Legume–Durum Wheat Cropping Systems for Sustainable Agriculture: A Life Cycle Assessment Systematic Literature Review

Abstract

Global sustainability challenges call for assessing the environmental impacts of agricultural production systems, which are crucial to meeting the nutritional demands of a growing global population. This study uses the PRISMA model and a checklist to provide a systematic literature review of LCA studies on durum wheat and legume cultivation; it highlights the impacts of monoculture cultivation with crop rotation on key environmental indicators. An analysis was conducted to examine the environmental burdens of these crops under conventional and organic systems and explored how using different functional units (mass- or area-based) influences the environmental outcomes. The results reveal that integrating legumes into crop rotations significantly enhances environmental sustainability by reducing reliance on synthetic nitrogen fertilizers through biological nitrogen fixation, resulting in substantial environmental benefits, reaching a reduction in GWP from 6 to 45% compared to monoculture durum wheat cultivation. Conventional agriculture achieves higher crop yields; however, its reliance on chemical inputs and substantial energy consumption results in greater overall environmental impact. Conversely, while organic farming has a lower impact per unit of land, its lower productivity results in higher emissions per unit of output.

1. Introduction

Global agriculture faces the dual challenge of ensuring food security for a rapidly growing population reaching 9.7 billion people in 2050 and peaking at 10.3 billion in the middle of 2080 [1], whilst mitigating its significant environmental impact on soil, water, and climate systems.

The intensification of agricultural production in recent decades has not only contributed to increased yields but also amplified environmental damages, such as greenhouse gas emissions, soil degradation, nutrient loss, and biodiversity decline. In particular, intensive agriculture uses large amounts of antibiotics, pesticides, energy, and water, amounting to about 70% of the total water resources for food production [2]. Furthermore, the agricultural food chain generates a carbon footprint (CF) equal to 12% of total greenhouse gas emissions (GHG) [3]. Therefore, in this context, the transition towards more sustainable cultivation systems has become a key priority in political agendas and scientific research on agri-environmental issues.

From a methodological perspective, Life Cycle Assessment (LCA) has established itself as a robust tool for quantifying the environmental performance of agricultural practices and identifying effective mitigation strategies along the entire production chain. Among these strategies, crop rotation, particularly between legumes and durum wheat (DW), has been recognized as pivotal practice for improving soil fertility, optimizing nutrient use efficiency, and reducing the overall environmental impact of cropping systems.

DW, one of the crops evaluated in this study, constitutes the predominant cereal crop cultivated across the Mediterranean basin [4]. In the agricultural year 2024/2025, global wheat cultivation reached over 819 million tons, with a forecast for 2025/2026 equal to 827 million tons on 220 million hectares (ha) [5,6], and within this production, DW reached 34 million tons on 13.7 million ha [7].

Meanwhile, global legume production hit around 97 million tons in the agricultural year 2024/2025 [8] with an average yield of 1 t/ha; these crops are significant for their nutritional value and for sustainable agronomic practices generated, such as nitrogen fixation, which reduces the amount of fertilizer needed and thus lowers GHG for the next crop from 11 to 25% depending on the legume considered [9].

Beyond these agronomic benefits, legumes represent a more sustainable raw ingredient than DW for producing pasta and other plant-based foods [10,11]. In this context, adopting sustainable practices based on legume cultivation can further support the Sustainable Development Goals (SDGs), from ensuring healthy lives (SDG 3) to promoting sustainable agriculture (SDG 2), combating climate change (SDG 13), and fostering inclusive labor (SDG 8) [12]. Therefore, the agri-food chain faces the dual objective of increasing production while reducing environmental impacts.

Since the use of cereals and legumes in crop rotation practices supports the environmental transition of agricultural systems, moving from conventional monocultures to diverse sustainable agroecosystems in Mediterranean, as underlined Denora et al. [13], the authors of the current revision paper opted to focus on both crops, DW and legumes. Furthermore, the crop rotation practices contribute to improve resource acquisition, enhance soil fertility, and mitigate soil erosion [14].

Hence, crop rotation significantly increases food production whilst enriching crop protection, climate resilience, and the overall sustainability of farming systems [15]. Cereal–legume intercropping practice has gathered significant consideration due to its capacity to improve crop productivity, optimize nitrogen availability, and contribute to the long-term maintenance of soil fertility [13].

Consequently, the present study aims to provide a comprehensive review of the scientific literature, applying LCA to agricultural systems including both DW and legume crops. Particular attention is paid to identifying consistent trends, methodological approaches, and data gaps among studies assessing the life cycle impacts of these crops. By integrating and comparing evidence from peer-reviewed sources, this review clarifies the extent to which crop diversification contributes to reduce the environmental impacts of DW production and to highlight promising strategies for promoting sustainable intensification in agricultural landscapes. In particular, the environmental performance of DW and legumes was assessed considering the crop growing phase but excluding the distribution of finished/processed products. LCA provided a comprehensive framework that allows a holistic and systematic assessment of environmental impacts and the crop growing cycle. This approach enabled us to identify critical points and define improvements [16] in conventional and organic farming practices.

The authors of the current paper collected a sample of eligible articles using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol [17] to investigate the environmental impacts of both DW and legumes cultivated using both conventional and organic cropping systems, considering the same time horizon of 100 years. Thus, the goal is to analyze the environmental impact of growing DW and legumes as single crops and how crop rotation practices influence the environmental performance of these systems under different conditions. Moreover, a brief comparative and sensitivity analysis explores how varying functional units can influence environmental impacts.

Hence, this paper is structured into several sections covering the research methodology, SLR (Systematic Literature Review) results, comparative and sensitivity analysis, conclusions, and limitations and practical implications. The findings provide insights for scholars, agri-food managers, public administrators, practitioners, and stakeholders involved in promoting more sustainable and resilient agricultural practices within the agricultural supply chain.

2. Research Methodology

To achieve the general goal outlined in the introductory section, the methodology was structured as five steps, as shown in Figure 1. In particular, in the first step, the authors focused on the identification of two crops linked by crop rotation, such as legumes and DW, a predominant cereal crop cultivated across the Mediterranean [4].

Subsequently, through Boolean research, the scholars collected a raw sample by consulting different databases and undertook SLR according to the PRISMA protocol, which was mainly used for reporting the review process and the article selection. The eligible products collected were divided into three main topics (DW, legumes, and crop rotations). In the fourth step, the main findings of the environmental impacts of the different cropping systems per crop selected and the advantages of the crop rotation are underlined. Finally, the scholars carried out a comparative and sensitivity analysis to investigate how changing FUs influences environmental damages.

2.1. Systematic Literature Review

This systematic review was performed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. In particular, the SLR methodology theorized by Page et al. [17] was used to select main articles that dealt with the application of the LCA methodology for the environmental assessment of DW and legume cultivation.

In accordance with the PRISMA guidelines, a systematic literature search was conducted using the predefined keywords reported in

Table 1. Keyword and string combination for quantitative analysis of review.

Keywords 1	Boolean Operator	Keywords 2	Boolean Operator	Keywords 3	Boolean Operator	Keywords 4	Covered Period	Number of Articles Found on Scopus	Number of Articles Found on WoS	Additional Records Identified Through Other Sources
Life Cycle Assessment	AND	durum wheat	AND	conventional	-	-	2022–2025	8	8	4
		durum wheat		organic		-	2022–2025	10	15
		agriculture		durum wheat		-	2022–2025	19	32
		raw material		durum wheat		-	2022–2025	1	0
		legumes	AND	conventional	AND	-	2016–2026	21	17
		legumes		organic		-	2016–2026	18	20
		legumes		agriculture		-	2016–2026	42	43
		agri-food		organic agriculture		-	2016–2026	5	5
		raw material		legumes		-	2016–2026	4	5
		durum wheat	AND	legumes	AND	crop rotation	2016–2026	1	1
TOTAL								129	146	4

. The corresponding search strings (mentioned after Table 1) were developed and applied to the Scopus and Web of Science databases. The search process started on 1 March 2025 and was carried out manually through targeted database queries, without the use of automated screening tools. Initial records were retrieved in March 2025, and the search was subsequently updated several times until 30 September 2025; however, only studies with online availability or a publication date up to 30 June 2025 were considered. In this study, the SLR methodology was applied, which supports systematic, replicable, and clear methods for identifying, selecting, and discussing relevant research and for building a dataset of the most relevant impacts related to the crop production supply chain [18,19]. For this reason, in the first step, the combination of several keywords to be used was identified, as well as relevant topics for a comprehensive search strategy [20].

The terminology “Life Cycle Assessment (LCA)” was identified as the most representative word fixed in the several string keywords developed using the Boolean search terms. Specifically, the keywords included in Table 1 were used for developing the strings.

Therefore, the following strings were used:

-

life cycle assessment AND durum wheat AND conventional,

-

life cycle assessment AND durum wheat AND organic,

-

life cycle assessment AND agriculture AND durum wheat,

-

life cycle assessment AND raw material AND durum wheat,

-

life cycle assessment AND legumes AND conventional,

-

life cycle assessment AND legumes AND organic,

-

life cycle assessment AND legumes AND agriculture,

-

life cycle assessment and agri-food and organic agriculture,

-

life cycle assessment and raw materials and legumes,

-

life cycle assessment AND durum wheat AND legumes.

In addition, as an inclusion criterion, only publications in English were considered, besides 4 records retrieved through other sources; thus, the authors of the current paper identified 279 records. Although the review protocol was not formally registered, the article search strategy and subsequent analytical approach was designed to be completely replicable, including studies addressing similar research topics.

Methodologically, the database search considered the study abstract, title, and full text to evaluate many details and thus obtain a systematic review of the literature that is objective, reproducible, and replicable, with a complete sample size appropriate for this research’s purposes and the most indexed studies.

Figure 2 displays the PRISMA diagram applied for developing an adequate and coherent sample of eligible articles to be analyzed in this SLR [21].

This kind of quali-quantitative screening protocol used for the literature review provides a rigorous and transparent review process [17]. This protocol ensures scholars obtain high levels of identification and screening and consider studies relevant to the topic analyzed [22,23], providing a comprehensive and coherent process focused on peer-reviewed journal articles, book chapters, and conference papers published in English [24].

Through this selection process, from a preliminary sample, 279 papers were found. Firstly, the authors of the current paper identified 135 duplicate records from both databases and removed them before the screening phase. From the 144 remaining records, 15 review articles were excluded prior to screening. In particular, 12 reviews were inconsistent with the study and the sample to be developed or were already included, and 3 represented the sample review papers in the current study, as shown in

Table 2. Summary of sample review papers.

Authors	Title	Journal	Keywords	Topics Analyzed
[25]	Ecological principles underlying the increase of productivity achieved by cereal-grain legume intercrops in organic farming. A review	Agronomy for Sustainable Development	Environmental resource use Eco-functional intensification Cereal–grain legume intercrop Protein concentration Weed Yield	- Revision of the potential advantages of eco-functional intensification in organic agricultural - Evaluation of the effects related to the intercropping of cereal and grain legume species
[26]	Representing crop rotations in life cycle assessment: a review of legume LCA studies	International Journal of Life Cycle Assessment	Legumes Crop rotations Functional units Allocation Multifunctionality Nitrogen cycling	- Attributional LCA of a single crop in a rotation and of an entire rotation sequence with simple or complex aggregated FU - Consequential LCA of introducing legumes into rotations
[4]	A systematic literature review of Life Cycle Assessment in the durum wheat sector	Science of the Total Environment	Agriculture Food production Durum wheat cultivation Life Cycle Assessment Climate change Environmental impact	- Evaluation of the main environmental impacts on the cultivation phase of the DW sector - Discussion of the methodological aspect to provide useful insight on performing LCA in the agri-food supply chain (e.g., pasta, bread)

, as they dealt with topics similar to those of this study.

Bedoussac et al. [25] performed a literature review with field data collected since 2001 under European pedo-climatic conditions to generalize the benefits of intercropping cereals and grain legumes in organic farming. Conversely, a few years later, Costa et al. [26] conducted a literature review on legumes and crop rotations, analyzing how intercropping system effects are represented in legume LCA studies, distinguishing attributional LCA on crop rotation and consequential LCA on introducing legumes into the rotation system. Zingale et al. [4] analyzed both conventional and organic DW cultivation, evaluating agronomic practices and environmental impacts. The LCAs covered pasta and bread supply chains, highlighting that the agricultural phase was the main environmental hotspot.

Therefore, through this SLR and lack of applications to our knowledge, a gap in the scientific literature remains due to several factors:

• The results of Bedoussac et al. [25] and Costa et al. [26] were not found following SLR methodology or produced following the PRISMA model subsequently theorized by Page et al. in 2021 [17].
• Bedoussac et al. [25] addressed the grain–legume rotation but not LCA studies, and Costa et al. [26] considered LCAs on crop rotation to analyze only legumes studies.
• The results of Zingale et al. [4] were found following the SLR methodology and the PRISMA model but only with regard to the DW sector. The same review has been adopted as a reference point for the topic of DW in this paper. Therefore, 14 articles, analyzed by Zingale et al. [4] (see Table S1 in Supplementary Materials), were excluded from the sample to avoid duplication of previously conducted analyses. Nevertheless, their review served as a starting point and reference for the present study.

Accordingly, two asymmetric time windows were defined during the literature search and screening process to capture relevant studies.

Given the aforementioned gap, the authors of this paper addressed LCAs based on crop rotation as a whole, analyzing the individual crops involved (DW and legumes) under both conventional and organic systems, highlighting the role of crop rotation in mitigating negative environmental impacts while optimizing crop productivity.

Moreover, since the current systematic review was restricted to peer-reviewed articles published in English, 2 other papers were identified as conference proceedings, short surveys, or conference proceedings and then excluded.

Subsequently, after a first reading of the titles and abstracts of the remaining 127 papers, 106 papers were identified that address themes different from those that the current study is interested in, use methodologies other than LCA, or provide no useful elements for the objectives of the review. Therefore, these 106 records were excluded. Then, another paper was removed because it did not include an LCA study. Therefore, 20 articles were considered in the current SLR as being consistent with the review’s objective.

Regarding the production timeline, the sample covered two asymmetric time windows that were defined during the literature search and screening process to capture relevant studies:

(a)

From 2022 to 2025 for DW: the review by Zingale et al. [4] published in June 2022, that analyzed 14 articles (see Table S1 in Supplementary Materials), serves as a basis for DW sector analysis and covers the years of publication from 2006 to 2022;

(b)

From 2016 to 2025: the authors considered this range for legume and crop rotation.

In summary, during the screening and eligibility phases, 259 records were excluded for the following reasons:

-

Publication type: 17 (review articles or conference abstracts/papers).

-

Papers not published in English: 0.

-

Not relevant to durum wheat (DW) or DW-derived food products: 106.

-

No application of Life Cycle Assessment (LCA), insufficient methodological details, or incomplete inventory data: 1.

-

Duplicate records: 135.

For further information on the applied PRISMA model, please consult the checklist in the Supplementary Materials (Table S2), which includes the various sections for each item considered. However, since the authors conducted a manual search without the use of automatic tools and did not archive the data on any platform, they deemed it appropriate not to record the SLR protocol.

Finally, the ROBIS tool [27] was used to assess the risk of bias in the SLR conducted. Therefore, a risk of bias assessment was assigned for each of the four domains (1. study eligibility criteria; 2. identification and selection of studies; 3. data collection and study appraisal; 4. synthesis and results). The questions should be answered as ‘‘yes’’, ‘‘probably yes’’, ‘‘probably no’’, ‘‘no’’, or ‘‘no information’’. The possible ratings were “high”, “low”, or “unclear”. An overall risk of bias rating was assigned to the review, as instructed by the tool. The results of the assessment are included in the Supplementary Materials (Table S3).

2.2. Comparative and Sensitivity Analysis on Functional Unit Change

At this stage, the authors carried out a comparative and sensitivity analysis to address the uncertainty in environmental impact assessments. When conducting LCA studies on agricultural products, it is recommended that both mass- and area-based FUs are used, particularly in evaluating crops with very different yields in conventional and organic systems [28,29].

A multifunctional approach was adopted, using two FUs, mass-based (kilogram of crop) and area-based (hectare of land), to ensure a holistic evaluation of these cropping systems. The area-based approach enabled the assessment of land use efficiency, while the mass-based approach evaluated environmental impact relative to crop yield. Both perspectives offer complementary insights into sustainability. For this analysis, the studies of Tidåker et al. [9] and Verdi et al. [30], which focus on conventional and organic farming, were selected because they compared conventional and organic cropping systems through three of the most significant environmental indicators in agriculture. Based on GWP, LU, and EP assessed by the authors and referring to 1 kg of crop (mass-based), the yield of DW and legumes was considered by the authors of this SLR to assess the aforementioned impacts, referred to 1 hectare of land (area-based).

3. Review of the LCA Literature

In this section, the authors of this SLR present and discuss the results. Since aggregating articles by topic [19,20] facilitates analysis by scholars, firstly, the authors of this study distinguished three main topics depending on the objectives of the reviewed studies and the LCAs, as reported in Table 3 and the subsequent sections:

(1)

LCA, durum wheat;

(2)

LCA, legumes;

(3)

LCA, crop rotation.

Table 3. Summary of selected articles based on three main topics.

Main Topics	Number of Articles	Authors
Life cycle assessment of DW	4	[30,31,32,33]
Life cycle assessment of legumes	8	[9,10,34,35,36,37,38,39]
Life cycle assessment of crop rotation	8	[40,41,42,43,44,45,46,47]

Table 3. Summary of selected articles based on three main topics.

Main Topics	Number of Articles	Authors
Life cycle assessment of DW	4	[30,31,32,33]
Life cycle assessment of legumes	8	[9,10,34,35,36,37,38,39]
Life cycle assessment of crop rotation	8	[40,41,42,43,44,45,46,47]

As shown in Table 3, 20 articles were reviewed, classified into the above-mentioned three main topics, and covered a time period from 2016 to 2025. Evidently, one (5%), three (15%), and sixteen (80%) papers were published in 2016, in 2017, and from 2019 to 2025, respectively; this shows that this topic is relatively of interest, especially in recent years.

Based on this bibliometric review, the following subsections present the impact analysis and findings from the current SLR of the LCA-based literature on DW, legume supply chains, and crop rotation, summarizing the results in Table 4, Table 5 and Table 6 for each topic discussed; in particular, the data concerned the agricultural phase, and hence, the other derived products (bread, pasta, and so on) were not considered. For the main results of this SLR, the authors compiled Table 7 and Table 8. Given the heterogeneity of the IAMs and FUs, only the midpoint impact categories expressed in the same units of measurement were retained for comparisons between studies or converted where possible in Table 8, with all other results being summarized qualitatively only in Table 7.

3.1. LCAs of DW

In this section, the authors analyzed the DW LCAs published after 2022, since Zingale et al. [4] in 2022 reviewed 14 papers on DW LCAs. Verdi et al. [30] analyzed a conventional and ancient organic wheat variety cultivation produced in Tuscany (Italy). In a cradle-to-grave LCA, considering 1 kg of an ancient Italian DW variety as an FU, lower emissions were observed from organic DW (0.360 kg CO₂eq./kg vs. 0.580 for conventional), although LU was higher (7.53 m² vs. 3.89 m² per kg of grain). Furthermore, organic systems display yields that are more than 40% lower than those of conventional systems, resulting in greater impacts; conversely, conventional systems show the worst environmental performance in almost all impact categories due to the production and consumption of non-renewable resources.

Also, Vinci et al. [31] assessed the environmental and social sustainability of organic DW cultivated in the Tuscany region (Italy) from a cradle-to-gate approach using 1 ha as the FU. The main environmental pressures arising from organic wheat production relate to LU, TET, and FET. The sowing phase accounted for over 70% of the impact in 13 of the 16 categories assessed, but a 40% reduction in GWP value was achieved through organic seed cultivation. This results in a positive environmental outcome in terms of GWP for wheat cultivation (−1.42 × 10² kg CO2eq./1 ha of land). Instead, Paolotti et al. [32] compared a conventional and organic cultivation system of DW for pasta production. The results revealed that the agricultural phase of DW cultivation contributes to the greatest impact within the overall pasta production systems. In particular, the scholars, according to the IPCC 2013 methodology and the cradle-to-gate approach, found that DW cultivation accounted for 76% of total emissions (1.042 kg CO₂eq./kg of pasta). Moreover, organic farming reduced CO₂ emissions by 38.1% compared to conventional methods. Indeed, excluding land occupation, organic cultivation had a lower impact for almost all impact categories. However, the DW system reduced synthetic fertilizers and pesticides, benefiting biodiversity, soil quality, and non-renewable energy conservation [32].

Moreover, different wheat farming systems were assessed by di Cristofaro et al. [33]. Comparing conventional, organic, and biodynamic agricultural systems of two old DW varieties, it emerged that conventional agriculture exhibited the lowest impacts per unit of biomass and the poorest performance per unit of area and income; organic agriculture showed intermediate environmental performance among the systems studied, both in terms of yield and per unit of area and lowest impacts and damages, in terms of gross income. Lastly, biodynamic agriculture (a self-sufficient agricultural system that considers the farm to be a living organism) recorded the highest impacts among the systems studied per unit of yield, primarily due to damage to ecosystem quality, and the best environmental performance in terms of resource depletion, representing the most environmentally friendly agricultural system, per unit of area [33]. Table 4 summarizes the main outcomes described above.

Table 4. Summary of main findings of DW articles.

Authors	Main Findings
[30]	- Comparison among conventional and ancient organic wheat variety cultivation; - Organic DW presents a decrease in DW equal to 37.93% (0.360 kg CO2eq./kg) compared to conventional DW (0.580 kg CO2eq./kg).
[32]	- Comparison among conventional and organic cultivation of DW for pasta production; - The agricultural phase is the most impactful (1.042 kg CO2eq./kg pasta); - Organic farming reduced CO2 emissions by 38.1%.
[33]	- Comparison of biodynamic, organic, and conventional cultivation of DW; - Biodynamic farming recorded the highest impact per unit of yield and in terms of resource depletion but the lowest per area, compared to conventional and organic systems.
[31]	- Assessment of organic durum wheat production; - The sowing phase was responsible for 70% of the impact in 13 out of 16 categories but had a positive environmental impact in terms of GWP (−1.42 × 102 kg CO2eq/ha).

Table 4. Summary of main findings of DW articles.

Authors	Main Findings
[30]	- Comparison among conventional and ancient organic wheat variety cultivation; - Organic DW presents a decrease in DW equal to 37.93% (0.360 kg CO2eq./kg) compared to conventional DW (0.580 kg CO2eq./kg).
[32]	- Comparison among conventional and organic cultivation of DW for pasta production; - The agricultural phase is the most impactful (1.042 kg CO2eq./kg pasta); - Organic farming reduced CO2 emissions by 38.1%.
[33]	- Comparison of biodynamic, organic, and conventional cultivation of DW; - Biodynamic farming recorded the highest impact per unit of yield and in terms of resource depletion but the lowest per area, compared to conventional and organic systems.
[31]	- Assessment of organic durum wheat production; - The sowing phase was responsible for 70% of the impact in 13 out of 16 categories but had a positive environmental impact in terms of GWP (−1.42 × 102 kg CO2eq/ha).

3.2. LCA of Legumes

Legumes (called pulses in many of the studies in this review) are a more sustainable and healthier alternative to DW. Nutritionally, legumes are highly valued for their protein and energy content [48].

Overall, legumes have significant environmental benefits, particularly nitrogen fixation, which improves soil health and biodiversity, resulting in reduced GHG and water pollution. Their smaller carbon footprint results from reduced resource use. Additionally, legumes used in crop rotation reduce the need for fertilization activities for subsequent crops, naturally enriching the soil [9]. Moreover, the cultivation of legumes, due to the biological fixation of atmospheric nitrogen, results in significantly lower emissions per kilogram (0.18 kg CO₂eq.) than DW and other food products. However, their yield (2014.5 kg/ha) is lower than that of DW (5340 kg/ha), leading to slightly higher LU per kg (0.53 m² vs 0.48 m²) [10].

More recently, comparative analyses by Svanes et al. [34], performing LCA through the ILCD 2011 Midpoint and ReCiPe 2016 Midpoint and Endpoint methodology, showed that dried legumes (peas and faba beans) generate just 0.55–0.57 kg CO₂eq./kg, which is far lower than meat (19–38 kg CO₂eq./kg), dairy (1.2–22 kg CO₂eq./kg), seafood (0.8–22 kg CO₂eq./kg), and even grain products (0.66–0.72 kg CO₂eq./kg). For instance, hamburgers generate 21.7 kg CO₂eq./kg, vastly exceeding peas (0.57 kg CO₂eq./kg) and faba beans (0.62 kg CO₂eq./kg). WC also favors legumes: peas and broad beans use 0.006 m³/kg and 0.003 m³/kg, respectively, compared to 0.25 m³/kg for hamburger production. This highlights legumes as a more sustainable protein source compared to animal-based alternatives.

Moreover, Treu et al. [35] compared CF and LU for plant- and animal-based products. They found that legumes have among the lowest emissions (0.32 kg CO₂eq./kg and 0.35 kg CO₂eq./kg for conventional and organic systems, respectively) and LU (0.36 m²/kg and 0.40 m²/kg, respectively). Overall, legumes present both environmental and health benefits, making them a key component of sustainable agriculture and diets.

Also, within the context of Northen Europe, as Svanes et al. [34] discussed above, Tidåker et al. [9] evaluated five Swedish legumes using the LCA methodology. Specifically, they investigated yellow peas, gray peas, faba beans, common beans, and lentils cultivated in conventional and organic farming systems. With an FU equal to 1 kg of legumes, the following environmental results for the cultivation of Swedish legumes based on the examined crop emerged:

• The GHG values fell within the range of 0.18–0.44 kg CO₂eq. in the conventional system;
• The LU value for conventional cultivation was 3.1–5.9 m²;
• The GHG values fell within the range of 0.18–0.26 kg CO₂eq. in the organic system;
• The LU for an organic cultivation system covered the range from 3.2 to 4.9 m²;
• No pesticides were used in organic agriculture, but 0.28–0.65 g of pesticides was used in conventional agriculture.

The most significant contributors to GHG in legume cultivation are direct and indirect N₂O emissions. Additionally, higher energy consumption and GHG have been assessed for the cultivation of faba beans and yellow peas due to their lower yields, leading to higher diesel consumption per kilogram of harvested product. However, the cultivation of legumes reduced the need for chemical fertilization for subsequent crops, with a 12–23% reduction in climate change damage, making intercropping very beneficial for soil health.

Similarly, Boakye-Yiadom et al. [36] compared the conventional and organic cultivation of peas and found a higher GWP impact in conventional cultivation (respectively, 0.98 kg CO₂eq and 0.88 kg CO₂eq.). These values were affected by the indicator of indirect land use change (ILUC), which has a significant influence on several environmental categories. Excluding ILUC, GWP values are reduced to 0.52 kg CO₂eq. (conventional) and 0.44 kg CO₂eq. (organic), aligning with previous studies. Using the CML-IA methodology and two different FUs, other studies, such as that of Araujo et al. [37], investigated the environmental impacts of common beans and pigeon peas grown in the Caribbean. Specifically, Araujo et al. [37] examined the environmental impact of common beans and pigeon peas grown in the Caribbean using the CML-IA methodology and considering two FUs: 1 ton of legumes and 1 hectare of cultivated land. The use of two FUs is in line with the methodology of the current study. They analyzed conventional cultivation with rhizobial inoculation, a technique that increases nitrogen fixation. The importance of this innovative agricultural method leads to a decrease of 19% in environmental loads by hectare and 21% by tons for common beans and a reduction of 12% by hectare and 32% by tons for peas. Definitively, these results demonstrate how higher crop yields lead to overall environmental benefits.

Pérez et al. [38] conducted a cradle-to-gate LCA of the small-scale organic cultivation of white “Faba Asturiana” beans (1 kg dry beans as a FU) in Northern Spain, with a CF of 1.20 kg CO₂eq./kg. This study proposed alternative energy scenarios to reduce electricity consumption by more than 40% and sustainable practices such as composting organic waste to make cultivation less impactful. Regarding this point, to minimize impacts, two key improvements are needed: increasing the productivity of legume crops, especially in organic production, and decreasing environmental loads through the adoption of renewable energy, which could reduce CF from 1.20 kg CO₂eq./kg to 0.70 kg CO₂eq./kg in more sustainable scenarios.

Finally, Narote et al. [39] analyzed three burger-based dishes (legume, chicken, and beef) to provide a holistic view of their impacts. In particular, focusing only on the agricultural stage related to legume production, as also noted earlier in the DW discussion section, the main impacts were observed to occur upstream, in the production of the raw materials for burgers. Specifically, in cultivation phase legume-based burgers (considering black chickpeas as raw ingredient), the documented GWP impact is lower than the other raw materials compared, as shown in Table 5, and it is equal to 12% of those recorded by chicken (3.60 × 10⁻¹ kgCO₂eq.) and equal to 1% of those quantified by beef (4.58 kgCO₂eq.). These results indicate that transitioning toward plant-based foods, such as legume-based burgers, could represent an effective strategy to mitigate climate and promote sustainable food systems [38].

Table 5 summarizes the main outcomes described above.

Table 5. Summary of main findings of legume articles.

Authors	Main Findings
[35]	- Comparison among plant- and animal-based products; - Legumes presented the lowest emissions (0.32 kg CO2eq./kg and 0.35 kg CO2eq./kg for conventional and organic systems, respectively) and LU (0.36 m2/kg and 0.40 m2/kg, respectively) compared to animal-based products.
[37]	- Comparison among conventional legume cultivation and legume cultivation with rhizobial inoculation; - Legume cultivation with rhizobial inoculation recorded the following decreases compared to conventional cultivation: (a) 19% in environmental loads by hectare and 21% by tons for common beans; (b) 12% by hectare and 32% by tons for peas.
[10]	- Comparison of chickpea pasta production with DW pasta production; - Focus only on the agricultural phase; - The LU per kg was compared between legumes and DW (0.53 m2 vs. 0.48 m2); - Chickpeas (Bulgaria) require higher LU than DW for significantly lower LU of chickpeas compared to DW.
[9]	- Comparison among conventional and organic cultivation of legumes; - Cultivation of 5 legumes with conventional and organic farming showed no significant differences: (a) GHG equals 0.18–0.44 kg CO2eq. (conventional) and 0.18–0.26 kg CO2eq. (organic); (b) LU equals 3.1–5.9 m2 (conventional) and 3.2–4.9 m2 (organic).
[34]	- Compared to legumes and other food protein sources, dried legumes showed an impact (GWP: 0.55–0.57 kg CO2eq./kg) that was lower than that of meat, seafoods, and cereals.
[36]	- Comparison of pea cultivation in conventional and organic systems; - GWP equals 0.98 kg CO2eq. and 0.88 kg CO2eq. for conventional and organic systems, respectively.
[38]	- Evaluation of white faba beans in different scenarios; - Reduction in GWP from 1.20 kg CO2eq./kg in the baseline scenario to 0.70 kg CO2eq./kg in more sustainable scenarios.
[39]	- Comparison of meat burgers and legume burgers; - The agricultural phase of legume cultivation for burgers showed a lower GWP than that of other raw materials (equal to 12% of the impacts recorded for chicken burgers and to 1% of those quantified for beef burgers).

Table 5. Summary of main findings of legume articles.

Authors	Main Findings
[35]	- Comparison among plant- and animal-based products; - Legumes presented the lowest emissions (0.32 kg CO2eq./kg and 0.35 kg CO2eq./kg for conventional and organic systems, respectively) and LU (0.36 m2/kg and 0.40 m2/kg, respectively) compared to animal-based products.
[37]	- Comparison among conventional legume cultivation and legume cultivation with rhizobial inoculation; - Legume cultivation with rhizobial inoculation recorded the following decreases compared to conventional cultivation: (a) 19% in environmental loads by hectare and 21% by tons for common beans; (b) 12% by hectare and 32% by tons for peas.
[10]	- Comparison of chickpea pasta production with DW pasta production; - Focus only on the agricultural phase; - The LU per kg was compared between legumes and DW (0.53 m2 vs. 0.48 m2); - Chickpeas (Bulgaria) require higher LU than DW for significantly lower LU of chickpeas compared to DW.
[9]	- Comparison among conventional and organic cultivation of legumes; - Cultivation of 5 legumes with conventional and organic farming showed no significant differences: (a) GHG equals 0.18–0.44 kg CO2eq. (conventional) and 0.18–0.26 kg CO2eq. (organic); (b) LU equals 3.1–5.9 m2 (conventional) and 3.2–4.9 m2 (organic).
[34]	- Compared to legumes and other food protein sources, dried legumes showed an impact (GWP: 0.55–0.57 kg CO2eq./kg) that was lower than that of meat, seafoods, and cereals.
[36]	- Comparison of pea cultivation in conventional and organic systems; - GWP equals 0.98 kg CO2eq. and 0.88 kg CO2eq. for conventional and organic systems, respectively.
[38]	- Evaluation of white faba beans in different scenarios; - Reduction in GWP from 1.20 kg CO2eq./kg in the baseline scenario to 0.70 kg CO2eq./kg in more sustainable scenarios.
[39]	- Comparison of meat burgers and legume burgers; - The agricultural phase of legume cultivation for burgers showed a lower GWP than that of other raw materials (equal to 12% of the impacts recorded for chicken burgers and to 1% of those quantified for beef burgers).

3.3. LCAs of Crop Rotation

In this section, the authors compare the effects of crop rotation in conventional and organic agriculture with monoculture. Crop rotation involves growing different crops in a specific order in the same field, such as legumes before DW. The use of legumes in crop rotation allows nitrogen to be supplied to the field. Rotating legumes with other crops offers the dual benefit of growing legumes without an additional nitrogen fertilizer, plus a nitrogen credit stabilized for the subsequent non-legume crop.

This benefit depends on the balance between the fixation of atmospheric nitrogen (N) and the removal of nitrogen in the form of grains. According to Loges et al. [49], the quantification of N fixation of a legume depends on the yield of the harvested grain (DM_legume), the concentration of N in the legume (N%), and the percentage of the fixed N of the total N of the legume (P_fix), as Equation (1) shows:

N_fix = DM_legume ∗ N % ∗ P_fix

(1)

P_fix depends on the percentages of fixed N in the legume shoot, in the legume root, transferred through soil and animals to the grasses, and immobilized in the soil (Equation (2)):

P_fix = P_shoot ∗ (1 + P_root + P_trans-soil + P_trans-animal + P_immobil)

(2)

Legumes play a crucial role in sustainable agriculture by fixing and accumulating nitrogen in the soil, reducing the need for synthetic fertilizers in subsequent crops such as DW. This practice significantly lowers GHG, with nitrogen fertilizer needs decreasing by 30 kg per hectare and emissions reducing by 12–23% [9]. Comparing the total GHG from monoculture systems reveals the following:

• Peas generated 529 kg CO₂eq./ha;
• DW produced 676 kg CO₂eq./ha;
• Rapeseeds had the highest emissions at 841 kg CO₂eq./ha.

The high emissions of DW monoculture were attributed to fertilizer production and transport (23–28% of total CO₂eq. emissions) and DW consumption (16–23%). However, shifting to an intercropping system significantly reduced emissions, with DW cultivated after peas emitted only 586 kg CO₂eq./ha [40]. Crop rotation also enhances DW yield. For instance, growing DW after broad beans improves yield compared to with monoculture.

Ali et al. [41] established that CF after crop rotation decreases from 1614.41 kg CO₂eq./ha to 1487.41 kg CO₂eq./ha. Moreover, conventional farming emits 80% more CO₂eq. per hectare than organic farming, primarily due to fertilizer use (2375.8 kg CO₂eq./ha vs. 1298.1 kg CO₂eq./ha).

In the same year, a six-year crop rotation experiment (FAST system) was carried out in Switzerland using the SALCA (Swiss Agriculture Life Cycle Assessment) method. It revealed that TET was significantly lower in conventional farming (972 m² ha⁻¹) than in organic farming (4891 m² ha⁻¹) due to manure application in organic systems [42]. Moreover, organic farming showed a reduction in GWP equal to 45% and in ME equal to 82% compared to conventional farming [41].

In Southern Italy, DW–vetch rotation outperformed DW monoculture in 16 out of 18 environmental impact categories (through the ReCiPe midpoint method), reducing climate change impacts by 24.3%. Fertilization was the most significant environmental factor (68.8% impact), but introducing vetch reduced the overall environmental impact by 36% due to nitrogen fixation [43].

Lago-Olveira et al. [45] used the ReCiPe midpoint method and analyzed DW–chickpea rotations, observing environmental impact reductions in GWP and freshwater ecotoxicity of 18–20%, while other impact categories decreased by 6–13%. An additional benefit was the increased organic matter input from chickpea straw, which improved soil health and further reduced GHG.

An interesting application by Costa et al. [44], carried out according to LCA methodology using the Nutrient Density Unit (NDU) as an FU, investigated the introduction of legumes in crop rotations across Scotland, Italy, and Romania. The results reported substantial sustainability improvements:

• In Scotland, GWP decreased from 4.99 to 2.87 kg CO₂eq.;
• In Italy, 9 out of 16 impact categories improved;
• In Romania, 14 out of 16 impact categories improved, with GWP decreasing from 6.81 to 4.78 kg CO₂eq.

Subsequently, Lago-Olveira et al. [46] evaluated the environmental performance of wheat cultivation in rotation with chickpeas and lentils compared to the conventional monoculture system in the Mediterranean region of Morocco. As shown in Table 5, crop rotation practices (R1 and R2) represent the most environmentally friendly cropping systems for most impact categories, regardless of the FU considered, compared to R3 (monoculture). However, trade-offs were observed between functional units, to the detriment of systems with lower grain yields when the productive functional unit was selected [45]. Thus, while rotation systems R1 (chickpea–wheat) and R2 (lentil–wheat) outperform monoculture in terms of hectares per year (sharing a similar impact), R1 shows the worst environmental profile in terms of ME and global Potential Species Loss (PDF) per kg of grain. In terms of land management, no significant differences were identified between cropping systems (R1, R2, and M) in terms of PDF. In contrast, crop rotations showed an improved SOC profile, suggesting additional ecosystem benefits associated with SOM (e.g., nutrient and water retention and climate change mitigation). Mineral fertilizer production and application appear to be the most critical aspects across all impact categories, except for WS, PDF, and SOC, for which irrigation, soil transformation, and land use were the determining factors, respectively [46]. This study highlights crop rotation benefits; in particular, it reduced stratospheric ozone depletion by 34% and water scarcity by 50%. Therefore, this study aims to provide valuable insights for more sustainable agriculture in Morocco and similar Mediterranean regions [46]. According to this article, the co-authors will focus on the multiple integration of ecosystem services in future applications. This will enable them to obtain a more comprehensive assessment that can be used to guide and inform decision-making for stakeholders and practitioners in the agricultural chain.

The results show that crop rotation practices represent the most environmentally friendly cropping systems for most impact categories, regardless of the FUs considered.

In the same year, Zingale et al. [47] evaluated two Sicilian DW intercropping systems: an organic, low-input system with an ancient DW species (rotated with nitrogen-fixing legumes like faba beans), and a conventional, high-input system with a modern variety (rotated with bare fallow land).

The ReCiPe methodology, using a cradle-to-farm-gate approach and multiple functional units (product-, land-, and price-based functional units), shows that the organic system benefited from nitrogen fixation, reducing fertilizer dependence and GHG (0.33 kg CO₂eq./kg and 780 kg CO₂eq./ha). However, lower yields required more land. The conventional system had higher emissions (0.55 kg CO₂eq./kg and 2260 kg CO₂eq./ha) due to intensive fertilization, mechanization, and bare fallowing, which was identified as an inefficient and environmentally harmful practice [47].

To enhance sustainability, suggested improvements include the following:

• Better nitrogen management;
• Optimized crop rotations;
• Reduced tillage;
• Adoption of renewable energy sources.

In particular, Zingale et al. [47] showed a reduction greater than 60% of GWP value in a conventional rotation system without legumes compared to rotation with legumes in an organic system; they considered the fixed N of legumes as a co-product and new input of the subsequent DW crop. These findings underscore the environmental benefits of integrating legumes into agricultural systems, particularly in reducing fertilizer use, lowering emissions, and improving soil health.

Table 6 summarizes the main outcomes described above.

Table 6. Summary of main findings of crop rotation articles.

Authors	Main Findings
[40]	- Evaluation effects of crop rotation based on DW and field peas. - Monoculture generated higher impacts than crop rotation: (a) Peas: 529 kg CO2eq./ha; DW: 676 kg CO2eq./ha. (b) Crop rotation of DW with legumes generated less environmental impact than DW grown in monoculture (GWP: 586 kg of CO2eq./ha).
[41]	- Evaluation effects of crop rotation based on DW and field peas; - CF after crop rotation decreases from 1614.41 to 1487.41 kg CO2eq./ha.
[42]	- Through the application of a six-year crop rotation experiment, comparing conventional and organic cultivation, organic farming recorded a decrease in GWP equal to 45% compared to the conventional system.
[43]	- Evaluation of the effects of DW monoculture and DW–vetch crop rotation; - DW–vetch rotation reduced the environmental impacts of all impact categories by 24.3%.
[44]	- Evaluation of the effects of rotation after the introduction of legumes in three countries: Scotland, Italy, and Romania. - The results of this cultivation system recorded some sustainability improvements: (a) In Scotland, GWP decreased from 4.99 to 2.87 kg CO2eq.; (b) In Italy, 9 out of 16 impact categories improved; (c) In Romania, GWP decreased from 6.81 to 4.78 kg CO2eq.
[45]	- In comparing DW–chickpea rotation with conventional agriculture based on DW monoculture, 9 environmental impact categories recorded a reduction of up to 18–20%.
[46]	- Evaluation of the effects of 3 crop rotations (chickpea–wheat, lentil–wheat, wheat–wheat); - Rotation reduced the stratospheric ozone by 34% and water scarcity by 50%.
[47]	- Evaluation of two Sicilian DW intercropping systems (organic, low-input system with an ancient DW species and a conventional, high-input system with a modern variety); - A decrease greater than 60% in the GWP value in the conventional rotation system was observed without legumes compared to rotation with legumes in the organic system.

Table 6. Summary of main findings of crop rotation articles.

Authors	Main Findings
[40]	- Evaluation effects of crop rotation based on DW and field peas. - Monoculture generated higher impacts than crop rotation: (a) Peas: 529 kg CO2eq./ha; DW: 676 kg CO2eq./ha. (b) Crop rotation of DW with legumes generated less environmental impact than DW grown in monoculture (GWP: 586 kg of CO2eq./ha).
[41]	- Evaluation effects of crop rotation based on DW and field peas; - CF after crop rotation decreases from 1614.41 to 1487.41 kg CO2eq./ha.
[42]	- Through the application of a six-year crop rotation experiment, comparing conventional and organic cultivation, organic farming recorded a decrease in GWP equal to 45% compared to the conventional system.
[43]	- Evaluation of the effects of DW monoculture and DW–vetch crop rotation; - DW–vetch rotation reduced the environmental impacts of all impact categories by 24.3%.
[44]	- Evaluation of the effects of rotation after the introduction of legumes in three countries: Scotland, Italy, and Romania. - The results of this cultivation system recorded some sustainability improvements: (a) In Scotland, GWP decreased from 4.99 to 2.87 kg CO2eq.; (b) In Italy, 9 out of 16 impact categories improved; (c) In Romania, GWP decreased from 6.81 to 4.78 kg CO2eq.
[45]	- In comparing DW–chickpea rotation with conventional agriculture based on DW monoculture, 9 environmental impact categories recorded a reduction of up to 18–20%.
[46]	- Evaluation of the effects of 3 crop rotations (chickpea–wheat, lentil–wheat, wheat–wheat); - Rotation reduced the stratospheric ozone by 34% and water scarcity by 50%.
[47]	- Evaluation of two Sicilian DW intercropping systems (organic, low-input system with an ancient DW species and a conventional, high-input system with a modern variety); - A decrease greater than 60% in the GWP value in the conventional rotation system was observed without legumes compared to rotation with legumes in the organic system.

From a qualitative point of view, for each selected article, Table 7 summarizes the LCA methodology adopted, all functional units (FUs), system boundaries, IAMs, all impact categories, and the quality of the inventory data. Similarly to Zingale et al. [4], some difficulties were encountered during data compilation (Table 8), mainly due to differences in IAMs and FUs among the selected studies. To address these issues and improve comparability, the authors harmonized the subset in Table 8 by considering only the common agricultural FUs (1 kg of crop and 1 ha) and only the impact categories that use the same measurement units, excluding other non-comparable functional units such as NDU or EUR 1 and impact categories with different units of measurement. For these reasons, di Cristofaro et al. [33] and Costa et al. [44] were not included in Table 8 because, respectively, they used DALY as the IAM and only NDU as the FU, making the environmental impacts incomparable with those of other studies, and Zingale et al. [47] only reports the impacts according to the mass- and area-based FUs, excluding the other two FUs shown in Table 7.

Table 7. Methodological aspects of the 20 studies reviewed.

Main Topic	Reference	Country	Methodology	FU	System Boundaries	Allocation Criteria	IAM	Impact Categories	Data Quality
									Primary Data	Secondary Data
LCA of DW	Verdi et al. (2022) [30]	Italy	LCA (SimaPro v.8.5)	1 kg of DW	From cradle to grave	No allocation	CML vs 3,06 (2016) and CED vs. 1.11 (2018)	GWP, EP, HCT, TA, MET, FET, TET, POF, WC, LU, NCR, RRC	Checklist developed ad hoc for wheat cultivation analyzed	Ecoinvent v.3.4 database
	Paolotti et al. (2023) [32]	Italy	LCA (SimaPro 9.0)	1 kg of DW pasta	From cradle to gate	Economic allocation	IMPACT 2002+	GWP	“Pastificio Mancini”, located in Marche region	Ecoinvent database
	di Cristofaro et al. (2024) [33]	Italy	LCA (SimaPro 9.4.0.2)	1 Mg of DW	From cradle to gate	No allocation	Endpoint (H) 1.07 ReCiPe 2008	GWP, HCT, ODL, POF, HCT, PM, TA, FET, TE, FE, ME, HnCT, LU, WC, HCT, IR, MRS, FRS, OFTE	Questionnaires to biodynamic and organic farms	Ecoinvent 3.3
				1 ha
				1 K €
	Vinci et al. (2025) [31]	Italy	LCA (SimaPro 9.5)	1 ha	From cradle to gate	Mass allocation	ILCD 2011+; V1.11 and ReCiPe 2016	GWP, SOD, PM, HCT, POF, TA, HnCT, HCT, FE, TET, FET, MET, LU, WS, IR	Farmer company in Tuscany region with questionnaires and interviews	Ecoinvent v3.8 and World Food LCA Database (WFLDB) database
LCA of legumes	Treu et al. (2017) [35]	Germany	LCA	1 kg of legumes	From cradle to gate	No allocation	N.A.	GWP and LU	NVS II (German National Nutrition Survey)	No secondary data
	Araujo et al. (2020) [37]	Dominican Republic	LCA	1 t of legumes	From cradle to gate	No allocation	CML-IA	GWP, HCT, FE, TE, POF, TA, MET, FET, TET, ODL, AD	No primary data	Ecoinvent v. 3.2 database
				1 ha
	Saget et al. (2020) [10]	Bulgaria, Spain	LCA and nutritional LCA (OpenLCA 1.10.2)	80 g of pasta	From cradle to grave	Economic allocation	PEF	TA, HCT, GWP, FE, MET, FET TET, LU, HnCT, ODL, WS, IR, FRS, MRS, PM	Bulgarian manufacturer of chickpea pasta Variva	Agrifootprint 3.0 and Ecoinvent 3.6
				NDU
	Tidåker et al. (2021) [9]	Sweden	LCA	1 kg of dried legumes	Not defined	Mass allocation (yield)	IPCC	GWP, MET, EP, LU	No primary data	GaBi database, national agricultural statistics for 2012–2018, Ecoinvent 3.4
	Svanes et al. (2022) [34]	Norway	LCA (SimaPro 9.3.0.)	1 kg dried legume	From agricultural production to finished product	Economic allocation	ILCD 2011 Midpoint ReCiPe 2016 Endpoint ReCiPe 2016 Midpoint	GWP, TA, TET, FET, MET AD, TE, TE, ME, HCT, HnCT, LU, WC, FR	Farmers based on a questionnaire	Ecoinvent (v. 3.8), AgriBalyse (v. 1.3) and AgriFootprint (v. 5.0)
				1kg legume protein
	Boakye-Yiadom et al. (2023) [36]	Italy	LCA-EASETECH v.3.4.	1 kg of legumes	From cradle to farm gate	No allocation	Environmental Footprint (EF) 3.0 midpoint life cycle impact assessment (LCIA)	TA, GWP, MET, FET, TET, ODL, WC, HCT	Agricultural joint-stock consortium in central Italy with 177 conventional and 10 organic peas fields	Ecoinvent database version 3.8
	Pérez at al. (2024a) [29]	Spain	LCA (Simapro 9.5.0)	1 kg of beans	From cradle to gate	No allocation	ReciPe midpoint V1.08	GWP, SOD, TA, FET, MET, TE, FE, ME, HCT, HnCT, PM, LU, WC, IR, OFHH, OFTE, FPMF, FRS, MRS	Farmer surveys	Ecoivent database and OECD iLibrary
	Narote et al. (2025) [39]	Italy	LCA (SimaPro 9.4.0.2)	100 kcal of energy	From cradle to gate	No allocation	CML-IA baseline (V3.09)	AD, GWP, ODL, HCT, FE, ME, TE, POF, TA, MET, TET, FET	Direct interviews with R&D team of the local company “Matarrese”	Ecoinvent 3.8
				100 g of a burger patty
LCA of crop rotation	Brock et al. (2016) [40]	Australia	LCA (SimaPro 8.0.4.30)	1 ha	From cradle to farm gate	No allocation	IPCC	GWP	No primary data	Australian LCI database (Life Cycle Strategies Pty Ltd. 2013) and from the Swiss Ecoinvent Database (v3)
				1 t of legumes
	Ali et al. (2017) [41]	Italy	LCA	1 kg of grain	From cradle to gate	No allocation	IPCC Tier 1	GWP	DW field experiments conducted in Policoro (Southern Italy)	No secondary data
				1 ha
	Prechsl et al. (2017) [42]	Switzerland	SALCA	1 kg of legumes	By the borders of a field	No allocation	SALCA	GWP, MET, TET, ME	Farming System and Tillage Experiment (FAST)	Secondary data from Ecoinvent database (v2.2), SALCA Database
				1 ha
				1 CHF
	Falcone et al. (2019) [43]	Italy	LCA (SimaPro 8.1)	1 ha	From cradle to farm gate	No allocation	ReCiPe Midpoint	ODL, HCT, GWP, TE, TA, ME, FE, MET, FET, WC	from experiment conducted in Foggia (Southern Italy) in collaboration with the Agricultural Research Council	No secondary data
	Costa et al. (2021) [44]	Scotland, Italy, Romania	Open LCA v1.9	NDU	From cradle to farm gate	Economic allocation	PEF	GWP, TET, MET, LU, TE, FE, HCT, WC, ODL, POF, HnCT, IR, TA, FR	No primary data	Ecoinvent v.3.5 database
				CU
				DP
	Lago-Olveira et al. (2023) [45]	Italy	LCA	1 ha	From cradle to farm gate	No allocation	ReCiPe Midpoint	GWP, SOD, TA, FET, MET, TE, FE, ME, FR	Questionnaire submitted to the main agricultural cooperatives in Puglia (Southern Italy)	Ecoinvent database 3.9v
				1 €
	Lago-Olveira et al. (2024) [46]	Morocco	Attributional LCA SimaPro v.9.3 Excel-MSO 365	1 ha	From cradle to farm gate	No allocation between products and co-products	ReCiPe 2016 V1.06 Hierarchist Midpoint method World (2010)	GWP, SOD, TA, FET, MET, TE, FE, ME, FR	Targeted interaction with farmers and supplemented by an agronomic report from the Moroccan Ministry of Agriculture and Rural Development (2000) ICARDA field studies on the agrochemical inputs applied in Morocco	Ecoinvent v3.9 database
				1 kg of grain
	Zingale et al. (2024) [47]	Italy	LCA (Simapro 9.1.0.11)	1 kg of grain	From cradle to farm gate	Economic allocation	ReCiPe 2016 v. 1.04	LU, PM, GWP, HnCT	Interviews and questionnaires submitted to farmers	Ecoivent v. 3.6 database
				1 ha
				1 €
				Quality-corrected

Table 7. Methodological aspects of the 20 studies reviewed.

Main Topic	Reference	Country	Methodology	FU	System Boundaries	Allocation Criteria	IAM	Impact Categories	Data Quality
									Primary Data	Secondary Data
LCA of DW	Verdi et al. (2022) [30]	Italy	LCA (SimaPro v.8.5)	1 kg of DW	From cradle to grave	No allocation	CML vs 3,06 (2016) and CED vs. 1.11 (2018)	GWP, EP, HCT, TA, MET, FET, TET, POF, WC, LU, NCR, RRC	Checklist developed ad hoc for wheat cultivation analyzed	Ecoinvent v.3.4 database
	Paolotti et al. (2023) [32]	Italy	LCA (SimaPro 9.0)	1 kg of DW pasta	From cradle to gate	Economic allocation	IMPACT 2002+	GWP	“Pastificio Mancini”, located in Marche region	Ecoinvent database
	di Cristofaro et al. (2024) [33]	Italy	LCA (SimaPro 9.4.0.2)	1 Mg of DW	From cradle to gate	No allocation	Endpoint (H) 1.07 ReCiPe 2008	GWP, HCT, ODL, POF, HCT, PM, TA, FET, TE, FE, ME, HnCT, LU, WC, HCT, IR, MRS, FRS, OFTE	Questionnaires to biodynamic and organic farms	Ecoinvent 3.3
				1 ha
				1 K €
	Vinci et al. (2025) [31]	Italy	LCA (SimaPro 9.5)	1 ha	From cradle to gate	Mass allocation	ILCD 2011+; V1.11 and ReCiPe 2016	GWP, SOD, PM, HCT, POF, TA, HnCT, HCT, FE, TET, FET, MET, LU, WS, IR	Farmer company in Tuscany region with questionnaires and interviews	Ecoinvent v3.8 and World Food LCA Database (WFLDB) database
LCA of legumes	Treu et al. (2017) [35]	Germany	LCA	1 kg of legumes	From cradle to gate	No allocation	N.A.	GWP and LU	NVS II (German National Nutrition Survey)	No secondary data
	Araujo et al. (2020) [37]	Dominican Republic	LCA	1 t of legumes	From cradle to gate	No allocation	CML-IA	GWP, HCT, FE, TE, POF, TA, MET, FET, TET, ODL, AD	No primary data	Ecoinvent v. 3.2 database
				1 ha
	Saget et al. (2020) [10]	Bulgaria, Spain	LCA and nutritional LCA (OpenLCA 1.10.2)	80 g of pasta	From cradle to grave	Economic allocation	PEF	TA, HCT, GWP, FE, MET, FET TET, LU, HnCT, ODL, WS, IR, FRS, MRS, PM	Bulgarian manufacturer of chickpea pasta Variva	Agrifootprint 3.0 and Ecoinvent 3.6
				NDU
	Tidåker et al. (2021) [9]	Sweden	LCA	1 kg of dried legumes	Not defined	Mass allocation (yield)	IPCC	GWP, MET, EP, LU	No primary data	GaBi database, national agricultural statistics for 2012–2018, Ecoinvent 3.4
	Svanes et al. (2022) [34]	Norway	LCA (SimaPro 9.3.0.)	1 kg dried legume	From agricultural production to finished product	Economic allocation	ILCD 2011 Midpoint ReCiPe 2016 Endpoint ReCiPe 2016 Midpoint	GWP, TA, TET, FET, MET AD, TE, TE, ME, HCT, HnCT, LU, WC, FR	Farmers based on a questionnaire	Ecoinvent (v. 3.8), AgriBalyse (v. 1.3) and AgriFootprint (v. 5.0)
				1kg legume protein
	Boakye-Yiadom et al. (2023) [36]	Italy	LCA-EASETECH v.3.4.	1 kg of legumes	From cradle to farm gate	No allocation	Environmental Footprint (EF) 3.0 midpoint life cycle impact assessment (LCIA)	TA, GWP, MET, FET, TET, ODL, WC, HCT	Agricultural joint-stock consortium in central Italy with 177 conventional and 10 organic peas fields	Ecoinvent database version 3.8
	Pérez at al. (2024a) [29]	Spain	LCA (Simapro 9.5.0)	1 kg of beans	From cradle to gate	No allocation	ReciPe midpoint V1.08	GWP, SOD, TA, FET, MET, TE, FE, ME, HCT, HnCT, PM, LU, WC, IR, OFHH, OFTE, FPMF, FRS, MRS	Farmer surveys	Ecoivent database and OECD iLibrary
	Narote et al. (2025) [39]	Italy	LCA (SimaPro 9.4.0.2)	100 kcal of energy	From cradle to gate	No allocation	CML-IA baseline (V3.09)	AD, GWP, ODL, HCT, FE, ME, TE, POF, TA, MET, TET, FET	Direct interviews with R&D team of the local company “Matarrese”	Ecoinvent 3.8
				100 g of a burger patty
LCA of crop rotation	Brock et al. (2016) [40]	Australia	LCA (SimaPro 8.0.4.30)	1 ha	From cradle to farm gate	No allocation	IPCC	GWP	No primary data	Australian LCI database (Life Cycle Strategies Pty Ltd. 2013) and from the Swiss Ecoinvent Database (v3)
				1 t of legumes
	Ali et al. (2017) [41]	Italy	LCA	1 kg of grain	From cradle to gate	No allocation	IPCC Tier 1	GWP	DW field experiments conducted in Policoro (Southern Italy)	No secondary data
				1 ha
	Prechsl et al. (2017) [42]	Switzerland	SALCA	1 kg of legumes	By the borders of a field	No allocation	SALCA	GWP, MET, TET, ME	Farming System and Tillage Experiment (FAST)	Secondary data from Ecoinvent database (v2.2), SALCA Database
				1 ha
				1 CHF
	Falcone et al. (2019) [43]	Italy	LCA (SimaPro 8.1)	1 ha	From cradle to farm gate	No allocation	ReCiPe Midpoint	ODL, HCT, GWP, TE, TA, ME, FE, MET, FET, WC	from experiment conducted in Foggia (Southern Italy) in collaboration with the Agricultural Research Council	No secondary data
	Costa et al. (2021) [44]	Scotland, Italy, Romania	Open LCA v1.9	NDU	From cradle to farm gate	Economic allocation	PEF	GWP, TET, MET, LU, TE, FE, HCT, WC, ODL, POF, HnCT, IR, TA, FR	No primary data	Ecoinvent v.3.5 database
				CU
				DP
	Lago-Olveira et al. (2023) [45]	Italy	LCA	1 ha	From cradle to farm gate	No allocation	ReCiPe Midpoint	GWP, SOD, TA, FET, MET, TE, FE, ME, FR	Questionnaire submitted to the main agricultural cooperatives in Puglia (Southern Italy)	Ecoinvent database 3.9v
				1 €
	Lago-Olveira et al. (2024) [46]	Morocco	Attributional LCA SimaPro v.9.3 Excel-MSO 365	1 ha	From cradle to farm gate	No allocation between products and co-products	ReCiPe 2016 V1.06 Hierarchist Midpoint method World (2010)	GWP, SOD, TA, FET, MET, TE, FE, ME, FR	Targeted interaction with farmers and supplemented by an agronomic report from the Moroccan Ministry of Agriculture and Rural Development (2000) ICARDA field studies on the agrochemical inputs applied in Morocco	Ecoinvent v3.9 database
				1 kg of grain
	Zingale et al. (2024) [47]	Italy	LCA (Simapro 9.1.0.11)	1 kg of grain	From cradle to farm gate	Economic allocation	ReCiPe 2016 v. 1.04	LU, PM, GWP, HnCT	Interviews and questionnaires submitted to farmers	Ecoivent v. 3.6 database
				1 ha
				1 €
				Quality-corrected

Table 8. Summary of the main LCA results based on comparable measure units.

Reference	Crop	Agricultural System	FU	GWP (kg CO2 Eq.)	TA (kg SO2 Eq.)	EP (kg PO4-3 Eq.)	CED (MJ)	TE (kg 1.4-DCB)	FE (kg 1.4-DCB)	ME (kg 1.4-DCB)	HCT (kg 1.4-DCB)	LU (m2 crop eq)	WC (m3/liter)
Verdi et al. (2022) [30]	DW	Conventional	1 kg of DW	0.580	3.90	5.41 × 10−3	N.A.	5.80 × 10−4	5.00 × 10−2	N.A.	1.10 × 10−1	3.89	6.06 × 10−1 liters
	DW	Organic	1 kg of DW	0.360	3.08	1.62 × 10−3		2.79 × 10−4	1.00 × 10−2		4.00 × 10−2	7.53	5.26 × 10−1 liters
Paolotti et al. (2023) [32]	DW	Conventional	1 kg of DW of pasta *	1.042	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Vinci et al. (2025) [31]	DW	Organic	1 ha	−1.42 × 102	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Treu et al. (2017) [35]	Legumes	Conventional	1 kg of legumes	0.32	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.	0.36	N.A.
		Organic	1 kg of legumes	0.35								0.40
Araujo et al. (2020) [37]	Legumes	Conventional	1 ha of common bean 1 ha of pigeon pea	1.44 × 103 7.15 × 102	7.13 3.55	2.28 1.14	2.09 × 104 1.04 × 104	1.80 0.893	1.98 × 102 9.83 × 101	N.A.	2.70 × 102 1.34 × 102	N.A.	N.A.
			1 t of common bean 1 t of pigeon pea	1.38 × 103 1.46 × 103	6.61 7.01	2.08E 2.20	2.08 × 104 2.20 × 104	1.70 × 100 1.80 × 100	2.25 × 102 2.38 × 102		2.65 × 102 2.80 × 102
Saget et al. (2020) [10]	DW	Conventional	80 g of pasta *	DW80%: 0.05	N.A.	N.A.	DW80%: 0.316	N.A.	N.A.	N.A.	N.A.	N.A.	DW80%: 0.031
	Chickpea	Conventional		Chickpea: Bulgaria 0.082			Chickpea: Bulgaria 0.475						Chickpea: Bulgaria 0.0518
Tidåker et al. (2021) [9]	Legumes	Conventional	1 kg of legumes	Faba bean: 0.18	N.A.	Faba bean: 2.5	Faba bean: 1.8	N.A.	N.A.	N.A.	N.A.	Faba bean: 3.1	N.A.
				Yellow pea: 0.18		Yellow pea: 3.8	Yellow pea: 1.7					Yellow pea: 3.2
				Gray pea: 0.20		Gray pea: 4.3	Gray pea: 4.3					Gray pea: 3.6
				Common bean: 0.44		Common bean: 7.8	Common bean: 1.9					Common bean: 5.9
		Organic	1 kg of legumes	Faba bean: 0.20	N.A.	Faba bean: 3.3	Faba bean: 2.0	N.A.	N.A.	N.A.	N.A.	Faba bean: 4.1	N.A.
				Yellow pea: 0.24		Yellow pea: 5.7	Yellow pea: 2.2					Yellow pea: 4.9
				Gray pea: 0.18		Gray pea: 3.8	Gray pea: 1.6					Gray pea: 3.2
				Lentils: 0.26		Lentils: 5.8	Lentils: 2.3					Lentils: 4.7
Svanes et al. (2022) [34]	Legumes	N.A.	1 kg of dried legumes	Pea 0.57	Pea 0.0027	Pea 0.005	Pea 4.8	Pea 0.77	Pea 0.02	Pea 0.02	Pea 0.03	Pea 2.9	Pea 0.006, faba beans 0.003
				Faba peans 0.62	Faba beans 0.0022	Faba beans 0.005	Faba beans 5.2	Faba beans 0.7	Faba beans 0.02	Faba beans 0.02	Faba beans 0.03	Faba beans 3
Boakye-Yiadom et al. (2023) [36]	Legumes (peas)	Conventional	1 kg of legumes	0.98	N.A.	N.A.	3.44	N.A.	N.A.	N.A.	N.A.	N.A.	0.26
		Organic	1 kg of legumes	0.88			2.70						0.22
Pérez et al. (2024b) [38]	Legumes (bean)	Organic	1 kg of legumes	1.2	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Narote et al. (2025) [39]	Black chickpeas	Conventional	100 g of a burger patty *	4.77 × 10−2	4.52 × 10−4	2.67 × 10−4	2.91 × 10−1	1.43 × 10−3	1.55 × 10−2	2.11 × 10−1	2.39 × 10−2	N.A.	N.A.
Brock et al. (2016) [40]	DW–DW	Not defined	1 ha	676	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
	DW–field peas		1 ha	586
	Field peas		1 ha	529
Ali et al. (2017) [41]	DW–DW	Conventional	1 ha	1614.54	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
	DW–faba beans		1 ha	1510.14
	DW–DW		1 kg of grain	0.322
	DW–faba beans		1 kg of grain	0.395
Prechsl et al. (2017) [42]	Cover crop, wheat, cover crop, maize, faba beans, wheat, and two years of grass–clover ley	Conventional	1 ha	2507.6	N.A.	N.A.	N.A.	N.A.	N.A.	797.4	N.A.	N.A.	N.A.
		Organic	1 ha	1366.4						142.4
Falcone et al. (2019) [43]	DW–DW	Conventional	1 ha	4.54 × 103	7.94 × 101	2.80 × 101	N.A.	3.53 × 100	2.02 × 101	3.08 × 101	1.23 × 103	1.48 × 100	6.51 × 101
	DW–vetch	Conventional	1 ha	3.44 × 103	5.58 × 101	2.49 × 101		3.03 × 100	1.30 × 101	3.06 × 101	1.06 × 103	1.07 × 100	8.55 × 101
Lago-Olveira et al. (2023) [45]	DW–DW–DW	Conventional	1 ha	5370	81.43	N.A.	N.A.	23,410	353.92	240.26	N.A.	N.A.	N.A.
	Chickpea–DW–DW	Conventional	1 ha	4420	82.90			21,010	283.92	223.15
Lago-Olveira et al. (2024) [46]	In 3 crop rotations: R1: chickpea–wheat R2: lentil–wheat R3: wheat–wheat	Conventional	Land management FU (1 ha/ year)	R1: 1.41 × 103 R2: 1.42 × 103 R3: 2.02 × 103	R1: 12.96 R2: 12.92 R3: 17.52	N.A.	N.A.	R1: 3.78 × 103 R2: 3.67 × 103 R3: 4.94 × 103	R1: 46.16 R2: 46.03 R3: 53.84	R1: 50.97 R2: 50.39 R3: 70.02	N.A.	N.A.	N.A.
			Productive FU (1 kg of harvested wheat)	R1: 1.05 R2: 0.95 R3: 1.30	R1: 9.60 × 10−3 R2: 8.62 × 10−3 R3: 11.30 × 10−3	N.A.	N.A.	R1: 2.80 R2: 2.45 R3: 3.19	R1:34.19 × 10−3 R2: 30.68 × 10−3 R3: 34.73 × 10−3	R1: 37.75 × 10−3 R2: 33.59 × 10−3 R3: 45.19 × 10−3	N.A.	N.A.	N.A.
Zingale et al. (2024) [47]	Faba beans–DW	Organic	1 kg	0.33	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.	0.371	5.87	N.A.
	Bare fallow–DW	Conventional		0.55							0.255	5.11
	Faba beans–DW	Organic	1 ha	7.80 × 1020							8.77 × 102	1.39 × 104
	Bare fallow–DW	Conventional		2.26 × 103							1.12 × 103	2.08 × 104

Legend: CED (Cumulative Energy Demand), DW (durum wheat), EP (eutrophication potential), equation (equivalent), FE (freshwater ecotoxicity), FU (functional unit), GWP (Global Warming Potential), LU (land use), ME (Marine Ecotoxicity), HCT (Human Carcinogenic Toxicity), N.A. (not available), NDU (Nutrient Density Unit), TA (Terrestrial Acidification), TE (Terrestrial Ecotoxicity), WC (Water Consumption). * For these studies, it was considered only the agricultural phase.

Table 8. Summary of the main LCA results based on comparable measure units.

Reference	Crop	Agricultural System	FU	GWP (kg CO2 Eq.)	TA (kg SO2 Eq.)	EP (kg PO4-3 Eq.)	CED (MJ)	TE (kg 1.4-DCB)	FE (kg 1.4-DCB)	ME (kg 1.4-DCB)	HCT (kg 1.4-DCB)	LU (m2 crop eq)	WC (m3/liter)
Verdi et al. (2022) [30]	DW	Conventional	1 kg of DW	0.580	3.90	5.41 × 10−3	N.A.	5.80 × 10−4	5.00 × 10−2	N.A.	1.10 × 10−1	3.89	6.06 × 10−1 liters
	DW	Organic	1 kg of DW	0.360	3.08	1.62 × 10−3		2.79 × 10−4	1.00 × 10−2		4.00 × 10−2	7.53	5.26 × 10−1 liters
Paolotti et al. (2023) [32]	DW	Conventional	1 kg of DW of pasta *	1.042	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Vinci et al. (2025) [31]	DW	Organic	1 ha	−1.42 × 102	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Treu et al. (2017) [35]	Legumes	Conventional	1 kg of legumes	0.32	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.	0.36	N.A.
		Organic	1 kg of legumes	0.35								0.40
Araujo et al. (2020) [37]	Legumes	Conventional	1 ha of common bean 1 ha of pigeon pea	1.44 × 103 7.15 × 102	7.13 3.55	2.28 1.14	2.09 × 104 1.04 × 104	1.80 0.893	1.98 × 102 9.83 × 101	N.A.	2.70 × 102 1.34 × 102	N.A.	N.A.
			1 t of common bean 1 t of pigeon pea	1.38 × 103 1.46 × 103	6.61 7.01	2.08E 2.20	2.08 × 104 2.20 × 104	1.70 × 100 1.80 × 100	2.25 × 102 2.38 × 102		2.65 × 102 2.80 × 102
Saget et al. (2020) [10]	DW	Conventional	80 g of pasta *	DW80%: 0.05	N.A.	N.A.	DW80%: 0.316	N.A.	N.A.	N.A.	N.A.	N.A.	DW80%: 0.031
	Chickpea	Conventional		Chickpea: Bulgaria 0.082			Chickpea: Bulgaria 0.475						Chickpea: Bulgaria 0.0518
Tidåker et al. (2021) [9]	Legumes	Conventional	1 kg of legumes	Faba bean: 0.18	N.A.	Faba bean: 2.5	Faba bean: 1.8	N.A.	N.A.	N.A.	N.A.	Faba bean: 3.1	N.A.
				Yellow pea: 0.18		Yellow pea: 3.8	Yellow pea: 1.7					Yellow pea: 3.2
				Gray pea: 0.20		Gray pea: 4.3	Gray pea: 4.3					Gray pea: 3.6
				Common bean: 0.44		Common bean: 7.8	Common bean: 1.9					Common bean: 5.9
		Organic	1 kg of legumes	Faba bean: 0.20	N.A.	Faba bean: 3.3	Faba bean: 2.0	N.A.	N.A.	N.A.	N.A.	Faba bean: 4.1	N.A.
				Yellow pea: 0.24		Yellow pea: 5.7	Yellow pea: 2.2					Yellow pea: 4.9
				Gray pea: 0.18		Gray pea: 3.8	Gray pea: 1.6					Gray pea: 3.2
				Lentils: 0.26		Lentils: 5.8	Lentils: 2.3					Lentils: 4.7
Svanes et al. (2022) [34]	Legumes	N.A.	1 kg of dried legumes	Pea 0.57	Pea 0.0027	Pea 0.005	Pea 4.8	Pea 0.77	Pea 0.02	Pea 0.02	Pea 0.03	Pea 2.9	Pea 0.006, faba beans 0.003
				Faba peans 0.62	Faba beans 0.0022	Faba beans 0.005	Faba beans 5.2	Faba beans 0.7	Faba beans 0.02	Faba beans 0.02	Faba beans 0.03	Faba beans 3
Boakye-Yiadom et al. (2023) [36]	Legumes (peas)	Conventional	1 kg of legumes	0.98	N.A.	N.A.	3.44	N.A.	N.A.	N.A.	N.A.	N.A.	0.26
		Organic	1 kg of legumes	0.88			2.70						0.22
Pérez et al. (2024b) [38]	Legumes (bean)	Organic	1 kg of legumes	1.2	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
Narote et al. (2025) [39]	Black chickpeas	Conventional	100 g of a burger patty *	4.77 × 10−2	4.52 × 10−4	2.67 × 10−4	2.91 × 10−1	1.43 × 10−3	1.55 × 10−2	2.11 × 10−1	2.39 × 10−2	N.A.	N.A.
Brock et al. (2016) [40]	DW–DW	Not defined	1 ha	676	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
	DW–field peas		1 ha	586
	Field peas		1 ha	529
Ali et al. (2017) [41]	DW–DW	Conventional	1 ha	1614.54	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.
	DW–faba beans		1 ha	1510.14
	DW–DW		1 kg of grain	0.322
	DW–faba beans		1 kg of grain	0.395
Prechsl et al. (2017) [42]	Cover crop, wheat, cover crop, maize, faba beans, wheat, and two years of grass–clover ley	Conventional	1 ha	2507.6	N.A.	N.A.	N.A.	N.A.	N.A.	797.4	N.A.	N.A.	N.A.
		Organic	1 ha	1366.4						142.4
Falcone et al. (2019) [43]	DW–DW	Conventional	1 ha	4.54 × 103	7.94 × 101	2.80 × 101	N.A.	3.53 × 100	2.02 × 101	3.08 × 101	1.23 × 103	1.48 × 100	6.51 × 101
	DW–vetch	Conventional	1 ha	3.44 × 103	5.58 × 101	2.49 × 101		3.03 × 100	1.30 × 101	3.06 × 101	1.06 × 103	1.07 × 100	8.55 × 101
Lago-Olveira et al. (2023) [45]	DW–DW–DW	Conventional	1 ha	5370	81.43	N.A.	N.A.	23,410	353.92	240.26	N.A.	N.A.	N.A.
	Chickpea–DW–DW	Conventional	1 ha	4420	82.90			21,010	283.92	223.15
Lago-Olveira et al. (2024) [46]	In 3 crop rotations: R1: chickpea–wheat R2: lentil–wheat R3: wheat–wheat	Conventional	Land management FU (1 ha/ year)	R1: 1.41 × 103 R2: 1.42 × 103 R3: 2.02 × 103	R1: 12.96 R2: 12.92 R3: 17.52	N.A.	N.A.	R1: 3.78 × 103 R2: 3.67 × 103 R3: 4.94 × 103	R1: 46.16 R2: 46.03 R3: 53.84	R1: 50.97 R2: 50.39 R3: 70.02	N.A.	N.A.	N.A.
			Productive FU (1 kg of harvested wheat)	R1: 1.05 R2: 0.95 R3: 1.30	R1: 9.60 × 10−3 R2: 8.62 × 10−3 R3: 11.30 × 10−3	N.A.	N.A.	R1: 2.80 R2: 2.45 R3: 3.19	R1:34.19 × 10−3 R2: 30.68 × 10−3 R3: 34.73 × 10−3	R1: 37.75 × 10−3 R2: 33.59 × 10−3 R3: 45.19 × 10−3	N.A.	N.A.	N.A.
Zingale et al. (2024) [47]	Faba beans–DW	Organic	1 kg	0.33	N.A.	N.A.	N.A.	N.A.	N.A.	N.A.	0.371	5.87	N.A.
	Bare fallow–DW	Conventional		0.55							0.255	5.11
	Faba beans–DW	Organic	1 ha	7.80 × 1020							8.77 × 102	1.39 × 104
	Bare fallow–DW	Conventional		2.26 × 103							1.12 × 103	2.08 × 104

Legend: CED (Cumulative Energy Demand), DW (durum wheat), EP (eutrophication potential), equation (equivalent), FE (freshwater ecotoxicity), FU (functional unit), GWP (Global Warming Potential), LU (land use), ME (Marine Ecotoxicity), HCT (Human Carcinogenic Toxicity), N.A. (not available), NDU (Nutrient Density Unit), TA (Terrestrial Acidification), TE (Terrestrial Ecotoxicity), WC (Water Consumption). * For these studies, it was considered only the agricultural phase.

3.4. Final Remarks and Research Gaps

This section analyzes the results of the 20 eligible papers included in the final sample, aiming to provide a replicable analysis to address the identified research gaps and methodological heterogeneity. The heterogeneity in LCA results across the studies is driven by multiple factors, which limits direct comparability and meta-analytic synthesis. Most studies adopted standard LCA methodologies such as the IPCC, CML, PEF, or ReCiPe methods. However, these methods are often not comparable because they use different units of measurement and have different impact analyses for midpoint and endpoint categories.

Furthermore, while many studies have a system boundary from cradle to gate, others have adopted a cradle-to-grave approach or have not explicitly defined their approach. Most studies selected the final output of the production process as the mass-based FU, but this analysis shows that several studies also opted for alternative FUs, such as 1 hectare of land [31,42,44,45,46], to assess the environmental impacts and soil management of crop rotations or the NDU to evaluate the nutritional quality of foods [10,26].

Methodological heterogeneity resulting from variable FUs, IAMs, and different system boundaries explains why it is difficult to compare the different studies reviewed. Contextual drivers, including pedo-climatic differences (e.g., arid Italian regions versus Swedish conditions) and cultivation practices, also contribute to variability between studies.

Moreover, analysis of the three main topics that cover this SLR revealed the following insights: First, many reviewed LCA studies on DW have focused on the comparison between conventional and organic farming systems. These studies show that organic farming practices can mitigate several environmental issues associated with cultivation, demonstrating greater sustainability than conventional farming systems. However, organic farming involves higher LU and significantly lower yields. In the organic system, mechanical practices were the main factor affecting GWP and other impact categories, accounting for 76% of the total. Fertilizer manufacturing and use provided a small contribution due to the limited adoption of synthetic fertilizers. In conventional farming, however, the main contributors to GWP and other impact categories were fertilizer manufacturing (39%), mechanical practices (29%), and fertilizer use (25%) [30].

Although legumes commonly present lower impact values, as shown in Table 8, clearly determining which crop is more sustainable than the other is not possible since the reviewed studies used different FUs and system boundaries, involved distinct agricultural practices, and often referred to crops grown in different climatic zones. Key variables influencing environmental impact include the use of fertilizers, agricultural production efficiency, and the amount of energy or water used with a significant impact on GWP, as well as other categories of environmental impact assessed through the life cycle analysis of cropping systems. These dynamics are affected by agronomic management practices such as tillage techniques and fertilization strategies, as well as by local soil and climate conditions, which impact yields and environmental impact [33].

In conventional DW systems, GWP was mainly influenced by fertilizers, which contribute to over 40% of total impact through production, application, and leaching and agricultural activities, while organic DW systems showed lower crop yields and required more agricultural treatments; unlike these systems, which demand significant synthetic N fertilizer inputs (typically over 200 kg N/ha, accounting for over 25% of environmental damage), leguminous crops rely on biological nitrogen fixation via root symbiosis with Rhizobium bacteria, often eliminating the need for external N inputs or requiring only minimal amounts; they tend to not require irrigation [33,34]. In leguminous crops, GWP is primarily driven by crop yield levels, nitrogen fixation efficiency, and soil management practices.

Moreover, crop rotation has been shown to reduce environmental impacts and improve agricultural sustainability by promoting legume N fixation, reducing the need for fertilization. In chickpea–DW crop rotation, 1100 kg/ha of mineral fertilizer is used, distributed in five applications (one application for chickpea cultivation and two applications for DW), while the wheat monoculture receives 1200 kg/ha of mineral fertilizer, distributed in six applications [45]. Analysis of the papers with crop rotation as the main topic, using 1ha as the FU, revealed that legumes in crop rotation allows the GWP impact to decrease from 6% [41] to 45% [43], compared to DW monoculture cultivation. This percentage difference is attributable not only to the different crops used in rotation and the different soil and climate conditions but also to the different years of crop rotation. Furthermore, only the GWP could be evaluated, as this was the only value present in each of the studies compared. Despite methodological differences in the various LCA studies reviewed, these percentages confirm the importance of crop rotation as a strategy for reducing the environmental burdens of agricultural systems.

However, some key limitations of the reviewed studies include the high complexity and variability of agricultural systems, which make it impossible to detect local environmental effects such as biodiversity, and the soil erosion and reliability of inventory data, which can be incomplete or standardized, using secondary data, and thus compromise result accuracy. Likewise, with a time-limited approach, many studies have focused on short observation periods that may not capture long-term impacts. More sustainable alternative scenarios have been hypothesized, often without verification through primary data.

From a territorial perspective, the reviewed LCA studies analyzed regional or national productions, with different yields and crop varieties (ancient vs. modern varieties grown in arid zones). Most of the studies concerned Italian production, with four observations for DW, two for legumes, and five for crop rotation, taken individually or as countries compared with others (Figure 3). In looking at the Mediterranean systems, Northern Europe, and smallholder tropics, distinct emission profiles are noted. This may be a limitation, but as Foerster et al. (2024) [50] noted in their comparative LCA of viticulture, different results may be observed in other climatic regions, where variations in environmental conditions, soil composition, and management practices could significantly influence the results.

Additionally, there is variability in the result interpretation, which can vary depending on the FU used. Many studies relied on mass-based FUs, which may overestimate the impact of organic farming due to lower yields. The use of land area-based FUs would provide complementary insights.

Also, variability in IAMs leads to different environmental impacts at the midpoint level with different units of measurement that are difficult to compare. The primary focus on cradle-to-gate assessments was also observed, which lacks downstream stages such as transport, distribution, and packaging and the application of incomplete models for estimating pesticide, fertilizer effects, and emission impacts using standard factors.

The current study provides valuable insights for practitioners and stakeholders adopting sustainable agricultural practices using the LCA approach, as suggested by Crovella et al. [51]. Despite its limitations, this research serves as a starting point for promoting the sustainable production of traditional Mediterranean foods, such as pasta and bread, and more nutritious alternatives like legumes as raw ingredients [52].

3.5. Comparative and Sensitivity Analysis on Functional Unit

Thus far, scholars have emphasized the significance of a holistic evaluation of the environmental impacts of DW and legumes under different cropping systems, comparing their LCAs based on two FUs: the mass unit of a given crop and the area of production. Thus, they carried out a brief sensitivity analysis to highlight changes in environmental impacts using different FUs. Lower yields in organic farming led to higher impact intensity per unit of product, so it was necessary to adopt a double-functional unit for more reliable evaluations and results [28]. In this regard, the papers by Tidåker et al. [9] and Verdi et al. [30] were selected, which analyzed conventional and organic legumes and DW cultivation, respectively. Based on the average GWP, LU, and EP assessed by these authors with regard to 1 kg of crop (mass-based), the authors of this review article used the yield of DW and legumes to analyze the aforementioned impacts about 1 hectare of land (area-based). These three impact categories were used because they are the most relevant environmental midpoint impacts for the evaluation of different cropping systems of DW and legumes. The average value of the environmental impacts of legumes was based on Tidåker et al. [9]. See Figure 4, Figure 5 and Figure 6 for the results.

The environmental impact of a crop can vary considerably depending on the FU adopted and the productivity of the crop. For example, considering the GWP (kg CO₂eq.) per kg of crops, the authors found that conventionally grown legumes had a greater impact (+13.60%) than those grown organically, whereas the GWP of conventional DW was 61.10% higher than one of organic DW due to the use of fertilizers and pesticides in conventional agriculture unlike in organic farming, despite organic farming, with its lower yield, requiring a greater amount of diesel for field operations per kg produced [9].

If, instead, the hectare of land was considered as the FU, the difference between the two agricultural systems would have been greater, with +68% and +197%, respectively, for conventional legumes and DW compared to organic crops, as shown in Figure 4. This is because when impacts are not influenced by yield, the use of pesticides and fertilizers in conventional agriculture becomes more visible.

Consequently, with 1 kg of crop, it is possible to observe, for instance, how the GWP of legumes is very similar. This is because it is greatly influenced by the different yields of the two agricultural systems and underestimates the environmental damage caused by conventional agriculture. However, when the impact per hectare of land is assessed, conventional farming causes greater environmental damage due to its greater use of treatments and fertilizers.

The impact related to LU is greatly influenced by the crop yield and the type of farming adopted. Organic farming has lower average yields, with reductions of about 10–20% compared to conventional farming, depending on the type of crop and location. Hence, to boost the yield for organic farming, more land is often required [53]. For example, 1 kg of DW production under organic conditions required about 93.6% more land than the same amount of DW produced under conventional conditions, whereas organic legume production required approximately 7% more land than conventional legume production.

The LU per hectare (m²/ha) differed between organic and conventional agriculture, as can be seen in Figure 5, since the measurement unit (m²) represents a surface unit, like ha. Thus, the LU per 1 ha of DW under organic conditions was higher than that under conventional conditions by 4.84%. The LU value of legumes per 1 ha was 38.1% greater under conventional conditions than under organic conditions. These results confirm that LU per hectare is much less significant than LU per 1 kg of grain, since the functional unit of mass is influenced by crop yield.

Eutrophication potential (EP) is affected by factors such as the release of nitrates, phosphates, ammonia in the fertilization activity, and the use of agricultural machinery [30]. According to Tidåker et al. [9], EP per hectare is influenced by yield, LU, and the type of cultivated land. The relationship between nitrogen and phosphorus application rates and the EP impact leads to higher impacts in conventional DW cultivation, with EP per kilogram being 234% higher than that of organic DW. When considering the FU as 1 hectare of land, the difference increases to over 516% (Figure 6). Verdi et al. [30] and Prechsl et al. [42] showed that organic agriculture, while using no synthetic pesticides and fertilizers, exhibits a slight increase in EP due to the use of manure and wastewater, which contributed to greater ammonia emissions; the EP per kilogram of organic legumes was observed to be slightly higher than that of conventional legumes by about 1.1%.

Regarding legumes, EP was also influenced by yield, with conventional systems showing a greater impact than organic systems. Specifically, EP under conventional conditions was 46.3% higher than under organic conditions.

Mass-based functional units (1 kg of harvested crops) are preferred for yield and productivity comparisons, as they directly attribute environmental inputs (such as fertilizers, pesticides, and irrigation) to marketable output, aligning with ISO 14040/44 [54] standards and finding application in downstream agri-food processes such as pasta or bread production. Area-based functional units (1 ha of cultivated land), by contrast, are ideal for evaluating land use efficiency, soil health benefits as for legume nitrogen fixation, and the trade-offs between intensive monocultures and sustainable crop rotations. As conclusive matter, this kind of analysis emphasized the importance of considering multiple FUs for future LCA evaluations, providing a comprehensive overview of crop multifunctionality, capturing both productivity and environmental sustainability in agricultural systems.

4. Limitations and Practical Implications

This section outlines the main limitations of the current systematic review and considers the implications for future research and policy. Some critical issues arise from the use of variable system boundaries, different LCA impact assessment methods, and non-comparable functional units across the reviewed papers. These factors collectively make it difficult to compare and evaluate LCA studies. For this reason, a protocol was not registered. A further key limitation is the reliance on secondary data used in most of the reviewed papers. Some bibliometric limitations refer to studies not written in English or published in non-indexed journals, difficult to find through these important databases, and with different units of measurement that do not always allow for an adequate comparison.

Expanding SLR-based analysis with more studies would increase robustness, and future research should explore other crops and different environmental contexts and integrate impact data through georeferencing and LCA.

This section also delivers prioritized, actionable recommendations for farmers, policymakers, and researchers to drive the sustainable transitions in DW and legume cultivation. For farmers and agri-food cooperatives, for example, evidence suggests that conventional systems have a higher environmental impact per hectare than organic systems, which have a higher impact per kilogram due to lower yields. This suggests the need to develop farm-level strategies that consider not only yield but also soil and land management through crop rotation planning and input use. For instance, integrating legumes with DW, as demonstrated by crop rotation studies, enables farmers to reduce mineral nitrogen inputs and lower environmental values such as GWP, while maintaining or marginally enhancing yields over a multi-year period. Therefore, advisory services and extension programs should support the co-design of rotation plans that optimize soil-based and product-based performance.

In addition to crop rotation and biological N fixation, innovative farming practices and fertilizer technologies offer substantial potential for reducing the environmental impact of both conventional and organic farming methods, aligning with key findings from LCA studies on DW and legumes. Slow-release and smart mineral fertilizers reduce nitrate, ammonium, and phosphate leaching while providing a gradual nutrient supply that stimulates soil microbial abundance, improves root development, and slightly lowers soil pH, thereby enhancing soil health and nutrient use efficiency [55]. Similarly, practices such as rhizobial inoculation and biodynamic farming systems significantly enhance sustainability: rhizobial inoculation increases legume yields and cuts GHG and resource use compared to traditional mineral fertilization [37], while biodynamic farming, through lower chemical inputs, low-input strategies, and soil management, achieves a 52% lower impact per hectare in GWP, eutrophication, and non-renewable resource use compared to conventional and traditional systems [33]. However, challenges persist with the biodegradability and higher costs of smart fertilizers [56], underscoring the need for future LCA research to quantify benefits of integrating these innovations into farming systems.

Secondly, environmental agricultural issues reinforce the need for policymakers to implement harmonized EU regulations (e.g., Regulation 2021/2115) [57], which support sustainable practices such as crop rotation and low-input agricultural systems.

By 2030, EU regulations will require most farmers to implement crop rotations to improve soil structure and fertility, reduce chemical inputs and cut pesticide and antibiotic use by 50% through precision farming and cover crops, and increase the amount of land dedicated to organic farming by 25%. To support this transition, the current reliance on secondary databases highlights the need to fund georeferenced primary LCI datasets for key crops on a regional scale. This would reduce uncertainty, enable accurate benchmarking of farming systems, and provide robust evidence of compliance with the EU’s 2030 sustainability objectives.

For researchers, to improve the robustness of future analyses, it is recommended that primary, geolocated LCI data be adopted and collected directly from target farms. This would reduce discrepancies arising from generic databases such as Ecoinvent and provide more accurate information on fertilizers, yields, and management practices specific to different agricultural contexts.

At the same time, the multifunctionality of agricultural systems must be addressed through advanced allocation methods or multiple functional units.

Technically, this SLR highlighted that LCA is a useful tool for agro-industrial companies, policymakers, and international organizations, providing data for sustainable policies. However, taken as a whole, it must be underlined that this modeling approach can be useful for analyzing different crops to provide a full evaluation of their performance, filling the gaps described above. Finally, this study supports farmers in producing sustainable crops, guides policymakers toward ecological models, and supports researchers to ensure data accuracy and comparability of results.

5. Conclusions

This study examined two different crops, DW and legumes, highlighting lower values in many impact categories for legumes, as shown in Table 5. However, the results do not allow for a clear identification of which crop is more sustainable, as the reviewed studies employed various FUs and applied different system boundaries, and the crops often affected geographical areas under different soil and climate conditions.

Notwithstanding, the results underline that crop rotation involving DW and legumes benefits from biological nitrogen fixation, requiring less fertilization and resulting in a reduction range from 6% [41] to 45% [43] in GWP values compared to monoculture DW cultivation, although these vary by cropping system and soil characteristic. The variation in GWP values is strongly influenced by fertilizer management, since monoculture systems typically require greater nitrogen inputs, particularly for wheat cultivation. In contrast, the need for synthetic fertilizers is reduced by crop rotation with legumes, thereby lowering GHG and improving the environmental performance of the overall cropping system.

Additionally, the sensitivity analysis boosts the use of multifunctional, which offer a more holistic perspective and improves comparability [28,58]. In particular, the analysis reveals that the environmental impact of a crop may vary depending on the FU (mass- or area-based) and the productivity and highlights the importance of using multiple FUs to obtain a comprehensive assessment of agricultural systems. Specifically, conventional agriculture shows a greater GWP difference when the FU is 1 ha, as illustrated in Figure 3. In contrast, organic agriculture exhibits a higher LU impact due to lower yields, which require greater land use per kilogram of crop. EP, largely driven by fertilizer application, is more pronounced in conventional systems, particularly in DW cultivation.

Overall, conventional farming is more productive, but it has a higher environmental impact due to the intensive use of chemicals and energy. Organic systems have lower environmental impacts per hectare, but they have higher emissions per kg due to reduced yields. In terms of resource efficiency, conventional systems are preferable, even though they rely heavily on non-renewables. Methodologically, this SLR provided valuable research opportunities since it allows for the design of study samples suitable for the purpose, including the selection of the period to be covered. It represents a replicable methodology that can be adapted to different topics.

These findings reinforce the importance of LCA as a tool for supporting decisions and identifying trade-offs between productivity and environmental performance. This will help foster the transition towards more sustainable and resilient agricultural systems.