Description and typology of dairy sheep farm management profiles in ...

Small Ruminant Research 164 (2018) 39–47

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Small Ruminant Research journal homepage: www.elsevier.com/locate/smallrumres

Description and typology of dairy sheep farm management profiles in Sardinia

T

⁎

Sandro Rolesua, Federica Loia, , Stefano Cappaia, Annamaria Coccollonea, Mario Cataldib, Piero Usalab, Antonio Poddab, Salvatore Deliperib, Paolo Oppiab, Antonio Nataleb, Alberto Laddomadac, Marino Contub a

Istituto Zooprofilattico Sperimentale della Sardegna, Osservatorio Epidemiologico Veterinario Regionale, Via XX Settembre 9, 09129 Cagliari, Italy Associazione Regionale degli Allevatori della Sardegna (ARA – Sardegna), Via Guido Cavalcanti 8, 09128 Cagliari, Italy c Istituto Zooprofilattico Sperimentale della Sardegna, Via Duca degli Abruzzi 8, 07100 Sassari, Italy b

A R T I C LE I N FO

A B S T R A C T

Keywords: Sheep farming system Sardinia Bluetongue Sheep farming comparison Multiple correspondence analysis Mediterranean livestock

In the current decade, the major risk factors that play a role in bluetongue disease development have been studied, with many of these studies being carried out in order to help understand the spread of the disease. Although these studies have focused on environmental factors and territory features, establishing their role in bluetongue outbreak probability, little attention has been given to the analysis of the characteristics of farms at a micro-level. The main objective of this study was to identify farm management and groups with similar hygiene practice habits, i.e. “farm management”, providing a micro-scale characterization that has not been studied in detail earlier. A total of 5547 Biosecurity Habits questionnaire, based on examining the respect of all suggested practices against bluetongue, was completed by specialized veterinarians or agronomists, each involving a Sardinian sheep farm. Since the biosecurity items were dichotomous, multiple correspondence analysis was performed to summarize a set of categorical variables into a small number of orthogonal variables called principal components, and provide graphical displays. As the first two dimensions of multiple correspondence analysis accounted for more than 50% of the variability between farms, these were retained for the analysis. The first dimension generally defined the good/poor farm management, where good management was particularly associated with large farms. The second dimension was able to explain differences in hygiene conditions. Finally, the association of these gradients with bluetongue was assessed using logistic regression. There was a significant association (p < 0.0001) between the first dimension and the probability of bluetongue at the farm level, showing an important role in generally improving the management to prevent the disease spread. The high hygiene conditions (dimension 2) reveal a strong association (p < 0.0001) in preventing bluetongue, with approximately 1.5 times less probability of disease development as compared to farms with low hygiene and cleaning. This innovative micro-analysis could be an important support for future strategic programs against this vector-borne disease, which to date affects our region.

1. Introduction The bluetongue virus (BTV), an RNA virus belonging to the genus Orbivirus (family Reoviridae), is the cause of the non-contagious, vectorborne bluetongue (BT) disease affecting domestic and wild ruminants, including sheep, cattle, goats, and deer (Maan et al., 2011). Twentynine different BTV serotypes are currently known globally, and the

epidemiology has varied greatly during the years (Lorusso et al., 2016). BT is classified as a notifiable disease by the Office International des Epizooties due to its potential rapid spread and serious economic consequences in the affected countries. To date, BTV has been demonstrated to be transmitted only by the vectors of genus Culicoides (Walton, 2004). Thus, the geographical distribution of the disease coincides with the areas suitable for the species of the Culicoides midge

Abbreviations: SIMAN, animal disease national informative system; BH-questionnaire, biosecurity habits questionnaire; BT, bluetongue; BTV, bluetongue virus; EC, commission regulation; C. imicola, Culicoides imicola; OIE, epizooties international office; VETINFO, informative veterinary system of health minister; ATSS, local health care sanitary agency; EFSA, European food safety authority; MASL, meters above sea level; MCA, multiple correspondence analysis; OR, odds ratio; OEVR, regional veterinary epidemiological observatory; ARASardinia, regional breeders association of sardinia; IZS, istituto zooprofilattico sperimentale ⁎ Corresponding author. E-mail address: [email protected] (F. Loi). https://doi.org/10.1016/j.smallrumres.2018.04.013 Received 5 February 2018; Received in revised form 23 April 2018; Accepted 25 April 2018 Available online 30 April 2018 0921-4488/ © 2018 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).


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Table 1 Overview of vaccination strategies associated to different epidemic season, with type of vaccine and number of vaccinated animals (sheep, goats and bovine), from 2002 to 2017. Serotypes Vaccination season

1

2002 2003 2004

2

4

8

2–4

3.836.228 (LAV) 17.462 (LAV)

2005 2006 2007

1.184.540 (LAV)

2008 2009

187.256 (LAV) 34.162 (IV); 108.971 (LAV)

67.454 (IV); 21.271 (LAV) 29.178 (IV); 10.826 (LAV) 5.071 (IV); 814 (LAV)

46.993 (LAV) 29.013 (IV); 12.554 (LAV) 5.071 (IV); 677 (LAV) 113.399 (IV)

2010 2011 2012 2013 2014 2015 2016 2017

23.817 (IV); 22.278 (LAV) 90.728 (IV); 23.368 (LAV) 170.748 (IV); 12.877 (LAV) 248.250 (IV) 173.603 (IV) 186.185 (IV) 49.697 (IV) 66.246 (IV)

1.776.812 (IV) 2.186.412 (IV) 2.681.646 (IV) 962.313 (IV)

2–4−16

1–2-4

1–8

824 (LAV) 1.066.325 (LAV) 109 (LAV)

7.487 (LAV)

58.906 (IV) 36.645 (IV) 46.323 (IV) 229.863 (IV); 165 (LAV)

1.465.025 (IV)

*LAV indicates type of live attenuate vaccine, IV indicates type of inactivated vaccine.

sheep and goat farms of Sardinia retain the traditional setup, and hence are different from those in the rest of Italy. Since 1941, Le Lannou extensively studied and discussed these features and defined the dairy sheep farming system in Sardinia as pasture-based and extensive; thus, agricultural practices are pushed into the background. The first BT occurrence in Italy is dated back to 2000 (Calistri et al., 2004) in Sardinia (Italy). Due to the spread of BTV-2 and BTV-4 several epidemics have occurred, the last one in 2017 caused 30.979 dead animals (Cappai et al., 2018). Following the EFSA guidelines (European Food Safety Authority, 2008), in Sardinia, farmers have always been encouraged by local veterinarians to follow BT preventive measures, to clean up the land, and avoid the formation of puddles or ponds in order to avoid creating environments favourable to the emergence and development of the insect vector. In fact, as C. imicola is confirmed to be responsible for the BT infection in Sardinia and as wetlands or water reservoirs are the ideal habitat for insect vector reproduction, keeping the soil and surfaces as dry as possible and free of organic material reduces the density of insects (Rolesu et al., 2013). Since 2004, the Sardinia region has initiated a multidisciplinary and integrated program to eradicate bluetongue, where vaccination for the circulating serotypes is associated with direct prophylactic control against the vector. Particularly, different vaccination measures were associated to different epidemic season, as described in Table 1. The Regional Decree (R.D.) 54/7 (December 30, 2013) re-defined and established direct measures against the insect vector to promote the disease prevention, control, and eradication. Article 3.2 of R.D. 54/7 rewards the veterinarians of Local Health Care Sanitary Agency (ATSS) and the agronomist technicians of Regional Breeders Association of Sardinia (ARASardinia) to promote and check the activities against the insect vector. However, large differences exist in terms of the sheep farming system, land use, and intensification level on the island, thus rendering the standardization of the biosecurity measures difficult. These differences basically depend on the geographical location, land availability, soil fertility, and water availability (Pirisi et al., 2001; Vagnoni et al., 2015). The general aim of this study was to define the farm characteristics and biosecurity and sanitary measures that are strongly associated with BToutbreaks, regardless of the serotype. Using multivariate analysis, the farm management conditions associated with exposure to BT in

and characterised by the specific range of rainfall, soil characteristics, photosynthetic activity, wind speed, and temperature (Gibbs and Greiner, 1994; Wittmann and Baylis, 2000; Verwoerd and Erasmus, 2004; Calvete et al., 2008; Tabachnick, 2004, 2010; Maclachlan and Mayo, 2013; Maclachlan and Guthrie, 2010; Cappai et al., 2018). A complete overview of the BTV transmission has been published by Maclachlan and Mayo in 2013, describing the global distribution of the disease since the late 1980s. Subsequent to the epidemics of BTV-8 in 2006, 2007, and 2008, the Commission Regulation (EC) No 1266/ 2007last amended in May 2012, prescribed the implementation of mandatory surveillance systems composed of passive clinical surveillance, sentinel surveillance, and a combination of serological and/or virological surveillance. As many previous studies have demonstrated, BT is associated with different well known risk factors such as temperature and precipitation (Wright et al., 1993; Ward, 1994, 1996; Ward and Thurmond, 1995; Ward and Carpenter, 1996a,b; Conte et al., 2003; Calvete et al., 2008, 2009; Faes et al., 2013; Loi et al., 2017; Cappai et al., 2018), wind (Baylis and Rawlings, 1998; Hendrickx et al., 2008), animal density (Calvete et al., 2009; Allepuz et al., 2010), altitude (Baylis et al., 2001; Conte et al., 2003; Loi et al., 2017; Cappai et al., 2018), and land use (Conte et al., 2007; Durand et al., 2010; Cappai et al., 2018). However, many control strategies are strongly recommended in order to avoid the insect vector reproduction and disease transmission (Carpenter et al., 2008; Maclachlan and Guthrie, 2010). In 2008, the European Food Safety Authority (EFSA), published the scientific opinion of the Panel on Animal Health and Welfare (Question No EFSA-Q-2007-201), which provides strategic guidelines for the urgent strengthening of insect vector control measures, as a key approach to preventing BT disease and responding to epidemics. The BT disease spread always leads to large economic losses, as previously described by Pinior et al. (2015) and Velthuis et al. (2010), including production losses, high mortality in sheep, goat, and cattle, and costs of vaccination programs. The high economic impact of the disease is even more serious in those countries where sheep farming and breeding are among the most important productive resources. Sardinia island has the highest number of sheep and goat farms in Italy with more than 3 million sheep currently; the entire Italy has only five times the sheep of Sardinia (Agriculture Regional Observatory, 2012). In addition, the 40


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Fig. 1. Maps of all the Sardinian sheep and goat farms, assisted by ARA-Sardinia.

2. Material and methods

Sardinia were identified, providing a micro-scale characterization that has not been studied in detail previously. As BT prevention measures are constantly under development and upgradation, this study provides biosecurity patterns and groups of subjects with similar farm management habits and a baseline for subsequent monitoring activities, and guides the application of interventions.

2.1. Study context All the Sardinian sheep and goat farms assisted by ARA-Sardinia, represented in Fig. 1, were included in the study. ARA-Sardinia, with 41


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farm data, (B) species of animals, (C) hygienic conditions, (D) sanitary conditions, (E) management, (F) treatments against insect vectors, (G) vaccination strategies applied, (I) BT outbreaks record. The BH-questionnaire has been developed originally in Italian language; an English language version is shown in Fig. 2.

Table 2 Questionnaire description. Block

Information

(A) Farm data

Year Owner Name Address (Municipality, Location) Farm code in National Database ATS code Latitude Longitude AMSL Number for each species Number of each species in milking General farm condition

(B) Species of animals (C) Farm hygienic condition

(D) Farm sanitary condition (E) Management

(F) Treatments against insect vector

(G) Vaccination strategies

(H) BT outbreaks record

2.3. Statistical analysis The electronic questionnaire provided to veterinarians and agronomists was built using specific web dynamic application (APACHE HTTP server version 2, 2013. The Apache Software Foundation.), and all data collected were stored in an electronic database using a closedresponse data collection instrument (Microsoft Excel, Microsoft Corporation, Redmond, WA), and was password protected. Before proceeding with the statistical analysis, data quality and completeness were tested, and extensive data check and verification were performed to evaluate the correspondence between data collected using the BHquestionnaire and that reported in the animal disease national informative system (SIMAN); the ill-formed variables were identified and corrected. For example, variables with categories less than 5 elements were re-categorized; if two variables belonging to the same block were strongly correlated, one of these was chosen or a combination of both was created. Analysis was carried out at the household level. Information on previous bluetongue episodes was used to construct the variable farm with BT in the last 17 years (POS/NEG). Since analysis of research data requires considerations depending on the type of data collected and/or on the main purpose of the research, preliminary descriptive analyses were performed. Sample data descriptions, based on number and percentage, were performed to explore the farm characteristics and evaluate the distribution at baseline, distinguishing freeBT farms and BT-outbreaks farms. Given the large number of variables collected, in order to pursue the principal aim of this study and propose a possible Sardinian farm management profile, method correspondence analysis has been used, since it allows simplification of complex data and provides a detailed description, yielding a simple and exhaustive analysis. According to BH-questionnaire structure and as descriptive preliminary analyses showed, most of the variables were collected in a categorical method (present/absent). When all the categorical variables are binary, the multiple correspondence analysis (MCA) is equivalent to a principal component analysis of the dummy variables describing the binary categorical variables (Lebart et al., 1984). MCA is a factor analysis method, which summarises a set of categorical variables into a small number of orthogonal variables called principal components (Tenenhaus and Young, 1985; Jobson, 1992). Technically, MCA is obtained by using a standard correspondence analysis on an indicator matrix (Abdi and Valentin, 2007). Its graphical display is used to summarise the proximities between the subjects and provides a structural organization for the variables and categories in a dimensional space that is useful for identifying patterns between dummy variables (Ayele et al., 2014). The contribution of the factor to χ2 statistics is higher with a longer distance from the 0,0 point. Correspondence between two or more values is represented by proximity of the points on the plot representing the values and lack of correspondence is represented by separation. To carry out the analysis, numerical variable were aggregated using the overall median values and establishing sheep as the main bred species as follows: low altitude (MASL ≤ 250); large number of sheep (presence of more than 200 sheep), large number of sheep in the milking period (more than 40 sheep in milking); presence of goats (yes/no), goats in milking period (yes/no); presence of cattle (yes/no), presence of cattle in milking period (yes/no); high mortality (more than 5 dead animals); high incidence of mastitis (more than 2 cases); current vaccination (last vaccination date not older than 8 months). Food distribution site had been re-categorized as 1 = paddock, 2 = pasture, 3 = barn, 4 = milk-room, 5 = two sites, 6 = three sites, 7 = all of these sites, 0 = other sites. All seasonal variables (overnight grazing period, pasture during the night period, light traps

Watering area hygiene (presence of water stagnations, mud and manure, regular cleaning, turnover of the ground) Waste management Flood channels cleaning Wells cleaning Dead animals during the current year Mastitis during the current year Food distribution site (paddock, pasture, barn, milk-room) Overnight grazing Overnight grazing season period Pasture during the night Pasture during the night period Treatment subject (animal, habitat, fold) Use of repellents on animals Use of larvicidal on risk area Number of Light traps Type of trap Trap placement Trap period Recommended vaccination strategy Last vaccination date Animals categories vaccinated (male, female, 1 year old, lambs) Epidemic season Number of infected animals Number of dead animals Number of slaughtered animals Number of vaccinated animals

members hired by Sardinia Region, operates under the general policy and organizational directives of Italian Breeders Association, in harmony with regional agricultural programs. ARA-Sardinia carries out its activity throughout the regional territory in order to promote and implement all regional measures aimed at improving animal productions, encourage studies and research, promote prophylactic actions aimed to fight infectious and implement any other useful initiative for the enhancement of regional animal husbandry. All the included farms were assigned to specialized veterinarians or agronomists and evaluated using different features and each control measures about biosecurity and management system. Each veterinary/agronomy control corresponded to each questionnaire compilation, named Biosecurity Habits questionnaire (BH-questionnaire), the results of which constitute the dataset of the present work. The included farms have been evaluated as places of BT-outbreak, during one of the epidemic seasons that previously occurred in Sardinia (from 2000 to 2017), or as those with historical freedom from BTV, with no BT-outbreaks from the time of the initial Sardinian cases in 2000. 2.2. The biosecurity questionnaire The BH-questionnaire involved the monitoring of direct prophylaxis against BT. The checklist, compiled by the operators during the farm control, consisted of 65 questions divided into 9 blocks (Table 2): (A) 42


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Fig. 2. ARA-questionnaire format.

(Bates et al., 2014) in R software version 3.4.1 (R Development Core Team, 2015).

period) have been re-categorized for none, autumn/winter, spring/ summer, or every year. Block (I), regarding BT epidemic season and number of infected, dead, slaughtered, or vaccinated animals was not entirely included in the MCA, but categorized as bluetongue in the last 17 years (yes/no) and this value was included as a supplementary variable. Supplementary variables do not contribute to the MCA but can be plotted together to provide useful visualization of the distribution of BT along the development gradients (Sourial et al., 2010). Variables were selected using the square cosine test (Lê et al., 2008), and all those with cos2 > 0.2 in at least one dimension were maintained. The association between each variable and the response variable (farm with or without BT in the last 17 years) was tested using the chi-squared test. When the chi-squared test result was not significant (at p-value ≤ 0.2), the variable was excluded. If some variable was known to be generally associated with BT development, different categorizations were attempted and the variable was discarded only if no alternative version attended the selection criterion. However, some variables considered important from the theoretical perspective, such as those associated with BT development, were maintained independently of these selection criteria. To retain the maximum number of MCA dimensions, the following criteria were considered: (a) screen test (Cattell, 1996); (b) Cronbach’s alpha score, considering at least 0.70 as the lower limit for Cronbach’s alpha acceptable (Johnson and Wichern, 2007); (c) eigenvalue inclusion of MCA dimensions with inertia above 0.2 (Hair et al., 1998). Based on those criteria, the first 2 MCA dimensions were maintained: the first accounting for 39.5% (0.177/0.448) of the inertia and the second for 12.6% (0.056/0.448), yielding a total cumulative inertia of 52.1. A mixed logistic regression model was fitted in order to map the probability distribution of bluetongue along the MCA gradients. The model has municipality as a random intercept to control for clustering of farms within localities and three fixed effects for each MCA dimension. Let Yij = report of BT in the last 17 years in farm i, at municipality j. The resultant model is:

3. Results The survey included 5547 Sardinia sheep and goat farms. Of these, 4647 (84%) were only sheep breeding, 438 (8%) bred only goats, and 462 (8%) both of these. No farm that bred only cattle was included, but 1074 (19%) farms bred cattle. At farm level, 740 (13%) reported at least one episode of bluetongue in the last 17 years. Table 3 shows the chi-squared association between the surveyed variables and the report of BT by the Sardinian farms. The variables most associated were the low altitude, large number of sheep, presence of cattle, flood channel cleaning, wells cleaning, high mortality, overnight grazing and its season, pasture during the night and its season, use of repellents on animals, and vaccination strategies. From the MCA analysis, a two-dimension MCA solution was considered the most adequate. The first two MCA dimensions were, eigenvalues of 0.487 and 0.226; inertia of 0.177 and 0.056; Cronbach’s alpha of 0.709 (CI 95% = [0.699-0.735]) and 0.696 (CI 95% = [0.665-0.726]) (Fig. 3). These two dimensions were retained for the analysis. Contributions measure (Table 4) and a joint plot representing the distribution of Sardinian farms along the first two MCA axes (Fig. 4) were obtained. In the MCA plot, the origin represents the farm’s average and the dispersion around it indicates how they differ in relation to this average. The most discriminant variables for dimension 1 were the number of animals, food site of distribution, overnight grazing, and all treatment and prevention practices. Sardinian farms did not spread as much along the second dimension, differed mainly in their general hygiene conditions, presence of water stagnations, mud and stool, cleaning, and waste management (Table 4 and Fig. 4). At the high end of the second axis, farms were characterized by good waste management and practiced turnover of the ground. Night pasture contributed to the development of both dimensions. At the low end of the second axis were farms with low hygiene condition and presence of mud and stool. From this feature and their graphical visualization, dimension 1 was termed “General Management and Prevention” and the dimension 2 was “Hygiene”.

logit (E (Yij )) = β0 + β1 dim1 + β2 dim2 + δj where β0 is the intercept, β1, β2, and β3 are the fixed effects of MCA dimensions, and δj is the random effect representing the dispersion among farms from the same municipality. All analyses were performed using the FactoMineR package (Lê et al., 2008) and the lme4 package 43


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Table 3 Summary of the variables characterizing Sardinian farms, organized by block, and the result of the chi-squared test for differences in distribution, between farms with (BT-case) and without Bluetongue (BT-free), in the last 17 years. Block description

Variables

Category

N (%)

X2 BT

(A) Farm data

Low altitude (MASL ≤ 250 m)a Large N° of sheepa

3031 (55) 2516 (45) 3216 (58) 2331 (42) 2762 (49) 2785 (51) 727 (13) 4820 (87) 351 (6) 5196 (94) 1073 (20) 4474 (80) 79 (1.5) 5468 (98.5) 146 (3) 2121 (45) 2473 (52) 782 (14) 4765 (86) 782 (14) 4765 (86) 4475 (80) 1072 (20) 1224 (22) 4323 (78) 316 (8) 2607 (62) 1257 (30) 2170 (39) 3377 (61) 1806 (32) 3741 (68) 2934 (53%) 2613 (47) 2638 (48%) 2909 (52%) 75 (1.5) 195 (3.5) 340 (6) 404 (7) 2182 (39) 1396 (25) 545 (10) 410 (7) 4205 (76) 1513 (27) 2853 (52) 770 (14) 393 (7) 1513 (27) 4346 (78) 1201 (22) 31 (0.5) 3559 (64) 756 (13.5) 1201 (22) 550 (10) 1170 (21) 879 (16) 2948 (53) 1234 (22) 4313 (78) 984 (18) 4563 (82) 5388 (97) 159 (3) 95 (60) 64 (40) 117 (73) 19 (12) 23 (15) 19 (12) 7 (5) 16 (10) 117 (73)

++

(B) Species of animals

Yes No Yes No Yes No Yes No Yes No Yes No Yes No Low Satisfactory Good Yes No Yes No Yes No Yes No Low Satisfactory Good Yes No Yes No Yes No Yes No Paddock Pasture Barn Milk-room 2 sites 3 sites All of these sites Other sites Yes No Autumn/Winter Spring/Summer Every time None Yes No Autumn/Winter Spring/Summer Each season None Animal Habitat Fold None Yes No Yes No None At least one Ultraviolet light White light Inside Outside Both of them Autumn Winter Spring Summer

Large N° of sheep in milking period

a

Presence of goats Presence of goats in milking period Presence of cattlea Presence of cattle in milking period (C) Hygienic condition

General farm conditiona

Watering area hygiene − presence of water stagnationsa Watering area hygiene − presence of mud and stool Watering area hygiene − regular cleaninga Watering area hygiene − turnover of the grounda a

Waste management

Flood channel cleaninga Wells cleaninga (D) Sanitary condition

High mortality High incidence of mastitis

(E) Management

Food distribution sitea

Overnight grazinga Overnight grazing season perioda

Pasture during the nighta Pasture during the night perioda

(F) Treatments against insect vector

Treatment subjecta

Use of repellents on animalsa a

Use of larvicidal on risk area Number of light trapa Type of traps Traps Placement

Traps Period

44

a

++ − + − ++ − −

+ − − − −

++ ++ ++ − +

++ ++

++ ++

−

++ + − − −

+

(continued on next page)


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Table 3 (continued) Block description

Variables

Category

N (%)

X2 BT

(G) Vaccination strategies

Recommended vaccination strategya

Yes No Yes No

3178 2369 2107 3439

++

Current vaccinationa

a

(57) (43) (38) (62)

++

Variables in the final model.

Table 4 Relative contributions of each variables to the two first dimensions of the MCA. Block description

(B) Species of animals

(C) Hygienic condition

Fig. 3. Plot of the MCA eigenvalues (black circles) and cumulative percentage of inertia (grey squares).

3.1. Bluetongue distribution along the two MCA dimensions (E)

The evaluation of distribution of farms with and without BT in the last 17 years in the two MCA dimensions, does not shows any BT clusters on specific region. According to the mixed logistic regression model results, the odds of observing a farm with BT outbreaks increased significantly along the first MCA dimension, which describes the general management and preventive measures such as vaccination, overnight grazing, pasture conditions (OR = 2.49, IC 95% [1.44–4.30], p < 0.0001). Along the second dimension, there was a significant positive effect (OR = 1.34, IC 95% [1.11–1.83], p = .05), with the odds of BT spread increasing in farms with low hygiene conditions. Variation in BT occurrence probability was lower in comparison with the first gradient (Table 5).

Variables

Dim 1

Dim 2

Contr.

R2

Contr.

R2

Large N° of sheep_y

1.05

−0.13

0.003

−

Large N° of sheep_n Cattle_y Cattle_n Farm condition_low

1.45 0.75 0.81 0.85

0.13 −0.21 0.21 0.31

0.005 0.008 0.003 5.77

− − − −0.59

Farm condition_mid Farm condition_good Water stagnations_y Water stagnations_n Mud and stool_y Mud and stool_n Regular cleaning_y Regular cleaning_n Turn–ground_y Turn–ground_n Waste manag_low Waste manag_mid Waste manag_good Flood ch. clean_y Flood ch. clean_n Wells clean_y Wells clean_n Food 1_site

0.44 2.50 0.17 0.03 0.45 0.07 1.12 4.70 2.71 0.77 0.69 0.61 3.10 6.28 4.03 6.59 3.19 0.99

−0.07 −0.35 −0.007 0.007 −0.02 0.02 −0.22 0.22 −0.18 0.18 0.22 −0.16 −0.38 −0.27 0.27 −0.36 0.36 0.17

2.46 1.62 8.52 1.40 8.61 1.41 0.70 0.16 1.56 0.44 8.03 0.46 3.25 3.10 1.98 4.35 2.11 0.02

0.02 0.24 −0.23 0.23 −0.23 0.23 0.06 −0.06 0.09 −0.09 −0.49 0.09 0.29 0.11 −0.11 0.14 −0.14 −

Food 2_site Food 3_site Food 4_site Overnight_grazing_y Overnight_grazing_n Overnight_grazing_aut/win Overnight_grazing_spr/sum Overnight_grazing_ever Overnight_grazing_none Night_pasture_y Night_pasture_n Night_pasture_aut/win Night_pasture_spr/sum Night_pasture_ever Night_pasture_none Trt_animal

0.03 1.87 0.74 3.22 10.1 2.27 0.72 0.17 9.87 1.26 4.56 0.01 2.99 1.23 4.47 0.01

−0.05 −0.26 0.12 −0.35 0.35 −0.18 −0.20 −0.14 0.52 −0.24 0.24 −0.15 −0.26 0.17 0.28 0.11

0.30 0.10 1.80 1.69 0.54 1.61 0.005 1.23 1.68 3.35 1.21 0.03 2.68 0.61 1.20 0.05

−0.06 −0.14 0.14 −0.09 0.09 −0.15 − 0.14 0.07 −0.24 0.24 − −0.12 −0.12 0.36 −0.04

Trt_habitat Trt_fold Trt_none Repellents on animals_y Repellents on animals_n Larvicidal on area_y Larvicidal on area_n Light trap_none Light trap_at least one Vaccination_y

0.99 0.91 1.18 0.39 0.11 1.35 0.29 0.01 0.38 0.71

−0.17 −0.10 0.19 −0.07 0.07 −0.14 0.07 0.16 −0.16 0.11

0.28 0.22 0.03 0.02 0.005 0.03 0.006 0.009 0.31 0.88

0.08 −0.14 0.01 − − 0.01 −0.01 −0.09 0.09 −0.07

Vaccination_n

0.96

−0.11

1.19

0.07

Management

4. Discussion (G) Treatments against insect vector

Although the effect of well-known farm characteristics and environmental risk factors (such as number of sheep, rainfall, wind, temperature) on the development of disease have been investigated in many studies, little attention has been given to the effect of overall management and hygienic conditions of the farms. Using data provided by the ARA-questionnaire completed by ARA veterinarians/agronomists, applying a MCA, we identified management and hygiene patterns, and looked for groups of farms with similar management habits, in order to define “bluetongue profiles”. MCA proves to be a relevant method of data analysis when an exploratory or even more in-depth analysis of categorical data is required, making it a particularly useful technique as it (i) is versatile, in part because no underlying distributional assumptions are required; (ii) provides a graphical output for representing the associations between the variables in a low-dimensional space, thus providing key exploratory insights on the

(H) Vaccination strategies

45


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Fig. 4. MCA plot showing the joint distribution of the categories in dimensions 1 × 2 on the factor map in the final model.

sites. The two developmental dimensions were strongly associated with the BT outbreak probability. Evidence of this relationship is necessary to elucidate whether particular farm characteristics belong to the construct of stronger as against poorer biosecurity performance, based on which further indications on BT safety can be provided by the competent authorities. Characterizing the micro-characteristics of the Sardinian farms where BT outbreaks occurred is essential for a better understanding of the factors that maintain transmission and to design adequate control strategies. This is the first study, which takes into account micro-management characteristics of sheep farms in relation to BT development, detecting a higher risk of disease in all the small and poorly managed farms with poor hygiene conditions. A previous macroanalysis lead on more than 12,000 farms in the same region revealed the fundamental role of some environmental risk factors such as meteorological features and certain characteristics of farm territory. The place and altimetry of the farm location represents a risk factor for BT, such as rainfall, amount of water, number of sheep, vaccination strategy (Cappai et al., 2018). The next goal of our project will be the integration of our predictive models with the two development dimensions found in the present study, in order to refine the model and all predictive maps provided every 10 days by our research organization (Osservatorio Epidemiologico Veterinario Regionale – OEVR), available at: http://www.izs-sardegna.it/oev_RischioBT.cfm. The refinement of this model with specific farm characteristics at the micro-level will provide further practical information on the action required to guarantee BT prevention and control, in the hope of staving off large BT spread and development.

Table 5 Results of mixed logistic regression model, represented as Odds ratio (OR), Confidence Intervals at 95% (IC 95%) and p-value, of having farm with bluetongue outbreaks along the two gradients derived from the MCA. Variable

OR

IC 95%

p-value

General Management and Prevention (Dim1) Hygiene (Dim2)

2.49 1.34

1.44–4.30 1.11–1.83

< 0.0001 0.005

relationships between the collected data; (iii) can be used in pair with other methods such as multidimensional analysis (Greenacre and Hastie, 1987; Hair et al., 1998; Tabachnick, 2010; Johnson and Wichern, 2007). From the Sardinian farm data analysis, and its graphical representation, two MCA dimensions of development, termed “General Management and Prevention” and “Hygiene”, characterizing different axes of variation, were identified. The first dimension describes an index of practical management, with well managed farms at the low end of the scale (preventive treatments on habits, no overnight grazing, use of larvicidal and light traps), while the ones at the high end were poorly managed. At the farm level, greater management was associated with a large farm size and animal’s productions (represented by sheep in milk), in agreement with the main findings of the Vagnoni study (2015). The second dimension found in the study describes the different farm conditions in terms of hygiene measures. It emphasizes the differences in hygiene measures and practices assessed by the owners of farms (flood and well cleaning, removal of mud and manure, turnover of the ground) that should therefore drastically reduce the population of C. imicola, as indicated by EFSA (2008). However, as specified in the same document, to date no quantitative evidence of the relation between specific practices and C. imicola reduction has been provided. Inferences could be drawn from the fact that manure removal from animal yards remarkably reduces the numbers of houseflies and it is reasonable to assume that it will also reduce the dung-breeding Culicoides and species that develop in dung contaminated development

Conflict of interest The authors declare no conflict of interest.

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Acknowledgement

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