structural characterization of extensive farms in andalusian dehesas

12 Pages·2010·0.25 MB·English

STRUCTURAL CHARACTERIZATION OF EXTENSIVE FARMS IN
ANDALUSIAN DEHESAS*
CARACTERIZACIÓN ESTRUCTURAL DE LOS SISTEMAS GANADEROS DE LAS
DEHESAS ANDALUZAS
García, A.1, Perea, J.1*, Acero, R.1, Angón, E.1, Toro, P.2, Rodríguez, V.1
and Gómez Castro, A.G.1
1Animal Production Department. University of Cordoba. Animal Production Building. Campus Rabanales.
14014 Cordoba. Spain. *[email protected]
2Pontificia Universidad Católica de Chile. Facultad de Agronomía e Ingeniería Forestal. Av. Vicuña Mackenna
4860. Santiago. Chile. *[email protected]
ADDITIONAL KEYWORDS PALABRAS CLAVE ADICIONALES
Factorial analysis. Farm typology. Livestock farming Análisis factorial. Tipología de explotaciones. Sis-
systems. temas ganaderos extensivos.
SUMMARY RESUMEN
Three types of livestock farming systems Mediante análisis multivariante se establecie-
are identified in Andalusian dehesas using ron tres tipos de sistemas ganaderos en las
multivariate analysis. One of two conservationist dehesas andaluzas. En 49% de las explotaciones
systems, both applying sustainable management se detectó un sistema ganadero denominado
criteria, was detected in most of farms: dehesa conservacionista de dehesa que corresponde a
farming system (49% of farms): small extensive pequeñas explotaciones de bovino y ovino con
cattle and sheep farms, which adapt stocking bajo nivel de intensificación y carga ganadera
rates to the availability of the land's natural ajustada a la disponibilidad alimenticia usando
resources, and occasional use of strategic food ocasionalmente suplementación estratégica. Las
supplementation; mountain farming system explotaciones (21%) del sistema conservacionista
(21%) also relative to small farms, with mainly de sierra y montaña son también de reducida
small ruminants and limited use of technology. dimensión, con predominio de pequeños rumian-
The third system was a yield targeted system tes y bajo empleo de tecnología. El sistema
(30%), corresponding mainly to large cattle productivista (30% de las explotaciones) corres-
farms, with greater use of technology and high ponde a ganaderías con predominio de bovinos,
levels of food supplementation caused by de mayor dimensión y nivel tecnológico que utili-
stocking rates that exceed the land's carrying zan elevados niveles de suplementación pues sus
capacity. Typology defined can be used as cargas ganaderas están por encima de la capa-
starting point to base technical and economic cidad de la dehesa. Los tipos establecidos pueden
characterization of farming systems taking into servir de punto de partida para la caracterización
consideration their current and future viability. técnica y económica de los sistemas ganaderos
considerando su viabilidad actual y futura.
*This study was carried out as part of the Research
INTRODUCTION
Project: Prospective study of pastureland in order to
develop an assessment methodology for extensive
The dehesa is a system of land use and
farming and its application to agricultural insurance,
management based mainly on livestock
developed by Consejería de Agricultura (Junta de
farming, as well as forest and agricultural
Andalucía) and Departamento de Producción Ani-
mal (Universidad de Córdoba). farming, in Mediterranean grassland areas
Recibido: 10-11-08. Aceptado: 5-3-09. Arch. Zootec. 59 (228): 577-588. 2010.
GARCÍA, PEREA, ACERO, ANGÓN, TORO, RODRÍGUEZ AND GÓMEZ CASTRO
which gives rise to an agrosystem in which plans that incorporate the rational and
the combination of agrosilvopastoral sustainable management of Andalusian
management fosters important environ- dehesa, it is necessary to understand the
mental values such as the sustainable use production systems associated with this
of land, a balanced landscape and high ecosystem (Gibon et al., 1999).
levels of diversity at different levels of Multivariate statistical techniques that
integration (Comisión Técnica de la Dehe- quantify relationships of similarity between
sa, 2006). structural variables (physical, size, intensifi-
There are 5.8 million ha of dehesa in the cation), can be useful for the identification
SW Iberian peninsula, 21% of which are in of systems and subsequent classification
Andalusia (Joffre et al., 1999). A large of farms, as proposed by Gibon et al. (1999)
proportion of dehesa is in natural parks and and Solano et al. (2000).
protected spaces (Junta de Andalucía, The aim of this study is to increase
2005). These very diverse region host a knowledge about the diversity and disper-
mixture of agrosilvopastoral production sion of livestock farms in order to group
systems, being livestock the primary product them according to their relationships of
(Plieninger and Wilbrand, 2001). The farms similarity, both in terms of physical and
on the dehesa, generally mixed, aim to extract technical characteristics, and to establish a
the widest variety of available resources typology of extensive livestock systems in
using as grazers different native species Andalusian dehesa ecosystem.
(Milán et al., 2006), which are very well
adapted to the harshness of the Medite- MATERIALS AND METHODS
rranean climate, the poor quality of the soils
Sample selection and survey data
and the seasonal nature of food availability
collection design. Following the environ-
(Martín et al., 2001). Hence, livestock grazing
mental characterization of dehesas (Junta
systems are characterized by high levels of
de Andalucía, 2005), these areas are grouped
complexity, diversity and variability,
into 5 geographical zones (table I) covering
conditioned by technical, social and
1.2 million ha of dehesa and around 10 000
environmental factors (Martín et al., 2001;
extensive livestock farms (IEA, 2003). A
Escribano et al., 2002).
randomized and stratified sampling was
Studies of dehesa systems in Extrema-
performed on 206 farms (2% of the
dura have revealed typological biodiversity
population) sampling was representative
(Escribano et al., 2001 and 2002). In
Andalusia, where conditions are very simi- and proportional to the distribution of
lar, the typification of livestock systems zones, species farmed (Iberian pigs, cattle,
has not been previously studied. The farms sheep and goats) and production system
commonly implement environmentally used (simple and multifunctional). Infor-
friendly production practices, like integrated mation was gathered in 2004 through direct
and organic livestock farming. Andalusian questionnaires to the farmers and visits to
dehesa farms are multifunctional systems, the farms, in accordance with the metho-
where pigs commonly graze alongside other dology used by Dobremez and Bousset
species (Porras et al., 2002). Most areas of (1995), Frías (1998), Acero (2001) and Milán
Andalusian dehesa are within zones that et al. (2003).
are protected by the Natura 2000 network Variable determination and database
(Junta de Andalucía, 2005), where farms construction. The database generated from
play an important role both in the develop- the questionnaires is made up of 34 varia-
ment of these areas and protection of the bles (table II), which represents aspects of
environment. In order to design strategic classification (4), agroclimatic factors (4),
Archivos de zootecnia vol. 59, núm. 228, p. 578.
CHARACTERIZATION OF EXTENSIVE FARMS IN AN ANDALUSIAN DEHESA
entageantes). ple% 2.03 2.17 2.14 2.06 2.23
cn m
ermi a
po S
and pred n 27 23 5 62 89
s s
ea
dehesa according to their predominant tree specie de dehesa según la densidad y las especies arbóre Predominant type of dehesa Normal dehesas with pasture and scrub, and densedehesas (Quercus suber and Quercus faginea) Mixed dense dehesas (Quercus pyrenaica,Quercus faginea and Quercus ilex) Normal dehesas with pasture and scrub,and dense dehesas (Olea europea) Normal dehesas with pasture and densedehesas (Quercus ilex andQuercus faginea) Open dehesas with pastures and normal dehesaswith pasture and scrub (Quercus ilex)
of ant
pe min
d it predominant tyestra y su tipo predo Dehesaha% 9144523.71 9963021.42 154222.56 43172727.20 58490832.01
Table I. Description of the sample anof wooded area. (Descripción de la mu ZonesAreas CampiñaCampiña,Campo de Gibraltar,La Janda Grazalema andAntequera,RondaLa Sierra Sur,Serranía de Ronda,Sierra de Cádiz Sierra SurPeníbetica,Sierra Sur Sierra Morena, WAndévalo Occidental,Andévalo Oriental,Costa,La Sierra Norte,La Vega,Sierra Sierra Morena, ECampiña Baja,Campiña del Norte,El Condado,La Sierra,Pedroches,Sierra Morena Junta de Andalucía (2005).Source:
Archivos de zootecnia vol. 59, núm. 228, p. 579.
GARCÍA, PEREA, ACERO, ANGÓN, TORO, RODRÍGUEZ AND GÓMEZ CASTRO
physical characteristics and size (19) and
Table II. Variable codes and description.
the intensification of the farms (7).
(Codificación y descripción de las variables).
The variables average annual rainfall
(AR) and temperature (AT) were obtained
Categories/Zone
from the closest weather station to each
1. Campiña; 2. Grazalema and Ronda;
farm. Data from 76 stations were used.
3. Sierra Sur; 4. West Sierra Morena;
5. East Sierra Morena Stocking rates (SR) were determined
Production system using the methodology proposed by Mar-
S. Simple, only one livestock species; tín et al. (1987) and developed by Escribano
M. Multifunctional, several livestock et al. (1996), transforming the stocking rates
species on the same farm into livestock units (LU) in order to make
Farm specialization comparisons between farms and species
1. Cattle; 2. Pigs; 3. Sheep; 4. Goats;
(Pulido and Escribano, 1994, Escribano et
5. Others (poultry, etc.)
al., 2002 and Acero et al., 2003). The
Vegetation
equivalent Livestock Units (LU) are as
1. Open dehesas; 2. Normal dehesas;
3. Dense dehesas; 4. Olive groves follows: For cattle; 1 LU: cow older than 24
months; 0.6: cow between 6 and 24 months,
Agro-climatology and 0.4: cow younger than 6 months. For
AR Average annual rainfall (mm) sheep and goats: 0.15 breeding females,
AT Average annual temperature (ºC) 0.12 adult males and 0.10 animals under a
AL Altitude (m) year old. For pigs: 0.5: breeding animals,
ASG Average slope gradient (%)
0.027: suckling pigs and 0.3: other pigs. For
poultry: 0.014: egg-laying hens and 0.007:
Physical and intensification variables
chickens bred for meat.
TSA Total surface area (ha)
The methodology proposed by Le
FSA Farming and livestock surface area (ha)
Houerou and Hoste (1977) and adapted by
POD Percentage of open dehesas (ha/TSA)
PDD Percentage of dense dehesas (ha/TSA) Jiménez (1986) was used to determine
PND Percentage of normal dehesas (ha/TSA) pasture production (PP). Finally, the
RO Percentage of olive groves (ha/TSA) production capacity (CP) of each farm was
FP Fodder production (kg DM/FSA) determined using the methodology descri-
CP Production capacity of the farm (LU /FSA) bed by Martín et al. (1986) for pastureland,
max
LU Livestock units (LU) which establishes the maximum stocking
CSU Cattle stock units (LU)
rate for a farm based on prior estimations of
PSU Pig stock units (LU)
pasture production and the feeding
SSU Sheep stock units (LU)
requirements of the different species of
GSU Goat stock units (LU)
livestock on the farm.
OSU Other stock units (LU of poultry, etc.)
Variable and factor selection. The most
PC Percentage of cattle (CSU/LU)
P P Percentage of pigs (PSU/LU) representative primary variables of livestock
PS Percentage of sheep (SSU/ LU) farming and the geophysical environment
PG Percentage of goats (GSU/LU) were selected (system response variables).
PO Percentage of other species (OSU/LU) The selection of variables was done using
SR Stocking rate (LU/FSA) the following the steps:
CSR Cattle stocking rate (CSU/FSA) - Determine the degree of association
PSR Pig stocking rate (PSU/FSA)
between variables analyzed, using the total
SSR Sheep stocking rate (SSU/FSA)
and partial correlation matrix, looking for
GSR Goat stocking rate (GSU/FSA)
original interrelated variables with no linear
OSR Other species stocking rate (OSU/FSA)
dependence (Uriel and Aldás, 2005; Castaldo
SUP Daily concentrated supplementation (kg/FSA)
et al., 2006).
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CHARACTERIZATION OF EXTENSIVE FARMS IN AN ANDALUSIAN DEHESA
- Statistical description of each variable. Newman-Keuls (SNK) multiple range test.
Variance, coefficients of variation, etc. were Statistical analyses were performed using
calculated; the variables with the greatest SPSS software, version 12.
discriminatory power for the construction
of groups were chosen, in accordance with RESULTS AND DISCUSSION.
Álvarez and Paz (1998), who recommended
The size of the farms selected ranged
the use of variables with coefficients of
from 6 to 1300 ha and the cattle load fluctuated
variation higher than 60%.
between 1 and 5000 livestock units (LU)
Factor analysis (FA) was used to reduce
used for meat production. Pasture is
dimensionality and to indicate the interre-
extremely important in the production
lationships between the original variables
system and contributes around 60-90% of
(Ness, 1997; Hair et al., 1995; Pérez, 2002).
the animals' energy requirements.
The fit of the data to the factorial model was
verified using Bartlett's test of sphericity
FACTOR SELECTION AND FARM CLASSIFICATION
and the Kaiser, Meyer and Olkin (KMO)
Twelve primary variables (FSA, LU, CP,
test. Once the model had been verified,
PP, PS, PG, PS, AR, AT, SR, PC, SUP), that
factors were extracted using the principal
met the criteria detailed in the methodology
components procedure. In order to facilitate
section, and representative of the dehesa
the interpretation of common factors, a
farm structure, were chosen. The factor
Varimax orthogonal rotation was performed
analysis determined 8 factors that explain
(Uriel and Aldás, 2005). Once the factors
95% of the total variance. From these, the
had been extracted, the farms were scored
first five factors were extracted, which
and classified.
accounted for 76% of the accumulated
Typification and characterization of
variance and presented eigenvalues greater
production systems. The farms were
than one. Table III shows the factors
classified using cluster analysis, esta-
selected, the factor loadings of the varia-
blishing groups of farms that were homoge-
bles, the correlation between the variables
neous within a group, but heterogeneous
and the resulting factors following axis
between different groups (Júdez, 1989; Castel
rotation. The farms were classified in
et al., 2003; Pérez, 2002). The hierarchical
cluster technique was used in accordance accordance with the score obtained for each
with Ward’s method, and the Euclidean of the factors.
Distance was used to measure similarity The first factor explains 21% of the total
between farms (Castaldo et al., 2006). variance and shows positive saturation with
The number of groups was established the variables stocking rate and daily food
based on the dendrogram and the distance supplementation per farm (table III). This
between clusters (DC) obtained during the factor differentiates the farms according to
grouping process (Sharma, 1996). Finally, the degree of intensification. So, 37% of the
the conglomerations obtained were vali- farms displayed high levels of intensification
dated by verifying the stability of the results and are located to the right of the axis;
through comparison with other distance whereas the remaining 63% are on the left of
measurements (Squared Euclidean Dis- figure 1 and represent a model with high
tance) and other grouping methods levels of extensification. Intensive farms
(centroid) (Jonson, 1998; Köbrich et al., are mainly located in the west of Sierra
2003). Once the typology of farms had been Morena and La Campiña; they mostly farm
defined, the groups were characterized and cattle and sheep and are predominantly sim-
compared using a single-factor analysis of ple farms, with only one productive activity.
variance (ANOVA) and the Student- They have large numbers of animals (99 LU)
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GARCÍA, PEREA, ACERO, ANGÓN, TORO, RODRÍGUEZ AND GÓMEZ CASTRO
Table III. Factor selection and degree of saturation of the variables. (Selección de factores
y grado de saturación con las variables).
Factor Eigenvalue Variance Accumulated Variable Saturation Estimated
(%) variance (%) communality
Factor 1 2.56 21.31 21.31 SR 0.98 0.97
SUP 0.94 0.92
Factor 2 2.18 18.20 39.51 CP 0.98 0.98
AR 0.90 0.98
Factor 3 1.80 15.05 54.56 PS -0.92 0.93
PC 0.86 0.83
AT 0.64 0.49
Factor 4 1.48 12.35 66.91 FSA 0.85 0.83
LU 0.80 0.77
P P -0.51 0.70
Factor 5 1.10 9.15 76.06 PG -0.97 0.97
and use a high stocking rate (0.68 LU/ha), inversely related to the proportion of cattle
which is sustained through the high use of stock and the average temperature (positive
food supplementation (0.29 kg/ha). The more saturation). Figure 1b shows the distri-
extensive farms, on the other hand, are bution of the farms in relation to the third
located in the west and east of Sierra More- factor: in the upper half, there are mostly
na and Grazalema, covering areas of 200 ha, cattle farms (48%) and average temperatures
with low stocking rates (0.25 LU/ha). The of 16ºC; the negative values for this factor
animals graze on pasture throughout the mostly correspond to sheep (34%) and goat
year, and their food is supplemented in (6%) farms with lower average temperatures
certain seasons or when there is a shortage (15ºC). Therefore, this factor determines the
of natural resources (0.01 kg/ha). substitution of activities and the proportion
The second factor explains 18.19% of of land used for cattle and sheep farming.
the total variance and presents positive This competitive and substitutive rela-
saturation with production capacity and tionship between different activities is in
rainfall, classifying the farms according to concordance with the findings of Escribano
the potential of the pasture. Hence, high et al. (2002) in Extremadura.
scores for this factor indicate farms with a The fourth factor explains 12.35% of the
high carrying capacity, whereas low values total variance and presents a positive single
correspond to areas with low potential. Fi- polarity with useful land surface area and the
gure 1a shows that 68% of the farms are number of livestock units. When comparing
located in areas with low carrying capacities, factors 1 and 4 (figure 1c), a proportional
in comparison with the 32% that have high relationship is observed, whereby increased
carrying capacities. Where the axes cross size corresponds to increased intensification.
(F1 vs. F2), 38% of the farms are extensive Large intensive farms have up to 331 ha and
with low carrying capacity in comparison 121 LU, with a marked predominance of cattle
with 7% of intensive farms that have a high stock (62%). Therefore, this factor defines
carrying capacity. the size of the farm.
The third factor explains 15.04% of the Finally, the fifth factor explains 9.15% of
total variance and is bipolar: the proportion the total variance and displays high
of sheep stock (negative saturation) is specificity and negative saturation with goat
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CHARACTERIZATION OF EXTENSIVE FARMS IN AN ANDALUSIAN DEHESA
stock. Figure 1d shows that most cattle sas, which is verified using the dendrogram
(41%) and sheep (34%) farms obtain positive and the distance between the clusters (figu-
values for this factor; whereas all goat farms re 2). Table IV shows the percentage
score negatively. distribution of the farms in relation to the
In line with the findings of Milan et al. category variables: type I farms are typical
(2003) intensification and farm specialization of Grazalema and Ronda; type II farms are
are the most relevant factors to explain mainly found in Sierra Morena (west and
heterogeneity among farms. Of the five east) and type III farms are frequently found
factors obtained, four refer to technical in Sierra Morena (west) and also in La Cam-
variables and farm specialization: the level piña.
of intensification (F1), carrying capacity Type I farms are less intensive, with
(F2), the substitution of activities (F3) and complex orography, poor access and a
the importance of goat production (F5). The greater presence of dense dehesa and
effect of the size of the farm is ranked fourth, Mediterranean woodland. They represented
and only explains 12% of the total variance. 40% of the farms. For farms in clusters II and
III, the topographical conditions are milder:
TYPIFICATION AND CHARACTERIZATION OF there is better access, widespread pasture
LIVESTOCK FARMING SYSTEMS and the production systems tend to be more
The cluster analysis, based on the five intensive.
factors selected, identifies three groups or Even though most farms are still largely
production systems in Andalusian dehe- owned and run by families, as we move from
(a) (b)
8 5
6
25% 7% 3
2 4
Factor 02 actor 3 -11
F
-2 38% 30% -3
-4
-3 -2 -1 0 1 2 3 4 5 -5
Factor 1 -3 -2 -1 0 1 2 3 4 5
Factor 1
(c) (d)
8 2
6 1
Factor 4 024 Factor 5 --210
-2 -3
-4 -4
-3 -2 -1 0 1 2 3 4 5
-3 -2 -1 0 1 2 3
Factor 1
Factor 1
Figure 1. Projection of farms according to factorial analysis scores. (Proyección de explota-
ciones según puntuación del AF).
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GARCÍA, PEREA, ACERO, ANGÓN, TORO, RODRÍGUEZ AND GÓMEZ CASTRO
800
600
e
c
n 400
a
st
Di
200
0
Farms
Figure 2. Dendrogram resulting from cluster analysis with three subsystems. (Dendrograma
resultante del análisis cluster con tres subsistemas).
type I to type III farms, there is an increased al., 2008). Several aspects explain the low
presence of hired laborers and the land is relation between the variables linked to pig
often leased rather than owned. farming and the typology of the dehesa:
In relation to livestock farming activity, Iberian pigs are present in most of Anda-
cattle and sheep stock compete for space, lusian pasture lands, making use of the
and as the system intensifies, cattle production of acorns to fatten the animals
production increases and sheep production on fodder (montanera); hence their
decreases. Different cattle production presence does not explain the variability
strategies are also observed; Type I farms observed moreover, the intensive pro-
mainly focus on rearing; whereas most type duction of suckling pigs in large stables and
III farms cover the entire production cycle the permanent stall housing of the mothers
(rearing and fattening). mean that this production stage is displaced
Sheep and goat stocks appear in less out of the pastoral system of grazing. As a
intensive systems (types I and II), reflecting consequence, the pigs only graze in dehesa
the food potential of the vegetation caused during the fattening stage (montanera), for
by the substitution of pasture for scrub and less than four months; whereas sheep and
dense dehesas. Extensive goat farming is cattle are always kept in the pasture; hence
absent in type III farms, which tend to focus pigs do not compete with other livestock
on milk production in farms without land. activities for space.
In Andalusia, Iberian pigs are associated
with dehesa and are an extremely important Group I. Conservationist mountain systems
economic product; however, this does not This groups represents 21% of farms
explain the heterogeneity between farms. and consists of small farms (around 32 LU
Similar results were found in dehesa farms and 127 ha). In these systems, rainfall is
of Extremadura (Milán et al., 2006; Gaspar et high, almost 1300 mm, and the average annual
Archivos de zootecnia vol. 59, núm. 228, p. 584.
CHARACTERIZATION OF EXTENSIVE FARMS IN AN ANDALUSIAN DEHESA
temperature is around 14.8ºC. The farms
Table IV. Characteristics of the three
included correspond to a model in which the
production subsystems based on cluster
land is extremely important in the production
analysis. (Caracteristicas de los tres subsistemas
of food. They are traditionally managed
productivos derivados del análisis cluster).
farms with limited use of technology. Even
though their production capacity is the Cluster
highest of the three groups (0-57 LU/ha), I II III
stocking rates are low, which indicates the
Distribution of farms (%) 21 49 30
scant availability of food resources,
fundamentally as the result of complex Zones
orography. Campiña - 9.7 25.0
These farms are chiefly located in steep Grazalema and Ronda 58.7 3.2 1.9
marginal areas: Antequera and Serranía de Sierra Sur 17.3 - -
Sierra Morena East - 18.3 9.6
Ronda in the province of Malaga and Sierra
Sierra Morena West 24.0 68.8 63.5
de Cadiz; where dense dehesa is increasingly
important (table IV). In this environment,
Production system
the farms mostly focus on one activity, or Simple 48.3 73.1 76.9
mixed farming with small ruminants, such as Multifunctional 51.7 26.9 23.1
goats, which are better able to use the natu-
ral resources, and contribute, through non- Farm specialization
productive functions, to a model of social Cattle 20.7 48.4 61.5
economy that is an effective way of Pig 10.3 6.5 2.5
Sheep 48.3 33.3 35.4
generating self-employment and persuading
Goat 17.2 10.7 0.5
the population from these areas to remain
Other 3.4 1.0 -
there, contributing to endogenous develop-
ment and sustainability over time (Milán et Vegetation
al., 2003). In some of the districts in these Open dehesas (pasture) 51.7 96.7 96.1
areas, farming is the only economic activity, Normal dehesas (scrub) 37.9 3.2 3.9
with no other alternative; hence their Dense dehesas 6.5 - -
disappearance would exacerbate its depopu- Olive groves 3.5 - -
lation and degradation.
Physical and intensification variables
Vegetation in these areas is extremely
FSA (ha) 127a 95a 376b
diverse, generally native and with abundant
LU (LU) 32a 35a 139b
flora: Mediterranean woodland, carob trees, CSU (LU) 29a 34a 129b
wild olives, oaks, cork oaks, fir, Spanish fir, SSU (LU) 27a 31a 109b
gall oaks, etc., with a predominance of scrub. GSU (LU) 8a 15a 50b
The pasture has scant tree cover and the PC (%) 29.3a 46.6ab 60.3b
herbaceous stratum is very highly developed. PP (%) 10.1b 8.9ab 0.8a
SR (LU/ha) 0.32a 0.39a 0.52b
Group II. Conservationist dehesa system
CSR (LU/ha) 0.11a 0.17a 0.33b
Group II accounts for 49% of the
PSR (LU/ha) 0.023b 0.028b 0.001a
population studied and corresponds to small
AR (mm/m2) 1287b 645a 616a
farms (around 35 LU and 95 ha) in comparison
AT (ºC) 14.8a 15.6b 15.9b
with the other groups, in particular group CP (LU/ha) 0.57b 0.41a 0.36a
III. These farms are mainly located in Sierra SUP (kg /ha) 0.28a 0.20a 0.84b
Morena; in East and West Andévalo, Costa
and Sierra (Huelva); La Campiña, La Sierra Different letters, in a row, indicates significant
differences between clusters (p<0.001).
Norte, La Sierra Sur and La Vega (Seville);
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GARCÍA, PEREA, ACERO, ANGÓN, TORO, RODRÍGUEZ AND GÓMEZ CASTRO
Campiña Baja, La Sierra and Pedroches (0.52 LU/ha) is higher than dehesa farms of
(Cordoba); Campiña del Norte, El Condado Extremadura (Gaspar et al., 2009).
and Sierra Morena (Jaen). Average rainfall The farms are mainly located in the west
and temperature are 645 mm and 15.5ºC of Sierra Morena (north of Cordoba and
respectively. Taking into account the Cabe- Jaen) and in the districts of La Janda, La
zas et al. (1986) climatological classification, Campiña and Campo de Gibraltar (Cadiz), in
the traditional system corresponds to the areas with an average rainfall of 616 mm, and
central group of pasture lands, with average an average temperature of 15.9ºC. This group
rainfall of 600-700 mm. represents 30% of the population studied
Average stocking rates in group II are and corresponds to specialized production
0.39 LU/ha, lower than the European avera- models that use a certain amount of
ge (Colson and Chatellier, 1996) and similar technology.
to the stocking rates indicated by Muslera These farms apply productivity based
(1992) and Escribano et al. (2002) for Extre- management criteria, with stocking rates
madura. above the carrying capacity of the land. As
These farms apply conservation manage- a consequence, farming activity depends
ment criteria, adjusting the stocking rates to largely on the contribution of food
the carrying capacity of the land (0.41 LU/ supplements. These farms tend to focus on
ha), using strategic or seasonal food cattle production, with high levels of
supplementation in periods of shortage. As intensification and large herds.
a consequence, farming activity is adapted In conclusion: five factors explain 76%
to the environment and presents low levels of the heterogeneity of dehesa farming in
of intensification; the animals are fed using Andalusia. The two main factors are: level
the natural resources available through of intensification and carrying capacity. The
pasture and fodder. most important factor variables are: stocking
These farms mostly focus on the indivi- rate, level of food supplementation, farm’s
dual production of sheep or cattle, which is production capacity and average rainfall.
determined by the resources produced by Three livestock farming systems are
the land and its orography. Combined identified in Andalusian dehesa. The
farming of several species is relatively Mountain and Dehesa systems (70% of the
infrequent (table IV). livestock farms), apply a conservation
management strategy, adjusting stocking
Group III. Yield targeted systems rates to the land's carrying capacity or even
Group III is made up of large farms (139 below this capacity. The Yield Targeted
LU and 376 ha), with values similar to those system (30% of livestock farms), corres-
recorded in Extremadura (Escribano et al., ponds to larger farms, which apply criteria
2002) and Portugal (Carvalho and Gama, of intensification, with stocking rates above
1999). However, the average stoking rate the land's carrying capacity.
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