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Article

Evaluation of Environmental Impacts in Legume Crops: A Case Study of PGI White Bean Production in Southern Europe

by
Reina Pérez
,
Cecilia Fernández
,
Amanda Laca
* and
Adriana Laca
Department of Chemical and Environmental Engineering, University of Oviedo, C/ Julián Clavería s/n, 33071 Oviedo, Spain
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(18), 8024; https://doi.org/10.3390/su16188024
Submission received: 28 June 2024 / Revised: 10 September 2024 / Accepted: 11 September 2024 / Published: 13 September 2024
(This article belongs to the Section Sustainable Agriculture)
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Abstract

:
A small-scale organic crop producing the protected geographical indication (PGI) cultivar “Faba Asturiana”, located in northern Spain, was considered to be a case study for analyzing the environmental impacts associated with the production of this legume (Phaseolus vulgaris L.). The life cycle assessment (LCA) methodology was employed for the analysis with a “cradle-to-gate” perspective, with 1 kg of dry beans as the functional unit. The results demonstrated that the main contributor to the environmental impacts was electricity consumption (with percentages above 75% for ionizing radiation, freshwater eutrophication, terrestrial ecotoxicity, and non-carcinogenic toxicity). A carbon footprint (CF) of 1.20 kg CO2eq per kg of dry beans was obtained (around 1000 kg CO2/ha·y). Electricity consumption was the major contributor to the CF, followed by atmospheric emissions from waste incineration and diesel use. Furthermore, some environmental improvements were suggested, and three alternative scenarios were investigated. In conclusion, it can be established that the CF of the studied PGI bean is within the range reported by other researchers for leguminous crops. The easiest way to reduce the CF for this particular crop would be to compost the organic waste instead of burning it. Additionally, the most effective strategy would be to reduce energy consumption or use renewable energy sources. For example, if the energy supply were obtained through in situ solar production, the CF could be reduced by more than 40%.

1. Introduction

A significant part of the environmental impacts caused by human activity is attributable to the necessity of food production. It has been estimated that 34% of total GHG (greenhouse gas) emissions, 59% of eutrophication emissions, and 26% of the overall primary energy demand are due to the food value chain, from production to consumption [1]. Many studies have shown that animal-based foods have a much higher environmental impact compared to plant-based products. Moreover, the environmental impacts derived from livestock systems are mainly associated with the production of animal feed [2,3,4,5]. In the agri-food sector, the highest environmental costs are associated with agricultural production. Additionally, the growing population, which is expected to pass 9 billion people by 2050, results in an increase in the global food demand by more than 50%. This prospect has intensified the pressure on agriculture to increase production and efficiency while maintaining and/or enhancing sustainability in the sector [6].
The implementation of sustainable agricultural methods is crucial not only for economic and social efficiency but also to mitigate the environmental impacts generated along the entire food chain. To achieve this goal, the first step is to conduct objective and comprehensive environmental studies that identify the major contributions to these impacts. In this context, life cycle assessment (LCA) provides a measurable and quantifiable framework to evaluate the environmental impacts on food production systems, offering valuable information for researchers, growers, and policymakers [7,8,9]. According to ISO 14040 and 14044 standards [10,11], an LCA study consists of four stages: goal and scope definition, inventory analysis, impact assessment, and interpretation. The quality of the results of an LCA depends on the robustness, reproducibility, and reliability of the procedures carried out during these four steps [12]. This methodology has proven useful for quantifying resource usage and emissions across a wide range of primary and secondary food sectors [2,3,9,13]. In particular, in agricultural systems, LCA has proved to be a valuable tool for both identifying the contribution of the life cycle stages to the overall environmental load and for comparing different alternatives, such as cultivars, crops, and agriculture practices, with the aim of prioritizing improvements and good environmental practices [14,15,16,17].
Moreover, the food industry demands a new generation of processed foods that are low-cost, tasty, and desirable while also being healthy and sustainable. In this context, legumes will play an important role [18]. Pulses, which include leguminous crops such as beans, peas, chickpeas, and lentils, can be an excellent source of protein in the human diet, as they contain 18–36% protein and are rich in fiber, vitamins, and minerals [7,8]. Legumes can be grown in a variety of soil types and climates, and in addition to their health benefits, they offer other advantages, such as a low environmental footprint during cultivation. Plant proteins, such as peas and beans, have lower land use requirements and GHG emissions per kg of protein obtained compared to other protein sources [19,20,21]. Furthermore, leguminous crops fix atmospheric nitrogen in the soil, which is then absorbed by the crops to form proteins, improving soil structure and favoring biodiversity. They are a sustainable option for farmers, as legumes require less water and fertilizers than other crops [21,22].
Substituting animal-based proteins for legume proteins has been proposed to move towards more sustainable and healthier diets. In the European Union (EU), increasing legume crops has been identified as a key strategy to achieve the objectives established by the European Green Deal [23,24]. Among European countries, Spain stands out as an important producer and consumer of legumes. This country is the leading producer of fodder legumes in the EU [25] and produces a wide variety of grain legumes, such as lentils, chickpeas, peas, and beans. The common bean (Phaseolus vulgaris L.) has been reported as the most important legume for direct human consumption, both as fresh pods and dried seeds [26]. It is cultivated worldwide, with an annual global production of over 26 million tons. Its nutritional importance lies in its high content of proteins, vitamins, dietary fiber, minerals, and antioxidants [27]. Spain produces around 17 thousand metric tons of dried beans per year [28], and the PGI “Faba Asturiana” is one of the most outstanding cultivars [29].
Before advocating the expansion of worldwide legume production as a future main source of proteins in the human diet, it is essential to understand the environmental impacts of leguminous crops and identify the subsystems with higher environmental loads and the most sustainable agriculture practices. Inventory data obtained from agricultural LCAs are greatly affected by temporal and spatial conditions. Therefore, it is necessary to have extensive and rigorous environmental information about case studies of cultivating different legumes in different climatic regions. Nevertheless, research on agricultural LCA methodology is quite limited compared to the industry sector [14], and studies on green beans are particularly scarce [30]. Very few LCA studies on bean production are available in Europe [20,30,31,32], and only one has been found in Spain, which analyzed a case study of green bean production in Southern Spain [33].
The aim of this work was to analyze the environmental impacts derived from dry bean production in northern Spain, using the PGI “Faba Asturiana” as a case study. A “cradle-to-gate” LCA was conducted with two main objectives: first, to increase the understanding of the environmental impacts associated with pulse production in Southern Europe, and second, to propose viable improvement measures to reduce the environmental burdens of grain legume production. It is expected that the results of this study could serve as a useful reference in this widespread but under-investigated sector.

2. Materials and Methods

2.1. System Description

The study area, namely the Principality of Asturias, is a region in northern Spain characterized by a maritime climate with temperate, cool winters and abundant rainfall throughout the year. The PGI “Faba Asturiana” refers to dried, shelled beans from the traditional “Granja Asturiana” variety (Phaseolus vulgaris L.) that originates from this particular region (MAPA, 2024). This legume, with organoleptic characteristics including creamy albumen and a buttery consistency on the palate, was recognized as PGI in the EU in 1996 by means of Commission Regulation (EC) 1107/96. The beans are creamy white, long and flat (with a minimum length of 18 mm, a maximum width of 11.5 mm, and a maximum thickness of 8.5 mm), kidney-shaped, and large (1000–1100 beans per kg). The typical composition of this product is summarized in Table 1 [34].
Sowing takes place from April to June, depending on weather conditions. The amount of seeds sown per hectare varies between 45 and 60 kg, and the crop is generally cultivated using an irrigation system. During cultivation, once seeds have sprouted, excess stalks are manually removed to favor crop development. Two or three passes with the cultivator are carried out during the production phase to remove weeds. Fertilizers are usually applied in autumn/winter; however, herbicide use is uncommon. This legume needs training, and the process of placing the stakes is very labor-intensive, as it is a manual task that must be conducted at a specific time during cultivation to maintain the vertical growth of the plants and prevent them from running along the ground. Harvesting is also done manually, either by pulling up the whole plant or harvesting pod by pod. After that, the harvested crops are left to air-dry in a well-ventilated place for at least a month. Finally, once the beans are air-dried, they are cleaned by aerating them, either manually or mechanically, to remove straw and defective grains [34].
The crop employed as a case study is an organic production system located in a sandy meadow in the west of the Principality of Asturias. It is situated at an altitude of 47 m, at coordinates 43°33′43.81” N and 6°8′45.2” W. The farm has three warehouses of 40 m2 each and a 10 m2 toilet with a tap water supply. Sowing, training, and harvesting are performed manually, whereas a brush cutter and a rotovator are used for weed removal. The cultivated land covers an area of 1.04 ha, with a total of 2900 plants, yielding 919 kg of commercialized beans and 210 kg of discarded beans during the study period (from January to December of 2021). In 2021, the grains were seeded in May, the crop was harvested in October, and the plants were watered by drip irrigation from May to September using river water.

2.2. Goal and Scope Definition

In this work, the environmental impacts derived from the production of PGI “Faba Asturiana” using LCA methodology were evaluated to enhance our understanding of the environmental impact of legume crop production in Europe, with the ultimate goal of establishing environmental improvements that could be implemented as sustainable practices. The functional unit (FU) used was 1 kg of commercialized dried beans at the orchard gate.
The system boundaries of this study encompass the entire process from raw material extraction to the orchard gate (“cradle-to-gate” approach). Specifically, this work included the inputs and outputs from soil preparation to the storage of the beans, without considering the packaging, labeling, and distribution to the points of sale (Figure 1).

2.3. Inventory Data

The inventory data includes primary and secondary data. Primary data were collected through detailed farmer surveys and correspond to the amount of resources used (water, electricity, plastic, rope, diesel, and land) and the wastes generated (wastewater and solid wastes), whereas emissions were calculated from this primary data employing reliable literature sources. Secondary data related to the production of consumed resources were obtained from the Ecoinvent database and OECD iLibrary [35]. The system considered the production phase and the fuel and electricity consumed during the shelling, cleaning, and freezing operations. The fertilizer production, water and electricity consumption, land occupation, and materials consumed (plastic net and rope) were also taken into account as inputs. The outputs included bean production, solid waste generation, wastewater, and emissions into the atmosphere derived from composting organic wastes (discarded beans and weeds), incineration (open burning) of vegetable residues (shelling and harvesting wastes), fertilizer application, and fuel consumption. The emissions into the atmosphere and the soil derived from fertilizer application were also included in the analysis (Table 2).
In accordance with PAS 2050 [36], the construction of infrastructures, buildings, and facilities existing at the crop production site were excluded from the LCA. Similarly, inputs/outputs that represented less than 1%, such as some minor fertilizer ingredients (for example, vitamins or enzymes), were also not included in the analysis.
Fertilizers were considered in the LCA by calculating the active ingredients of each product [37]. Three different products were employed: an organic fertilizer (10 L per year), a mineral amendment based on calcium and magnesium (125 kg per year), and skimmed milk (80 L per year). In agreement with Pérez et al. [38], the emissions to the soil from fertilization were included, considering that 20% of any applied product leaches into the ground. Additionally, emissions into the atmosphere (NH3, NO2, and N2O) from fertilizer application were estimated using methods employed by the Intergovernmental Panel on Climate Change (IPCC) and the Ministry of Agriculture, Fisheries and Food (MAPA) of Spain, i.e., the emissions factors used were 0.09, 0.003 and 0.1 for NH3, NO2, and N2O, respectively [29,38]. Moreover, emissions from the incineration of shelling and harvesting wastes were determined through the methodology described by the Regional Government of Andalusia [39]. The composting emissions from the in situ decomposition of organic wastes, specifically weeds removed during cleaning and discarded beans, were calculated based on the emission rates of 4 g CH4, 0.24 g of NO2, and 0.24 g of NH3 per kg of wet-treated waste [40].
It should be noted that the net CO2 capture in the system was not taken into account, as the CO2 is not retained in the plant, i.e., the plant grows and degrades within the annual cycle. Additionally, the shelling and harvesting residues are either incinerated or composted, as previously indicated.

2.4. Impact Assessment

An impact assessment was conducted using the LCA Simapro 9.5.0 software [35] with the ReCiPe 2016 Midpoint (H) V1.08 method, which includes 18 impact category indicators: global warming (GW), stratospheric ozone depletion (SOD), ionizing radiation (IR), ozone formation human health (OFHH), fine particulate matter formation (FPMF), ozone formation terrestrial ecosystems (OFTE), terrestrial acidification (TA), freshwater eutrophication (FE), marine eutrophication (ME), terrestrial ecotoxicity (TEC), freshwater ecotoxicity (FEC), marine ecotoxicity (MEC), human carcinogenic toxicity (HCT), human non-carcinogenic toxicity (HNCT), land use (LU), mineral resource scarcity (MRS), fossil resource scarcity (FRS), and water consumption (WC) [41]. This method is frequently used in the literature to evaluate different agricultural products from crops [42].
The carbon footprint was calculated using the Greenhouse Gas Protocol (GHG) V1.03 method, which allows for obtaining fossil CO2eq. (from the combustion of fossil resources), biogenic CO2eq. (from biological sources), CO2eq. from land transformation and CO2 uptake. According to ISO 14067 guidelines [43], this study considered only fossil and biogenic CO2 to obtain the CF value.

2.5. Alternative Scenarios

Based on the results from this study, alternative scenarios were simulated to evaluate the effects of different strategies on greenhouse gas emissions performance.

3. Results and Discussion

3.1. Environmental Impacts

As can be seen in Figure 2, the subsystem with the highest environmental impacts is clearly the electricity consumption, which contributed over 30% to 13 of the 18 categories analyzed. Electricity consumption was particularly damaging in several areas: ionizing radiation (96%), freshwater eutrophication (78%), non-carcinogenic toxicity (75%), mineral resource scarcity (68%), fine particulate matter formation (55%), and in categories related to ecotoxicity (terrestrial, freshwater and marine) (>50%). It is well known that different electricity-generating technologies produce ionizing radiation [44]. Additionally, the impact of electricity on the human toxicity category depends on its source; for example, it has been reported that this impact is lower when electricity is generated from shale gas compared to coal [45].
Ozone formation categories are strongly affected by electricity consumption (>30%) and by emissions from diesel use (>40%). This is due to the formation of photochemical smog, a brownish-gray haze caused by the action of solar ultraviolet radiation on an atmosphere polluted with hydrocarbons (HC) and oxides of nitrogen (NOx), both of which are emitted during fossil fuel combustion. Photochemical smog primarily contains ozone, which can affect human health and cause damage to plants [46].
The effect of fertilizer consumption on the marine eutrophication category (92%) is noteworthy. This effect may be due to the composition of the wastewater from the chemical fertilizer industry, which mainly contains organics, alcohols, ammonia, nitrates, phosphorous, heavy metals (such as cadmium), and suspended solids [47]. Fertilizer consumption is also an important contributor to the human carcinogenic toxicity (67%) and freshwater ecotoxicity (40%) categories. These results align with those reported by Rebolledo-Leiva et al. [48], who indicated that the amount of fertilizer consumed by a crop has a significant effect on toxicity-related impact categories.
Nitrogen is one of the main plant nutrients and inputs in crop production, so it is important to note that since legumes are capable of fixing N2, the use of nitrogen fertilizer can be minimized for these crops [49]. In the case study analyzed here, milk is employed as a source of nitrogen. Milk is rich in organic carbon and nitrogen, making it a commonly used fertilizer in organic farming [50]. Nitrogen in milk is present in two forms: protein (3.5%) and non-protein nitrogen (3–8%) [51]. According to these values, approximately 3.5 kg of nitrogen per ha are used, a significantly lower amount than those reported in the literature for other non-legume crops such as sunflower, maize, millet, and wheat (35–99 kg N/ha) [52].
Regarding the stratospheric ozone depletion category, the main subsystems responsible for the environmental impacts are fertilizer consumption (36%) due to emissions that originated during its production and emissions to the air from composting (50%). In particular, nitrogen emissions in the form of nitrous oxide (N2O) have a strong impact on global warming and the depletion of stratospheric ozone [53]. Rebolledo-Leiva et al. [48], who analyzed the incorporation of lupin into the cultivation of autochthonous winter wheat in a crop rotation system with potato, maize, and rapeseed in Galicia (NW Spain), also noted that N2O emissions from fertilizers were highly relevant in the global warming category. Additionally, electricity and fertilizer consumption, incineration, diesel use, and composting emissions are the most critical subsystems in the global warming category (36%, 17%, 12%, 12%, and 10%, respectively).
Moreover, electricity, plastic, and diesel consumption, which contribute 39%, 30%, and 24%, respectively, had a significant effect on the fossil resource scarcity category. Other subsystems, such as land occupation and water consumption, were key contributors to the land use and water depletion categories, respectively. In Figure 2, it can also be observed that recycling plastic wastes had a favorable impact on 7 of the 18 categories analyzed (fossil resource scarcity, global warming, ozone formation (human health and terrestrial ecosystems), terrestrial acidification, fine particulate matter formation, and water consumption). Similarly, the wastewater subsystem had a beneficial effect on the water consumption category.

3.2. Carbon Footprint (CF)

Figure 3a shows the CF value obtained for the analyzed system, which, according to ISO 14067, was calculated considering fossil and biogenic CO2eq emissions. The carbon footprint was 1.20 kg CO2eq per kg of dried beans at the orchard gate. This value falls within the range reported by other researchers for different leguminous crops (between 0.1 and 1.24 kg CO2eq per kg of seeds), as can be seen in Table 3. The CF was primarily due to fossil CO2 emissions, with electricity being the major contributor, followed by emissions from waste incineration and diesel combustion. Emissions from organic waste composting, fertilizer consumption, and plastic consumption subsystems also had a substantial impact on GHG emissions associated with common bean production. Finally, it should be noted that recycling plastic waste has a beneficial effect on the CF value.
Regarding the carbon footprint of legumes on a global level, as can be seen in Table 3, the studies reviewed were primarily conducted in North America (Canada and USA), Asia (India, Iran, and China), and Europe (including only one in Spain, as well as the current study), whereas few were conducted in South America (Argentina and Brazil). The top five countries in terms of legume production are Brazil, USA, China, Argentina, India, and Canada [54]; hence, with the aim of expanding the knowledge of this topic, more research should be conducted in these countries. Moreover, there is very limited information about the environmental impacts associated with legume cultivation in Africa and Oceania. Additionally, it is important to note that many of the studies shown in Table 3 were conducted employing databases rather than primary data (directly measured or collected data that represents activities at specific facilities or sets of facilities). It is well known that when the life cycle inventory (LCI) is obtained, including the compilation and quantification of all inputs and outputs within the system boundaries, primary data should always be favored over secondary data when accessible [55]. This makes the LCA more reliable and objective by ensuring the use of raw data [56]. Therefore, future research should prioritize primary data over secondary data when possible. Additionally, legumes are important components of smallholder farming systems, especially in sub-Saharan Africa and Asia [57]. Furthermore, small farms play a significant role in developed countries as well. In the EU, the average farm size is significantly smaller than in the rest of the developed world, and small farms make up the vast majority of the EU’s 10 million farms [58]. Although small farms dominate the European agricultural landscape, they are underrepresented in agricultural decision-making structures compared to larger farms. This underrepresentation has constrained small farms’ contributions to the food sector and its sustainability. To overcome the lack of recognition of small farms’ realities, roles, and needs in European agriculture, researchers have a key role to play in producing new knowledge while also stimulating a dialogue between practice and policy [59]. In this context, future research should address this limitation by incorporating new studies on legume production at a small scale, not only in Europe but worldwide.
It is noteworthy that the CF value of common beans found in the present work was generally higher than those reported for other pulses, such as peas, flaxseed, or soybeans. In this context, it should be noted that many of the cited authors do not specify whether the CF is calculated per kg of fresh or dry product, making comparisons between different studies challenging. Additionally, if the CF of the PGI assessed in this work is expressed per kg of fresh seeds, which have an average moisture content of 60% [60], the value would be much lower (0.56 kg CO2eq per kg of product). This CF is similar to that reported by Tidåker et al. [32] in a study carried out in Sweden for this particular species of bean (Phaseolus vulgaris L.); however, it is slightly higher than the values found in open-field cropping systems by Romero-Gámez et al. [33] (Table 3). It is important to note that the latter authors excluded certain aspects of the LCA, such as the containers and packaging used in the supply of fertilizers and other farming materials, which were included in the present study. Furthermore, the slightly higher value calculated here compared to the literature data could also be attributed to the electricity required for irrigation (water is obtained from a river). This is consistent with Pratibha et al. [61], who indicated that the contribution of CO2 emissions from farm operations to the total CF was higher in irrigated crops compared to rain-fed crops.
When using the LCA methodology, the choice of the FU is decisive, as it is essential that this reference unit aligns with the goal of the analysis. Generally, agricultural LCA studies calculate environmental impacts based on mass (e.g., kg), as in the present work and the studies summarized in Table 3. However, the environmental performance of a particular crop can also be quantified based on the cultivated area (e.g., ha) [16]. For this reason, the CF results are discussed below in terms of cultivated area.
Fernández-Luqueño et al. [62] analyzed the effect of different forms of N fertilizer, such as urea, wastewater sludge, and vermicompost, on CO2 and N2O emissions in soil cultivated with common beans (Phaseolus vulgaris L.) in Mexico. These authors reported CO2 production rates between 340 and 839 kg CO2 per ha per year, which are lower than the value obtained in the present work (1060 kg CO2/ha·y). In contrast, this value is similar to that reported for this species by Allard et al. [63] in France (750–970 kg CO2/ha·y) and falls within the range found by Wilson and Al-Kaisi [64] in Iowa (USA) (964–1250 kg CO2/ha·y).
Productivity is also a key factor when calculating the carbon footprint. In the case study analyzed here, 884 kg of PGI “Faba Asturiana” beans were obtained per ha during 2021. According to García et al. [65], a crop of this specific PGI, when adequately managed, should produce 1500–2300 kg/ha annually. Therefore, the productivity in the analyzed case was notably lower than the theoretical values. Notwithstanding the above, the PGI Regulatory Council [34] asserts that most systems included in this protected geographical indication typically obtain yields between 600 and 1000 kg/ha·y, which aligns with this case study. These variations in productivity can be attributed to different factors, such as genetic, socio-economic, soil, and climatic constraints that limit legume crop production [66].
Additionally, it is well known that productivity in organic farming is lower than in conventional crops (as was observed in the case study). For example, Jelínková et al. [67] analyzed oat production yields in Central Europe from 2007 to 2014 and found that conventional crops had approximately twice the productivity of organic farming. Similarly, Bernas et al. [68], who carried out a study on sustainable legume cultivation in Austria, indicated that organic production could lead to a decrease in oat grain yield. More recently, De Notaris et al. [69] assessed the productivity of the common faba bean (Vicia faba L.) variety Boxer, grown over four years (2015–2018) in a long-term crop rotation field experiment in Denmark, and observed that this crop was more productive under conventional treatments compared to organic ones, where the occurrence of pests and diseases limited yield. Furthermore, Sánchez-Navarro et al. [70] compared GHG emissions from a Spanish cultivar (Muchamiel) of the former legume species using organic and conventional fertilizers, reporting that organic fertilizers led to higher N2O and CO2 emissions, resulting in higher CF values.
Furthermore, repeatedly planting a specific crop on the same land negatively affects productivity, primarily due to the increase in pathogens and the proliferation of weed species. Specifically, for PGI “Faba Asturiana” beans, it is recommended to plant different crops sequentially on the same plot of land to improve soil health, optimize soil nutrients, and combat pest and weed pressure [65]. Moreover, crop rotation in legume cultivation has been shown to reduce GHG emissions and improve yields [64,71].

3.3. Strategies for Reducing CF

As electricity consumption has been found to be the most impactful subsystem, two alternative scenarios have been proposed, recommending potential measures to reduce the CF. The data employed for the LCA was obtained from the database Ecoinvent v 3.9 and corresponds to the mix of electricity supplied in Spain in 2014. For this year, the percentage of electricity coming from renewable sources was 40.5%, a similar value to that described for the year of this study (2021) (46.7%) [28]. According to the Integrated National Energy and Climate Plan (2021–2030) of the Spanish government (MITECO, 2024), 74% of electricity is to come from renewable sources by 2030. Therefore, this percentage was considered in Scenario 2 (Scenario 1 is the real case). In Scenario 3, it was assumed that all the electricity was self-generated in the system through solar panels. A fourth scenario was considered, whereby all organic residues generated were composted, i.e., no vegetable wastes were incinerated, as emissions derived from waste incineration have a notable effect on the CF value (Scenario 4).
As can be seen in Figure 3, all the alternatives considered reduced the CF value in comparison to Scenario 1, specifically, 0.94, 0.70, and 1.07 kg CO2eq per kg of dry beans for scenarios 2, 3, and 4, respectively. It should be noted that, as expected, in scenarios 2 and 3, the decrease in CO2eq emissions was due to a reduction of fossil CO2, as the burning of coal, natural gas, and oil for electricity and heat is the largest source of global greenhouse gas emissions [72]. In contrast, in the fourth scenario, avoiding the incineration of vegetable wastes entails a decrease in biogenic CO2eq.
Finally, it was found that a reduction of more than 40% in the CF value could be achieved if all the energy supply comes from in situ solar production. However, it is important to consider that the impacts of the production and disposal of solar panels are not included in Scenario 3. It is known that these systems have some harmful effects due to their construction process and end-of-life stages [73]. A solar panel has an approximate lifespan of 25–30 years [74], so increasing the longevity of the modules and exploring recycling possibilities have been proposed as good options to reduce the damaging effects associated with the life cycle of these systems [73]. Additionally, solar energy is an interesting alternative to consider in food production. Specifically, Hamidinasab et al. [75] recently conducted a review of the environmental burdens associated with solar technology applications in the agri-food sector. Although the former authors concluded that photovoltaic systems are the most environmentally friendly among all solar systems analyzed, they also highlighted several gaps in the utilization of solar energy in the agri-food sector that should be further investigated.
Table 3. Overview of the carbon footprint values reported in the literature for different legumes considering a “cradle-to-gate” perspective (d.: dried; n.d.: non-dried, -: not indicated).
Table 3. Overview of the carbon footprint values reported in the literature for different legumes considering a “cradle-to-gate” perspective (d.: dried; n.d.: non-dried, -: not indicated).
CropCountryCF (kg CO2eq/kg)Reference
Chickpea
(Cicer arietinum L.)		Canada0.254, 0.406 (-)[76]
India0.53 (-)[77]
Common bean
(Phaseolus vulgaris L.)		Spain0.56 (n.d.), 1.20 (d.)This work
0.136, 0.356 (open-field) (n.d.)
0.102, 0.169, 0.289 (screenhouse) (n.d.)		[33]
Sweden0.44 (d.)[32]
Faba bean
(Vicia faba L.)		Europe0.23–0.58 (-)[20]
Finland0.31, 0.37 *[31]
Sweden0.18 (conventional) (d.)
0.20 (organic) (d.)		[32]
Flaxseed
(Linum usitatissimum L.)		Canada0.456, 0.658, 0.727 (-)[76]
Lentil
(Lens culinaris M.)		Canada0.164, 0.237 (-)[76]
0.21 (-)[71]
Sweden0.26 (organic) (d.)[32]
Mung bean
(Vigna radiata)		India0.66 (-)[77]
Iran1.14 (-)[78]
Oat
(Avena sativa L.)		Europe
Germany
Denmark
Poland
Finland
Romania		0.55 (-)
0.33 (-)
0.39 (-)
0.49 (-)
0.61 (-)
0.68 (-)		[20]
Finland0.58, 0.63 *[31]
Czech Republic0.359 (conventional) (-)
0.302 (organic) (-)		[67]
USA0.58–1.24 (d.)[79]
Pea
(Pisum sativum L.)		Canada0.189, 0.287 (d.)[76]
0.188 (d.)[71]
Finland0.43, 0.49 *[31]
Sweden0.20 (gray pea) (conventional) (d.)
0.18 (gray pea) (organic) (d.)		[32]
USA0.12 (-)[80]
Peanut
(Arachis hypogaea L.)		Argentina0.237 (-)[81]
India0.25 (-)[82]
Iran0.327, 0.302 (-)[83]
Soybean
(Glycine max L.)		Argentina0.09–0.58 (-)[84]
Brazil0.13–0.44 (-)
China0.10 (-)[85]
0.17 (-)[86]
USA0.25 (-)[80]
0.27–0.58 (d.)[79]
Yellow pea
(Lathyrus aphaca L.)		Sweden0.18 (conventional) (d.)
0.24 (organic) (d.)		[32]
* Expressed per kg of feed (as fed basis) for Finnish pork and broiler.

4. Conclusions

Employing an LCA methodology, this work has evaluated the environmental impacts of a small-scale organic crop production site in the North of Spain producing a PGI white bean. The primary subsystem contributing to most of the assessed categories was electricity consumption, which involved a mix of energy supplies, including 40% of electricity coming from renewable sources. Electricity consumption was also one of the main contributors to the high CF value, followed by emissions to the atmosphere derived from waste incineration and diesel use.
To reduce the CF and other environmental impacts in these types of crops, two types of improvements can be assessed: those aimed at increasing legume productivity and those focused on reducing the environmental loads. It is well known that yield in organic farming is usually lower than in conventional systems, as seen in this case study, primarily due to the lack of suitable pest and disease management. Therefore, researching green methodologies that enable better pest management practices in organic bean crops will have a direct positive effect not only on economic but also on environmental outcomes. Additionally, genetic improvement is a promising option for achieving more economically viable and sustainable bean production by increasing productivity. In the particular case of the PGI “Faba Asturiana”, selecting for genetic resistance to diseases while maintaining the strong performance traits of this elite cultivar is a challenging task.
With respect to the implementation of strategies that reduce the environmental impacts of agricultural activities, the first aspect to consider would be electricity consumption. However, since some energy is always required, energy-saving measures should be paired with the use of renewable energy sources. For example, in the case studied in this work, if all the energy supply came from in situ solar production, the CF value would decrease from 1.20 (real scenario) to 0.70 kgCO2 per kg of dry beans. Additionally, a very harmful agricultural practice, which is still used in smallholdings, is the open burning of vegetable wastes, which emit CO2, CO, CH4, NOx, and other atmospheric pollutants. This uncontrolled incineration should be banned, while composting these organic wastes should be encouraged instead.
LCA is an important methodology to promote the green and low-carbon development of agriculture. This case study offers insight into the organic production of PGI “Faba Asturiana” in Spain and contributes to the overall knowledge of bean production. However, as the literature available on this topic is quite scarce, further studies based on primary data are needed to establish regional similarities and variations in bean crops. This will help achieve general conclusions that can support decision-making, from individual farms to regional or (inter)national agricultural organizations, for the sustainable future of legume production.

Author Contributions

Conceptualization and formal analysis, R.P., A.L. (Amanda Laca) and A.L. (Adriana Laca); Methodology, investigation and data curation, R.P. and C.F.; Writing—original draft preparation, R.P. and A.L. (Amanda Laca); Supervision and writing-review and editing, A.L. (Adriana Laca); Funding acquisition, A.L. (Amanda Laca) and A.L (Adriana Laca). All authors have read and agreed to the published version of the manuscript.

Funding

The authors are grateful for the financial support from the Employment, Industry, and Tourism Office of the Principality of Asturias (Spain) through the project GRUPIN AYUD/2021/51041.

Institutional Review Board Statement

Ethical review and approval were waived for this study because the data used were voluntarily supplied by the owner of the crop, and the study does not involve humans or animals.

Informed Consent Statement

Not applicable.

Data Availability Statement

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

Acknowledgments

The authors would like to express their special gratitude to Paula Álvarez and Santiago González from the Regulatory Council for sharing their knowledge about “Faba Asturiana” PGI. José Carlos Rubio from “Finca El Ribeiro” is also gratefully acknowledged for his kind collaboration in supplying the data used in this work.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. System boundaries.
Figure 1. System boundaries.
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Figure 2. Contribution analysis based on the characterization of results obtained using the Recipe Midpoint (H) method (FU: 1 kg dried beans): global warming (GW), stratospheric ozone depletion (SOD), ionizing radiation (IR), ozone formation human health (OFHH), fine particulate matter formation (FPMF), ozone formation terrestrial ecosystems (OFTE), terrestrial acidification (TA), freshwater eutrophication (FE), marine eutrophication (ME), terrestrial ecotoxicity (TEC), freshwater ecotoxicity (FEC), marine ecotoxicity (MEC), human carcinogenic toxicity (HCT), human non-carcinogenic toxicity (HNCT), land use (LU), mineral resource scarcity (MRS), fossil resource scarcity (FRS), and water consumption (WC).
Figure 2. Contribution analysis based on the characterization of results obtained using the Recipe Midpoint (H) method (FU: 1 kg dried beans): global warming (GW), stratospheric ozone depletion (SOD), ionizing radiation (IR), ozone formation human health (OFHH), fine particulate matter formation (FPMF), ozone formation terrestrial ecosystems (OFTE), terrestrial acidification (TA), freshwater eutrophication (FE), marine eutrophication (ME), terrestrial ecotoxicity (TEC), freshwater ecotoxicity (FEC), marine ecotoxicity (MEC), human carcinogenic toxicity (HCT), human non-carcinogenic toxicity (HNCT), land use (LU), mineral resource scarcity (MRS), fossil resource scarcity (FRS), and water consumption (WC).
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Figure 3. Carbon footprint obtained using the Greenhouse Gas Protocol (FU: 1 kg dried beans): (a) real scenario, (b) considering the goals for 2030 regarding electrical consumption, (c) considering solar electricity as the exclusive energy supply, and (d) considering that all the organic wastes are composted.
Figure 3. Carbon footprint obtained using the Greenhouse Gas Protocol (FU: 1 kg dried beans): (a) real scenario, (b) considering the goals for 2030 regarding electrical consumption, (c) considering solar electricity as the exclusive energy supply, and (d) considering that all the organic wastes are composted.
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Table 1. Characteristic composition of PGI “Faba Asturiana” [34].
Table 1. Characteristic composition of PGI “Faba Asturiana” [34].
ParameterContent (g) in 100 g
Moisture<15
Carbohydrates50–60
Proteins20–30
Fat0.3–1.5
Total fiber4
Ash3–5
Magnesium0.09
Calcium0.04
Iron66 *
* mg/kg.
Table 2. Inventory data of the analyzed system, expressed per functional unit (FU = 1 kg of dried beans).
Table 2. Inventory data of the analyzed system, expressed per functional unit (FU = 1 kg of dried beans).
Inputs
1. Water consumption (m3)
        a.
Tap water
0.013
        b.
Irrigation water (from a river)
0.294
2. Land occupation (m2/y)11.3
3. Electricity consumption (kWh)1.278
4. Plastic consumption (g)55.1
5. Rope consumption (g)1.088
6. Diesel (g)55.406
7. Fertilizer consumption (g)
        a.
Organic carbon
4.146
        b.
Calcium oxide
50.362
        c.
Magnesium oxide
19.042
        d.
Milk
87.051
        e.
Organic matter
128.042
Outputs
1. Plastic wastes (to recycling) (g)58.13
2. Cardboard wastes (to recycling) (g)0.011
3. Urban solid wastes (to landfill) (g)1.088
4. Wastewater (to treatment) (m3)0.174
5. Emissions to the atmosphere derived from vegetable waste incineration (g)
        a.
CO2
276.92
        b.
CH4
0.517
        c.
CO
10.86
        d.
N2O
0.014
        e.
NOx
0.498
        f.
SOx
0.124
        g.
Butane
0.543
        h.
Propane
0.543
        i.
Ethane
0.543
        j.
NH3
0.140
6. Emissions to the atmosphere derived from organic waste composting (g)
        a.
CH4
2.394
        b.
N2O
0.144
        c.
NH3
0.144
7. Diesel emissions to the atmosphere (derived from diesel combustion) (g)
        a.
CO2
177.35
        b.
CH4
0.009
        c.
N2O
0.009
        d.
N2
630.13
        e.
H2O
66.51
        f.
O2
107.49
        g.
CO
0.423
        h.
HC
0.786
        i.
NOx
2.318
        j.
SO2
0.645
8. Emissions to soil derived from the application of fertilizers (g)
        a.
Magnesium oxide
3.808
        b.
Calcium oxide
10.065
9. Emissions to the atmosphere derived from the application of fertilizers (g)
        a.
NH4+
0.104
        b.
NO2
0.011
        c.
N2O
0.012
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MDPI and ACS Style

Pérez, R.; Fernández, C.; Laca, A.; Laca, A. Evaluation of Environmental Impacts in Legume Crops: A Case Study of PGI White Bean Production in Southern Europe. Sustainability 2024, 16, 8024. https://doi.org/10.3390/su16188024

AMA Style

Pérez R, Fernández C, Laca A, Laca A. Evaluation of Environmental Impacts in Legume Crops: A Case Study of PGI White Bean Production in Southern Europe. Sustainability. 2024; 16(18):8024. https://doi.org/10.3390/su16188024

Chicago/Turabian Style

Pérez, Reina, Cecilia Fernández, Amanda Laca, and Adriana Laca. 2024. "Evaluation of Environmental Impacts in Legume Crops: A Case Study of PGI White Bean Production in Southern Europe" Sustainability 16, no. 18: 8024. https://doi.org/10.3390/su16188024

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