Mondina Francesca Lunesu
Maria Francesca Caratzu
Giuseppe Pulina
Gianni Battacone- Dipartimento di Agraria, Università degli Studi di Sassari, Sassari, Italy
This study aimed to estimate the net carbon footprint (Net CFP) of Mediterranean cow–calf systems and to assess the role of cultivation intensity in shaping environmental performance. Eighteen beef farms adopting a cow–calf grazing system with an open productive cycle were considered. Farms were selected according to cultivation rate (CR) and classified as high (high-CR; >15% of cultivated land) or low (low-CR;<15% of cultivated land). Data were collected through farmer interviews using a cradle-to-farm-gate approach, in accordance with ISO 14040:2006 and ISO 14044:2006 standards. The average of five production years was used as the temporal boundary. Emission intensity was expressed as kilograms of CO2 equivalent (CO2e) per kilogram of live weight (LW) sold from yearling beef, per kilogram of total LW sold from end-career cows, bulls, and yearling beef, and per hectare of land. Soil carbon sequestration was estimated by accounting for above- and belowground biomass residues and organic carbon from manure deposition. Carbon sequestration from Meriagos, forests, and Mediterranean scrubland was also included. Gross CFP was lower in low-CR farms than in high-CR farms (19.80 vs. 26.75 kg CO2e/kg LW sold). Enteric methane was the main contributor, accounting for 68.3% and 74.2% of total greenhouse gas emissions in high- and low-CR farms, respectively. When carbon sequestration was included, Net CFP was significantly lower (P<0.01) in low-CR farms. In conclusion, lower cultivation intensity reduced the CFP of cow–calf systems by generating higher carbon credits that could fully offset emissions from the fattening phase. However, this potential is based on farm-level estimates and does not automatically translate into certified carbon credits. Therefore, further work is needed to assess eligibility and practical implementation within formal carbon crediting frameworks.
1 Introduction
The global livestock sector emits about 6.2 Gt CO2e/year, representing approximately 12% of total anthropogenic greenhouse gas (GHG) emissions. Cattle are the main contributors within the sector, accounting for more than 60% of livestock emissions (3.8 Gt CO2e/year; GLEAM 3 dashboard, GLEAM, 2015).
At the European level, livestock systems have changed markedly in recent years. These changes are driven by new regulations, structural adjustments, and shifts in consumer preferences; environmental concerns have also increased (Adams et al., 2025). Despite the progress made, European livestock production remains far from sustainability targets (Guyomard et al., 2021; Adams et al., 2025).
Within the European context, Italy represents a relevant case, as it is the fourth-largest beef producer in Europe after France, Germany, and Spain. In 2023, per capita Italian beef consumption was 16.4 kg, while the self-sufficiency rate was 40.3% (ISMEA, 2024). Both indicators underscore a substantial production deficit. This gap could be partially addressed through more economically and environmentally sustainable farming practices. Promising options include innovative technologies, sustainable feeding strategies, and regenerative agricultural practices. The integration of agroforestry systems is also relevant (Dick et al., 2015; Eldesouky et al., 2018; Caprarulo et al., 2022).
Among Italian regions, Sardinia (southern Italy) represents a particularly relevant context. The cattle sector is the second most important livestock activity in the region, including about 9,000 farms and 283,000 head (BDN, 2025). Beef production involves 80% of farms and 70% of animals (Lunesu et al., 2024, 2025a). In this area, breeding is mainly based on the cow–calf system, in which calves stay with their mothers at pasture until weaning. After weaning, calves are either integrated into the herd or sold for fattening (Lunesu et al., 2024). Large marginal areas are used during the first phase of production and are mainly represented by Meriagos, which are typical agroforestry pasture systems where herbaceous and arboreal species coexist (Pulina et al., 2020). These systems are comparable to the Dehesas in Spain and Montados in Portugal (Eldesouky et al., 2018; Laporta et al., 2021). Their role may be relevant for carbon dioxide (CO2) removal through vegetation and soil (Laporta et al., 2021; Carranca et al., 2022; Pan et al., 2025).
Therefore, considering that livestock systems both emit and remove CO2, their environmental impact should be assessed by balancing emissions and sequestration (Kim et al., 2016; Günther et al., 2024; Lunesu et al., 2025b). In this context, the Sardinian cow–calf system may represent a potential model of environmentally sustainable livestock farming (Lunesu et al., 2022). However, the extent of carbon removal may vary with cultivation intensity. Intensive practices, characterized by high inputs of fertilizers, irrigation, and mechanization, generally increase emissions from soil respiration, fuel use, and synthetic input production (Grossi et al., 2020). In contrast, extensive or low-input systems rely more on natural grasslands and local feed resources, which can enhance soil organic carbon storage and reduce indirect emissions (Soussana et al., 2010).
To our knowledge, no studies have quantified the carbon footprint (CFP) of the Sardinian cow–calf system. Therefore, this study aimed to estimate the net carbon footprint (Net CFP) of this system. We hypothesize that farms with a lower cultivation rate (CR; i.e., the proportion of cultivated land relative to total farm area) have a lower Net CFP per unit of live weight (LW) sold than farms with a higher CR. This is expected because extensive systems, with a greater proportion of natural and semi-natural grasslands, have a higher carbon sequestration capacity. We further expect that farms with a higher CR generate more crop-related GHG emissions from soil management, fuel use, and input application. These emissions are only partially offset by higher technical efficiency and per-cow productivity. This hypothesis meets the criteria for a sound experimental hypothesis as defined by Pulina (2025).
The specific objective of this study was to assess how the degree of land cultivation influences the Net CFP in a Mediterranean cow–calf system. Insights from this research can be used to identify management strategies that improve the environmental sustainability of these livestock systems.
2 Materials and methods
2.1 Carbon footprint approach
The quantification of CFP, GHG emissions per kg product, was performed using a life cycle assessment (LCA) approach in accordance with ISO 14040 (2006) and ISO 14044 (2006) guidelines.
2.1.1 System boundary and functional unit
The system boundary was defined as cradle to farm gate. The system included all relevant stages of production, from feed production (grass and hay) to the farm gate, where yearling beef are sold to fattening farms (Figure 1). Specifically, cattle management, manure management, energy and fuel use, feed production, and the production and transportation of purchased feeds were considered. Indirect land-use change, fattening, slaughtering, meat processing, and consumption were excluded from the study.
Figure 1. Schematic representation of the system boundary, defined as a cradle-to-farm-gate perspective. The system boundary included the following stages: cattle management, manure management, energy and fuel use, feed production, and production and transportation of purchased feeds to the farm. Indirect land-use change, fattening, slaughtering, meat processing, and consumption were excluded. Net greenhouse gas (GHG) emissions were calculated as the difference between total GHG emissions and total carbon sequestered.
The temporal boundary was defined as the annual average of five production years, from 1 October to 30 September, covering the period from 2016 to 2021.
The global warming potentials of methane (CH4), CO2, and nitrous oxide (N2O) were included, considering both direct and indirect emissions.
The following functional units (FUs) were considered: (i) 1 kg of LW sold from yearling beef; (ii) 1 kg of total LW sold (TLW), calculated as the sum of LW sold from end-career cows, bulls, and yearling beef; and (iii) 1 hectare (ha) of land.
2.1.2 Data collection
Data were collected from 18 cow–calf farms located in the Sardinia region. Farms were randomly selected from a pool of commercial cow–calf farms representative of the typical cattle management systems in Sardinia. Farm contacts were provided by a regional fattening company that regularly purchases calves from cow–calf farms to complete the production cycle. Selection was based on data availability and farmers’ willingness to participate.
Farms were classified according to their cultivation rate (CR) into two groups: high-CR (>15% of cultivated land; n = 9 farms) and low-CR (<15% of cultivated land; n = 9 farms). The CR was defined as the proportion of cultivated land relative to the total farm area. High-CR farms had a greater proportion of cultivated land (>15% of the total farm area), mainly consisting of annual and perennial crops, typically used to support on-farm feed availability. Low-CR farms had less than 15% of their total area under cultivation. Their land use was dominated by natural grasslands, Mediterranean scrubland, and forest areas. Apart from CR, no additional selection criteria related to farm performance were applied.
Farms in both groups operated under comparable cow–calf production systems representative of the Sardinia region. This system is primarily extensive or semi-extensive. Calves are raised with their mothers at pasture until 6–9 months of age (200–300 kg LW). Calves are fed exclusively on maternal milk and represent the main product of cow–calf farms. After this period, animals are sold to fattening companies to complete the production cycle (Lunesu et al., 2024).
These farms are generally small, with fewer than 50 cattle per farm. Herds mainly consist of autochthonous breeds (e.g., Sarda, Sardo-Bruna, and Sardo-Modicana), raised as purebreds or crossed with specialized breeds such as Limousine or Charolaise to produce crossbred animals with intermediate characteristics (Cesarani et al., 2018).
The production system relies on large areas mainly consisting of permanent pastures and silvopastoral areas, especially in hilly and marginal zones of central and northern Sardinia. Stocking rates are generally low, reflecting the limited forage productivity of Mediterranean pastures and the seasonal availability of feed resources (Lunesu et al., 2024). Grazing is the primary feeding strategy for most of the year. Hay and concentrates are provided during periods of pasture shortage, particularly in summer and winter.
Overall, productivity levels are typical of extensive cow–calf systems. Reproductive performance and growth rates are moderate, reflecting both genetic background and environmental conditions. As a result, productivity per hectare and per animal is lower than in intensive beef systems; however, it is consistent with low input use and efficient exploitation of marginal lands.
Data collection was standardized using a structured questionnaire. The survey included information on farm size, land use, fertilizer application rates, pasture management, herd size, animal category numbers, fertility rate, mortality rate, feeding practices, and related variables. In addition, invoices for purchased inputs (e.g., energy and fuel consumption, fertilizer, and seed use) and outputs (LW sold) were collected.
2.1.3 Inventory analysis
The CFP was estimated by considering both primary and secondary emission sources. Primary emissions included enteric CH4, manure CH4, and manure N2O. Secondary emissions included CO2 emissions from purchased feeds, energy, and fuel. On-farm feed CO2 emissions were assessed by accounting for both primary and secondary emission sources.
Primary emissions were estimated using the standard equations of the 2019 Refinement to the 2006 Intergovernmental Panel on Climate Change Guidelines for National Greenhouse Gas Inventories (IPCC, 2019a, b), Chapters 10 and 11 of Volume 4, following the Tier 2 procedure.
Enteric CH4 emissions were estimated by considering gross energy intake (GE) for each animal category, based on net energy requirements and diet digestibility (Equation 10.21; IPCC, 2019a). The animal categories considered were cows, heifers, yearling beef, and bulls, along with the number of animals in each category and their respective body weights (BW; on average, 495 kg for cows, 311 kg for yearling beef, 435 kg for heifers, and 865 kg for bulls). The diet consisted of more than 75% forage, with a digestibility (DE) ≤ 62%. The methane conversion factor (Ym = 7%) was set according to IPCC (2019a) values for forage-based diets (Table 10.12; IPCC, 2019a). A methane conversion factor of zero (Ym = 0) was assumed for calves (IPCC, 2019a).
Manure CH4 emissions were estimated from the excretion of volatile solids following Equation 10.23 of IPCC (2019a), while manure N2O emissions were estimated by considering both direct and indirect N2O emissions. Direct N2O emissions from manure were calculated per animal category (cows, heifers, calves, yearling beef, and bulls), considering the number of animals in each category and the allocation of excreta within the management system. According to Escribano et al. (2022), emissions from manure management are minimal in extensive cattle systems, as these systems rely primarily on grazing. Therefore, the estimation assumed that most nitrogen is returned directly to the soil through droppings and urine deposited on pasture. Indirect emissions were also considered, including nitrogen losses through volatilization, leaching, and runoff.
For primary on-farm feed emissions, both direct and indirect N2O emissions were calculated. Direct N2O emissions from managed soils were estimated using Equation 11.1 of IPCC (2019b), while indirect N2O emissions were calculated as the sum of atmospheric deposition of volatilized nitrogen (Equation 11.9; IPCC, 2019b) and nitrogen lost through leaching and runoff (Equation 11.10; IPCC, 2019b).
Secondary on-farm feed emissions associated with fertilizer and seed use were estimated using emission coefficients reported in the literature (Table 1). In addition, emission factors were applied to quantify emissions from energy, fuel, and purchased feed consumption (Table 1), including GHG emissions related to both production and transportation. For purchased feeds, Italian-specific emission factors were used.
Table 1. Emission factors used to estimate on-farm and off-farm greenhouse gas emissions in cow–calf farms.
2.1.4 Global warming potential
Total GHG emissions were expressed as CO2 equivalents (CO2e) using global warming potentials relative to CO2, over a 100-year time horizon, where 1 kg CH4 = 27.9 kg CO2e and 1 kg N2O = 273 kg CO2e (IPCC, 2021).
2.1.5 Carbon sequestration and net greenhouse gas emissions
Soil carbon sequestration and carbon sequestration from Meriagos, forests, and Mediterranean shrubland were quantified. For this purpose, the total farm area was classified by land use and cultivation. Each land-use category was treated separately. Total farm carbon sequestration was calculated by summing the contributions of each category, avoiding overlap and ensuring that each area was counted only once.
Carbon sequestered by farm soils was estimated using the method proposed by Petersen et al. (2013). A soil profile of 0–100 cm was considered (topsoil: 0–25 cm; subsoil: 25–100 cm). A Mediterranean climate with mild, wet winters and hot, dry summers was assumed, with an annual average temperature of approximately 16°C. According to this method, 9.7% of the organic carbon added to the soil in the first year is sequestered over a 100-year time horizon. Thus, the 9.7% coefficient was applied to the organic carbon remaining in the soil at the end of the production year.
The remaining soil carbon was estimated by considering: (i) carbon from aboveground and belowground biomass residues, and (ii) carbon from manure deposition during grazing.
Biomass residues were quantified according to Lunesu et al. (2025b). Specifically, aboveground biomass residues were estimated in relation to farm area, crop temporal boundaries, crop use (e.g., grass and hay), and crop yield. The corresponding equations and coefficients are reported in Table 2.
Table 2. Equations and indices/coefficients used to quantify above- and belowground biomass residues.
Belowground biomass residues, including roots and rhizodeposition biomass, were calculated using shoot-to-root ratios from the literature (Table 2). Rhizodeposition was assumed to be 0.65 of root biomass (Bolinder et al., 2007). Total aboveground and belowground biomass residues were expressed in t dry matter (DM)/ha and converted to t C/ha using a conversion factor of 0.40 (Dos Santos et al., 2011).
Carbon from manure deposition was estimated in relation to the amount of nitrogen returned to the soil. This was calculated as the difference between N excretion and N volatilization and then converted to C using a C:N ratio of 13.4 (Escudero et al., 2012).
Carbon sequestration by Meriagos, forests, and Mediterranean shrubland was estimated using data from the National Report on Forests in Italy (RAFITALIA, 2019), which reports an average annual organic carbon sequestration rate of 5 t CO2/ha/year. For this estimation, canopy cover was assumed to be 20% for Meriagos and 100% for forests and shrubland. Meriagos, forests, and Mediterranean shrubland areas were treated separately from soil carbon estimates, with no overlap with C-tool values derived from crop residues and manure.
For each farm, the Net CFP was calculated as the difference between total GHG emissions (expressed in CO2e) and total carbon sequestered (also expressed in CO2e).
An index of the carbon offset ratio (COR) was calculated as the ratio between sequestration intensity (carbon sequestered per functional unit; C sequestration/FU) and emission intensity (CFP; total emissions/FU).
To address the asymmetry of the sequestration-to-emissions ratio (i.e., values >1 indicate sequestration exceeding emissions, whereas values<1 indicate the opposite), a natural logarithmic transformation (log COR) was applied. This transformation centers the indicator around zero: a neutral balance yields a value of zero, positive values reflect net sequestration, and negative values indicate net emissions. This approach allows for a symmetric interpretation and improves the statistical robustness of the analysis.
To facilitate interpretation, a simple numerical example is provided. A COR value of 1 indicates a neutral balance, where carbon sequestration exactly offsets GHG emissions. A COR greater than 1 (e.g., COR = 3) indicates net sequestration, with carbon removals three times higher than emissions. A COR lower than 1 (e.g., COR = 0.5) indicates net emissions, with sequestration covering only half of total emissions. After logarithmic transformation, log COR equals 0 when COR = 1, is positive for COR > 1 (e.g., log COR ≈ 1.10 for COR = 3), and negative for COR< 1 (e.g., log COR ≈ −0.69 for COR = 0.5). This transformation centers the indicator around zero and allows a symmetric interpretation of net emissions and net sequestration.
2.2 Statistical analysis
Data were analyzed using the ANOVA procedure of SAS (Version 9.4; SAS Institute Inc., Cary, NC, US) to test differences between CR classes. However, the limited sample size (n = 18 farms; 9 per group) restricts statistical power and the generalizability of the findings. ANOVA assumptions (i.e., normality and homoscedasticity) were not tested, as the analysis was exploratory and descriptive.
Two significance thresholds were predefined. For farm characteristics, P<0.10 was considered significant. For main outcome variables (Gross CFP and Net CFP), P<0.05 was used.
This approach balances type I and type II errors. A more lenient threshold for ancillary variables reduces the risk of overlooking potential sources of heterogeneity (type II error), while a stricter threshold for primary outcomes decreases the likelihood of false positives (type I error), thereby enhancing the robustness of the conclusions.
To evaluate the sensitivity of Net CFP to key assumptions, a one-way sensitivity analysis was performed on two parameters. First, annual carbon sequestration was varied by ±20% of the aggregated sequestration outcome, while keeping the attribution to individual land-use categories unchanged. Second, the methane conversion factor (Ym) was varied by ±1 percentage point around the baseline value of 7%.
3 Results
3.1 Characteristics of the footprinted farms
Data collected from Sardinian cow–calf farms are reported in Table 3. The selected farms adopted a cow–calf grazing system with an open productive cycle. On average, cow–calf farms were 122 ha in size, stocked at 0.75 livestock units (LU)/ha, and produced 10.71 t of TLW. The mean herd size was 41 cows, and the replacement rate averaged 14%.
Table 3. Main characteristics, annual inputs, and outputs of cow–calf farms.
Calves were raised on pasture with their mothers, suckling milk while gradually starting to eat grass. During this period, dams were fed forages and concentrates to support adequate milk production. Concentrate input averaged 126 kg DM cow−1, while purchased forage averaged 147 kg DM cow−1. Calves did not receive additional supplements, except during periods of low grass availability, such as summer or winter. At 10 months of age and 331 kg LW, yearling beef were sold to fattening companies to complete the production cycle.
Land use and herd structure differed between high- and low-CR farms. High-CR farms had a higher proportion of cultivated land than low-CR farms (P = 0.001). However, the area of natural grassland in low-CR farms (68 ha) was nearly twice that of high-CR farms (33.67 ha), while the extent of Meriagos was comparable between the two groups. High-CR farms managed larger herds, with a higher number of cows (P = 0.076), a higher stocking rate (P = 0.086), and a numerically higher number of yearling beef sold per year (P = 0.11) compared with low-CR farms. However, high-CR farms had a lower percentage of bulls (P = 0.095), due to the higher livestock concentration on a smaller total area.
No significant differences were observed between the two farm types in terms of diesel, energy, and fertilizer use. Similarly, purchased concentrates and forages did not differ significantly between groups (P = 0.79 and P = 0.52, respectively). In contrast, seed use was significantly higher in high-CR farms (P = 0.04), reflecting greater cultivation intensity.
3.2 Carbon sequestration
Results on carbon sequestration are presented in Table 4. On average, carbon input amounted to 8 t C ha-1 yr-1 from crop residues and 111 kg C ha-1 yr-1 from manure deposition during grazing. High-CR farms showed numerically higher carbon input from crop residues, both in terms of dry matter and carbon content, than low-CR farms (P = 0.10). In contrast, nitrogen and carbon inputs from manure deposition were comparable between the two farm types (P = 0.35).
Table 4. Annual carbon inputs from crop residues and manure deposition during grazing, soil C sequestration, and C sequestration from Meriagos, forests, and Mediterranean scrubland in cow–calf farms.
Soil carbon sequestration averaged 1.5 t CO2e ha-1 yr-1, while C sequestration from Meriagos, forests, and Mediterranean scrubland accounted for 4.3 t CO2e ha-1 yr-1. In both cases, values were numerically higher in low-CR farms than in high-CR farms (P = 0.12).
3.3 Gross and net carbon footprint
Emission and sequestration intensities, Net CFP, and COR indices are presented in Table 5. The distribution and percentage contribution of different emission sources are shown in Figures 2 and 3.
Table 5. Gross and net emission intensity, sequestration intensity, and carbon offset ratio (COR) index in cow–calf farms.
Figure 2. Emission intensity (expressed as kg CO2e kg LW sold-1) of cow-calf farms with high and low-cultivation rate (CR).
Figure 3. Percentage contribution of the different emission sources to total greenhouse gas emissions in cow-calf farms with high and low-cultivation rate (CR).
Emission intensity was affected by cultivation rate. Gross CFP, expressed per kg TLW sold and per hectare, was significantly lower in low-CR farms than in high-CR farms (P< 0.01). When expressed per kg of LW sold, Gross CFP was numerically lower in low-CR farms (P = 0.064).
Specifically, in low-CR farms, Gross CFP was 19.80 kg CO2e/kg LW sold, 13.55 kg CO2e/kg TLW sold, and 1,056 kg CO2e/ha. In high-CR farms, Gross CFP was 26.75 kg CO2e/kg LW sold, 21.15 kg CO2e/kg TLW sold, and 2,722 kg CO2e/ha.
Emissions from CH4 largely exceeded those from N2O and CO2 (Figure 2). The main on-farm contributor to GHG emissions was enteric methane (high-CR: 68.3%; low-CR: 74.2%; Figure 3), while manure deposited on pasture contributed to a lesser extent (high-CR: 4.6%; low-CR: 5.6%). The second-largest contributor to CFP was on-farm feed production in high-CR farms (17.68%) and fuel use in low-CR farms (8.37%).
The inclusion of soil C sequestration reduced GHG emissions. Net CFP values were more negative in low-CR farms than in high-CR farms. Sequestration intensity was affected by cultivation rate, and Net CFP across all functional units (kg LW sold, kg TLW sold, and hectare) was significantly lower (i.e., more negative) in low-CR farms. For the same reason, COR and log COR indices were significantly higher in low-CR farms than in high-CR farms.
4 Discussion
4.1 Gross carbon footprint
This study analyzed the environmental impact of the cow–calf system in Sardinia, a region located in the western Mediterranean Sea. The system showed GHG intensities of 26.75 kg CO2e/kg LW sold in high-CR farms and 19.80 kg CO2e/kg LW sold in low-CR farms. These values are similar to those reported for extensive beef production in Italy (Bragaglio et al., 2018; Grossi et al., 2020; Sabia et al., 2025) and in EU and non-EU countries (Picasso et al., 2014; De Vries et al., 2015; Alemu et al., 2017; Reyes-Palomo et al., 2022).
As a percentage of total GHG emissions, enteric methane accounted for 71%. This value was lower than that reported in Podolian and specialized extensive systems, in which methane emissions accounted for 85% and 75%, respectively (Bragaglio et al., 2018). However, lower values have also been reported by other authors (59%–66%; Reyes-Palomo et al., 2022; Sabia et al., 2025).
Methane emission is the most important factor affecting the CFP of cattle, accounting for 55%–92% of total GHG emissions (Desjardins et al., 2012). Enteric methane production depends mainly on feed, animal characteristics, and interactions between them (Jaurena et al., 2015). Moreover, it represents a loss of energy (Johnson and Johnson, 1995). The fraction of GE converted into methane is defined as the methane conversion factor (Ym; IPCC, 2019). In extensive grazing systems, Ym tends to be higher than in intensively managed systems. This is mainly due to less optimized diets and greater variability in forage quantity and quality, which directly affect the amount of methane produced (Jaurena et al., 2015; IPCC, 2019a; Beauchemin et al., 2022).
In this study, the substantial contribution of enteric methane was mainly attributed to the forage-based diet (i.e., diets with more than 75% forage). Methane production increases with high-roughage diets, which are characterized by higher fiber content and lower digestible energy compared with concentrate-based diets (Nguyen et al., 2010; McAuliffe et al., 2018; Reyes-Palomo et al., 2022; Tinitana-Bayas et al., 2024). Such diets also increase the time required to reach final weight at the farm gate. For the same reason, methane emissions are generally higher in grass-fed than in grain-fed feedlot cattle (Desjardins et al., 2012; De Vries et al., 2015), because more hydrogen is available for the methanogenesis process (Janssen, 2010; Escobar-Bahamondes et al., 2017).
In high-CR farms, the second-largest source of emissions was associated with on-farm feed production, mainly due to the inputs required to manage cultivated land. Similarly, feed production was identified as the second major emission source in Portuguese beef farms (Dos Santos et al., 2024). Other studies have shown that emissions related to on-farm activities exceed those from off-farm sources (Berton et al., 2017; Reyes-Palomo et al., 2022). However, in low-CR farms, the second main contributor to CFP was fuel use. This confirms that low-CR farms rely less on on-farm feed production. The higher relative contribution of energy use to total emissions was mainly related to herd movement and handling.
4.2 Net carbon footprint
The inclusion of carbon sequestration significantly reduced the CFP, leading to negative Net CFP values. On average, Net CFP was −7.30 kg CO2e/kg LW sold in high-CR farms and −48.02 kg CO2e/kg LW sold in low-CR farms. This suggests that lower cultivation intensity more effectively reduces the CFP of cow–calf systems, resulting in higher carbon credits that could fully offset emissions generated during the fattening phase.
The generation of carbon credits could allow beef farmers to comply with new EU sustainability policies that support access to carbon credit markets (Frascarelli et al., 2025). However, these values should be interpreted with caution, as they represent a temporary carbon sink rather than a permanent state of carbon neutrality or certified carbon credits. Moreover, carbon credits for policy or market purposes require more than biophysical carbon balances. Eligible credits must demonstrate additionality, long-term permanence, and be subject to independent monitoring and verification.
Lower cultivation intensity was associated with higher COR and log COR indices. This further confirms that more extensive systems have a significantly lower environmental impact than systems with a high-CR. Previous studies have developed similar environmental impact indices, showing that grassland-based beef systems have a lower impact than seeded pastures and that grazing-based systems are less impactful than feedlots (Picasso et al., 2014).
Research on the environmental impact of extensive grassland-based cattle systems has primarily focused on GHG emissions and deforestation, while soil carbon sequestration has received comparatively less attention. This is mainly due to methodological limitations (Brandão et al., 2011, 2013; Aguilera et al., 2021). Direct quantification methods are expensive and labor-intensive, require long-term observation periods, and are not yet standardized (Maillard et al., 2017; Nayak et al., 2019).
Quantification methods provide a valid alternative to direct measurements and are suitable for inclusion in LCA studies (Nayak et al., 2019; Lunesu et al., 2025b). One of the most widely used approaches is the method proposed by Petersen et al. (2013), which uses a C-tool to simulate soil carbon dynamics as a function of soil characteristics, climate, and C inputs. This method has been applied to quantify the Net CFP of beef (Buratti et al., 2017; Eldesouky et al., 2018; Mogensen et al., 2023; Sabia et al., 2025), sheep (Batalla et al., 2015; Escribano et al., 2020; Vagnoni et al., 2024; Lunesu et al., 2025b), goats (Gutiérrez-Peña et al., 2019; Horrillo et al., 2020), and pigs (Horrillo et al., 2020).
Nevertheless, soil carbon sequestration is subject to important uncertainties related to permanence and saturation. Soil organic carbon (SOC) accumulation is finite and tends to slow as soils approach a new equilibrium. Accumulated carbon may also be released if land use or management practices change. In addition, the definition of baseline SOC stocks represents a key source of uncertainty. The indirect modeling approach applied here estimates SOC dynamics under current land use and management but does not allow direct observation of counterfactual scenarios, such as SOC stocks in the absence of livestock production.
In Mediterranean extensive systems, grazing contributes to grassland persistence and limits shrub expansion and land abandonment, potentially leading to alternative carbon trajectories. Therefore, the estimated sequestration should be interpreted as management-related SOC changes rather than strictly additional carbon uptake. However, regional data on SOC in Sardinian agro-pastoral systems indicate substantial potential for further carbon storage. Measurements at multiple sites show moderate SOC levels in the topsoil (0–20/30 cm) and high spatial variability related to soil depth, texture, and land use (Zucca et al., 2010). Most Sardinian pasture soils have not reached carbon saturation and may still accumulate organic carbon under appropriate management. In this context, annual sequestration rates estimated using the approach of Petersen et al. (2013) are reasonable over medium-term time horizons. The EU carbon farming framework (Reg. UE 2024/3012) requires carbon storage to be maintained for at least 20 years; these requirements are compatible with the estimated rates, provided that land use and management remain stable and that no major reversals occur.
Potential double counting with national GHG inventories should also be considered. Soil and biomass carbon sequestration may already be partly accounted for under the Land Use, Land-Use Change, and Forestry (LULUCF) sector. Consequently, the Net CFP values reported here should not be interpreted directly as tradable carbon credits without alignment with national accounting frameworks. Moreover, carbon sequestration from forests and Mediterranean scrubland was included only when these lands were functionally connected to the farm. Nevertheless, attributing the full sequestration of woody biomass to the cow–calf enterprise may overestimate its mitigation potential. Overall, including carbon sequestration highlights the climate mitigation potential of extensive cow–calf systems and contributes to lowering their CFP. However, the resulting negative Net CFP values should be considered indicative rather than definitive.
In this study, the same model estimated a carbon sequestration rate of 1.45 t CO2e/ha per year for high-CR farms and 1.58 t CO2e/ha per year for low-CR farms. In both cases, values were consistent with the range reported in previous studies (O’Brien et al., 2014; Batalla et al., 2015; Lunesu et al., 2025b). Similarly, C sequestration rates from Meriagos, forests, and Mediterranean scrubland were in line with values previously reported for Dehesa agroforestry systems (Reyes-Palomo et al., 2022). In that study, a sequestration rate of 3.57 t CO2e/ha per year, considering both soils and tree biomass, reduced the CFP of extensive cattle farms from 20 to 6.41 kg CO2e/kg LW (Reyes-Palomo et al., 2022).
In other comparable studies, the inclusion of carbon sequestration in LCA reduced the environmental impact of extensive beef farms in Portugal by 16% (Dos Santos et al., 2024). Similarly, Batalla et al. (2015) reported GHG reductions ranging from 3% to 41% using the same model developed by Petersen et al. (2013).
The one-way sensitivity analysis showed that under a −20% sequestration scenario, Net CFP per hectare in low-CR farms remained strongly negative, decreasing from −2,326 to approximately −1,650 kg CO2e/ha. This indicates that the negative balance is robust even under conservative sequestration assumptions. In contrast, the high-CR group shifted further toward net emissions, approaching a neutral balance. Increasing Ym from 7% to 8% (equivalent to a 14.3% increase in enteric CH4 emissions) increased total emissions by approximately 10%, given that enteric CH4 accounts for around 70% of total emissions. Low-CR farms nevertheless remained negative, with a Net CFP of approximately −2,215 kg CO2e/ha. Decreasing Ym to 6% had the opposite effect, further strengthening the negative Net CFP in low-CR farms. Overall, these scenarios suggest that negative Net CFP values in low-CR farms are robust to plausible uncertainties in both sequestration and enteric methane, whereas results for high-CR farms are more sensitive due to their smaller net balance.
4.3 Mitigation strategies
Our results indicate that enteric methane is the main source of GHG emissions in Sardinian cow–calf farms, accounting for over 70% of total emissions. Therefore, mitigation efforts should focus primarily on reducing enteric methane. One effective approach is to improve forage quality. Enhanced pasture management, selection of forage species, and monitoring forage maturity can increase digestibility and reduce methane emissions (Thompson and Rowntree, 2020).
However, the application of these mitigation strategies in the Sardinian cow–calf system may be challenging. This is mainly because cattle are often allowed to roam freely (Acciaro et al., 2022), resulting in selective and heterogeneous animal distribution, which in turn leads to widespread over- and under-grazing (Probo et al., 2014). A recent regional study showed that cattle grazing activity is homogeneous only during spring (Acciaro et al., 2022), likely due to greater availability of lush herbage and higher rainfall compared with other seasons. Improving pasture management through rotational grazing practices can increase grazing uniformity and reduce selectivity (Acciaro et al., 2024). In addition, rotational grazing can indirectly reduce methane production by up to 22% compared with continuous stocking systems (DeRamus et al., 2003).
Improving forage quality can also be achieved by evaluating forage species. The inclusion of perennial ryegrass, clover, or brassicas in grazing beef systems has been shown to improve forage quality (Smith et al., 2022). In the Sardinia region, the use of legume species could contribute to reducing methane emissions, mainly due to their higher nutritional quality and digestibility compared with grasses. Moreover, their inclusion can reduce the need for nitrogen fertilizers, thereby decreasing N2O emissions and overall on-farm emissions.
Forage maturity also plays a key role in methane mitigation (Thompson and Rowntree, 2020). As forages mature, their neutral detergent fiber (NDF) and acid detergent lignin (ADL) contents increase, resulting in lower digestibility (Jung and Allen, 1995). Forage digestibility in Sardinian beef farms could be improved not only by harvesting at the optimal phenological stage but also through physical processing methods such as chopping and grinding (Hristov et al., 2014).
Additional mitigation strategies should focus on improving reproductive performance, genetic selection, and animal welfare (Beauchemin et al., 2020, 2022). In the Sardinian cow–calf system, the average fertility rate is 84%, and the annual replacement rate is 14%. Therefore, improving reproductive efficiency (Becoña et al., 2014) and optimizing age at first calving are valid mitigation strategies (O’Brien and Shalloo, 2021). Previous studies have shown that increasing reproductive efficiency from 0.5 to 1 calf per year can reduce CFP by up to 39% (Davis and White, 2020; Baruselli et al., 2023). The use of artificial insemination can also reduce age at first calving and improve weaning performance (Abreu et al., 2022; Baruselli et al., 2023). However, genetic selection remains the most reliable long-term mitigation strategy (Thompson and Rowntree, 2020; Pulina et al., 2022).
In summary, mitigation strategies in the Sardinian cow–calf system that focus on improving forage quality and animal performance show substantial potential to reduce environmental impact. These approaches reduce emission intensity and optimize resource use, contributing to a more sustainable and productive beef cattle sector.
5 Conclusions
This study confirms the hypothesis that cultivation intensity influences the Net CFP of Mediterranean cow–calf systems. The two farm clusters, defined by the proportion of cultivated land relative to total farm area, were largely homogeneous in terms of structural characteristics, supporting the validity of the classification criterion. Farms with lower cultivation rates, characterized by larger areas of grassland and semi-natural habitats, exhibited a markedly lower Net CFP.
When carbon sequestration was included, offsets in low-CR farms appeared sufficient to compensate for a large share of the expected emissions during the fattening phase, estimated at 8–15 kg CO2e per kilogram of average daily gain. In extensive systems, biophysical calculations indicate a potential surplus of carbon credits. However, this potential is based on farm-level estimates and does not automatically result in certified carbon credits. Certification under EU carbon removal and carbon farming schemes requires specific rules, monitoring, and constraints.
Overall, while low-CR farms show a promising contribution to carbon mitigation, further work is needed to assess their eligibility and practical implementation within formal carbon crediting frameworks. Therefore, LCA-based estimates alone do not guarantee compliance with carbon market rules or certification standards.
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethics statement
Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The participants or participants’ legal guardian/next of kin provided their written informed consent to participate in this study.
Author contributions
ML: Writing – review & editing, Formal Analysis, Methodology, Writing – original draft, Data curation, Conceptualization. MC: Formal Analysis, Data curation, Writing – review & editing, Methodology. SS: Formal Analysis, Data curation, Investigation, Writing – review & editing. GP: Conceptualization, Funding acquisition, Writing – review & editing, Validation. GB: Funding acquisition, Validation, Writing – review & editing. AN: Writing – review & editing, Validation, Funding acquisition.
Funding
The author(s) declared that financial support was received for this work and/or its publication. This research was funded by RESTART-UNINUORO project (“Actions for the Valorization of Agroforestry Resources in Central Sardinia”; FSC 2014–2020), PROBOVIS (“Beef and Sheep Production: Enhancement & Innovation in Sardinia”. Rural Development Programme fund 2014-2020, sub-measure 16.2), and BOVARIA (“Knowledge and Sustainable Management of Agricultural and Forest Systems with the Sustainable Improvement of Primary production: the Case of Cattle Farming in Sardinia”; Interdisciplinary Research Projects – Ministerial Decree 737/2021).
Acknowledgments
The authors are grateful to farmers for their collaboration and hospitality during the data collection.
Conflict of interest
The authors declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
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Keywords: agroforestry, cow-calf system, life cycle assessment, net carbon footprint, soil carbon sequestration
Citation: Lunesu MF, Caratzu MF, Sechi S, Pulina G, Battacone G and Nudda A (2026) Role of cultivation intensity in shaping the net carbon footprint of Mediterranean cow-calf systems. Front. Anim. Sci. 6:1748147. doi: 10.3389/fanim.2025.1748147
Received: 17 November 2025; Accepted: 29 December 2025; Revised: 23 December 2025;
Published: 23 January 2026.
Edited by:
Luca Muzzioli, Sapienza University of Rome, ItalyReviewed by:
Ioanna Poulopoulou, Agricultural University of Athens, GreeceRégio Marcio Toesca Gimenes, Federal University of Grande Dourados, Brazil
Copyright © 2026 Lunesu, Caratzu, Sechi, Pulina, Battacone and Nudda. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Sara Sechi, c3NlY2hpMUB1bmlzcy5pdA==