# Grassland-Based Farming Systems Targeting Agroecology: Which Indicators Should Be Used for On-Farm Assessment?

Elena Benedetti del Rio, Audrey A. Michaud, Gilles Brunschwig, Enrico Sturaro

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Elena Benedetti del Rio, Audrey A. Michaud, Gilles Brunschwig, Enrico Sturaro. Grassland-Based Farming Systems Targeting Agroecology: Which Indicators Should Be Used for On-Farm Assessment?. Sustainability, 2025, 17 (6), pp.2720. 10.3390/su17062720. [https://hal.inrae.fr/hal-05153577v1](https://hal.inrae.fr/hal-05153577v1)

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Article

# Grassland-Based Farming Systems Targeting Agroecology: Which Indicators Should Be Used for On-Farm Assessment?

Elena Benedetti del Rio <fnref n="1" />*, Audrey Michaud <fnref n="2" /> ID, Gilles Brunschwig <fnref n="2" /> ID and Enrico Sturaro <fnref n="1" /> ID

**Abstract:** This study investigates grassland-based farming systems within the framework of agroecology (AE), focusing on the identification of relevant indicators for on-farm assessment. The purpose of this research is to test indicator compliance with AE at the farming system level in grassland farms, particularly in High-Nature-Value (HNV) areas. Seventeen farms in France and Italy were selected for this study, and data were collected through semi-structured interviews. These interviews explored various indicators across environmental, economic, and social dimensions. Principal Component Analysis (PCA) was employed to analyze the quantitative indicators, while qualitative data offered insights into farm management and learning practices. The results highlighted the importance of forage self-sufficiency (livestock production dimension) and revenue (economic dimension) as key indicators of successful agroecological management. The study also found that increasing forage self-sufficiency was linked to higher farmer satisfaction, an indicator related to the social dimension. Additionally, qualitative data underscored the significance of self-sufficiency, workload management, and social interaction and continuous learning as critical elements in grassland-based farming. In conclusion, this research proposes self-sufficiency as an indicator that can facilitate the assessment of grassland-based systems, aiding in the broader adoption of agroecological practices in compliance with European policies.

**Keywords:** agroecology; indicator; grassland-based farming systems; assessment

## 1. Introduction

In recent decades, the agroecological transition has been a topic of great importance for European policies. Within them, we can find the Farm to Fork and the biodiversity strategies, inextricably linked to the development and practical application of agroecology (AE), in addition to the 2030 Agenda of the United Nations and the SDGs (Sustainable Development Goals) [1]. Our study directly contributes to multiple SDGs, particularly SDG 2 (Zero Hunger), SDG 12 (Responsible Consumption and Production), and SDG 15 (Life on Land) [2]. Agroecology can be defined as “the science of applying ecological concepts and principles to the design and management of sustainable food systems” [3]. It also encompasses the pillars of sustainability but widens them, including the political involvement of ecology [4], and opens up a modern development paradigm, pursuing changes in conventional thinking regarding food and agricultural systems. In fact, it was proposed as a possible approach to reduce the impact of LFSs (livestock farming systems), involving the contemporaneous consideration of the three dimensions of sustainability in

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Academic Editor: Georgios Koubouris

Received: 15 January 2025

Revised: 13 February 2025

Accepted: 11 March 2025

Published: 19 March 2025

**Citation:** Benedetti del Rio, E.; Michaud, A.; Brunschwig, G.; Sturaro, E. Grassland-Based Farming Systems
Targeting Agroecology: Which Indicators Should Be Used for On-Farm Assessment? *Sustainability* **2025**, *17*, 2720. https://doi.org/
10.3390/su17062720

**Copyright:** © 2025 by the authors.
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<footnote n="1">¹ Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Viale dell'Università, 16, 35020 Legnaro, Italy</footnote>

<footnote n="2">² INRAE, VetAgro Sup, UMR Herbivores, Université Clermont-Auvergne, 63122 Saint-Genès-Champanelle, France</footnote>

<footnote>* Correspondence: elena.benedettidelrio@unipd.it</footnote>
farming systems, namely the environmental, economic, and social dimensions, in addition to collaborations between experts from different fields [5]. However, the inter-relation between the aspects of AE also represents an obstacle to its practical assessment, requiring an objective evaluation of the real farm management to determine its application potential [5]. In this regard, assessing if and to what extent a system is managed agroecologically can benefit from the use of indicators with the following characteristics: easy to gather, objective, time-efficient, and cost-effective.

Grassland- and grazing-based livestock farming systems, when effectively managed, can enhance on-farm self-sufficiency [6] and have positive environmental outcomes, thus having a high potential to be agroecological [7]. In Europe, permanent grasslands represent nearly one-third of the agricultural surface [8], and ruminants—animals capable of using such a resource as feed—represent 44% of the European livestock population [9]. Grassland systems are, therefore, relevant to the agroecological transition, but their importance can change depending on how they are integrated into farming management.

In recent years, multiple tools have been developed to assess farming systems, from mountain dairy systems in France (Diagagroeco, DIAM, Botreau et al. (2014)) [10], targeting different aims, to the “world-wide” TAPE, addressing the transition of family farming to agroecology. However, specific indicators for the evaluation of grassland-based farming systems in the frame of agroecology still show a gap in knowledge despite AE’s relevance in the European context. Regarding this issue, Benedetti del Rio et al. [11] explored the literature in order to propose indicators for on-farm testing, finding that both quantitative and qualitative indicators are necessary and can be combined, in addition to considering the possibility of merging indicators from different dimensions (e.g., merging management criteria—qualitative and social—with economic indicators—quantitative). This could favor a deeper understanding of agroecology in practice. Indeed, a single indicator may be a proxy for different dimensions and management decisions interacting in a system [11].

Thus, the aim of this article is to identify indicators that are able to describe farm management and are effective at the farm level in farms located on High-Nature-Value (HNV) farmland and normally operate under AE principles. In fact, HNV farmland links the conservation of diversity in the countryside to the necessity of maintaining a certain type of farming activity to preserve these areas [12]. As such, testing the indicators can reveal their level of compliance with AE using a quantitative approach, and what this means for farmers and at the farming system level using a qualitative approach. To this end, a sample of 17 grassland-based farms was tested and surveyed in terms of previously identified indicators. The results of this study will help us to propose effective indicators for grassland-based farming management while complying with the European regulations in place. This research also aims to identify indicators to be used in future assessments, in order to facilitate the agroecological management of grassland-based farming systems.

## 2. Materials and Methods

### 2.1. Survey Data Collection

This study involved grassland-based farming systems in the areas of the Regional Natural Park Livradois-Forez (Puy de Dome, France, 140,000 ha) and Alpago (Veneto, Italy, 17,000 ha). The farms were selected on the basis of the following criteria: full-time farmers, the use of pasture in mountain areas operating under AE principles, and different ruminant species. These criteria were chosen to target farms with proven experience, highlighting possible evolution paths and needs of the livestock sector from the perspective of different types of farmers (ovine, bovine). A total of 10 farms were surveyed in the French Regional Park; 7 farms were sampled in the Italian mountain area.
Every farm has been surveyed with a semi-structured questionnaire designed to collect a broad range of classical indicators, composed of qualitative and quantitative data, which are considered critical to understanding the management of the farm systems. The survey (Supplementary File S1) leverages the dimensions and indicators (in brackets) identified in Benedetti del Rio et al. [11], structuring them through a systemic approach: Livestock Pro- ductions (Animal production; Grassland management; Farm practices); Environment (Land and soil quality); Economic (Technical economic performances); Social (Farmer-related social aspects). The survey was structured in the following sections: descriptive farm indicators on livestock (LU, breeds), soil management (intensity of tractor use), output and input (yields/ha, product destination); system management (grassland management, type of grazing, stocking density, grazing time); management practices (fertilization schedule, management of weeds); biodiversity (breeds and species, ecological infrastructure, number of plots); economy (selling price, sold production); social (satisfaction degree on workload, revenue, cooperation with local community); strategies and adaptation. Diving in further detail, indicators included the following: detailed information on livestock size, breeds, intensity of medicine use; management practices on crops, grasslands, and livestock; out- put and input data, covering yields, the use of fertilizers, pesticides, and any other input, including possible innovative alternative. Additionally, system management aspects were examined to understand animal biodiversity relevance, decision-making processes, and overall farm management strategies, focusing on grassland management. The economic dimension covered revenue, revenue satisfaction (Lickert scale 1-5), and economic chal- lenges faced by farmers. Social indicators included farmer personal data, education levels, community engagement, and workload satisfaction (Lickert scale 1-5). Lastly, the survey explored the management criteria employed and the learning and innovative aspects for farmers, both considered in relation to grazing.

The questionnaires were conducted in the summers of 2022 and 2023, respectively, in Italy and France. The interviews lasted a minimum of 2 h; they were conducted in person and were authorized, recorded, and transcribed. To proceed with the data analysis, a database was created containing all the data collected. The next step was to separate indicators by dimension of agroecology. Quantitative data were only used if all farms provided official data, following the criterion of objectivity. This criterion substantially reduced the number of indicators that could be used. Qualitative data were categorized into classes to understand the role of grazing in farm management.

## 2.2. Data Analysis

From the data analysis, the database included the indicators that were provided by all the 17 farms sampled. A Principal Component Analysis (PCA; PROC PRINCOMP in SAS statistical software, version 9.4; SAS, 2013) was performed on the indicators listed in the next paragraph to see the relationship between indicators and the PCs (Principal Components). The results considered for our discussion include components above or below the reference threshold of 0.4, as used in Berton et al. [13] to assess PCA results. Relevant indicators were retained and then further discussed. The PCA aimed at facilitat- ing the identification of key indicators representing the interactions among the different dimensions and with the potential of influencing the agroecological level and success of grassland-based farming systems.

The indicators and unit of measurement used for the analysis are the following: breeds (N), LU (N), total feed self-sufficiency (%), forages self-sufficiency (%), alternative practices (N), tractor use—permanent pasture (N), tractor use—temporary pasture (N), pasture (%), LU/UAA (N/ha), LU/pasture (N/ha), revenue/UAA (€/ha), revenue/LU (€/N), revenue and workload (satisfaction degree).
Furthermore, qualitative data on "management criteria" and "learning and innovative aspects of farmers" in relation to grazing were studied to enlarge the understanding of the systems studied and to disprove further conclusions. Each farmer's answer was used to categorize their perception and show differences and similarities between countries. Qualitative data are reported as percentage of the total interviewees, divided by country.

# 3. Results

## 3.1. *Description of the Sample*

A total of 10 farms were selected in France: 5 cow farms, 2 sheep farms, 3 mixed farms. A total of 7 farms were sampled in Italy: 4 dairy cow farms and 3 sheep farms. Table 1 shows the main characteristics of the sampled farms. As previously stated, LU and UAA are quite variable among the sampled farms. This is due to the different geographical conformation of the mountain areas in France and Italy. However, the percentage of grassland in the total agricultural area is higher than 90%, highlighting its relevance in both countries' mountain areas and, consequently, for the sustainable management of the local livestock farms located in those areas. As expected, total self-sufficiency is lower than forage self-sufficiency because of Natura 2000 Network rules that prohibit tillage and only allow re-seeding to maintain a semi-natural pasture (e.g., Cansiglio Plateau in the Alpago area, Italy). As a consequence, for some of these farms, it is not possible to self-produce cereals for livestock, making them a good example of agroecological and extensive farming management, as these farms mainly rely on local resources and adapt LU depending on the available pasture.

<table><caption>**Table 1.** Description of the farms studied (N 17).</caption><thead><tr><th rowspan="2">Variable</th><th colspan="2">Total (N 17)</th><th colspan="2">France (N 10)</th><th colspan="2">Italy (N 7)</th></tr><tr><th>Mean</th><th>SD</th><th>Mean</th><th>SD</th><th>Mean</th><th>SD</th></tr></thead><tbody><tr><td>LU <sup>1</sup> (N)</td><td>60.75</td><td>32.77</td><td>48.69</td><td>26.71</td><td>77.97</td><td>34.72</td></tr><tr><td>UAA <sup>2</sup> (ha)</td><td>69.18</td><td>24.17</td><td>62.70</td><td>28.67</td><td>78.43</td><td>12.45</td></tr><tr><td>Grasslands (%)</td><td>98.35</td><td>4.92</td><td>97.20</td><td>6.29</td><td>100.00</td><td>0.00</td></tr><tr><td>Pasture (%)</td><td>55.71</td><td>24.90</td><td>58.45</td><td>24.09</td><td>51.79</td><td>27.44</td></tr><tr><td>Meadow (%)</td><td>40.06</td><td>28.47</td><td>24.35</td><td>19.80</td><td>62.50</td><td>23.97</td></tr><tr><td>Total self-sufficiency (%)</td><td>86.77</td><td>11.05</td><td>92.02</td><td>6.80</td><td>79.29</td><td>12.05</td></tr><tr><td>Forages self-sufficiency (%)</td><td>93.57</td><td>11.43</td><td>93.23</td><td>14.38</td><td>94.07</td><td>6.11</td></tr><tr><td>Workers (N)</td><td>1.89</td><td>0.73</td><td>1.92</td><td>0.90</td><td>1.86</td><td>0.35</td></tr></tbody></table>

The indicators are divided by dimension and category across meat-focused (N = 4) and milk-focused (N = 13) farms (Table 2). The results revealed distinct differences between these two types of farms. Milk-focused farms exhibited greater breed diversity (2.77 vs. 1.25), slightly larger livestock units (61.12 vs. 59.53), and significantly higher economic returns, both in terms of revenue per utilized agricultural area (1520.88 vs. 450.41) and revenue per livestock unit (1693.47 vs. 436.53). Despite these economic advantages, milk farms showed similar or lower levels of feed self-sufficiency compared to meat farms (85.18% vs. 91.94%), while forage self-sufficiency is similar. Both farm types reported comparable pasture percentages (55.31 vs. 57), even though milk farms had a higher LU per pasture ratio (1.78 vs. 1.26).

With respect to social qualitative aspects, the satisfaction degree in revenue is higher for milk farms (4.12 vs. 3.50), while workload satisfaction is similar.

<footnote n="1">¹ LU: livestock unit; ² UAA: utilized agricultural area.</footnote>
<table><caption>**Table 2.** Descriptive analysis of the indicators' dimensions included and related categories.</caption><thead><tr><th rowspan="2">Dimension</th><th rowspan="2">Category</th><th rowspan="2">Indicator</th><th colspan="2">Total (N 17)</th><th colspan="2">Meat (N 4)</th><th colspan="2">Milk (N 13)</th></tr><tr><th>Mean</th><th>SD</th><th>Mean</th><th>SD</th><th>Mean</th><th>SD</th></tr></thead><tbody><tr><td rowspan="7">Livestock productions</td><td rowspan="2">Animal production</td><td>Breeds (N)</td><td>2.41</td><td>1.97</td><td>1.25</td><td>0.50</td><td>2.77</td><td>2.13</td></tr><tr><td>LU (N)</td><td>60.75</td><td>32.77</td><td>59.53</td><td>17.54</td><td>61.12</td><td>36.80</td></tr><tr><td rowspan="2">Grassland management</td><td>Total feed self-sufficiency (%)</td><td>86.77</td><td>11.05</td><td>91.94</td><td>5.73</td><td>85.18</td><td>11.96</td></tr><tr><td>Forages self-sufficiency (%)</td><td>93.57</td><td>11.43</td><td>92.44</td><td>15.13</td><td>93.92</td><td>10.79</td></tr><tr><td rowspan="3">Farm practices</td><td>Alternative practices <sup>1</sup> (N)</td><td>0.47</td><td>0.72</td><td>0.25</td><td>0.50</td><td>0.54</td><td>0.78</td></tr><tr><td>Tractor use—permanent pasture (N)</td><td>1.00</td><td>1.58</td><td>0.75</td><td>0.96</td><td>1.08</td><td>1.75</td></tr><tr><td>Tractor use—temporary pasture (N)</td><td>4.29</td><td>2.54</td><td>5.25</td><td>5.19</td><td>4.00</td><td>1.22</td></tr><tr><td rowspan="3">Environment</td><td rowspan="3">Land and soil quality</td><td>Pasture (%)</td><td>55.71</td><td>24.90</td><td>57.00</td><td>14.28</td><td>55.31</td><td>27.84</td></tr><tr><td>LU/UAA (N/ha)</td><td>0.84</td><td>0.30</td><td>0.87</td><td>0.36</td><td>0.83</td><td>0.29</td></tr><tr><td>LU/pasture (N/ha)</td><td>1.66</td><td>1.58</td><td>1.26</td><td>0.48</td><td>1.78</td><td>1.79</td></tr><tr><td rowspan="2">Economic</td><td rowspan="2">Technical economic performances</td><td>Revenue <sup>2</sup>/UAA (€/ha)</td><td>1269.01</td><td>1159.39</td><td>450.41</td><td>481.60</td><td>1520.88</td><td>1200.91</td></tr><tr><td>Revenue/LU (€/N)</td><td>1397.72</td><td>1008.71</td><td>436.53</td><td>278.77</td><td>1693.47</td><td>966.70</td></tr><tr><td rowspan="2">Social</td><td rowspan="2">Farmer-related social aspects</td><td>Revenue (Sat. degree)</td><td>3.97</td><td>1.26</td><td>3.50</td><td>1.73</td><td>4.12</td><td>1.12</td></tr><tr><td>Workload (Sat. degree)</td><td>3.79</td><td>1.08</td><td>3.75</td><td>1.26</td><td>3.81</td><td>1.07</td></tr></tbody></table>

## 3.2. *Description of the PCA Scatter Plot*

The PCA shows a percentage inertia of 53.04%. Component 1 (PC1) explains 35.66% of the variance, while Component 2 (PC2) accounts for 17.38% of the variance.

From the exploratory scatter plots (Figure 1) we can see that mixed livestock farming is grouped separately in PC1 from specialized cows and sheep farming and in opposition to Italian cow farms. Italian sheep are grouped within the French cluster of farms. In PC1, we can find the Italian dairy cluster (only dairy cows), which is separated from dairy cows in France and from sheep in Italy. Interestingly, we cannot identify particular clusters through PC2, apart from two French farms producing meat, which remain at the edge of PC2 and near the center of PC1.

<figure><img src="image_3.png" /><figcaption>**Figure 1.** Scatter plot of the surveyed farms' characteristics: main livestock species (a) and country (b).</figcaption></figure>

<footnote n="1">¹ Alternative practices mentioned by farmers and included in the analysis: mixed grazing, before heading grazing, after heading grazing. ² Revenue is the total amount of products sold, multiplied by the selling price.</footnote>
3.3. *Analysis of the Indicators Through PCA Loadings Plot*

Figure 2 shows that variables like “LU/UAA”, “LU”, “revenue/ha”, “LU/UAA-
pasture”, and “revenue/LU” have positive correlations with PC1, suggesting their signif-
icant contribution to the variance captured by PC1. On the other hand, “self-sufficiency
on total feed”, “alternative practices”, “pasture %” and “N breeds” show a negative corre-
lation with PC1. The variables “forage self-sufficiency” and “revenue satisfaction” have
a strong positive correlation with PC2, highlighting their significant contribution to the
variance captured by PC2. Moreover, “total feed self-sufficiency”, “workload satisfaction”,
and “LU/UAA” also show a positive correlation with PC2. In contrast, “tractor access on
temporary pasture” shows a negative correlation with PC2. Interestingly, “tractor use on
permanent pasture” is opposite to “alternative practices and “self-sufficiency on total feed”,
showing a complex influence on the data structure, which needs to be further studied.
However, PC1 highlights the separation between components, where the positive side
represents the farm’s productive factors. An increase in these factors contributes to the in-
tensification and specialization of the productive system (LU, LU/UAA, LU/UAA pasture,
revenue/LU, revenue/ha), in contrast to extensification and diversification variables.

<figure><img src="image_4.png" /><figcaption>**Figure 2.** PCA including the studied indicators.</figcaption></figure>

For PC2, Figure 2 highlights positive loadings on variables linked to self-sufficiency, social indicators of farmers’ perception, and LU/UAA in contrast to the use of tractors in temporary pasture, emphasizing the relevance of feed production alongside economic and social aspects, such as revenue and workload satisfaction. So, ruminants represent a sustainable way to reduce the use of tractors, increase on-farm self-sufficiency, and enhance farmer satisfaction regarding economic and social aspects.

From the component pattern in Figure 2, we retain those indicators positioned above
0.4 or below -0.4 in order to select pairs of variables showing moderate to high correlation
and to discuss their relevance in the assessment of grassland-based farming systems. In
this case, the indicators are the following three: "revenue/ha", "forage self-sufficiency",
and "revenue satisfaction".

Taking into account these three indicators, we can notice that revenue/ha is not
clearly correlated with the satisfaction degree on revenue, nor with forage self-sufficiency.
Interestingly, as self-sufficiency increases, revenue satisfaction also tends to rise. How-
ever, achieving high forage self-sufficiency does not necessarily correlate with the highest revenue per hectare.

### 3.4. Qualitative Information on Grazing: Management Criteria and Learning and Innovative Aspects of Farmers

The last part of the survey included the investigation of the qualitative aspects of grazing in both countries: “management criteria” and “learning and innovative aspects of farmers”. The aim of this section of the survey was to study the criteria applied by farmers for grazing management and to highlight farmers’ ability to cope with the challenges posed in the management of their farming systems. The management criteria for grazing in France include extensification (70%), such as the possibility of using fewer external resources and favoring independence from the big enterprises that dominate agriculture and livestock production. For both France and Italy, the main effect includes self-sufficiency (FR: 60%; IT: 43%), confirming the PCA results. Italy differs from France by adding the quality of products (43%) as one main effect of grazing, which is also mentioned in French farms but a lower percentage (30% of interviewees). The learning aspects offered by grazing to farmers are also similar between the two countries: production and climate and workload (respectively, IT: 86%; 57%; FR: 50%; 50%). The main challenge for France concerns the management of grassland (70%).

## 4. Discussion

### 4.1. Agroecological Definition Is Represented by Principal Components

Due to the heterogeneity of farming systems in mountain areas, the sampling cannot be fully representative of the different areas; however, the selected group of farms, which complies with the three criteria, can be considered a good case study to test agroecological indicators in mountain livestock farms. The results of this study highlighted relevant indicators for the assessment and differentiation of systems managed agroecologically. Our PCA highlighted two central aspects of agroecology in the management of a farming system: the contrast between intensification factors (LU, LU/UAA, LU/pasture, revenue/ha, and revenue/LU) and extensification and diversification factors (number of breeds, pasture %, total feed self-sufficiency, alternative practices) within a farm, underscoring the economic relevance of feed supply and its management. The intensification factors are mostly represented by classical indicators of “Animal production”, “Land and soil quality”, and “Technical economic performances” categories; the extensification factors are identified by less common indicators belonging to the “Farm practices”, “Animal production”, and “Land and soil quality” categories. Accordingly, the indicators that had the most significant impact were forage self-sufficiency (Farm practices), revenue satisfaction (Farmer-related social aspects), and revenue/UAA ratio (Technical economic performances). Although less relevant, the results also highlighted the importance of workload satisfaction (Farmer-related social aspects), which in agroecology is often considered a complex aspect [14,15]. Thus, agroecology is represented along the four dimensions studied, which are deeply interlinked. The application of agroecology to evaluate grassland-based farming systems shows the relevance of studying forage resource supply and the involvement of farmers’ satisfaction in management choices.

### 4.2. Choice and Explanation of Indicators

From the results, we can conclude that revenue satisfaction increases with rising self-sufficiency, suggesting its positive impact on farmers’ income. Therefore, increasing self-sufficiency appears to positively impact revenue satisfaction and could be a strategic target for enhancing farmer satisfaction with financial returns. However, data suggest that
increasing forage self-sufficiency might not significantly boost revenue. In fact, since the indicator does not include the total feed self-sufficiency but only forage self-sufficiency, we can hypothesize that a potential increase in revenue per hectare is mostly affected by feed supplements than by forage. However, we expect that the amount of feed supplementation— not forage—in the diet of ruminants reared on grasslands is a low percentage of the total. Then, this indicator provides a broader perspective on where the feed is produced and consumed, as well as its role in feed-food competition within a given diet. In addition, this indicator might highlight the ability of farmers to balance grazing with the available grassland surface. In fact, the management of grassland can be analyzed through this indi- cator, suggesting that the most satisfied farmers, having high forage self-sufficiency, might manage a higher amount of grasslands than those farmers who are required to purchase hay, thereby reducing their revenue per hectare. So, these indicators could be coupled with stocking rate, in addition to testing and assessing the grazing management type and its implementation. Stocking rate represents another significant and transformative indicator that in grassland-based farming systems may show important aspects of their management, changing depending on the context, and which must be adapted to avoid misinterpreting overgrazing or undergrazing without considering specific plot productivity. It can be a very punctual, cost-effective, and time-efficient indicator.

Self-sufficiency in grassland-based farms reduces dependence on external suppliers, an important factor in agroecology, as well as production costs [16], transportation costs, and their related impact [17,18]. The latter was calculated to be 8.3% of the total emissions in an intensively managed Japanese beef cow-calf system [19] and accounted for around 4% in French-Italian cow-calf production systems [20]. In a global overview of the livestock sector, feed transportation is the primary cause of deforestation [21,22]. In this case study, the interviewed farmers confirmed the importance of grazing for self-sufficiency and extensification [23]. Furthermore, the effects of using their own forages can be observed in the final products, as it is known that, for example, milk quality directly reflects the ruminants' diets and the biodiversity present in pastures [24]. However, Pornaro et al. [25] found that pasture biodiversity is negatively correlated with pasture yields, which may consequently lower on-farm self-sufficiency. Thus, pasture biodiversity represents an important aspect to be evaluated in each context.

The findings of this study align with previous research on agroecological transitions in grassland-based farming systems across different countries. Studies conducted in France, the United States, and Brazil, for example, have highlighted the role of self-sufficiency in en- hancing sustainability and farmer resilience [6,7]. The positive correlation between forage self-sufficiency and revenue satisfaction in our research echoes findings from Latin Ameri- can agroecological farms, where local resource optimization has led to greater economic and social stability [26]. Similarly, studies in Nordic countries emphasize the importance of diversified livestock farming in increasing resilience against climate variability and market fluctuations [9]. The global applicability of these indicators suggests that self-sufficiency is a key determinant of agroecological success across various contexts.

4.3. *Importance of Quantitative and Qualitative Indicators in Agroecology*

When exploring the self-sufficiency indicator, previously classified as a quantitative measure related to grassland management, this study reveals its link with a qualitative social indicator, such as the satisfaction level. Delving deeper into this connection, the results suggest self-sufficiency as a mixed indicator, encompassing both qualitative and quantitative aspects.

In detail, the results of the qualitative analysis of the management criteria identified by farmers are consistent with the PCA findings. In grassland-based farming systems, self-
sufficiency is a key factor for the sustainability of system management. The contribution of self-sufficiency extends, directly or indirectly, to multiple aspects of agroecology. It serves as an indicator not only for revenue satisfaction but also for workload, encompassing both economic and social aspects of the farming system. Moreover, it represents the farming system through farmers' choices, adapting the herd size and requirements to forage quality and quantity and adapting to climate change variability.

Managing grasslands, both for grazing and hay-making, presents challenges as part of the learning and innovation process for farmers, particularly climate variability and lower yields compared to intensive farming. Indeed, the primary goal of farmers in producing their own hay or moving herds to pasture is to provide the best quality fiber for their animals and maintain the health of their grasslands (Table 3) [27], overcoming potentially lower yields due to high biodiversity of mountain grasslands [25]. This way, farmers can reduce their dependence on fluctuating external resource prices and become more aware of the quality of their production throughout the year. However, this requires the farmer to optimize workload management, allowing maximum attention to all the farm's productive factors (pasture, meadow, livestock) for the sustainability of agroecological management over time. Therefore, the relationship between self-sufficiency and agroecologically managed grassland-based farming systems is evident, as it serves as a proxy for multiple aspects of a farming system.

<table><caption>**Table 3.** Percentage of the French and Italian farmers identifying criteria and learning aspects of grazing in their farm system.</caption><thead><tr><th>Grazing</th><th>Farmers Perception</th><th>France</th><th>Italy</th></tr></thead><tbody><tr><td rowspan="6">Management criteria<br>(% of farmers)</td><td>Extensification <sup>1</sup></td><td>70</td><td>14</td></tr><tr><td>Self-sufficiency</td><td>60</td><td>43</td></tr><tr><td>Biodiversity</td><td>30</td><td>14</td></tr><tr><td>Quality of products</td><td>30</td><td>43</td></tr><tr><td>Resilience</td><td>30</td><td>14</td></tr><tr><td>Weed management</td><td>0</td><td>29</td></tr><tr><td rowspan="4">Learning and innovative<br>aspects of farmers<br>(% of farmers)</td><td>Workload <sup>2</sup></td><td>50</td><td>57</td></tr><tr><td>Management of grasslands</td><td>70</td><td>43</td></tr><tr><td>Production problems and climate</td><td>50</td><td>86</td></tr><tr><td>Social</td><td>10</td><td>29</td></tr></tbody></table>

A response to these challenges, referred to as “learning and innovative aspects of farmers”, can be found in the knowledge related to grassland management [15]. In France, many of the surveyed farmers reported having limited knowledge of pasture management. Despite trying to do their best to provide high-quality fiber to their animals, they are forced to slightly adapt their management each year [28] due to climate variability and changing grass growth patterns. As a result, their knowledge increases slowly over time. In this regard, acquiring the necessary expertise through on-farm experience is a long and continuous process [29]. For example, the impact of a specific agricultural practice on soil health may take decades to become evident after transitioning from one practice to another [30]. In fact, qualitative results confirm that the most relevant aspect for farmers is not always economic, as also found by Dumont and Baret [31], who highlight that agroecological farms often prioritize environmental and social factors, sometimes at the expense of economic considerations. A study by Bouttes et al. [32] found that social satisfaction among farmers increases when they engage with one another, emphasizing the importance of social interaction and the interest in continuous learning. Facilitating

<footnote n="1">¹ Extensification includes the reduction of expenses, forage waste, plastic, mechanization rate, stocking rate.</footnote>

<footnote n="2">² Workload includes time for diversifying activities, time to change parcels to graze, scattered parcels, maintaining fences.</footnote>
European farmers' access to training programs dedicated to grassland management could introduce a new approach to livestock farming. An example is in the case of succession [33], where new farmers may find it difficult to transition from the old conventional production-based paradigm to an agroecological one.

Focusing on livestock that can convert natural resources (such as grass, by-products, crop residues) into energy that humans cannot directly utilize is crucial for achieving the required paradigm shift [34]. This approach minimizes external input while promoting natural processes by reconnecting animals and soil. Promoting grazing activities and their study can optimize the use of forage resources, facilitate the return of indigestible carbon to the soil [35], and serve as natural method for weed and pest control, reducing the need for chemical inputs [36].

## 4.4. A First Step for On-Farm Assessment According to Agroecology in Grassland-Based Farming Systems

Our data suggest that for an on-farm assessment in agroecologically managed grassland-based farming systems, a holistic approach is needed, integrating both quantitative and qualitative indicators. For this aim, self-sufficiency is an indicator providing a metric for an initial assessment of whether farm management is agroecological or not. Indeed, establishing and testing thresholds for multiple external inputs and on-farm self-sufficiency (e.g., feed or fertilizers) can demonstrate if a farm is transitioning towards agroecology, in addition to assessing the levels of efficiency, recycling, and circularity of an agroecological grassland-based farming system. In addition, thresholds on feed integration in animal diets can be proposed as discriminating factors for assessing whether a system is agroecological or not, as already applied in product certification (see organic legislation for animal products, EU 2018/848).

In relation to self-sufficiency, revenue and workload satisfaction can also be considered. Their relationship could be further investigated through the quantitative composition of total farm income, which, for farms in mountain areas, is often based on European contributions, such as the Common Agricultural Policy (CAP) for less favored areas [37].

From a social point of view, an important indicator can be the level of interaction at both local and broader scales. The qualitative aspects, such as farmers' knowledge, workload satisfaction, and adaptability to climate variability, further underscore the importance of continuous learning and innovation in managing these systems. While financial incentives play a critical role in encouraging farmers to adopt sustainable practices, socio-environmental demands such as the need for knowledge exchange, participatory governance, and policy support also remain essential, as highlighted by the 2030 Agenda [38]. The European Commission CAP could favor farmers' networks in order to facilitate knowledge exchange on innovative practices, related results, and challenges that the scientific community can analyze and are able to disseminate.

These indicators can be helpful both for farmers and for further investigation in experimental farms. Therefore, to advance from the present situation toward the European policies objectives, integrating the proposed indicators into existing tools for grassland-based farming systems assessment represents a crucial first step for on-farm assessments.

# 5. Conclusions

This study highlighted that self-sufficiency serves as both a qualitative and quantitative indicator in agroecological grassland-based farming systems. It is closely linked to revenue satisfaction and workload management, offering insights into the overall sustainability of a farming system. Self-sufficiency reflects a farm's independence from external inputs, leading to lower production costs, reduced environmental impact of products,
and improved product quality. Moreover, self-sufficiency also emphasizes a farmer's ability to dynamically cope with climate change by adapting a farm's elements and farm management strategies.

In agroecological management, farmers emphasized the need for support to enhance their knowledge of grassland management. This is particularly important for adapting to challenges such as climate variability and optimizing the use of natural resources. Furthermore, the study underscored the significance of social interaction among farmers as a key driver for continuous learning and innovation. Thus, this study contributes to the theoretical identification of useful indicators that can facilitate the assessment of the agroecological level of a farming system and supports the practical application of agroecological principles in farm management. In fact, by utilizing the proposed indicators, farmers can autonomously test the agroecological status of their systems. However, the limits of the study can be found in the heterogeneity and the decreasing number of farms in mountain areas. This collaborative approach can help farmers develop more resilient and sustainable practices, further promoting the agroecological transition in line with European policies.

**Supplementary Materials:** The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su17062720/s1, Supplementary File S1: Summary of data collection based on survey.

**Author Contributions:** Conceptualization, E.B.d.R., A.M. and E.S.; methodology, E.B.d.R., A.M., G.B. and E.S.; validation, E.B.d.R., E.S. and A.M.; formal analysis, E.B.d.R.; investigation, E.B.d.R.; data curation, E.B.d.R.; writing—original draft preparation, E.B.d.R.; writing—review and editing, A.M., G.B. and E.S.; visualization, E.B.; supervision, E.S. and A.M.; project administration, E.S.; funding acquisition, E.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 872328.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** The data presented in this study are available from the corresponding author upon reasonable request.

**Acknowledgments:** We want to thank the farmers for their availability and kindness in participating in the survey process.

**Conflicts of Interest:** The authors declare no conflicts of interest.

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