Using Cluster Analysis to Characterize the Goat Farming

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Using cluster analysis to characterize the goat farmingsystem in Sardinia

M.G. Usai *, Sara Casu, G. Molle, M. Decandia, S. Ligios, A. Carta Istituto Zootecnico e Caseario per la Sardegna, Loc. Bonassai, Km 18.6 S.S. Sassari-Fertilia, 07040 Olmedo (SS), Italy

Received 4 October 2005; received in revised form 2 March 2006; accepted 13 March 2006

Abstract

This study is a large-scale survey based on interviews with owners of 151 Sardinian goat farms. The aim was to provide anup to date description of the goat production chain on the island. A multivariate statistical approach was applied to exploit thegreat number of available variables in the best way. The statistical analysis was carried out in two steps: principal component analysis and successive cluster analysis. In general, Sardinian goat farming showed a remarkable backwardness compared todairy sheep farming. This is particularly true for farm facilities and productivity. Cluster analysis allowed us to identify fiveclusters, which corresponded to three principal farming systems. Firstly, a traditional system with little infrastructure and low

management and productive levels was identified. Here the most frequent genotype was basically the native Sardinian breed.Secondly, there is a group of farms mainly located in the southwest of Sardinia where the facilities and the management were poor and production was on a low level than dairy sheep farming but generally better developed than the previous cluster.Thirdly, there is a group of farms using a sort of semi-intensive farming system, which was more similar to that of dairy sheep,with relatively high productive and reproductive performance.

In conclusion, this study identified different goat farming systems in Sardinia and emphasizes the need to develop strategies,which are able to take this diversity into account.D 2006 Elsevier B.V. All rights reserved.

Keywords: Sardinian goat; Cluster analysis; Farming systems; Development strategies

1. Introduction

The domestic goat ( Capra hircus ) has played acrucial role in human history. Since it can adapt tovery different environmental conditions, the goat is

the most geographically widespread livestock species(Luikart et al., 2001 ). Today, goat farming is a veryimportant economic resource in several developingcountries. These countries own most of the globalgoat stock (756 million head or 96% of the total worldstock; FAO, 2004 ). On the whole, the traditional roleof goats in these countries has been to satisfy the foodrequirements of the family rather than for commercial purpose (Morand-Fehr et al., 2004 ). In Europe, goats

1871-1413/$ - see front matter D 2006 Elsevier B.V. All rights reserved.doi:10.1016/j.livsci.2006.03.013

* Corresponding author. Tel.: +39 079387318; fax: +39079389450.

E-mail address: [email protected] (M.G. Usai).

Livestock Science 104 (2006) 6376

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are found mainly in Mediterranean countries (Greece,Spain, France and Italy). Their most important product is milk, which is used both for cheese makingand for direct consumption ( Haenlein, 2001 ).

There are 850,000 head of goats in Italy, with 23%of these being found in Sardinia ( ISTAT, 2000 ).Sardinia has a typical Mediterranean climate withmost rainfall concentrated between October andMarch. The average annual rainfall is 756 mm withlarge variations between years and areas depending onthe altitude. During the year, the average temperaturesrange between 1 8 C in winter and 26 8 C in summer, but in the marginal mountain areas the temperature inwinter is lower than the average. As a result of the

above-mentioned climatic conditions, there are usual-ly two peaks of herbage growth. There is a minor peak between October and December after the first autumnrains but the main peak (about three-quarters of thetotal herbage yield) occurs between March and Junewhen the herbage starts to mature and dry out (Rivoira, 1976 ). In marginal mountain areas, the pasture is mainly scrubland and consists of ligneousspecies, and there is little herbage available, due to theabove-mentioned climatic and topographic con-straints. Under these conditions, the goats mainly browse on the leaves and twigs of ligneous species,which often have high condensed tannin contents(Decandia et al., 2000 ). The reproductive cycle of goats is determined by the previously mentionedenvironmental conditions. Adult goats kid mainly inlate autumn or early winter and primiparous goats inearly spring. Drying off occurs in early summer for both adult and primiparous females ( Macciotta et al.,2005 ).

Sheep breeding has always been the most impor-tant livestock production system in Sardinia. Howev-er, goat farming has always been of some importance,

particularly in hilly or mountainous marginal areas.This is because goats are considered to be the onlyspecies able to exploit marginal areas covered byligneous vegetation and unsuitable for tillage or for raising other domestic herbivore species ( Brandano,1980 ), as it is also the case in other Mediterraneanregions. Goats in Sardinia have often been raised inconjunction with sheep or cattle. The other speciesgraze on the herbaceous vegetation while the goats browse the leaves and twigs of bushes. In the last century, social and economic changes, and the

consequent decline in agriculture, led to the progres-sive abandonment of the areas used for goat farming(Brandano and Piras, 1978 ). In addition, Sardiniansheep dairy products supported by EU subsidies weresuccessfully marketed, in particular the d PecorinoRomano T cheese, which was exported in massivequantities to the USA. This success, combined withthe lack of marketable goat products, led localfarmers, advisers, scientists and politicians to focuson the dairy sheep production chain. Today, partly because of cuts in EU subsidies, the market for dairysheep products is in decline and as a result there isnow more interest in alternative livestock species.Goats are still of economic relevance in Sardinia. It is

the region with the highest goat population in Italy(209,000 head; ISTAT, 2000 ). The current populationis a crossbreed of autochthonous animals withimproved Mediterranean goats, mainly of the Maltese breed (Ligios et al., 2004; Usai et al., 2004 ). TheSardinian goats are very variable in their morpholog-ical (Macciotta et al., 2002 ), productive and geneticcharacteristics. The main product is milk, which ismostly used to make cheese. Traditional homemadecheeses are produced directly in the farms, and aremainly destined to domestic consumption or smalllocal markets ( Scintu et al., 1998 ). Most of the milk isdelivered to cheese making factories where it isusually mixed with sheep milk and used to makesheepgoat cheeses ( Pirisi et al., 1995 ). Suckling kidsare used for meat, a seasonal product that is locallymuch appreciated ( Carta et al., 2001 ).

The objective of this study was to characterize precisely the Sardinian goat farming system byapplying a multivariate statistical approach to analysedata coming from interviews with farmers. Discussionof the results will focus on possible strategies for developing goat-based enterprises within the frame-

work of new CAP objectives.

2. Materials and methods

2.1. Data collection

The survey was carried out in the main Sardiniangoat farming areas (Nuorese, Ogliastra, Sarrabus-Gerrei and Sulcis-Iglesiente; Fig. 1). Representativefarms of the regions were chosen. The number of

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Nuorese23 farms

4,574 head

Ogliastra59 farms

9,288 head

Sarrabus-Gerrei

16 farms6,104 head

Sulcis-Iglesiente53 farms

13,311 head

Total151 farms

33,277 head

Fig. 1. Number of farms and head of goats.



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farms and number of head sampled in each area wereapproximately proportional to the totals for each area.Each farmer was asked about the basic farm character-istics, farm facilities, herd management, feedingtechniques, breeding management and its history,total stock and association with other animal speciesand productive and reproductive performances. Theanswers were recorded on forms. The main aspectsconsidered in the interviews are shown in Table 1 . Thedescriptive variables used for the multivariate analysisare listed in Table 3. The tilled surfaces the totalsurface of the farm that was cultivated. The timeoutdoor in winter, spring and dry season (summer andearly autumn) represents the daily hours devoted to

outdoor feeding-related activities. The fertility wascalculated as the ratio between the number of kiddedgoats and the number of mated goats. The prolificacywas the number of kids per kidded goat. Thefecundity was calculated as the number of kids per mated goat. The Sardinian blood is an estimate bythe farmer of the percent of original Sardinian blood.The milk yield in winter, spring and summer was theaverage daily milk yield per goat measured by thefarmer. Some variables were not directly included inthe forms but resulted from calculation based on theoriginal variables. The total private land of each herd-owner (TPL) was calculated as the sum of owned andrented land surfaces. For farms using public lands,

Table 1Main aspects considered in the interview form

Basic farm characteristicsAltitude (m) Rented land (ha) Tilled surface (ha)Total land (ha) Total private land (ha)Owned land (ha) Public land (ha)

FacilitiesRoad connection (yes/no) Generator (yes/no) Milking machine (yes/no)Water availability (yes/no) Sheds (yes/no) Milk tank (yes/no)Potable water (yes/no) Housing facilities (yes/no) Facility scoreElectrification (yes/no) Storehouse (yes/no)

Feeding management Time outdoor in winter (h/day) Cereal grain (yes/no) Homemade hay (yes/no)Time outdoor in spring (h/day) Leguminous seeds (yes/no) Purchased hay (yes/no)Time outdoor in dry season (h/day) Commercial concentrate (yes/no)

Size of the goat herd and association with other speciesTotal goats ( n ) Adult goats (n ) Total sheep (n )Males (n) Primiparous ( n ) Total cattle (n )Replacement males ( n ) Replacement females ( n ) Conventional stocking rate (LU/ha) a

Breeding management Main breed of the herd Sardinia blood (%) Breed-crossbreed in the past

Pure breed (yes/no) Other pure bred Type of males (internal/external)Crossbreed (yes/no) Other crossbreed Choice of the breed for the future

ReproductionArtificial insemination (yes/no) Adult kidding period Primiparous fertility rateEstrus synchronization (yes/no) Primiparous kidding period Adult prolificacy rateMales used ( n ) Adult fertility rate Primiparous prolificacy rate

Milk and meat productionMilk yield in winter (l/day/head) Weight of milk on the income (%) Kid slaughter ing weight (kg/head)Milk yield in spring (l/day/head) Weight of meat on the income (%) Kid weaning age (days)Milk yield in summer (l/day/head) Kid slaughtering age (days) Kid weaning weight (kg/head)

a LU: livestock units.



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TPL corresponded only to the private property. Thefacility score, ranged from 0 to 10, was calculated asthe total number of farm facilities (road connection,water availability, potable water, electrification,generator, sheds, housing facilities, storehouse, milk-ing machine and milk tank). The stocking rate wasestimated as the ratio between the total livestock units (LU), inclusive of cattle and sheep, and TPL.For farms based on public land, it was not possibleto estimate the actual surface exploited by animals,which was anyway very large; thus, a conventionalstocking rate (CSR), obtained by setting to zerofarms based on common land, was used in theanalysis.

2.2. Statistical analysis

Means and standard deviations (continuous variables) or frequencies (categorical variables) werecalculated for the most informative variables. Amultivariate approach was used in order to exploit the large amount of recorded variables in the most efficient way. Statistical analysis was carried out intwo steps: principal component analysis (PCA) andsuccessive cluster analysis (CA). PCA extracts linear combinations (PC) of the original variables whoseweights correspond to the eigenvectors of the corre-lation matrix. Similar approaches were used byWeigel and Rekaya (2000) and Zwald et al. (2003)to cluster cattle herds. Only the PC with eigenvaluesgreater than 0.5 were considered ( Weigel and Rekaya,2000). This approach allows a large part of the totalvariation to be concentrated in a small number of standardized uncorrelated variables. The PCA was performed using the SAS PRINCOMP procedure(SAS, 1989 ). The process used for the CA was basedon the nearest centroid sorting method ( Anderberg,

1973). This performs a disjoint cluster analysis basedon Euclidean distance and guarantees that distancesamong observations in the same cluster are less thanthe distances between observations in different clus-ters. The analysis was performed using the SASFASTCLUS procedure ( SAS, 1989 ). The optimalnumber of clusters was chosen on the basis of thecubic clustering criterion (CCC) statistic ( Zwald et al.,2003 ). In order to characterize and compare theidentified clusters, the main descriptive statistics werecalculated for each of them.

3. Results

A preliminary check of the forms led to theinformation for six farms being eliminated becausedata was missing. The data for further 14 farms withless than 25 head of goats were also removed, as thesewere considered to be farms kept as a hobby rather than for commercial ones.

The variables used for PCA are reported in Table 3 .Twelve PC had eigenvalues greater than 0.5 and wereretained for the successive CA. The eigenvalues of these PC ranged from 0.52 to 5.19 and on the wholethey explained 87.5% of the total original variation(Table 2). The eigenvectors of weights of original

variables on the new standardized variables arereported in Table 3 . The cluster analysis was carriedout from 1 to 15 clusters and maximization of CCCwas obtained with 11 clusters ( Fig. 2).

Statistics of CA are summarized in Table 4 . Thenumber of farms by cluster ranged from 1 to 51. Someof the identified clusters showed a small number of farms (less than five). Generally, they were charac-terized by very specific features such as a very largegoat stock, private land or tilled surface or acompletely absent time outdoor and relatively high production levels. In order to limit the discussion to

Table 2Eigenvalues corresponding to each principal component (PC) andrelative proportion of variation

PC Eigenvalues Proportion of variation

1 5.191 0.2602 2.503 0.1253 1.747 0.0874 1.393 0.0705 1.173 0.0596 1.136 0.0577 0.948 0.047

8 0.884 0.0449 0.741 0.03710 0.660 0.03311 0.610 0.03112 0.516 0.02613 0.472 0.02414 0.432 0.02215 0.379 0.01916 0.317 0.01617 0.300 0.01518 0.228 0.01119 0.224 0.01120 0.147 0.007



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the most representative farming systems, results will be showed only for the five clusters with more thanfive farms. The root mean square (RMS) of standarddeviations, which measures the degree of dispersionwithin each cluster, ranged from 0.715 to 1.141. Someoverlapping between clusters can be deduced bycomparing the maximum distance between a seedand the observations of its cluster, with the distance between the two centroids of two close clusters. Thisis shown in Fig. 3, where the distribution of farmswith respect to the two first PC, which explain 38.5%of the total variance, is reported.

Tables 58 show descriptive statistics of originalvariables with reference to the database as a whole(average results in the first column) and to eachretained cluster (following columns).

3.1. Average goat farm

The results of the survey showed that goat-basedenterprises were sited at different altitudes with about 40% at more than 500 m ( Table 5 ). Although most

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b i c C l u s t e r

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Fig. 2. Cubic clustering criterion according to number of clusters.

Table 4Main statistics of the cluster analysis (CA)

Cluster Number of farms

RMS a

of S.D.Max distanceseed-observation

Nearest cluster

Distance betweencluster centroids

1 51 0.849 4.372 9 2.7142 3 0.877 3.457 10 5.4773 1 0.000 11 8.8984 4 0.821 3.926 10 5.5395 3 0.997 4.791 8 5.8956 2 0.727 1.782 11 5.9097 4 1.055 4.565 10 3.8658 9 0.954 4.542 9 3.0709 25 0.715 3.854 10 2.40510 24 0.774 4.532 9 2.40511 5 1.141 5.626 10 5.537

Table 3Eigenvectors (weights) for each of the 20 descriptive variables according to the 12 principal component (PC) retained for the cluster analysis

PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12

Altitude (m) 0.258 0.167 0.171 0.310 0.145 0.240 0.231 0.022 0.025 0.192 0.371 0.058Total private land (ha) 0.265 0.247 0.316 0.024 0.000 0.099 0.071 0.052 0.333 0.040 0.087 0.129Tilled surface (ha) 0.234 0.166 0.279 0.118 0.272 0.029 0.408 0.016 0.054 0.313 0.166 0.073Facility score 0.291 0.215 0.230 0.029 0.090 0.110 0.109 0.064 0.101 0.095 0.353 0.095Time outdoor winter (h/day) 0.066 0.401 0.334 0.042 0.047 0.065 0.285 0.019 0.069 0.510 0.076 0.126Time outdoor spring (h/day) 0.184 0.438 0.051 0.072 0.222 0.150 0.168 0.159 0.042 0.106 0.337 0.026Time outdoor dry season (h/day) 0.250 0.366 0.064 0.145 0.217 0.072 0.187 0.258 0.007 0.036 0.177 0.097Total goats ( n ) 0.174 0.235 0.286 0.055 0.382 0.152 0.394 0.273 0.083 0.176 0.004 0.148Total sheep ( n ) 0.205 0.260 0.233 0.224 0.218 0.132 0.056 0.174 0.287 0.015 0.069 0.457Total cattle ( n ) 0.002 0.061 0.200 0.636 0.081 0.092 0.255 0.247 0.286 0.032 0.335 0.262Conventional stocking rate (LU a /ha) 0.226 0.029 0.148 0.052 0.520 0.104 0.148 0.149 0.215 0.075 0.497 0.430Adult fertility rate 0.153 0.027 0.117 0.124 0.037 0.690 0.037 0.112 0.487 0.237 0.022 0.175Adult prolificacy rate 0.297 0.105 0.174 0.107 0.128 0.156 0.220 0.030 0.204 0.348 0.087 0.163Sardinian blood (%) 0.278 0.046 0.312 0.008 0.253 0.072 0.229 0.126 0.261 0.114 0.049 0.264Milk yield in winter (l/day/head) 0.336 0.146 0.023 0.125 0.270 0.144 0.115 0.128 0.034 0.326 0.022 0.048Milk yield in spring (l/day/head) 0.286 0.244 0.029 0.086 0.295 0.038 0.140 0.220 0.172 0.326 0.088 0.151Milk yield in summer (l/day/head) 0.309 0.166 0.201 0.059 0.162 0.119 0.055 0.298 0.054 0.078 0.122 0.035Kid weaning age (days) 0.123 0.020 0.112 0.415 0.068 0.195 0.261 0.724 0.103 0.199 0.162 0.093Kid slaughtering age (days) 0.009 0.085 0.363 0.412 0.237 0.325 0.146 0.061 0.510 0.023 0.007 0.401Kid slaughtering weight (kg/h) 0.071 0.306 0.312 0.070 0.004 0.377 0.383 0.047 0.000 0.309 0.357 0.365

a LU: livestock units.



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used public land and tillage was not frequent, theaverage TPL and tilled surfaces for the involved farmswere high, albeit with great variability from farm tofarm. Most of the farms did not have basic facilitiessuch as electricity, milking machine or housingfacilities, and so the facility score was generally low(Table 5 ). The feeding system and herd management (Table 6 ) were based on traditional techniques. Most of the day was devoted to outdoor feeding-relatedactivities. The goats were housed for more time inwinter than the other seasons (approximately 10 h/ day). Diet supplementation with concentrates wascommon, in particular with leguminous grains,

whereas hay was less widely used. General statisticson the breeding, herd size and association with other species are reported in Table 7 . The average herd sizewas 224 goats, with the flocks ranging from 55 to 900head. In about half of the sample the goats were raisedin conjunction with dairy sheep and/or suckling cows.In these conditions, there were generally many moresheep and cattle than goats. On the other hand, onsome farms, only a few dairy sheep or suckling cowswere kept and these were purely used to meet thefamilys needs. The average percentage of Sardinian

blood estimated by the farmer was higher than onehalf with 43 farmers claiming that they bred pureSardinian goats. Three farmers raised pure exotic breeds. The age and weight of kids at slaughteringwere quite homogeneous across the farms ( Table 8 ).The reproductive and productive performances weregenerally low, with an average fertility of 83% and amilk yield of 1.1 l/head in spring. The weight of meat production on the gross income was relatively high(Table 8 ).

3.2. Retained clusters and identified goat farming systems

3.2.1. Cluster 1: b Ogliastra Q traditional extensive system (ES)

Cluster 1 was the largest, with 51 farms locatedmainly in the Ogliastra region ( Table 9 ). This area is at a high altitude and has harsh environmental condi-tions. The farms exploit public land. Private plotswere rarely added and in such cases these plots werenot always cultivated. The facility score was relativelylow because of the absence of the main facilities, andespecially electricity, milking machines and water

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PC 1 (26%)

P C 2 ( 1 3 % )

cluster 1 : extensive system (ES) cluster 8: mixed grazing cluster (MGC) cluster 9: in termediate cluster (IC)

cluster 10: semi-exstensive system (SES) cluster 11: semi-intensive system (SIS)

Fig. 3. Distribution of farms according to the two first principal components (PC).



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(Table 5 ). The herds were kept indoors for long periods during the cold winter, although often inrudimentary sheds. They spent the dry seasons of

summer and early autumn outdoors. Grain andleguminous seed feed supplements were widely used, but hay supplements were only used on about 50% of

Table 6Feeding management: mean F S.D. for the continuous variables and frequencies (%) for the categorical variables

Total Clusters

1 10 9 8 11

ESa SES b ICc MGC d SIS e

N farms 131 51 24 25 9 5Time outdoor winter (h/day) 14.4 F 6.3 12.1 F 5.7 16.5 F 4.0 18.1 F 5.6 22.2 F 3.5 10.4 F 3.1Time outdoor spring (h/day) 21.2 F 5.4 21.8 F 4.7 21.2 F 4.1 23.7 F 0.9 24.0 F 0.0 13.0 F 6.2

Time outdoor dry season (h/day) 22.0 F 4.5 23.6 F 1.9 20.9 F 4.5 23.7 F 0.9 24.0 F 0.0 13.0 F 6.2Cereal grain (%) 72 76 71 60 67 60Leguminous seeds (%) 85 94 75 84 89 80Commercial concentrate (%) 9 2 4 8 22 20Total concentrate (%) 93 100 83 88 89 100Homemade hay (%) 10 2 4 4 11 40Purchased hay (%) 67 55 79 68 78 60Hay supplementation (%) 71 55 79 72 78 100

a ES: extensive system. b SES: semi-extensive system.c IC: intermediate cluster.d MGC: mixed grazing cluster.e SIS: semi-intensive system.

Table 5Basic farm characteristics and facilities: mean F S.D. for the continuous variables and frequencies (%) for the categorical variables

Total Clusters

1 10 9 8 11


N farms 131 51 24 25 9 5Altitude (m) 424 F 318 682 F 230 140 F 150 390 F 281 200 F 179 322 F 345Tillage (%) 21 4 33 20 22 40Tilled area (ha) 20.8 F 19.6 4.5 F 2.1 19.0 F 16.5 8.1 F 7.2 25.0 F 21.2 30 F 0.0Public land (%) 70.2 100.0 41.7 88.0 11.1 40.0Private land (%) 37 4 63 32 89 60Total private land (ha) 164.1 F 171.4 42.5 F 53.0 171.5 F 97.8 37.3 F 40.4 115.0 F 78.7 40.0 F 10.0Road connection (%) 68 86 46 40 89 80Water availability (%) 83 67 100 84 100 100Potable water (%) 19 25 8 16 11 20Electrification (%) 40 14 83 20 56 60Generator (%) 23 14 38 8 22 40Sheds (%) 97 94 100 96 100 100Housing facilities (%) 11 2 8 4 11 40Storehouse (%) 50 22 75 36 89 80Milking machine (%) 27 12 42 4 44 40Milk tank (%) 26 12 38 12 44 0Facility score 4.3 F 2.1 3.4 F 1.5 5.2 F 1.4 3.2 F 1.5 5.7 F 2.1 5.4 F 2.1

a ES: extensive system. b SES: semi-extensive system.c IC: intermediate cluster.d MGC: mixed grazing cluster.e SIS : semi-intensive system.



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the farms (Table 6 ). This cluster included small andmedium sized herds (from 55 to 466 head). They werefrequently raised in conjunction with suckling cows but rarely with sheep ( Table 7 ). CSR was zero becauselarge portions of public land were generally used. The

farmer claimed that their goats had very high levels of original Sardinian blood. In almost all cases, they saidthat they intended to maintain or increase this high proportion in the future. Reproductive performanceswere low, particularly the fertility rate ( Table 8 ). This

Table 8Reproductive and productive performances: mean F S.D. for the continuous variables and frequencies (%) for the categorical variables

Total Clusters

1 10 9 8 11


N farms 131 51 24 25 9 5Adult fertility rate 0.83 F 0.15 0.76 F 0.18 0.83 F 0.11 0.87 F 0.11 0.86 F 0.16 0.96 F 0.06Adult prolificacy rate 1.43 F 0.29 1.21 F 0.18 1.65 F 0.23 1.45 F 0.25 1.47 F 0.20 1.90 F 0.09Adult fecundity rate 1.19 F 0.36 0.93 F 0.30 1.36 F 0.24 1.26 F 0.28 1.28 F 0.32 1.82 F 0.17

Milk yield in winter (l/day/head) 0.9 F 0.5 0.6 F 0.4 0.9 F 0.2 0.9 F 0.3 0.7 F 0.3 2.0 F 0.4Milk yield in spring (l/day/head) 1.1 F 0.5 1.0 F 0.3 1.0 F 0.2 1.1 F 0.2 1.1 F 0.2 2.7 F 0.6Milk yield in summer (l/day/head) 0.6 F 0.4 0.4 F 0.1 0.6 F 0.2 0.6 F 0.4 0.6 F 0.3 1.2 F 0.4Kid weaning age (days) 116 F 29 124 F 25 128 F 17 111 F 32 102 F 30 82 F 44Kid slaughtering age (days) 37 F 6 38 F 6 40 F 3 34 F 4 32 F 4 33 F 5Kid slaughtering weight (kg/head) 6.7 F 0.8 6.9 F 0.7 6.6 F 0.5 6.3 F 0.6 6.2 F 0.3 6.5 F 0.5Weight of milk on the income (%) 58 F 12 54 F 13 63 F 9 55 F 11 62 F 11 58 F 11Weight of meat on the income (%) 42 F 12 46 F 13 37 F 9 45 F 11 38 F 11 42 F 11


Table 7Size of the herd, associations with other species and prevalent genotype of the goats: mean F S.D. for the continuous variables and frequencies(%) for the categorical variables

Total Clusters1 10 9 8 11


N farms 131 51 24 25 9 5Only goat (%) 51.1 58.8 54.2 60.0 33.3 40.0Total goat ( n ) 224.2 F 136.1 178.4 F 106.6 295.9 F 121.1 181.7 F 71.9 228.7 F 87.2 166.6 F 170.8Sheep (%) 27.5 5.9 37.5 28.0 44.4 40.0Total sheep ( n ) 199.1 F 184.4 36.7 F 15.3 182.2 F 162.4 86.6 F 82.8 225.0 F 125.8 85.0 F 106.1Cattle (%) 29.8 37.3 12.5 20.0 55.6 20.0Total cattle ( n ) 29.6 F 30.6 22.6 F 23.7 12.3 F 15.3 11.8 F 9.2 43.6 F 26.4 1.0 F 0.0Conventional stocking rate (LU/ha)* 0.51 F 0.42 0 0.33 F 0.10 0.23 F 0.01 0.80 F 0.48 0.74 F 0.62Sardinian blood (%) 63 F 25 84 F 21 52 F 12 48 F 13 58 F 10 32 F 20Preference for Sardinian bucks** 56 83 42 40 44 20

a ES: extensive system. b SES: semi-extensive system.c IC: intermediate cluster.d MGC: mixed grazing cluster.e SIS: semi-intensive system.* LU: livestock units.

** Percentage of farmers who declare their will to use Sardinian bucks in the future.



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indicates that there are management, nutritional and probably also health problems. The milk yield wasalso low, especially in winter and summer. The kidsfor replacement were traditionally weaned at aroundfour months and the age of kids at slaughtering of kidswas similar to that of the overall goat population. The percent of income from meat production was slightlyhigher than in other clusters. On the basis of the aboveresults, we defined this cluster as extensive system(ES).

3.2.2. Cluster 10: semi-extensive goatsheep system(SES)

Cluster 10 was composed of 24 farms sited at lowaltitudes ( Table 5 ), mainly in the hills of the southwest Sardinia (Sulcis-Iglesiente; Table 9 ). 62.5% of farmshad private property and tillage of quite large surfaceswas relatively frequent. Farms had a good facilityscore and 42% of them owned milking machines.Herd management appeared to be less traditional thanin ES: the time spent outdoor in the dry season wasshorter and hay supplements were more widely used

(Table 6). The average herd was larger than in ES.One third of farms also had dairy sheep flocks. Insome cases, these were quite large ( Table 7 ). Thegenetic type of goats corresponded to a cross level of about 50% but a high percentage of farmers said that they intended to use pure Sardinian bucks in the futureto return to the original Sardinian genetic type ( Table7). The reproductive performances were undoubtedly better, milk yield was higher and weaning was earlier than in ES (Table 8 ). However, the farmers said that they obtained a higher percentage of their income

from milk than did farmers in the previous cluster.Although this seems to indicate a less traditionalfarming system, the age at weaning was similar to ES.This cluster included the farms with a more developedfarming system and we defined it as semi-extensivesystem (SES).

3.2.3. Cluster 9: goatsheep intermediate cluster (IC)Cluster 9 shared several common features either

with ES or SES. The 25 farms of this cluster werespread over different regions ( Table 9 ) and found at medium altitudes ( Table 5 ). TPL was low with tillageonly slightly more common than in ES. More basicfacilities were found here than in ES, with the

exception of milking machines. In general, the herdmanagement was similar to ES, whereas hay supple-ments were used as frequently as they were in SES(Table 6 ). The average herd size was almost the samein ES but association with dairy sheep was much morefrequent. The cross level and the percentage of farmers who intended to use pure Sardinian bucks inthe future was similar than SES ( Table 7 ). Reproduc-tive performances were similar to those of SES, withthe exception of prolificacy that was lower. Milk yieldwas almost the same as in SES ( Table 8 ). Given the better animal management than in ES, particularly thefeeding management and productive and reproductive performances, this group of farms was defined as anintermediate cluster (IC).

3.2.4. Cluster 8: semi-extensive with mixed grazing (goatsheepcattle) cluster (MGC)

The nine farms of this cluster were sited mainly inthe Sulcis-Iglesiente region as SES. The cluster couldnot be precisely characterized except for the fact that alarge percentage of farms raised cattle, sheep andgoats simultaneously. The presence of often substan-

tial sheep flocks and cattle herds meant that thestructural level of these farms was good. Theygenerally had the same level of facilities as those of the SES and often possessed private land. However,the levels of Sardinian blood in the flocks were higher than in SES and the production performances werelower and more similar to IC.

3.2.5. Cluster 11: semi-intensive goat system (SIS)Cluster 11, even though it only contained five

farms, was very different from the others. There were

Table 9 Number of farms for each region according to each cluster

Region Clusters Total

1 10 9 8 11 Outliers


Nuorese 5 1 9 1 1 3 20Ogliastra 45 0 4 0 2 2 53Sarrabus-Gerrei 1 5 1 1 1 4 13Sulcis-Iglesiente 0 18 11 7 1 8 45Total 51 24 25 9 5 17 131




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farming system (SIS) with high productive andreproductive performances.

Beyond this level of intensification, there are afew farms, not surveyed in this study, which can beregarded as being the top level of goat farming inSardinia. These basically use the housing systemtypical of northern Europe (top of Fig. 4). Thedifferences between the two main clusters (ES andSES) in terms of altitude, private ownership, tilledsurface areas and association with dairy sheepsuggest that they are suitable for different types of sustainable development. The b Ogliastra Q system(bottom of Fig. 4) is located in an area of great interest for environmental conservation with great

possibilities for developing for nature and archeology based tourism. It does, however, suffer from certainshortcomings, such as its distance from the maincities (Cagliari and Sassari) and insufficientlyupgraded electricity, road networks and infrastruc-ture. Considering the climatic and topographicconditions and the constraints related to the publicowning of land, it is difficult to envisage animprovement of management techniques and pro-ductive and reproductive performances. The devel-opment of this system should be based on traditional

products such as meat (suckling kids) and farm-madedairy products, combined with a strategy aimed at conserving not only the native Sardinian goat and itstraditional herd management techniques, but also therural culture as a whole. However, some prereq-uisites have to be satisfied in order to establish asustainable development strategy for ES: (i) using anintegrated approach, including molecular genetics, tocharacterize the native Sardinian breed ( Sechi et al.,2005 ) and to organize a breeding scheme aimed at preserving its genetic distinctiveness and variability;(ii) the adoption of a well-designed policy aimed at an adequate management of the Mediterraneanforests and bushland; (iii) the characterization,

labeling and traceability of the traditional goat products. Given the new CAP objectives, a decou- pling approach with cross-compliance premiumsrelated to environmental targets seems the most sensible system to adopt. This is fully in line withEU policy guidelines, which are designed to support biodiversity and the conservation of autochthonous breeds.

The Sulcis-Iglesiente and partially the Sarrabus-Gerrei regions where the SES and SIS are locatedare different from Ogliastra where ES is prevalent.

+ Percentage of goat farm -

Cluster and farm number

Extra survey

Cl. 11 (SIS) n=5

Cl. 10 (SES) n=24

Cl. 9 (IC) n=25;Cl. 8 (MGC) n=9

Cl. 1 (ES) n=51

% o f n

a t i v

e b l

o o d

L e v e l o f i n t e n s i f i c a t i o n

Envisaged development strategies

Intensive System Stall feeding of high merit continentalbreeds Milk for direct consumption

Semi-Intensive System (SIS) Supplement + grazing Cross-bred 75% Cheese making in factories

Semi-Extensive System (SES) Grazing/browsing + Supplement

Cross-bred 50% Cheese making in factories

Intermediate Cluster (IC) +Mixed Grazing Cluster (MGC)

Extensive System (ES) Browsing + Supplement Native goat population

Meat (kids) + farm-made cheese+ nature conservation

Fig. 4. Structure of goat farming systems in Sardinia and envisaged development strategies.



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They are closer to the main regional city (Cagliari)and have, on the whole, better infrastructure. Theseareas are more suitable for the development of a goat farming system, which is more similar to that of dairy sheep, i.e. based on well-organized farms,which deliver their milk to medium or high-technology cheese factories. In this type of system, breeding improved animals through large-scale adop-tion of artificial insemination, combined with a semi-intensive farming system, would justify some cheesefactories specializing in the production of high-quality, standardized goat cheese for internal andexternal markets.

In order to sustain such development, a semi-

coupled scheme used in conjunction with the adoptionof premium schemes aimed at improving hygiene andmilk technology can be envisaged. Pasteurized milk from intensive farms based on improved-milk breeds is putatively free of off-flavors, and in particular thegouty flavor, if the goats are fed with hay andconcentrates ( Delacroix-Buchet and Lamberet, 2000;Landau and Molle, 2004;Coulon et al., 2005;Decandiaet al., in press ).

There could also be a non production-drivensystem outside the outline shown in Fig. 4. InSardinia, as it is probably also the case in other European Mediterranean regions, goats are not raisedonly for production. In 14 goat enterprises with lessthan 25 goats, they were also raised as b pets Q byenthusiasts. They were part of small zoos of domes-ticated animals and farm-house educational programsor agricultural tourism. This shows how this speciescould easily be integrated into multi-functionalagricultural schemes.

In conclusion, this study clearly demonstrates howvaried goat farming is in Sardinia and emphasizes theneed for development strategies which can take this

diversity into account as well as economic and socialaspects that warrant further research.

Acknowledgements

The authors wish to thank Mr. Salvatore Masala, Mr.Costantino Saccu and Mr. Sebastiano Porcu for their help with the survey and all interviewed farmers for their kind collaboration. This work was supported bythe Italian Ministry of Agriculture (MiPAF) and the

Regional Government of Sardinia (RAS), inside theframework of the d Biodiversity and Genetic Resources T

program.

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Using Cluster Analysis to Characterize the Goat Farming