Organic dairy sheep farms in south-central Spain: Typologies according to livestock management and economic variables

Small Ruminant Research 104 (2012) 28–36

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Organic dairy sheep farms in south-central Spain: Typologies according to livestock management and economic variables P. Toro-Mujica ∗ , A. García, A. Gómez-Castro, J. Perea, V. Rodríguez-Estévez, E. Angón, C. Barba Departamento de Producción Animal, Facultad de Veterinaria, Universidad de Córdoba, Campus de Rabanales, 14071 Córdoba, Spain

a r t i c l e

i n f o

Article history: Received 13 August 2011 Received in revised form 27 October 2011 Accepted 7 November 2011 Available online 30 November 2011 Keywords: Organic production Clusters Characterization

a b s t r a c t Organic dairy sheep farms have been analyzed by multivariate analysis to identify and characterize typological groups in organic dairy sheep systems; with the aim of evaluating their technical and economic performance, and social implications, to propose the corresponding measures of improvement or support. This analysis was conducted on 30 farms in the Spanish region of Castilla-La Mancha, where 164 technical, economic and social variables were analyzed. This analysis allowed the selection of 4 principal components related to size, use of labour, land use, level of supplementation and productive and economic performance. The subsequent cluster analysis classified the farms into three groups. Group I called the Family of Subsistence, has the smallest flocks (24.9 LU) with the lowest stocking rate (0.12 LU/ha) and the lower productivity of labour per animal (0.72 UTA/100 sheep). Group II with larger flocks (138.7 LU) is a system semi-intensive commercial, with higher levels of technology and less use of family labour (51.9%). Group III consists of family farms with a commercial profile, medium-sized flocks (72.6 LU), which has the best performance in terms of global sustainability, given the ability of farmers in organic productions with competitive vision. These systems show notable technical weaknesses due to the lack of agriculture and livestock integration; besides, the high stocking rates exceed carrying capacities and lead to an increased of supplementary feed and, consequently, of feeding cost. Hence the studied farms have a high cost of production and low profitability. As a result of this, the continuity of Groups I and II depends on a profitable result, enough to support the family economy, and is dependent on subsidies. © 2011 Elsevier B.V. All rights reserved.

1. Introduction The central region of Spain (Castilla-La Mancha) has a long tradition of sheep production, with a census which reaches 2.6 million heads, spread over 5.434 farms (16% and 7.8% of the respective national totals). In 2007, 32% of ewes were dairy sheep, most of them Manchega breed (more than 1.5 million head) (INE, 2009). This autochthonous

∗ Corresponding author. Tel.: +34 679859373. E-mail address: [email protected] (P. Toro-Mujica). 0921-4488/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.smallrumres.2011.11.005

breed has been selected for its rusticity, adaptability to the environment and higher milk yield compared to other autochthonous breeds (Serrano et al., 1996; Smulders et al., 2007). Their milk production is entirely intended to cheese making and, since 1985, all is for the denomination of origin “Queso Manchego”, which allows for a differential pay for quality, contributing to the protection of this breed and the preservation of its production systems (Cabezas et al., 2007). As a result of the loss of profitability and the “pay per head” subsidy system from reform of the Common Agricultural Policy in 1992, a high number of producers opted

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to increase production through the following measures: abandonment of grazing, permanent housing of animals, use compound feed and external forage, intensification of the system and ultimately the transformation of traditional grazing farms in semi-intensive systems with little use of land (García et al., 1999; Basset-Mens et al., 2009). Today, concern over environmental issues, food security, and the abandonment of rural areas (which results in a loss of traditional systems of production and cultural heritage) (Paniagua, 2009), than cause the society choose to link the future of livestock in Europe to sustainable systems, as is the case of organic farming (EC, 2007; Bernués et al., 2011; Oudshoorn et al., 2011). In this context, the continuity of traditional pastoral systems is the key to the sustainability of rural areas (Aldanondo et al., 2007). This coupled with the recognition of the productive, environmental and social aspects of traditional grazing systems, by agricultural policies, is a boost for organic farming (Gibon, 2005). Thus, although organic dairy sheep production in Spain has a national census of less than 50 farms (MARM, 2009), it is an emerging sector that has experienced an annual growth of 26% over the period 2001–2009. Dairy sheep production is a traditional mixed cereal-vine–sheep system characterized by its diversity of production systems (land utilization, farm structure, livestock practices, etc.) (Toro-Mujica et al., 2011, Caballero and Fernández-Santos, 2009). The analysis of structural, economics and social variables it is useful to distinguish groups (Gaspar et al., 2011; Perea et al., 2011) and to discriminate while establishing typologies (Gibon et al., 1999). The establishment of these typologies is of great interest in animal production research as a tool to identify the structural features that define every system. Furthermore, from the obtained typologies it is possible to propose improvement measures and specific policies for each of the groups identified. By other side, to establish and characterize groups in ruminants production systems, are commonly used multivariate methods, mainly whole cluster analysis with factorial analysis or principal components analysis (Milán et al., 2003, 2011; Maseda et al., 2004; Riedel et al., 2007; Barrantes et al., 2009; Costa et al., 2010). Given the lack of knowledge about the organic dairy sheep systems in the central region of Spain, this study has double objective: firstly the description of the typology of the farms in Castilla-La Mancha (the region where these are more abundant) in relation to their livestock management, economic and social variables; and secondly to propose the corresponding measures of improvement or support to these farms. 2. Materials and methods 2.1. Study area and data collection The study was conducted in the region of Castilla La Mancha, central Spain, between parallels 38◦ and 41◦ N and meridians 1 and 5W, with an area of 8 × 106 ha of which 53.2% are farmland, 23.5% forest land and 10% pastures (Gallego et al., 1998). The climate in the region is Mediterranean continental with cold winters and few rainfall, hot and very dry summers. The rainy seasons are spring and autumn, with irregular rainfall, which ranges from 400 to 1000 mm per year (Gallego et al., 1998). The annual temperature variation

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is moderate (about 20 ◦ C). In winter, the average minimum temperatures are recorded between 3 and 7 ◦ C and the average maximum summer temperatures reaches values above 25 ◦ C. The winter minimum temperatures are below 0 ◦ C and maximum higher than 31 ◦ C (MAPA, 2005; Brunet et al., 2006). 31 farms were selected, whose main activity relates to the production of sheep milk: 10 organic (total census of the region) and 21 at the stage of conversion to organic (85% of existing). Farms in conversion are considered those that meet more than 80% of the requirements necessary to access the category of “organic” according to the European legislation (EC, 2007). The farms in conversion were selected through stratified random sampling with proportional allocation by province and farm size according to Milán et al. (2003). The information was obtained through collection of primary data from direct interviews with the producers. The interview questionnaire included 226 questions (69% of open answers), relative to the following aspects: sociology (26), facilities (16), reproduction (29), feeding (21), farm structure (27), animal health (9), market and economy (98) according to the methodology proposed by FAO (1989) and used to study organic farming by Mata (2011). The data used correspond to the 2007–2008 years and were obtained during 2008. 2.2. Statistical analysis The development of the typology is made from the methodology proposed by Escobar and Berdegué (1990), used by Gaspar et al. (2011) and Giorgis et al. (2011), which consists of three stages: review and selection of variables; principal component analysis and Cluster analysis. 164 variables were analyzed; these are related to the production and economic structure, size, use and land possession, diversification of production, organization and flock management, productivity, socioeconomic aspects and farm management (Solano et al., 2000). Fifty variables were obtained directly from the information collected through the survey. All other variables come from a combination of original variables or estimated from data collected in the survey, according to the work of Gaspar et al. (2008) and Ruiz et al. (2008). In a first stage, 64 variables were selected, those with a coefficient of variation higher than 60%. Then we analyzed the correlation matrix to eliminate uncorrelated variables and the one with lowest coefficient of variation of each pair with linear dependence (Uriel and Aldás, 2005). Through the selection process were obtained the following 16 variables: farm surface area (ha), pasture area per ewe (ha/ewe), flock size (Livestock Unit: LU, 1 ewe = 0.15 LU), stocking rate (LU/ha), work units (WU) per animal (WU/100 ewes), work unit per area (WU/100 ha), total income (D ), unit cost of fixed labour (D /ewe), total cost of fixed labour (D /year), sheep amortization (% of total cost), machinery amortization (% of total cost), unit gross margin (D /l), net margin (D /year), unit net margin (D /l), unit cost supplementary feed cost (D /l) and land in ownership (%). In a complementary manner, the following technical variables, considered of interest for the description of dairy systems, were included: supplementary feed (kg/ewe and year), milk productivity (l/ewe and year), experience in the activity (years) and technical efficiency (%) (Toro-Mujica et al., 2011). In a second stage, principal component analysis was used in order to reduce the number of variables and summarize the most variability (Uriel and Aldás, 2005). The variables were standardized to avoid influence by the use of different scales (Hair et al., 1999; Picón et al., 2003). Once the components were selected, the orthogonal varimax rotation was applied to relate more easily the selected variables to the extracted factors. The Bartlett sphericity test and the Kaiser–Meyer–Olkin index were applied to verify sample adequacy (KMO > 0.7) (Malhotra, 2004). In a third stage, the farms were classified into groups using Sequential cluster analysis (Peng et al., 2010). Firstly hierarchical groupings were developed based on Ward’s method, using the Euclidean, squared Euclidean and Manhattan distances (Anderberg, 1973). The following nonhierarchical groupings were developed using as centroids and the number of groups those obtained in each of the hierarchical groupings with each distance. Six solution groups were tested using discriminant analysis and analysis of variance. As final solution, it was chosen the non-hierarchical clustering because its discriminant function classified correctly the highest percentage of farms and generated significant differences in the largest number of original variables. This procedure maximizes the homogeneity within groups and heterogeneity between groups (Uriel and Aldás, 2005). For the development of statistical analysis it was used SPSS 11.5.

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3. Results and discussion 3.1. Characteristics of organic dairy sheep farms The variables used in the discriminant analysis together with other 10 useful to characterize organic dairy sheep systems are described in Table 1. It shows that organic dairy sheep production in Castilla-La Mancha has an average stocking rate of 0.38 sheep LU/ha (2.53 ewes/ha), although with high variability (0.22–0.55 LU/ha) with management systems from that extensive to semi-intensive. These results are consistent with what was found by Riedel et al. (2007) to classify sheep farms of Huesca in the Northeast of Spain. Regarding to the farm surface area, the farm type has 300 ha, used by a flock about 80 LU (533 ewes) (Table 1), a 65% higher than that found by Caballero (2001) in traditional sheep systems of the same area. The variability is also present in the flock size (between 15 and 198 LU, or between 99 and 1300 ewes). The stocking rate is negatively correlated with the farm surface area (r = −0.45, p < 0.01). Moreover, there is positive correlation between the farm surface area and the flock size (r = 0.42, p < 0.05). The stocking rate is negatively correlated with the farm surface area (r = −0.45, p < 0.01). Moreover, there is positive correlation between the farm surface area and the flock size (r = 0.42, p < 0.05) in other word, as the surface increases the number of animals increases, although its growing is at lower rates than the livestock surface. This represents a decrease of the stocking rate in the larger area farms, as stated by Gaspar et al. (2008) for sheep flocks in dehesa farms in Extremadura. A 63.18% of the area is for grazing and other agricultural uses such as winter cereals, olives, vines and sunflowers, among others. Also, pastures are dominated by perennial grasses, (Stipa tenacissima, Festuca ovina, Festuca hystrix, Poa pratensis angustifolia, P. bulbosa) and legumes (Medicago spp., Astragalus spp., Trifolium spp.) (Caballero, 2001). The pasture area per ewe did not correlate with supplementary feed per ewe, or with the stocking rate. However, this variable was negatively correlated with total income (r = −0.42, p < 0.05) and flock size (r = −0.49, p < 0.01) variables, which, while suggesting further intensification in larger flocks, reflects the variability of the production system, both in land use and use of external feed. The use of pastures, crop residues and stubbles is very heterogeneous, and it is only a limited portion of the nutritional content of the ration (Molle et al., 2008). This is the reason because all the farms use supplementary feed, reaching an average of 228.7 kg/ewe/year; a very high amount compared with 194.2 kg/ewe/year of concentrate reported by Pérez et al. (1996) for conventional sheep systems. However, no significant correlations between milk productivity and supplementary feed or supplementary feed cost were found, indicating an inefficient use of supplementary feed and the need to improve the nutritional management. Sheep have a milk productivity of 97.3 l/year, while produce 1.06 lambs/year, values similar to those obtained in traditional systems described for the same breed by Caballero (1998) and Gallego et al. (1998). Milk productivity is positively correlated with total income (r = 0.3, p < 0.1), with technical efficiency (r = 0.86, p < 0.01) and unit net

margin (r = 0.39, p < 0.05), indicating its importance for the profitability of the farms. The traditional flock management of the Manchega breed includes two rearing strategies, breeding-milking systems, which is the breeding of lamb and milking sheep simultaneously, and the short-term lactation with weaning at 25–35 days (Gallego et al., 1998). The farms under study follow the system of short-term lactating with weaning average at 42 days, 3 days less than required by the regulation of organic farming (EC, 2007). The milking period on the organic farms is between 60 and 150 days, similar to the traditional ones (Gallego et al., 1998). Farmers have great experience in the activity exceeding 25 years in more than 50% of farms, although the amount of land in ownership only reaches 24%. The farms are essential as a source of family employment, since 64% of the labour is from the owner family. Lobley et al. (2009) find a similar result in small ruminants systems depending on agroecosystems. Furthermore this percentage rises to 74.1% if one considers only the fixed labour. The family labour, which represents 85.2% of labour costs, increase their relative importance as the flock size decreases (r = −0.83, p < 0.01). The labour productivity, with an average of 0.46 WU/100 ewes, is negatively correlated with the flock size (LU) (r = −0.44, p < 0.05), showing a higher productivity of labour as the flock grows. The labour productivity per hectare is 20.91 WU/100 ha and noted for its high variability between farms, which is consistent with different levels of intensification and land uses, given also the positive correlation (r = 0.51, p < 0.01) between the stocking rate and the labour productivity (WU/100 ha). Similar relations were found by Milán et al. (2003) in traditional sheep farms of Catalonia (Spain). The average farm has a total income of 91,577D /year. The incomes are distributed in 54% from the sale of milk, 26% from the sale of lambs and 14% from subsidies; the remaining 6% is generated by the sale of culling, the difference in inventory and the sale of wool. The total cost per farm amounted to average of 90,138D . Three items grouped 82% of the costs: food (37%), labour (33%) and depreciation (12%); a similar result is indicated by Tzouramani et al. (2011) for organic sheep farming in Greece. The remaining 18%, correspond to items independent professional services, repairs and maintenance, supplies, leases, expense interests, insurance, etc. Economic values have high variability, generating 95% of farms a net margin within the range 1439 ± 9136D /year. When comparing the net margin without subsidies, it can be appreciated the importance of these on farm viability (Tzouramani et al., 2011). Similarly, looking at the gross margin, it is clear that the continuity of these systems arises from the generation of income for the household (Table 1). Unit net margin reaffirm the above, showing both negative values with (−0.18D /l) or without (−0.46D /l) incorporation of subsidies. 3.2. Principal components characterizing the farms The KMO test of sampling adequacy showed a value of 0.7 while the Bartlett’s sphericity test showed a satisfactory

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Table 1 Descriptive statistics for technical, economic and social variables. Variables

Mean

Standard deviation

Coefficient of variation

Farm surface area (ha)a Pasture area per ewe (ha/ewe) Flock size (LU)b Stocking rate (LU/ha) Supplementary feed (kg/ewe/year) Work unit per animal (WU/100 ewes) Work unit per area (WU/100 ha) Milk production (l/year) Milk productivity (l/ewe and year) Technical efficiency (%) Total incomes (D /year) Unit income (D //l) Total incomes without subsidies (D /year) Unit income without subsidies (D //l) Total cost (D /year) Unit cost (D /l) Net margin (D /year) Net margin without subsidies (D /year) Unit net margin (D /l) Unit net margin without subsidies (D /l) Cost of fixed labour (D /ewe) Unit cost of fixed labour (D /l) Total cost of fixed labour (D /year) Sheep amortization (% of total cost) Machinery amortization (% of total cost) Unit cost of supplementary feed cost (D /l) Gross margin (D /year)c Unit gross margin (D /l) Experience in the activity (years) Land in ownership (%)

359 0.79 77.9 0.38 228.7 0.46 20.9 1.19 97.3 66.2 91,577 2.0 79,208 1.76 90,138 2.22 1439 −10,930 −0.46 −0.18 48.59 0.82 27,383 8.36 4.79 0.82 16,614 0.54 24.7 23.6

261 0.92 60.2 0.53 144.9 0.40 77.0 2.22 31.0 17.9 71,685 0.2 63,112 0.15 74,065 0.97 24,909 26,018 0.84 0.65 43.04 0.39 24,896 6.94 5.01 0.43 26,653 0.93 15.7 36.3

72.7 116.7 77.2 137.2 63.4 86.3 368.3 185.8 31.8 27.1 78.3 10.0 79.6 8.73 82.2 43.7 1731 238.0 −180.3 361.1 88.6 47.5 90.9 83.0 104.42 52.9 160.4 174.0 61.1 153.7

a b c

Total area: agricultural area + livestock area + grassland area. 1 Adult sheep = 0.15 LU (EC, 2007). Gross margin: net margin + familiar labour cost.

probability value (p < 0.001), indicating the suitability of the analysis. The first four factors that accounted for 70.7% of the original variability (Table 2) were selected as indicated by Malhotra (2004). The first principal component explains 29.3% of the variability and it is associated with indicative variables of the farm dimension, its technological level and its relation

to the use of labour. Higher scores on this factor correspond to the largest flocks, proportionally with increased investment in ewes and machinery which generates high revenues, and optimize the use of labour. The second principal component justifies 18% of the variance, and explains the intensification of land use and its relation to farm surface area, land in ownership and

Table 2 Principal components (PC) selected, eigenvalue, variance explained and accumulated, and correlation coefficients of the variables with each PC. PC

Eigenvalue % variance explained (% variance accumulated)

Variables

Correlation with the PC

1

5.9 29.3 (29.3)

2

3.6 18.0 (47.3)

3

2.8 13.8 (61.1) 1.9 9.6 (70.7)

Total incomes (D /year) Flock size (LU) Sheep amortization (% of total cost) Total cost of fixed labour (D /year) Cost of fixed labour (D /ewe) Machinery amortization (% of total cost) Work unit per animal (WU/100 ewes) Work unit per area (WU/100 ha) Stocking rate (LU/ha) Land in ownership (%) Farm surface area (ha) Pasture area per ewe (ha/ewe) Experience in the activity (years) Unit cost of supplementary feed cost (D /l) Supplementary feed (kg/ewe and year) Unit gross margin (D /l) Technical efficiency (%) Milk productivity (l/ewe/year) Net margin without subsidies (D /year)

0.91 0.95 0.83 0.78 −0.76 0.61 −0.58 0.86 0.74 0.60 −0.53 −0.52 −0.48 −0.81 −0.75 0.74 0.81 0.70 0.61

4

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experience in the activity. The oldest farmers use more land area, although they have proportionately less surface property and a less intensive use. Farms that have the higher scores for this factor shows the highest percentage of land in ownership and less experience in the activity, which is indicative of the commercial character of these farms. The third principal component shows the inverse relation between the use of external feed and the economic result of the operation, accounting for 13.8% of the original variability. Higher scores on this factor indicate a reduced use of external food, improving the economic performance of the farm. The fourth principal component, expresses the relation between technical variables and the economic variability of farms, accounting for 9.6% of the variance. Thus, higher factor scores correspond with high milk productivity and technical efficiency which translate into economic performance. 3.3. Establishment of the typology Cluster analysis that presented the most significant results was the solution of three groups. Clusters I and II consisted of 9 farms each, and Group III was 13 farms. Fig. 1 shows the distribution of farms considering the first two components, where scores of farms distinguishing the 3 groups formed. Groups I and III showed greater homogeneity in regards to the first two principal components, while Group II exhibited greater dispersion of data. Tables 3–5 show the differences between groups; to the 20 variables used to perform the cluster analysis were added 23 others classified as techniques (Table 3), economic (Table 4) and social (Table 5). According to the found differences and similarities between the groups obtained it is possible to describe these as follows. 3.4. Group I: family subsistence system This group concentrates 29% of farms, with a traditional production system with low productivity. Flocks are smallest (on average 24.9 LU), which together with the largest area of pasture available determine low stocking rate. The level of feed supplementation is intermediate and production per ewe is 30% lower than Group III, despite being close to the average 70 l/ewe per year recorded in the region (de Rancourt et al., 2006). Generally, this group corresponds to farms with only family labour, high experience in the activity, rooted in local customs and traditional livestock culture, which base their production on grazing systems guided with shepherd in community surfaces. This is the group with lower labour productivity per animal which is due to low investment and low mechanization, usually with hand milking (Perea et al., 2008). This initially causes high costs of labour and subsequently lower net margins per animal. A comparison of net margin and gross margin, allows to appreciate the social function of this type of production that acts as generator of employment (Lobley et al., 2009) (Table 4), as exemplified by Gaspar et al. (2009) in extensive pasture systems of dehesa in Spain, Mata (2011) in organic dairy cow systems in the

Northwest of Spain and Riedel et al. (2007) in livestock mountain farms in Huesca (Spain), where 58% of farmers economy was totally dependent on sheep production. From the environmental point of view, extensive management, together with the use of marginal pastoral resources and crop residues, presents three aspects of interest. On one hand, as pointed out by Caballero and Fernández-Santos (2009), these are an important food source for sheep, reducing dependence on external inputs and thus exploitation food costs, and on the other hand, these help to reduce erosion and maintain soil quality by preventing the burning of stubble (McCool et al., 2008; Caballero and FernándezSantos, 2009) and finally act as a basis for the generation of differentiated products with certified quality by the Denomination of Origen “Queso Manchego” (Cabezas et al., 2007). This group receives less income from subsidies (Table 4), since these are assigned predominantly per animal (as the decoupling of production subsidies introduced by the reform of the Common Agricultural Policy of 2003 has not been fully applied in the region). To try to improve the conditions of the group, measures must be aimed at: increasing the productivity of the ewes, through a plan of selection and improvement of the Manchega breed; improving the nutritional balance in the key moments of the production cycle (the last third of gestation and lactation); incorporating the production of cereals, which considering the ecological character of the farms, would have a fundamental role and it is possible in a lot of the area (Caballero, 2001). Government support policies should encourage the sowing of cereals or legumes for animal feeding and encourage the incorporation of producers to the Manchega Sheep Breed Scheme Selection (ESROM) (Pérez-Guzmán, 2010). Finally, it is necessary to encourage the transformation on cheese of the milk and the development of specific marketing channels, because 77.4% of farms delivered their milk production to wholesalers. Considering the production volume of this group, a recommended measure is to encourage the creation of Cooperatives, which collect milk, process it and commercialize the product. 3.5. Group II: semi-intensive commercial system The second system consists of 29% of farms. These are of average size farms (359.2 ± 95.8 ha) mainly directed at cultivating cereals for sheep feeding. The animals are intensively managed in the main part of the farms remaining housed most of the year. They have a higher stocking rate (0.68 LU/ha) than the rest of the groups identified. The food is based on conserved forages, produced on the farms and high use of supplementary feed. Both the labour productivity as the sheep, are intermediate in relation to Groups I and III. This group records the lowest presence of family labour (52%), the highest percentage of land owned (51%), larger flocks (138 LU), higher levels of investment (306D /ewe/year), consistent with the commercial nature of the farms, and higher level of technology. The high stocking rate makes difficult the handling of organic sheep farms, although it is within the limits established by the European regulation for organic farming (EC, 1999).

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12 10 8 6

PC 2

4 2 0 -15

-13

-11

-9

-7

-5

-3

-1 -2

1

3

5

7

9

11

13

-4 -6

PC 2 Group I

Group II

Group III

Fig. 1. Positioning of the farms according to the scores obtained for principal component 1 (PC1) and principal component 2 (PC2) where Groups I, II and III are family subsistence system, semi-intensive commercial system and family commercial system respectively.

The farms show high production costs, with lower margins, weakening the techno-economic scope, despite the high productivity in terms of milk and lambs (Tables 4 and 5). Several authors show how the intensification strategy (Riedel et al., 2007; Castel et al., 2011) has invaded the production systems of small ruminants, in response to the reduced costs. In contrast, authors such as Rosati and Aumaitre (2004), Lu et al. (2010) and Tzouramani et al. (2011), show the possibility of increasing profitability through quality and price.

It is necessary to reorganize the management system, increasing mainly the efficiency in use of supplementary feed and the complementation with the use of grazing resources (Toro-Mujica et al., 2011). On the other hand, considering the high investment in infrastructure and machinery, the productivity of labour should be increased through training of staff. Government measures to mitigate the problems must be directed to support technical advice and staff training, particularly with regards to monitoring and recording of revenues and costs, nutrient balance and

Table 3 Mean values and significance level of technical variables for all farms and groups identified. Technical variables

Number of farms Flock size (LU) Stocking rate (LU/ha) Pasture area per ewe (ha/ewe) Farm surface area (ha) Agricultural area (ha) Pasture area (ha) Work unit per animal (WU/100 ewes) Work unit per area (WU/100 ha) Milk production (l/year) Milk productivity (l/ewe and year) Lamb mortality (%) Replacement rate (%) Ewes/Ram Supplementary feed (kg/ewe/year) Supplementary feed (kg/l) Land in ownership (%) Familiar labour (%) Sheep with subsidies (%) Total lamb production (lambs sold/year) Commercial lamb index (lambs sold/100 sheep) Technical efficiency (%) * **

Groups* Mean

I

II

III

p

10.3 77.9 0.38 0.78 359.2 109.2 227.4 0.47 1.19 49,133 97.4 12.9 21.0 48.4 274 3.1 23.6 83.3 90.9 528.0 106.6 66.2

9 24.9c ** 0.12c 1.6a 280.2 12.4 267 0.71a 0.65b 11,186c 75.5b 11.8 21.5 52.5 226b 3.6ab 1.7b 100a 90.4 202.5b 118.8a 55.4b

9 138.7c 0.69a 0.16b 371.9 203.8 165.8 0.53ab 2.5a 88,911a 103.5a 16.9 19.0 47.6 386a 4.1a 50.5a 51.9b 83.5 801.6a 81.1b 69.8a

13 72.6a 0.39b 0.62b 404.9 110.5 242.1 0.27b 0.62b 47,301b 108.3a 10.7 22.1 46.1 229b 2.2b 20.2b 93.4a 96.3 565.5 b 116a 71.7a

– 0.00 0.00 0.00 0.55 0.11 0.59 0.00 0.06 0.00 0.03 0.77 0.76 0.60 0.00 0.03 0.01 0.00 0.18 0.00 0.01 0.09

Group I: family subsistence system, Group II: semi-intensive commercial system, Group III: family commercial system. Means with different letters show significant differences between groups.

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Table 4 Mean values and significance level for economic variables for all farms and groups identified. Groups*

Economic variables

Number of farms Total cost of fixed labour (D /year) Unit cost of fixed labour (D /ewe) Unit cost of total labour (D /ewe/year) Sheep amortization (% of total cost) Machinery amortization (% of total cost) Investment (D ) Unit investment (D /ewe) Supplementary feed cost (D /year) Unit cost of supplementary feed (D /l) Subsidies incomes (D /year) Total costs (D /year) Total incomes including subsidies (D /year) Lamb sales income (D /ewe/year) Net margin without subsidies (D /year) Net margin (D /year) Net margin without subsidies per ewe (D /ewe and year) Net margin per ewe (D /ewe/year) Gross income (D /year) Gross income per ewe (D /ewe/year) Unit gross income (D /l) * **

Mean

I

II

III

p

10.3 27,978 48.6 65.3 8.4 4.8 133,913 277.5 37,149 2.64 12,369 90,138 91,577 47.2 −10,930 1439 −31.5 −7.2 16,614 53.6 0.53

9 16,960b ** 91.5a 107.0a 2.7c 0.95c 40,150c 241.6 10,566c 2.83ab 3613c 34,873b 26,947c 50.2 −11,539b −7926b −72.0b −49.8b 6473b 41.8b 0.56a

9 52,289a 31.6b 59.7b 13.6a 8.41a 256,634a 305.7 80,378a 3.41a 21,366a 174,552a 158,063b 39.5 −37,855b −16,489b −62.1b −38.3b 1709b 34.6c 0.26b

13 17,357b 30.5b 40.4c 8.6b 4.94b 113,865b 282.9 25,626b 1.98b 12,201b 69,957b 90,292b 50.4 8132a 20,334a 17.8a 43.8a 33,953a 74.9a 0.71a

– 0.02 0.00 0.00 0.00 0.00 0.00 0.69 0.00 0.17 0.00 0.00 0.00 0.17 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Group I: family subsistence system, Group II: semi-intensive commercial system, Group III: family commercial system. Means with different letters show significant differences between groups.

management, handling and use of products (manure, crop residues, etc.). At the same time, considering the group productive dimension, they could provide a steady supply throughout the whole year, essentially to support the development of quality cheese and the development of direct marketing channels for organic products.

milk productivity (108 l/ewes), are the highest when comparing the three groups. The favorable performance of these variables, in addition to the ability of farmers in organic production of dealing with more competitiveness, results in positive net margins, significantly different from the other two groups formed (p < 0.05). Mechanization on these farms includes the addition of mechanical milking machines and modernization of facilities. This kind of exploitation is the one with the overall best performance. From a technical-economic point of view this performance is due to the high productivity of sheep and labour, good management of pastoral resources and complementing of animal feed with the grain produced on the farm that deliver a positive economic balance. The correct sheep stocking rate and use of sheep grazing supports the environmental dimension (Gibon et al., 1999). However, as in Group I, grazing on lands outside of their own property (not necessarily organic) is a constraint on shortterm continuity. Thus both at farm and government levels, they should encourage the incorporation of technological improvements aimed at the production and use of own exploitation resources (improving pastures, strategies of

3.6. Group III: family commercial system Group III concentrates 42% of organic dairy sheep farms and it is the predominant production system in Castilla La Mancha. It corresponds to family farms with commercial profile and skilled workforce, supported by favorable stocking rate, in both technical and economic terms (Tables 4 and 5). These are farms with mediumsized flocks (72.6 LU) and intermediate stocking rates (0.38 LU/ha). Supplementary feed per liter produced is lower than the previous groups, because is complemented by guided grazing over large areas. The territorial basis, in which 20% is owned by farmers, is used for crop of cereals, whose purpose is to animal feed (Caballero, 2001). The labour productivity (0.27 WU/100 ewes), as well as

Table 5 Mean values and significance level for social variables for all farms and groups identified. Social variables

Number of farms Experience of activity (years) Age of manager (years) Number of children Farm dependent people * **

Groups* Mean

I

II

III

p

10.3 25 46.8 1.7 4.6

9 36.1a ** 56.1a 2.3 3.4

9 22.0b 45.0b 1.8 5.0

13 18.6b 40.9b 1.4 5.2

– 0.01 0.00 0.17 0.28

Group I: family subsistence system, Group II: semi-intensive commercial system, Group III: family commercial system. Means with different letters show significant differences between groups.

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conservation and use of forage). In the commercial area, this group, like Group I should opt for the cooperative strategy for making and sale cheese, since despite to possess an intermediate volume, this is not guarantee the return on investment in infrastructure, nor the labour associated to a cheese plant at farm level. 4. Conclusions The organic dairy sheep farming systems in CastillaLa Mancha showed high heterogeneity, but generally are based on familiar labour, Manchega breed, use of external feeding resources and local agricultural byproducts. Size, intensification of production and social factors has more discriminating power for classifications of farms. The groups identified differ mainly in the variables related to the economic dimension (income and costs) and the flock, stocking rate, labour productivity, productivity of the sheep, profitability, land in ownership and the farm profile (commercial or family). All these systems should try to integrate agricultural and livestock activities (mixed crop-livestock operation), looking for their positive interactions, in order to reduce the external inputs dependence. The called family subsistence and semi-intensive commercial systems show serious problems of viability, and need a high increase of their gross margin; because their continuity depends on family labour without demands for payment of work hours. Improvement measures and supportive government policies should be geared to the specific problems of each group. So, in family subsistence system, it is of priority access for funding the improvement of structures, in semiintensive commercial system it is necessary to improve the technical advice and labour training, and finally in family commercial system, there should be an increased access to grazing and fodder resources. Also, the three systems must reduce its dependence on external resources through improved feeding of nutritional management and optimization of production. On the other hand, it requires the development of the marketing chain of organic cheese, promoting the short channel. Acknowledgement The main author would like to thank the National Commission on Science and Technology (Spanish initials CONICYT) of Chile for financial support during the last period of her postgraduate studies. References Aldanondo, A., Casanovas, V., Almansa, C., 2007. Explaining farm succession: the impact of farm location and off-farm employment opportunities. Spanish Journal of Agricultural Research 5, 214–225. Anderberg, M., 1973. Cluster Analysis for Applications. Academic Press, New York, United States. Barrantes, O., Ferrer, C., Reine, R., Broca, A., 2009. Categorization of grazing systems to aid the development of land use policy in Aragon, Spain. Grass and Forage Science 64, 26–41. Basset-Mens, C., Ledgard, S., Boyes, M., 2009. Eco-efficiency of intensification scenarios for milk production in New Zealand. Ecological Economics 68, 1615–1625.

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