
treatment (Baillet and Renaud-Gentié 2024).

4. Discussion

This study analyses and compares different technologies of frost
protection methods while considering external drivers thanks to a new
conceptual framework. To achieve this, scenarios have been combined
and integrated into an attributional LCA, which is a step towards what
we refer to as context-specific LCA. The contextualisation of LCA, as
described by Aissani (2023), involves accounting for the drivers be-
tween the system under study and its context. These drivers can stem
from various sources such as political, economic, environmental, and
societal. In this framework, climatic variables and decision-making
strategies have been identified as sources of the context-specific
drivers for ASFPMs. The proposed context-specific LCA is an
easy-handle modeling approach more adjusted to the problematic of
comparing technologies influenced by different drivers than a proper
system dynamic, as described by Yi, Wu et al. (Yi et al., 2023), that is
more appropriate for policies definition aiming at improving complex
systems that evolve over time. Nonetheless, the context-specific LCA
enables the inclusion of context-specific and external drivers' variation.
Thus, it allows a more relevant comparison between the different
technologies of ASFPMs than a simple attributional LCA approach that
could mislead decision-maker. This approach is adapted to incorporate
contextual elements that are outside of the attributional LCA bound-
aries, such as climatic drivers. Other approaches that combine scenarios
with LCA were primarily designed around stakeholders' strategies,
changing the LCI of the studied systems without other external pressure
(Suh and Rousseaux 2002; Spielmann et al., 2005, Cluzel et al., 2014,
Steubing et al., 2016). The proposed approach facilitates the rapid
handling of scenarios within the results of the LCA, specifically focusing
on the reference flows of environmental impacts, and conducts sensi-
tivity analysis involving variations of external drivers.

Based on the literature, viticulture is one of the highest impactful
phases of the wine life cycle in environmental terms. The key factors
causing these high environmental impacts include the fossil fuel con-
sumption for agricultural machinery during field operations, soil man-
agement practices, and the use of fertilisers and pesticides. Fuel
consumption alone contributes from 40 to 20% of the global carbon
footprint for viticulture stage (Ferrara and De Feo 2018). However,
previous studies reviewed in this context have largely overlooked the
potential contribution of ASFPMs. Contrary to this oversight, our study
demonstrates that the environmental impact of ASFPMs can vary
significantly depending on the technology employed and the prevailing
climatic conditions, and could greatly contribute to the environmental
performance of the viticulture stage (Thiollet-Scholtus and Bockstaller
2015; Liu and Sherif 2019, Frota de Albuquerque Landi et al., 2021). For
instance, anti-frost candles have the potential to emit between 1 and 13
tonnes of CO2 equivalent per hectare depending on the frequency of
events. Conversely, wind machines emit between 0.1 and 0.5 tonnes of
CO2 equivalent per hectare.

The hierarchy of carbon footprint for the ASFPM technologies from
this study differs from the technical paper by Pauthier et al. (2022),
because of the ranks of sprinklers and wind machines. In the
context-specific LCA, sprinklers and wind machines show similar levels
of CO2 equivalent per hectare, with the wind machine potentially sur-
passing sprinkler score if more frost hours occur. The carbon footprint of
the wind machine is lower than in the study by Pauthier et al. (2022)
mainly due to a larger protected area. In this study, one wind machine is
considered to protect 4 ha of vine plots, compared to 2 ha in the tech-
nical paper by Pauthier et al. (2022). Additionally, sprinklers require a
large number of aluminium poles per hectare of vine plots, along with
plastic pipes to connect the water source to the vineyard. These

aluminium poles are fragile and require frequent replacement, espe-
cially during frost events, where consistent and thorough monitoring of
irrigation is crucial to prevent significant damage. Consequently, in this
study, the overall lifespan of sprinklers is considered shorter than that of
a wind machine. Moreover, the amount of required equipment to protect
1 ha of vine plot with sprinklers is larger compared to using a wind
machine.

A study by Boschiero et al. (2018) evaluates various types of plastic
covers used as insect and pest control systems in organic apple orchards.
Despite differences in support structures and primary function, these
systems bear similarities to winter cover systems. In terms of key factors
contributing to the carbon footprint, plastic production remains the
most significant contributor, with overall scores ranging between 0.65
and 1.5 tonnes of CO2 equivalent per hectare. In the context-specific
LCA, the carbon footprint of the winter cover presents a higher
average score of 1.9 tonnes of CO2 equivalent per hectare, with the same
most significant contributor as Boschiero et al. (2018), i.e., plastic pro-
duction and its waste treatment. In this study, the lifespan of the plastic
cover is considered to be 3 spring seasons on average, considering its
susceptibility to breakage against vineyard end posts.

While most environmental indicators present relatively low absolute
uncertainties, significant exceptions include the water scarcity indicator
across all ASFPMs and the human toxicity cancer indicator specifically
for the winter cover practice. This highlights the need for careful
consideration and possibly further data refinement in these particular
areas to improve the reliability of the environmental impact
assessments.

The present study focused on examining how the environmental
impacts of ASFPMs and their subsystems respond to various context-
specific factors, i.e., climatic and technical drivers. Furthermore, a
comprehensive sensitivity analysis of the attributional LCA could be
essential in identifying priorities for future data collection and eco-
design improvements for ASFPMs, as well as in highlighting the
robustness of the study's findings.

For future research, it would be relevant to integrate scenarios of
ASFPM efficiency into the context-specific approach proposed in this
paper, as a third level of contextualisation. This approach, based on
technical and scientific literature, would enable the inclusion of
ASFPMs' environmental contributions within the LCA of a Pathway of
Technical Operations (PTO). PTOs encompass the overall technical op-
tions employed in a given year to produce grapes (Renaud-Gentié et al.,
2014). Thus, incorporating efficiency scenarios in this approach would
enable the assessment of the environmental impacts of ASFPMs within
the broader context of grape production, accounting for both yield losses
caused by frost and the saved yield maintained thanks to ASFPMs.
Moreover, the different ASFPMs have different potential drawbacks
when involving additional technical operations such as the application
of specific phytosanitary products. For instance, the high quantity of
water brought by sprinklers in the vineyard can increase the incidence of
cryptogamic diseases, necessitating to operate more phytosanitary
treatment. Furthermore, such an approach would facilitate the ident-
fication of ASFPMs' significance in the overall environmental footprint
of grape production. Additionally, incorporating future climatic sce-
narios into the analysis would allow for an evaluation of the climate
change effect on ASFPMs' environmental contributions. This is partic-
ularly relevant considering the projected increase in the risk of frost
damage in wine-producing regions in the near future (Santos et al.,
2020; Viveros Santos et al., 2023). By accounting for future climatic
scenarios, further research could better understand the resilience and
effectiveness of ASFPMs in mitigating frost-related risks while consid-
ering their environmental implications. In the review by Drepper et al.
(2022), a lack of reporting standards for extensive correlations between
efficiency of ASFPMs and ambient factors is highlighted. However,
among the ASFPMs, sprinkler systems seemed to perform best. Other
heating systems such as wind machine, and anti-frost candles did not
outperform low-resource techniques as winter cover systems. In several
viticulture regions, the state provides subsidies to wine-growers for certain types of ASFPMs. This underscores the importance of identifying the best ASFPMs to guide subsidies towards the most environmentally beneficial practices. However, it is crucial to recognise that not all ASFPMs are suitable for all vine plot structures. Therefore, it would be valuable to study all ASFPMs in contrasting contexts, considering both vine plot structures and climatic conditions.

Uncertainty is often identified as a significant limit in LCA studies, particularly within deterministic comparisons (Ross et al., 2002). The integration of context-specific scenarios adds complexity in handling uncertainties within LCA results. Therefore, identifying specific guidelines to address and mitigate these uncertainties within the framework could offer valuable insights for advancing decision-making support in future research endeavours in environmental assessments.

The comparison of ASFPM's environmental impacts aims to inform wine-growers' choices of anti-frost solutions from an environmental perspective. However, other dimensions which lie outside of the scope of the current study, such as socio-economic considerations, are also crucial in their decision-making process. Therefore, an in-depth economic analysis of ASFPMs should encompass labour and resource consumption costs, subsidies, taxes, economic depreciation, and benefits from potential saved yield. This analysis could adopt a life cycle cost approach, such as the one proposed by Falcone et al. (2016), but with additional context-specific drivers as this study proposed for ASFPMs' environmental analysis. Additionally, farm-level work organisation and ensuring manageable workload for workers during frost periods may also be significant factors influencing wine-growers' decisions. To address these considerations, incorporating a context-specific approach in social life cycle assessment could further provide a more holistic understanding of the social implications associated with ASFPMs. The outcomes of these analyses might be influenced by similar external factors such as frost hours, which impact fuel consumption and labor activities, thereby affecting the cost of ASFPM use and workload organisation. Future research should aim to integrate these dimensions to provide a more comprehensive evaluation of the socio-economic and environmental impacts of ASFPMs from a context-specific perspective.

## 5. Conclusion

The conceptual framework presented in this study has demonstrated its relevance in the context of ASFPM technologies through a concrete application in Loire Valley viticulture. The integration of contextual elements has broadened the scope beyond the system boundaries typically addressed in attributional LCA, facilitating the analysis and comparison of various technologies such as ASFPMs. The different environmental impacts of ASFPMs are influenced to varying degrees by local climate conditions. Among the ASFPMs examined, anti-frost candles presented the highest variability across all environmental impacts, whereas the winter cover displayed the lowest. The variability in environmental scores was mainly due to the direct application of ASFPMs during frost events, except for anti-frost candles. The hierarchy of environmental scores for ASFPMs shifted in response to changes in frost hours. In this study, wind machines presented the best environmental scores, closely trailed by sprinklers, except in the case of the Water scarcity indicator. Future research endeavours may explore additional context-specific elements, such as the ASFPM efficiencies and other ASFPM technologies. It would allow for further refinement and development of the framework, particularly in assessing the climate change consequences on PTOs. Such an approach could find applicability across different fields of study, enabling the analyse and comparison of environmental impacts of technologies responding to diverse external drivers.

### CRediT authorship contribution statement

Vincent Baillet: Writing – review & editing, Writing – original draft,

Visualization, Software, Methodology, Investigation, Formal analysis, Data curation. **Ronan Symoneaux:** Writing – review & editing, Supervision, Project administration. **Christel Renaud-Gentié:** Writing – review & editing, Validation, Supervision, Project administration, Methodology, Funding acquisition, Conceptualization.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Data availability

Data will be made available on request.

### Acknowledgement

The authors thank Thomas Chassaing from the Maine-et-Loire Chamber of agriculture for identifying wine-growers using different ASFPM technologies, and the interviewees for sharing their knowledge about frost protection systems and strategies. This project is funded by Pays de la Loire region, France.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.cesys.2024.100209.

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