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Article

LCA-Based Environmental Performance of Olive Cultivation in Northwestern Greece: From Rainfed to Irrigated through Conventional and Smart Crop Management Practices

1
Laboratory of Pomology, School of Agricultural Sciences, University of Thessaly, Fitoko Str., 38446 Volos, Greece
2
Department of Agriculture, University of Ioannina, Kostakii Campus, 47100 Arta, Greece
3
International Center for Advanced Mediterranean Agronomic Studies (CIHEAM-Bari), Via Ceglie 9, Valenzano, 70010 Bari, Apulia, Italy
4
Faculty of Agriculture, Forestry and Natural Environment, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
5
Department of Agriculture, University of Patras, Theodoropoulou Terma, 27200 Amaliada, Greece
*
Authors to whom correspondence should be addressed.
Water 2021, 13(14), 1954; https://doi.org/10.3390/w13141954
Received: 22 May 2021 / Revised: 7 July 2021 / Accepted: 13 July 2021 / Published: 16 July 2021

Abstract

:
Olive cultivation is expanding rapidly in the northwestern part of Greece, under both rainfed and irrigated practices. Irrigation can result in larger yields and economic returns, but trade-offs in the water–energy–pollution nexus remain a controversial and challenging issue. This study presents an environmental Life Cycle Assessment (LCA) of Greek olive orchard systems in the plain of Arta (Epirus), comparing rainfed (baseline), Decision Support System (DSS)-based (smart) irrigation practices and farmer experience-based (conventional) irrigation practices. The contributions in this paper are, first, to provide a first quantitative indication of the environmental performance of Greek olive growing systems under different management strategies, and second, to detail the advantages that can be achieved using smart irrigation in olive cultivation in the Greek and Mediterranean contexts. Eighteen midpoints (e.g., climate change, water scarcity, acidification, freshwater eutrophication, etc.), two endpoints (damages on human health and ecosystem quality), and a single score (overall environmental impact) were quantified using the IMPACT World+ life cycle impact assessment method. The LCA model was set up using the OpenLCA software v1.10.3. The functional units were 1 ton of product (mass-based) and 1 ha of cultivated area (area-based) on a cradle-to-farm gate perspective. Irrigated systems had the lowest impacts per mass unit due to higher yields, but showed the highest impacts per cultivated area. The DSS-based irrigation management could reduce water and energy use by 42.1% compared to conventional practices. This is translated into a reduction of 5.3% per 1 ton and 10.4% per 1 ha of the total environmental impact. A sensitivity analysis of impact assessment models demonstrated that the benefits could be up to 18% for 1 ton of product or 22.6% for 1 ha of cultivated land. These results outline that DSS-based irrigation is a promising option to support less resource-intensive and sustainable intensification of irrigated agriculture systems in the plain of Arta.

1. Introduction

The olive (Olea europaea L.) is one of the most important perennial Mediterranean crops with overriding importance in terms of employment and contribution to farm income [1]. It possesses multiple significance for Greece in financial, social, and ecological terms [2]. Greece is the world’s third-largest producer of olive-based products, after Spain and Italy. It encompasses 1 million hectares of olive groves, corresponding to 17% of the total world production of olive fruit. Regarding table olives, Greece produces annually more than 200,000 tons, which account for 25% and 7% of EU and global production, respectively [3]. The plain of Arta (Region of Epirus, Greece) is one of the most significant areas for table olive production in Greece. Olive groves account for 12% of the total and 22% of the irrigated agricultural land of the plain [4].
Although the olive tree is a well-adapted species to the Mediterranean-type climate, new challenges are predicted to arise from climate change, threatening this traditional crop [5]. In the future, in some Mediterranean areas including Greece, rainfed cultivation could not be economically feasible and wherever water is available, it would be replaced by irrigated olive cultivation [6]. Irrigation is a valuable agricultural practice for table olive cultivation since it affects not only final yields and fruit size but also qualitative characteristics of the olive fruit [7]. However, current changes and future climatic scenarios indicate significant and increasing water demand [8], underlining the specialized guidance needed to rationalize the use of irrigation water use and application rates. Agronomic management practices in developed countries are generally based on farmers’ empirical experience, resulting in over-application of irrigation water and nutrients [9]. On the other hand, extra energy is required not only to convey water from the delivery point to the crops [10] but also for the production and transportation of fertilizers [11]. The imbalance of water supply and demand may aggravate water exploitation and depletion, leading to unsustainable exploitation of water resources. Energy consumption for irrigation has major environmental implications due to fossil fuel combustion or higher fossil energy use in electrical grids [12]. Many over-intensified farming systems and false practices contribute to ineffective water use (consumptive and degradative), ammonia volatilization, and greenhouse gas emissions, causing multiple environmental burdens [13].
The increasing use of smart farming is repeatedly described as a panacea that contributes to the sustainability of agricultural production [14]. For this purpose, a variety of sensors and Decision Support Systems (DSS) have been proposed to provide efficient use of natural resources [15,16]. Nonetheless, only a comprehensive integrated assessment along the life cycle stages of a product may ensure a robust analysis of the benefit of the innovation [17]. The Life Cycle Assessment (LCA) has received increasing attention over the years for evidencing and analyzing the environmental impacts along the life cycle of a product. Olive cultivation and olive oil production have been largely studied through LCA, as presented in the review by Espadas-Aldana et al. [18]. Latterly, LCA was applied by Fernandez-Lobato et al. [19] to compare traditional rainfed, traditional irrigated, and intensive Spanish irrigated olives, including the processing phase. Maffia [20] evaluated the environmental impacts of the production of olive oil in the Italian region of Campania by comparing six olive oil production systems (two organic certified, two integrated, and two organic hobbyists). Ben Abdallah et al. [21], using LCA, compared traditional, intensive, and super-intensive systems under conventional or organic practice in the olive growing systems in Tunisia. Yet, across the international literature, very few studies have documented and used LCA directly linking the effect of smart farming to environmental impacts [14]. For instance, Mehmeti et al. [22] conducted an eco-efficiency analysis of a real-time irrigation management tool for more precise on-farm inputs in a large irrigation scheme. Balafoutis et al. [23] analyzed variable rate water and nutrient applications in grape cultivation but focused its LCA only on greenhouse gas (GHG) emissions. Palacios et al. [24] proposed a DSS for the correct fertilization and used LCA to quantify its environmental contribution in sugarcane agriculture. Vatsanidou et al. [25] used LCA to analyze different N fertilizer application systems in a Greek pear orchard. Recently, Bacenetti et al. [26] performed LCA to evaluate variable rate nitrogen fertilization in paddy rice under farmer perceptions of crop needs and a new smart app coupled with satellite data. The need for further LCA studies on smart farming and technologies is evident to further verify benefits using the best available knowledge and practice.
In this paper, we present an environmental LCA of rainfed and irrigated olive systems in the Plain of Arta, northwestern Greece. We analyzed two different irrigation practices: conventional irrigation based on farmer perceptions of crop needs and smart irrigation based on a web-based irrigation decision support system. Our leading research questions are first whether producing olive with irrigation would lead to higher, or lower, impacts than producing under rainfed; and second whether, and to which extent smart irrigation may contribute to the reduction of the environmental impact. The main contribution of our work lies in broadening current limited LCA knowledge on the performance of olive-based systems and products in Greece [27,28]. The results provide quantitative information on the product life cycle performance of Greek olive orchards and scientific support on the environmental benefits of assisted irrigation management in crop cultivation. Additionally, provide a useful state-of-the-art reference on the LCA performance of olive cultivation in the Mediterranean contexts.

2. Materials and Methods

2.1. Study Area

The plain of Arta (Figure 1) is located in the northwestern part of Greece, in the Region of Epirus, and has a total area of about 45,000 ha [5]. The climate of Arta’s plain is of Mediterranean type, characterized by hot summers and rainy moderate winters. The climatic annual precipitation is about 1100 mm, concentrated mainly during winter months, rendering irrigation a necessity during summer [29,30]. Two large rivers (Arachthos and Louros) traverse the plain, being its main irrigation water source.
The prevailing crops in the plain consist of citrus, olive, and kiwifruit trees, along with some arable crops. Olive is one of the most important crops in terms of surface area at a regional level, with about 5500 ha of cultivated land, of which 30% is irrigated [5]. The area of table olive groves that are registered for Common Agricultural Policy (CAP) subsidies occupy almost 3200 ha [31]. The average planting density is 250 trees per ha. The main inputs during the olive life cycle are energy (electricity and fuel) consumption, water consumption, and the use of chemical products (pesticides and fertilizers). Fertilization is carried out early in the year (January–March) in both rainfed and irrigated groves. Plant protection is applied from late spring to September aiming at the control of pests such as Prays oleae and Dacus oleae and diseases such as olive leaf spot (Spilocaea oleagina). Weeds are controlled using mechanical (mowing) rather than chemical means. Many olive groves in the area are rainfed, but a significant part is irrigated. The climate-based crop water requirement for olive crops during the irrigation period was estimated using FAO’s CropWAT [32] to be almost 500 mm, while the corresponding effective rainfall was about 400 mm. The average gross irrigation volume that was measured in selected conventional groves at a representative area for olive culture at the plain of Arta (Village of Grammenitsa, 39.184 °N, 20.981 °E) during 2019 and 2020 was 318.5 mm/year while the corresponding average applied quantity, as derived from interviews, was almost 400 mm/year. The climatic data for the study area are given in Table 1.
Irrigation is performed using micro-sprinklers operating at a pressure of 1.0–2.0 bars. Surface water (SW) and, in some cases, groundwater (GW) are mainly used as irrigation water sources. The share of SW/GW is 95%/5%. Electricity is the main power source for pump operation (90% of farms), while diesel is also used. The average pumping depth is 35–40 m. The soil in the area is characterized according to the USDA classification as clay loam.

2.2. Methodology

Figure 2 shows the methodology steps adopted to evaluate the potential environmental impacts. LCA is based on four main phases: (1) goal and scope, (2) inventory analysis, (3) impact assessment, and (4) interpretation. The first phase defines the start with goal and scope statement defining functional unit and system boundaries, intended application, and audience. Agricultural life cycle inventory data were collected using the data collection template of the Agricultural Life Cycle Inventory Generator [33] and modeled following AusAgLCI [34] and WFLDB guidelines [35]. For impact assessment, a consistent midpoint-damage framework was used [36]. The methodology is explained in detail in the following sections.

2.2.1. Goal and Scope Definition

The goal of this study was the LCA-based impact assessment of rainfed and irrigated olive orchards. The rainfed cropping system was utilized as a baseline scenario. Two irrigation strategies were compared. In the conventional scenario, the irrigation water was supplied based on farmer experience based on the perception of soil water status and crop reactions. For the smart scenario, the performance of IRMA_SYS DSS [37] was analyzed. IRMA_SYS DSS combines actual agrometeorological data along with crop parameters and has provided crop-specific recommendations for irrigation in the whole plain of Arta since 2015. The target group of this study includes local stakeholders (farmers, irrigation managers, and local government officials) and agriculture-related LCA practitioners. The scope of this study was defined as a cradle-to-farm gate (Figure 3). The following activities were included in the analysis: raw materials extraction (e.g., fossil fuels), manufacture of the agricultural inputs (fertilizers, pesticides, electricity, diesel, etc.), use of the agricultural inputs (water emissions, fertilizers emissions, diesel fuel emissions, and pesticide emission), and maintenance and final disposal of machines. Irrigation, tractors, fertilizers, and pesticides were combined to produce the overall product system footprint. Two functional units (FU) were defined: 1 ton (mass-based) of the freshly harvested olives at the farm exit gate and 1 ha of cultivated land (area-based). In this way, both eco-efficiency of production and farm impact intensity and under irrigation and rainfed conditions are addressed [38].

2.2.2. Life Cycle Inventory

Table 2 reports the main inventory data about the cultivated crops. The Life Cycle Inventory (LCI) in this LCA is a mixture of measured, collected from the questionnaire surveys, and calculated data. This study refers to two cultivation cycles, 2019 and 2020. These data were collected from producers involved in this study. The orchard density was 240 trees per hectare. The lifespan of the orchard was considered 25 years. Secondary data were retrieved from databases, literature, or estimated using specific models. N-related emissions were estimated, based on the registered use of fertilizers and using specific models and IPCC guidelines (2006). Ammonia (NH3) accounted for 10% of the applied N. Direct N2O emissions were calculated as 1% (0.01 kgN2O-N/kgN) and 2.1% (0.021 kgN2O-N/kgN) of the applied N for rainfed and irrigated crops, respectively. Nitrogen oxides (NOx) emissions to air were calculated as 21% of the direct N2O [39]. The fraction of N synthetic fertilizers that volatilizes as NH3 and NOx (FracNGASF) was 10% (kg N volatilized/kg of the applied N). The fraction of N lost through leaching and runoff (FracNLEACH) was assumed 0.3 (kg N/kg N additions). Phosphorus emissions included phosphorus leaching to groundwater, run-off to surface waters, and emissions through erosion by water to surface waters. These emissions were modeled according to available guidelines [34,39]. Diesel combustion and pesticide emissions were sourced from Ecoinvent Database 3.1 (2014). Indirect inventory data (emission profile) for the production of inputs were retrieved from Ecoinvent v.3.1 database [40].

2.2.3. Life Cycle Impact Assessment

The life cycle impact assessment (LCIA) was performed using IMPACT World+ (IW+), a novel method [36] combining characterization, normalization, damage assessment, and weighting (Table 3). We firstly computed 18 midpoint impacts (climate change, human and eco-toxicity, particulate matter acidification, and eutrophication, but also impacts due to the use of water, land, and resources). Then, the endpoints (damage to human health and ecosystem quality) and a single score assessment assisted the analysis. Endpoint and single score condensed the complexity of the multiple impact indicators, recognized the interdependency of indicators, and allowed easier communication of results to non-LCA experts. The OpenLCA software v.1.10.3 [41] was used to conduct LCIA.
A Monte Carlo uncertainty analysis (1000 runs) with sampling from a lognormal distribution was conducted to determine the influence of data quality on the significance of the study results. Uncertainty scores were assigned to input-output data based on the criteria presented in the Ecoinvent Pedigree matrix (Table A1, Appendix A). Moreover, a sensitivity analysis was performed to determine if different impact assessment methods may lead to different conclusions. The ReCiPe 2016 [42] and Environmental Footprint [43] model results were used for sensitivity analysis.

3. Results

3.1. Comparative Results at the Midpoint Level

The results of impact category indicators at the midpoint level for the FU of 1 ton of olives are presented in Table 4. The results show that large increases in yield as a result of irrigation offset the footprint increases in most of the impact categories, except for water scarcity, which is due to the use of blue water (irrigation water). Crop yields and blue water use under irrigated treatments were greater than those of the rainfed system. This is because rainfed cropping has a zero on-farm blue water footprint with no water extracted for irrigation [44]. Similar ranges for the majority of life cycle environmental impacts (the change is less than 10%) were found for both conventional and smart irrigation. Smart irrigation via DSS resulted in better environmental performance compared to conventional for water scarcity (−27%), fossil and nuclear energy use (−13%), and human toxicity (−8%) due to associated water and energy savings. For several other impact categories, the conventional system resulted in a lower footprint than the DSS-based one. This variation in the environmental impacts of irrigation per unit of crop production between conventional and smart irrigation mostly results from differences in crop yield. It is well known that the impacts of irrigated versus rainfed crops depend on the functional unit [21,44,45,46]. Per 1 ha, in comparison with rainfed, the irrigated olives showed the highest environmental impacts in all categories as they require greater use of inputs of water and energy (Table A2, Appendix A). This finding is consistent with previous relevant findings [21,47]. Obviously, for 1 ha, smart irrigation has less impact than conventional irrigation due to a lower input intensity. Our findings reinforce the importance of the examination of multiple functional units to provide a better understanding of the benefits and trade-offs of practices on the environmental impacts.
We attempted to compare midpoint results across studies since midpoint modeling is widespread across studies. Nevertheless, the values of each study cannot be directly compared because they are estimated using different LCIA methods. However, it was a useful step to establish basic benchmarking information. Pergola et al. [47] estimated global warming potential referring to 1 ton of olives in Italy to be 110 and 80 kg CO2 eq for rainfed and irrigated systems, respectively. The eutrophication potential was 0.0595 and 0.0494 kg PO43 eq while acidification potential 0.43 and 0.63 kg SO2 eq, respectively. Romero-Gámez et al. [48], comparing olive growing practices in Spain, found that the climate change of rainfed and irrigated conventional olives per 1 ton of product was 277 kg CO2 eq and 260 kg CO2 eq, respectively. The eutrophication potential was 0.052 and 0.0548 kg P eq while acidification was 3.27 and 2.88 molc H+ eq per ton of product. Ben Abdallah et al. [21], comparing rainfed and irrigated conventional olives in Tunisia, found the following impacts per 1 ton of product: climate change, 630.9 and 420.8 kg CO2 eq; eutrophication, 0.09 and 0.128 kg P eq; acidification, 4.35 and 6.72 molc H+ equation. Fernández-Lobato et al. [19] found that for 1 ton of olive oil, climate change was 239 kg CO2 eq, ozone depletion 1.78 × 10−4 kg CFC-11 eq, particulate matter 1.65 kg PM2.5 eq, freshwater eutrophication 0.574 kg P eq, and water resource depletion 52.9 m3 water eq. The impact varies widely across the reviewed literature and agricultural systems. Russo et al. [27] compared the environmental performance of different olive farming systems in a European context and found that the environmental performances of the olive cultivation in Greece were the best for 14 out of 16 impact categories.
Figure 4 shows the process contribution analysis. At the midpoint level, our findings largely confirm those of other relevant olive-based LCA studies [21,48] in terms of the identification of the main impacts and hotspots. Fertilization and irrigation were the agricultural practices that implied the major contribution in most of the categories considered. In the case of farmer-led practices, the irrigation impacts ranged from 0.018% (land occupation) to 83.3% (water use). In the case of the smart scenario, the irrigation impacts ranged from 0.01% (land occupation) to 73.3% (water use). It was observed that energy consumption in irrigation had a higher contribution to fossil and nuclear energy, ionizing radiation, and human toxicity impacts. Irrigation water use for cultivation had the highest contribution to blue water consumption and therefore to water scarcity. The field emissions such as ammonia and dinitrogen monoxide had the highest contribution to impact categories of particulate matter formation acidification, marine eutrophication, and climate change. Pesticide emissions lea to toxicity impacts and ozone layer depletion.

3.2. Endpoint and Overall Environmental Impact

The interpretation of LCA results was extended to the endpoint and single score analysis (Figure 5) to provide an additional basis for a better understanding of the trade-offs between cultivation systems and environmental impacts. It should be reiterated that the higher the score, the more the environmental impact of a crop. Considering impact for 1 ton, it is clear that the irrigated cropping system is more eco-compatible than the rainfed system thanks to its higher olive productivity. The level of damage to human health ranged from 0.0013 DALY/ton for DSS irrigation to 0.0021 DALY/ton for rainfed. The level of damage to ecosystem quality ranged from 361.9 PDF·m2·yr/ton for DSS irrigation to 520.6 PDF·m2·yr/ton for rainfed. The final impact as a single score for 1 ton of olives at farm gate were 228.3, 158.4, and 149.9 Euro2003 for the rainfed, farmer-led, and DSS-based irrigation, respectively.
The final results demonstrate that smart irrigation via DSSs produced the lowest environmental impact in terms of damage to human health, ecosystem quality, and overall environmental impact. When the focus is only on irrigation process impacts, the implementation of DSS-based technology benefits translated to 42.2% less damage to human health and 37.5% less damage to ecosystem quality. The final impact could be reduced by 15.2 Euro2003/ton or 40.01%. Overall, however, the DSS-based irrigation management could reduce the product life cycle impacts by 5.3% (1 ton) and 10.4% (1 ha) in comparison with farmer-led irrigation practices. The benefits were limited due to yield and water input trade-offs of DSS versus farmer-led irrigation practices. This was affected also by fertilizers which were identified as the highest contributor to life cycle impact (Figure 5). These results suggest that optimization of fertilization should be the major target to improve LCA results for olive cultivation. This is particularly relevant for rainfed cropping systems. Looking at the impact for 1 ha of olive production (Table A3, Appendix A), the rainfed system had the lowest environmental impact. Based on the weighted results, climate change, particulate matter formation, acidification, and water scarcity were the contributing impact categories.
The Monte Carlo analysis (Figure 6) revealed that the uncertainty was relatively low for most impact categories with a 10−20% fluctuation range (See Table A4 for numerical results). The uncertainty analysis indicates that the environmental impacts could be around 18% lower for photochemical oxidation formation and 20% higher for particulate matter formation.

3.3. Sensitivity Analysis of Life Cycle Assessment Method

Besides the Impact World+ method, the potential environmental impacts were further computed with ReCiPe 2016 [42] and Environmental Footprint [43] to validate the credibility of the above results. The results as a single score are shown in Figure 7 (See Table A5 and Table A6 for detailed LCIA results). At the midpoint level, a similar trend to the IW+ method was found, confirming that irrigated crops have a lower footprint per 1 ton and higher per 1 ha. As expected, substance contributions in each LCIA method were different. Nevertheless, the overall findings from the primary analysis and the sensitivity analysis both confirm that DSS irrigation is the strategy with the lowest environmental impact. The overall benefits of DSS-based irrigation vs. farmer-led irrigation for 1 ton of product were different among methods: 5.3% (IW+), 10.7% (ReCiPe 2016), and 18.1% (EF method). Considering yield data, these benefits for 1 ha become 10.4% (ImpactWorld+), 17% (ReCiPe 2016), and 22.6% (EF method). In all methods, the results showed that the fertilizers remain a great source of impact. The EF method provides a higher benefit since it attributes higher weights to the water use impact category and thereby to irrigation. ReCiPe 2016 confirms fertilizers as the main contributor, with particulate matter, global warming, and human toxicity as the main contributors.

4. Conclusions

This is one of the few LCA studies performed so far for table olive growing systems in Greece. Along with the numerical reference for Greek olives, it provided a better understanding of the benefits of using DSSs for irrigation management. The results were studied for two functional units, a mass-based (reflecting production efficiency) and a surface-based unit (reflecting production intensity). The large increases in yield resulting from irrigation offset the increases in footprint due to increased resource inputs compared to the rainfed system. This confirmed that high-yield farming reduces the global environmental impact compared to low-yield olive farming systems. On the other hand, irrigated orchards are likely to increase impacts per unit area of farmland. The use of DSS-based irrigation compared to conventional farmer practices allowed achieving a considerable decrease in water and electricity use of about 42.1%. This reduced the total environmental impacts of the irrigated process by 40% per unit of product and 43% per unit area. However, environmental benefits were drastically reduced when considering all agricultural activities. This is because fertilization was the highest contributor to environmental impacts. Overall, this assessment showed that the total environmental impact could be reduced by 5.3% per 1 ton and 10.4% per 1 ha by changing from conventional to smart irrigation practices. A sensitivity analysis of the LCA method demonstrated that benefits could be higher. This highlights that promising environmental benefits could be achieved using DSS-based irrigation, which can reduce impact intensity and increase efficiency due to more efficient use of inputs. To further enhance the life cycle environmental benefits, the focus should be placed on the development of DSSs optimizing both irrigation and nutrient management. Further studies will be conducted to analyze the sustainability aspect of smart technologies considering cross-cutting economic, environmental, and social effects.

Author Contributions

Conceptualization, A.M., K.F., and I.T.; methodology, A.M.; software, A.M.; validation, M.T.; investigation, resources, and data curation, I.T., K.F., N.M., and P.B.; writing—original draft preparation, K.F. and A.M; writing—review and editing, K.F., A.M., I.T., G.N., A.P.M., N.M., P.B., and M.T.; visualization, G.N., A.M., N.M., and P.B.; supervision, G.N., A.M., I.T., and M.T.; project administration, I.T. and M.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not available.

Acknowledgments

A.M. thanks Sergio Ulgiati from Università Degli Studi di Napoli “Parthenope” for allowing access to LCA databases. The datasets of 2019 and 2020 were created through measurements and interviews that were conducted in the framework of the Interreg–IPA CBC Greece–Albania 2014–2020 project, OLIVE_CULTURE/Contribution to the enhancement of olive sector by promoting certified good cultivation practices, applying precision agriculture technologies, creating innovative local products, and supporting relevant SMEs (http://www.interreg-oliveculture.eu/ accessed on 30 April 2021).

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Data Quality Indicators (DQI) assigned to each input-output flow for uncertainty analysis.
Table A1. Data Quality Indicators (DQI) assigned to each input-output flow for uncertainty analysis.
ParameterDQIGeometric Standard Deviation
Electricity2;1;1;1;21.0714
N-fertilizer2;1;1;1;21.0714
P-fertilizer2;1;1;1;21.0714
K-fertilizer2;1;1;1;21.0714
Pesticides2;1;1;1;21.0714
Diesel2;1;1;1;21.0714
Tractors2;1;1;1;21.0714
Tractor oil3;1;1;1;21.0714
Land occupation3;1;1;1;21.1155
Ammonia3;2;1;2;21.1155
Dinitrogen monoxide3;2;1;2;21.1155
Nitrous oxide3;2;1;2;21.1155
Nitrates3;2;1;2;21.1155
Phosphorus3;2;1;2;21.1155
Phosphates3;2;1;2;21.1155
Pesticide emissions3;2;1;2;21.1155
Combustion emissions *3;2;1;2;21.1155
* Note: See Table 7.1 page 62 at Necemek and Kagi (2007).
Table A2. LCA-based metrics of 1 ha of olive cultivation in the plain of Arta under different management strategies (graded color scale: green = lowest impact, orange = midpoint, red = highest impact).
Table A2. LCA-based metrics of 1 ha of olive cultivation in the plain of Arta under different management strategies (graded color scale: green = lowest impact, orange = midpoint, red = highest impact).
Impact CategoriesUnitRainfedFarmer IrrigationDSS Irrigation
Climate change, long termkg CO2 eq (long)3252.24747.094426.15
Climate change, short termkg CO2 eq (short)3331.54851.334520.30
Fossil and nuclear energy MJ deprived14,900.524,217.7520,011.88
Freshwater acidificationkg SO2 eq3.85 × 10−54.89 × 10−54.43 × 10−5
Freshwater ecotoxicityCTUe165,476.5174,578.17170,469.62
Freshwater eutrophicationkg PO4 P-lim eq0.3010.3040.302
Human toxicity, cancerCTUh2.62 × 10−63.60 × 10−63.16 × 10−6
Human toxicity, non-cancerCTUh1.42 × 10−41.96 × 10−41.72 × 10−4
Ionizing radiationsBq C-14 eq18,324.50222,723.27020,737.646
Land occupation, biodiversitym2 arable land eq .yr262.2263.4262.9
Land transformation, biodiversitym2 arable land eq1.261.311.28
Marine eutrophicationkg N N-lim eq1.461.501.49
Mineral resources usekg deprived18.2118.7918.53
Ozone layer depletionkg CFC-11 eq6.44 × 10−47.08 × 10−46.79 × 10−4
Particulate matter formationkg PM2.5 eq1.831.831.83
Photochemical oxidant formationkg NMVOC eq11.3813.1712.58
Terrestrial acidificationkg SO2 eq0.0930.1010.098
Water scarcitym3 world eq51,419.16277,937.8919,2942.53
Table A3. Endpoint and single scores indicators of 1 ha of olive cultivation in the plain of Arta under different management strategies.
Table A3. Endpoint and single scores indicators of 1 ha of olive cultivation in the plain of Arta under different management strategies.
Impact CategoriesUnitRainfedFarmer IrrigationDSS Irrigation
Damage to human health per 1 ton
MechanizationDALY4.18 × 10−42.00 × 10−42.11 × 10−4
FertilizersDALY1.47 × 10−38.13 × 10−48.58 × 10−4
PesticidesDALY1.93 × 10−49.24 × 10−59.75 × 10−5
Land occupationDALY---
IrrigationDALY0.002.89 × 10−41.67 × 10−4
Damage to human health per 1 ha
MechanizationDALY2.37 × 10−32.37 × 10−32.37 × 10−3
FertilizersDALY8.35 × 10−39.63 × 10−39.63 × 10−3
PesticidesDALY1.09 × 10−31.09 × 10−31.09 × 10−3
Land occupationDALY---
IrrigationDALY0.003.42 × 10−31.88 × 10−3
Damage to ecosystem per 1 ton
MechanizationPDF·m2·yr93.244.647.0
FertilizersPDF·m2·yr349.9191.3201.9
PesticidesPDF·m2·yr46.722.323.6
Land occupationPDF·m2·yr30.714.715.5
IrrigationPDF·m2·yr-118.273.9
Damage to ecosystem per 1 ha
MechanizationPDF·m2·yr527.7527.7527.7
FertilizersPDF·m2·yr1980.72265.12265.1
PesticidesPDF·m2·yr264.5264.5264.5
Land occupationPDF·m2·yr173.7173.7173.7
IrrigationPDF·m2·yr-1399.7829.5
Total environmental impact per 1 ton
MechanizationEURO200344.021.0322.19
FertilizersEURO2003158.186.9691.76
PesticidesEURO200320.99.9710.52
Land occupationEURO20034.32.052.17
IrrigationEURO2003-37.9222.72
Total environmental impact per 1 ha
MechanizationEURO2003248.9248.9248.9
FertilizersEURO2003894.81029.61029.6
PesticidesEURO2003118.0118.0118.0
Land occupationEURO200324.324.324.3
IrrigationEURO2003-449.0255.0
Table A4. Results of the uncertainty analysis with use of the Monte Carlo simulation.
Table A4. Results of the uncertainty analysis with use of the Monte Carlo simulation.
Impact CategoriesUnit/tonRainfedFarmer-Led
Irrigation
DSS
Irrigation
5%95%5%95%5%95%
Climate change, long termkg CO2 eq (long)501.7658.4347.6461.1336.8459.7
Climate change, short termkg CO2 eq (short)514.1674.2355.4471.0344.2469.2
Fossil and nuclear energy MJ deprived2334.82967.11820.52301.41586.32011.6
Freshwater acidificationkg SO2 eq5.98 × 10−67.80 × 10−63.64 × 10−64.71 × 10−63.47 × 10−64.51 × 10−6
Freshwater ecotoxicityCTUe25,817.333,174.813,029.816,721.513,422.817,238.2
Freshwater eutrophicationkg PO4 P-lim eq0.0460.061590.0220.029710.0230.03123
Human toxicity, cancerCTUh4.10 × 10−75.24 × 10−7 2.70 × 10−73.43 × 10−72.50 × 10−73.18 × 10−7
Human toxicity, non-cancerCTUh2.22 × 10−52.85 × 10−51.47 × 10−51.87 × 10−51.36 × 10−51.74 × 10−5
Ionizing radiationsBq C-14 eq2874.03650.71706.52162.11642.72084.7
Land occupation, biodiversitym2 arable land eq .yr41.3651.418.9426.119.4326.5
Land transformation, biodiversitym2 arable land eq0.1970.250.0980.120.1020.13
Marine eutrophicationkg N N-lim eq0.2220.300.1090.150.1140.16
Mineral resources usekg deprived2.8553.61.4091.81.4661.9
Ozone layer depletionkg CFC-11 eq1.02 × 10−41.27 × 10−45.35 × 10−50.05.41 × 10−56.77 × 10−5
Particulate matter formationkg PM2.5 eq0.2720.40.1300.20.1370.2
Photochemical oxidant formationkg NMVOC eq1.8492.40.9831.30.9881.3
Terrestrial acidificationkg SO2 eq1.41 × 10−21.94 × 10−27.34 × 10−31.00 × 10−27.45 × 10−31.02 × 10−2
Water scarcitym3 world eq8064.49110,261.220,897.16326,349.013,980.64917,576.5
Damage to human healthDALY1.82 × 10−32.39 × 10−31.22 × 10−31.60 × 10−31.15 × 10−31.54 × 10−3
Damage to ecosystem qualityPDF·m2·yr455.6597.3342.0447.3308.9410.2
Total environmental impactEURO2003198.4260.8137.9180.9128.6171.5
Table A5. LCA results with the ReCiPe 2016 model.
Table A5. LCA results with the ReCiPe 2016 model.
UnitUnit/haRainfedFarmer IrrigationDSS Irrigation
Fine particulate matter formationkg PM2.5 eq1.240.740.72
Fossil resource scarcitykg oil eq87.9266.2361.51
Freshwater eco-toxicitykg 1,4-DCB eq27.9418.3217.32
Freshwater eutrophicationkg P eq0.190.240.18
Global warmingkg CO2 eq398.89327.74246.10
Human carcinogenic toxicitykg 1,4-DCB eq8.2010.648.27
Human non-carcinogenic toxicitykg 1,4-DCB eq540.80395.60359.67
Ionizing radiation kBq Co-60 eq22.5614.5614.05
Land usem2a crop eq5.603.283.31
Marine eco-toxicitykg 1,4-DCB eq20.1316.5314.48
Marine eutrophicationkg N eq2.201.121.17
Mineral resource scarcity kg Cu eq4.001.972.09
Ozone formation, human health kg NOx eq1.290.760.72
Ozone formation, terrestrial ecosystemskg NOx eq1.310.770.73
Stratospheric ozone depletionkg CFC11 eq5.73 × 10−35.23 × 10−32.80 × 10−3
Terrestrial acidificationkg SO2 eq6.163.303.38
Terrestrial eco-toxicitykg 1,4-DCB989.15589.35580.90
Water consumptionm3 consumed7.09214.18127.96
Human health DALY1.41 × 10−39.47 × 10−48.49 × 10−4
Ecosystemsspecies.yr3.54 × 10−62.86 × 10−62.39 × 10−6
ResourcesUSD201333.2019.8920.22
Single scorepoint (pt)25.9317.6915.78
Table A6. LCA results with the EF model.
Table A6. LCA results with the EF model.
UnitUnit/haRainfedFarmer IrrigationDSS Irrigation
Acidificationmol H+ eq12.26.06.3
Climate changekg CO2 eq584.2315.7316.2
Ecotoxicity, freshwaterCTUe24,803.812,084.612,665.4
Eutrophication, freshwaterkg P eq0.680.330.35
Eutrophication, marinekg N eq52.7025.4426.78
Eutrophication, terrestrialmol N eq9.474.774.92
Human toxicity, cancerCTUh1.03 × 10−65.04 × 10−75.28 × 10−7
Human toxicity, non-cancerCTUh7.79 × 10−63.85 × 10−64.01 × 10−6
Ionizing radiationkBq U-235 eq30.54820.7518.99
Land usePt3798.6292146.091957.21
Ozone depletionkg CFC11 eq1.10 × 10−45.79 × 10−55.86 × 10−5
Particulate matterdisease inc.0.0000.000.00
Photochemical ozone formationkg NMVOC eq1.9841.031.05
Resource use, fossilsMJ4172.0672516.582408.72
Resource use, minerals and metalskg Sb eq2.46 × 10−31.19 × 10−31.25 × 10−3
Water usem3 depriv.22,909.29631,260.2823,352.64
Single scorePoint0.320.3080.252

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Figure 1. Map of Greece, Epirus region, and plain of Arta (green spot) in northwestern Greece.
Figure 1. Map of Greece, Epirus region, and plain of Arta (green spot) in northwestern Greece.
Water 13 01954 g001
Figure 2. Diagram of the LCA framework used in this study.
Figure 2. Diagram of the LCA framework used in this study.
Water 13 01954 g002
Figure 3. System boundary for LCA of olive production systems.
Figure 3. System boundary for LCA of olive production systems.
Water 13 01954 g003
Figure 4. Process contribution analysis on impact categories for rainfed, RF; farmer-led irrigation, F-IRR; DSS-based irrigation, DSS-IRR. Note: CC = climate change; FEU = fossil energy and nuclear use; FA = freshwater acidification; FET = freshwater ecotoxicity; FE = freshwater eutrophication; HTc = human toxicity cancer; HTnc = human toxicity non cancer; IR = ionizing radiation; LO = land occupation; LT = land transformation; ME = marine eutrophication; MRU = mineral resource use; OD = ozone depletion; PM = particulate matter; POF = photochemical ozone formation; TA = terrestrial acidification; WS = water scarcity.
Figure 4. Process contribution analysis on impact categories for rainfed, RF; farmer-led irrigation, F-IRR; DSS-based irrigation, DSS-IRR. Note: CC = climate change; FEU = fossil energy and nuclear use; FA = freshwater acidification; FET = freshwater ecotoxicity; FE = freshwater eutrophication; HTc = human toxicity cancer; HTnc = human toxicity non cancer; IR = ionizing radiation; LO = land occupation; LT = land transformation; ME = marine eutrophication; MRU = mineral resource use; OD = ozone depletion; PM = particulate matter; POF = photochemical ozone formation; TA = terrestrial acidification; WS = water scarcity.
Water 13 01954 g004aWater 13 01954 g004b
Figure 5. The human health, ecosystem, and overall environmental impact scores of olive cultivation under different management strategies.
Figure 5. The human health, ecosystem, and overall environmental impact scores of olive cultivation under different management strategies.
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Figure 6. Monte Carlo uncertainty analysis of olive cropping system representing uncertainty per impact category using the IW+ method.
Figure 6. Monte Carlo uncertainty analysis of olive cropping system representing uncertainty per impact category using the IW+ method.
Water 13 01954 g006
Figure 7. Process contribution to the overall environmental impact of olive cultivation as single score (points) using EF 3.0 (adapted) and ReCiPe 2016 life cycle assessment methods.
Figure 7. Process contribution to the overall environmental impact of olive cultivation as single score (points) using EF 3.0 (adapted) and ReCiPe 2016 life cycle assessment methods.
Water 13 01954 g007
Table 1. Climate data for the study area.
Table 1. Climate data for the study area.
MonthPrc.Wet Days.Tmp. min.Tmp. max.Tmp. MeanRel. Hum.Sun- shineWind (2 m)ETo
mm/mdays°C°C°C%%m/smm/d
Jan13112.73.511.97.773.248.410.9
Feb13012.24.113.18.671.849.71.11.3
Mar9211.15.915.810.869.353.412
Apr7410.98.719.41468.357.512.9
May508.812.624.118.36566.10.93.9
Jun245.315.628.221.960.276.70.84.9
Jul143.51831.124.556.986.80.85.4
Aug183.518.131.124.657.784.40.84.9
Sep44515.628.321.962.975.80.83.6
Oct1159.611.821.716.768.662.40.82.1
Nov16912.6817.212.675.451.20.71.1
Dec17913.94.913.1975.844.80.90.8
Table 2. Inventory data for LCA performance of olive growing systems in the plain of Arta.
Table 2. Inventory data for LCA performance of olive growing systems in the plain of Arta.
Parameter.UnitAverage Rainfed [min;max]Average Farmer-Led Irrigation [min;max]Average DSS Based Irrigation [min;max]
Crop yieldton/ha5.66 [3.4;7.9]11.84 [7;16.6]11.23 [6.6;15.8]
Irrigation waterm3/ha-3560 [2962.5;4155]1953 [1885;2020.4]
Electricity for irrigation (Greek mix)kWh/ha-687 [802;571.7]377 [363.5;389]
Nitrogen fertilizer, as Nkg/ha135 [96;172.8]135 [96;172.8]135 [96;172.8]
Phosphorus fertilizer, as P2O5kg/ha84 [48;120]84 [48;120]84 [48;120]
Potassium fertilizer, as K2Okg/ha60 [54;60]60 [54;60]60 [54;60]
Pesticides, unspecifiedkg/ha21.621.621.6
DieselMJ/ha391339133913
Tractor, 4-wheel, agricultural kg/ha10.5610.5610.56
Tractor lubricating oilkg/ha2.12.12.1
Land Occupation, permanent cropm2 ∗ a44.1621.122.3
Table 3. IMPACT World+ midpoint-damage impact categories and normalization/weighting factors.
Table 3. IMPACT World+ midpoint-damage impact categories and normalization/weighting factors.
CategoryAbbreviationDamage to
Human Health
Damage to Ecosystems
Climate change, long termCC_lt++
Climate change, short termCC_st++
Fossil and nuclear energy useFEU
Freshwater acidificationFA +
Freshwater ecotoxicityFET +
Freshwater eutrophicationFE +
Human toxicity cancerHTc+
Human toxicity non-cancerHTnc+
Ionizing radiationsIR+
Land occupation, biodiversityLO +
Land transformation, biodiversityLT +
Marine eutrophicationME +
Mineral resources useMRU
Ozone layer depletionOD
Particulate matter formationPM+
Photochemical oxidant formationPOF+
Terrestrial acidificationTA +
Water scarcityWS++
Normalization factor-13.70.000101
Weighting factor-5401.4601386.139
Table 4. LCA-based metrics of 1 ton of olive cultivation in the plain of Arta under different management strategies (graded color scale: green = lowest impact, orange = midpoint, red = highest impact).
Table 4. LCA-based metrics of 1 ton of olive cultivation in the plain of Arta under different management strategies (graded color scale: green = lowest impact, orange = midpoint, red = highest impact).
Impact CategoriesUnit/tonRainfed
(Reference)
Farmer-Led Irrigation
(Conventional)
DSS Based Irrigation
(Smart)
Climate change, long termkg CO2 eq (long)574.6 400.9394.5
Climate change, short termkg CO2 eq (short)588.6409.7402.9
Fossil and nuclear energy MJ deprived2632.62045.41783.6
Freshwater acidificationkg SO2 eq6.80 × 10−64.13 × 10−63.95 × 10−6
Freshwater ecotoxicityCTUe29,236.114,744.7815,193.4
Freshwater eutrophicationkg PO4 P-lim eq5.31 × 10−22.56 × 10−22.69 × 10−2
Human toxicity, cancerCTUh4.64 × 10−73.04 × 10−7 2.82 × 10−7
Human toxicity, non-cancerCTUh2.52 × 10−51.66 × 10−51.53 × 10−5
Ionizing radiationsBq C-14 eq3237.541919.201848.3
Land occupation, biodiversitym2 arable land eq. yr46.322.2523.43
Land transformation, biodiversitym2 arable land eq0.220.110.114
Marine eutrophicationkg N N-lim eq0.2580.1270.132
Mineral resources usekg deprived3.21.591.65
Ozone layer depletionkg CFC-11 eq1.14 × 10−4 5.98 × 10−56.05 × 10−5
Particulate matter formationkg PM2.5 eq0.3230.1550.163
Photochemical oxidant formationkg NMVOC eq2.011.1121.121
Terrestrial acidificationkg SO2 eq1.64 × 10−2 8.55 × 10−38.70 × 10−3
Water scarcitym3 world eq9084.6626,131.1817,196.30
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Fotia, K.; Mehmeti, A.; Tsirogiannis, I.; Nanos, G.; Mamolos, A.P.; Malamos, N.; Barouchas, P.; Todorovic, M. LCA-Based Environmental Performance of Olive Cultivation in Northwestern Greece: From Rainfed to Irrigated through Conventional and Smart Crop Management Practices. Water 2021, 13, 1954. https://doi.org/10.3390/w13141954

AMA Style

Fotia K, Mehmeti A, Tsirogiannis I, Nanos G, Mamolos AP, Malamos N, Barouchas P, Todorovic M. LCA-Based Environmental Performance of Olive Cultivation in Northwestern Greece: From Rainfed to Irrigated through Conventional and Smart Crop Management Practices. Water. 2021; 13(14):1954. https://doi.org/10.3390/w13141954

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Fotia, Konstantina, Andi Mehmeti, Ioannis Tsirogiannis, George Nanos, Andreas P. Mamolos, Nikolaos Malamos, Pantelis Barouchas, and Mladen Todorovic. 2021. "LCA-Based Environmental Performance of Olive Cultivation in Northwestern Greece: From Rainfed to Irrigated through Conventional and Smart Crop Management Practices" Water 13, no. 14: 1954. https://doi.org/10.3390/w13141954

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