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Article

Comparative Assessment of Environmental/Energy Performance under Conventional Labor and Collaborative Robot Scenarios in Greek Viticulture

by
Emmanouil Tziolas
1,
Eleftherios Karapatzak
1,
Ioannis Kalathas
1,
Chris Lytridis
1,
Spyridon Mamalis
2,
Stefanos Koundouras
3,
Theodore Pachidis
1 and
Vassilis G. Kaburlasos
1,*
1
Human-Machines Interaction (HUMAIN) Lab, Department of Computer Science, International Hellenic University (IHU), 65404 Kavala, Greece
2
Department of Management Science and Technology, School of Economics and Business Administration, International Hellenic University (IHU), 65404 Kavala, Greece
3
Laboratory of Viticulture, Faculty of Agriculture, Forestry and Natural Environment, School of Agriculture, Aristotle University of Thessaloniki (AUTh), 54124 Thessaloniki, Greece
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(3), 2753; https://doi.org/10.3390/su15032753
Submission received: 5 January 2023 / Revised: 20 January 2023 / Accepted: 30 January 2023 / Published: 2 February 2023
(This article belongs to the Collection Environmental Assessment, Life Cycle Analysis and Sustainability)
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Abstract

:
The viticultural sector is facing a significant maturation phase, dealing with environmental challenges to reduce agrochemical application and energy consumption, while labor shortages are increasing throughout Europe and beyond. Autonomous collaborative robots are an emerging technology and an alternative to the scarcity of human labor in agriculture. Additionally, collaborative robots could provide sustainable solutions to the growing energy demand of the sector due to their skillful precision and continuous labor. This study presents an impact assessment regarding energy consumption and greenhouse gas emissions of collaborative robots in four Greek vineyards implementing a life cycle assessment approach. Eight scenarios were developed in order to assess the annual production of four Vitis vinifera L. cultivars, namely, Asyrtiko, Cabernet Sauvignon, Merlot, and Tempranillo, integrating data from two wineries for 3 consecutive years. For each conventional cultivation scenario, an alternative was developed, substituting conventional viticultural practices with collaborative robots. The results showed that collaborative robots’ scenarios could achieve a positive environmental and energy impact compared with conventional strategies. The major reason for lower impacts is fossil fuel consumption and the efficiency of the selected robots, though there are limitations regarding their functionality, lifetime, and production. The alternative scenarios have varying energy demand and environmental impact, potentially impacting agrochemical usage and requiring new policy adjustments, leading to increased complexity and potential controversy in farm management. In this context, this study shows the benefits of collaborative robots intended to replace conventional practices in a number of viticultural operations in order to cope with climate change impacts and excessive energy consumption.

1. Introduction

Climate change is one of the main global challenges for viticulture, since direct (e.g., temperature, rainfall distribution, and CO2 concentration) and indirect impacts (e.g., pests’ population, energy efficiency, and invasive species availability of food) affect an assortment of production factors (yield, quality, etc.). Early flowering and maturity of grapes are already a worldwide problem [1], while wine-producing regions may face issues related to land suitability for growing grapevines [2]. Predictions of climate change scenarios have depicted significant raises in average growing season temperature in several wine-growing regions over the past 50 years [3], though recent studies have indicated that the barrier is surpassed in specific areas in Europe and in the USA [4]. Apart from the abovementioned, budbreak, flowering, and véraison dates of different grapevine varieties are expected to differ significantly due to climate change impacts in the 21st century [5].
Spain, France, and Italy are major producers of viticulture, accounting for over 50% of the wine production worldwide while covering about one-third of the global area under vines [6]. According to the latest Eurostat data [7], Spain has the largest agricultural areas under vines in Europe, whereas France is second. Nevertheless, Italy produces more wine than either Spain or France, holding the lead in Europe since 2017 [8]. The importance of grape production in the European area is enhanced by an assortment of studies implementing life cycle assessment (LCA) to evaluate the environmental, societal, and economic performance of manifold locations and objectives. In this context, the investigation of energy consumption and environmental sustainability in viticulture is more crucial than ever.
The energy and environmental evaluation of the current industry should follow a strict protocol and a credible methodological framework to ensure the homogeneity of results. LCA is an established tool, integrating the ISO 14040 and 14044 guidelines [9,10] and defining a standardized methodological framework for the life cycle of a product or a procedure. Nevertheless, the results’ legitimacy is questionable, since the standards do not detail a step-by-step procedure, but rather, they describe a broader range of choices that could lead to dubious assumptions [11].
One the one hand, LCA has been included in environmental legislation around the world, while recognizing process issues related to recent developments [12], system boundaries set by researchers [13], consequences of the appropriate impact assessment method [14], and translation of the functional unit to the real world [15]. On the other hand, LCA is an integrated methodological framework for the evaluation of the environmental performance of a system (product or procedure), taking into account all the relevant inputs and outputs throughout its lifetime [16]. Furthermore, LCA is considered a competent decision support system incorporating scientific data [17] and a policy decision-making tool [18]. Consequently, the LCA methodological framework could be used with other quantitative methods for better data management and validation of results especially to assess agricultural sustainability [19,20]. The evolution of LCA incorporates an interdisciplinary framework with economic, social, and environmental aspects, formulating an integrated approach, namely, life cycle sustainability analysis (LCSA) [21]. In this context, the life cycle methodological framework consists of four interrelated stages, namely, (i) goal and scope definition, (ii) life cycle inventory (LCI), iii) life cycle impact assessment, and (iv) interpretation [22].
LCA has also been implemented to assess the environmental impacts of irrigation systems for vineyard cropping systems in southern Italy [23] and in southern France [24]. In addition, freshwater scarcity and footprint profile for the production of a Portuguese wine (vinho verde) have been evaluated following four freshwater use LCA methods [25]. Furthermore, water-focused LCA has been used to assess the impacts on water resources for the production of a typical red Italian wine [26]. Identifying critical life cycle stages and comparing environmental performance among wine production is another domain of several LCA studies in Spain [27,28,29], in Italy [30,31], and in Portugal [32,33]. Roselli et al. [34] assessed the environmental impacts of three table grape production schemes related to harvesting dates in Italy. Moreover, the environmental sustainability of four vineyard production scenarios, mixing cultivation techniques (conventional and organic) with training systems (gobelet and espalier), in a protected designation of origin (PDO) wine-growing area in Calabria (southern Italy) was investigated by Falcone et al. [35] and extended with the integration of multicriteria analysis to rank the scenarios’ environmental and economic sustainability in the same area [36]. In a similar manner, two viticultural management techniques (integrated and organic) were assessed via LCA in Loire Valley, France [37].
In Greece, the estimation of environmental performance for the wine production industry is relatively recent and limited to PDO and protected geographical indication (PGI) red and white varieties in several areas [38,39]. Greek viticulture is changing and aligning with EU directives for quality products over quantity, and LCA is considered a methodological tool for identifying environmental performance and environmental and energy weak points throughout the production process. Furthermore, Greek viticulture is characterized by the production of (i) PDO wines, (ii) PGI wines, and (iii) currants. According to environmental impact assessment studies in the area, Corinthian currant cultivation is a human-labor-intensive production procedure, and the relevant impacts are mainly caused by processing [40]. On the other hand, Balafoutis et al. [41] identified field energy (tractor fuel use and electricity for irrigation) as the most significant activity related to greenhouse gas (GHG) emissions between conventional and precision viticulture techniques in the region of Eastern Macedonia and Thrace, Greece. In this context, viticulture management and, more importantly, production techniques related to on-field agricultural activities play one of the most significant roles in environmental performance in viticulture.
Robots are increasingly being used in the viticulture industry to improve efficiency and accuracy in tasks, such as vineyard mapping, pruning, and harvesting. The use of robots in vineyards can provide several advantages, such as continuous work day and night, reduced labor costs, and task precision, especially in areas where human labor is scarce or expensive. Replacing conventional labor with robots in order to cope with labor shortages, especially when the demand for human labor cannot be satisfied, is a promising solution, though agricultural robots for commercial use focus mainly on weeding and harvesting operations [42]. The “VineRobot” is another example of a robotic system used in viticulture, which is equipped with cameras and sensors that allow it to map the vineyard and identify individual vines. This information is used to prune the vines with high precision, while also collecting data on the health and growth of the vines [43]. Therefore, farmers take rational decisions on when and how to prune their vines, which can lead to improved yields and higher-quality grapes. A similar project is the “FLEXIGROBOTS”, which integrates precision agriculture operations based on intelligent automation. Nevertheless, this is an ongoing project, and the first trial regarding harvesting operations was positive [44]. Robotic systems such as these can also help to reduce the environmental impact of viticulture. This was the case in studies by the University of California–Davis (UC Davis), where a robot could simultaneously collect soil moisture samples and adjust irrigation emitters [45,46]. Furthermore, the use of robots can reduce the need for pesticides, herbicides, and fungicides, which can be harmful to the environment [47,48].
Consequences to the environment, GHG emissions, and energy consumption of an integrated robotic system for agricultural operations are difficult to quantify, and very few studies have reported on those issues. In particular, some studies address the impact of autonomous weeding systems [49,50] and autonomous electric tractors [51] or focus on economic impacts [52]. Therefore, the main aim of the current study is a holistic environmental and energy assessment of conventional and collaborative robots [53] (cobot) scenarios in Northeastern Greece following a LCA framework. In particular, on-field activities during grapevine production by human labor are compared with activities by cobots regarding energy consumption and overall efficiency in two private vineyards in the region of Eastern Macedonia and Thrace, Greece. The study highlights for the first time the potential of cobots in an assortment of agricultural operations against climate change impacts and excessive energy consumption.
The paper is structured as follows: The methodology section (Section 2) includes eight subsections describing the methodological framework, the case study area, and the cobot description. Section 3 includes the results of the study regarding the impacts of cobots on the environment and the relevant indices. Finally, Section 4 and Section 5 summarize the contribution of this work, including discussions, conclusions, and potential future work.

2. Materials and Methods

2.1. Case Study Area and Selected Vineyards’ Description

The wine-producing vineyards assessed herein during the 2019–2021 vintages are located in Northern Greece, regional unit of Drama in the region of Eastern Macedonia and Thrace, at two different locations that are approximately 30 km apart, with similar terroir parameters. Note that the wider area of Drama hosts a plethora of wineries and vineyards producing several PGI-labelled wines. The first location of the current study, denoted as LOC1, namely, Ktima Pavlidis winery (41.200400 N, 23.953084 E, 200 m elevation), includes Vitis vinifera L. cvs Tempranillo (grafted onto Berlandieri X Rupestris 110R rootstock) and Asyrtiko (grafted onto Berlandieri X Rupestris 1103P rootstock), whereas the second location, denoted as LOC2, namely, Nico Lazaridi winery (41.127832 Ν, 24.275972 Ε, 190 m elevation), includes Vitis vinifera L. cvs Cabernet Sauvignon and Merlot (both grafted onto Berlandieri X Riparia SO4 rootstock).
The cultivars Tempranillo and Asyrtiko in LOC1 are part of a 40 ha vineyard under conventional crop management. The planting distance is 2.2 m between rows and 1.2 m along each row (3780 vines/ha) with planting in both cultivars being NE to SW orientated following the low slope of the terroir. Likewise, the cultivars Cabernet Sauvignon and Merlot in LOC2 are part of a 35 ha vineyard under conventional crop management. The planting distance is 2.5 m between rows and 1.2 m along each row (3330 vines/ha) with planting for Cabernet Sauvignon being NW to SE and for Merlot N to S orientated (Table 1). All cultivars in both locations are managed under very similar conventional management schemes employed by most wineries in the wider area. Those include composite winter pruning following the bilateral cordon training of the vines, followed by summer pruning operations, including budding, topping, defoliation, and crop load reduction. All vegetation and crop load management operations follow the course of the phenological stages of the vines that may differ between years targeting a relatively low final crop load for optimum crop quality (in the order of ≈10 t/ha). In addition, the vegetation between rows is managed mechanically, implementing plant protection practices via targeted sprays for pests and diseases.

2.2. Collaborative Robots in Agriculture

Cobots in agriculture integrate specific equipment and technology, which may include drones and wheel robots, in order to be effective [54]. Before activating the robots, the area of interest was mapped by a drone. In particular, the drone captures geographic data (digital images of the vineyard) and feeds them to the computing base station to calculate the optimal path for the ground robots within the vineyard [55]. The robots communicate through the base station, as illustrated by the autonomous mobile robot “VINBOT” [56]. The novelty of the SVtech project is based on the collaboration among master and slave robots, as well as on the enhanced number of viticultural operations by cobots. Each master robot has at least one robotic arm equipped with a robotic hand attached to it and various electronic sensing instruments, including cameras, while the slave robot has up to one robotic arm equipped with a gripper. Note that a gripper is much less dexterous than a robotic hand, but typically, a gripper can lift more than 2 kg. A master robot carries out viticultural tasks either alone or cooperatively with the other master robot in selected viticultural tasks, such as in vine tying, whereas the slave robot is used to transport materials produced by the master robot, such as grapes (during harvest). In addition, a master robot can direct the slave robot as needed. The cobot’s technology is based on the interaction and coordination between robots and humans during production.
A robotic hand can handle a manual viticultural tool per operation, such as pruning/spraying/ tying, thus significantly reducing the cost of using an expensive specialized robotic arm per operation. Furthermore, a robotic hand can replace the human hand, such as during harvest. The effective use of robotic hands will be pursued by innovative artificial intelligence (AI) techniques tackling embodiment issues [57]. The pilot project SVtech of autonomous cooperative robots is focusing on the following basic viticultural operations: (i) cutting (see defoliation, pruning, and harvesting), (ii) spraying (precautionary), and (iii) tying. The aforementioned multiple operations and innovations are also supported using a new AI technology, called “lattice computing” [58,59,60], toward making the robots autonomous.

2.3. Selected Cobots’ Description

The current project uses two types of robots: an expert-type robot and a helper-type robot; both are supplied by Robotnik Automation S.L.L. company in Valencia, Spain RB-EKEN (helper) is a ragged robot with a weight of 270 kg and a payload of 300 kg, equipped with a UR10e robotic arm and a payload of 16 kg (Figure 1). The robot moves with 4 motors (each has a maximum power of 1.2 kW). It can reach a maximum speed of 2 m/s. RB-EKEN is powered by LiFePO4 (48 V, 60 Ah) batteries with a maximum autonomy of 4 h of continuous motion. The maximum reachable slope of RB-EKEN is 60%.
RB-VOGUI (expert) is an autonomous base robot with a weight of 165 kg and a payload of 150 kg (Figure 2). Τhe robot moves with four traction motors (each has a maximum power of 500 W) and 2 or 4 steering motors (each has a maximum power of 100 W). It can reach a maximum speed of 2.5 m/s. RB-VOGUI is powered by LiFePO4 (48 V, 30 Ah) batteries with a maximum autonomy of 8 h of continuous motion.

2.4. Goal and Scope Definition

The main goal of the current study is a holistic environmental assessment and the comparison of environmental performances between conventional and cobot labor scenarios of four vineyards in the region of Eastern Macedonia and Thrace, Greece. The process includes an assortment of operations, namely, harvesting, pruning, spraying, tying, weed control, and defoliation performed either conventionally or by cobots. More attention is paid to farming activities and machinery operations in order to highlight core differences between conventional and cobot labor. System boundaries are depicted in Figure 3, including all the relevant phases and the respective actions and inputs from annual grape production, while the transportation of products to the wine processing plant is also included.
A cradle-to-factory gate variation is selected since the study focuses on the on-field activities and the integration of robotic labor in the production phase, neglecting the planting and disposal phase. A 3-year framework is proposed as a minimum timeline for GHG accounting, as suggested by the International Organisation of Vine and Wine (OIV), to minimize uncertainty [63]. Therefore, data from 3 consecutive years were collected, and the functional unit was set to 1 ha of agricultural land in order to minimize deviations among grapevine varieties. Furthermore, it is a commonly used functional unit that is easily transmuted to 1 metric ton of table grapes produced on 1 ha if needed [34,64].
Transportation of all the inputs necessary for the table grapes’ cultivation was set on trucks for a distance of 200 km. Biomass produced by the pruning and defoliation activities involved the transportation of grapes to a specified storage area in order to continue to the processing stage, while biomass residues were used again as fertilizers, as stated by the two firms. Therefore, four management cultivation schemes were thoroughly analyzed, comparing GHG emissions, performances, and labor substitutions by cobots toward sustainable wine growing grape cultivation.
The collected data for the environmental/energy assessment included fertilizers (kg/ha), fungicides (kg/ha), herbicides (kg/ha), machinery usage (h/ha), human labor (h/ha), electrical energy (kWh/ha), irrigation needs (m3/ha), other inputs (kg/ha), diesel, and petrol (l/ha) per management cultivation scheme.

2.5. Inventory Analysis

Formulating an LCI is a crucial stage that involves the development of a directory for input and output flows for the relevant system [65]. Flows include inputs of agrochemicals, energy and raw materials, emissions, and primary energy consumption. The inventory is based on literature analysis, and all the relevant parameters for calculating GHGs and consumption of primary energy for grapes’ cultivation are given in Table 2. Emissions from land use change are excluded, and the remarks are focused on the cultivation phase, since the major goal of the study is the assessment of conventional and cobot’s labor. Data related to harvested yield, growing area, agrochemical application, number of pesticide applications, transportation of supplies, and biomass are equal for the respective cobot’s scenarios. The robots have built-in LiFePO4 technology batteries inside their shell to meet their needs, namely, the movement in the field, the use of sensors, computing systems, and the powering of the telecommunication systems with the base station. If batteries reach a low energy state, the robot should reach the location of recharge in a protected area with a sufficient supply of energy, ideally within walking distance of the field of work.
Quality and consistency are considered major factors for the inventory analysis, especially in the primary sector and, hence, in sustainable agricultural production toward lower GHG emissions. The connection between LCA and agricultural production systems has grown stronger over the past years [66] and has generated a number of public and private inventories [67]. The reference system of the present study is based mainly on the BioGrace-II greenhouse gas (GHG) standard values [68] following European Directive 2018/2001 [69]. The Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) integrates the latest radiative efficiencies and metrics, meaning that global warming potential (GWP100) in CO2 equivalents has been calculated as follows: CO2 = 1, CH4 = 27.9, and N2O = 273. The time horizon of 100 years was selected considering short-term and mid-term implications of agricultural production systems and wide application in relevant studies as well [6]. Lubricants, biomass, and supplies transportation are estimated as well, and more specifically, lubricants represent 0.7% of the consumed diesel fuel [70], while the assumption for the distance travelled in order to acquire the required materials (lubricants, fertilizers, pesticides, etc.) is set to 200 km. Furthermore, indirect emissions from nitrogen fertilization are considered as 1% of the N2O direct emissions [71].

2.6. Carbon Footprint and Energy Consumption Impact Assessment

Differences among conventional and cobot practices were assessed in terms of GHG emissions following the approach of “emission factors” [87]. Although this approach has been criticized for deficiency of field measurements and background datasets, comparing similar systems and quantifying GHG fluxes as a function of farming activity could illustrate trade-offs among them. The formulation of one unified indicator converting climate pollutants into CO2 equivalents is based on two factors, namely, the conversion factor and the respective quantity of each pollutant, as follows [88]:
C F i = ∑ n = 1 i E M i , j × G W P 100
where CFi is the carbon footprint in CO2 equivalents for each scenario i, EMj,i is the emissions of each pollutant j related to each scenario i, and GWP100 is the global warming potential conversion factor of each pollutant for a specified time horizon (100 years). Furthermore, the calculation of consumed energy for each scenario is based on the multiplication of a primary energy factor with the quantity of energy consumed per functional unit, which is based on the following equation:
E C i = ∑ n = 1 i P E i , j × P E F j
where ECi is the consumed energy for each scenario i, PEj,i is the primary energy of each action j related to each scenario i, and PEFj is the primary energy factor of each input to the system. The parameters considered for calculating energy and environmental impacts include the agricultural practices as described in the goal and scope definition section. Furthermore, energy used to produce every piece of machinery, including mining, manufacture, and transport, is calculated according to embodied energy (EI) equation [83]:
E I = ∑ w i × e c i L i i × H o p i
where wi represents the machinery’s weight in kilograms, ec is the respective energy coefficient, Lii is the total hours of each machinery’s lifetime in hours, and Hopi is the hours of agricultural operations for the on-field cultivation practices. Consequently, indirect impacts from the machinery used in the agricultural operations are calculated.

2.7. Energy Efficiency and Emission Intensity of the Investigated System

Energy efficiency is of the utmost importance in agriculture, as the primary sector is vulnerable to energy cost fluctuations, and the depiction of energy consumption per production unit could elicit important conclusions for farm management strategies [89]. Nevertheless, the concept of energy efficiency is considered a ratio between a sum of outputs (energy or not) per sum of energy inputs of a process as well [90]. Whichever energy efficiency indicator is chosen, the energy content of all the relevant inputs should be determined following a robust protocol. The EU highlights the importance of a life cycle approach in order to cover gaps toward energy efficiency [91]. As a result, the energy efficiency (EFi) of the relevant scenarios is calculated as follows:
E F i , j = E N i C Y j
where EN is the consumed energy per scenario i in MJ, and CY is the crop yield for each cultivar j in kg. A similar indicator presenting the efficiency of new technologies and their impact on the reduction of emissions and costs is GHG emission intensity [92]. Emission intensity is a parameter that represents the impact of innovative technologies on agricultural production and their respective effect on climate change. Emission intensity is calculated by the amount of CO2 equivalents emitted in kg per produce in kg, which could be used for a broader spectrum of production systems [93]. Nevertheless, for the selected vineyards, the GHG emission intensity indicator (EIi) is measured as follows:
E I i , j = G H G   e m i s s i o n s i C Y j
Calculations for data analysis and illustrations presented in the Results section were elaborated via RStudio 2022.07.1+554 [94].

3. Results

In the context of our study, the calculation of GHG emissions for each scenario was carried out based on the inventory and the provided data from the wineries. The two firms integrate different management strategies for the selected vineyards in relation to the amount and type of applied agrochemicals, hours of labor, and energy consumption. A brief description of the management schemes per hectare for the four cultivars is presented in Table 3.
Factors that significantly alter the GHG totals, expressed in CO2 equivalents, are classified in separate categories, indicating critical hotspots of each management system. In this context, five subcategories are created: (i) agrochemicals (fertilizers, fungicides, and herbicides, (ii) electrical energy, (iii) fossil fuels (petrol and diesel), (iv) machinery (direct and indirect), and (v) other (transportation of inputs, indirect N2O, lubricants, etc.). Electrical energy is a discrete category since the cobots are powered by rechargeable batteries, thus lowering the usage of human and agricultural machinery labor.
All agricultural activities, such as pruning, tying, and harvesting, are connected to an assortment of impacts and energy consumption, alternating the total needs of energy and emissions per management scheme. The abbreviations of the scenarios use the first letter of the respective cultivar (e.g., for Asyrtiko, it is A), followed by the letter C if the operations are performed conventionally or CB if the operations are performed by cobots (e.g., AC for conventional practices and ACB for cobot simulation for the Asyrtiko cultivar). All the relevant scenarios integrating cobots illustrate lower energy consumption in comparison with their respective conventional scenarios. In Figure 4, a dual Y-axis bar chart is presented, depicting the consumed energy in MJ ha−1 and the total GHG emissions in kg CO2-eq ha−1 for each scenario. Verifying the abovementioned, all the scenarios integrating cobots (namely, ACB, CCB, MCB, and TCB) are less energy demanding in comparison with their corresponding conventional scenarios(AC, CC, MC, and TC, respectively). On the one hand, regarding only the conventional scenarios, AC consumes the highest amount of energy (27,281.79 MJ ha−1), while MC is the least energy intensive scenario (25,014.93 MJ ha−1). Nevertheless, agricultural activities adopted in CC and TC systems do not deviate significantly from the lowest energy consumption scenario (+2.51% and +2.53%, respectively).
On the other hand, TCB is the least energy-demanding system with 14,703.07 MJ ha−1, followed by ACB with 16,304.33 MJ ha−1, illustrating the potential positive impact of cobots in vine-growing systems. Regarding GHG emissions, scenarios under conventional practices form two groups, one group exceeding the threshold of 2,000 kg CO2eq ha−1 and the other one below this threshold (Table 4). More specifically, CC and MC emit lower levels of GHGs with 1,428.75 and 1,678.68 kg CO2eq ha−1, respectively, while the results for AC and TC are 2,570.57 and 2,308.90 kg CO2eq ha−1, respectively. Cobots performing agricultural activities could lower the carbon footprint of agriculture, since the results show GHG emissions under 1,500 kg CO2eq ha−1 for all the relevant scenarios. More specifically, CCB accounts for only 980.16 kg CO2eq ha−1, which is the lowest value, while ACB emits 1,465.57 kg CO2eq ha−1, which is the highest value among the cobots’ scenarios.
The differences among the four cultivars are due to the different management practices implemented by the two wineries. In fact, the main difference is the fossil fuel consumption, which is higher for the Asyrtiko and Tempranillo cultivars, since the agricultural activities are performed by the same winery (Figure 4).
Indeed, conventional agricultural practices highlight significant environmental impacts due to fossil fuel consumption ranging from 50.13% for CC to 69.39% for TC, as shown in Figure 5. The substitution of conventional with cobot’s labor reduces the share of fossil fuels for the cobot’s scenarios, especially for the Merlot management scheme in which the percentage is cut in half (MC—52.38% and MCB—26.64%) (Table 5). Conventional labor techniques consume more fossil fuels due to machinery usage (e.g., tractor) for some viticultural operations, whereas cobots consume only electrical energy much more efficiently.
Although the replacement of conventional labor by robots increases the share of impacts due to the constant need for charging the batteries of the cobots. Electrical energy accounts for lower GHG emissions since the cobots’ energy consumption is low, though the share of electrical energy emissions for TCB and ACB is quite significant (21.38% and 18.21%, respectively). On the other hand, the “Other” category, integrating transportation of inputs, indirect N2O, and lubricants illustrate minimum impact on the environment with percentages below 10% for each scenario.
Consumed energy is yet another aspect creating an uneven situation and complicating the decision making between efficiency and environmental protection. The impact of each category, as a share for each scenario, is depicted in Figure 6. As a matter of fact, agrochemicals are of the upmost importance especially for the Cabernet Sauvignon and the Merlot cultivars, presenting a range of percentages between 43.85% for MC and 64.63% for CCB (Table 6). Nevertheless, the impact of fossil fuels is approximately the same for AC and TC in comparison with the shares of GHG emissions (61.92% and 65.86%, respectively). Regarding the electric energy consumed, the major difference apparently hinges on the usage of cobots, which is responsible for the high shares of the respective impact category in the relevant scenarios. Following the same pattern with Figure 5, Machinery and Other categories illustrate a very low energy impact, under 15%.
The results of the research indicate that agricultural activities performed by cobots alter the total GHG and energy consumption pattern, reducing the impact of fossil fuels. Nevertheless, the impact of agrochemicals and electric energy differentiate the critical points between the conventional and the cobot’s scenarios. Especially in the ACB and TCB scenarios, the proportion of electric energy consumed has increased by over 10% in comparison with conventional scenarios.
The estimation of energy efficiency and GHG emissions per kg of production could illustrate the real potential of cobots in agriculture. In particular, Table 7 depicts deviations between the selected vine-growing systems, indicating the positive impact of cobots regarding energy consumption, as well as GHG emissions. More specifically, the CO2 eq per kg of produce indicator could approach a value of 0.151 kg CO2-eq kg−1, achieving 47.58% less emissions to the environment. Deviations for the Asyrtiko, Merlot, and Cabernet Sauvignon cultivars are −43.34%, −32.96%, and −31.40%, respectively. Furthermore, deviations in energy efficiency are effective to a higher degree for the Asyrtiko and Tempranillo cultivars (−40.24% and −42.67%, respectively) and to a lesser degree for the Merlot and Cabernet Sauvignon cultivars (−25.51% and −15.60%, respectively).
In comparison with the Asyrtiko and Tempranillo cultivars, Merlot and Cabernet Sauvignon present minor discrepancies (especially for energy efficiency). The latter is explained by the more intensive usage of agrochemicals and particularly by the poultry manure and olive pomace residue application. As depicted in Figure 6, the shares of agrochemicals for the two cultivars have a significant impact on the total mixture of energy needed; thus the cobot scenarios could not alter the efficiency aspect to the same degree as in the Asyrtiko and Tempranillo scenarios. The same pattern applies for the GHG emission per kg of produce for the cobot scenarios, although the impact is more meaningful in comparison with energy efficiency.

4. Discussion

The integration of cobots performing viticultural tasks on the field is not an energy and environmentally impact-free step toward agricultural sustainability. An LCA approach has been implemented to investigate the environmental and energy impact of various real-life and simulated agricultural management scenarios. Furthermore, energy efficiency and GHG emission intensity indicators have been calculated as well to highlight deviations between conventional practices and the cobot’s integration to the agricultural practices. Vineyard planting and disposal stages were neglected in order to focus on the impacts of annual production and the respective pros and cons of cobots, which could substitute conventional viticultural practices. Merlot and Cabernet Sauvignon scenarios during the production phase are heavily affected by the amount and nature of specific inputs (e.g., poultry manure and olive pomace residues), which usually cause considerable impacts on the environment [95].
The Merlot and Cabernet Sauvignon cultivars illustrate a better environmental profile in absolute terms than the Asyrtiko and Tempranillo cultivars, though these latter had higher yields, better energy efficiency, and GHG emissions intensity. The results for MJ per hectare fall into the range of Steenwerth et al. [96] with average values of 20,000 MJ. Human-induced emissions mainly due to soil management and diesel consumption accounted for 0.26 kg CO2-eq kg−1 per kg of grape yield in the south of Sardinia in Italy [97]. This value is quite similar to the respective emission intensity indicator for the conventional practices applied to the Asyrtiko, Cabernet Sauvignon, and Tempranillo cultivars of the current study. Nevertheless, Gierling and Blanke [98] presented higher GHG emissions per hectare, investigating the difference between steep and flat terrains of vineyards. Their findings for steeper terrains (2990 kg CO2 ha−1), in which human labor is preferred over mechanical labor, match to a certain degree with the findings of the GHG emission of AC and TC scenarios. In addition, Gierling and Blanke [98] highlight the relationship between lower emissions due to human labor in steep terrains, though lower productivity of human labor in comparison with mechanical labor along with scarcity of manual labor when needed, developing a paradox stalemate for sustainable viticulture.
In relation to fossil fuel production and consumption as the main source of GHG emissions in viticulture, the findings of the current study are consistent with other surveys [96,97,98,99], while this is the case as well for a table grape variety named Soultanina in Cyprus [100]. Moreover, Balafoutis et al. [41] reported field energy use as the main factor of GHG emissions for two different cultivars in Greece, while fertilizers are the second most important factor. Nevertheless, other studies present fertilizer production as the major contributor to the carbon footprint shares among management practices and more specifically the Xynisteri and Cabernet Sauvignon varieties in Cyprus [100]. Indeed, GHG emissions associated with annual production of multiple grape training systems in North Tajikistan have been linked to impacts mainly due to ammonium nitrate application [101]. Roselli et al. [34] reported agrochemicals as factors of significant environmental impact as well, especially regarding the cultivation phase, while the same perspective applies for Gazzula et al. [27].
Inventory parameters, estimation methods for impact assessment, and methodological options complicate the comparability of the results among LCA studies [32]. Extending the system boundaries of the cultivation phase in vineyards, by including cobots performing agricultural operations and substituting human and mechanical labor, is an innovative approach that should be assessed. Nonetheless, significant hotspots have been identified, which would not be taken into account otherwise, though there are few available cobot-related LCA studies focusing on autonomous weed mowing. Weeding management strategies were assessed by Pradel et al. [49], concluding to overall lower environmental impacts in comparison with conventional solutions. Autonomous lawn mowing has illustrated even better performance when the path planning is optimized [50].
However, the implementation of these technologies comes with maintenance costs, which can include regular check-ups, repairs, and replacement of parts. Despite the potential benefits, the high cost of purchasing and maintaining agricultural robots remains a major barrier to their widespread adoption [102]. The investment and annual costs of real-time kinematics Global Positioning Systems along with the small battery capacity of robots hinder the economic viability to a significant degree [103]. Autonomy is also a concern for agricultural robots. These machines are often required to operate in unstructured environments, which can make it difficult for them to navigate and perform complicated tasks [104]. Additionally, the use of robots in agriculture often requires them to work in close proximity to humans, animals, and other equipment, which can increase the risk of accidents and injuries, creating gray areas in autonomy regulations [105].
Furthermore, the use of robots in agriculture has the potential of significantly impacting the job market, both positively and negatively. On the one hand, the use of robots can lead to job losses as they automate tasks that were previously performed by human workers, creating key ethical debates [106]. On the other hand, the use of robots can also create new jobs as they increase productivity and efficiency, leading to growth in the agriculture industry [107]. Nevertheless, these new jobs could include positions in areas such as robot design, programming, maintenance, and data analysis. Software maintenance and updates are crucial for the proper functioning of robots in agriculture. A key aspect of software maintenance is troubleshooting and resolving any issues that arise, especially for robots in agriculture [108], thus meaning that the cost of software maintenance and updates can be another aspect of troubleshooting for farmers and other agricultural operators.
Nonetheless, the present study focuses on the environmental performance and energy consumption of an assortment of agricultural activities performed for the first time by cobots [109]; thus the concluding remarks focus on the key parameters never published before and on suggestions for future research.

5. Conclusions

This paper examined the energy consumption and GHG emissions of conventional and cobot agricultural practices of selected vineyards in Northern Greece. The methodological framework of LCA is implemented to identify main hotspots of four different cultivars and to highlight the most sustainable and environmentally friendly scenarios. The study implies that the use of cobots for several agricultural operations in vineyards emits lower GHG emissions than conventional practices performed by human and conventional mechanical labor. The reduction of GHG emissions is mainly due to the fossil fuel consumption, which is significantly decreased when the cobots are used. Furthermore, GHG emission intensity deviations between scenarios present a greater environmental impact, achieving reductions of kg CO2 eq kg−1 of grapes from 31.40% to 47.58%. However, the implementation of cobots’ labor in agriculture changes the potential energy and environmental shares of inputs used, which develops a new mixture of energy demand and GHG emissions. Specifically, all the cobot’s scenarios demonstrate higher shares of impacts due to agrochemical application and machinery usage.
Although cobots decrease impacts in absolute terms, at the same time, a new mixture of energy needs and environmental impacts alternates the perspective of hotspots in viticultural systems. Therefore, the functionality of cobots should be further investigated, integrating an actual lifetime period for cobots, as well as all impacts connected to the manufacturing of electronic components, cables, and motors. Additionally, while cobots consume less energy than other agricultural machines (e.g., tractors), the results should be viewed with optimism as the study assumes ideal conditions (e.g., flat terrain, exposure to fungicides/climatic conditions, and a lack of data on cobot failures). Finally, the results were evaluated regarding the cultivation phase, neglecting on purpose the planting, training, and disposal phase; thus impacts regarding these phases could alter the magnitude of the total positive impacts of cobots.
Future research should integrate economic data to highlight the eco-efficiency management philosophy, mitigating simultaneously climate change and economic impacts. Furthermore, additional data for the whole lifecycle of vineyards could be integrated, including the planting, training, and disposal stages of viticultural operations performed by cobots, since the environmental impacts of batteries’ production, use, and recycling/disposal could be significant [110].

Author Contributions

Conceptualization, E.T. and V.G.K.; data curation, E.T., E.K., and I.K.; formal analysis, E.T., E.K., and I.K.; investigation, E.T., E.K., and I.K.; methodology, T.P. and V.G.K.; supervision, V.G.K.; validation, E.T. and I.K.; visualization, E.T.; writing—original draft, E.T., E.K., I.K., and V.G.K.; writing—review and editing, E.T., C.L., S.M., S.K., T.P., and V.G.K. All authors have read and agreed to the published version of the manuscript.

Funding

We acknowledge support for this work by the project “Technology for Skillful Viniculture (SVtech)” (MIS 5046047), which is implemented under the action “Reinforcement of the Research and Innovation Infrastructure” funded by the operational program “Competitiveness, Entrepreneurship, and Innovation” (NSRF 2014–2020) and cofinanced by Greece and the European Union (European Regional Development Fund).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data is contained within the article.

Acknowledgments

The authors would like to thank Ioannis Chronis from Ktima-Pavlidis winery and Nikolaos Tsipouridis from Nico Lazaridi winery for data provision regarding all the relevant viticultural operations, hours of labor, inputs, energy consumption, and transportation of yield.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 15 02753 g001 550
Figure 1. RB-EKEN blueprints (source: [61]).
Figure 1. RB-EKEN blueprints (source: [61]).
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Figure 2. RB-VOGUI blueprints (source: [62]).
Figure 2. RB-VOGUI blueprints (source: [62]).
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Figure 3. System boundaries.
Figure 3. System boundaries.
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Figure 4. GHG emissions and consumed energy per hectare for each scenario.
Figure 4. GHG emissions and consumed energy per hectare for each scenario.
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Figure 5. Stacked bars comparing the shares of GHG emissions.
Figure 5. Stacked bars comparing the shares of GHG emissions.
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Figure 6. Stacked bars comparing the shares of consumed energy.
Figure 6. Stacked bars comparing the shares of consumed energy.
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Table
Table 1. Location, cultivars, and vineyard details of the two assessed case studies.
Table 1. Location, cultivars, and vineyard details of the two assessed case studies.
Cultivar/RootstockTempranillo/110 RichterAsyrtiko/1103 PaulsenCabernet Sauvignon/SO4Merlot/SO4
WineryLOC1
Ktima Pavlidis winery		LOC1
Ktima Pavlidis winery		LOC2
Nico Lazaridi winery		LOC2
Nico Lazaridi winery
Coordinates (HGRS87/EGSA87)
(Lat, Lon)		41.200400 N, 23.953084 E41.127832 Ν, 24.275972 Ε
Elevation200 m190 m
Planting distance/orientation2.2 X 1.2/NE–SW2.2 X 1.2/NW–SE2.2 X 1.2/N–S
Vines/ha37803330
Table
Table 2. Inventory for GHGs and primary energy consumption.
Table 2. Inventory for GHGs and primary energy consumption.
InputsUnitEnergy ContentGHG UnitGHGsRemarks
Agrochemicals
ΝMJ/kg48.99gCO2eq/kg4524.41[68]
ΡMJ/kg15.23gCO2eq/kg541.67[68]
ΚMJ/kg9.68gCO2eq/kg416.67[68]
Olive pomace resMJ/kg20.75gCO2eq/kg67.00[72,73,74]
Poultry manureMJ/kg8.40gCO2eq/kg148.62[73,75,76]
FungicidesMJ/kg99.00gCO2eq/kg3,900.00[77,78]
HerbicidesMJ/kg418.00gCO2eq/kg9,100.00[77,79]
Energy
LubricantsMJ/kg53.28gCO2eq/kg947.00[68]
DieselMJ/kg56.80gCO2eq/MJ95.10[68,80]
PetrolMJ/kg60.20gCO2eq/MJ93.30[68,80]
ElectricityMJ/MJ2.73gCO2eq/MJ243.49[68]
Operations, maintenance, and manufacturing
TractorMJ/h16.42gCO2eq/h9800[81,82]
HumanMJ/h1.80gCO2eq-[81]
MachineryMJ/h0.10–35.05gCO2eq/h0.10–190[73,83,84]
RB-EKENMJ/h2.59gCO2eq--
RB-VOGUIMJ/h0.65gCO2eq--
Irrigation systemMJ/ha373.7gCO2eq-[85]
Use of dieselMJ-gCO2eq/MJ0.9[68]
Transportation
Supplies MJ/t.km0.87gCO2eq/t.km71[68,86]
Biomass MJ/t.km0.81gCO2eq/t.km71[68,86]
Table
Table 3. Management scheme for the selected vineyards.
Table 3. Management scheme for the selected vineyards.
InputsUnitAsyrtikoTempranilloCabernet SauvignonMerlot
Acreageha2.92.22.41.9
Crop yieldt/ha9.638.025.325.05
Irrigation (energy)MJ/ha884.52884.52442.26442.26
Borehole depthm1801809090
N-based fertilizerskg/ha3331510.5
P-based fertilizerskg/ha331510.5
K-based fertilizerskg/ha3321.514.88
Poultry manurekg/ha--244940
Olive pomace reskg/ha--1100-
Fungicideskg/ha252515.448
Herbicideskg/ha---3.5
Diesellt/ha255238111.8142
Petrollt/ha40402020
Residuest/ha4.23.91.51.5
Human laborh/ha447.5450308.66401.25
Tractorh/ha32.53023.732.75
Table
Table 4. Analytical presentations of GHG emissions and consumed energy per hectare.
Table 4. Analytical presentations of GHG emissions and consumed energy per hectare.
ScenarioUnitACACBCCCCBMCMCBTCTCB
Consumed energy MJ ha−127,281.8016,304.3325,643.9521,642.9525,014.9318,633.6525,648.4714,703.07
GHG emissionskg CO2-eq ha−12570.571456.571428.74980.161678.681125.432308.901210.23
Table
Table 5. Shares of GHG emissions per scenario.
Table 5. Shares of GHG emissions per scenario.
ScenarioAgrochemicalsElectric EnergyFossil FuelsMachineryOther
AC9.71%8.38%62.32%14.60%4.98%
ACB17.14%18.21%44.13%12.35%8.18%
CC17.85%7.54%50.13%18.87%5.62%
CCB26.02%15.17%37.97%12.94%7.90%
MC15.62%6.41%52.38%22.15%3.43%
MCB23.29%13.02%26.64%32.44%4.61%
TC4.94%9.33%69.39%15.05%1.29%
TCB9.42%21.38%54.01%13.46%1.74%
Table
Table 6. Shares of consumed energy per scenario.
Table 6. Shares of consumed energy per scenario.
ScenarioAgrochemicalsElectric EnergyFossil FuelsMachineryOther
AC15.27%8.85%61.92%10.14%3.82%
ACB25.55%18.24%41.46%11.48%3.28%
CC54.54%4.71%29.46%8.43%2.86%
CCB64.63%7.70%18.08%6.94%2.65%
MC43.85%4.83%37.06%11.40%2.86%
MCB58.87%8.82%16.92%13.28%2.11%
TC10.51%9.41%65.86%10.34%3.87%
TCB18.34%19.73%46.74%11.83%3.36%
Table
Table 7. Energy efficiency and GHG emission intensity per vine-growing scenario.
Table 7. Energy efficiency and GHG emission intensity per vine-growing scenario.
Scenariokg CO2 eq kg−1Deviation (%)MJ kg−1Deviation (%)
AC0.267−43.34%2.833−40.24%
ACB0.1511.693
CC0.269−31.40%4.820−15.60%
CCB0.1844.068
MC0.332−32.96%4.953−25.51%
MCB0.2233.690
TC0.288−47.58%3.198−42.67%
TCB0.1511.833
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Tziolas, E.; Karapatzak, E.; Kalathas, I.; Lytridis, C.; Mamalis, S.; Koundouras, S.; Pachidis, T.; Kaburlasos, V.G. Comparative Assessment of Environmental/Energy Performance under Conventional Labor and Collaborative Robot Scenarios in Greek Viticulture. Sustainability 2023, 15, 2753. https://doi.org/10.3390/su15032753

AMA Style

Tziolas E, Karapatzak E, Kalathas I, Lytridis C, Mamalis S, Koundouras S, Pachidis T, Kaburlasos VG. Comparative Assessment of Environmental/Energy Performance under Conventional Labor and Collaborative Robot Scenarios in Greek Viticulture. Sustainability. 2023; 15(3):2753. https://doi.org/10.3390/su15032753

Chicago/Turabian Style

Tziolas, Emmanouil, Eleftherios Karapatzak, Ioannis Kalathas, Chris Lytridis, Spyridon Mamalis, Stefanos Koundouras, Theodore Pachidis, and Vassilis G. Kaburlasos. 2023. "Comparative Assessment of Environmental/Energy Performance under Conventional Labor and Collaborative Robot Scenarios in Greek Viticulture" Sustainability 15, no. 3: 2753. https://doi.org/10.3390/su15032753

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