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Article

Associations of Climatic Variables with Health Problems in Dairy Sheep Farms in Greece

by
Eleni I. Katsarou
1,†,
Daphne T. Lianou
1,†,
Charalambia K. Michael
2,
Natalia G. C. Vasileiou
3,
Elias Papadopoulos
4,
Efthymia Petinaki
5 and
George C. Fthenakis
1,*
1
Veterinary Faculty, University of Thessaly, 43100 Karditsa, Greece
2
School of Veterinary Medicine, European University of Cyprus, Nicosia 2404, Cyprus
3
Faculty of Animal Science, University of Thessaly, 41110 Larissa, Greece
4
Laboratory of Parasitology and Parasitic Diseases, School of Veterinary Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
5
University Hospital of Larissa, 41110 Larissa, Greece
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Climate 2024, 12(11), 175; https://doi.org/10.3390/cli12110175
Submission received: 21 September 2024 / Revised: 28 October 2024 / Accepted: 31 October 2024 / Published: 1 November 2024
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Abstract

:
This study aimed to study the potential effects of climatic conditions prevalent at the locations of sheep farms in the country. The specific objectives were to explore associations between climatic variables and the incidence of four clinical problems in sheep farms and, moreover, to compare these to the health management practices applied in the farms. Our hypothesis was that climatic factors may be associated with the prevalence of diseases in sheep farms; this will provide information regarding potential weather effects, to take into account in the efforts for control of the diseases. Data were obtained during a large cross-sectional investigation performed across Greece involving 325 sheep flocks. Climatic variables prevailing at the location of each farm were derived from ‘The POWER Project’. The annual incidence rate for abortion was 2.0% (95% confidence intervals: 1.9–2.1%), for clinical mastitis 3.9% (3.8–4.0%), for lamb pneumonia 1.4% (1.3–1.4%) and for lamb diarrhoea 7.9% (7.8–8.1%). In multivariable analyses, climatic variables emerged as significant predictors for abortion—high annual precipitation at the farm location (p = 0.024)—and for lamb diarrhoea—high average annual temperature range at the farm location (p < 0.0001)—but not for clinical mastitis or lamb pneumonia. The potential effects of climatic variables were found to be more important in lambs than in adult animals. Future studies may focus on how variations in temperature and precipitation can be translated into on-farm metrics to understand the impacts on sheep health and welfare.

1. Introduction

Changes in the environmental conditions in sheep farms may interfere with the health and welfare of the animals and, consequently, with their productivity. Sheep, due to the nature of their management, which includes a significant period of grazing, are exposed to climate-related factors to a greater degree than other livestock species [1,2,3]. Among the various climatic factors that may affect sheep, greater attention has been given to the potential effects of temperature. Sheep can adapt to the stress caused by extreme temperatures and by temperature fluctuations by means of responses, which enable various mechanisms; however, their production output is often reduced. The relevant scientific literature has focused on the effects of climatic parameters on sheep production; for example, it has been reported that high environmental temperatures could lead to decreased milk production and altered milk composition [4,5,6] and to lower daily weight gain of lambs [7,8,9].
With regard to the potential effects of climate factors on sheep health, the international literature refers mostly to the effects of temperature and precipitation on endoparasitic infections and lameness. There is a clear and well-documented effect of temperature and precipitation on helminth biology and, consequently, on infection of grazing animals [10,11]. Moreover, there is also relevant literature regarding the effects of precipitation on foot-rot; prolonged exposure of sheep feet to wet conditions can lead to devitalization of the interdigital skin and a higher incidence rate of lameness on sheep farms [12,13].
A recent (August 2024) literature search in the Web of Science platform using the topic search string [sheep OR ovine OR Ovis aries] AND [climat* OR weather] AND [abortion OR mastitis OR pneumonia OR [diarrhoea OR diarrhea]] revealed a total of 85 research articles on the associations of climate variables with these four important clinical problems of sheep. The individual assessment of these articles revealed that climatic variables were reported to be associated with the transmission of various abortifacient pathogens (including Border disease virus [14], Nairobi disease virus [15] and Toxoplasma gondii [16]), as well as Mycoplasma agalactiae [17]. Climatic factors have also been reported to have an effect on the development of enterotoxaemia [18] and tick-borne fever [19]. Moreover, climate-related factors can also adversely affect efforts for control of various diseases (e.g., respiratory infections [20], peste des petits ruminants [21]). Approximately 20% of the world’s sheep population is farmed for milk production.
Global sheep milk production has increased significantly in recent years; a clear tendency for further increase is also evident [22]. In contrast to cow milk, sheep milk is not consumed directly but is more frequently used in the manufacturing of dairy foods, primarily yogurt or cheese products [23]. The largest proportion of sheep milk globally is produced in Asia (approximately 45%) (mainly in China and Turkey); smaller proportions are produced in Europe (approximately 30%) (mainly in France, Greece, Italy and Spain) and Africa (approximately 25%) (mainly in Algeria); minimal proportions are produced in the Americas (mainly in Mexico) and Oceania (overall less than 2%) [22,24]. Globally, dairy sheep farms are mainly located in temperate and tropical areas. The increased concentration of sheep farms around the Mediterranean and Black Sea regions has been considered a consequence of the Greek or Roman cultural heritage of these areas [25]. In those regions, dairy products from sheep milk are significant ingredients in the diet of local people.
In Greece, sheep are farmed primarily for dairy production; over 98% of sheep in the country are milked. The industry is the most significant branch of the animal production sector in the country and contributes 0.6% to the country’s total annual gross domestic product [26]. In this system, lambs are moved away from ewes, mainly at 40 to 60 days of age, and are taken for slaughter.
This study was performed as part of a detailed mapping of the sheep industry in Greece and aimed to study the potential effects of climatic conditions prevalent at the locations of sheep farms in the country. The specific objectives of the study were to explore associations between climatic variables and the incidence of four clinical problems in sheep farms and, moreover, to compare these to the health management practices applied in the farms. Our hypothesis was that climatic factors may be associated with the prevalence of diseases in sheep farms; this will provide information regarding potential weather effects to take into account in the efforts toward the control of the diseases.

2. Materials and Methods

2.1. Visits to Sheep Farms and Interviews of Farmers

The data were obtained during a large cross-sectional investigation performed across Greece (April 2019–June 2020) in all 13 regions of the country (Figure 1). The study involved 325 sheep flocks. The protocols for the participation of farms in the investigation have been detailed previously [27].
No relationship had existed between the researchers and any of the farmers in the visited farms prior to the visit. The researchers visited all 325 farms in the study for the collection of all relevant information. The objectives and the details of the study were presented to the farmers and were discussed with them.
Then, an interview was conducted by means of a structured detailed questionnaire. The questionnaire included 442 questions, which covered all the details of the farms. Questions were of general context (n = 8) or specifically related to infrastructure (n = 119), animals (n = 85), production characteristics (n = 19), health management applied in the farm (n = 150), nutrition (n = 37) or human resources (n = 21). Open, multiple-choice, dichotomous and scaling questions were included in the questionnaire (Table S1). This was conceived and developed in Greek at the Veterinary Faculty of the University of Thessaly, with input from colleagues at the Faculty or at the Faculty of Veterinary Medicine of the Aristotle University of Thessaloniki [27]. After completion of the draft questionnaire, an initial pilot test was carried out with 11 farmers. Thereafter, the questionnaire was assessed on two repeated occasions with 27 farmers; the objective was to assess the consistency of the replies. Cronbach’s coefficient alpha was ultimately found to be 0.987. Finally, visits were made to the 325 farms, which were selected on a convenience basis (i.e., the acceptance of farmers to a visit by a scientific team). The interview of each farmer was always carried out by the same researcher (author D.T.L.) [27]. When farmers requested clarifications about the questions during the interview, these were provided immediately. Finally, information obtained from the farmers was verified during a physical evaluation of the farm site and from the veterinarians supervising the farms [27]. After completion of the interview, no repeat visits were made to the farms.

2.2. Data Management and Analysis

During the visit to each farm, geo-location data were taken using hand-held Global Positioning System Garmin units. The geo-references were resolved to the specific farm level.
Climatic variables prevailing at the location of each farm were derived from ‘The POWER (Prediction of Worldwide Energy Resources) Project’ (NASA Langley Research Center (LaRC), Hampton, VA, USA), which collects and makes available meteorological datasets from NASA research specifically for the support of agricultural needs and relevant research work. The following settings were used for obtaining the data: ‘agroclimatology’, ‘daily & annual’, ‘geo-references of each farm’, ‘ASCII’. Data for the following parameters were extracted: ‘temperature at 2 m’ (i.e., at a height of 2 meters above the ground), ‘temperature of Earth skin’, ‘minimum temperature at 2 m’, ‘maximum temperature at 2 m’, ‘temperature range at 2 m’, ‘relative humidity at 2 m’, ‘total precipitation’ and ‘wind speed at 10 m’ (i.e., at a height of 10 meters above the ground) [28]. For the evaluation of climatic variables, the averages for the above parameters for the year that preceded each visit were taken into account.
Data were entered into Microsoft Excel (versions 1901-2409) (Microsoft Corporation, Redmond, WA, USA) and analyzed using SPSS v. 21 (IBM Analytics, Armonk, NY, USA). Initially, descriptive analyses were carried out and exact binomial confidence intervals (CI) were obtained.
The outcomes ‘Annual incidence rate of xxx’ were considered (where xxx = each clinical problem under evaluation: abortion, clinical mastitis, lamb pneumonia and lamb diarrhoea). In total, up to 38 parameters (classified in variables related to infrastructure, production characteristics, health management practices, human resources and climatic factors in farms; Table A1) were evaluated for potential association with each of these outcomes. These variables were either taken directly from the answers obtained at the interview performed during the visit or they were calculated based on these answers. For the outcomes of the clinical problems, relevant details were also collected (in addition to the answers received during the interview) from the veterinarians, who were supervising the farms and were accompanying the researchers during the respective visits [27].
Initially, the significance of predictors was assessed in univariable analysis by using Spearman rank correlation and with simple logistic regression with the results of the various parameters assessed. Then, a multivariable analysis model was developed, with the initial inclusion of variables found with p < 0.2 in the univariable analyses and the subsequent elimination of variables found with p > 0.2 during the analysis process. The variables included in the final assessment in each multivariable model are described in Table S2.
Within each of the above outcomes, the variables found to be significant in the univariable analyses were classified into one of the above categories (i.e., infrastructure, production characteristics, health management practices, human resources and climatic factors). The proportions of variables found to be significant in each of the five categories were compared between them by using the Pearson chi-square test. Separate comparisons were also made for outcomes present in adult animals (abortion, clinical mastitis) and in lambs (pneumonia, diarrhoea).
For outcomes, in which climatic factors were found to be significant predictors, these significant factors were subsequently assessed separately for farms under intensive/semi-intensive and for farms under semi-extensive/extensive management system. The Fisher r-to-z transformation was applied to compare correlation coefficients and analysis of covariance to compare the respective slopes.
In all analyses, statistical significance was defined at p < 0.05.

3. Results

3.1. Descriptive Findings

The reported annual incidence rates of the clinical problems under study are shown in Table 1 and Table 2. For abortion, clinical mastitis and lamb pneumonia, the respective incidence rates were significantly higher in farms with intensive or semi-intensive management systems; for lamb diarrhoea, the incidence rate was significantly higher in farms with the semi-extensive or extensive system (Table 1).

3.2. Predictors

Details of the univariable analyses for associations of the various variables with the outcomes under study are in Tables S3–S6.
In the multivariable analysis, high annual precipitation at the farm location emerged as the only significant predictor for a high annual incidence rate of abortion (p = 0.024) (Table 3, Figure 2). There was no difference in the significance of annual precipitation at the farm location for farms under intensive/semi-intensive or under semi-extensive/extensive management systems (rsp = 0.040 and 0.129, respectively, z = 0.79, p = 0.43; slope = 0.013 ± 0.012 and 0.029 ± 0.014, respectively, p = 0.59).
In the multivariable analysis, the small number of ewes in a flock emerged as the only significant predictor for a high annual incidence rate of clinical mastitis (p = 0.009) (Table 4, Figure 3).
In the multivariable analysis, (a) the lack of a separate barn for lambs (p = 0.0001) (Figure 4), (b) the proximity of the farm to industrial sites (p = 0.020) and (c) the younger age of lambs when removing them from their dam (p = 0.020) (Figure 5) emerged as significant predictors for a high annual incidence rate of lamb pneumonia (Table 5).
In the multivariable analysis, the high average annual temperature range at the farm location emerged as the only significant predictor for a high annual incidence rate of lamb diarrhoea (p < 0.0001) (Table 6, Figure 6). There was also a tendency for significance for the routine prophylactic administration of antibiotics to newborn lambs (p = 0.056) (Figure S1). There was no difference in the significance of the average annual temperature range at the farm location for farms under intensive/semi-intensive or under semi-extensive/extensive management systems (rsp = 0.067 and 0.268, respectively, z = 1.84, p = 0.07; slope = 0.004 ± 0.002 and 0.006 ± 0.002, respectively, p = 0.75).
Overall, in clinical problems associated with adult animals (abortion, mastitis), health management practices were more often found with significant associations with the respective annual incidence rate—in 15.4% of univariable analyses made. In contrast, in clinical problems associated with lambs (pneumonia, diarrhoea), climatic variables were more often found with such associations—in 38.9% of univariable analyses made (Table 7).

4. Discussion

4.1. Preamble

The current study explored the potential importance of variables present in sheep farms in the occurrence of four clinical problems. The study used a participatory epidemiology approach [29,30], which has been defined as ‘the use of participatory techniques for the harvesting of epidemiological intelligence contained within community observations, existing veterinary knowledge and traditional oral history’ [31], by means of which it was possible to obtain information from farms across Greece. During the investigation, we evaluated concurrently farm-related and farmer characteristics, as well as climatic factors, in order to study potential interactions between them. Participatory epidemiology methods are fully recognized techniques in veterinary medicine (for example, the Food and Agriculture Organization has published relevant material) that can be employed to gather relevant information. Advantages of the approach include, among others, the possibility of collecting large amounts of data and the flexibility in field work, as well as the empowerment of rural people to participate in issues that concern them [30,31]. Participatory epidemiology is a proven technique that can contribute to overcoming many of the constraints of conventional epidemiological approaches; in this respect, it has been used to study various problems in animal health surveillance and research [32].
Some constraints can emerge, nonetheless, in the application of this approach [33]. For example, knowledge of local systems and interests should apply; for this reason, local veterinarians selected the farms for the present study, accompanied the researchers during the visits and introduced them to the farmers. Moreover, the targets of the study should align with the priorities of local farmers, and further, the emphasis of scientists on data extraction may limit the extent to which farmers can participate and propose problems and potential solutions. However, through the inclusion of farms from all administrative regions of the country, climate conditions and health management practices prevailing and taking place throughout the country were taken into account, and thus, factors of regional importance weighed less. Moreover, climatic conditions occurring at locations throughout the country were also taken into account.
The current study combined, for the first time, the evaluation of many management-related and climate-related practices. Hence, the former were compared versus the latter, and their potential interactions were evaluated. In this approach, the extensive coverage of the country has allowed us to consider the diversity of climate conditions occurring at the locations of the farms.
The results contribute to the identification of parameters that could be important for the development of clinical problems in sheep. That way, efforts for control could be appropriately directed. The emergence of climate-related factors as predictors for the increased incidence rate of two clinical problems should also be viewed within the general context of climate-related issues. In this, professionals should take into account (a) the short- and long-term changes in environmental conditions at the location of a farm and (b) the increases in extreme events, e.g., higher daily maximum temperatures, more frequent and longer heat waves and changes in rainfall patterns. All these can have an impact on the clinical problems that develop on farms. Thus, management practices should be applied appropriately to contribute to the efficient control of the respective clinical problems.

4.2. Significance of Climatic Variables

The results have indicated that for two clinical problems (abortion, lamb diarrhoea), climatic conditions are of greater importance than the management practices. Climatic conditions can influence pathogens directly; for example, the multiplication and dissemination of bacteria and protozoa are better at higher temperatures, which can result in increased pathogen populations in the animal environment [34,35,36,37]. In turn, this can lead to enhanced infection of animals, as found with the increased incidence rate of lamb diarrhoea.
Humid environmental conditions have been reported to be a risk factor for an increased rate of abortion caused by Chlamydia abortus [38] (previous name: Chlamydophila abortus), a primary causal agent of the pathological condition in sheep, which has also been found to have a seasonal pattern occurrence [39], possibly as an effect of the changes in climate conditions. Toxoplasma gondii, another important cause of abortion in ewes, also thrives in areas with humid climate [40], which explains the more frequent detection of the protozoon in sheep in areas with high humidity [41]. In cattle, increased rainfall was reported to be associated with a higher risk for abortion by Neospora caninum [42]. Presence of coccidian oocysts in the environment of a farm is related to precipitation at the farm location, as oocyst sporulation and persistence depend on moisture [43]. Hence, the present findings of the association of higher overall incidence of abortion (as a clinical entity) in farms with higher total precipitation at the respective location align with the previously published studies of potential climate effects on various abortifacient agents [38,39,40,41,42,43].
Although the incidence of clinical mastitis was not found to be associated with the climate variables assessed, studies have indicated that dairy production can be affected by climate-related factors more than other types of animal production [44]. In cases of heat stress, dairy animals usually reduce feed dry matter intake, which results in a significant decrease in milk production. Rojas-Downing et al. [45] have suggested that larger animals would emit a higher amount of metabolic heat; based on this, one may postulate that ewes with a large body type, which usually belong to high-milk-producing breeds (e.g., Lacaune, Assaf, Chios), may be more sensitive to heat stress than smaller animals of indigenous breeds, which are also typified by low milk production. Hence, milk losses from heat stress would be more pronounced in animals of the former breeds. Indeed, Finocchiaro et al. [46] have implied that genetics for milk yield traits are antagonistic with heat tolerance, which supports the above suggestion. High environmental temperatures increase the energy requirements of lactating ewes by 7 to 25%, partly due to accelerated respiration, which, consequently, could lead to reduced milk production [47]. During high environmental temperatures, reduced food intake and increased energy requirements stimulate the mobilization of body reserves and body proteins to provide amino acids for protein synthesis and carbon sources for gluconeogenesis [48]. However, it is noted that the precise mechanisms through which heat stress adversely affects milk metabolism and synthesis have not been fully elucidated [49,50]. In this respect, the present findings contribute that the possible reduction in milk production would not be regulated through inflammatory pathways.
The potential effects of climatic factors were found to be more important in lambs than in adult animals. This can be a direct effect of the reduced immunocompetence of young animals, which can be more prone to clinical disease as the result of infection by various pathogens. Further, it may also be the result of better tolerance of adult animals to adverse climate conditions in comparison to lambs [1]. Hence, in the context of climate-related changes, as previously discussed, professionals should be alerted to implement, as a priority, appropriate management practices in order to protect lambs.
Overall, in the evaluation (and potential quantification) of the risk of development and presence of a clinical problem in sheep farms, it is important to consider the burden from pathogens, which may accumulate over time in the diverse environmental conditions prevalent in the farms.

4.3. Role of Management Practices

For the other two clinical problems (mastitis, lamb pneumonia), management factors applied in the farms were found to be more important than climatic factors. Often, this was in line with established knowledge.
With regard to clinical mastitis, the increased incidence rate in flocks with smaller size abides with the previous finding of higher somatic cell counts in bulk-tank milk (a significant indicator of mastitis prevalence in a flock) in farms with less than 500 ewes [51]. This may indicate better management and hygiene standards in larger, industrial-type farms rather than in small, family-run flocks [52]. However, it may also reflect better monitoring of animals in the latter flocks due to the small number of animals, which allowed easier and more careful recording of mastitis cases.
With regard to lamb pneumonia, in the absence of a separate barn for lambs, these would be housed in the same buildings as the adult animals, resulting in an increased stocking rate therein. This creates an increased risk for pathogen transmission between animals; also, higher ammonia concentrations may develop within buildings in such cases. Ammonia is a respiratory irritant, and in increased concentrations, it can lead to the impediment of local defenses of animals as the result of nasal mucosal hyperplasia and cilia dysfunction [53]. These two factors combined can contribute to the increased incidence of respiratory infections in such cases.
With regard to the finding of a tendency for antibiotic administration as a predictor for an increased incidence rate of diarrhoea, it is noted that, in Greece, farmers would mostly attempt to treat diarrhoea with antibiotic administration [54]. That is an incorrect practice that must be discouraged, as it is a significant factor in the development of antibiotic resistance among bacteria on the farms [55]. Moreover, diarrhoea in lambs can be caused by various pathogens, among them viruses and protozoa (e.g., Rotavirus, Giardia spp., Cryptosporidium spp.) [56], against which antibiotics are not effective. Therefore, the routine prophylactic administration of antibiotics to newborn lambs, rather than the targeted implementation of appropriate health management practices, would not effectively control the clinical problem, whilst at the same time would also contribute to the development of antibiotic resistance.

5. Conclusions

The study, performed during an extensive countrywide investigation in Greece, provided evidence for the importance of climatic conditions for abortion and lamb diarrhoea. In contrast, management-related variables were significant predictors for clinical mastitis and lamb pneumonia. In general, the potential effects of climatic conditions were found to be more important in lambs than in adult animals. In farms where climate conditions were found to have an impact on clinical problems, management practices should be applied to contribute to the efficient control of the respective clinical problems. Future studies may focus on how variations in temperature and precipitation can be translated into on-farm metrics to understand the impacts on sheep health and welfare.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cli12110175/s1, Table S1: The questionnaire used for collection of information during visits to 325 sheep farms in Greece; Table S2: Details of multivariable models (n = 4) employed for the evaluation of associations with the incidence rate of mastitis, abortion, lamb pneumonia or lamb diarrhoea in 325 sheep flocks in Greece; Table S3: Results of univariable analysis for predictors of incidence rate of abortion in 325 sheep flocks in Greece; Table S4: Results of univariable analysis for predictors of incidence rate of clinical mastitis in 325 sheep flocks in Greece; Table S5: Results of univariable analysis for predictors of incidence rate of lamb pneumonia in 325 sheep flocks in Greece; Table S6: Results of univariable analysis for predictors of incidence rate of lamb diarrhoea in 325 sheep flocks in Greece; Figure S1: Cross-plot of the incidence rate of lamb diarrhoea in 325 sheep farms in Greece in accordance with the annual temperature range at the location of the farms and the routine administration of antibiotics to lambs.

Author Contributions

Conceptualization, E.I.K. and G.C.F.; methodology, E.I.K., D.T.L., C.K.M. and G.C.F.; formal analysis, E.I.K. and G.C.F.; investigation, E.I.K., D.T.L. and C.K.M.; data curation, E.I.K. and D.T.L.; writing—original draft preparation, E.I.K., N.G.C.V. and G.C.F.; writing—review and editing, E.I.K., D.T.L., C.K.M., N.G.C.V., E.P. (Elias Papadopoulos), E.P. (Efthymia Petinaki) and G.C.F.; visualization, E.I.K. and D.T.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The protocols of the study were approved by the academic board of the Veterinary Faculty of the University of Thessaly, meetings 34/03.04.2019 and 82/04.11.2020.

Informed Consent Statement

Not applicable.

Data Availability Statement

Most data presented in this study are in the Supplementary Materials. The remaining data are available upon request from the corresponding author. The data are not publicly available, as they form part of the PhD thesis of the first author, which has not yet been examined, approved and uploaded in the official depository of Ph.D. theses from Greek universities.

Acknowledgments

The climatic data were obtained from the NASA Langley Research Center (LaRC) POWER Project funded through the NASA Earth Science/Applied Science Program. Eleni I. Katsarou receives a scholarship from the Unit of Research, Innovation and Excellence of the University of Thessaly.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. List of variables (related to infrastructure, animals, production characteristics, health management, human resources and climatic conditions) evaluated for potential association with the incidence rate of abortion, clinical mastitis, lamb pneumonia or lamb diarrhoea in 325 sheep flocks in Greece.
Table A1. List of variables (related to infrastructure, animals, production characteristics, health management, human resources and climatic conditions) evaluated for potential association with the incidence rate of abortion, clinical mastitis, lamb pneumonia or lamb diarrhoea in 325 sheep flocks in Greece.
Variables Used
Variables related to animals in farms
Management system applied in farm (EFSA classification: shepherding/intensive/semi-intensive/semi-extensive/extensive/very extensive/mixed)
Altitude at the location of farm (m)
Availability of a separate barn for lambs (yes/no)
Availability of a dedicated lambing area (yes/no)
Proximity to industrial sites (10 km) (yes/no)
Total grazing land by the farm animals (acres)
Availability of a milking parlor (yes/no)
Variables related to animals in farms
No. of ewes on farms (no.)
Breed of animals (description)
Variables related to production characteristics in farms
Month of the start of the lambing season (description)
Total milk quantity obtained during the preceding milking period (liters)
Average number of lambs born per ewe during the preceding lambing season (no.)
Presence of cats on farm (yes/no)
No. of cats on farm (no.)
Variables related to health management in farms
Common grazing of sheep with wildlife ruminants (yes/no)
Duration of grazing annually (no. of months)
Average age of culling ewes (years)
Application of reproductive management (no hormonal control/administration of melatonin/administration of progestagens)
Collaboration with a veterinarian (yes/no)
Total visits made annually by veterinarians to the farm during the preceding season (no.)
Use of laboratory diagnostic examinations in samples of milk (yes/no)
Administration of oxytetracycline to pregnant animals (yes/no)
Administration of selenium to pregnant animals (yes/no)
Administration of selenium to newborn animals (yes/no)
Source of replacement animals (own animals/purchase)
Daily number of milking sessions (no.)
Use of teat disinfection after milking (yes/no)
Method for drying-off at the end of the lactation period (abrupt/progressive)
Administration of ‘dry-ewe’ treatment at the end of the lactation period (yes/no)
Duration of the dry-period (months)
Newborn care and specific monitoring (yes/no)
Maintenance of a colostrum bank (yes/no)
Lamb fostering to female animals other than their dams (yes/no)
Age for lamb removal from their dams (days)
Administration of milk replacer to lambs (yes/no)
Routine prophylactic administration of antibiotics to newborn lambs (yes/no)
Vaccination against staphylococcal mastitis (yes/no)
Vaccination against contagious agalactia (yes/no)
Vaccination against Chlamydia infection (yes/no)
Vaccination against bacterial respiratory infections (yes/no)
Vaccination against clostridial infections (yes/no)
Annual frequency of systemic disinfections in the farm (no. of occasions)
Variables related to human resources in farms
Age of farmer (years)
Length of previous animal farming experience (years)
Highest general education level achieved (primary/secondary/tertiary)
Farmer by profession (yes/no)
Daily period of presence at the farm (hours)
Family tradition in farming (yes/no)
Presence of working staff at the farm (yes/no)
Variables related to climatic conditions at the locations of farms
Temperature at 2 m for the year preceding the visit (°C)
Temperature of Earth skin for the year preceding the visit (°C)
Minimum temperature at 2 m for the year preceding the visit (°C)
Maximum temperature at 2 m for the year preceding the visit (°C)
Temperature range at 2 m for the year preceding the visit (°C)
Relative humidity at 2 m for the year preceding the visit (%)
Precipitation for the year preceding the visit (kg m−2 s−1)
Wind speed at 10 m for the year preceding the visit (m s−1)

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Climate 12 00175 g001
Figure 1. Map showing the locations of the 325 sheep farms across Greece that were visited during the investigation (red dots: locations of farms, black arrow in top right corner: pointing to the North).
Figure 1. Map showing the locations of the 325 sheep farms across Greece that were visited during the investigation (red dots: locations of farms, black arrow in top right corner: pointing to the North).
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Climate 12 00175 g002
Figure 2. Cross-plot of annual precipitation at farm location and annual incidence rate of abortion in 325 sheep farms in Greece (dashed line is respective trendline).
Figure 2. Cross-plot of annual precipitation at farm location and annual incidence rate of abortion in 325 sheep farms in Greece (dashed line is respective trendline).
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Climate 12 00175 g003
Figure 3. Cross-plot of flock size and annual incidence rate of clinical mastitis in 325 sheep farms in Greece (dashed line is respective trendline).
Figure 3. Cross-plot of flock size and annual incidence rate of clinical mastitis in 325 sheep farms in Greece (dashed line is respective trendline).
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Climate 12 00175 g004
Figure 4. Box and whisker plot of annual incidence rate of lamb pneumonia in 325 sheep farms in Greece, in accord with the availability of a separate barn for lambs (green: yes, grey: no).
Figure 4. Box and whisker plot of annual incidence rate of lamb pneumonia in 325 sheep farms in Greece, in accord with the availability of a separate barn for lambs (green: yes, grey: no).
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Climate 12 00175 g005
Figure 5. Funnel-type graph of the average within-farm incidence rate of lamb pneumonia, in accord with the age when lambs were removed from their dams.
Figure 5. Funnel-type graph of the average within-farm incidence rate of lamb pneumonia, in accord with the age when lambs were removed from their dams.
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Climate 12 00175 g006
Figure 6. Cross-plot of the incidence rate of lamb diarrhoea in 325 sheep farms in Greece, in accord with the annual temperature range at the location of the farms (dashed line is trendline).
Figure 6. Cross-plot of the incidence rate of lamb diarrhoea in 325 sheep farms in Greece, in accord with the annual temperature range at the location of the farms (dashed line is trendline).
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Table 1. Annual incidence rate (95% confidence interval) of clinical cases for clinical problems reported in 325 sheep farms in Greece.
Table 1. Annual incidence rate (95% confidence interval) of clinical cases for clinical problems reported in 325 sheep farms in Greece.
All Flocks (n = 325)Flocks with Intensive or Semi-Intensive Management (n = 184)Flocks with Semi-Extensive or Extensive Management (n = 141)
Abortion2.0% (1.9–2.1%)2.3% (2.1–2.4%) a1.5% (1.4–1.6%) a
Clinical mastitis3.9% (3.8–4.0%)4.0% (3.9–4.2%) a3.7% (3.5–3.9%) a
Lamb pneumonia1.4% (1.3–1.4%)1.5% (1.4–1.5%) a1.2% (1.1–1.3%) a
Lamb diarrhoea7.9% (7.8–8.1%)7.7% (7.6–7.9%) a8.2% (7.8–8.3%) a
a p < 0.04 for comparisons between management systems.
Table 2. Annual median within-farm incidence rate (interquartile range) of clinical cases for clinical problems reported in 325 sheep farms in Greece.
Table 2. Annual median within-farm incidence rate (interquartile range) of clinical cases for clinical problems reported in 325 sheep farms in Greece.
All Flocks (n = 325)25% of Flocks with Higher Incidence Rate (n = 81)
Abortion2.7% (2.7%)4.7% (4.5%)
Clinical mastitis0.0% (4.4%)9.6% (7.6%)
Lamb pneumonia0.0% (0.8%)3.3% (5.8%)
Lamb diarrhoea2.2% (10.0%)22.2% (21.6%)
Table 3. Results of multivariable analysis for predictors for incidence rate of abortion in 325 sheep farms in Greece.
Table 3. Results of multivariable analysis for predictors for incidence rate of abortion in 325 sheep farms in Greece.
VariablesOdds Risk (±se) 1p
Annual precipitation at farm location 0.024
Per unit (kg m−2 s−1) increase1.020 ± 1.0090.024
1 se: standard error.
Table 4. Results of multivariable analysis for predictors for incidence rate of clinical mastitis in 325 sheep farms in Greece.
Table 4. Results of multivariable analysis for predictors for incidence rate of clinical mastitis in 325 sheep farms in Greece.
VariablesOdds Risk (±se) 1p
Number of ewes in flock 0.009
Per unit (animal) decrease1.00006 ± 1.000020.005
1 se: standard error.
Table 5. Results of multivariable analysis for predictors for incidence rate of lamb pneumonia in 325 sheep farms in Greece.
Table 5. Results of multivariable analysis for predictors for incidence rate of lamb pneumonia in 325 sheep farms in Greece.
VariablesOdds Risk (±se) 1p
Availability of separate barn for lambs 0.0003
Yes (0.0% (0.5%)) 2reference-
No (0.0% (2.4%))1.014 ± 1.0040.0008
Proximity of the farm to industrial sites 0.012
Yes (0.0% (2.2%)) 21.013 ± 1.0050.008
No (0.0% (0.5%))reference-
Age of removing lambs from their dam 0.020
Per unit (day) decrease1.0002 ± 1.00010.008
1 se: standard error; 2 median (interquartile range) annual incidence rate among respective farms.
Table 6. Results of multivariable analysis for predictors for incidence rate of lamb diarrhoea in 325 sheep farms in Greece.
Table 6. Results of multivariable analysis for predictors for incidence rate of lamb diarrhoea in 325 sheep farms in Greece.
VariablesOdds Risk (±se) 1p
Average annual temperature range
at farm location		 <0.0001
Per unit (°C) increase1.005 ± 1.0010.0001
1 se: standard error.
Table 7. Proportion of variables found with significant associations with the annual incidence rates of four clinical problems in 325 sheep farms in Greece, in accordance with the category of variables.
Table 7. Proportion of variables found with significant associations with the annual incidence rates of four clinical problems in 325 sheep farms in Greece, in accordance with the category of variables.
Category of Variables AssessedAdultsLambs
Infrastructure0.0%33.3%
Production characteristics0.0%0.0%
Health management practices14.3%8.8%
Human resources7.1%21.4%
Climatic variables6.3% a38.9% a
a p = 0.025 between respective proportions.
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Katsarou, E.I.; Lianou, D.T.; Michael, C.K.; Vasileiou, N.G.C.; Papadopoulos, E.; Petinaki, E.; Fthenakis, G.C. Associations of Climatic Variables with Health Problems in Dairy Sheep Farms in Greece. Climate 2024, 12, 175. https://doi.org/10.3390/cli12110175

AMA Style

Katsarou EI, Lianou DT, Michael CK, Vasileiou NGC, Papadopoulos E, Petinaki E, Fthenakis GC. Associations of Climatic Variables with Health Problems in Dairy Sheep Farms in Greece. Climate. 2024; 12(11):175. https://doi.org/10.3390/cli12110175

Chicago/Turabian Style

Katsarou, Eleni I., Daphne T. Lianou, Charalambia K. Michael, Natalia G. C. Vasileiou, Elias Papadopoulos, Efthymia Petinaki, and George C. Fthenakis. 2024. "Associations of Climatic Variables with Health Problems in Dairy Sheep Farms in Greece" Climate 12, no. 11: 175. https://doi.org/10.3390/cli12110175

APA Style

Katsarou, E. I., Lianou, D. T., Michael, C. K., Vasileiou, N. G. C., Papadopoulos, E., Petinaki, E., & Fthenakis, G. C. (2024). Associations of Climatic Variables with Health Problems in Dairy Sheep Farms in Greece. Climate, 12(11), 175. https://doi.org/10.3390/cli12110175

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