Sustainability Indicators of Different Production Systems of a Greek Local Sheep Breed

Abstract

This study develops a toolkit of sustainability indicators to analyze the economic, environmental, and social performance of various pasture-based production systems rearing Karagkouniko sheep (both specialized and mixed), and compares it with the intensive Lacaune production system in the same region. The analysis showed that despite the lower milk productivity, the group of specialized livestock Karagkouniko farms was more profitable compared to the Lacaune (35% higher net profit) production system, mainly due to savings in purchased feedstuff (64% lower expenses). This implies that grazing—if properly managed—can indeed enhance the profitability of farms. The group of mixed Karagkouniko farms—cultivating crops for both feedstuff and markets—was the least profitable group (−144.76 per ewe) as well as the least efficient in terms of use of energy (EUR 4.66 of output per EUR 1 of energy cost) and agrochemical inputs (537.2 kg of fertilizers and 3.3 liters of pesticides per ha). This suggests that strong organizational skills are required to effectively manage both crop and livestock production. Trade-offs were also observed between the sustainability dimensions. To address these trade-offs and ensure a transition to more sustainable agriculture, a comprehensive framework should be developed, integrating a mix of socioeconomic and agro-environmental schemes.

1. Introduction

Livestock systems worldwide have been facing severe transformations during the past decades. In the sheep and goat sector, the marked heterogeneity of production systems—ranging from extensive pasture-based systems (PBSs) to intensive confined ones—is actually declining as a result of a continuous intensification and specialization process [1]. PBSs are primarily characterized by low input use and, on many occasions, relate to extensive grazing and rearing of local breeds [1,2,3], while the more intensive confined ones aim to maximize yields and profits through the high use of inputs (particularly feedstuff) with minimum or no grazing [3,4]. Semi-extensive production systems integrate elements and practices from both PBSs and intensive systems [5]. However, this broad classification often fails to capture the vast diversity and unique characteristics of many farms within each category, which include differences in diversification and orientation of production, engagement in off-farm activities, etc.; see [5].

Today, in the face of various challenges, such as the increase in worldwide population and climate change, the sustainability (economic, environmental, and social) of livestock systems is a topic that has received significant attention [6]. Intensive systems have faced significant criticism for their environmental impact (excessive use of antibiotics, increased greenhouse gas emissions, biodiversity loss, etc.) [7,8], while an increasing body of literature has pointed out that higher productivity does not always lead to higher economic performance [1] and relates to lower resilience [9]. Many researchers agree that PBSs are crucial for maintaining areas of High Natural Value [3,10,11]. However, opinions vary regarding their economic performance, as a part of the literature highlights their cost-effectiveness [1,12,13,14], while another one underscores their low production volumes [9,15]. In contrast with intensive confined ones, PBSs have higher requirements for specialized labor [16]—which is very scarce—while in the cases that they are not properly managed, PBSs can lead to overgrazing [17]. Other studies are in favor of mixed production systems [1,18], highlighting their overall sustainability. However, due to their complicated management, which requires high organizational skills, mixed farms often fail to reach their full potential, thus undermining their economic and environmental comparative advantages [19].

In any case, the assessment of the overall performance of livestock systems can provide valuable information for the transition toward more sustainable production, ensuring food security for both current and future generations. Thus, an increasing body of literature has analyzed the overall sustainability performance of livestock systems—or some of its aspects—in different regions around the globe. Some examples are the studies of [1], who investigated the economic performance of different sheep production systems in Spain; [7], who analyzed the performance of bovine farms in Italy; [20], who investigated factors that affect the environmental sustainability of livestock farms in Alpes; and [21], who classified dairy Irish farms based on their overall sustainability.

Due to the heterogeneity of livestock systems and the complexity of the sustainability concept [22], a wide variety of sustainability indicators (SIs) [23] as well as indicator-based methods and frameworks—such as the Sustainability Assessment of Food and Agriculture guidelines (SAFA), the Response Inducing Sustainability Evaluation (RISE) tool and Public Goods Tool (PGT)—have been developed and implemented for livestock systems [24]. In Greece, there is a lack of literature on the development of SI tailored to the specific needs and characteristics of the Greek livestock farms and agriculture in general. Moreover, the majority of Greek studies have focused on the economic aspect of livestock production systems [25,26,27,28], while only a few have delved into their environmental and social dimensions [29,30]. Notably, [29] conducted a comparative assessment of the overall sustainability of sheep farms in Greece and Spain, using general farm-level indicators derived from the FLINT (Farm Level Indicators for New Topics in Policy Evaluation) research project.

Using data from a farm management survey, this study aims to fill this gap by developing a toolkit of SIs that reflects some of the most relevant economic, environmental, and social aspects of different PBS-rearing Karagkouniko sheep in the Thessaly region, located in Central Greece. To our knowledge, this is the first study in Greece that developed an SI toolkit tailored to the characteristics of PBS-rearing local sheep breeds (LSBs). The complete list of SIs was derived based on previous research [1,7,26,31,32,33] and was validated by experts. In order to gain further insights into the strengths and weaknesses of these PBSs, a benchmarking activity was conducted, comparing the SI of different Karagkouniko PBSs with SIs of the intensive Lacaune breed production system in the same region. The results of this study could provide valuable insights to support the design of targeted policies and schemes that promote the transition to more sustainable livestock production.

2. Materials and Methods

2.1. Case Study

Thessaly is one of the main agricultural regions in Greece, with considerable crop and livestock production [34]. Climate—which is characterized by cold winters and hot summers [35]—and soil conditions have formulated a significant heterogeneity of livestock systems, with 12,477 livestock farms (specialized and mixed), of which 66% rear sheep [36]. PBSs—which vary from sedentary to transhumant with different levels of grazing according to weather conditions—coexist alongside intensive confined ones, which are, however, increasing rapidly.

The region generally receives moderate rainfall, with the majority of precipitation occurring during autumn and winter. However, climate change has increased the intensity and frequency of extreme weather events, making rainfall more unpredictable [37]. For instance, Storm Daniel, which occurred in September 2023, unleashed unprecedented amounts of rain across Thessaly, with 400 to 600 mm falling over a couple of days, and some areas receiving nearly 750 mm in just 24 h [38]. This storm severely impacted both livestock and crop production. With regards to livestock, 250,000 animals were lost, of which 30% were sheep [39]. To support the recovery of agricultural production, a strategic plan was formulated [40], which, among other objectives (rational management of water resources, crop restructuring, etc.), aims to enhance the competitiveness and overall sustainability of PBS-rearing local breeds.

Karagkouniko is the main local sheep breed in the region [41], with high adaptability to local conditions and exceptional functional traits, such as high longevity (more lactation periods per ewe), strong resistance to diseases, and relatively low requirements for both fixed and variable capital. These characteristics can contribute to increases in the economic performance of farms under proper management [9]. In addition, the production traits of Karagkouniko, such as an average annual milk production of 188 kg per ewe and a prolificacy rate of 1.36, are superior to those of other unimproved local sheep breeds in the same region (e.g., Kalaritiko and Orino Epiru) [42], highlighting the potential for further genetic improvement.

Farms rearing this breed are mainly located in the western part of Thessaly and particularly in the regional units of Karditsa and Trikala (Figure 1).

Like most farms rearing local breeds, Karagkouniko flocks graze on natural and, less frequently, artificial pastures, usually from April to the end of November. Then, just before the lambing period, sheep are kept indoors and fed with harvested forage and concentrates due to harsh climate conditions and limited vegetation. However, a key difference between the PBSs of Karagkouniko and other systems that rear local breeds (e.g., Kalaritiko and Orino Ipirou) in the same region is that many Karagkouniko farms also engage in crop production [41], not only for feedstuff production, but also for markets (mainly cotton). Therefore, the agro-pastoral production system of the Karagkouniko breed is diverse with different farm types, as follows:

• Specialized Livestock farms (SLf): Pasture-based farms specializing in milk and meat with no cultivated land, so all feedstuff (concentrates and forage in winter) is purchased from markets.
• Partially Mixed farms (Mixed [L]): Livestock farms that cultivate crops for feedstuff (forage and concentrates).
• Mixed Livestock–Crop farms (Mixed [L + C]): Farms combining livestock and crop production both for feedstuff and for markets. Apart from corn, barley, and alfalfa, Mixed [L + C] farms engage in cotton production which is a crop of strategic importance for Thessaly.

However, as in the case of other LSB in Greece, an increasing number of Karagkouniko farmers started to intensify, replacing and/or crossbreeding this breed with exotic improved ones, particularly Lacaune [41]. Lacaune Farms (LF) are larger than the ones rearing Karagkouniko; they graze ewes for fewer hours, mainly on artificial pastures; they are more intensive in the use of inputs, and most of them produce at least one part of their feedstuff on-farm. This situation has led to a decline in the population of purebred Karagkouniko sheep. Indeed, according to the population data of the Livestock Genetic Resources Centre of Karditsa (

Table 1. Number of Karagkouniko farms and purebred sheep.

Year	Purebred Breed Karagkouniko Sheep Registered in the Herdbook	Number of Karagkouniko Farms
2011	8451	77
2012	5714	60
2015	4482	48
2017	4412	51
2019	4709	42
2020	4511	43
2021	4653	46
2022	4319	44

Source: Livestock Genetic Resources Centre of Karditsa (Data processed by authors).

) [45], the number of purebred ewes, registered in the herd books, has decreased significantly in the last decade (a reduction of 49%). Due to the significant decline in their population, Karagkouniko sheep was recently officially recognized as a rare and endangered breed (Ministerial Decision 973/251361/22-8-2024/). Thus, it will be supported through the Strategic Plan of the Common Agricultural Policy of Greece 2023–2027, under the intervention I3-70-1.5, which aims to compensate farmers for economic losses caused by rearing endangered local breeds.

Pricing strategies of dairies, which prioritize milk quantity over quality, are among the main causes of this adverse situation [41], as they have led farmers to focus principally on increasing milk yields, rather than on functional traits, which, however, formulate its potential benefits for livestock farms. Ref. [41] has also highlighted several other factors contributing to the decline of Karagkouniko sheep, including (i) the limited availability of purebred Karagkouniko rams in contrast to the availability of exotic breeds, particularly Lacaune, and (ii) low accessibility to communal pastures.

2.2. Development of the Sustainability Indicators Toolkit

For the purpose of this study, a consistent ‘toolkit’ of SI was developed (Table 2), which accounted for economic, environmental, and social aspects of management and operation of all types of Karagkouniko farms as well as for LF. In order to ensure consistency and avoid subjectivity and repetition, a well-defined and transparent approach was implemented based on an adaptation of the SI selection process applied by [23] as described below:

Step 1—Literature review: An extensive literature review was undertaken to identify SI that accurately depict the complexity and multifunctional nature of different PBSs, resulting in a comprehensive list of potential economic, environmental, and social SI that have been used in previous relevant studies.

Step 2—Evaluation of the initial list: Eighteen stakeholders (researchers and professors familiar with the subject matter and the implemented methodology) were identified and invited to participate in an online survey. They were asked to rate the initial list of SI on a scale from −3 to 3, where −3 indicated the least important and 3 the most important, based on the selection criteria presented by [46]. These criteria refer to:

• “Relevance”: Ensures that the selected SI aligns with the contextualization of the study (e.g., objectives, spatial and temporal scale, etc.).
• “Applicability”: The selected SIs are applicable in other frameworks and settings, and data for calculation can be easily retrieved.
• “End-user values”: The SIs are easily understandable by the target audience.

Then, stakeholders’ responses were analyzed, prioritizing indicators that scored highest across the three criteria.

Step 3—Identification of correlations and trade-offs as well as formulation of the final list: Following the relevant recommendations of [46], we investigated potential correlations between SI. Based on this assessment, redundant SI (i.e., those correlated and depict the same sustainability aspect) were removed. In addition, despite the fact some SI received lower scores from stakeholders, we decided to include them in the toolkit in order to ensure consistency and comprehensiveness. The final list consisted of 10 economic, 9 environmental, and 6 social SI, which were further grouped into themes.

Step 4—Selection of measurement units: A thorough literature review was carried out to choose the measurement units presented in Table 2. It was decided to express most SI per ewe to facilitate comparisons.

Table 2. The ‘toolkit’ of the sustainability indicators.

Sustainability Dimension	Sub-Theme	Indicator	Definition/Calculation Formula	Unit	Threshold	Source
Economic	Profitability	Gross revenues	The sum of revenues, which was calculated with and without income support from the Common Agricultural Policy (CAP) 2014–2020	EUR/ewe	min–max	[26]
		Total production cost	The sum of production expenses (i.e., total expenses for Labor, Land and Capital)	EUR/ewe	min-max	[1,26]
		Purchased feedstuff expenses	Cost of purchased feedstuff	EUR/ewe	min–max	[26]
		Crop production expenses	Cost of seeds, fertilizers, pesticides, fuel, etc.	EUR/ewe	min–max	[1,26]
		Net profit/loss	Gross revenues minus total production costs. Net profit loss was calculated with and without income support from CAP 2014–2020	EUR/ewe	min–max	[1,26]
		Farm Family income	Gross revenues minus explicit costs. Farm Family income was calculated with and without income support from CAP 2014–2020	EUR/ewe	min–max	[1,26]
	Productivity	Milk yield	This indicator refers to the total milked quantity per ewe	liters/ewe	min–max	[26]
	Diversification	No. of income sources	Number of different income sources (e.g., income from livestock products, cotton, corn, alfalfa, etc.). Livestock products constitute a single category	Scale	1–max	[1]
	Self-resilience	Subsidy dependence	Subsidies/total income	%	min–max	[1]
		Feedstuff self-sufficiency	On-farm feeding cost/total feeding cost 1	%	min–max	[1]
Environmental	Use of energy	Efficiency of energy use	Gross output/Cost of energy (electricity, fuel, etc.)/ 2	EUR/EUR gross output	min–max	[47]
	Agrochemical Inputs	Fertilizers use	-	Kg/ha	min–max	[7]
		Pesticides use	-	Lt/ha	min–max	[7]
	Nutrients	Nitrogen	-	Kg/ha	min–max	[7]
		Phosphorus	-	Kg/ha	min–max	[7]
	Irrigation practices	Total irrigated area	-	ha	min–max	[32,33,47]
		Low efficiency irrigation systems coverage 3	Total ha irrigated by surface irrigation systems/Total irrigated area	%	0–100	[31,47]
		Medium efficiency irrigation sytems coverage 3	Total ha irrigated by overhead (e.g., center pivot) and sprinkler systems/Total irrigated area	%	0–100	[31,47]
		Highly efficiency irrigation sytems coverage 3	Total ha irrigated by drip irrigation system/Total irrigated area	%	0–100	[31,47]
Social	Social Welfare	Satisfaction with quality of life	We asked farmers to rate their quality of life on a scale from 0 to 5, where 0 = ‘very poor’ and 5 = ‘very good’.	Median	0–5	[31,48]
		Job Satisfaction	We asked farmers to rate their job satisfaction on a scale from 0 to 5, where 0 = ‘not at all’ and 5 = ‘very much’	Median	0–5	[31,48]
		Holidays per year	We asked farmers how many times per year they go on vacation	Median	0–max	[31,48]
	Employment and working conditions	Working conditions: Replacement during illness	Percentage of farmers that have a family member to replace them in case of illness or injury.	%	0–100	[31]
	Continuity of farm	Continuity of farm in the next 5–10 years	We asked farmers whether they plan to remain in the sector over the next 5 to 10 years.	% of positive answers	0–max	[31]
		Continuity of farm in the long term (over ten years)	We asked farmers if they plan to remain in the sector in the long term.	% of positive answers	0–max	[31]

¹ [1] Calculated feedstuff self-sufficiency based on energy content (MJ) of the feedstuffs. Due to lack of data, in our study, we approached feedstuff self-sufficiency from an economic perspective, calculating the ratio of on-farm feeding cost to total feeding cost. ² Contrary to [40], due to lack of data regarding MJ consumption, we divided the gross output (EUR) by the total cost of energy, which, as in the study of [7], includes the sum of costs for both fuel and electricity. ³ The classification of irrigation systems into low, medium, and high efficiency was based on the classification presented by [49].

Table 2. The ‘toolkit’ of the sustainability indicators.

Sustainability Dimension	Sub-Theme	Indicator	Definition/Calculation Formula	Unit	Threshold	Source
Economic	Profitability	Gross revenues	The sum of revenues, which was calculated with and without income support from the Common Agricultural Policy (CAP) 2014–2020	EUR/ewe	min–max	[26]
		Total production cost	The sum of production expenses (i.e., total expenses for Labor, Land and Capital)	EUR/ewe	min-max	[1,26]
		Purchased feedstuff expenses	Cost of purchased feedstuff	EUR/ewe	min–max	[26]
		Crop production expenses	Cost of seeds, fertilizers, pesticides, fuel, etc.	EUR/ewe	min–max	[1,26]
		Net profit/loss	Gross revenues minus total production costs. Net profit loss was calculated with and without income support from CAP 2014–2020	EUR/ewe	min–max	[1,26]
		Farm Family income	Gross revenues minus explicit costs. Farm Family income was calculated with and without income support from CAP 2014–2020	EUR/ewe	min–max	[1,26]
	Productivity	Milk yield	This indicator refers to the total milked quantity per ewe	liters/ewe	min–max	[26]
	Diversification	No. of income sources	Number of different income sources (e.g., income from livestock products, cotton, corn, alfalfa, etc.). Livestock products constitute a single category	Scale	1–max	[1]
	Self-resilience	Subsidy dependence	Subsidies/total income	%	min–max	[1]
		Feedstuff self-sufficiency	On-farm feeding cost/total feeding cost 1	%	min–max	[1]
Environmental	Use of energy	Efficiency of energy use	Gross output/Cost of energy (electricity, fuel, etc.)/ 2	EUR/EUR gross output	min–max	[47]
	Agrochemical Inputs	Fertilizers use	-	Kg/ha	min–max	[7]
		Pesticides use	-	Lt/ha	min–max	[7]
	Nutrients	Nitrogen	-	Kg/ha	min–max	[7]
		Phosphorus	-	Kg/ha	min–max	[7]
	Irrigation practices	Total irrigated area	-	ha	min–max	[32,33,47]
		Low efficiency irrigation systems coverage 3	Total ha irrigated by surface irrigation systems/Total irrigated area	%	0–100	[31,47]
		Medium efficiency irrigation sytems coverage 3	Total ha irrigated by overhead (e.g., center pivot) and sprinkler systems/Total irrigated area	%	0–100	[31,47]
		Highly efficiency irrigation sytems coverage 3	Total ha irrigated by drip irrigation system/Total irrigated area	%	0–100	[31,47]
Social	Social Welfare	Satisfaction with quality of life	We asked farmers to rate their quality of life on a scale from 0 to 5, where 0 = ‘very poor’ and 5 = ‘very good’.	Median	0–5	[31,48]
		Job Satisfaction	We asked farmers to rate their job satisfaction on a scale from 0 to 5, where 0 = ‘not at all’ and 5 = ‘very much’	Median	0–5	[31,48]
		Holidays per year	We asked farmers how many times per year they go on vacation	Median	0–max	[31,48]
	Employment and working conditions	Working conditions: Replacement during illness	Percentage of farmers that have a family member to replace them in case of illness or injury.	%	0–100	[31]
	Continuity of farm	Continuity of farm in the next 5–10 years	We asked farmers whether they plan to remain in the sector over the next 5 to 10 years.	% of positive answers	0–max	[31]
		Continuity of farm in the long term (over ten years)	We asked farmers if they plan to remain in the sector in the long term.	% of positive answers	0–max	[31]

¹ [1] Calculated feedstuff self-sufficiency based on energy content (MJ) of the feedstuffs. Due to lack of data, in our study, we approached feedstuff self-sufficiency from an economic perspective, calculating the ratio of on-farm feeding cost to total feeding cost. ² Contrary to [40], due to lack of data regarding MJ consumption, we divided the gross output (EUR) by the total cost of energy, which, as in the study of [7], includes the sum of costs for both fuel and electricity. ³ The classification of irrigation systems into low, medium, and high efficiency was based on the classification presented by [49].

2.3. Data Collection and Analysis

For the calculation of the SIs presented in Table 2, a farm management questionnaire survey was conducted. The interviews with farmers, each of which lasted approximately 3 h, were conducted either at their houses or at their farm facilities. In a few cases, interviews also took place in public locations, such as cafeterias or restaurants. The questionnaire consisted of seven parts, recording (i) available labor (family and hired), (ii) use of natural resources and energy (e.g., electricity, fuel, water and irrigation expenses, manure production, etc.), (iii) land use (cropland and pastures) and ownership, (iv) livestock production (number of sheep, yields, sales of products, use of inputs, etc.), (v) crop production (cultivated crops, e.g., alfalfa, barley, corn, etc.] etc.), (vi) fixed capital endowments (machinery, buildings, land reclamation) and their use by each farm sub-sector and (vii) farmers’ sociodemographic characteristics and perceptions.

Initially, our aim was to include in our analysis all farms rearing purebred Karagkouniko sheep (Table 1). To achieve this, we collaborated with the Livestock Genetic Resources Centre of Karditsa, which provided us with a comprehensive list of farmers rearing Karagkouniko sheep. We then directly contacted each of these farmers, inviting them to participate in our research. Despite our extensive efforts to reach all farmers, a small percentage refused to participate. From an initial pool of 44 farms, a total of 32 pasture-based farms rearing Karagkouniko sheep were surveyed, representing 84% of the breed’s total population.

Out of these farms, four (4) were SLf, eleven (11) were Mixed (L), and seventeen (17) were Mixed (L + C). In addition to the Karagkouniko farms, data were also collected from seven LF, which formulated a fourth group. These typical farms were chosen by experts as representative of the intensive Lacaune breed production system in the region of Thessaly. Based on the classification of farms, SIs included in the ‘toolkit’ were calculated for the average farm of each type.

3. Results—Discussion

According to the results presented in

Table 3. The economic performance of the groups.

Indicators	SLf	Mixed (L)	Mixed (L + C)	LF
Gross revenues with Income Support (EUR/ewe)	EUR 286.77	EUR 281.03	EUR 551.37	EUR 481.53
Income Support (EUR/ewe)	EUR 43.47	EUR 59.61	EUR 112.67	EUR 46.88
Total production cost (EUR/ewe)	EUR 280.25	EUR 359.36	EUR 696.13	EUR 476.73
Purchased feedstuff expenses (EUR/ewe)	EUR 99.87	EUR 84.83	EUR 43.85	EUR 276.67
Other production expenses (EUR/ewe)	EUR 180.38	EUR 274.53	EUR 652.28	EUR 200.06
Net profit/loss with Income Support (EUR/ewe)	EUR 6.51	EUR −78.36	−144.76 €	4.80 €
Farm family income with Income Support (EUR/ewe)	EUR 93.08	EUR 70.77	78.70 €	63.88 €
Milk yield (liters/ewe)	186.3	160.3	160.0	383.4
No. Income sources (Scale)	1.0	1.0	3.4	1.0
Subsidy dependence (%)	15%	21%	20%	10%

, specialized farms were more profitable than mixed ones, which aligns with the findings of relevant literature [19,50]. Both types of specialized farms (SLf and LF) achieved net profits, while mixed farms operated with net losses. However, when income support from CAP was excluded from the calculation of gross revenues, severe losses were reported for all groups. This indicated that farms were highly dependent on EU support and thus vulnerable to policy changes. This is in agreement with other studies [51,52], which have also pointed out that the abolishment of income support may result in the shrinkage of the sector, with further repercussions on the constant depopulation of rural areas [53]. On the other hand, farm family income remained positive across all groups, indicating that farms were able to support their livelihoods in the short run and did not face immediate risks in terms of viability [22,54].

Among specialized farms, SLf were more profitable compared to LF, as they achieved higher net profit and higher family income despite their lower milk yields, mainly due to lower expenses for feedstuff (EUR 99.87 and EUR 276.67 cost of purchased feedstuff per ewe, respectively) (Table S1). This illustrated the extent to which grazing could lead to savings in feeding costs [55,56], which were the main cost drivers for sheep farms in this study but also in previous ones (for instance, in [26]). However, increasing grazing did not always lead to higher profitability. For example, Ref. [13] noted that during the economic crisis, the transition of Greek sheep farms from intensive sedentary patterns to grazing led to reduced economic performance and net losses. This aligns with the findings of [26], who also indicated that inefficient use of labor and unbalanced feeding patterns were the two main bottlenecks that reduced the socioeconomic performance of PBSs.

Mixed farms depended more on CAP income support, which accounted for about 20% of their revenues (Table 3). Mixed (L + C) achieved the highest gross revenues, with and without income support (EUR 551.37 and EUR 438.70 per ewe, respectively). This indicates that the diversification of production can increase the gross revenues of farms [57], while also providing crucial flexibility, particularly during times of crises or volatile market conditions [1]. Indeed, depending on market prices, Mixed (L + C) farmers have the flexibility to either sell the forage and concentrates or feed them to their sheep. However, according to [9], this flexibility between crop and livestock production could pose threats to the continuation of the livestock enterprise, as higher profits from crops (either from markets or income support from policy measures) could motivate farmers to focus on crops. This observation can be supported by some statements of the surveyed Mixed (L + C) farmers. In particular, during personal interviews, a Mixed (L+ C) farmer mentioned: “Since corn prices are rising, it would be better for me to sell it and reduce the number of sheep rather than feed it to them”. Another Mixed (L+ C) farmer stated: “I no longer earn anything from sheep. Instead, I lose by keeping them. I only keep them because I inherited them from my father. My wife is pressuring me to sell them”.

Despite the high gross revenues, Mixed (L + C) was the least profitable group (EUR −144.76/ewe) (Table 3), as the high gross revenues were offset by high production costs (EUR 696.13/ewe). In addition to Mixed (L +C), Mixed (L) also had 28% higher production costs compared to SLf despite higher feed self-sufficiency (38%) (Figure 2). Therefore, contrary to the findings of [1], our study showed that higher feed self-sufficiency did not always lead to lower production costs, as savings from reduced purchased feedstuff were counterbalanced by the variable expenses of crop production (fertilizers, pesticides, fuel, etc.). In addition, both mixed farms had lower milk yields compared to SLf, a fact that could not be associated with breed or environmental conditions (see Section 2.1). The complex management of mixed farms could be a possible reason for lower productivity, as farmers, in some cases, may struggle to accommodate both crop and livestock production [19].

Apart from lower profitability, the complexity of mixed systems also resulted in less efficient use of energy (lower gross output per euro of energy cost) compared to specialized ones (Figure 3). Mixed (L + C) was the less energy-efficient group, (EUR 4.66 of output per EUR 1 of energy cost), followed by Mixed (L) (EUR 11.60 of output per EUR 1 of energy cost). This aligns with [58], who also observed that mixed farms—particularly those cultivating crops for markets—generally ‘trailed’ behind specialized PBSs in terms of energy use, mainly due to the high use of inputs, particularly fertilizers. These observations, in combination with the result presented in Table 3, indicate that low-input production systems, such as SLF, may align better with the objectives of the Sustainable Development Goal 2 (SDG2) of the United Nations [59], which aim to promote sustainable agricultural practices to enhance food security and resilience.

The excessive use of fertilizers by mixed farms can also be supported by the results of our study (Figure 4). Indeed, Mixed (L + C)—was the group with the highest use of fertilizer (537.2 per ha) and pesticides (3.3lt per ewe), followed by Mixed (L) (Table S2). A possible reason for the highest use of agrochemical inputs in Mixed (L + C) is that this group specializes more in crop production, including cotton, which is a crop with high nutrient and pesticide requirements [60]. Introducing legumes into crop rotations or transitioning to organic farming practices could reduce mixed farms’ dependence on nitrogen fertilizers, thus improving energy efficiency [58] and supporting the EU’s 2030 energy targets [61].

Among groups, Mixed (L + C) also had the largest irrigated area (11.7 hectares), most of which (89%) was irrigated with medium efficiency systems (e.g., center pivot irrigation and sprinkler irrigation) (Figure 5). The use of drip irrigation was low in both mixed farms (around 6–7%). Due to numerous economic and environmental benefits (water and energy savings, increased crop production, etc.) [62], the establishment of drip irrigation is currently subsidized by the Rural Development Plan 2014–2020, under sub-measure 4.1.2, which is expected to be included in the new programming period (CAP 2023–2027). However, additional initiatives are required, including (i) information campaigns, (ii) pilot programs and demonstration plots, and (iii) constraints or requirements in water supply (e.g., guaranteed water supply) [63].

All the above highlight the importance of the reconfigured system for Agricultural Knowledge and Innovation (AKIS), within CAP 2023–2027. Through a well-design AKIS, farmers could receive advisory support from qualified consultants on a wide range of farm management practices (implementation of rational feeding patterns, adoption of strategies and tools for efficient water, energy and nutrient management, adoption of Information and Communication Technologies, etc.), enhancing both their economic and environmental performance. Apart from economic and environmental benefits, this initiative could also have a positive social impact, as it could make pastoral farming more attractive, particularly to young farmers, who, according to [16,64], tend to leave the sector because of harsh working conditions.

Another finding of this study is that there were trade-offs between the social and economic SI [3], as the most profitable group, SLf, had the lowest score of satisfaction regarding their job as well as quality of life (Figure 6). During the interviews, many SLf farmers expressed dissatisfaction related to working conditions and lifestyle, stating that they would consider leaving the sector if they had any other alternative. Lower annual leaves per year (0.25) combined with the lower possibility for farmers to be replaced in case of illness (25%) are two possible justifications. Low satisfaction, for both job and quality of life, is also reflected in the indicator regarding the continuity of the activity, as none of the SLf see themselves remaining in the sector after 10 years (Table S3).

Hence, initiatives focusing on improving overall social welfare are crucial for ensuring food security for future generations (SDG2). An example of such an initiative is the project “Pastores de Emergencia” (https://pastoresdemergencia.com/) in Spain. The main objective of this project is to find experienced farmers (with more than ten years of experience) in order to temporarily replace their colleagues who are in need and, for various reasons (injury/illness, social obligations, vacations, etc.), want to take some days off. There are no similar services available in Greece. During personal interviews, many farmers stated they rely on informal networks (e.g., colleagues and/or other residents in their village) in order to find workers. However, due to a shortage of skilled labor in the area [65], they often hire individuals who require important training, particularly at the beginning of their employment [66]. This situation increases the workload of farmers and, in some cases, reduces the operational effectiveness of farms [67].

4. Conclusions

One of the main findings of the analysis was that the SLf—despite the lower productivity compared to LF—was the most profitable group due to cost savings in purchased feedstuff. This indicates that grazing—if properly managed—can indeed increase the competitiveness and profitability of farms. Furthermore, it was shown that higher feedstuff self-sufficiency does not always lead to lower feeding costs. For instance, Mixed (L) had higher feeding costs compared to SLf, as savings from purchased feedstuff were offset by excessive expenses related to on-farm feed production (such as fertilizers, pesticides, seeds, etc.). This observation, combined with the fact that both types of mixed farms had lower milk productivity and were the only ones operating with net loss, suggests that their management may be more complex and require higher organizational skills to accommodate both livestock and crop production.

Apart from the lower economic performance, both mixed types, particularly Mixed (L + C), applied higher volumes of agrochemical inputs and made less efficient use of energy. However, the analysis in this paper focuses on the farm level and, therefore, future research is required to include in the analysis also the inputs required for the production of purchased feedstuff within a more holistic approach to the production system.

In addition, the analysis indicated some trade-offs between different sustainability dimensions. In particular, we found that the focus of mixed farmers on increasing crop yields could limit their environmental performance due to the excessive use of inputs, particularly agrochemicals. Moreover, the higher economic performance does not necessarily result in increased social welfare, as trade-offs were also observed between economic and social SI. Hence, the overall performance of farms should be addressed through a holistic mechanism that considers both trade-offs and synergies between indicators [3,68,69]. Within this mechanism, economic and agri-environmental measures should work synergistically with schemes toward the enhancement of social welfare. This holistic approach could play a vital role in the transition to more sustainable production, ensuring food security for current and future generations (SDG 2).

Further examination of initiatives that foster the diffusion of innovation and optimize farm management is also essential, as it could provide valuable insights that will facilitate this transition. Moreover, due to the observed low continuity of pasture-based farms in the long term (over 10 years), there is a clear need for more research and policy initiatives focused on family succession and generational renewal.