STRUCTURAL CHARACTERIZATION OF EXTENSIVE FARMS IN ANDALUSIAN DEHESAS

Three types of livestock farming systems are identified in Andalusian dehesas using multivariate analysis. One of two conservationist systems, both applying sustainable management criteria, was detected in most of farms: dehesa farming system (49% of farms): small extensive cattle and sheep farms, which adapt stocking rates to the availability of the land's natural resources, and occasional use of strategic food supplementation; mountain farming system (21%) also relative to small farms, with mainly small ruminants and limited use of technology. The third system was a yield targeted system (30%), corresponding mainly to large cattle farms, with greater use of technology and high levels of food supplementation caused by stocking rates that exceed the land's carrying capacity. Typology defined can be used as starting point to base technical and economic characterization of farming systems taking into consideration their current and future viability. RESUMEN


SUMMARY
Three types of livestock farming systems are identified in Andalusian dehesas using multivariate analysis.One of two conservationist systems, both applying sustainable management criteria, was detected in most of farms: dehesa farming system (49% of farms): small extensive cattle and sheep farms, which adapt stocking rates to the availability of the land's natural resources, and occasional use of strategic food supplementation; mountain farming system (21%) also relative to small farms, with mainly small ruminants and limited use of technology.The third system was a yield targeted system (30%), corresponding mainly to large cattle farms, with greater use of technology and high levels of food supplementation caused by stocking rates that exceed the land's carrying capacity.Typology defined can be used as starting point to base technical and economic characterization of farming systems taking into consideration their current and future viability.

INTRODUCTION
The dehesa is a system of land use and management based mainly on livestock farming, as well as forest and agricultural farming, in Mediterranean grassland areas which gives rise to an agrosystem in which the combination of agrosilvopastoral management fosters important environmental values such as the sustainable use of land, a balanced landscape and high levels of diversity at different levels of integration (Comisión Técnica de la Dehesa, 2006).
There are 5.8 million ha of dehesa in the SW Iberian peninsula, 21% of which are in Andalusia (Joffre et al., 1999).A large proportion of dehesa is in natural parks and protected spaces (Junta de Andalucía, 2005).These very diverse region host a mixture of agrosilvopastoral production systems, being livestock the primary product (Plieninger and Wilbrand, 2001).The farms on the dehesa, generally mixed, aim to extract the widest variety of available resources using as grazers different native species (Milán et al., 2006), which are very well adapted to the harshness of the Mediterranean climate, the poor quality of the soils and the seasonal nature of food availability (Martín et al., 2001).Hence, livestock grazing systems are characterized by high levels of complexity, diversity and variability, conditioned by technical, social and environmental factors (Martín et al., 2001;Escribano et al., 2002).
Studies of dehesa systems in Extremadura have revealed typological biodiversity (Escribano et al., 2001 and2002).In Andalusia, where conditions are very similar, the typification of livestock systems has not been previously studied.The farms commonly implement environmentally friendly production practices, like integrated and organic livestock farming.Andalusian dehesa farms are multifunctional systems, where pigs commonly graze alongside other species (Porras et al., 2002).Most areas of Andalusian dehesa are within zones that are protected by the Natura 2000 network (Junta de Andalucía, 2005), where farms play an important role both in the development of these areas and protection of the environment.In order to design strategic plans that incorporate the rational and sustainable management of Andalusian dehesa, it is necessary to understand the production systems associated with this ecosystem (Gibon et al., 1999).
Multivariate statistical techniques that quantify relationships of similarity between structural variables (physical, size, intensification), can be useful for the identification of systems and subsequent classification of farms, as proposed by Gibon et al. (1999) and Solano et al. (2000).
The aim of this study is to increase knowledge about the diversity and dispersion of livestock farms in order to group them according to their relationships of similarity, both in terms of physical and technical characteristics, and to establish a typology of extensive livestock systems in Andalusian dehesa ecosystem.

MATERIALS AND METHODS
Sample selection and survey data collection design.Following the environmental characterization of dehesas (Junta de Andalucía, 2005), these areas are grouped into 5 geographical zones (table I) covering 1.2 million ha of dehesa and around 10 000 extensive livestock farms (IEA, 2003).A randomized and stratified sampling was performed on 206 farms (2% of the population) sampling was representative and proportional to the distribution of zones, species farmed (Iberian pigs, cattle, sheep and goats) and production system used (simple and multifunctional).Information was gathered in 2004 through direct questionnaires to the farmers and visits to the farms, in accordance with the methodology used by Dobremez and Bousset (1995), Frías (1998), Acero (2001) and Milán et al. (2003).
Variable determination and database construction.The database generated from the questionnaires is made up of 34 variables (table II), which represents aspects of classification (4), agroclimatic factors (4), Table I.Description of the sample and it predominant type of dehesa according to their predominant tree species and percentage of wooded area.(Descripción de la muestra y su tipo predominante de dehesa según la densidad y las especies arbóreas predominantes).Junta de Andalucía (2005).

Dehesa
physical characteristics and size ( 19) and the intensification of the farms (7).
The variables average annual rainfall (AR) and temperature (AT) were obtained from the closest weather station to each farm.Data from 76 stations were used.
Stocking rates (SR) were determined using the methodology proposed by Martín et al. (1987) and developed by Escribano et al. (1996), transforming the stocking rates into livestock units (LU) in order to make comparisons between farms and species (Pulido and Escribano, 1994, Escribano et al., 2002and Acero et al., 2003).The equivalent Livestock Units (LU) are as follows: For cattle; 1 LU: cow older than 24 months; 0.6: cow between 6 and 24 months, and 0.4: cow younger than 6 months.For sheep and goats: 0.15 breeding females, 0.12 adult males and 0.10 animals under a year old.For pigs: 0.5: breeding animals, 0.027: suckling pigs and 0.3: other pigs.For poultry: 0.014: egg-laying hens and 0.007: chickens bred for meat.
The methodology proposed by Le Houerou and Hoste (1977) and adapted by Jiménez (1986) was used to determine pasture production (PP).Finally, the production capacity (CP) of each farm was determined using the methodology described by Martín et al. (1986) for pastureland, which establishes the maximum stocking rate for a farm based on prior estimations of pasture production and the feeding requirements of the different species of livestock on the farm.
Variable and factor selection.The most representative primary variables of livestock farming and the geophysical environment were selected (system response variables).The selection of variables was done using the following the steps: -Determine the degree of association between variables analyzed, using the total and partial correlation matrix, looking for original interrelated variables with no linear dependence (Uriel and Aldás, 2005;Castaldo et al., 2006).-Statistical description of each variable.Variance, coefficients of variation, etc. were calculated; the variables with the greatest discriminatory power for the construction of groups were chosen, in accordance with Álvarez and Paz (1998), who recommended the use of variables with coefficients of variation higher than 60%.
Factor analysis (FA) was used to reduce dimensionality and to indicate the interrelationships between the original variables (Ness, 1997;Hair et al., 1995;Pérez, 2002).The fit of the data to the factorial model was verified using Bartlett's test of sphericity and the Kaiser, Meyer and Olkin (KMO) test.Once the model had been verified, factors were extracted using the principal components procedure.In order to facilitate the interpretation of common factors, a Varimax orthogonal rotation was performed (Uriel and Aldás, 2005).Once the factors had been extracted, the farms were scored and classified.
Typification and characterization of production systems.The farms were classified using cluster analysis, establishing groups of farms that were homogeneous within a group, but heterogeneous between different groups (Júdez, 1989;Castel et al., 2003;Pérez, 2002).The hierarchical cluster technique was used in accordance with Ward's method, and the Euclidean Distance was used to measure similarity between farms (Castaldo et al., 2006).
The number of groups was established based on the dendrogram and the distance between clusters (DC) obtained during the grouping process (Sharma, 1996).Finally, the conglomerations obtained were validated by verifying the stability of the results through comparison with other distance measurements (Squared Euclidean Distance) and other grouping methods (centroid) (Jonson, 1998;Köbrich et al., 2003).Once the typology of farms had been defined, the groups were characterized and compared using a single-factor analysis of variance (ANOVA) and the Student-Newman-Keuls (SNK) multiple range test.Statistical analyses were performed using SPSS software, version 12.

RESULTS AND DISCUSSION.
The size of the farms selected ranged from 6 to 1300 ha and the cattle load fluctuated between 1 and 5000 livestock units (LU) used for meat production.Pasture is extremely important in the production system and contributes around 60-90% of the animals' energy requirements.

FACTOR SELECTION AND FARM CLASSIFICATION
Twelve primary variables (FSA, LU, CP, PP, PS, PG, PS, AR, AT, SR, PC, SUP), that met the criteria detailed in the methodology section, and representative of the dehesa farm structure, were chosen.The factor analysis determined 8 factors that explain 95% of the total variance.From these, the first five factors were extracted, which accounted for 76% of the accumulated variance and presented eigenvalues greater than one.Table III shows the factors selected, the factor loadings of the variables, the correlation between the variables and the resulting factors following axis rotation.The farms were classified in accordance with the score obtained for each of the factors.
The first factor explains 21% of the total variance and shows positive saturation with the variables stocking rate and daily food supplementation per farm (table III).This factor differentiates the farms according to the degree of intensification.So, 37% of the farms displayed high levels of intensification and are located to the right of the axis; whereas the remaining 63% are on the left of figure 1 and represent a model with high levels of extensification.Intensive farms are mainly located in the west of Sierra Morena and La Campiña; they mostly farm cattle and sheep and are predominantly simple farms, with only one productive activity.They have large numbers of animals (99 LU) and use a high stocking rate (0.68 LU/ha), which is sustained through the high use of food supplementation (0.29 kg/ha).The more extensive farms, on the other hand, are located in the west and east of Sierra Morena and Grazalema, covering areas of 200 ha, with low stocking rates (0.25 LU/ha).The animals graze on pasture throughout the year, and their food is supplemented in certain seasons or when there is a shortage of natural resources (0.01 kg/ha).
The second factor explains 18.19% of the total variance and presents positive saturation with production capacity and rainfall, classifying the farms according to the potential of the pasture.Hence, high scores for this factor indicate farms with a high carrying capacity, whereas low values correspond to areas with low potential.Figure 1a shows that 68% of the farms are located in areas with low carrying capacities, in comparison with the 32% that have high carrying capacities.Where the axes cross (F1 vs. F2), 38% of the farms are extensive with low carrying capacity in comparison with 7% of intensive farms that have a high carrying capacity.
The third factor explains 15.04% of the total variance and is bipolar: the proportion of sheep stock (negative saturation) is inversely related to the proportion of cattle stock and the average temperature (positive saturation).Figure 1b shows the distribution of the farms in relation to the third factor: in the upper half, there are mostly cattle farms (48%) and average temperatures of 16ºC; the negative values for this factor mostly correspond to sheep (34%) and goat (6%) farms with lower average temperatures (15ºC).Therefore, this factor determines the substitution of activities and the proportion of land used for cattle and sheep farming.This competitive and substitutive relationship between different activities is in concordance with the findings of Escribano et al. (2002) in Extremadura.
The fourth factor explains 12.35% of the total variance and presents a positive single polarity with useful land surface area and the number of livestock units.When comparing factors 1 and 4 (figure 1c), a proportional relationship is observed, whereby increased size corresponds to increased intensification.Large intensive farms have up to 331 ha and 121 LU, with a marked predominance of cattle stock (62%).Therefore, this factor defines the size of the farm.
Finally, the fifth factor explains 9.15% of the total variance and displays high specificity and negative saturation with goat  (2003) intensification and farm specialization are the most relevant factors to explain heterogeneity among farms.Of the five factors obtained, four refer to technical variables and farm specialization: the level of intensification (F1), carrying capacity (F2), the substitution of activities (F3) and the importance of goat production (F5).The effect of the size of the farm is ranked fourth, and only explains 12% of the total variance.

TYPIFICATION AND CHARACTERIZATION OF LIVESTOCK FARMING SYSTEMS
The cluster analysis, based on the five factors selected, identifies three groups or production systems in Andalusian dehe-sas, which is verified using the dendrogram and the distance between the clusters (figure 2).Table IV shows the percentage distribution of the farms in relation to the category variables: type I farms are typical of Grazalema and Ronda; type II farms are mainly found in Sierra Morena (west and east) and type III farms are frequently found in Sierra Morena (west) and also in La Campiña.
Type I farms are less intensive, with complex orography, poor access and a greater presence of dense dehesa and Mediterranean woodland.They represented 40% of the farms.For farms in clusters II and III, the topographical conditions are milder: there is better access, widespread pasture and the production systems tend to be more intensive.
Even though most farms are still largely owned and run by families, as we move from type I to type III farms, there is an increased presence of hired laborers and the land is often leased rather than owned.
In relation to livestock farming activity, cattle and sheep stock compete for space, and as the system intensifies, cattle production increases and sheep production decreases.Different cattle production strategies are also observed; Type I farms mainly focus on rearing; whereas most type III farms cover the entire production cycle (rearing and fattening).
Sheep and goat stocks appear in less intensive systems (types I and II), reflecting the food potential of the vegetation caused by the substitution of pasture for scrub and dense dehesas.Extensive goat farming is in type III farms, which tend to focus on milk production in farms without land.
In Andalusia, Iberian pigs are associated with dehesa and are an extremely important economic product; however, this does not explain the heterogeneity between farms.Similar results were found in dehesa farms of Extremadura (Milán et al., 2006;Gaspar et al., 2008).Several aspects explain the low relation the variables linked to pig farming and the typology of the dehesa: Iberian pigs are present in most of Andalusian pasture lands, making use of the production of acorns to fatten the animals on fodder (montanera); hence their presence does not explain the variability observed moreover, the intensive production of suckling pigs in large stables and the permanent stall housing of the mothers mean that this production stage is displaced out of the pastoral system of grazing.As a consequence, the pigs only graze in dehesa during the fattening stage (montanera), for less than four months; whereas sheep and cattle are always kept in the pasture; hence pigs do not compete with other livestock activities for space.

Group I. Conservationist mountain systems
This groups represents 21% of farms and consists of small farms (around 32 LU and 127 ha).In these systems, rainfall is high, almost 1300 mm, and the average annual  temperature is around 14.8ºC.The farms included correspond to a model in which the land is extremely important in the production of food.They are traditionally managed farms with limited use of technology.Even though their production capacity is the highest of the three groups (0-57 LU/ha), stocking rates are low, which indicates the scant availability of food resources, fundamentally as the result of complex orography.
These farms are chiefly located in steep marginal areas: Antequera and Serranía de Ronda in the province of Malaga and Sierra de Cadiz; where dense dehesa is increasingly important (table IV).In this environment, the farms mostly focus on one activity, or mixed farming with small ruminants, such as goats, which are better able to use the natural resources, and contribute, through nonproductive functions, to a model of social economy that is an effective way of generating self-employment and persuading the population from these areas to remain there, contributing to endogenous development and sustainability over time (Milán et al., 2003).In some of the districts in these areas, farming is the only economic activity, with no other alternative; hence their disappearance would exacerbate its depopulation and degradation.
Vegetation in these areas is extremely diverse, generally native and with abundant flora: Mediterranean woodland, carob trees, wild olives, oaks, cork oaks, fir, Spanish fir, gall oaks, etc., with a predominance of scrub.The pasture has scant tree cover and the herbaceous stratum is very highly developed.

Group II. Conservationist dehesa system
Group II accounts for 49% of the population studied and corresponds to small farms (around 35 LU and 95 ha) in comparison with the other groups, in particular group III.These farms are mainly located in Sierra Morena; in East and West Andévalo, Costa and Sierra (Huelva); La Campiña, La Sierra Norte, La Sierra Sur and La Vega (Seville); Different letters, in a row, indicates significant differences between clusters (p<0.001).

Table II .
Variable codes and description.

Table III .
Factor selection and degree of saturation of the variables.(Selección de factores y grado de saturación con las variables).

Table IV .
Characteristics of the three production subsystems based on cluster analysis.(Caracteristicas de los tres subsistemas productivos derivados del análisis cluster).