Type: Article
Diversity of dairy farming systems in Tunisia and their effects on milk yield and quality traits
1. National Agronomic Institute of Tunisia, Research Lab of Aquatic and Animal Eco-Systems and Resources (UCAR), 43 Avenue Charles Nicolle, 1082 Tunis, Tunisia
2. International Center for Agricultural Research in the Dry Areas Tunisia, Rue Hédi Karray, 1004 El Menzah 1, Tunis, Tunisia
Abstract: This study aimed to evaluate the effects of farming systems, feeding strategies, and bioclimatic conditions on milk yield and milk quality in Tunisian dairy farms. A total of 102 farms were surveyed across 17 governorates located in northern, central, and coastal regions of Tunisia, which represent the main dairy production areas of the country. These farms were distributed across four bioclimatic zones, namely humid, sub-humid, semi-arid, and arid conditions. Data were collected through structured surveys conducted between 2022 and 2024, combined with official milk recording data. The recorded parameters included daily milk yield, fat content, protein content, milk urea concentration, and somatic cell count, allowing a comprehensive assessment of both production and milk quality. Farms were classified according to feeding strategies into four ration types and according to farming systems as above-ground, rain-fed, irrigated, and mixed systems. The results showed that milk yield was significantly influenced by bioclimatic conditions, with higher production observed in humid and sub-humid regions compared to arid areas. Feeding strategies also had a significant effect, as rations including green forage and silage resulted in higher milk yield and improved milk composition. In addition, farming systems influenced performance, with mixed and irrigated systems showing better productivity and efficiency. Milk quality parameters, particularly fat content, milk urea concentration, and somatic cell count, were significantly affected by the studied factors, reflecting differences in feeding balance, environmental stress, and management practices. Overall, dairy performance in Tunisia is strongly influenced by the interaction between climatic conditions, feeding strategies, and farming systems. Improving forage availability and adapting feeding practices to local conditions are key elements to enhance productivity and sustainability of dairy farms.
Keywords: Dairy cattle, feeding system, farming system typology, milk production, milk quality, Tunisia
Article Info.
Submitted: 03-22-2026; Revised: 04-29-2026; Accepted: 05-04-2026; Online: 05-11-2026
Cite as: Hamzaoui, S., Frija, A., Moujahed, N. (2026). Diversity of Dairy Farming Systems in Tunisia and Their Effects on Milk Yield and Quality Traits. Animal Reports, 2(1), 140-152.
https://doi.org/10.64636/ar.49
Copyright: © 2026 by the author(s). This work is licensed under the CC BY 4.0.
1 Introduction
In Tunisia, livestock production, particularly dairy cattle production, represents a key component of the agricultural sector and plays an essential role in food security, rural employment, and farmers’ income. Dairy cattle production refers to the management of cows for milk production under various production systems, including intensive, semi-intensive, and extensive systems, depending on resource availability and management practices (Attia et al., 2017; Darej et al., 2019).
The dairy sector contributes significantly to the national economy, accounting for an important share of agricultural production and agri-food value chains. According to recent reports from the Office de l’Élevage et des Pâturages (OEP, 2023), Tunisia produces approximately 1.4 million tonnes of milk annually, with a per capita consumption exceeding 100 liters per year. The national dairy herd is mainly composed of Holstein cows and their crossbreds, which dominate milk production systems (OEP, 2023).
Over the past two decades, the dairy sector has faced increasing challenges due to climate variability, water scarcity, rising feed costs, and market instability. These constraints have negatively affected farm profitability and production stability. In addition to drought and feed shortages, other factors such as increasing input prices, reduction in herd size, inadequate feeding practices, and weak technical support have contributed to the observed decline in milk production in recent years (Darej et al., 2019; OEP, 2023).
Although genetic improvement programs have been implemented, milk yield remains below the expected potential. Average production is estimated at around 4500 liters per lactation for purebred cows under Tunisian conditions, which is lower than the performance observed in more intensive production systems. This difference can be explained by suboptimal feeding strategies, environmental stress, and variability in management practices (Hammami et al., 2015).
The diversity of dairy farming systems in Tunisia is strongly influenced by the wide range of bioclimatic conditions, extending from humid regions in the north to arid areas in the south. This variability affects forage availability, water resources, and farm management strategies, leading to significant differences in productivity and milk quality (Jouili, 2008).
Several studies have investigated dairy farming systems at the regional level in Tunisia. However, there is still a lack of comprehensive studies that simultaneously analyze the effects of feeding strategies, farming systems, and bioclimatic conditions on both milk yield and milk quality at the national scale. To our knowledge, this study is among the first to provide an integrated analysis of farming systems, feeding strategies, and bioclimatic conditions at a national scale in Tunisia (Brahmi et al., 2026).
Therefore, the objective of this study is to evaluate the influence of farming systems, feeding strategies, and bioclimatic conditions on dairy performance and milk quality parameters in Tunisian dairy farms. By combining field survey data with production records, this study aims to provide an integrated understanding of the main factors affecting dairy productivity and to identify possible pathways for improving the sustainability of dairy systems under Tunisian conditions.
2 Materials and methods
2.1 Study area
The study was conducted in the main dairy production regions of Tunisia, covering 17 governorates distributed across five major geographical areas: Northwest (Béja, Bizerte, Jendouba, Siliana, Kef), Northeast (Ben Arous, Manouba, Ariana, Tunis, Nabeul, Zaghouan), Central-East (Sousse, Monastir, Mahdia), Central-West (Kairouan, Sidi Bouzid), and Southeast (Sfax).
These regions belong to four bioclimatic zones (humid, sub-humid, semi-arid, and arid) as defined by Jouili (2008). Together, they represent the main dairy production areas in Tunisia and host the majority of the national dairy herd. The geographical distribution of surveyed farms is presented in Fig. 1, while the distribution by bioclimatic zone is summarized in Table 1.
Fig. 1. Geographic distribution of the surveyed dairy farms across bioclimatic zones in Tunisia.
Table 1. Distribution of farms by governorates and bioclimatic zone.
|
Bioclimatic zone |
Number of surveyed farms |
Governorates |
|
6 |
Bizerte |
|
|
Sub-humid |
12 |
Beja, Jndouba |
|
Semi-arid |
48 |
Nabeul, Zaghouan, Tunis, Ariana, Manouba, Ben Arous, Kef, Siliana |
|
Arid |
36 |
Sousse, Monastir, Mahdia, Sidi Bouzid, Sfax, Kairouan |
2.2 Farms survey and data collection
Data were collected through a structured survey conducted between November 2022 and June 2024, involving a total of 102 dairy farms. A stratified sampling approach was used to ensure the representation of different dairy production systems and bioclimatic zones. Farms were selected based on criteria including geographical location, farm size, production system, and accessibility. In addition, the selection was supported by regional databases from the Office de l’Élevage et des Pâturages (OEP) to reflect the dominant dairy systems in each region.
A standardized questionnaire was administered to all farmers to ensure consistency of data collection across regions. The questionnaire included four main sections:
- General characteristics of farmers and farms (age, education level, farm structure, and environmental conditions)
- Herd characteristics (herd size, breed composition)
- Cropping systems and forage resources
- Feeding practices and seasonal feeding strategies
In addition to survey data, milk production and quality parameters were obtained from official records provided by regional livestock authorities. The following variables were collected:
- Milk yield (kg day⁻¹)
- Fat content (FC, %)
- Protein content (PC, %)
- Urea concentration (UC, mg dL⁻¹)
- Somatic cell count (SCC, ×10³ mL⁻¹)
2.3 Feeding systems and farm typology
Farms were classified according to feeding practices and production systems. Rations were categorized into four types (TR1 to TR4) based on the relative contribution of concentrate, dry forage, green forage, and silage. This classification allowed the identification of contrasting feeding strategies and their association with milk production and quality parameters.
2.4 Statistical analysis
Descriptive statistics were computed using the PROC MEANS and PROC FREQ procedures in SAS (version 9.0, SAS Institute Inc., Cary, NC, USA). The effects of farming system, ration type, and bioclimatic zone on milk production and milk quality traits were analyzed using a General Linear Model (GLM) according to the following model:
Yijkl = μ + Fi + Rj + Bk + eijkl
where Yijkl is the observed variable, μ is the overall mean, Fi is the effect of farming system, Rj is the effect of ration type, Bk is the effect of bioclimatic zone, and eijkl is the residual error. Interactions between the main effects (farming system × ration type, farming system × bioclimatic zone, and ration type × bioclimatic zone) were tested. As these interactions were not statistically significant (P > 0.05), they were excluded from the final model. Prior to analysis, data normality and homogeneity of variances were checked. Analysis of variance (ANOVA) was used to test the significance of fixed effects, and mean comparisons were performed using Tukey’s test at P < 0.05. In addition, multivariate analyses were conducted using XLSTAT (Version 2016, Addinsoft, Paris, France) to explore relationships among variables and to support the interpretation of the results
3 Results
3.1 Characterization of farms
3.1.1 identification of breeders based on age, gender, and educational level
The dairy farmers surveyed were predominantly middle-aged. More than 64% were between 40 and 50 years old, while farmers older than 55 years represented 25% of the sample. Younger farmers were underrepresented, with only 7% aged between 26 and 40 years and 4% under 25 years. Women accounted for 8.8% of the respondents. Regarding education level, 41% of farmers had higher education, 29% had secondary education, 23% had primary education, and 7% were illiterate. Overall, nearly 70% of farmers had at least a secondary level of education.
3.1.2 Classification of farms based on herd size and forage area
Descriptive statistics of farm and herd characteristics are presented in Table 2. On average, farms covered 20.54 hectares and included 34.6 dairy cows. However, large variability was observed, with herd size ranging from 1 to 501 cows and farm area from 0 to 389 hectares. Purebred cows represented 59.8% of the herd, while crossbred animals accounted for 40.2%. Irrigation was practiced on 63% of the agricultural land, whereas 37% relied on rainfall.
Table 2. Classification of Farms Based on Herd Size and Forage Area.
|
Setting |
Mean |
SD |
Minimum |
Maximum |
|
Number of heads |
34.6 |
60,77 |
1 |
501 |
|
Area |
20.54 |
47,76 |
0 |
389 |
3.1.3 Characteristics of dietary rations
The average composition of dairy cow diets is illustrated in Fig. 2.
Across all farms, diets consisted of 39% concentrate, 33% dry forage, 23% green forage, and 5% silage on a dry matter basis.
Four main ration types were identified based on ingredient combinations, as shown in Table 3. The most used ration was TR2, representing 41.18% of farms, followed by TR1 with 34.31%. Rations including silage were less frequent.
Fig. 2. Average composition of dairy cow diets based on survey data (% of dry matter).
Table 3. Identified types of rations and their frequency.
|
Type of ration |
Ingredients combination |
Use frequency, % |
|
TR1 |
CC+ DF |
34.31 |
|
TR2 |
CC+GF+DF |
41.18 |
|
TR3 |
CC+GF+DF+S |
14.71 |
|
TR4 |
CC+DF+S or CC+GF+S |
9.8 |
3.1.4 Distribution of breeders according to water resources
Four farming systems were identified according to water availability and land use, as presented in Table 4. The mixed system was the most common, representing 41.18% of farms, followed by rain-fed systems at 28.43%, above-ground systems at 21.57%, and irrigated systems at 8.82%. The mixed system had the largest farm size and herd size, while the above-ground system had no agricultural land and relied entirely on purchased feed.
Table 4. Farming Systems.
|
Items |
Above-ground System |
Rain-fed System |
Irrigation System |
Mixed System |
||||
|
Mean |
SD |
Mean |
SD |
Mean |
SD |
Mean |
SD |
|
|
Farm surface area (ha) |
- |
- |
8.10 |
20.07 |
21.78 |
19.59 |
38.80 |
66.98 |
|
Number of present cows (head) |
18.64 |
18.18 |
21.69 |
41.48 |
38.44 |
34.25 |
51.07 |
82.14 |
|
MPPC (kg/year) |
4659.54 |
96 |
4872.18 |
237.78 |
4928.11 |
285.98 |
5061.40 |
251.55 |
|
MPLC (kg/year) |
5663.09 |
49.82 |
5845.11 |
193.79 |
5976.56 |
175.88 |
6102.02 |
185.75 |
|
Concentrate, %DM |
50.00 |
6.00 |
36.00 |
16.10 |
45.00 |
12.70 |
35.00 |
13.20 |
|
Dry Forage, %DM |
43.00 |
22.40 |
52.00 |
32.00 |
39.40 |
24.10 |
32.00 |
21.90 |
|
Green Forage, %DM |
- |
- |
11.00 |
23.00 |
18.40 |
18.50 |
28.00 |
17.10 |
|
Silage, %DM |
- |
- |
- |
- |
- |
- |
5.00 |
8.30 |
|
Frequency distribution of farms distribution (%) |
22.00 |
28.00 |
9.00 |
41.00 |
||||
3.2 Factors influencing dairy performance and main quality parameters
3.2.1 Effect of bioclimatic stage on dairy performance and main quality parameters
The bioclimatic zone significantly affected milk production and milk quality parameters, as shown in Table 5. Milk yield was highest in humid and sub-humid zones, reaching 19.61 and 18.42 kg per day, respectively. Lower values were observed in semi-arid and arid regions, with the minimum recorded in the arid zone at 15.10 kg per day.
The milk-to-concentrate ratio followed a similar pattern, with higher values in humid areas and lower values in arid regions. Milk fat content differed significantly between zones, with the highest value recorded in the humid region at 3.84%. Protein content showed smaller variations, ranging between 2.95% and 3.01%. Milk urea concentration varied significantly, with the highest values observed in the humid zone. Somatic cell count also differed strongly across regions, with the lowest values in humid areas and the highest in arid zones.
Table 5. Effect of bioclimatic zone on milk yield and milk quality parameters.
|
Bioclimatic zone |
MP |
M/C
|
FC |
PC |
UC |
SC |
|
Humid |
19.61 a |
2.45 a |
3.84 a |
2.99 ab |
44.78 a |
904.89 c |
|
Sub-humid |
18.42 a |
2.30 a |
3.44 b |
2.95 b |
42.21 ab |
1664.27 a |
|
Semi-arid |
16.86 b |
2.10 b |
3.19 c |
3.01 a |
38.19 b |
1075.36 b |
|
Arid |
15.10 c |
1.85 c |
3.36 bc |
2.97 b |
41.91 ab |
1762.14 a |
|
SEM |
0.31 |
0.03 |
0.06 |
0.03 |
0.55 |
52.44 |
|
p value |
<0.0001 |
<0.0001 |
<0.0001 |
<0.05 |
<0.05 |
<0.0001 |
3.2.2 Effect of ration type on dairy performance and main quality parameters
Ration type had a significant effect on milk yield and milk quality traits, as presented in Table 6 and Fig 3. The highest milk production was observed for TR2, with an average of 20.26 kg per day, while the lowest was recorded for TR1 at 15.57 kg per day. The milk-to-concentrate ratio ranged from 1.85 to 2.45 across ration types. Milk fat content was highest in TR4 and lowest in TR1 and TR2. Protein content did not differ significantly among ration types. Milk urea concentration showed significant variation, with the highest value observed in TR2 and the lowest in TR1. Somatic cell count was lowest in TR4 and highest in TR1.
Table 6. Effect of ration type on milk yield and milk quality parameters.
|
Type of ration |
MP |
M/C |
FC |
PC |
UC |
SC |
|
TR1 |
15.57 C |
2.45 a |
3.29 b |
2.97 |
34.64 c |
1433.83 a |
|
TR2 |
20.26 a |
2.30 a |
3.33 b |
2.97 |
43.07 a |
1341.31 a |
|
TR3 |
18.4 b |
2.10 b |
3.48 b |
2.91 |
39.26 b |
1217.47 ab |
|
TR4 |
17.2 b |
1.85 c |
3.87 a |
3.11 |
38.76 b |
1010.50 b |
|
SEM |
0,26 |
0.03 |
0.05 |
0.023 |
1.02 |
42.55 |
|
p value |
<0.0001 |
<0.0001 |
<0.0001 |
NS |
<0.0001 |
<0.0001 |
Fig. 3. Effect of ration type on milk yield in Tunisian dairy farms.
3.2.3 Effect of farming system on dairy performance and main quality parameters
The farming system significantly influenced milk production and milk quality parameters, as shown in Table 7. The highest milk yield was recorded in the mixed system at 19.9 kg per day, followed by the irrigated system. The lowest production was observed in the above-ground system. Feed efficiency, expressed as the milk-to-concentrate ratio, was highest in mixed and irrigated systems and lowest in above-ground systems. Milk fat content was higher in irrigated and mixed systems, while lower values were observed in rain-fed and above-ground systems. Protein content showed significant variation, with the highest values recorded in irrigated systems. Milk urea concentration varied between systems, with lower values in rain-fed and irrigated farms. Somatic cell count was highest in the above-ground system and lowest in the rain-fed system.
Table 7. Effect of systems on milk yield and milk quality parameters.
|
Systems |
MP |
M/C |
FC |
PC |
UC |
SC |
|
S 1 |
15.09 c |
1.78 c |
3.36 b |
2.97 |
41.91 a |
1762.14 a |
|
S 2 |
17.72 b |
1.99 b |
3.17 b |
2.98 |
38.83 b |
807.55 c |
|
S 3 |
18.00 b |
2.31 a |
3.73 a |
3.11 |
41.00 b |
1349.33 b |
|
S 4 |
19.90 a |
2.56 a |
3.57 a |
2.96 |
42.62 a |
1441.81 b |
|
SEM |
0.28 |
0.02 |
0.07 |
0.03 |
0.42 |
48.37 |
|
p value |
<0.0001 |
<0.0001 |
<0.0001 |
<0.0001 |
<0.05 |
<0.0001 |
4 Discussion
4.1 Influence of bioclimatic conditions on dairy performance
The results clearly demonstrate that bioclimatic conditions strongly influence dairy performance and milk quality in Tunisia. Higher milk yields observed in humid and sub-humid regions are mainly explained by better forage availability and more favorable feeding conditions. These environments generally support higher-quality pastures and more stable feed resources, which enhance nutrient intake and animal productivity (Hammami et al., 2015; Singh et al., 2015).
In contrast, the lower productivity recorded in semi-arid and arid regions reflects the combined effects of feed scarcity and heat stress. Elevated temperatures are known to reduce feed intake, alter rumen fermentation, and impair metabolic efficiency, ultimately decreasing milk production (Rojas-Downing et al., 2017; West, 2003).
Milk composition was also affected by climatic conditions. Variations in fat content can be linked to differences in forage quality and fiber intake, which directly influence rumen fermentation pathways and acetate production (Piantoni et al., 2013). Furthermore, the higher somatic cell counts observed in drier regions suggest increased physiological stress and potential udder health issues. These conditions are often associated with poor housing environments, limited water availability, and heat stress, which negatively affect immune function (Stocco et al., 2023; Winther et al., 2023).
Overall, these findings confirm that climate is a key driver of dairy system performance in Mediterranean environments and highlight the importance of adaptive strategies to mitigate environmental stress.
4.2 Effect of feeding strategies on milk production and quality
Feeding practices emerged as one of the main determinants of dairy performance. Rations including green forage and silage were consistently associated with higher milk yields and improved milk composition compared to diets dominated by concentrate and dry forage.
This effect can be explained by the fundamental role of forage in maintaining rumen function and ensuring a balanced nutrient supply. Fresh forage improves fiber intake and stimulates microbial activity, which is essential for efficient digestion and milk synthesis (Piantoni et al., 2013; Nichols et al., 2019).
In contrast, diets heavily dependent on concentrate may lead to imbalances in rumen fermentation when forage quality is insufficient. Although such rations may sometimes show higher milk-to-concentrate ratios, they do not necessarily translate into better overall productivity. This highlights that feed efficiency should be evaluated based on the balance and quality of the diet rather than concentrate use alone (McCarthy et al., 2018).
Milk quality parameters also responded to feeding strategies. Higher fat content and lower somatic cell counts observed in diversified rations indicate improved metabolic status and better animal health. Balanced diets enhance nutrient utilization and reduce metabolic disorders, contributing to improved milk quality (Zhao et al., 2025; Winther et al., 2023).
These results underline the importance of optimizing forage inclusion and improving feed resource management in dairy systems, particularly under variable climatic conditions.
4.3 Effect of farming systems on dairy performance
The differences observed among farming systems further emphasize the importance of resource availability and management practices in shaping dairy performance. Mixed and irrigated systems achieved the highest productivity levels, likely due to better access to diverse and high-quality feed resources. These systems benefit from more stable forage production, allowing farmers to maintain consistent feeding strategies throughout the year. Similar findings have been reported in Tunisian dairy systems, where access to irrigated forage significantly improves milk production (Darej et al., 2019; Hammami et al., 2015).
In contrast, above-ground systems, which rely mainly on purchased feed, showed lower productivity and poorer feed efficiency. This reflects limitations in diet diversity and a higher dependence on external inputs, which can negatively affect both economic and technical performance. Rain-fed systems showed intermediate results, highlighting the variability associated with rainfall-dependent production. Seasonal fluctuations in forage availability often led to inconsistent feeding conditions and variable milk output (Attia et al., 2017).
In addition to production, farming systems also influenced milk quality and animal health. Lower somatic cell counts observed in systems with better resource availability suggest improved animal welfare and management conditions, including better hygiene, ventilation, and feeding balance (Stocco et al., 2023).
4.4 Implications for dairy system sustainability
The combined effects of climate, feeding practices, and farming systems illustrate the complexity of dairy production in Tunisia. Improving system performance requires an integrated approach that considers both environmental constraints and farm management strategies.
Enhancing on-farm forage production, promoting ration diversification, and improving water management are key levers to increase system resilience and productivity. In Mediterranean conditions, optimizing the use of local feed resources and adapting feeding strategies to seasonal variability are essential to mitigate the impacts of climate change (Rojas-Downing et al., 2017). Furthermore, improving efficiency and reducing dependence on external input can contribute to both economic sustainability and environmental performance of dairy farms.
4.5 Study limitations
This study is based on survey data and cross-sectional observations, which may limit the ability to establish causal relationships between variables. In addition, variability in farm management practices and data recording may have influenced the observed results. Future research should incorporate longitudinal data and more detailed nutritional assessments to better understand the interactions between feeding strategies, environmental conditions, and dairy performance. Integrating experimental approaches would also help to validate the relationships identified in this study.
5 Conclusion
This study provides an overview of the diversity of dairy farming and feeding systems in Tunisia and their influence on milk yield and milk quality. Based on a survey of 102 farms across 17 governorates, significant differences were observed among ration types, farming systems, and bioclimatic zones. Rations including green forage and silage were associated with higher milk yield and better feed efficiency, while diets mainly based on concentrate and dry forage resulted in lower productivity. Similarly, mixed and irrigated farming systems showed higher performance compared with above-ground systems due to better access to forage resources.
Climatic conditions also played an important role in shaping dairy productivity. Farms located in humid and sub-humid regions generally achieved higher milk yield and improved milk composition, whereas arid regions showed lower productivity and higher somatic cell counts. Overall, these findings highlight the importance of improving forage availability, optimizing feeding strategies, and adapting farm management to local climatic conditions in order to enhance the sustainability and productivity of Tunisian dairy systems.
Acknowledgements
The authors would like to thank the Office de l’Élevage et des Pâturages, both at the central and regional levels, for their valuable support and collaboration in data collection and fieldwork.
Funding:
No specific funding was available for this study
Author contributions:
Sarra Hamzaoui: Conceptualization, Methodology, Software, Formal analysis, Investigation, Data curation, Writing-original draft preparation. Aymen Frija: Validation, Data curation, Writing-review and editing. Nizar Moujahed: Conceptualization, Validation, Writing-review and editing, Supervision. All authors have read and agreed to the published version of the manuscript.
Ethical approval
Not applicable
Informed consent
None.
Conflicts of interest
The authors declare that there is no conflict of interest.
Data availability statement
The authors declare that data can be provided by the corresponding author upon reasonable request.
Declaration of using AI Technology
The authors declare that they used ChatGPT to improve the clarity and readability of the manuscript. The content was carefully reviewed and edited by the authors, who take full responsibility for the final version of the manuscript.
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