The peanut complex

The Argentine peanut (Arachis hypogaea L.), which is mostly concentrated in the province of Córdoba, has shown a notable capacity to adapt to adverse climatic conditions. With a total of 532,991 hectares planted nationally and an estimated production of 1.81 million tons, the 2024/25 season achieved historic records, positioning Argentina among the top peanut-exporting countries globally. At the provincial level, Córdoba stands out as the leading peanut-producing region, accounting for 70% of the cultivated area in the country, with over 374,000 hectares. It is followed by the province of Buenos Aires, which covers nearly 85,000 hectares (16% of the national total), and La Pampa with 37,000 hectares (7%). This growth not only reflects improved yields (4.13 t ha⁻¹ in Córdoba), but also a strategic decision by producers to diversify in response to the declining profitability of crops such as soybean and corn (Bolsa de Comercio de Rosario, 2025).

According to recent data from the Argentine Peanut Chamber (CAM) and USDA‑FAS, Argentina reached record peanut production levels of approximately 1.8 million tons in the 2024/25 season and remains the world’s leading peanut exporter, with minor differences among sources explained by reporting scope and timing. India holds second place, with 0.98 million tons of projected exports, and China ranks third, with 0.65 million tons. Exported products from Argentina included shelled peanuts (USD 527 million), roasted peanuts and peanut butter (USD 81 million), peanut oil (USD 64 million) and pellet/expeller (USD 5.1 million). The European Union is the main importer from Argentina, accounting for 60% of the total Argentine exports (Ferrari et al., 2025).

The peanut industry in Argentina has become a significant regional economic driver, providing more than 12,000 jobs and with exports exceeding USD 1 billion annually. The industrial sector has state-of-the-art processing plants, with many of them having installed production capacity higher than the actual production, enabling an increase in processing volume (Cámara Argentina del Maní, 2022).

Global context and sustainability challenges in peanut production

The growing global awareness about climate change and sustainability has exerted unprecedented pressure on agri-food supply chains. International markets, regulators and consumers demand transparency of the environmental impact of products; in this context, quantification tools have turned into a strategic imperative. Thus, the Life Cycle Assessment (LCA) has become the principal method to evaluate the environmental impacts of a product along its entire value chain.

Environmental Product Declarations (EPDs) emerged as a standardized and independently verifiable mechanism to communicate Life Cycle Assessment (LCA) results, based on internationally recognized and voluntary standards such as ISO 14025 (2006), ISO 14040 (2006) and ISO 14067 (2018). These standards do not constitute mandatory regulations, but provide harmonized methodological frameworks that are widely referenced in environmental policies, green procurement schemes and market-based sustainability requirements. A Type III EPD represents a transparent and comparable disclosure of the environmental performance of a product and serves as a strategic tool for product differentiation in highly competitive international markets and for addressing the sustainability demands of global value chains.

The LCA has been the focus of studies in the agricultural sector in diverse geographic regions. However, no complete LCA has been conducted for peanuts in the world. Argentina, one of the world’s leading peanut producers and exporters, lacks a thorough “cradle-to-grave” study. There is no accurate knowledge about the complete environmental profile of a vertically integrated supply chain, one that simultaneously evaluates agricultural production and industrialization of several byproducts, from raw peanut to the most complex processed products. The lack of regional data of life cycle inventory and emission factors for Argentina is a critical gap in scientific knowledge, especially regarding the influence of soil management practices (e.g., conventional tillage and its impact on carbon emissions) as these studies often focus on geographical areas where energy matrices and agricultural practices differ significantly from the local ones. Conversely, there are some reference studies of peanut products that have quantified environmental footprints in highly productive areas and that have provided valuable reference information for industry and academia. In the United States, the American Peanut Council (2022), based on the Sustainable U.S. Peanuts Initiative, reports field-level greenhouse gas emissions for the 2021 growing season averaging 0.167 kilograms of CO₂ equivalents per kilogram of product (kg CO₂ eq kg⁻¹) of raw peanuts at the farm gate. This value is derived from grower-reported data and represents the first pilot-year results of the program.

For context, these results are compared against the Field to Market national benchmark of approximately 0.300 kg CO₂ eq kg⁻¹ of peanuts, which is not specific to a single year but reflects a broader reference value developed from national datasets and modeling frameworks. Therefore, the difference between 0.300 and 0.167 kg CO₂ eq kg⁻¹ should not be interpreted as a year-to-year reduction, but rather as a comparison between a benchmark reference and recent measured performance, indicating that most participating fields (approximately 90%) performed below the national benchmark.

On the other hand, a mean carbon footprint of 2.88 kg CO₂ eq kg⁻¹ of peanut butter consumed was reported by McCarty et al. (2014). This value corresponds to a cradle-to-grave life cycle assessment, in which the system boundary extends from agricultural production through processing, retail, consumer use (including household activities), and final disposal of packaging (McCarty et al., 2014, 2016a, 2016b). Likewise, an impact of 0.670 kg CO₂ eq kg⁻¹ was calculated for blanched peanuts in Brazil (Ramos et al, 2023; Embrapa Meio Ambiente, 2021). These values provide useful contextual reference points, since peanut cropping practices in both countries are similar (Table 1).

Table 1

Comparative Carbon Footprint of Peanut Products

It is important to note that most published studies on peanuts quantify carbon footprint (CF, or Global Warming Potential, GWP) rather than performing a complete LCA covering multiple environmental impact categories. Therefore, the international comparison presented here refers exclusively to carbon footprint values expressed as kg CO₂ eq kg⁻¹ of product, under comparable system boundaries. Full LCA-based comparisons remain largely unavailable in the literature.

Results of a cradle-to-grave analysis for peanuts in Argentina would make a great contribution as quantifying environmental footprints results in a roadmap for continuous improvement. Identifying the stages of greatest impact (i.e., hotspots), allows producers or industries to direct their investments to cleaner technologies, optimize processes and reduce costs, thereby strengthening their competitiveness in international markets. Knowledge of the environmental profile of local products boosts transparency for consumers and validates the commitment of the peanut sector to sustainability; thus, this knowledge becomes a differentiating factor and an added value, both crucial aspects in international trade.

The aim of this study was to determine the environmental impacts along the value chain of seven peanut products originating from the central region of Argentina, from agricultural production to the end-of-life. An LCA was performed, which can inform EPD and support continuous improvement and sustainability validation. This work follows the authors’ research line (Bongiovanni et al., 2016), using the LCA method to evaluate the entire value chain, from cradle-to-grave of peanut products, but also including the environmental impact categories required by EPD as well as the carbon footprint.

Life Cycle Assessment.

LCA is a methodological frame standardized by ISO 14040 (2006) and ISO 14044 (2006), which allows one to account for the environmental burden generated by a product or service along its life cycle. Its theoretical basis lies in a systematic approach that goes beyond the emission measurements at a specific point in the production process, encompassing the extraction of raw materials (cradle) to a product’s end-of-life (grave). This comprehensive approach contrasts sharply with the partial environmental impact assessments, such as a simple measurement of emissions at the farm gate or factory gate, offering a holistic and objective view of environmental externalities. The products evaluated in this study are commercialized under a Business-to-Business (B2B) model.

The LCA’s robustness lies in its capacity to identify the transfer of environmental burdens along the value chain, a phenomenon that specific measurements fail to detect. For example, an analysis of the emissions at the processing plant might detect a low impact but would ignore the high impacts generated during agricultural production or long-distance transportation. The LCA’s cradle-to-grave approach allows an accurate diagnosis of the environmental hotspots, providing a roadmap for continuous improvement.

Declared Units.

The declared units followed the Central Product Classification (CPC), including CPC 21421 (1 kg of shelled peanuts and 1 kg of blanched peanuts) and CPC 21495 (1 kg of roasted peanuts, peanut granules, fried peanuts, peanut paste, and peanut butter). In all cases, the declared unit refers to 1 kg of product excluding packaging. The CPC provides a comprehensive and internationally harmonized framework for classifying goods and services (United Nations, 2015).

Geographical and temporal scope.

Data were obtained from three farms representative of peanut production in Argentina: two in the province of Córdoba (Tercero Arriba and General Roca departments) and one in Buenos Aires province (Lincoln district). The industrial complex is located in Hernando, Córdoba province, and products produced are mostly exported to Europe. The analysis covers the 2023/2024 peanut crop season and the calendar year 2024 for industrial data. The temporal representativeness of the study corresponds to current production conditions, with primary data reflecting the most recent complete production cycle available.

Primary peanut production on the farm

The following factors were included: production and use of agricultural inputs (agrochemicals, fertilizers, fuel, seeds), production of their packaging, emissions from the use of fuels for transport and agricultural activities (planting, spraying, digging and harvesting), emissions from the application of nitrogen fertilizers, and those from above- and below-ground residue decomposition. Data of use of fuels and inputs were taken from the records of each farm.

Weighted averages of the results obtained from the farms were calculated so that the input to the industry represents output from the field.

For the agricultural stage, direct emissions from the application of nitrogen fertilizers -which were used only in low rates as foliar feeding- as well as from decomposition of above- and below-ground crop residues, were considered. These emissions were calculated using Intergovernmental Panel on Climate Change (IPCC) tier 2 methodology, with default equations and emission factors specific for Argentina, taken from the Biennial Update Reports of the Ministry of Environment and Sustainable Development (MAyDS, 2023). This approach improves accuracy, since it uses disaggregated activity data.

On the farm located in Tercero Arriba department, Córdoba, yield was 4000 kg ha⁻¹ of in-shell peanut. Fungicide/insecticide-treated seeds were planted at a density of 175 kg ha⁻¹ using a paratill, weeder, double action disc harrow and conventional planter. Crop fertilization consisted of 1.57 kg ha⁻¹ of compound fertilizer and 0.63 kg ha⁻¹ of micronutrients (i.e., a total rate of 2.2 kg ha⁻¹). Crop protection included the application of herbicides (4.88 kg ha⁻¹ of active ingredient, ai), insecticides (0.091 kg ha⁻¹ ai) and fungicides (3.95 kg ha⁻¹ ai). The amount of agrochemical packaging waste was 0.54 kg ha⁻¹ of plastic material, which was destined for recycling. Fuel consumption was 67.67 L ha⁻¹ of diesel; for technical assistance, a pick-up truck travelled 2 km per cultivated ha. Input transport to the field required 8 km by truck (3.5 to 7.5 t load capacity). Transport of in-shell peanuts from the farm to the processing plant was also 8 km by truck (16 to 32 t load capacity). Transport of agrochemical packaging waste covered 115 km by truck (3.5 to 7.5 t load capacity). Crop residues were left on the soil surface as cover and no cover crops were used.

On the farm in General Roca department, Córdoba, yield was 6200 kg ha⁻¹ of in-shell peanut. Treated seeds were planted at a density of 175 kg ha⁻¹, using weeder and no-till planter. No fertilization was applied in the cases studied. Crop protection included herbicides (7.09 kg ha⁻¹ ai), insecticides (0.088 kg ha⁻¹ ai) and fungicides (3.12 kg ha⁻¹ ai). The amount of agrochemical packaging waste was 0.62 kg ha⁻¹ of plastic material, which was destined for recycling. Gas oil consumed amounted to 48.99 L ha⁻¹, and 12.47 km were travelled by pick-up truck for technical assistance per cultivated ha. Input transport covered 6 km by truck (3.5 to 7.5 t load capacity). Transport of in-shell peanuts from the farm to the processing plant covered 300 km by truck (16 to 32 t load capacity). Transport of packaging waste covered 82 km by truck (3.5 to 7.5 t load capacity). Crop residues were left on the surface as cover and no cover crops were used.

On the farm in Lincoln district, Buenos Aires, yield was 5,700 kg ha⁻¹ of in-shell peanuts. Fungicide/insecticide-treated seeds were planted at a density of 175 kg ha⁻¹, using a double action disc harrow, and conventional tillage. No fertilization was applied to the crop. Crop protection consisted of the application of herbicides (6.14 kg ha⁻¹ ai), insecticides (0.10 kg ha⁻¹ ai) and fungicides (3.39 kg ha⁻¹ ai). The amount of agrochemical packaging waste was 0.58 kg ha⁻¹ of plastic material, which was destined for recycling. Fuel consumed amounted to 58.23 L ha⁻¹ of gas oil; for technical assistance, a pick-up truck travelled 23.34 km per cultivated ha. Transport of inputs covered 28.75 km by truck (3.5 to 7.5 t load capacity). Transport of in-shell peanuts from the farm to the processing plant covered 505 km by truck (16 to 32 t load capacity). Transport of agrochemical packaging waste covered 65 km by truck (3.5 to 7.5 t load capacity). Crop residues were left on the soil surface as cover and no cover crops were used.

Peanut processing industry

Peanut processing primary data was obtained for products, like shelled peanuts, blanched peanuts and their byproducts, from a company representative of the sector, located in Hernando, Córdoba. For each product, the company identified the use of inputs, fuel and energy, the suppliers involved, the process losses and the byproducts obtained.

Regarding logistics, all types of packaging materials used for the commercialization of each product were included; they were calculated according to the percentage of participation in the total volume exported. The analysis considers the terrestrial transportation by truck to the port and the marine transportation to the final destination. Lastly, final disposal of packaging materials was considered, thereby completing the product’s life cycle.

Figure 1 shows the stages included in the study, with the division between upstream, core and downstream processes, as requested by the Product Category Rules (PCR). All the declared units and their relationships are shown.

Figure 1. Stages of the production system and system boundaries, including upstream, core and downstream processes.

The industrial process starts with the reception of in-shell peanuts, which are dirty and moist, in bulk in trucks. Upon arrival at the plant, each truck is weighed, and samples are collected for further laboratory analysis, following quality rules. If peanuts comply with the requirements, they are received at the plant. Once unloaded, peanuts are subjected to mechanical pre-cleaning using aspiration and screening procedures to remove large foreign materials (i.e., stones, stems, leaves and other visible debris). Then, to avoid fungal development, peanuts are dried, if necessary, to reach less than 10% moisture. This process is conducted in double-floor drying wagons, through which air at ambient or high temperature –depending on the environmental humidity– is blown. Once dried, peanuts are stored in bins until processing. These processes entail losses due to dirt, foreign materials and moisture.

At the cleaning stage, in-shell peanuts pass through a series of devices that remove heavy elements (i.e., stones, dust and leaves) through aspiration. After this process, the in-shell peanuts are introduced into the shelling machine, where the shell is removed by some rollers and the shell remains are separated from the grain. Then the grains undergo a two-type sorting process: sorting by size and color. At this initial stage, the shelled peanuts are obtained, which are stored in industry standard (APC, 2023) one-ton flexible intermediate bulk containers (big-bags) until the next stage of commercialization. In addition, shells obtained as a byproduct are sold for energy generation; peanuts rejected during processing are sold to the oil industry, and there are losses due to dirt or foreign materials. In accordance with the applicable Product Category Rule (PCR) and the General Program Instructions (GPI) of the EPD system, the environmental burdens of the shared processes were allocated among the main product and the valorized byproducts (e.g. shells used for energy generation and rejected peanuts sold to the oil industry), and no environmental credits were assigned.

Shelled peanut kernels are first subjected to a dry thermal treatment in an oven, followed by controlled cooling to promote differential contraction between the kernel and the testa, thereby loosening the skin. The kernels then pass through abrasive rollers with rough surfaces that mechanically rub and detach the skin, while an air aspiration system removes the released skin particles. Subsequently, the kernels are conveyed over vibrating screens, where remaining skin fragments and fines are separated. A final sorting stage separates whole kernels from splits and classifies them by size. Byproducts recovered from the sorter are obtained and diverted to other lines; the skin is sold for energy generation, and discolored sorter rejects are used in the oil industry. There are also losses due to moisture. Blanched peanuts are stored in big-bags.

Roasted peanuts can be produced using both blanched and unblanched peanuts, with the former being the most used. Roasting involves putting the peanuts in an oven at a controlled temperature (160 to 180 C). After this process, peanuts are rapidly cooled with air to stop the roasting process. Besides roasted peanuts with or without skin, salted peanuts can also be obtained; in this case, oil is added before roasting. Once the products are cooled, they are packed and stored.

Fried peanuts can also be produced using both shelled and blanched peanuts, most commonly using peanuts without skin. Unlike roasted peanuts, peanuts are fried in oil at about 160 C, then placed on a mesh conveyor where they are cooled, and the excess oil is drained. Finally, peanuts are salted in a rotating cylinder. At the end of the process, fried peanuts pass along a belt for packaging and further storage.

Peanut butter is produced with roasted peanuts without skin. Peanut grains are ground with rollers, until a creamy consistency is obtained. Then salt, sugar and stabilizer are added, and the butter is homogenized. Finally, the product is packaged using the different packaging options offered by the company.

Peanut paste is produced in a similar way to that of peanut butter, but no ingredients are added. Once the desired consistency is obtained by grinding, it can be packed as a finished product, or granules can be added to obtain a crunchier paste.

Finally, peanut granules are produced by partially grinding roasted peanuts without skin; then they are packaged and stored.

Models for the characterization of environmental impact

Selection of models for the characterization of environmental impact used are in line with the scientific principles that describe the environmental phenomena evaluated. The impact categories recommended by the PCR of the EPD system ( https://www.environdec.com/pcr/env-perf-indic/gpi5) applied in this study are detailed in Table 2.

Table 2

Categories of environmental impact, parameters evaluated and models applied.

Climate change (total), expressed as Global Warming Potential (GWP), quantifies the cumulative radiative forcing of greenhouse gas emissions over a 100‑year time horizon, aggregating contributions from fossil, biogenic, and land‑use‑related CO₂, as well as other greenhouse gases, and expressing them as kilograms of CO₂ equivalents (kg CO₂ eq) according to the IPCC baseline characterization model.

Eutrophication potential describes the contribution of nutrient emissions, mainly nitrogen and phosphorus compounds, to excessive enrichment of aquatic and terrestrial ecosystems, which may result in algal blooms, oxygen depletion and loss of ecological balance.

Ozone depletion potential (ODP) quantifies the contribution of emissions to the destruction of stratospheric ozone, by accounting for substances that reduce ozone concentrations relative to a reference compound and is expressed as kilograms of CFC-11 equivalents (kg CFC-11 eq).

Acidification potential (AP) quantifies the contribution of emissions to the increase of acidity in soils and water bodies through the release of acidifying substances, such as sulfur dioxide, nitrogen oxides and ammonia, which lead to proton formation (acid rain), and is expressed as moles of H⁺ equivalents (mol H⁺ eq).

Photochemical ozone creation potential (POCP) quantifies the contribution of emissions, mainly nitrogen oxides (NOₓ) and volatile organic compounds (VOCs), to the formation of tropospheric (ground-level) ozone through photochemical reactions in the presence of sunlight and is expressed as kilograms of NMVOC equivalents (kg NMVOC eq).

Depletion of abiotic resources quantifies the consumption of nonrenewable natural resources, including minerals, metals, and fossil fuels, by accounting for the extraction of finite stocks and their potential scarcity. This impact reflects the reduction of resource availability for future use and is expressed using antimony equivalents (kg Sb eq) for minerals and metals, and energy content (MJ, net calorific value) for fossil fuels, following the CML characterization method.

Water deprivation potential quantifies the potential impact of freshwater consumption on water availability by accounting for the reduction of locally available water resources relative to demand. It reflects the risk of water scarcity induced by water use and is expressed as cubic meters of worldequivalent deprived water (m³ world eq), based on the AWARE (Available Water Remaining) method in accordance with ISO 14046.

Allocation of environmental burdens

Within the framework of the LCA, allocation of environmental burdens is a crucial theoretical challenge in modelling production of processed peanuts to avoid over- or underestimating impacts. Allocation theory determines that environmental burdens generated during a shared process should be distributed among the different co-products and byproducts that have economic value. In this study, the allocation method was based on a biophysical criterion, specifically by mass of products and byproducts (e.g., shell, skin), following the guidelines of the PCR for “Food and Beverage Products” (International EPD System, 2025). The selected method ensures an equal distribution of environmental burdens, reflecting the actual material burden of the production system.

Figure 2 shows the processes of allocation. Since all the products share some part of the production chain, all the declared units are affected by at least one allocation. Table 3 presents the allocation of environmental burden to the products and byproducts.

Figure 2. Diagram of product integration with identification of allocation points. Numbers in parenthesis correspond to numbers of products in Table 3.

Table 3

Percentage of environmental burden allocated based on mass in the industry.

For peanut butter and paste, environmental burdens were also allocated based on mass because they generate byproducts for the oil industry. However, they were not included in the table because the amount was lower than 0.1%.

After the input and output inventories of each operation and stage were generated, the unit emissions associated with each input were obtained: inputs, raw materials and energy referring to the corresponding declared unit. Then, the amounts used of each input were associated with their unit emissions to obtain the environmental impact for each inventory element.

Life Cycle Assessment modeling and impact calculations were performed using SimaPro software version 9.6, and environmental profiles for processes not covered by primary data collection were sourced from the Ecoinvent database version 3.10 (Wernet et al., 2016). These tools provided background data and methodological support essential for completing the life cycle inventory and impact assessment in accordance with ISO standards.

Environmental impact parameters for each declared unit

Global Warming Potential

Global Warming Potential (GWP) results for each declared unit were obtained through a cradle-to-grave LCA following ISO 14040/14044 and ISO 14067 standards. For each product, greenhouse gas emissions were first quantified at the lifecycle inventory level by compiling activity data for agricultural production, industrial processing, packaging, transportation and end-of-life. Emissions of CO₂, CH₄ and N₂O were calculated using primary data and IPCC Tier 2 equations with Argentina-specific emission factors. Inventory flows were then converted into GWP using the IPCC 100-year characterization factors (IPCC, 2021).

The resulting impacts were aggregated by lifecycle stage and expressed per declared unit (1 kg of product, excluding packaging weight), after allocation of shared processes based on mass, as described in the Methods section. Final GWP values reported here therefore represent the sum of fossil, biogenic and landuserelated CO₂ equivalents across all stages of the product life cycle.

Based on the aggregation of stage-level GWP results, this parameter was considerably higher in the products with greater degree of industrial processing, with values ranging from 0.631 kg CO₂ eq kg⁻¹ for shelled peanuts (Table 4) to 1.009 kg CO₂ eq kg⁻¹ for peanut paste (Table 9). In all cases, most of the GHG emissions were concentrated in the agricultural production stage, accounting for 40% to 66% of the total (Table 11). In the field, 80% of the emissions were from carbon loss through soil management and from harvest residues. On two of the farms, conventional planting was used, which has a greater impact on soil carbon content than no-till.

Table 11

Distribution of the GWP impact by product and by stage.

The second most significant stage in terms of GWP was distribution of finished products, with a 16%-24% contribution to the total (Table 11). Environmental burden at this stage was distributed between marine and land transport, with the former being up to 2% higher than the latter. Emissions from products with higher added value tend to concentrate at the industrial stage, with the use of electricity and burning of fossil fuels being the main factors increasing total emissions, besides the addition of other inputs, like salt, sugar and other additives.

For clarity, the international comparisons in Table 1 are limited to the carbon footprint indicator (Global Warming Potential, GWP) and do not encompass the full range of environmental impact categories assessed in the complete LCA.

Based on this analysis, a direct comparison was performed using published carbon footprint values of comparable peanut products, with clearly defined system boundaries. The comparison contrasts Argentine peanut butter produced locally and exported to international markets (including overseas maritime transport), as quantified in this study, with peanut butter produced and consumed within the United States, as reported in the cradle-to-grave assessment by McCarty et al. (2014).

Although some reference studies used for international comparison were conducted more than a decade ago, the results presented in this study are not based on historical secondary data, but on primary activity data collected during the 2023/2024 agricultural season and the 2024 industrial and logistics operations. Changes in production processes, management practices, energy use and logistics patterns were therefore explicitly captured through updated farm records, industrial energy and fuel consumption data, and current transportation distances and modes. Older studies are used exclusively for contextual comparison and benchmarking purposes; differences observed should thus be interpreted not only as geographical contrasts, but also as the result of technological progress, improved efficiency and structural changes in the Argentine peanut value chain over time.

Despite the inclusion of long-distance marine transportation in the Argentine case, results show that the cradle-to-grave carbon footprint of Argentine peanut butter is substantially lower than the U.S. national benchmark. Similarly, blanched peanuts produced in Argentina exhibit a 21% lower carbon footprint than those reported for Brazil under comparable cradle-to-gate system boundaries. Distribution includes truck transport (620 km) to port and international maritime transport (11,928 km to Europe). Marine transport represents between 60% and 75% of the distribution stage GWP, but less than 14% of total cradle-to-grave emissions. These results reinforce that production system characteristics may outweigh transport distance in determining total life cycle impacts.

The comparative results between Argentina and the United States highlight that differences in life cycle environmental performance are primarily driven by structural agronomic and supply chain factors rather than by isolated management practices.

In the Argentine system, lower impacts at the farm stage are associated with a combination of characteristics that reduce both direct and indirect emissions. Peanut production is predominantly rainfed, avoiding the energy requirements and emissions associated with irrigation. In addition, fertilization rates are minimal, as the crop relies largely on biological nitrogen fixation, which substantially reduces upstream emissions linked to synthetic inputs. Crop rotations and favorable agroecological conditions contribute to lower disease pressure, limiting the need for agrochemical applications. Together, these factors result in a relatively low-input production system with moderate fuel requirements and reduced overall emission intensity.

Beyond field-level management, post-harvest handling represents an additional source of differentiation. In Argentina, peanut drying is primarily performed at the industrial stage using centralized, large-scale facilities, which are more energy-efficient than decentralized on-farm drying systems. This contrasts with systems where drying is conducted at the farm level, typically with lower energy efficiency per unit of product.

Recent data from the American Peanut Council’s Sustainable U.S. Peanuts initiative indicate a clear downward trend in farm-level greenhouse gas intensity in the United States, reflecting continuous improvements in agronomic efficiency. However, structural differences between production systems remain relevant. These include a relatively greater reliance on irrigation and external inputs in the U.S., as well as differences in post-harvest configurations and supply chain organization.

As a result, even under ongoing efficiency gains, these underlying structural characteristics continue to influence the overall life cycle performance. The findings suggest that system-level attributes -such as input intensity, water management, and process configuration- play a more decisive role than transport distance in determining total cradle-to-grave environmental impacts.

Sensitivity analysis

This analysis consists of modifying a variable of interest and analyzing the effects of this change on the result of each environmental impact category. Sensitivity analyses were performed by modifying yield (scenarios 1 and 2) and management type (scenario 3). A 25% increase in yield reduced total GWP by 8% to 12%, but a 25% reduction increased GWP by 13% to 20%; yield losses caused larger penalties than the benefits of yield increase (Table 12 and Table 13). Table 14 shows the change of agricultural practices, with no-tillage and cover crops. Changes in yield strongly affect the results because most agricultural inputs and emissions are related to land area (ha); therefore, higher yields dilute impacts per kilogram of product, whereas lower yields concentrate them.

Table 12

Scenario 1: Effects of mean 25% INCREASE in peanut harvest yield per ha, in absolute and relative values.

Table 13

Scenario 2: Effects of mean 25% DECREASE in peanut harvest yield per ha, in absolute and relative values

Table 14

Scenario 3: The relative change to global warming (kg CO₂ eq kg⁻¹) due to use of no-tillage and cover crops.

Comparative analysis

This comparative analysis is illustrative rather than exhaustive and is restricted to the carbon footprint indicator (Global Warming Potential), reflecting the limited availability of published LCA’s that are methodologically comparable at the product level. The objective is not to derive global averages or rank countries, but to contextualize the results obtained for Argentine peanut products within the existing peer‑reviewed literature on comparable peanut products.

Two product‑level benchmarks were identified as suitable for comparison based on system boundary definition, declared unit consistency and transparency of methodological documentation: (i) blanched peanuts produced in São Paulo State, Brazil, evaluated under a cradle‑to‑gate system boundary by Ramos et al. (2023), and (ii) peanut butter produced and consumed in the United States, evaluated under a cradle‑to‑grave system boundary with production data collected from Georgia by McCarty et al. (2014).

Other major peanut‑producing countries (e.g., China and India) were not included because, at the time of writing, no peer‑reviewed LCA studies with clearly defined system boundaries, declared units and inventory transparency were available for comparable peanut products. This exclusion is therefore related to data availability and methodological compatibility rather than to geographic relevance. Although Brazil was excluded from generic background‑database benchmarks, it is explicitly included here through the product‑specific LCA of blanched peanuts (Ramos et al., 2023), who reported a cradle-to-gate carbon footprint of 0.67 kg CO₂ eq kg⁻¹ of product. Under comparable system boundaries, the results obtained in the present study show values approximately 21% lower. In a similar context, for peanut butter, McCarty et al. (2014) reported mean cradle-to-grave emissions of 2.88 kg CO₂ eq kg⁻¹, including downstream stages such as retail, consumer use, and end-of-life. According to that study, emissions associated with retail, consumer use and end‑of‑life stages amount to approximately 1.35 kg CO₂ eq kg⁻¹, resulting in an industry‑gate value of 1.53 kg CO₂ eq kg⁻¹.

In the present study, greenhouse gas emissions for peanut butter up to the industry gate, corresponding to the agricultural and industrial processing stages defined as the core production system, amount to 0.781 kg CO₂ eq kg⁻¹.

When comparing the entire life cycle, Argentine peanut butter exhibits a total carbon footprint of 0.951 kg CO₂ eq kg⁻¹, despite the inclusion of long‑distance terrestrial and maritime transport to export markets.

It is important to emphasize that the differences observed in this comparative analysis should not be interpreted as a definitive ranking of environmental performance across countries, but rather as contextual insights derived from studies with differing methodological approaches, temporal scopes, and system configurations. In particular, the U.S. reference study represents a geographically distributed, national-scale system combining data from multiple production regions and processing facilities, whereas the Argentine system analyzed here reflects a more regionally concentrated and vertically integrated supply chain. These differences in data structure and system representation limit the robustness of direct quantitative comparisons.

Within this context, the lower values observed in the Argentine system are associated with specific sources of efficiency along the production chain. At the farm level, gains arise primarily from reduced energy and input requirements, including the absence or limited use of irrigation, low fertilization rates linked to biological nitrogen fixation, and comparatively lower agrochemical intensity. In addition, crop management practices adapted to local agroecological conditions contribute to relatively stable yields with moderate use of machinery and fuel.

At the industrial stage, efficiency gains are associated with process integration and scale, particularly in post-harvest operations such as drying and primary processing, which are concentrated in specialized facilities rather than dispersed across multiple locations. This configuration may contribute to lower energy use per unit of processed product.

Overall, these factors help explain the differences observed in this study; however, given the heterogeneity of methodological frameworks and data sources across the literature, such comparisons should be interpreted as indicative of underlying system characteristics rather than conclusive evidence of superior environmental performance.