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Article

Life Cycle Assessment of Key Mediterranean Agricultural Products at the Farm Level Using GHG Measurements

by
Georgios Bartzas
1,
Maria Doula
2 and
Konstantinos Komnitsas
1,*
1
School of Mineral Resources Engineering, Technical University of Crete, University Campus, Kounoupidiana, 73100 Chania, Greece
2
Laboratory of Non Parasitic Diseases, Soil Resources and Geoinformatics, Department of Phytopathology, Benaki Phytopathological Institute, Kifissia, 14561 Athens, Greece
*
Author to whom correspondence should be addressed.
Agriculture 2025, 15(14), 1494; https://doi.org/10.3390/agriculture15141494
Submission received: 18 June 2025 / Revised: 4 July 2025 / Accepted: 10 July 2025 / Published: 11 July 2025
(This article belongs to the Section Ecosystem, Environment and Climate Change in Agriculture)
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Abstract

Agricultural greenhouse gas (GHG) emissions contribute significantly to climate change and underline the importance of reliable measurements and mitigation strategies. This life cycle assessment (LCA)-based study evaluates the environmental impacts of four key Mediterranean agricultural products, namely olives, sweet potatoes, corn, and grapes using GHG measurements at four pilot fields located in different regions of Greece. With the use of a cradle-to-gate approach six environmental impact categories, more specifically acidification potential (AP), eutrophication potential (EP), global warming potential (GWP), ozone depletion potential (ODP), photochemical ozone creation potential (POCP), and cumulative energy demand (CED) as energy-based indicator are assessed. The functional unit used is 1 ha of cultivated land. Any potential carbon offsets from mitigation practices are assessed through an integrated low-carbon certification framework and the use of innovative, site-specific technologies. In this context, the present study evaluates three life cycle inventory (LCI)-based scenarios: Baseline (BS), which represents a 3-year crop production period; Field-based (FS), which includes on-site CO2 and CH4 measurements to assess the effects of mitigation practices; and Inventoried (IS), which relies on comprehensive datasets. The adoption of carbon mitigation practices under the FS scenario resulted in considerable reductions in environmental impacts for all pilot fields assessed, with average improvements of 8% for olive, 5.7% for sweet potato, 4.5% for corn, and 6.5% for grape production compared to the BS scenario. The uncertainty analysis indicates that among the LCI-based scenarios evaluated, the IS scenario exhibits the lowest variability, with coefficient of variation (CV) values ranging from 0.5% to 7.3%. In contrast, the FS scenario shows slightly higher uncertainty, with CVs reaching up to 15.7% for AP and 14.7% for EP impact categories in corn production. The incorporation of on-site GHG measurements improves the precision of environmental performance and supports the development of site-specific LCI data. This benchmark study has a noticeable transferability potential and contributes to the adoption of sustainable practices in other regions with similar characteristics.

Graphical Abstract

1. Introduction

Agricultural greenhouse gas (GHG) emissions, primarily CO2, CH4, and N2O, contribute significantly to global climate change [1]. Recent data show that agriculture, forestry and other land use (AFOLU) activities contribute approximately 22% of global GHG emissions, while in Europe, agriculture itself accounts for about 10% of total emissions [2]. The agricultural-based emissions are mainly due to activities such as (i) soil management, which involves crop rotation, tillage, and the use of synthetic and organic fertilizers; (ii) livestock production, with a focus on enteric fermentation; and (iii) land-use changes, such as deforestation for agricultural expansion and soil loss due to intensive farming. In this context, precise data on GHG emission are essential to guide policy makers, support effective mitigation plans, and ensure sustainable agriculture in line with major initiatives and strategies, such as the Paris Agreement and the European Green Deal [3].
The last decade, various techniques have been developed for the quantification of GHG emissions in agriculture, including chamber (open and closed) and micrometeorological methods for point sources and stable isotope techniques for process-specific measurements [1,4,5]. The non-flow-through static chamber method, which offers a cost-effective and harmonized approach for consistent data collection in short periods, is the most widely used for on-site GHG measurements [6]. Despite their ease of use, static chambers tend to underestimate actual gas fluxes, by up to 55% for N2O and 10–30% for CO2 and therefore they require strict compliance with sampling protocols and careful data analysis to minimize measurement errors and improve accuracy [7,8]. In contrast, open chambers are well-suited for continuous or high-frequency measurements and are particularly valuable in studies where preservation of natural environmental conditions is required. However, they are more complex and require precise control of airflow and calibration [9].
The use of ground-based light detection and ranging (LiDAR) enables high-resolution, non-destructive, and accurate assessments of carbon stocks and GHG emissions and fluxes. LiDAR outperforms traditional methods and ensures repeatable, objective measurements, rapid data collection in large fields, and reliability in dense canopies. In addition, it avoids saturation issues and is considered ideal for consistent, large-scale environmental monitoring [10,11]. However, crop type and soil management practices influence significantly GHG emissions, as demonstrated in a recent Latvian study that used a portable gas analyzer across 24 experimental fields for two tillage types and four different crops [12]. Emission patterns are also strongly affected by soil properties, such as organic content and moisture levels, while temporal variations are also closely linked to crop growth stages and prevailing meteorological conditions [12,13].
While emissions quantification in agriculture remains challenging and resource-intensive, strategic GHG reduction programs/investments and development of integrated frameworks can lead to significant improvements in GHG estimation at different levels, i.e., farm, regional, and national [4,14]. In this sense, field measurement methodologies are important components to enhance national GHG inventories related to agricultural output, encourage the adoption of sustainable farm practices, and aid the production of green and certified products [1,15]. Moreover, the integration of these measurements into national and international climate policies enables the establishment of realistic reduction targets, the implementation of reliable life cycle assessments (LCA) to quantify emissions at various scales and ultimately the transition to low-emission agriculture. LCA approaches along with reliable data on CO2, CH4, and N2O emissions can be essential components for evaluating GHG mitigation strategies in agriculture, including optimization of inputs, reduction in energy consumption, and increase in soil carbon stocks [16,17]. However, to date, only one study has evaluated GHG emissions from cropping systems including maize, faba beans, alfalfa, spring wheat, and canola, demonstrating that direct measurement methods, such as static chambers and micrometeorological techniques, are effective tools to incorporate CO2 and N2O emissions into agricultural-based LCA models [18].
Recent advancements involve the integration of innovative technologies such as LiDAR and Internet of things (IoT) into low-carbon certification frameworks, thus allowing for more precise measurements of GHG emissions at field-level [15,19]. Modern and site-specific GHG quantification methods can reduce inputs and GHG emissions and increase productivity and economic benefits [15,20]. The synergy between on-site GHG measurements and advanced data technologies may provide a solid basis for the certification and promotion of low-carbon agricultural practices.
This LCA study aims to (i) evaluate the environmental impacts of four key Mediterranean agricultural products, i.e., olives, vegetables (sweet potatoes), cereals (corn), and grapes based on data obtained from four pilot fields across Greece, using both conventional and organic cultivation systems; and (ii) assess the carbon reduction potential of these cropping systems. It compares baseline cultivation conditions over a three-year period with a subsequent reference year that incorporates GHG measurements at farm level and estimates carbon offsets from implemented mitigation practices. To the best of our knowledge, no similar LCA studies exist in the literature for these key Mediterranean crops. Therefore, our approach aims to increase the reliability of environmental assessments with the use of on-site GHG quantification and ultimately assist in the development of effective low-carbon agricultural strategies.

2. Materials and Methods

The present LCA study quantifies GHG emissions (CO2, CH4, NOX, VOCs, SO2, etc.) into the air, water, and soil [21] by taking into account the consumption of raw materials, such as fertilizers, pesticides, irrigation water, energy, as well as agricultural waste. It follows the guidelines and specific requirements of the ISO 14040-14044 standard series [22,23]. In this context, the four fundamental steps of LCA are followed: (i) goal and scope definition, where the objectives and system boundaries are established; (ii) life cycle inventory (LCI) analysis, which involves data collection for material and energy inputs, as well as emissions; (iii) life cycle impact assessment (LCIA), where potential environmental impacts are evaluated; and (iv) interpretation, where results are analyzed to enable GHG mitigation and support decision-making.

2.1. Goal and Scope Definition

The goals of this LCA study are (i) to quantify the energy consumption and GHG footprint of the existing life cycle of four key Greek agricultural products and (ii) to evaluate/benchmark the incorporation of primary data related to GHG emissions at the farm level in the establishment of a reliable life cycle inventory (LCI) that can be used for certification of agricultural products as well as for other auditing purposes [15]. In this context, agricultural activities were recorded at four pilot fields across Greece, involving both conventional and organic production of key crops, namely olives, vegetables (sweet potatoes), cereals (corn), and grapes (Figure 1), that are cultivated under varying climatic conditions and have a significant role in both national and regional economies.
According to the ISO standard series [22,23], the functional unit (FU) is a measurable representation of the function of the system under study and serves as a reference unit to enable a consistent comparison of environmental impacts. In this context, the functional unit selected in this study is 1 ha of land cultivated annually per crop category/associated pilot field selected. This reference functional unit allows for a more direct comparison of the environmental performance of each orchard, in order to assess the relative impacts of the agricultural practices used in each field [24,25]. All calculated emissions related to energy consumption, raw material production and transportation, as well as waste management, are determined using the selected functional unit.

System Boundaries

This study adopted the “cradle-to-gate” methodology for each fresh product, taking into account all related cultivation stages from raw material extraction (i.e., the cradle) to the point where the fresh product leaves the farm (i.e., the gate), incorporating both direct and indirect emissions in the assessment (Figure 2).
Based on the defined system boundaries, the foreground and background processes/flows were grouped into seven distinct phases to facilitate data compilation and comparative assessment (Figure 3). These phases varied depending on the cultivation system and included the production of inorganic or organic fertilizers, the production of pesticides, the irrigation systems, the application of inorganic or organic fertilizers, the application of pesticides, and all main field management activities.
Field management phase involves various farm-level operations, such as planting/sowing, land preparation/plowing, tillage, pruning, and harvesting, where applicable. Alao, waste management was considered (if needed). For the transportation of raw materials, including fertilizers and pesticides, road transport by a 16-t truck/lorry combined with sea transport (for Crete) and the associated diesel consumption were taken into account [26].

2.2. Life Cycle Inventory

2.2.1. Data Collection

Data collection during agricultural production was made through a detailed survey of the four pilot fields assessed. This approach enables the provision of the required data for the LCA analysis and defines local agricultural practices. As a result, primary site-specific data were obtained for operations performed at the olive, cereal, vegetable, and grape orchards (the foreground system). To complete the life cycle inventory, data associated with the operations performed in the background system (related to agro-chemicals production and transportation) were drawn from literature and the available life cycle inventory (LCI) databases, i.e., Professional 2023 and Ecoinvent v.3.9 [26,27]. In all cases, emissions derived from combustion of fossil fuels used in agricultural machinery and transportation, as well as heavy-metal emissions from tire abrasion, have also been included.

2.2.2. Scenario Analysis

Scenario analysis was implemented to identify differences in key parameters/production stages, and provide a comprehensive assessment of how different assumptions or changes in inputs might impact LCA results. This approach allows for a more robust understanding of the potential range of outcomes in terms of carbon credits/offsets and supports the identification of critical factors that influence the accuracy of the on-site GHG measurements for the establishment of a reliable LCI.
In this LCA study, two scenarios based on primary data and an additional inventoried one were investigated:
(i).
Baseline production scenario—Baseline conditions (BS). It includes common farm management and processes carried out at farm level during the implementation of agricultural activities, by considering data recorded from a three-year cropping cycle in each selected pilot field (Table 1). This scenario represents the cultivation practices and waste/by-product management strategies employed during the three-year period preceding the reference production year (Table S1). It serves as a benchmark to evaluate carbon changes, identify differences in GHG emissions, and provide a comprehensive assessment of the effectiveness of mitigation strategies at farm level [28,29]. Primary data obtained through questionnaires distributed to farmers; all data were cross-checked against farm records, when available, or after discussions with farmers, to ensure accuracy.
(ii).
Reference production scenario—Field measurement conditions (FS). In this scenario, an integrated framework is used to accurately evaluate carbon changes resulting from practices implemented during the reference production year (Table 1), in comparison to baseline conditions from the past three years, as defined in the BS scenario [15]. The integrated framework uses on-site measurements of CO2 and CH4 emissions in real time, incorporating innovative technologies such as LiDAR and Internet-of-Things (IoT) telemetry as well as a web-based platform that allows farmers to keep records of their practices on a frequent basis (daily or weekly). This platform records and evaluates activities such as tillage, use of equipment, manure/compost management, irrigation water consumption, use of renewable sources, and other fuel or electricity-consuming activities. Typical on-site LiDAR measurements of CO2 and CH4 emissions from organic olive production in Kato Valsamonero pilot field (Rethymno Prefecture) during the reference year (2023) are presented in Figure S1a,b, respectively (Supplementary Material). Additionally, all data for the reference production year per pilot field is cross-checked with the farming practices recorded at the farmers’ platform as well as with questionnaires, in order to ensure that the inventory reflects the practices carried out during the period measurements are taken. This approach provides a detailed and synchronized recording of both emissions and agricultural practices, establishes a comprehensive LCI for each pilot field under study, and offers an accurate basis for assessing environmental impacts at farm level.
The carbon-mitigation cultivation practices implemented in the four pilot fields included composting of manure and plant debris to reduce ammonia emissions and phytotoxicity, as well as the disposal of prunings on soil to avoid burning. While the burning of prunings/residues may temporarily increase nutrient availability in the short term, it may not sustain the soil organic carbon density in the long term compare to un-burnt soils [30]. Conservation tillage was adopted to minimize soil disturbance, while crop residues such as corn stalks were cut into smaller pieces (4–5 cm) and incorporated into the soil to accelerate their decomposition. The use of nitrogen-rich amendments such as manure or compost enhances microbial activity and increases the content of soil organic matter (SOM). Additionally, green manure and legume cultivation were introduced to improve soil fertility, alongside fertilization plans tailored to crop-specific nutrient requirements.
(iii).
Comparison production scenario—Fully inventoried conditions (IS). This scenario, sourced from Ecoinvent v.3.9, is built upon a fully inventoried dataset of conventional/traditional or organic agricultural production practices [26]. The Ecoinvent database is a comprehensive and widely recognized LCI database that provides extensive data on the environmental impacts of various processes, including those related to agricultural production. More details regarding the well-established inventories selected in this LCA study per crop category are provided in Table 2. All selected Ecoinvent-based inventories were adapted/modified, when needed, to reflect the specific agricultural practices associated with each cultivation system per pilot field for the reference production year.

2.3. Life Cycle Impact Assessment

Modeling Approach and Impact Categories

The software used for the life cycle impact assessment was the commercial “LCA for experts” version 10.7 [27], by taking into account the phases of classification and characterization, as defined by the standards of ISO 14040-14044 series [22,23]. In the classification phase, each environmental burden was assigned to one or more impact categories. During the characterization phase, the contribution of each burden to the respective impact category was quantified by applying characterization factors. The demand for energy as well as waste production was also estimated based on primary data (survey) and background data (Ecoinvent), if required. Five mid-point environmental impact categories, defined according to the CML 2001 (April 2016 version) impact assessment method developed by the Centre of Environmental Science of Leiden University, as well as the impact category of cumulative energy demand (CED) as an energy flow indicator, were assessed (Table 3).
The CML method was selected due to its broad recognition and frequent application in agricultural LCA studies [31]. It enables precise quantification across a wide range of relevant impact categories, allows clear identification of environmental hotspots, and provides a more transparent impact profile. Compared to endpoint methods, the CML approach has a lower level of uncertainty and is particularly suitable for decision-making in agricultural contexts.

2.4. Uncertainty Analysis

Uncertainty in LCA can arise from input data (parameter uncertainty), scenario choices, and modeling approaches [32,33]. Therefore, LCA outputs in terms of impact categories can exhibit high uncertainty due to variability in inventory data and the use of simplified models to estimate complex environmental cause–effect relationships [34,35]. In this study, a parameter uncertainty analysis was conducted using the well-established Monte Carlo simulation technique [36] to evaluate the robustness of LCA results across the analyzed scenarios. Uncertainty propagation was performed with a 95% confidence interval based on 1000 simulation runs, utilizing the lognormal data distribution curve. Standard deviations of all input data and emissions were incorporated into the Monte Carlo simulation for the three investigated scenarios.

3. Results and Discussion

3.1. Benchmarking of Environmental Impacts—Comparison of LCI-Based Scenarios

3.1.1. Olive Production

The environmental impacts (AP, EP, GWP, ODP, POCP and CED) of organic olive production per hectare in the Kato Valsamonero (Rethymno Prefecture) pilot field for three different LCI-based scenarios are presented in Table 4.
Compared to the baseline scenario, the real-time field-based scenario (FS) presents reduced impacts across all categories, with the most substantial reduction observed for EP (−11.4%), followed by POCP (−9.4%). The fully inventoried scenario (IS), however, results in an increase for all impact categories, with the most pronounced rise in EP (+10.5%). AP and EP in organic olive production are affected by emissions such as sulfur dioxide and phosphate, which contribute to nutrient enrichment in water bodies. In the baseline scenario, AP is 27.56 kg SO2-eq per ha, and EP is 24.92 kg PO4-eq per ha. The field measurements scenario (FS) shows a slight reduction in AP to 26.40 kg SO2-eq (−4.2%) and a greater decrease in EP to 22.08 kg PO4-eq (−11.4%). These results indicate that the carbon emissions management practices implemented in the pilot field during the reference production year effectively reduced nutrient pollution compared to the previous three-year cultivation period. These practices included the disposal of prunings on soil to avoid combustion, as well as the proper application of organic manure (2.5–4 t/ha) under the olive canopy.
In contrast, the fully inventoried scenario (IS) using data from the Ecoinvent-based olive production in Italy indicates significantly increased AP and EP values equal to 30.28 kg SO2-eq (+9.9%) and 30.13 kg PO4-eq (+10.5%), respectively. The GWP for rainfed organic olive production under the baseline scenario in the Kato Valsamonero pilot field is estimated at 1993 kg CO2-eq per hectare, a value similar to those reported in the literature for such rainfed organic systems. Fotia et al. [37] reported a GWP of 3252 kg CO2-eq/ha in northwestern Greece, representing 63.2% higher impact, whereas a considerably lower value of 549 kg CO2-eq/ha is reported for rainfed organic olives in Portugal [38], corresponding to a 72.5% lower impact compared to Kato Valsamonero. These discrepancies can be attributed to a combination of factors, including variations in agricultural practices, yield levels, local soil quality, and climatic conditions, as well as the proper application of organic manure (2.5–4 t/ha) beneath the olive canopy. Additionally, differences in methodological approaches, such as LCA system boundaries and underlying assumptions, also contributed to the observed variability in results. In this context, comparison studies on conventional and organic olive cultivation in the Mediterranean region have shown mixed environmental impact results [39,40]. Generally, conventional systems exhibit lower GWP per ton of olives, primarily due to reduced mechanical operations and differing fertilization methods [41]. However, organic cultivation, despite having a higher GWP associated with manure use (about 80% of total emissions in organic vs. 45% in conventional), promotes soil carbon sequestration, leading to a more negative net carbon flux and greater CO2 mitigation potential [42].
In the FS scenario, the reduction in GWP to 1822 kg CO2-eq (−8.7%) and in POCP from 0.96 to 0.87 kg C2H4-eq (−9.4%) suggest that incorporating olive prunings into the soil in the reference year, rather than open burning them as happened in the previous three years, reduced direct CO2 emissions and CH4 releases from biomass combustion and, as well as emissions from volatile organic compounds (VOCs) and carbon monoxide (CO), which are precursors in the formation of ground-level ozone, as measured by POCP [43]. On the other hand, GWP in the IS scenario showed values as high as 1392 kg CO2-eq (+10.4%), while POCP increased to 1.00 kg C2H4-eq (+4.2%). These higher emissions are due to more comprehensive data coverage by the Ecoinvent database, as expected in this case. In this context, the CED value is also higher in the IS compared to the BS scenario, 22,914 MJ instead of 20,458 MJ per FU, underlining the importance of the detailed inventory accounting. Overall, the incorporation of on-site LiDAR measurements in establishing the LCI for the FS scenario confirmed an average 8% reduction in all impact categories assessed for the olive production in the Prefecture of Rethymno, in Crete, during the reference production year (2023). This is mainly due to carbon-mitigation cultivation practices implemented during the previous 3-year baseline period.

3.1.2. Vegetables (Sweet Potato) Production

The environmental impacts (AP, EP, GWP, ODP, POCP, and CED) of organic sweet potato production in the Tympaki (Heraklion Prefecture) pilot field for three different LCI-based scenarios are presented in Table 5. Compared to the baseline scenario (BS), the real-time field-based scenario (FS) presents lower impacts in all categories, indicating improvements due to sustainable soil and crop management practices implemented during the reference production year (2021). The most significant reductions in the FS scenario are observed for EP, which decreased from 63.32 to 58.36 kg PO4-eq per FU (−7.8%), and AP, which dropped from 49.29 to 46.35 kg SO2-eq per FU (−6.0%). These improvements reflect reduced nutrient runoff and more balanced fertilization in 2021, resulting in lower AP and EP emissions. GWP under FS reached 5121 kg CO2-eq per FU, a 5.5% decrease compared to BS (5417 kg CO2-eq), mainly due to improved residue management applied and the cease of open burn practices. Recent LCA studies for organic potato production, for different farm systems and management methods in Italy and Egypt, reported GWP values of 5014 and 7110 kg CO2-eq per hectare [44,45]. The GWP estimated in the present study for the FS scenario is at the lower end of that range, indicating improved environmental performance and strong GHG reduction potential. Under the FS, POCP declined by 4.8%, from 1.321 to 1.258 kg C2H4-eq, while ODP also decreased marginally, from 0.035 to 0.033 g CFC-11-eq (−5.7%). Moreover, CED decreased from 51,234 MJ to 48,963 MJ per ha (−4.4%), reflecting more energy-efficient practices and input reductions, especially related to improved irrigation and fieldwork activities. In terms of CED, energy requirements for potato cultivation in the Mediterranean region are significant and range from 74,270 MJ/ha to 112.3 GJ/ha, depending on local farming practices and input intensity [45,46]. Recent studies have examined the environmental impacts of conventional and organic potato cultivation within the Mediterranean region. In this context, organic cultivations generally led to higher CO2 emissions than conventional ones due to fertilization application (0.77 vs. 0.60 g m−2 h−1), although spading tillage proved more climate-friendly [47]. Climate change may sustain a 3 t/ha yield gap between conventional and organic systems, and while organic farming aligns with Green Deal goals, yield improvements are critical for scalability [44,48].
As anticipated, the IS scenario results in increased values (5% on average) in all impact categories, which may reflect the more detailed data collection and energy-intensive nature of the Ecoinvent-based inventory. More specifically, AP increased to 51.73 kg SO2-eq (+4.9%), EP to 66.89 kg PO4-eq (+5.6%), and GWP to 5696 kg CO2-eq (+5.1%). These increases are due to the broader system boundaries that include more inputs from energy, machinery, and transport sources. ODP and POCP also increased by 5.7% and 4.1%, respectively, while CED reached its highest value among all scenarios used at 53,568 MJ per FU (+4.6%).
As a result of carbon-mitigation cultivation practices that were followed in the period 2018–2020, the incorporation of on-site GHG measurements into the development of LCI for the associated FS scenario confirmed an average 5.7% reduction for all impact categories during the reference production year (2021). These reductions were obtained by improved residue management, optimized irrigation, and efficient fieldwork which promote resource efficiency and mitigate associated GHG emissions.

3.1.3. Cereals (Corn) Production

Table 6 presents the environmental impacts (AP, EP, GWP, ODP, POCP, and CED) of corn production in the Ag. Ioannis (Pyrgos, Ilia Prefecture) pilot field for three different LCI-based scenarios. Compared to the BS (period 2020–2022), the IS scenario demonstrated for 2023 reductions for all environmental impact categories, while the most notable decreases were observed for GWP (−6.1%) and AP (−5.6%). These improvements resulted from the implementation of a controlled-release fertilization program (NPK 8-5-5) and the incorporation of corn residues into the soil during the reference production year. The GWP for the FS scenario was estimated at 1305 kg CO2-eq/ha, a value that is similar with those reported in the literature for such crops in the Mediterranean region. [49,50]. Among the fertilization types assessed in a crop production case study in Spain [50], traditional fertilization (NPK 8-15-15 and urea) resulted in the highest emissions (3251 kg CO2-eq/ha), while urea accounted for 79% of the total. In contrast, controlled-fertilization and pig slurry treatments resulted in much lower emissions (~2191 and 2160 kg CO2-eq/ha, respectively) due to the reduced urea input, while the pig slurry alternative, using even less urea, resulted in the lowest emissions (1030 kg CO2-eq/ha).
On the other hand, the IS scenario resulted in an increase in impact in all categories, with the most notable for AP (+9.3%). In the BS scenario, AP and EP were 16.05 kg SO2-eq and 21.6 kg PO4-eq per hectare of cultivated land, respectively. In corn production, AP and EP are particularly influenced by emissions of N2O and NH3, due to the excessive application of nitrogen fertilizers in an inefficient timed manner [43]. In the FS scenario, AP was slightly reduced to 15.15 kg SO2-eq (−5.6%), while EP decreased to 20.54 kg PO4-eq (−5.2%). These results suggest that emission management strategies such as optimized fertilization, residue management, proper irrigation, and conservation tillage can effectively mitigate nutrient pollution and enhance the overall environmental performance.
Furthermore, the IS scenario, when applied to corn production in Hungary, resulted in considerable increase in environmental impacts; more specifically, AP increased to 17.54 kg SO2-eq (+9.3%) and EP to 23.02 kg PO4-eq (+6.6%). Also, GWP and CED, are estimated at 1475 kg CO2-eq/ha and 5375 MJ/ha, 6.1% and 5.6% higher, respectively, compared to the BS scenario. These increases are mainly due to the additional data used and the complexity of IS, which accounts for emissions or resource use not captured in the BS scenario. The results suggest that while more comprehensive inventories may indicate higher environmental impacts, they can also highlight areas where improvements in emissions reduction practices are possible. Overall, the LCA results obtained from the FS scenario indicate an average 4.5% reduction for all assessed impacts during corn production in the Ag. Ioannis pilot field during the 2023 reference production year; this was mainly due to the implementation of carbon-mitigation cultivation practices at farm level compared to the previous 3-year baseline period.

3.1.4. Grapes Production

The environmental impacts associated with grape production in the Thourio (Viotia Prefecture) pilot field are presented in Table 7. The GWP value for rainfed grape production under the BS scenario is estimated at 2520 kg CO2-eq/ha, which is below the typical range reported for similar conventional systems in the Mediterranean region [51,52,53,54]. Depending on grape variety and farming practices, GWP values range from 2961 kg CO2-eq/ha in Drama, NE Greece to 6330 kg CO2-eq/ha in Cyprus. This lower-than-average carbon footprint shows that the grape production system in the Viotia Prefecture of Central Greece benefited from rainfed conditions and more efficient, less intensive farm management. These practices included a significant reduction in NPK input, from 170-170-240 to nearly half. The lower nitrogen application, split between pre-bloom and berry development stages, does not allow excessive vegetative growth.
Compared to the BS (period 2020–2022), the FS scenario that incorporates real-time on-site GHG measurements and more accurate site-specific practices, results in consistent reductions for environmental impacts in all categories during the reference production year (2023). The most substantial improvements were observed for AP, which decreased from 21.23 to 19.52 kg SO2-eq per ha (−8.1%), and GWP, which also decreased from 2520 to 2349 kg CO2-eq per ha (−6.8%). The reduction in GWP was mainly due to management of pruning waste, which was crushed and disposed of on soil [53]. Reductions were also recorded for EP (−6.6%, from 15.43 to 14.42 kg PO4-eq), POCP (−6.4%, from 0.562 to 0.526 kg C2H4-eq), and ODP (−5.4%, from 0.221 to 0.209 g CFC-11-eq). Additionally, CED also decreased by 5.3%, from 26,893 to 25,325 MJ per ha, indicating improved energy efficiency due to the adoption of more sustainable vineyard practices (15% lower consumption of fuels for tillage). It is mentioned that grape production in the Mediterranean region shows wide variation in energy use, depending on the farming system: organic systems range from 16,366 to 23,663 MJ/ha, integrated from 32,965 to 35,428 MJ/ha, whereas conventional can reach values as high as 43,592 MJ/ha [51,52,55]. These differences indicate the higher energy intensity of conventional viticulture, largely due to greater reliance on agrochemicals, mechanization, and irrigation.
Also, the IS scenario showed increased environmental burdens for all impact categories. Specifically, AP increased to 22.73 kg SO2-eq (+7.1%), EP to 16.39 kg PO4-eq (+6.2%), and GWP to 2674 kg CO2-eq (+6.1%). ODP and POCP increased to 0.232 g CFC-11-eq (+5.0%) and 0.593 kg C2H4-eq (+5.5%), respectively. CED exhibited the highest value, 28,585 MJ per FU (+6.3%), mainly due to the inclusion of additional energy inputs, machinery usage, and upstream emissions. Consistent with findings from previous crop systems, the LCA results of this study show for rainfed grape production with the use of the FS scenario an average 6.5% decrease across all assessed impact categories for the 2023 reference year.

3.2. Uncertainty Analysis

The results of the uncertainty analysis obtained for the three LCI-based scenarios investigated and the six impact categories analyzed for the four key Mediterranean crops are given in Tables S2–S5 in the Supplementary Material. The uncertainty is represented by the standard deviation (SD) and the coefficient of variation (CV%), which indicate the degree of dispersion around the mean value for each impact category. In general, uncertainty analysis indicates that the variations in the values for all impact categories are low (≤10%) (except for AP and EP), and thus the BS and the FS scenarios have good reliability [21,56,57]. The low CV values indicate that the LCA results are not very sensitive to input uncertainties, thus enabling comparisons of different scenarios.
Across all crops and impact categories, CV indicates that the IS scenario consistently exhibits the lowest uncertainty, reflecting the robustness and advantages of comprehensive data collection and modeling. Conversely, the FS scenario often shows the highest variability, especially for AP and EP—the two impact categories most affected by uncertainty. In terms of CV, the AP reached 15.71% in the FS scenario for corn and 13.85% for grapes, while the EP peaked at 14.70% for corn and 14.23% for grapes. Regarding GWP, variation across all crops remained relatively low: the CV ranged for the IS from 3.21% to 5.89%, and for the FS from 8.05% to 9.58%. The grape production in Thourio exhibited a CV for GWP of 6.67% for the BS, 8.05% for the FS, and 3.48% for the IS, with the values aligning with the general trend of decreasing uncertainty from BS to IS scenarios. Regarding CED, the IS scenario again showed minimal variability, with CVs below 4% for all crops. In contrast, the FS scenario exhibited the highest CVs, up to 8.34% for corn production in Ag. Ioannis and 9.18% for sweet potato production in Tympaki.
Comparing the BS and FS scenarios with the IS using the Monte Carlo simulation, it is evident that the FS exhibits the highest uncertainty, with CV values ranging from 6.82% to 16.71% across all impact categories and pilot fields. As expected, the IS scenario presented the lowest uncertainty, with CV values as low as 0.52% for CED in olive production and less than 7.31% across all impact categories. This high level of reliability is due to the comprehensive data collection in this scenario and the detailed life cycle assessment approach, that is also supported by process data retrieved and validated from Ecoinvent 3.9. On the other hand, the uncertainty in the FS scenario is mainly due to the incorporation of on-site GHG measurements during the construction of the associated LCI.
Overall, the uncertainty analysis reveals that the integration of on-site GHG measurements into the construction of associated LCIs results in slight to moderate differences compared to conventional database or background data. The results of the Monte Carlo simulations demonstrate that the LCA-based framework adopted in this study has strong potential for reliable determination of carbon offsets resulting from mitigation practices applied in the four key Mediterranean-based crops. Based on on-site GHG measurements that enhance the precision of environmental impact assessments, this integrated approach supports the development of reliable, site-specific LCI data and offers a valuable reference for similar agricultural systems in Greece and other Mediterranean countries willing to adopt sustainable practices. However, it should be noted that the discussion of the results obtained from the uncertainty analysis has been conducted at the impact category level for the three scenarios analyzed, in order to clearly assess and compare their reliability, rather than at the source level of individual input parameters.

4. Conclusions

This LCA study constructs, for the first time, a detailed environmental profile of four key Mediterranean crops, namely, olives, sweet potatoes, corn, and grapes, based on LCI data collected from four pilot fields located in different regions of Greece with the use of on-site GHG measurements. Based on an integrated framework developed for the low-carbon certification of agricultural production, which uses innovative technologies such as LiDAR and IoT, this study evaluates the carbon offsets achieved through the implementation of specific mitigation practices in both conventional and organic cultivation systems.
Based on the cradle-to-gate methodology, the environmental impacts (AP, EP, GWP, ODP, POCP, and CED) of crop production per functional unit (1 ha) in each pilot field, under three different LCI-based scenarios, namely BS, FS, and IS, are calculated. The LCA results show noticeable reductions in environmental impacts for all crops, due to the carbon-mitigation practices applied at farm level. The FS scenario that implemented carbon-mitigation practices, olive production in Rethymno Prefecture (2023), exhibits an 8% reduction, while organic sweet potato production in Heraklion Prefecture (2021) exhibits a 5.7% reduction. Corn production in Ilia Prefecture (2023) has a 4.5% reduction, while grape production in Viotia Prefecture exhibits a 6.5% reduction. The IS scenario exhibits consistently the lowest uncertainty, with CV values ranging from 0.52% (CED in olive production) to 7.31% across all impact categories. In contrast, the FS scenario shows higher variability, with CV values up to 15.71% for AP and 14.70% for EP in corn production. The Monte Carlo simulation further demonstrates that the FS scenario has CV values ranging from 6.82% to 16.71% across all categories, while the IS scenario has significantly lower CVs ranging from 0.52% to 7.31%.
It is concluded that the LCA-based framework used in this study is highly effective in accurately estimating carbon offsets from mitigation practices applied in the four key Mediterranean crops. The incorporation of on-site GHG measurements offers an integrated and effective approach that improves the precision of the results in terms of environmental impacts, supports the creation of reliable site-specific LCI, and provides a valuable reference for similar agricultural systems in Greece and other Mediterranean regions in the pursuit for sustainable practices towards a greener agriculture.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agriculture15141494/s1, Table S1 Main agronomic characteristics and LCI data of the associated crop production in each pilot area (Baseline scenario); Figure S1: (a) CO2 and (b) CH4 emissions (in ppm) from organic olive production in the Kato Valsamonero (Rethymno Prefecture) pilot field, based on-site measurements conducted using LiDAR technology during the reference production year (2023); Table S2: Uncertainty analysis results for the three scenarios and the six impact categories studied during the organic olive production in Kato Valsamonero (Rethymno Prefecture) pilot field; Table S3: Uncertainty analysis results for the three scenarios and the six impact categories studied during the sweet potato production in the Tympaki (Heraklion Prefecture) pilot field; Table S4: Uncertainty analysis results for the three scenarios and the six impact categories studied during the corn production in the Ag. Ioannis (Pyrgos, Ilia Prefecture) pilot field; Table S5: Uncertainty analysis results for the three scenarios and the six impact categories studied during the grape production in the Thourio (Viotia Prefecture) pilot field.

Author Contributions

Conceptualization, G.B. and K.K.; methodology, G.B. and K.K.; software, G.B.; validation, G.B. and M.D. formal analysis, G.B. and K.K.; investigation, G.B., M.D. and K.K.; resources, K.K.; data curation, G.B. and M.D.; writing—original draft preparation, G.B.; writing—review and editing, G.B., M.D., and K.K.; supervision, K.K.; project administration, K.K.; funding acquisition, K.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the European Union (LIFE+ Environment Policy & Governance) in the framework of the LIFE17 CCM/GR/000087 project “Innovative technologies for climate change mitigation by Mediterranean agricultural sector” https://life-climamed.eu/ (accessed on 21 November 2024).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The data presented in this study are available upon reasonable request from the corresponding authors.

Conflicts of Interest

The authors declare no conflicts of interest.

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Agriculture 15 01494 g001
Figure 1. Pilot fields of conventional and organic cultivations assessed in this study.
Figure 1. Pilot fields of conventional and organic cultivations assessed in this study.
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Figure 2. System boundaries adopted in this study.
Figure 2. System boundaries adopted in this study.
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Figure 3. Analytical system boundaries per production phase adopted in this study.
Figure 3. Analytical system boundaries per production phase adopted in this study.
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Table 1. Main characteristics of the 4 pilot fields per crop category/agricultural product investigated in this LCA study.
Table 1. Main characteristics of the 4 pilot fields per crop category/agricultural product investigated in this LCA study.
Pilot FieldsCrop Category/
Agricultural
Product		Holding Size (ha)3-Year
Cropping
Period		Product Yield (t/ha) (3-Years)Reference
Production Year		Product Yield (t/ha) (Reference)
Kato Valsamonero (Rethymno
Prefecture)		Olives 11.22020–20222.920234
Tympaki
(Heraklion Prefecture)		Vegetables 1 (Sweet potatoes)12018–202030202130
Ag. Ioannis (Pyrgos, Ilia Prefecture)Cereals (corn)22020–202211.4202310
Thourio (Viotia
Prefecture)		Grapes12020–202240202340
1 organic cultivation.
Table 2. Fully Ecoinvent-based inventories selected in this LCA study per crop category evaluated.
Table 2. Fully Ecoinvent-based inventories selected in this LCA study per crop category evaluated.
Pilot FieldsCrop CategoryEcoinvent 3.9
Type of
Production		Production Data Characteristics (in Other Countries)
Kato Valsamonero (Rethymno Prefecture)Olives 1Conventional/
traditional
production		Puglia and Sicily regions, Italy. Modified from conventional to organic production (chemical fertilization was excluded). Inventory data: Yield: 4.3 t ha−1, Organic fertilizers: 2 m3 ha−1of liquid manure and 2 t ha−1of solid manure.
Tympaki (Heraklion Prefecture) Vegetables 1
(Sweet potatoes)		OrganicSwitzerland. Average production of organic potatoes in the Swiss lowlands. Modified from rain-fed to irrigated crops. The yield is 22.9 t ha−1at a moisture content at storage of 78%.
Ag. Ioannis (Pyrgos, Ilia Prefecture)Cereals (corn)Conventional/
traditional
production		Hungary. The data are representative of sweet corn production from Soroksar, southern part of Budapest. The average yield is 17.8 t ha−1.
Thourio (Viotia
Prefecture)		GrapesConventional/
traditional
production		Rest of the world: This dataset represents table grape production worldwide. Yield: 30 t ha−1.
1 organic production.
Table 3. Environmental impact categories assessed in this study.
Table 3. Environmental impact categories assessed in this study.
Impact CategoryAcronymUnits 1
Acidification potential APkg SO2-eq∙FU−1
Eutrophication potentialEPkg PO4-eq∙FU−1
Global warming potential (100 years) GWPkg CO2-eq∙FU−1
Ozone layer depletion potential ODPkg CFC-11-eq∙FU−1
Photochemical oxidant creation potential POCPkg C2H4-eq∙FU−1
Cumulative energy demandCEDMJ-eq∙FU−1
1 FU: Functional unit.
Table 4. Environmental impact of organic olive production in the Kato Valsamonero (Rethymno Prefecture) pilot field.
Table 4. Environmental impact of organic olive production in the Kato Valsamonero (Rethymno Prefecture) pilot field.
Impact Category 1LCI-Based Scenarios
Baseline (BS)Field-Based (FS)Fully Inventoried (IS)
AP [kg SO2-eq∙FU−1]27.5626.40 (−4.2%)30.28 (+9.9%)
EP [kg PO4-eq∙FU−1]24.9222.08 (−11.4%)30.13 (+10.5%)
GWP [kg CO2-eq∙FU−1]19931822 (−8.6%)1392 (+10.4%)
ODP [g CFC-11-eq∙FU−1]0.02250.0208 (−7.6%)0.0122 (+6.2%)
POCP [kg C2H4-eq∙FU−1]0.960.87 (−9.4%)1.00 (+4.2%)
CED [MJ∙FU−1]20,45819,023(−7.0%)22,914 (+8.1%)
1 FU: Functional unit; the values in parentheses indicate the relative difference between the scenarios (FS and IS) compared to the Baseline scenario (BS).
Table 5. Environmental impacts of sweet potato production in the Tympaki (Heraklion Prefecture) pilot field.
Table 5. Environmental impacts of sweet potato production in the Tympaki (Heraklion Prefecture) pilot field.
Impact Category 1LCI-Based Scenarios
Baseline (BS)Field-Based (FS)Fully Inventoried (IS)
AP [kg SO2-eq∙FU−1]49.2946.35 (−6.0%)51.73 (+4.9%)
EP [kg PO4-eq∙FU−1]63.3258.36 (−7.8%)66.89 (+5.6%)
GWP [kg CO2-eq∙FU−1]54175121 (−5.5%)5696 (+5.1%)
ODP [g CFC-11-eq∙FU−1]0.0350.033 (−5.7%)0.037 (+5.7%)
POCP [kg C2H4-eq∙FU−1]1.3211.258 (−4.8%)1.375 (+4.1%)
CED [MJ∙FU−1]51,23448,963 (−4.4%)53,568 (+4.6%)
1 FU: Functional unit; the values in parentheses indicate the relative difference between the scenarios (FS and IS) compared to the Baseline scenario (BS).
Table 6. Environmental impacts of corn production in the Ag. Ioannis (Pyrgos, Ilia Prefecture) pilot field.
Table 6. Environmental impacts of corn production in the Ag. Ioannis (Pyrgos, Ilia Prefecture) pilot field.
Impact Category 1LCI-Based Scenarios
Baseline (BS)Field-Based (FS)Fully Inventoried (IS)
AP [kg SO2-eq∙FU−1]16.0515.15 (−5.6%)17.54 (+9.3%)
EP [kg PO4-eq∙FU−1]21.620.54 (−4.9%)23.02 (+6.6%)
GWP [kg CO2-eq∙FU−1]13901305 (−6.1%)1475 (+6.1%)
ODP [g CFC-11-eq∙FU−1]0.08650.0843 (−2.5%)0.0883 (+2.1%)
POCP [kg C2H4-eq∙FU−1]0.2320.226 (−2.6%)0.240 (+3.4%)
CED [MJ∙FU−1]50904820 (−5.3%)5375 (+5.6%)
1 FU: Functional unit; the values in parentheses indicate the relative difference between the scenarios (FS and IS) compared to the Baseline scenario (BS).
Table 7. Environmental impacts for grape production in the Thourio (Viotia Prefecture) pilot field.
Table 7. Environmental impacts for grape production in the Thourio (Viotia Prefecture) pilot field.
Impact Category 1LCI-Based Scenarios
Baseline (BS)Field-Based (FS)Fully Inventoried (IS)
AP [kg SO2-eq∙FU−1]21.2319.52 (−8.1%)22.73 (+7.1%)
EP [kg PO4-eq∙FU−1]15.4314.42 (−6.6%)16.39 (+6.2%)
GWP [kg CO2-eq∙FU−1]25202349 (−6.8%)2674 (+6.1%)
ODP [g CFC-11-eq∙FU−1]0.2210.209 (−5.4%)0.232 (+5.0%)
POCP [kg C2H4-eq∙FU−1]0.5620.526 (−6.4%)0.593 (+5.5%)
CED [MJ∙FU−1]26,89325,325 (−5.8%)28,585 (+6.3%)
1 FU: Functional unit; the values in parentheses indicate the relative difference between the scenarios (FS and IS) compared to the Baseline scenario (BS).
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Bartzas, G.; Doula, M.; Komnitsas, K. Life Cycle Assessment of Key Mediterranean Agricultural Products at the Farm Level Using GHG Measurements. Agriculture 2025, 15, 1494. https://doi.org/10.3390/agriculture15141494

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Bartzas G, Doula M, Komnitsas K. Life Cycle Assessment of Key Mediterranean Agricultural Products at the Farm Level Using GHG Measurements. Agriculture. 2025; 15(14):1494. https://doi.org/10.3390/agriculture15141494

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Bartzas, Georgios, Maria Doula, and Konstantinos Komnitsas. 2025. "Life Cycle Assessment of Key Mediterranean Agricultural Products at the Farm Level Using GHG Measurements" Agriculture 15, no. 14: 1494. https://doi.org/10.3390/agriculture15141494

APA Style

Bartzas, G., Doula, M., & Komnitsas, K. (2025). Life Cycle Assessment of Key Mediterranean Agricultural Products at the Farm Level Using GHG Measurements. Agriculture, 15(14), 1494. https://doi.org/10.3390/agriculture15141494

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