Gastrointestinal infection in goat farms in Lombardy (Northern Italy): Analysis on community and...

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Small Ruminant Research 88 (2010) 102–112

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Small Ruminant Research

journa l homepage: www.e lsev ier .com/ locate /smal l rumres

Gastrointestinal infection in goat farms in Lombardy (Northern Italy):Analysis on community and spatial distribution of parasites�

Anna Rita Di Cerboa, Maria Teresa Manfredia,∗, Sergio Zanzania, Katia Stradiottob

a Dipartimento Patologia Animale, Igiene e Sanità Pubblica Veterinaria, Sez. Patologia Generale Veterinaria e Parassitologia, Università degli Studi di Milano,via Celoria 10, 20133 Milan, Italyb Servizio di Assistenza Tecnica (SATA), Sezione Ovini e Caprini, Associazione Regionale degli Allevatori della Lombardia, Crema, Italy

a r t i c l e i n f o

Keywords:GoatGastrointestinal parasitesLombardyGIS

a b s t r a c t

We conducted a study with the aim of setting out the composition of gastrointestinal par-asite communities within the goat farm system in Lombardy, a region in northern Italy. AGIS was set and all the goat farms in the study area were geo-referenced. Our investiga-tion for gastrointestinal parasite infections involved 110 farms where 2554 individual perrectum faecal samples were collected and tested. Almost all the examined goats resultedto be positive to parasites (P = 96%, 95% CI: 95.1–96.7%). The taxa identified are Monieziabenedeni, Strongyloides spp., Strongylida, Nematodirus spp., Skrjabinema spp., Trichuris spp.,Capillaria spp., Marshallagia spp. and Eimeria spp. Co-infection supported by more than asingle parasite was observed in almost all the farms; goats could even harbour 6 taxa simul-taneously. A highly positive correlation emerged between the number of parasite taxa perfarm and pasture as well as extensive breeding (both showing p < 0.001). When consideringour data spatially, we found a clustered pattern distribution of M. benedeni, Strongylida, Skr-jabinema while Strongyloides spp., Nematodirus spp., Trichuris spp. and Eimeria spp. resultedto be randomly distributed among the farms. We also assessed spatially the positive rela-tionship of M. benedeni, Trichuris spp. and Strongylida to the pasture. As to Nematodirus, thistaxon showed to be particularly linked to altitude. Assessing the widespread distributionof gastrointestinal parasites in the goat farms of Lombardy, was of particular interest to ourstudy. In fact, even if the parasitic risk mainly derives from pasture, in some areas the roleof goats as biological control agents for the renovation of pastures is getting more and moreimportant. Nevertheless, several autochthonous goat breeds can survive in different areasof both western and eastern Lombardy where farming on extensive pasture is still carriedout. In order to support the smallest farmers with keeping such breeding, we think that thecontrol of parasites should be emphasized in these farms. Eradication of gastrointestinalparasites from the environment is generally impractical; though, infections can be limited,and control programmes should mainly minimize the deriving economic losses. For suchpurpose, we would suggest a regional project of permanent sanitary monitoring of goatfarms all over Lombardy.

© 2010 Elsevier B.V. All rights reserved.

� This paper is part of a special issue entitled “SIPAOC Congress 2008”Guest Edited by Antonello Carta, Giovanni Garippa and Giuseppe Moniello.

∗ Corresponding author. Tel.: +39 02 50318098; fax: +39 02 50318095.E-mail address: [email protected] (M.T. Manfredi).

1. Introduction

The goat (Capra hircus) is among the earliest speciesthat have been domesticated since about 9000 B.C. (Peterset al., 1999); at present it is distributed worldwide withhigher concentrations in tropical areas, in dry zones and

0921-4488/$ – see front matter © 2010 Elsevier B.V. All rights reserved.doi:10.1016/j.smallrumres.2009.12.017


A.R.D. Cerbo et al. / Small Ruminant Research 88 (2010) 102–112 103

Fig. 1. Choroplet map on the abundance of goat farms according to GRIDED/the grid zone (10 km × 10 km of length) and distribution of the examined farms(dots) within the studied provinces.

in developing countries. In 2004, over 743 million goatswere estimated to be present in the world. Asia, Pacificareas, and Africa harbour up to 75% of their world popu-lation. Europe houses only 4% of the total amount, but ithas the largest share of goat genetic resources (33%) andproduces about 18% of the world goat milk (Galan, 2005;Dubeuf, 2007). In northern European countries, the goatindustry has developed mainly in the Netherlands and inNorway. In Great Britain, Belgium, and Germany the dairygoat sector is more limited. It is in the southern and east-ern European countries that the goat industry producesmore than 80% of the total European yields (Dubeuf, 2007).In the Mediterranean basin, the major productive country

is Greece (5,000,000 goats) followed by Spain (3,046,716goats), Italy (1,330,000 goats) and France (1,214,276 goats)(FAOSTAT, data 2004). The Mediterranean farming systemrelated to small ruminant shares some specific character-istics, such as: utilization of marginal lands, prevalenceof pastoral system, availability of commonage, produc-tion of typical cheeses and a low level of mechanization(Abecia, 2008; Santos-Silva and Carolino, 2008). Nonethe-less, in the last 30 years as to milk production it has beenhighlighted a progressive decline of the traditional pas-toral system substituted by a slow evolution towards moreintensive systems (Ronchi and Nardone, 2003). In centraland southern Italy, the goat farm system plays an impor-

Fig. 2. Climatic maps of Lombardy referring to the year 2005 (A and B) (data source APAT: www.scia.sinanet.apat.it/home.asp). (A) Isopleths map on annualmean temperature and (B) Isopleths map on annual mean relative humidity.


104 A.R.D. Cerbo et al. / Small Ruminant Research 88 (2010) 102–112

tant economic role. The island of Sardinia holds the nationalleadership with its total population of about 300,000 goats(Garippa et al., 2008). Lombardy region hosts 53,575 goatsbred in 3386 farms representing about 38% of the totalamount of goats living in the north of the country. In thelast 35 years the total number of goats has increased by3.3% here (Istat, data 2005). Nevertheless, the sector hasbeen significantly less supported than other zootechnicalactivities. Particularly, the monitoring of parasitic diseaseappears not proportionate to the expansion of goat farmsin northern Italy. In Lombardy, only few data are availableon the occurrence of gastrointestinal parasites and on theirreal impact on milk and meat production in small rumi-nant farms. Moreover, they are not updated or limited tosmall marginal areas (Genchi et al., 1984, 1985; Traldi andFranchi, 1987; Attili et al., 2004; Di Cerbo et al., 2006). Inconducting the present investigation within the goat farmsystem in Lombardy, we aimed mainly to (i) set out thecomposition of gastrointestinal parasite community and(ii) perform a descriptive analysis of the spatial distributionof parasitic infections using a GIS approach.

2. Materials and methods

2.1. Sampling, GIS construction and spatial statistics

The survey was carried out from June 2005 to June 2006 in the north-ern Italian region of Lombardy (latitude: 45◦40′N; longitude: 9◦30′E). Ourstudy area mainly included its most suitable provinces as to goat breeding,i.e. are Bergamo (BG), Brescia (BS), Como (CO), Lecco (LC), Sondrio (SO),and Varese (VA). In addition, it also considered a group of farms locatedin the province of Pavia (PV), southernmost of Lombardy. A geographi-cal information system (GIS) was set using the administrative boundariesof Lombardy at both provincial and municipal levels (Dati Geografici diSintesi of Lombardy Region, vers. 1.2, 2003). So, all the goat farms (3086)located in the study area resulted to be geo-referenced. For this purpose,a grid of 10 km × 10 km in length was set up where farms included ineach zone could be counted to see the areas most densely populated bygoats (Fig. 1). All goat farms (3086) located in the study area were geo-referenced. A grid of 10 km × 10 km in length was constructed and thefarms included in each grid zone were counted to visualize which areaswere more densely populated by goats (Fig. 1). From the regional databaseour sampling units (110 farms) were selected, mainly farms members ofSATA, a regional association offering technical support to breeders. In theGIS we also included the locations (2558) of all sheep farms. To assign themtopographical attributes, we used thematic layers such as “land use” and“altitude”. To assess the potential risk of parasite infection for small rumi-nants other than goats living near the farms under consideration, a bufferzone with a radius of 1.3 km was set up around each goat farm, and thenboth goat and sheep farms falling inside the buffer zones were counted.The length of the radius represents the mean pasture area of the farmswhere grazing animals were present.

Spatial distribution of farms and parasites was investigated by thePattern Point Analysis (PPA). A preliminary neighbourhood analysis wasperformed to explore the spatial distribution of the studied farms. Particu-larly, Average Nearest Neighbour Distance measures the distance betweeneach feature centroid and its nearest neighbour’s centroid location andaverages all these neighbour distances. If average distance among thefarms is less than the average for a hypothetical random distribution,the distribution is considered clustered (ratio of observed/expected dis-tance < 1), whereas if the observed distance is greater than the expecteddistance (ratio of observed/expected distance > 1), the distribution isdefined dispersed. A spatial autocorrelation with local Moran’s Index (I)was also performed to describe patterns of locations of parasites and totest whether there was a significant occurrence of clustering of pointevents in the study area. Local Moran’s I measures spatial autocorrelationbased both on feature locations and attribute values (parasites) simulta-neously. Given a set of features (i.e. farms) and an associate attribute (i.e.mean eggs excretion of a certain parasite), it evaluates whether the pat- Ta

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Table 2Summarized statistics on distribution of parasites in the sampling units (110 farms).

Parasite Index Mean ± SD Min–max Skewness Kurtosis 1st quartile Median 3rd quartile

M. benedeni A – – – – – – –P (%) 8.37 ± 14.26 0–58.33 1.81 5.31 0 0 11.43

Strongyloides A 9.32 ± 20.92 0–131.81 3.76 18.87 0 1.5 7.94P (%) 15.46 ± 21.39 0–84.62 1.82 5.71 0 7.59 23.08

Strongylida A 212.77 ± 505.07 0–2984.8 3.39 14.93 0 17.17 137.04P (%) 39.66 ± 40.01 0–100 0.33 1.39 0 20.55 82.72

Nematodirus A 1.88 ± 6.18 0–48.36 5.34 35.35 0 0 1.08P (%) 11.85 ± 20.52 0–80 1.97 5.99 0 0 14.3

Skrjabinema A – – – – – – –P (%) 24.41 ± 23.18 0–87.5 0.90 3.09 5 20 39.39

Trichuris A 6.79 ± 12.18 0–59 2.75 10.44 0 2.22 7.15P (%) 12.12 ± 15.44 0–70 1.68 5.58 0 6.67 18.18

Capillaria A 0.17 ± 0.65 0–4.76 4.64 27.10 0 0 0P (%) 0.54 ± 2.34 0–14.29 4.78 25.90 0 0 0

Marshallagia A 0 0 0 0 0 0 0P (%) 0.07 ± 0.76 0–8 10.34 108.01 0 0 0

Eimeria A – – – – – – –P (%) 91.94 ± 17.26 8.33–100 −3.18 13.29 92 100 100

Total A 230.94 ± 503 0–2990.4 3.36 14.80 7.7 43.03 161.99P (%) 92.03 ± 23.55 0–100 −3.41 13.28 97.44 100 100

A: Mean abundance; P: prevalence; SD: standard deviation.

Fig. 3. Quantitative map on prevalence of Moniezia benedeni and Nematodirus spp. in the Lombard farms.



Fig. 4. Quantitative map on prevalence of Strongylida and Trichuris spp. in the Lombard farms.

Fig. 5. Quantitative map on prevalence of Skrjabinema spp. and Strongyloides spp. in the Lombard farms.



Table 3Spatial autocorrelation (Moran’s I) on prevalence of parasites from goat farms.

Parasite Moran’s I Variance Z-score p-Value Pattern

M. benedeni 0.0765 0.00055 3.663 ** ClusteredStrongyloides 0.0198 0.00054 1.243 n.s. RandomStrongylida 0.1200 0.00057 5.421 ** ClusteredNematodirus −0.0148 0.00054 −0.243 n.s. RandomSkrjabinema 0.0515 0.00056 2.567 * ClusteredTrichuris 0.0093 0.00054 0.791 n.s. RandomEimeria −0.0044 0.00050 0.211 n.s. RandomTotal prevalence 0.1488 0.00055 0.791 ** Clustered

* p < 0.05.** p < 0.01.

tern expressed is clustered (value of Moran’s I near +1.0), dispersed (valueof Moran’s I near −1.0), or random.

Furthermore, a non-parametric density estimator, the kernel den-sity estimation, was used to create maps on parasite density byweighing the farms by the mean eggs excretion (Gatrell et al.,1996). Climatic data on Lombardy were consulted in the SCIA (Sis-tema nazionale raccolta, elaborazione, diffusione dati climatologici diinteresse ambientale), a website created by APAT (Agenzia per la Pro-tezione dell’Ambiente e per i Servizi Tecnici). In particular, data onannual mean, minimum and maximum temperatures and mean rel-ative humidity referring to the year 2005 were queried and mapswere created (http://www.scia.sinanet.apat.it/sciaweb/scia mappe.html)(Fig. 2).

2.2. Parasitological analyses

Faecal samples from 2554 females were taken directly from the rec-tum before anthelminthic treatments. Flocks were not treated at leastduring the previous two months. Qualitative and quantitative parasitolog-ical exams were performed by flotation techniques following the standardprocedures. The former analysis was achieved to calculate prevalence inthe farm by considering data on the presence/absence of each parasite inindividual goats. The identification of eggs was up to genus or order leveland to species only for cestode. Eimeria spp. infection was only evalu-ated by qualitative analysis. The individual faecal eggs counts (FECs) wereperformed using a modified McMaster method (MAAF, 1986; Raynaud,1970) utilizing a sodium nitrate and sucrose-based flotation solution (den-sity = 1.300) and the epg (eggs per gram) were calculated for all nematodesbut for Skrjabinema and the cestode Moniezia.

2.3. Statistical analyses

The epidemiological indices were calculated for each parasite accord-ing to Bush et al. (2001). Mean abundance (A) was considered as thearithmetic mean value of epg of the entire goat population or of examinedgoats in a farm (mean abundance in the farms), including both positiveand negative animals. Mean intensity (I) was calculated as the arithmeticmean of epg of the only infected animals. According to Ròzsa et al. (2000),in order to give a more detailed characterization of parasite distribution,standard deviation (SD) and percentiles were also calculated. Confidencelevel was held at 95% and p < 0.05 was set for significance. Geodatabasewere exported in the software SPSS (vers. 16.0) and analysed statistically.

3. Results

Examined goat farms showed a clustered pattern (aver-age nearest neighbour test: observed mean distance:5293.73 m, expected mean distance: 6750.98 m, nearestneighbour ratio = 0.78; Z-score = −4.33 standard devia-tions; p < 0.01). The surrounding territories of the farmswere identified on the map as cultivated and pasture areas(30% and 25.4% of farms, respectively), deciduous forest(21.8%), urban areas (9.1%), coniferous and other type offorest or uncultivated areas (14.6%) (Table 1). No relation-

ship statistically significant between size category of farmsand land use was found (Kruskal–Wallis test: p > 0.05).

Almost all the examined goats were positive to theparasitological exams (P = 96%, 95% CI: 95.1–96.7%). The fol-lowing gastrointestinal parasites were identified Monieziabenedeni, Strongyloides spp., Strongylida, Nematodirus spp.,Skrjabinema spp., Trichuris spp., Capillaria spp., Marshalla-gia spp. and the coccidian Eimeria spp. The distribution ofgastrointestinal parasites was assessed according to thesampling units (110 farms). The statistics are summarizedin Table 2.

The protozoan Eimeria was widespread in the study areawith prevalence in the farm varying between 8.3% and100%. Skrjabinema, Strongylida and M. benedeni showed anuneven distribution (Kruskal–Wallis test: p < 0.01, p < 0.01and p < 0.05, respectively), with higher prevalence val-ues in the western provinces of Lecco, Varese and Como(Figs. 3–5).

Co-infection due to several gastrointestinal par-asite taxa was observed in almost all the farmswith goats harbouring up to 6 taxa simultaneously(mean ± SD = 1.99 ± 1.04, median = 2.0; variance = 1.08). Adetailed representation related to the distribution ofthe parasite richness within the study area is shownin Fig. 6. Differences were found among the provinces(Kruskal–Wallis test: p < 0.001). Farms showing goats withhigher parasite richness were mainly located in the north-ern portion of the study area.

The maximum number of parasite taxa per farm was cal-culated and its frequency of distribution was investigatedin the study area (Fig. 7).

A highly positive correlation emerged between thenumber of parasite taxa per farm and pasture as wellas extensive breeding (Spearman correlation test: bothp < 0.001). Considering these data spatially, a clustered pat-tern distribution resulted by the Moran’s Index (I = 0.0673;p < 0.01). As to the single parasite, an aggregate distribu-tion of M. benedeni, Strongylida, Skrjabinema was found,too. Whereas, Strongyloides spp., Nematodirus spp., Trichurisspp. and Eimeria spp. were randomly distributed among thefarms (Table 3).

Furthermore, a mapping of distributional trend ofparasites was performed to create new feature classes con-taining elliptical polygons of parasite distribution whichcover approximately 68% of all imputed feature centroidsof farm locations (1 standard deviation) in order to identifythe core area and the directional distribution of each par-



Fig. 6. Parasite richness within the farms and at a province level. (A) The bars represent the number of goats belonging to each number of taxa categorynormalized by the total number of animals examined in that farm. (B) The box-plot showing the median, 1st and 3rd quartiles, minimum and maximumvalues of the distribution of parasite richness (number of taxa per goat) according to the province.

Table 4Measures (length and surface) of elliptic polygons of parasites and pasture core areas.

Variable XstdDist (km) YstdDist (km) Rotation Area (km2) Overlap. area (km2) %

Trichuris 55.88 26.88 101.67 4718.89 3199.73 67.81Skrjabinema 52.09 30.18 102.71 4939.79 2990.25 60.53Strongyloides 46.75 38.90 117.03 5712.91 2725.80 47.71Nematodirus 51.69 31.48 101.77 5111.78 2996.87 58.63Strongylida 58.72 29.20 94.56 5385.62 3503.84 65.06M. benedeni 49.17 20.87 95.31 3223.07 2786.13 86.44Pasture 54.40 20.50 92.60 3503.84 – –

XstdDist: standard distance in the X direction; YstdDist: standard distance in the Y direction.



Fig. 7. Frequency distribution of the parasite richness per farm, with summary statistics.

asite. The X and Y standard distances (the half length of Xand Y axes, respectively) and rotation (that is the rotationof the long axis measured clockwise from noon) were cal-culated. The same procedure was applied to the pasture byconsidering data on the presence/absence of grazing in thestudied farms (Fig. 8). Polygon of each parasite was super-imposed to the pasture ellipse and the overlapping areawas calculated (Table 4). Strongyloides ellipse covered thelargest surface on the whole, and it showed a scarce over-lapping with the pasture zone (47.7%). On the contrary, M.benedeni was the parasite most related to the grazing area(above 86.4% of its area was superimposed to the pasture

ellipse), followed by Trichuris spp. and Strongylida (67.8%and 65.06% respectively). The core area of Nematodirusellipse included the 58.6% of the pasture ellipse, extend-ing more along the north-south axis (Y standard distance:31.48 km) and in the eastern sector of the study area.

As to the quantitative analyses, the best contribution tothe parasitic infection of goats in the farms was given byStrongyloides and the group of Strongylida (Table 2). Ker-nel density maps of these parasites were created to showwhere they were more concentrated spatially. Graphically,a percent volume contour represents the boundary of thearea that contains x% of the volume of a probability density

Fig. 8. Prevalence direction of parasites and pasture core areas.



Fig. 9. Kernel density maps of Strongylida (left) and Strongyloides spp. (right).

distribution. For instance, the 95% volume contour should,on average, contain 95% of the points used to generate thekernel density estimate (Fig. 9).

The positive relationship of some parasites to thepasture was spatially assessed (Fig. 4). Thus, the buffergeneration was used to construct circular buffer zonesof 1.3 km radius around each studied farm to locate spa-tially areas at major risk of parasite infection. Particularly,farms with pasture and positive to Strongylida (42) andM. benedeni (29) were considered as possible source ofegg contamination. The 5.2% (160/3086) of goat and 5.4%(138/2558) of sheep farms fell into the buffer zones positiveto Strongylida, 3.3% (103/3086) of goat and 3.2% (83/2558)of sheep farms were located nearby a farm positive to M.benedeni (at a linear distance < 1.3 km). Unfortunately, nodata are available on the presence of parasites in thesefarms; thereby no further analysis was performed. How-ever, in this preliminary phase, the buffer generation wasuseful to identify which areas can be at major risk of infec-tion, a further, more focused monitoring. Differences werefound among the provinces (Kurskal–Wallis test, p < 0.01).Particularly, Brescia province included more farms at riskof infection by Strongylida than the other provinces.

4. Discussion

Our survey assessed that all the studied farms werepositive to one or more taxa of parasites. The ubiquitar-ious presence of coccidians in almost all farms, with aninfection level up to 100% observed in many flocks, isremarkable because infections by Eimeria can have seri-ous clinical signs and acute infections with loss of animalshave been recorded in a few farms, during the study period.On the other hand, coccidiosis produced by Eimeria speciesis one of the most ubiquitous and spread enteric diseasesof goats worldwide, being reported in a large number ofregions and countries of Europe, Africa, Asia and America(Ruiz et al., 2006). However, the high prevalence valuesof Eimeria spp. recorded in Lombard farms were simi-lar to other data collected in insular Italy (Scala, 2008).Furthermore, a positive correlation between the number

of parasite taxa and pasture was found. M. benedeni andNematodirus spp. are also typical parasites of grazing rumi-nants. In the present survey, the cestode was confirmedto be strictly related to pasture, spatially. On the contrary,the distribution of Nematodirus was more linked to altitude(Di Cerbo et al., this volume). Indeed, this parasite shows astrong adaptation to low temperatures as its larvae are ableto survive winter staying inside their egg shell. Further,results from this study confirm the capability of Nema-todirus spp. to tolerate a sharp temperature decrease muchbetter than other nematodes. In fact, higher mean valuesof epg were recorded for the species where mean and min-imum values of temperature were the lowest, that is inthe northern and in the eastern provinces of Sondrio andBrescia (Fig. 2A). On the other hand, Strongylida appearedmore abundant in the western sector of Lombardy evenif the map indicates a lower mean humidity in this area(Fig. 2B). The environmental characteristics and microcli-matic conditions are important factors in the occurrenceand dynamic transmission of parasitic infections (Gasnieret al., 1997). Particularly, temperature and humidity influ-ence the development, distribution and survival of larvaeon the pasture (Manfredi, 2006). However, the optimalsurvival levels of L3 are different among trichostrongyles:warm and humid habitats for Trichostrongylus colubriformisand Haemonchus contortus, cold and humid conditions forTeladorsagia circumcincta and Ostertagia ostertagia (Gasnieret al., 1997; Papadopoulos et al., 2003). Further, T. circum-cincta is particularly susceptible to the dry microclimate(Manfredi, 2006). No closer analyses on microclimatic con-ditions and the occurrence of parasites could have beenperformed locally so far.

Strongylida are strictly influenced by pasture and theirimportant role in the composition of helminths communityof goat was also confirmed by our results. A preliminarysurvey on gastrointestinal parasites by necroscopy of goatsfrom some of the studied farms allowed to define the par-asite composition at a specie level and to investigate theirrole in the helminth community in the Lombard goat farms(Di Cerbo et al., 2006). T. circumcincta, Trichostrongylusaxei and H. contortus were identified as dominant species



of the gastrointestinal tract. Particularly, T. circumcinctawas the only dominant species in the abomasum of goatsfrom semi-extensive farms. Again, some helminths foundin these goats can also infect wild ruminants. For instance,T. circumcincta and Marshallagia marshalli were the mostdominant species in Alpine chamois, Rupicapra rupicapra(Genchi et al., 1984; Zaffaroni et al., 2000). While Spicu-lopteragia spiculoptera, Spiculopteragia mathevossiani andOstertagia leptospicularis are common parasites in Cervuselaphus and Capreolus capreolus (Zaffaroni et al., 2000;Manfredi, 2006). Some more generalist nematodes with arecognized pathogenic effect (i.e. H. contortus and T. axei)have also an important role in the transmission of parasiticdiseases among various hosts. For instance, H. contortushas been observed in wild ruminant populations mainlyin areas where sheep grazing is widespread (Zaffaroni etal., 1996). However, the occurrence of these helminths ingoats and the widespread distribution of wild ruminants, asassessed in Lombard Alps and Prealps (Prigioni et al., 2001),suggests possible interactions from a sanitary point of viewbetween domestic and wild hosts and also within domes-tic species, as goats and sheep have and share numerousgastrointestinal parasites. So far, only very few gastroin-testinal nematodes have shown to be genetically differentdepending on host species such as T. circumcincta repre-senting a species complex made up by a sheep and a goatline (Leignel et al., 2002; Gruner et al., 1998). Furthermore,the possibility of a parasite exchange between goats andsheep increased in our study area mainly where sheepfarms resulted to be located near goat farms positive toStrongylida and/or M. benedeni, as showed by the buffergeneration of the pasture zones.

Assessing the widespread distribution of gastrointesti-nal parasites in the goat farms of Lombardy, was ofparticular interest to our study. In fact, even if the para-sitic risk mainly derives from pasture, in some areas therole of goats as biological control agents for the renova-tion of pastures is getting more and more important. This isdue to both environmental concern and high costs of othercontrol methods, e.g. mechanical cutting and herbicideapplication (Luginbuhl et al., 1999). Nevertheless, severalautochthonous goat breeds can survive in different areasof both western and eastern Lombardy where farming onextensive pasture is still carried out. Some of these breeds,e.g. Nera di Verzasca, Orobica, are strictly connected to typi-cal products somehow linked to rural culture and traditionsor even to certified products, so-called PDO, Protected Des-ignation of Origin. In order to support the smallest farmerswith keeping such breeding, we think that the control ofparasites should be emphasized in these farms. Althoughthe eradication of gastrointestinal parasites from the envi-ronment is generally impractical, infections can be limited.Thus, the main objective of control programmes shouldconcern the reduction of the associated economic lossesand the containment of animal population within levelswhich cannot affect the production of their hosts signif-icantly (Ambrosini, 2000; Vallade et al., 2000; Silvestreet al., 2000; Rinaldi et al. 2007). As a suggestion, controlcould be achieved by a combination of sanitary treatment– based on synthetic chemotherapeutic drugs or on bio-logical control methods – with a proper management of

farms and grazing (Silvestre et al., 2002; Gorski et al., 2004;Waller, 2006; Genchi, 2006). Furthermore, just recentlysome authors emphasized a hypothesis based on histor-ical data for which climate change could be responsiblefor the increased cases of parasitic gastroenteritis in sheepin the UK (van Dijk et al., 2008). Actually, in the northernregions of Italy the mean temperature raised more than 1 ◦Cfrom 1860 to 2000, which might have influenced the wormburden in goats from Lombardy (Buffoni et al., 2003). Notethat the temperature increase is significant during winterso extending the at-risk period for parasitic infections.

Summing up, the results from our investigation suggestthe need for a well-coordinated, permanent sanitary mon-itoring all over the goat farm system of region Lombardy.

Acknowledgments

The research was funded by a grant from Regione Lom-bardia, Programma regionale di ricerca in campo agricolo2004–2006 (Project no. 845). We gratefully thank theregional institution ARAL and the province sections of APAfor their support in the survey.

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