Assessment of Greenhouse Gas Emissions and Carbon Footprint in Mountainous Semi-Extensive Dairy Sheep and Goat Farms in Greece

Abstract

Livestock contributes to global warming through greenhouse gas (GHG) emissions. Reducing these emissions is an ongoing challenge for the small ruminant sector. Despite its significant role in national economies, limited studies on the carbon footprint (CF) of dairy small ruminants in Mediterranean countries exist. The study aimed to achieve the following: (a) estimate the GHG emissions of eleven semi-extensive sheep and goat farms in a mountainous region of southern Greece, using the Tier 1 and Tier 2 methodologies; (b) compare the outcomes of both methods; and (c) calculate farms’ CF, as a means of their environmental impact evaluation. All on-farm activities (except machinery or medicine use) related to sheep or goat production were considered to estimate GHG emissions. The results show differences between Tier 1 and Tier 2 estimates, reflecting the simplified computational approach of Tier 1. The average CF values estimated via Tier 1 for goat and sheep farms were 2.12 and 2.87 kg CO₂-eq./kg FPCM, respectively. Using Tier 2, these values increased to 2.73 and 3.99 kg CO₂-eq./kg FPCM. To mitigate environmental impact, farms could enhance productivity by improving herd management and feeding strategies.

1. Introduction

Climate change is an ongoing environmental challenge with significant transnational implications, particularly for human activities and biodiversity. Among anthropogenic activities, livestock production represents a significant driver of climate change, primarily due to its contribution to greenhouse gas (GHG) emissions, which are directly linked to global temperature increases. Specifically, GHG emissions from the livestock sector are estimated at approximately 7.1 Gt CO₂-equivalent, accounting for 14.5% of global human-induced GHG emissions [1,2]. Ruminants are the primary contributors, particularly through enteric fermentation and manure management [3]. In fact, ruminant farming is responsible for 80% of livestock-related emissions, while small ruminants account for 12% of global methane emissions and 19% of global nitrogen monoxide emissions [1]. However, ruminant farming systems vary based on environmental factors [4], such as climate, soil type, altitude, and landscape, as well as species (e.g., goats, sheep) and production purposes (e.g., dairy, meat, wool). These variations influence the GHG emissions generated by different farming systems.

The EU’s “2030 Climate Target Plan” seeks to reduce GHG emissions by 40% by 2030 and 55% by 2050, ultimately achieving net-zero emissions by mid-century [5]. Within this framework, it is acknowledged that certain livestock production systems significantly contribute to the emissions of major greenhouse gases that drive global warming. For instance, sheep and goats are responsible for approximately 6.5% of emissions from the global livestock sector [6]. Thus, promoting more sustainable farming practices is essential.

In Europe, most small ruminants are reared in Mediterranean countries, where they hold significant socioeconomic and ecological importance. These systems utilize vast grazing areas and are well adapted to local climates and available forage. For example, they can consume low-quality forage from regions with limited agricultural productivity, such as mountainous areas [7]. Among production orientations (milk, meat, wool), dairy sheep and goat farming are particularly relevant in European Mediterranean countries [8]. This sector encompasses a range of farming systems, from extensive grazing to intensive livestock operations [9]. In many inland regions, these systems represent the only viable economic activity, particularly those relying on grazing. They play a crucial role in sustaining rural communities, preserving traditions, and addressing environmental challenges.

Greece ranks first among EU-27 countries in ovine milk production (943,970 t) and fourth in goat milk production (348,920 t), with a total sheep and goat population of approximately 7.9 and 2.7 million, respectively [10]. Most Greek sheep and goat farming systems are extensive or semi-extensive, operating in marginal areas where animals graze throughout the year with minimal supplemental feed. However, intensive farming systems also contribute significantly to total milk production, relying entirely on feed-based nutrition rather than grazing [11]. Although both sectors yield high-quality, value-added products, they face challenges in terms of competitiveness. Improving their environmental performance could enhance their sustainability and competitiveness, especially in marginal areas [8,12]. Consequently, assessing their environmental impact and identifying key areas for mitigation is essential [13].

While numerous studies have evaluated sheep and goat milk production footprints, most have focused on farms in Spain or Italy [4,14,15,16]. Other studies have examined meat and wool production systems, which have different technical characteristics from dairy farms (e.g., [17,18]) or have explored dairy intensive farms rearing the East Friesian breed in Australia [19] or implementing a different level of grazing in Spain [20]. In Greece, information on GHG emissions from sheep and goat farms remains scarce, with most available studies analyzing intensive or semi-intensive production systems [4,14]. This highlights the need for more research on the small ruminant sector, particularly in (semi)-extensive production systems located in mountainous areas, which represent traditional dairy sheep and goat farming practices.

Accordingly, this study aims to address this knowledge gap by estimating GHG emissions from sheep and goat dairy semi-extensive farms in mountainous areas of Greece. Therefore, the study primary focuses on estimating the GHG emissions of sheep and goat farms in mountainous areas that implement (semi)-extensive production systems, using the Tier 1 and Tier 2 methodologies recommended by the IPCC [21,22]. Further objectives were to compare the outcomes of both methodologies and to further characterize the carbon footprint of these farms as a measure of their environmental impact. The research examines the distinctive characteristics of the small Mediterranean ruminant sector, where (semi)-extensive systems remain prevalent. It is expected that Tier 1 estimates will be higher than Tier 2 estimates, according to the animal population reared. Additionally, the estimated footprint values are anticipated to be lower in higher-productivity systems or those with fewer animals. Ultimately, this research will help identify practices that can reduce environmental impacts and support more sustainable livestock production.

2. Materials and Methods

2.1. Area of the Study

This study was conducted on the area of Vamvakou Village, located in Peloponnese in the Laconia region (37°14′38.9″ N 22°33′06.2″ E). Vamvakou is a mountain village built at an altitude of 900 m on the slopes of Mount Parnon, northeast of Sparta. The climate of the village is mild, and generally warm and temperate (classified as Csa by Köppen and Geiger system). The rain in Vamvakou falls mostly in the winter, with relatively little rain in the summer. The average annual temperature is 16.0 °C (60.7 °F), while the average minimum and maximum temperatures are 5.2 °C (41.4 °F) and 27.5 °C (81.5 °F), respectively. Each year, approximately 676 mm (26.6 inch) of precipitation occurs. The lowest precipitation is usually observed in July, with an average of 27 mm (1.1 inch), and it reaches its peak in December, with an average of 85 mm (3.3 inch) [23]. The economy of the region is based on farming (walnut and chestnut trees, potatoes, other vegetables) and the livestock sector (sheep and goats). In the region, 11 livestock farms exist, which all participated in the study.

2.2. Case Study and Data Collection

This study was conducted during the first semester of the year 2022 and the collected data from each farmer refer to the previous productive year, thus the reference year is the productive year (January–December) of 2021. The collected data also mirror data on the two previous consecutive years (2019, 2020), since there was no change in animal numbers and farming practices according to the farmers and their records. The study involved 11 participating farmers, who conducted livestock activities rearing goats and sheep. All relevant primary data for the estimations of productive indices and greenhouse gas emissions were collected through field visits and interviews with farmers, using structured questionnaires. The questionnaire included indicators about the farm’s characteristics and management practices, i.e., information about the farm size and structure, animal management and reproduction, feed management, and manure treatment. Indicatively, the questionnaire was structured into four major sections including: (a) animal species and animal category data referring to the species reared and the total number of existing animals, as well as the total number of animals in each animal category (i.e., ewes, rams, replacement animals, etc.); (b) animal productivity and reproduction data referring to milking production, average milk fat and protein content, lactation period, milking period, fertility rate, weaning period, and mortality; (c) grazing and feed use, referring to the hours of grazing in each farm and forages and concentrate feeds that may be supplementarily given to animals; (d) manure treatment, referring to the treatment of manure produced by the animals during their housing. The data collected were verified through the farm records and further recall from farmers for records that were not well clarified.

Table 1. Number of animals per species reared by the farmers of the study (Ø = Not applicable).

Farmer	Species		Total Number of Animals
	Sheep	Goats	Sheep	Goats
1	Ø	yes	Ø	570
2	yes	Ø	125	Ø
3	Ø	yes	Ø	450
4	Ø	yes	Ø	560
5	Ø	yes	Ø	530
6	Ø	yes	Ø	440
7	Ø	yes	Ø	273
8	yes	Ø	91	Ø
9	yes	yes	300	22
10	Ø	yes	Ø	680
11	yes	yes	80	560

shows the animal species and the number of animals reared by each farmer. Considering diet intake, the farming system was characterized as semi-extensive, since the animals were grazing almost all year (>355 days) and for over 8.5 h daily. Lactating ewes were provided with a standard concentrate lactation ration (1 kg/animal) exclusively during the lactation period, and only upon their return to the farm following daily grazing. The grazing area was on the west side of the Parnon mountain.

The goats reared by the farmers belonged to (i) the breed of Greek autochthonous goat (farmers 1, 3, 6, and 7), (ii) Damaskou breed (farmers 5, 10, and 11); or (iii) Alpine breed (farmers 4 and 9). Sheep populations reared by the farmers belonged to the Lacaune (farmer 2 and 9) or Friesian (farmer 8 and 11) breeds.

2.3. Zootechnical Indexes

The following parameters were retrieved for each farm unit per species reared according to the data collected by the questionnaires: (i) number of milking animals; (ii) lactation period (months); (iii) milking period (months); (iv) fertility index (number of fertilized female animals/number of total female animals); (v) prolificacy rate (number of lambs or kids born/number of ewes or goats gave birth); (vi) lambs’ or kids’ mortality (number of dead lambs or kids before weaning/total number of lambs or kids); (vii) weaning period; (viii) duration of female animals’ productive life (years); (ix) duration of males’ productive life (years); (x) ratio of male to female animals.

2.4. Greenhouse Gas Emissions (GHG) Estimations

The estimation of GHG emissions (methane and nitrous oxide) was conducted using the Tier 1 and 2 approaches for each gas category according to the IPCC recommendations and guidelines [21,22]. The target period was the year 2021, referring to the productive year of that period (January–December 2021). The system boundaries were considered to be from cradle to farm gate and specifically included all on-farm activities related to sheep or goat production to estimate methane (CH₄) emissions through intestinal fermentation and manure management, as well as direct (N₂O) and indirect (NOx, NH₃, NO₃-) nitrogen emissions. Machinery, buildings, medicines, and energy sources related to feed production were excluded. For the correct computation of GHG emissions for animals living less than one calendar year (i.e., suckling/weaned lambs or goats), the exact population number was estimated using Equation (1) [22]:

$$\mathrm{N}=\mathrm{D}\mathrm{a}\mathrm{y}{\mathrm{s}}_{\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{v}\mathrm{e}}\times \left({\displaystyle \frac{\mathrm{N}\mathrm{A}\mathrm{P}\mathrm{A}}{365}}\right)$$

(1)

where

N = the number of heads of livestock species/animal category in the examined farms;

NAPA = number of animals produced annually.

Further, to use the equations reported by the IPCC, basic characteristics related to the area’s climate, manure treatment, and feeding practices were required to choose proper values for the parameters required by the equations or further estimations [21]. Therefore, the climate zone for the target area was considered warm temperate (dry). Regarding the treatment of manure, considering that all the farmers implemented the same farming system in the area, the animals were grazing for over 6 h per day, and farmers also used a solid storage system for manure allocation when animals were farmed after grazing, the whole implemented manure system was considered as daily spread of manure in pastures (50% allocated) and solid storage (50% allocated). The productive system was characterized as low productivity, considering the typical characteristics of pastoral systems, including grazing throughout the year.

2.4.1. Tier 1 Approach

The following equation was applied in accordance with the IPCC recommendations to estimate the total annual methane emissions from intestinal fermentation [22]:

$$\mathrm{E}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{i}\mathrm{c} {\mathrm{C}\mathrm{H}}_4 \mathrm{E}\mathrm{m}\mathrm{i}\mathrm{s}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{s}=\sum \left(\mathrm{E}\mathrm{F}\times \left({\displaystyle \frac{\mathrm{N}}{{10}^6}}\right)\right)$$

(2)

where

CH₄ Emissions = emissions derived from enteric fermentation (Gg CH₄);

EF = defined country emission factor for the livestock population (kg CH₄·head⁻¹·yr⁻¹);

N = the number of heads of livestock population.

To determine methane emissions derived from manure, the following equations were used [18]:

$$\mathrm{M}\mathrm{a}\mathrm{n}\mathrm{u}\mathrm{r}\mathrm{e} {\mathrm{C}\mathrm{H}}_4 \mathrm{e}\mathrm{m}\mathrm{m}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{s}=\left[\sum {\displaystyle \frac{\left(\mathrm{N}\times \mathrm{V}\mathrm{S}\times \mathrm{A}\mathrm{W}\mathrm{M}\mathrm{S}\times \mathrm{E}\mathrm{F}\right)}{1000}}\right]$$

(3)

where

CH₄ Emissions = emissions derived from manure management (kg CH₄);

N = the number of heads of livestock population;

VS = the annual average volatile solid excretion per head of species (kg VS·animal⁻¹·yr⁻¹);

AWMS = the fraction of total annual VS for livestock species that is managed in manure management system (dimensionless);

EF = emission factor for direct CH₄ emissions from manure management systems, by animal species (g CH₄·kg VS⁻¹).

The annual average volatile solid (VS) excretion was estimated using the following equation:

$$\mathrm{V}\mathrm{S}=\left({\mathrm{V}\mathrm{S}}_{\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{e}}\times {\displaystyle \frac{\mathrm{T}\mathrm{A}\mathrm{M}}{1000}}\right)\times 365$$

(4)

where

VS_rate = default VS excretion rate for the productivity system (kg VS·(1000 kg animal mass)⁻¹·day⁻¹);

TAM = typical animal mass for livestock species (kg·animal⁻¹).

All appropriate factors and parameters used in the equations following the recommendations of the IPCC [18] are presented in

Table 2. Values of the parameters used for estimating GHG emissions (Tier 1 and 2 methodologies), according to the IPCC refined recommendations [22].

Parameter	Methodology	Value	Equation
EFCH4-enteric	Tier 1	Sheep and goats: 5 kg CH4·head−1·yr−1 (low productivity system)	(2)
AWMS	Tier 1/2	Sheep: 42% (solid storage); 0% pasture/range Goats: 28% (solid storage); 0% pasture/range	(3)/(7)/(9)/(14)/(16)
EFCH4-manure	Tier 1	Sheep and Goats: 3.5 g CH4·kg VS−1	(3)
VSrate	Tier 1	Sheep: 8.2 (kg VS·(1000 kg animal mass)−1·day−1) Goats: 9 (kg VS·(1000 kg animal mass)−1·day−1)	(4)
TAM	Tier 1/2	40 kg·animal−1	(4)/(10)
Ym	Tier 2	Sheep: 6.7 (dimensionless); Goats 5.5 (dimensionless)	(5)
Bo	Tier 2	Sheep: 0.19 m3 CH4·kg−1 of VS excreted Goats: 0.18 m3 CH4·kg−1 of VS excreted	(7)
MCF	Tier 2	Sheep and Goats: 4% (solid storage) or 0.55% (pasture/range)	(7)
DE	Tier 2	67.5%	(8)
UE	Tier 2	0.04×GE	(8)
ASH	Tier 2	0.08	(8)
EF3	Tier 1	Sheep and Goats: 0.01 kg N2O-N/kg N	(9)
Nrate	Tier 1	Sheep: 0.43 kg N·(1000 kg animal mass)−1·day−1 Goats: 0.42 kg N·(1000 kg animal mass)−1·day−1	(10)
Nretention_frac	Tier 2	Sheep and Goats: 0.10 (dimensionless)	(11)
CP%	Tier2	Sheep: 8.2%; Goats: 8.1%	(12)
EF4	Tier 1/2	Sheep and Goats: 0.01 kg N2O-N/kg N	(13)
FracGasMS	Tier 1/2	Sheep and Goats: 0.12 (dimensionless)	(14)
EF5	Tier 1/2	Sheep and Goats: 0.011 kg N2O-N/kg N	(15)
FracLeachMS	Tier 1/2	Sheep and Goats: 0.02 (dimensionless)	(16)

.

2.4.2. Tier 2 Approach

Methane emissions from enteric fermentation within each animal category were calculated using Equation (2), but for the EF parameter, instead of using default values for the target region, it was calculated using the following equation [22]:

$$\mathrm{E}\mathrm{F}={\displaystyle \frac{\mathrm{G}\mathrm{E}\times \left(\frac{{\mathrm{Y}}_{\mathrm{m}}}{100}\right)\times 365}{55.65}}$$

(5)

where

EF = emission factor (kg CH₄·head⁻¹·yr⁻¹);

GE = gross energy intake (MJ·head⁻¹·day⁻¹);

Y_m = methane conversion factor, per cent of gross energy in feed converted to methane.

The factor 55.65 (MJ/kg CH₄) is the energy content of methane.

The GE was calculated using the following equation based on the reported guidelines of the IPCC [21,22]:

$$GE=\left[{\displaystyle \frac{\left(\frac{{NE}_m+{NE}_a+{NE}_l+{NE}_p}{REM}\right)+\left(\frac{{NE}_g}{REG}\right)}{DE}}\right]$$

(6)

where

GE = gross energy (MJ·day⁻¹);

NE_m = net energy required by the animal for maintenance (MJ·day⁻¹);

NE_a = net energy for animal activity (MJ·day⁻¹);

NE_l = net energy for lactation (MJ·day⁻¹);

NE_p = net energy required for pregnancy (MJ·day⁻¹);

REM = ratio of net energy available in a diet for maintenance to digestible energy;

NE_g = net energy needed for growth (MJ·day⁻¹);

REG = ratio of net energy available for growth in a diet to digestible energy consumed;

DE = digestibility of feed expressed as a fraction of gross energy.

The estimation of parameters related to GE calculation (NE_m, NE_a, NE_l, NE_work, NE_m, NE_g) was conducted following the guidelines and respective equations reported by the IPCC [22] and analytically presented by Azoukis et al. [24].

Regarding the methane emissions derived from manure within each animal category, Equations (2) and (3) were used, but the EF and VS factors were estimated as follows [22]:

$$\mathrm{E}\mathrm{F}=\left(\mathrm{V}\mathrm{S}\times 365\right)\ast \left[{\mathrm{B}}_0\times 0.67\times \sum {\displaystyle \frac{\mathrm{M}\mathrm{C}\mathrm{F}}{100}}\times \mathrm{A}\mathrm{W}\mathrm{M}\mathrm{S}\right]$$

(7)

where

EF = annual CH₄ emission factor for livestock population (kg CH₄·animal⁻¹·yr⁻¹);

VS = daily volatile solid excreted for livestock population (kg dry matter·animal⁻¹·day⁻¹);

365 = basis for calculating (days·yr⁻¹);

B0 = maximum methane-producing capacity for manure produced by livestock population (m³ CH₄·kg⁻¹ of VS excreted),

MCF = methane conversion factors for each manure management system in the climate region (percent);

AWMS = the fraction of total annual VS for livestock species that is managed in the manure management system (dimensionless).

The VS parameter was estimated by using Equation (6), as follows:

$$\mathrm{V}\mathrm{S}=\left[\mathrm{G}\mathrm{E}\times \left(1-{\displaystyle \frac{\mathrm{D}\mathrm{E}}{100}}\right)+\left(\mathrm{U}\mathrm{E}\times \mathrm{G}\mathrm{E}\right)\right]\times \left[\left({\displaystyle \frac{1-\mathrm{A}\mathrm{S}\mathrm{H}}{18.45}}\right)\right]$$

(8)

where

VS = volatile solid excretion per day on a dry organic matter basis (kg VS·day⁻¹);

GE = gross energy intake (MJ·day⁻¹);

DE = digestibility of the feed in percent;

(UE × GE) = urinary energy expressed as fraction of GE;

ASH = the ash content of feed calculated as a fraction of the dry matter feed intake;

18.45 = the conversion factor for dietary GE per kg of dry matter (MJ·kg⁻¹).

For the estimation of GE, the recommendation of the IPCC [21,22] for lactating sheep was followed, using Equation (6) as previously described.

Similarly to the Tier 1 methodology, all appropriate factors and parameters used in the equations for the Tier 2 calculations followed the recommendations of the IPCC [21,22] and are presented in Table 2.

2.4.3. Estimations of Nitrous Oxide (N₂O) Emissions from Manure Management

Nitrous oxide (N₂O) is produced, directly and indirectly, during the storage and treatment of manure. Calculation of the respective emissions was based on N excretion, emission factors for N₂O emissions, and volatilization and leaching factors (IPCC, 2019). Direct N₂O emissions are produced by the nitrification and denitrification of nitrogen contained in manure. Volatile nitrogen losses result in indirect emissions that occur primarily in the forms of ammonia and NO_x.

Direct Estimations (Tier 1 and 2 Methodology)

Direct nitrogen gases (N₂O) derived from manure management were calculated according to the following equation [22].

$$\mathrm{M}\mathrm{a}\mathrm{n}\mathrm{u}\mathrm{r}\mathrm{e} {\mathrm{N}}_2\mathrm{O} \mathrm{e}\mathrm{m}\mathrm{i}\mathrm{s}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{s}=\left[\sum \left[\sum \left(\left(\mathrm{N}\times \mathrm{N}\mathrm{e}\mathrm{x}\right)\times \mathrm{A}\mathrm{W}\mathrm{M}\mathrm{S}\right)\right]\times {\mathrm{E}\mathrm{F}}_3\right]\times {\displaystyle \frac{44}{28}},\mathrm{K}\mathrm{g}{\mathrm{N}}_2\mathrm{O}$$

(9)

where

N = the number of heads of livestock species;

Nex = the annual average N excretion per head of species (kg N·animal⁻¹·yr⁻¹);

AWMS = the fraction of total annual nitrogen excretion for the livestock species managed in the manure management system (dimensionless);

EF₃ = the emission factor for direct N₂O emissions from manure management system (kg N₂O-N/kg N);

44/28 = factor for the conversion of N₂O-N(mm) emissions to N₂O(mm) emissions. The implementation of the equation required calculating the excreted nitrogen per animal (Nex), whereas the emission factor (EF) is provided by the IPCC guidelines [21,22].

A.

Tier 1 methodology

For the Tier 1 approach, the excreted nitrogen (Nex) was computed using the following equation [21]:

$$\mathrm{N}\mathrm{e}\mathrm{x}={\mathrm{N}}_{\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{e}}\times {\displaystyle \frac{\mathrm{T}\mathrm{A}\mathrm{M}}{1000}}\times 365$$

(10)

where

N_rate = default N excretion rate (kg N·(1000 kg animal mass)⁻¹·day⁻¹);

TAM = typical animal mass for livestock species (kg·animal⁻¹).

B.

Tier 2 methodology

The Tier 2 methodology follows a more complex approach, considering parameters related to productivity characteristics. Therefore, the following equation, which considers the nitrogen ingested and retained by an animal according to its productive stage, was used to determine the Nex factor [22].

$$\mathrm{N}\mathrm{e}\mathrm{x}={\mathrm{N}}_{\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{a}\mathrm{k}\mathrm{e}}\times (1-{\mathrm{N}}_{retention\_frac})\times 365$$

(11)

where

N_intake = the daily N intake per head of animal of species (kg N·animal⁻¹·day⁻¹);

N_{retention_frac} = fraction of daily N intake that is retained by animal (dimensionless);

365 = number of days in a year.

The N_intake was estimated using the following equation [21,22]:

$$N_{intake}={\displaystyle \frac{GE}{18.45}}\times \left({\displaystyle \frac{\frac{CP\%}{100}}{6.25}}\right)$$

(12)

The GE was determined as previously described, and the CP was determined based on the respective information provided by the IPCC guidelines (2019), considering the characteristics of the target region, the farming systems, and the animal category (Tabe 2). Similarly, regarding N_{retention_frac}, the respective values were determined according to the recommendations of IPCC [21,22].

Indirect Estimations (Tier 1 and 2 Methodology)

The calculation of indirect nitrogen gases (N₂O) refers to the emissions that are volatized and leached from the manure management system and is estimated using the following equations for both methodologies applied (Tier 1, 2), according to the IPCC [21,22].

$${\mathrm{N}}_2\mathrm{O}=\left({\mathrm{N}}_{\mathrm{v}\mathrm{o}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{l}\mathrm{i}\mathrm{z}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \mathrm{M}\mathrm{M}\mathrm{S}}\times {\mathrm{E}\mathrm{F}}_4\right)\times {\displaystyle \frac{44}{28}}$$

(13)

where

N₂O = indirect N₂O emissions due to volatilization of N from manure management (kg N₂O·yr⁻¹);

EF₄ = emission factor for N₂O emissions from atmospheric deposition of nitrogen on soils and water surfaces (kg N₂O-N·(kg NH₃-N + NO_x-N volatilized)⁻¹);

N_{volatilization MMS} = the amount of manure nitrogen that is lost due volatilization of NH₃ and NO_x, (kg N·yr⁻¹);

44/28 = factor for the conversion of N₂O-N (mm) emissions to N₂O (mm) emissions.

And,

$${\mathrm{N}}_{\mathrm{v}\mathrm{o}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{l}\mathrm{i}\mathrm{z}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{M}\mathrm{M}\mathrm{S}}=\sum \left[\sum \left[\left(\left(\left(\mathrm{N}\times \mathrm{N}\mathrm{e}\mathrm{x}\right)\times \mathrm{A}\mathrm{W}\mathrm{M}\mathrm{S}\right)+{\mathrm{N}}_{\mathrm{c}\mathrm{d}\mathrm{g}}\right)\times {\mathrm{F}\mathrm{r}\mathrm{a}\mathrm{c}}_{\mathrm{G}\mathrm{a}\mathrm{s}\mathrm{M}\mathrm{S}}\right]\right]$$

(14)

where

N = number of heads of livestock species;

Nex = annual average N excretion per head of species, as estimated for Tier 1 or Tier2 methodology (kg N·animal⁻¹·yr⁻¹);

AWMS = fraction of total annual nitrogen excretion for each livestock species that is managed in manure management (dimensionless);

Frac_GasMS = fraction of managed manure nitrogen that volatilizes as NH₃ and NO_x in the manure management system.

The nitrogen gases (N₂O) related to the emissions that are leached were estimated using the following equations [21,22]:

$${\mathrm{N}}_2\mathrm{O}=\left({\mathrm{N}}_{\mathrm{l}\mathrm{e}\mathrm{a}\mathrm{c}\mathrm{h}\mathrm{i}\mathrm{n}\mathrm{g}\_\mathrm{M}\mathrm{M}\mathrm{S}}\times {\mathrm{E}\mathrm{F}}_5\right)\times {\displaystyle \frac{44}{28}}$$

(15)

where

N₂O = indirect N₂O emissions due to leaching and runoff from manure management (kg N₂O·yr⁻¹);

EF₅ = emission factor for N₂O emissions from nitrogen leaching and runoff, (kg N₂O-N/kg N);

N_{leaching MMS} = amount of manure nitrogen that is lost due to leaching (kg·N·yr⁻¹);

44/28 = factor for the conversion of N₂O-N (mm) emissions to N₂O (mm) emissions.

And,

$${\mathrm{N}}_{\mathrm{l}\mathrm{e}\mathrm{a}\mathrm{c}\mathrm{h}\mathrm{i}\mathrm{n}\mathrm{g}\_\mathrm{M}\mathrm{M}\mathrm{S}}=\sum \left[\sum \left[\left(\left(\mathrm{N}\times \mathrm{N}\mathrm{e}\mathrm{x}\times \mathrm{A}\mathrm{W}\mathrm{M}\mathrm{S}\right)\right)\times {\mathrm{F}\mathrm{r}\mathrm{a}\mathrm{c}}_{\mathrm{L}\mathrm{e}\mathrm{a}\mathrm{c}\mathrm{h}\mathrm{M}\mathrm{S}}\right]\right]$$

(16)

where

N_leachingMMS = amount of manure nitrogen that is lost due to leaching (kg N·yr⁻¹);

N = number of heads of livestock species;

Nex = annual average N excretion per head of species (kg N·animal⁻¹·yr⁻¹);

AWMS = fraction of total annual nitrogen excretion for livestock species (dimensionless);

Frac_LeachMS = fraction of managed manure nitrogen for livestock animal category that is leached from the manure management system.

All appropriate factors and parameters used in the above equations followed the recommendations of the IPCC (2019) and are presented in Table 2.

2.5. Data Formatting, Analysis, Function Unit and Calculations

All appropriate estimations were conducted in an Excel sheet using the appropriate calculations and formulas. Upon the analysis being completed, descriptive statistics and graphical interpretations followed. The emissions are presented as net emissions per estimated gas (CH₄, N₂O) or in Gg CO₂ equivalent (Gg CO₂-eq) for all estimated gases. The conversion of non-CO₂ GHG emissions, thus of CH₄ and N₂O, was conducted considering the 100-year global warming potentials of 25- and 298-times CO₂, respectively. The carbon footprint was expressed as kg CO2-eq/kg milk and kg CO₂-eq of 1 kg fat and protein-corrected milk (FPCM). Milk was corrected at 6.5% fat and 5.8% protein, according to Pulina et al. [25], with the raw milk yield corrected at 6.5% and 5.8% fat and protein content, respectively.

3. Results

3.1. Analysis of Basic Zootechnical Indexes of Sheep and Goat Farms

The major zootechnical indexes of the targeted farms are presented in

Table 3. Zootechnical parameters of the examined farms. Mean represents the average value for each parameter. The symbol “Ø” represents non-applicable values for the respective farms. Many of these parameters (1–5, 9, 11) together with total animal population per species were used to define specific values and parameters recommended by the IPCC [21,22] for calculating the GHG emissions.

Parameters		Farms
Zootechnical Index	Species	1	2	3	4	5	6	7	8	9	10	11	Mean
1. Number of milking animals	Ewes	Ø	110	Ø	Ø	50	320	Ø	70	180	Ø	77	134.5
	Goats	350	Ø	350	350	300	68	220	Ø	20	380	300	259.8
2. Weaning (months after birth)	lambs	Ø	2	Ø	Ø	2	2.5	Ø	5	2.5	Ø	2	2.67
	kids	2.5	Ø	2.5	2.5	2	2.5	2.5	Ø	2.5	2	2	2.33
3. Milking period (days)	Ewes	Ø	195	Ø	Ø	195	165	Ø	100	210	Ø	210	179.17
	Goats	195	Ø	180	180	180	180	210	Ø	0	210	210	171.67
4. Fertility (%)	Ewes	Ø	100%	Ø	Ø	70%	94	Ø	96	100	Ø	100	93.33
	Goats	97%	Ø	99%	94%	97%	100	82	Ø	99	100	95	95.89
5. Prolificacy	Ewes	Ø	1.64	Ø	Ø	2	1.27	Ø	1.79	1.22	Ø	2	1.65
	Goats	1.47	Ø	1.16	1.59	1.21	1.03	1.39	Ø	1.1	1.61	1.09	1.29
6. Mortality (%)	lambs	Ø	3	Ø	Ø	0	37	Ø	29	4	Ø	9.4	13.73
	kids	8	Ø	15	10	11	14	3	Ø	10	16	26	12.56
7. Female productive life (years)	Ewes	Ø	6	Ø	Ø	6	8	Ø	8	6	Ø	7	6.83
	Goats	5	Ø	6	6	6	8	7	Ø	5	8	7	6.44
8. Male productive life (years)	Rams	Ø	3	Ø	Ø	5	5	Ø	6	5	Ø	5	4.83
	Bucks	5	Ø	4	4	5	5	5	Ø	4	4	5	4.56
9. Female mortality (%)	Ewes	Ø	9	Ø	Ø	6	10	Ø	5.7	5	Ø	24	9.95
	Goats	9	Ø	7	10	20	1	9	Ø	10	4	10	8.89
10. Male: Female (ratio)	Sheep	Ø	1:22	Ø	Ø	1:10	1:40	Ø	1:11	1:12	Ø	1:25	1:20
	Goats	1:9	Ø	1:9	1:6	1:6	1:17	1:12	Ø	1:20	1:8	1:20	1:12
11. Total milk production (t)	Sheep	Ø	22	Ø	Ø	5.4	17.4	Ø	2	27.25	Ø	15	14.84
	Goats	47	Ø	52	50	28.55	6.6	28	Ø	0	20.25	70	33.60

. The number of animals milking on the examined farms ranged from 50 to 320 ewes and 68 to 380 goats, with mean values of 134.5 ewes and 259.8 goats. According to the farmers’ responses, lambs and kids after birth remained with their mothers until weaning. The weaning of young suckling animals was implemented at 2–2.5 months in both species. The milking period in the case of ewes ranged from 100 to 210 days, while for goats it ranged from 180 to 210 days. Ewes’ fertility ranged from 70 to 100%, while the respective values in the case of goats were between 82% and 100%. The prolificacy ranged from 1.27 to 2 in the case of ewes, while the respective values for goats ranged from 1.03 to 1.61. Lamb and kid mortality was noted high in both cases, with mean values of 13.73% (range: 3–37%) and 12.56% (range: 3–26%), respectively. High mortality was also noted for adult ewes and goats, with mean values of 9.95% (range: 5–24%) and 8.89% (range: 1–20%), respectively. The productive life of ewes and goats ranged from 6 to 8 years and 5 to 8 years, respectively. Rams and bucks were kept in farms for 3–5 years and 4–5 years, respectively. The ratio of male to female animals varied between 1:40 and 1:11 (mean value 1:20) in the case of sheep units, and between 1:20 and 1:6 in the case of goat units (mean value 1:12). The total milk production varied between 2 and 27.25 t of ovine milk (mean value: 14.84 t), while the respective values for goat milk varied between 6.6 t and 52 t (mean value: 33.60 t). On all farms, milking was conducted by hand. The birth season was between the end of October to the end of January for ewes, and from mid-October to mid-February for goats. According to the farmers, none of them implemented artificial insemination or estrous synchronization, and all followed natural matings. Finally, none of them were in favor of increasing the number of animals that were bred on their farms in the next five years.

3.2. GHG Emissions’ Using Tier 1 Methodology

Table 4. Estimated methane emissions derived from enteric fermentation and manure management at farm level using the Tier 1 methodology. Emissions are presented in net values (kg CH₄) and in carbon dioxide equivalents (kg CO₂-eq). The symbol “Ø” represents non-applicable values for the respective farms.

Farm	Methane Emissions—Enteric Fermentation				Methane Emissions—Manure Management
	kg CH4		kg CO2-eq		kg CH4		kg CO2-eq
	Goats	Sheep	Goats	Sheep	Goats	Sheep	Goats	Sheep
1	3681.51	Ø	92,037.67	Ø	47.41	Ø	1185.19	Ø
2	Ø	2025	Ø	50,625	Ø	35.64	Ø	890.94
3	2599.32	Ø	64,982.88	Ø	34.81	Ø	870.27	Ø
4	3257.19	Ø	81,429.79	Ø	41.94	Ø	1048.59	Ø
5	2497.26	395.21	62,431.51	9880.14	32.16	6.96	803.94	173.88
6	467.53	2.125,62	11,688.36	53,140.41	6.02	37.41	150.51	935.21
7	1571.16	Ø	39,279.11	Ø	20.23	Ø	505.80	Ø
8	Ø	555,68	Ø	13,892.12	Ø	9.78	Ø	244.49
9	210	1607.88	5250	40,196.92	2.70	28.30	67.61	707.42
10	3819.18	Ø	95,479.45	Ø	49.18	Ø	1229.1	Ø
11	2872.26	557.88	71,806.51	13,946.92	36.99	9.82	924.67	245.45

presents the estimated methane emissions (in Kg CH₄ and in kg CO₂-eq) of the targeted farms using the Tier 1 methodology. The methane emissions derived from the enteric fermentation were higher compared to the respective emissions derived from manure management on all farms for both species (goats, sheep). Specifically, the methane emissions derived through the digestive process of goats in the respective farms ranged from 5250 kg CO₂-eq. to 95,479.45 kg CO₂-eq., while the respective sheep emissions were between 9880.14 kg CO₂-eq. and 50,625 kg CO₂-eq. Regarding the methane emissions derived from manure management, the goats’ contribution ranged from 67.61 kg CO₂-eq. to 1185.19 kg CO₂-eq. The sheep’s contribution varied between 173.88 kg CO₂-eq. and 935.21 kg CO₂-eq.

The nitrous oxide emissions for all the analyzed farms are presented in

Table 5. Estimated nitrous oxide emissions derived from manure management at farm level using Tier 1 methodology. Emissions are presented in net values (kg N) and in carbon dioxide equivalents (kg CO₂-eq.). The symbol “Ø” represents non-applicable values for the respective farms.

Farm	Direct Emissions—N2O				Indirect Emissions—N2O
	kg N		kg CO2-eq.		Volatized				Leached
					kg N		kg CO2-eq.		kg N		kg CO2-eq.
	Goats	Sheep	Goats	Sheep	Goats	Sheep	Goats	Sheep	Goats	Sheep	Goats	Sheep
1	10.88	Ø	3241.94	Ø	1.31	Ø	389.03	Ø	5.50	Ø	1640.42	Ø
2	Ø	7.02	Ø	2093.34	Ø	0.84	Ø	251.20	Ø	0.15	Ø	46.05
3	7.68	Ø	2288.96	Ø	0.92	Ø	274.68	Ø	3.89	Ø	1158.21	Ø
4	9.63	Ø	2868.29	Ø	1.16	Ø	344.19	Ø	4.87	Ø	1451.35	Ø
5	7.38	1.37	2199.09	408.54	0.89	0.16	263.89	49.03	3.73	0.03	1112.74	8.99
6	1.38	7.38	411.71	2197.36	0.17	0.88	49.41	263.68	0.70	0.16	208.33	48.34
7	4.64	Ø	1383.57	Ø	0.56	Ø	166.03	Ø	2.35	Ø	700.09	Ø
8	Ø	1.93	Ø	574.44	Ø	0.23	Ø	68.93	Ø	0.04	Ø	12.64
9	0.62	5.58	184.93	1662.14	0.07	0.67	22.19	199.46	0.31	0.12	93.57	36.57
10	11.29	Ø	3363.18	Ø	1.35	Ø	403.28	Ø	5.71	Ø	1701.77	Ø
11	8.49	1.94	2529.32	576.71	1.02	0.23	303.52	69.20	4.29	0.04	1279.83	12.69

. Direct emissions were noted as higher in both species compared to the respective indirect emissions. The direct emissions ranged from 411.71 kg CO₂-eq. to 3363.18 CO₂-eq. for goat farms, while the respective emissions for sheep farms varied between 408.54 kg CO₂-eq. and 2197.36 CO₂-eq. Lower values were estimated for vitalized nitrogen emissions (49.03–263.68 CO₂-eq. in sheep farms and 22.19–403.28 CO₂-eq. in goat farms). The amount of nitrogen emitted from leaching procedures ranged from 93.57 kg CO₂-eq. to 1701.77 CO₂-eq. and from 8.99 kg CO₂-eq. to 48.34 CO₂-eq. for goat and sheep farms, respectively.

Finally,

Table 6. Total GHG emissions (kg CO₂-eq) estimated at farm level and carbon footprint of produced milk (kg CO₂-eq/kg FPCM) per farm using the Tier 1 methodology. The symbol “Ø” represents non-applicable values for the respective farms.

Farms	Total GHG Emissions (kg CO2-eq.)		Carbon Footprint (kg CO2-eq./kg FPCM)
	Goats	Sheep	Goats	Sheep
1	98,494.26	Ø	2.08	Ø
2	Ø	53,016.44	Ø	2.39
3	69,575	Ø	1.33	Ø
4	87,142.22	Ø	1.73	Ø
5	66,881.17	10,346.86	2.33	1.91
6	12,508.31	55,650.68	1.88	3.18
7	42,034.60	Ø	1.49	Ø
8	Ø	14,548.36	Ø	7.23
9	5618.30	42,095.75	0	1.53
10	102,177.48	Ø	5.01	Ø
11	76,880.84	14,605.75	1.09	0.96
Mean	62,356.13	31,710.64	2.12	2.87

summarizes the total GHG emissions (CO₂-eq.) for the targeted farms, as well as the estimated carbon footprint (CO₂-eq./kg FPCM) of the milk produced by each farm. The average total GHG emissions for goats were 62,356.13 kg CO₂-eq, ranging from 5618.30 kg CO₂-eq. to 102,177.48 kg CO₂-eq. The respective average value for sheep was 31,710.64 CO₂-eq. and varied between 10,346.86 kg CO₂-eq. and 55,650.68 kg CO₂-eq. Regarding the goats’ milk footprint, it ranged from 1.09 kg CO₂-eq./kg FPCM to 5.01kg CO₂-eq./kg FPCM, with a mean value of 2.12 kg CO₂-eq./kg FPCM. Referring to ovine milk, the carbon footprint varied between 0.96 kg CO₂-eq./kg FPCM and 7.23 kg CO₂-eq./kg FPCM, with a slightly higher mean value compared to caprine milk, estimated at 2.87 kg CO₂-eq./kg of FPCM.

3.3. GHG Emissions Using Tier 2 Methodology

Regarding the GHG emissions estimated by the Tier 2 methodology,

Table 7. Estimated methane (CH₄) emissions derived from enteric fermentation and manure management at farm level using the Tier 2 methodology. Emissions are presented in net values (kg CH₄) and in carbon dioxide equivalents (kg CO₂-eq). The symbol “Ø” represents non-applicable values for the respective farms.

Farm	Methane Emissions—Enteric Fermentation				Methane Emissions—Manure Management
	kg CH4		kg CO2-eq		kg CH4		kg CO2-eq
	Goats	Sheep	Goats	Sheep	Goats	Sheep	Goats	Sheep
1	5137.51	Ø	128,437.82	Ø	0.15	Ø	3.75	Ø
2	Ø	1820.42	Ø	45,511.05	Ø	0.02	Ø	0.52
3	4318.28	Ø	107,957	Ø	0.11	Ø	2.66	Ø
4	5209.91	Ø	130,247.84	Ø	0.16	Ø	3.88	Ø
5	1558.08	624.5	38,951.99	15,606.24	0.07	0.003	1.79	0.08
6	703.22	3065.27	17,580.59	76,631.63	0.003	0.081	0.08	2.01
7	2476.01	Ø	61,900,16	Ø	0.04	Ø	0.91	Ø
8	Ø	7,40.58	Ø	18,514.48	Ø	0.005	Ø	0.13
9	278.31	2707.32	6957.82	67,682.98	0.001	0.05	0.01	1.35
10	5519.64	Ø	137,991.06	Ø	0.19	Ø	4,74	Ø
11	5054.27	1133.01	126,356.80	28,325.16	0.13	0.01	3.29	0.19

presents the methane emissions derived from enteric fermentation and manure management, and

Table 8. Estimated nitrous oxide (N₂O) emissions derived from manure management at farm level using the Tier 2 methodology. Emissions are presented in net values (kg N) and in carbon dioxide equivalents (kg CO₂-eq.). The symbol “Ø” represents non-applicable values for the respective farms.

Farm	Direct Emissions—N2O				Indirect Emissions—N2O
	kg N		kg CO2-eq.		Volatized				Leached
					kg N		kg CO2-eq.		kg N		kg CO2-eq.
	Goats	Sheep	Goats	Sheep	Goats	Sheep	Goats	Sheep	Goats	Sheep	Goats	Sheep
1	44.44	Ø	13,242.70	Ø	5.33	Ø	1589.12	Ø	0.98	Ø	291.34	Ø
2	Ø	6.20	Ø	1848.75	Ø	0.74	Ø	221.85	Ø	0.14	Ø	40.67
3	31.51	Ø	9388.89	Ø	3.78	Ø	1126.67	Ø	0.69	Ø	206.56	Ø
4	45.89	Ø	13,675.88	Ø	5.51	Ø	1641.11	Ø	1.01	Ø	300.87	Ø
5	21.13	0.87	6297.93	257.94	2.54	0.10	755.75	30.95	0.46	0.02	138.55	5.67
6	0.94	22.86	278.68	6812.14	0.11	2.74	33.44	817.46	0.02	0.50	6.13	149.87
7	10.81	Ø	3222.43	Ø	1.30	Ø	386.69	Ø	0.24	Ø	70.89	Ø
8	Ø	1.44	Ø	430.26	Ø	0.17	Ø	51.63	Ø	0.03	Ø	9.47
9	0.16	15.27	48.42	4551.17	0.02	1.83	5.81	546.14	0.004	0.34	1.07	100.13
10	56.07	Ø	16,709.80	Ø	6.73	Ø	2005.18	Ø	1.23	Ø	367.62	Ø
11	38.89	2.14	11,589.34	637.77	4.67	0.26	1390.72	76.53	0.86	0.05	254.97	14.03

shows the respective nitrous oxide emissions from manure management. Similarly to the Tier 1 methodology, the methane emissions from enteric fermentation were higher than those from manure management. The same was noted for the direct emissions compared to the respective indirect emissions of nitrous oxide. Specifically, methane emissions from enteric fermentation ranged from 17,580.59 kg CO₂-eq. to 137,991.06 kg CO₂-eq. on goat farms, while the respective sheep emissions were between 15,606.24 kg CO₂-eq. and 76,631.63 kg CO₂-eq. Regarding the methane emissions derived from manure management, the goats’ contribution ranged from 0.01 kg CO₂-eq. to 4.74 kg CO₂-eq. The sheep’s contribution varied between 0.13 kg CO₂-eq. and 2.01 kg CO₂-eq. (Table 8).

According to Table 8, the direct nitrous emissions ranged from 48.42 to 16,709.80 Kg CO₂-eq. for goat farms, while the respective emissions for sheep farms varied between 257.93 and 6812.14 Kg CO₂-eq. Lower values were estimated for the vitalized nitrogen emissions (5.67–149.87 kg CO₂-eq. on sheep farms and 5.81–2005.18 kg CO₂-eq. on goat farms). The amount of nitrogen emitted from the leaching procedure ranged from 1.07 kg CO₂-eq. to 300.87 Kg CO₂-eq. and from 5.67 kg CO₂-eq. to 149.87 kg CO₂-eq. for goat and sheep farms, respectively (Table 8).

Considering the total GHG emissions derived from the targeted farms (

Table 9. Total estimated GHG emissions (kg CO₂-eq.) at farm level and carbon footprint of produced milk (kg CO₂-eq/kg FPCM) per farm using the Tier 2 methodology. The symbol “Ø” represents non-applicable values for the respective farms.

Farm	Total GHG Emissions (kg CO2-eq.)		Milk Carbon Footprint (kg CO2-eq./kg FPCM)
	Goat	Sheep	Goat	Sheep
1	143,561.51	Ø	3.04	Ø
2	Ø	47,622.9	Ø	2.15
3	118,681.73	Ø	2.27	Ø
4	145,869.57	Ø	2.9	Ø
5	46,146.01	15,900.88	1.61	2.93
6	17,898.92	84,413.11	2.69	4.82
7	65,581.09	Ø	2.33	Ø
8	Ø	19,005.96	Ø	9.45
9	7013.13	72,881.75	0	2.66
10	157,078	Ø	7.71	Ø
11	139,595.12	29,053.69	1.98	1.92
Mean	93,491.68	44,813.05	2.73	3.99

), they ranged from 7013.13 to 145,869.57 Kg CO₂-eq. for goat farms (mean value of all farms: 93,491.68 kg CO₂-eq.) and from 15,900.88 kg CO₂-eq. to 72,881.75 kg CO2-eq. for sheep farms (mean value of all farms: 44,813.05 kg CO₂-eq.). Regarding the goat milk’s footprint based on the Tier 2 approach, it ranged from 1.61 to 7.71 kg CO₂-eq./kg FPCM, with a mean value of 2.73 kg CO₂-eq./kg FPCM. Referring to ovine milk, the carbon footprint varied between 1.92 kg CO₂-eq. and 9.45 kg CO₂-eq./kg of produced milk, resulting in a mean value of 3.99 kg CO₂-eq./kg FPCM.

3.4. Comparison of Carbon Footprint Between Tier 1 and Tier 2 Methodologies

The differences between the estimations of carbon footprint between the two methodologies are presented in

Table 10. Difference (%) in the estimated milk carbon footprint at farm level between the Tier 1 and Tier 2 methodologies. Emissions are presented in carbon dioxide equivalents/kg produced milk (kg CO₂-eq./kg FPCM). The symbol “Ø” represents non-applicable values for the respective farms.

Farm	Goats			Sheep
	Tier 1	Tier 2	% Difference	Tier 1	Tier 2	% Difference
1	2.08	3.04	31.5	Ø	Ø	Ø
2	Ø	Ø	Ø	2.39	2.15	−11.1
3	1.33	2.27	41.4	Ø	Ø	Ø
4	1.73	2.9	40.3	Ø	Ø	Ø
5	2.33	1.6	−45.6	1.91	2.93	34.81
6	1.88	2.7	30.3	3.18	4.82	34.02
7	1.49	2.33	36.1	Ø	Ø	Ø
8	Ø	Ø	Ø	7.23	9.45	23.49
9	Ø	Ø	Ø	1.53	2.66	42.81
10	5.01	7.71	35.01	Ø	Ø	Ø
11	1.09	1.98	44.94	0.96	1.92	50
Mean	2.12	2.73	22.21	2.87	3.99	28.04

. Mean differences of 22.21% and 28.04% in the carbon footprints of goat and sheep milk, respectively, were noted between the Tier 2 and the Tier1 methodologies.

4. Discussion

Livestock farming plays a crucial role in the global agricultural economy. According to projections, the world population is expected to reach 10.3 billion by the mid-2080s [26], leading to increased demand for animal products and, consequently, a greater environmental impact from animal husbandry. Specifically, more natural resources will be required to meet these demands, which may negatively affect the environmental impact and sustainability of animal production. As a result, greenhouse gas (GHG) emissions from livestock activities are becoming increasingly significant, given their substantial contribution to total anthropogenic GHG emissions [27].

This study analyzes GHG emissions and the carbon footprint of sheep and goat farms in the Vamvakou region of Greece, a mountainous area that exemplifies (semi-)extensive farming systems for small ruminants. The research applies the Tier 1 and Tier 2 methodologies according to the guidelines proposed by the Intergovernmental Panel on Climate Change [21,22].

The findings reveal fluctuations in the total estimated GHG emissions across the farms examined, regardless of the methodology used. This outcome aligns with our expectations, as variations stem from differences in herd size, bread, and productivity levels. According to Toro-Mujica et al. [28], breed plays a key role in reducing carbon footprints. Additionally, discrepancies were observed between the Tier 1 and Tier 2 GHG emissions and carbon footprint estimates, primarily due to the distinct mathematical approaches employed by each method to calculate emission factors.

The Tier 1 methodology, known for its simplicity, estimates emissions based on total animal numbers and species-specific emission factors for methane and nitrous oxide, without accounting for detailed herd composition [21,22]. Consequently, Tier 1 estimations reflect changes in overall animal population size only, overlooking variations in age, productivity, production stage (e.g., lactation, dry season), diet composition, or farm infrastructure (e.g., manure management systems). In contrast, Tier 2 provides a more refined assessment by incorporating parameters related to herd structure, management practices, diets, and manure treatment [21,29]. This approach factors in specific details such as the number and category of animals (e.g., rams, ewes, replacement animals, lambs), animal weight, manure management system, diet composition, energy requirements, and productivity levels. Accordingly, this methodology considers specific information related to herd structure, animal categories and their respective numbers (i.e., rams, ewes, replacement animals, lambs), the animal weight, the characteristics of the production system, considering, i.e., the type of manure system used, and diet characteristics, as well as energy requirements and productivity levels.

Similar discrepancies between Tier 1 and Tier 2 estimations have been reported in studies assessing national greenhouse gas (GHG) emissions from pig and beef cattle sectors. For example, GHG emissions from pig farming estimated using the Tier 2 methodology were significantly lower than those obtained through Tier 1, often reaching approximately half of the Tier 1 estimates [29]. Comparable trends in GHG emission discrepancies between Tier 1 and Tier 2 estimates have also been observed in the Greek beef sector [24]. Since the Tier 1 methodology relies exclusively on the total number of animals reared and predefined regional emission factors, it inherently risks misestimations compared to Tier 2 assessments. Tier 1 emission factors have an uncertainty range of ±30–50% [21]. However, some cases, particularly in developing countries, have reported higher total emissions in Tier 2 compared to Tier 1 inventories [30]. In any case, the IPCC recommends that countries should adopt higher-tier methodologies for more accurate GHG emission assessments [22].

In the current study, extreme (very high) values of carbon footprint (CF) were observed across both applied methodologies. Notably, the same farms exhibited the highest CF regardless of the method used. Specifically, farm 10 recorded the highest CF among goat farms, while farm 8 showed the highest CF among sheep farms. In both instances, reduced productivity contributed to the elevated CF values. This consistency suggests that the choice of methodology did not influence the ranking of the farms with the highest emissions, aligning with findings reported in previous studies [24,29]. Furthermore, among farms rearing the same species (i.e., goats; farms 1,3,4) with comparable milk productivity levels (e.g., 47, 52, 50 tn), substantial variation in the carbon footprint (CF) values was observed. This divergence can be attributed to differences in herd management practices, reflected in herd structure (i.e., animal numbers and categories), and operational strategies, such as replacement rates, prolificacy, and fertility.

Comparing the findings of the present work with previous studies presents several challenges and limitations. Differences in the units used to express results, production system boundaries, and methodologies for estimating emissions introduce significant variability in reported outcomes [30,31,32]. Despite these variations, the emission intensity results from this study fall within the ranges reported in previous research. On average, the emission intensity for small ruminants in the dairy sector is 6.5 kg CO₂-eq/kg FPCM, with goat milk exhibiting lower average emissions (5.2 kg CO₂-eq/kg FPCM) compared to sheep milk average emissions (8.4 kg CO₂-eq/kg FPCM) based on the Tier 1 methodology [33]. Another study [14] using IPCC equations reported a variation in ovine milk carbon footprint from 0.7 to 5.2 kg CO₂-eq/kg FPCM, with an average of 3.2 kg CO₂ -eq/kg FPCM. These values could be reduced through increased milk productivity and the inclusion of values of carbon sequestration from grazing in calculations. Vagnoni et al. [12] used the Tier 1 methodology and reported a carbon footprint of 2.0–2.3 kg CO₂-eq/kg FPCM for two different farming systems (semi-extensive and semi-intensive) in Sardinia. Similarly, Vagnoni and Franca [15] reported a respective value of 3.3 kg CO₂-eq/kg FPCM for semi-intensive production systems of sheep in Sardinia, having considered in their estimations all inputs and outputs related to sheep milk production. More recently, Sabia et al. [16] reported an average carbon footprint of 3.78 kg CO₂-eq/kg FPCM across four farms in the Province of Potenza including all emissions derived from on-farm activities and the use of fuels and electricity, while Ravani et al. [34] estimated that the respective value for intensive sheep farming systems in Western Macedonia (Greece) was 3.58 kg of CO₂-eq/kg FPCM, using a Tier 2 approach, employing different system boundaries including not only on-farm activities but also crop-related activities and CO₂ emissions from the production process, using the SimaPro software. Sheep husbandry in EU-27 countries has been reported to have an emissions range of 2.6–4.1 kg CO₂-eq/kg of corrected milk (7% fat) [35]. In terms of absolute emissions, Plaza et al. [20] documented a range of 352.11 kg CO₂-eq/sheep/year to 768.12 kg CO₂-eq/sheep/year in the region of Castilla-Leon in Spain, considering all the on-farm and off-farm emissions excluding machinery, buildings, medicines, and other minor stable supplies as system boundaries. However, their findings indicate that more productive systems tend to reduce carbon footprints when emissions are expressed per kilogram of FPCM.

Regarding dairy goat farming, studies are relatively limited. Gutiérrez-Peña et al. [36], based on a Tier 2 approach which considered all on-farm and off-farm emissions apart from those related to machinery, buildings, and medicines, reported that carbon footprint variated according to farm productivity, ranging from 2.36 kg CO₂-eq/kg FPCM to 1.76 kg CO₂-eq/kg FPCM for low- and high-productivity grazing systems, respectively. A carbon footprint of 2.6 kg CO₂-eq per kg of FPCM for a typical semi-intensive farm in southern Spain was also reported, including in the estimation all emissions derived from inputs and outputs related to animal housing, crop cultivation, feed processing, and transport [37]. A recent study [38] considering farm emissions as well as those related to feeds and land use reported comparable carbon footprint values for four different dairy goat systems: indoor systems without associated crops and those with associated crops had carbon footprints of 1.42 kg CO₂-eq/kg FPCM and 1.04 kg CO₂-eq/kg FPCM, respectively. Further, the authors found that grazing systems with high feed supply and pastoral systems had carbon footprints of 1.15 kg CO₂-eq/kg FPCM and 1.17 kg CO₂-eq/kg FPCM, respectively.

Greenhouse gas mitigation solutions should be primarily directed at their point sources, enabling more effective and targeted reduction strategies across sectors [1]. Considering the characteristics of the farming systems studied, several strategies could help reduce emission intensity by improving animal productivity [1,39,40]. One approach is the implementation of appropriate crossbreeding schemes within farms, or the selection and maintenance of high-yielding animals. This can assist in increasing milk productivity and thus lowering the carbon footprint of the milk produced. An additional solution could be the rational management of nutrition and the proper coverage of animal needs per production stage. Usually, in extensive systems where grazing is implemented all year, assessing whether animals receive sufficient nutrition can be very challenging. The proper formulation and implementation of balanced diets for all animal categories (e.g., rams, lactating or/and dry ewes/goats, etc.), while accounting for seasonal variations in nutrient availability across different grazing areas, can support effective dietary management at the farm level and prevent both under- and over-nutrition in livestock. Therefore, optimized diet management can enhance productivity and lower the carbon footprint per unit of the final product.

5. Conclusions

This study assessed the GHG emissions and the carbon footprint of (semi-)extensive sheep and goat farms located in a mountainous region of Greece, highlighting the distinctive characteristics of small ruminant production in the Mediterranean, where (semi-)extensive systems remain prevalent. Emission estimates were conducted using both Tier 1 and Tier 2 methodologies, revealing notable differences between the two approaches, primarily attributable to the simplified and less specific computational framework of the Tier 1 method. The average carbon footprints estimated via Tier 1 for goat and sheep farms were 2.12 and 2.87 kg CO₂-eq./kg FPCM, respectively, whereas Tier 2 estimations resulted in corresponding values of 2.73 and 3.99 kg CO₂-eq./kg FPCM. Enhancing productivity through improved herd management and optimized feeding strategies could help reduce the overall carbon footprint of these farms