Author’s Accepted Manuscript

Sheep production systems in semi-arid zone:Changes and simulated bio-economic performancesin a case study in Central Chile

Paula Toro-Mujica, Claudio Aguilar, Raúl Vera,José Rivas, Antón García

PII: S1871-1413(15)00317-0DOI: http://dx.doi.org/10.1016/j.livsci.2015.07.001Reference: LIVSCI2785

To appear in: Livestock Science

Received date: 7 November 2014Revised date: 22 June 2015Accepted date: 2 July 2015

Cite this article as: Paula Toro-Mujica, Claudio Aguilar, Raúl Vera, José Rivasand Antón García, Sheep production systems in semi-arid zone: Changes andsimulated bio-economic performances in a case study in Central Chile, LivestockScience, http://dx.doi.org/10.1016/j.livsci.2015.07.001

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Sheep production systems in semi-arid zone: Changes and simulated bio-economic

performances in a case study in Central Chile

Paula Toro-Mujicaa*

, Claudio Aguilara, Raúl Vera

a, José Rivas

b and Antón García

b

aDepartamento de Ciencias Animales, Facultad de Agronomía e Ingeniería Forestal, Pontificia Universidad Católica

de Chile. Av. Vicuña Mackenna 4860, Santiago, Chile.

bDepartamento de Producción Animal, Facultad de Veterinaria, Universidad de Córdoba, Campus de Rabanales,

14071 Córdoba, España

ABSTRACT

A sheep farm typology was developed to describe the evolution of sheep farming between the

censuses of 1997 and 2007 in the semiarid zone of Central Chile. The typology yielded three

groups (I to III) that accounted for 81, 17 and 2.5% of the farms respectively, differing in farm

size and in the ratio of sheep to cattle and other agricultural activities. Sheep represented 80-86%

of the livestock units in small farms, as opposed to 53% in the larger, more diversified, ranches.

Farm-based technical and economic parameters were not available. Stochastic mathematical

simulation of bioeconomic performance for prototype farms representative of each of the three

groups showed differences accounted for by farm size, farm diversification and animal breed.

Large between-farms within group variation in performance suggest the existence of room for

incorporation of technology. Smaller flock sizes in I and II were associated with larger

production costs and less income per lamb and per kg live weight. Larger farms carrying Merino

produced more lambs per ewe and had lower unitary costs. Unaccounted for costs of family labor

in the smaller farms, together with some evidence of their gradual decapitalization, explain the

continued existence of the small sheep farm sector. Implications for the future development of

these farms are discussed.

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Keywords: small farms, land use, typology, simulation modeling

1. INTRODUCTION

The Mediterranean region of Chile extends for about 1,000 km between 30° and 37° S,

ranging from arid areas in the N to humid in the S (Le Houerou, 2004), and covers some of the

most important agricultural regions of the country. Within this area, the central, most populated,

semi-arid portion includes a variety of land uses, and has a long history of agricultural occupation

(Cáceres, 2010). The valleys and flatlands are dedicated to intensive, irrigated horticulture,

viticulture and pomology, whereas hilly lands are typically dominated by a woody savanna

(Moreira-Muñoz, 2011), and are largely in the hands of family farms whose main source of

income is meat and wool sheep raised in extensive low input-low output systems. These dry

lands, degraded by decades of cereal monocropping (Brunel et al., 2011; Kapur and Ersahin,

2014), are covered by low yielding naturalized annual pastures (Ovalle and Squella, 1996) thus

resulting in low animal and per ha performance (Vera et al., 2013). Grassland-based sheep

production systems are of historical, social, environmental and economic importance throughout

the world’s Mediterranean regions (Cosentino et al., 2014). Sheep systems are frequently based

on sub humid to semiarid degraded rangelands that limit physical productivity and are highly

seasonal (Specht et al., 1988), but may also be associated with multifunctionality characteristics

(Hadgigeorgiou et al., 2005), including conservation of the landscape and biodiversity (Baumont

et al., 2014; Casasús et al., 2012; García-Martínez et al., 2011; Winkler, 1999). The forage

resources of Mediterranean environments and the environmental services that they provide have

been the subject of numerous reviews (e.g. Arroyo and Zedler, 1995; Baumont et al., 2014), but

their future is uncertain (de Rancourt et al., 2006).

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Not surprisingly, between-farm variability is large in Mediterranean regions, compounded by

changes in policies, national and international market opportunities, and variable institutional

support over time (Carmona et al., 2010).

Numerous authors have advanced the view that government policies should ideally consider

this wide variation so as to skew policies towards the weaker sectors (Boyazoglu and Morand-

Fehr, 2001; Boyazoglu et al., 2002; Daskalopoulou and Petrou, 2002), but neither the EU

subsidies (Caballero, 2001) nor the credits and policies available in Chile differentiate between

production systems. In consequence, the importance of sheep production systems in the

Mediterranean continues to decrease (Bernués et al., 2011), a trend replicated in Chile where

sheep farms decreased 17% in the 1997-2007 period (INE, 2007, 1997). Sheep flocks in Central

Chile are mostly based on Suffolk and Merino, and are owned mostly by small and medium

farmers. Reduction in wool prices, particularly for medium wool, have forced farmers to favor

meat production via the gradual introduction of new breeds that give rise to larger, easier to cut

carcasses, and with larger added value. This process further increased between-farms variability,

and modified their production and profitability. In this context the development of farm

typologies constitutes a tool to identify structural features of production systems, generate a

framework within which policies may address the needs of specific farm categories, and identify

farms with a need or potential to adopt new technologies.

Targeting policies, technical assistance, and innovative technologies for fragile environments

can be helped through the identification of farms clusters and recommendation domains as

carried out by typological studies (Barrantes et al., 2009; Cortez-Arriola et al., 2015;

Daskalopoulou and Petrou, 2002; Girard et al., 2008; Madry et al., 2013; Ripoll-Bosch et al.,

2014) together with a variety of information technologies (Blackmore and Apostolate, 2011;

Carmona et al., 2010; Dallies et al., 2009) to support farmer decision making. For example,

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Meyland et al. (2013) recognizing the ecosystem importance of soils, combined a farm typology

with a conceptual cropping system with the purpose of identifying system-appropriate

alternatives to promote soil conservation. Similarly, Bohnet et al., (2011a) developed a farm

typology of cattle systems in the Bowen-Broken watershed, to understand the relationships

between ranchers, land, and the capacity to incorporate sustainable practices in order to decrease

the rate of sedimentation and nutrient loading of the Great Barrier Reef.

Mathematical modeling techniques that integrate economic and agro ecological information

allow the early assessment of possible policies and technical interventions, due to their ability to

rapidly focus on specific farm types and production systems at low cost, as exemplified among

others by Bohnet et al. (2011b), Buysse et al. (2007), Cacho et al. (1995), Catrileo et al. (2009)

Dalgliesh et al. (2009), Ruben et al. (1998), Toro et al. (2009) and Zimmermann et al. (2009).

Thus, the joint use of these tools allows prioritization of support policies for farm groups

based on technical and economic indicators. They also permit to value environmental services

and societal values, including the generation of opportunities for farm labor (Olaizola et al.,

2015), so that marginal farms may reach profitability levels compatible with their continuing

existence.

The objective of the present paper was to develop a typology of sheep production systems in

the Mediterranean area of Central Chile to identify and quantify changes that have taken place in

the interval between the last two agricultural surveys, in terms of structural, and aggregated

economic and technological variables. Since surveys do not identify technical and economic

parameters for production systems, nor was data available elsewhere, the bio-economic

performance of prototype farms representative of each typological group was simulated to

identify groups of farms marginally profitable, and to assess interventions that would increase

their performance and continued livelihood.

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2. MATERIALS AND METHODS

2.2. Study area

The study was carried out in the central, semi-arid zone of Chile (33° to 35° 01’ S, 70° 02' W

to the Pacific Ocean). The surface area is 16,387 km2, 29.5% of which is under native forests,

24% in rangelands, 17% croplands and 9% forestry (INE, 2007). The topography is varied, with

altitudes varying between 0 to 5135 masl, and encompasses four agro ecological regions: Coastal

areas, a Central Valley with ocean influences, an interior Valley, and the Andean foothills. The

average yearly rainfall varies between 518 mm in the interior Valley to 580 mm in the foothills

(Santibañez, 1993) but annual rainfall has been decreasing over the last 20 years (IPPC, 2014).

Winter rainfall predominates, and June and July are the wettest months but with a highly erratic

rainfall ranging between 100 and 300 mm per month (Pizarro, 2007).

2.2. Data selection and population

Farms that raise ruminant animals cover 1.6 million ha in the study region, and 18% of them

carry sheep (INE, 2007). Data available in the 1997 and 2007 surveys for sheep farms include

dimensional and social variables, land tenure, and farmer-related characteristics. Survey data

were then processed to obtain derived quantitative and qualitative variables (Ruiz et al., 2008).

The number of farms carrying at least some sheep was 3,466 in 1997, and 2,793 in 2007 (INE,

2007, 1997). Some farms had nominal numbers of sheep and therefore only sheep farms with a

minimum of 5 ha, stocking rates of at least 0.1 sheep LU/ ha and a minimum of 40% of the LU as

sheep were kept for further analysis. Initially, farms were grouped into four classes, according to

the number of sheep animal units in each, as shown in Table 1. Subsequently, noncommercial

farms with less than 5 LU were excluded from the analysis. These farms represented a large

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proportion of the total, but with a trend towards decreasing absolute numbers. The number of

farms classified as T1 increased over the period 1997-2007, whereas that of T0 decreased,

possibly due to increases in flock size of the latter that allowed them to reclassify as T1 farms in

2007. Excluding category T0 from the study population left a total (T1+T2+T3) of 352 farms for

1997 and 395 for 2007 (Table 1).

To characterize sheep production systems, quantitative variables extracted and/or generated from

the 2007 Census were classified into three categories as follows: dimensional, intensity and

diversification, and social variables. The dimensional variables showed a wide range of variation

in terms of crop area and flock size (Table 2). Variables related to intensification and

diversification were relatively less variable. Sheep numbers as proportion of total livestock, and

the area covered by natural grasslands, showed coefficients of variation less than 50%. Although

with a high coefficient of variation, 95% of the farm population had sheep stocking rates ranging

between 0.25 and 0.32 sheep LU. The persistence of extensive sheep systems in the region is

evidenced by variable, but low, sheep stocking rates, large proportion of native pastures, and low

areas of sown annual and perennial forages. Social variables showed a high percentage of tenured

areas, advanced age of farmers, and low permanent and total labor use, all of which coincide with

the extensive nature of these systems.

The geographical location of sheep farms (Figure 1) showed larger concentrations in the

Coastal area and Central Valley. The municipalities of Litueche, La Estrella and, Marchigüe had

65 and 67% of the commercial farms reported for 1997 and 2007 respectively, and showed an

increasing number of farms owning 30-100 LU.

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2.2.1. Typological classification and Inter-census evolution

The method proposed by Escobar and Berdegué (1990) and used by Guillem et al., (2012),

and Toro-Mujica et al., (2012) was applied to farm classification. It includes three stages, as

follows: review and selection of variables, factor analysis, and cluster analysis. This approach

was applied to the 2007 data yielding a number of farm types as well as equations to estimate

factors for each farm. Quantitative variables included dimension, land use and tenure, and the

composition of the farm crop and livestock portfolio. An initial descriptive analysis led to

elimination of variables with a coefficient of variation less than 60%. This was followed by

calculation of the correlation matrix between variables, to eliminate those highly correlated

among each other (r>90%) and those totally uncorrelated. Bartlett’s chi-square test was used to

ensure adequate correlations, and the Kaiser-Meyer-Olgin index was calculated to determine

sampling adequacy (Uriel and Aldás, 2005). The remaining standardized variables were subject

to a factor analysis using principal components to extract the factors (Hair et al., 2009). Factors

with eigenvalues larger than 1 were considered significant (Uriel and Aldas, 2005). The varimax

rotation was applied to these factors to maximize simplification (Aggelopoulos et al., 2009; Hair

et al., 2009).

A hierarchical cluster analysis was used to separate farms into distinct groups, using the Ward

and centroid methods to delimit them (Álvarez-López et al., 2008; Riveiro et al., 2013). The

Euclidean, square Euclidean and Manhattan distances were calculated for each of these methods.

Selection of the number of groups was based on observation of the respective dendograms and

variation in the cluster coefficients in successive stages (Caballero and Fernandez-Santos, 2009).

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The resolution of the groups was tested with analysis of variance and discriminant analyses, and

the final solution accepted was that for which the discriminant analysis classified the majority of

the farms, and in addition generated significant differences in the largest number of original,

observed, variables.

Contingency and chi-square tests were used to determine the dependence of the typological

groups on flock size prior to further comparisons. When significant associations were found, the

adjusted residues allowed determination of combinations of typological groups with flock size

that differed from expectations (Caballero, 2001). Comparisons between typological groups

identified for the 2007 Census were carried through analysis of variance or Kruskal-Wallis test,

of the original quantitative variables and by contingency and chi-square tests of the qualitative

variables. The variables loadings or weights for each factor in the analysis of the 2007 survey

data were then applied to the 1997 farm data to make the two datasets compatible with each

other. The 1997 data thus transformed, were subjected to a discriminant analysis and grouped in

the typological groups defined for 2007. Changes between the two censuses were assessed by

statistical comparison of the selected variables.

All statistical analyses were performed with SPSS 11.5 (Pérez, 2005).

2.3. Bio-economic performance

Due to lack of on-farm technical and economic parameters for sheep systems in the study

region, a mathematical, dynamic, simulation model was use to estimate the above parameters.

Aguilar et al., (2006) developed a well tested and validated model to estimate lamb production

under grazing conditions with and without supplementation of ewes and lambs, parameterized for

the target region. The model is energy-driven, and all energy transactions use the CSIRO (2007,

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1990) equations with appropriate corrections for the effect of breed. Body condition score (CS)

was used to calculate reproductive performance as in CSIRO (2007), but CS was estimated from

body weight using the regression proposed by Frutos et al., (1997) for Churra ewes in Spain,

corrected based on the standard reference weight (CSIRO, 2007) of the local breeds. CS thus

calculated were input into the logistical regression of Molina et al., (1994) developed for

Manchega ewes in Mediterranean environments, to estimate ewe fertility at mating. Lamb birth

weights were estimated as proposed by Donald and Russell (1970), corrected for the breed effect,

and modified to include the effect of feeding level as in Kelly et al., (2006). The latter authors

also showed a “U” type of relationship between lamb birth weight and lamb death rate, and data

for Merino were derived from Oldham et al., (2011). All other animal parameters were calculated

as in CSIRO (2007). Lambs were sold when they reached 25 kg liveweight and 120 d of age.

The model included three stochastic variables, namely, the voluntary forage intake by grazing

sheep with a normal distribution for random factors as proposed by Naylor et al., (1966), and

ewe and lamb death rates with respective means of 3 and 5% yearly, in a geometric distribution.

The model also calculates costs associated with labor use, feeds, and other minor inputs, and

simulates lamb births and deaths and their weight gain, thus providing estimates of farm outputs

and profitability. Input data consisted of the dimensional and other variables supplemented with

average climate data for the region. Following detailed examination of the large number of output

variables, some of them were selected and summarized to provide data indicative of the scale of

production (Yearly lamb production per farm), the most important farmer-controlled management

variable (Stocking rate), data on reproductive performance as influenced by feeding strategy and

ewe condition, the degree of farm diversification (Sheep-derived income; Sheep LU), and lastly,

estimates of economic farm performance (Net income, Operational income, Average cost, Mean

operational cost). Sheep-derived income is the ratio between income originating in sheep

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production and total farm income. All economic results are expressed per unit lamb produced.

Sheep livestock units (LU) is intended to represent the importance of the sheep flock relative to

all farm livestock (Gaspar et al., 2008; Milan et al., 2006). Net income is the total farm income

minus the market value of all fixed and variable costs. The mean operational cost equals net

income plus the opportunity cost of all animals and the cost of forage consumed. The average

cost is the ratio of total costs divided into the number of lambs sold. The average operational cost

is the ratio between the total operational cost and the number of lambs sold (Aguilar et al., 2006).

The model was employed to simulate a random sample of farms. The sample consisted of 8, 5

and 3 farms for Groups I, II, and III respectively, representing a minimum of 2.5% of the

respective totals. For each farm 20 replicates were performed to accommodate stochastic

variables previously described.

3. RESULTS AND DISCUSSION

3.2. Farm typology, 2007 Census

3.2.1. Characterization variables

Ten of the 35 census quantitative variables were discarded due to low CVs. The correlation

matrix was used to eliminate 11 additional variables, thus leaving 14 for further analysis. The

KMO test was 0.7 suggesting an adequate sample, and Bartlett’s chi-square test was highly

significant (P<0.001) indicating also the adequacy of the correlation matrix for the subsequent

factor analysis (Uriel and Aldas, 2005). Four factors were selected that accounted for 71.7% of

the variance (Table 2).

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The first factor accounted for 36.5% of the variance and was related to the majority of the

dimensional variables examined, including farm size, irrigated area, cropped area, flock size and

size of the labor force. These results partially resembled those of García-Martínez et al. (2009)

who found that the first factor accounting for 23% of the variance was closely correlated with

flock size (r=0.74) in Spain’s mountain cattle systems. In the present study, the second factor

accounted for 13.5% of the variance and was related to the relative size of the sheep component

in the farming systems examined, where low factor values indicated the large proportion of LU as

sheep. The third factor accounted for 11.3% of the variance and was related to high value orchard

and vineyard crops; high values for this factor are indicative of non-sheep related agricultural

activities. Lastly, the fourth factor explains 10.3% of the variance and was related to degree of

farm intensification. High values for this factor suggest larger than average sheep stocking rates,

associated with a larger relative area of sown perennial forages.

3.2.2. Typology

The definitive cluster analysis yielded three groups or clusters using Ward’s method and

estimation of the Euclidean distance. A discriminant analysis using the cluster groups as

classification variable, correctly classified 97% of the farms. Most of the erroneously assigned

farms corresponded to farms of group I that were classified as group II. Groups I, II, and III

accounted for 81, 17, and 2.5% of the farms respectively. Figure 2 shows farm distribution based

on the two first clusters. Differences between Groups I and II are mostly accounted for by the

weights of factor 2, indicative of Group I farms specializing in sheep production. Group III on the

other hand shows high weights for factor 1, indicative of larger farms and higher sheep stocks.

Groups I and II are more homogenous for factor 2, relative to factor 1.

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Table 3 shows between group differences and similitude for the 34 variables related to farm size,

degrees of specialization and intensification, and social variables. Farms in Group III were

significantly larger than in Groups I and II. Differences between farm sizes gave rise to

significant differences in areas of annual and perennial forages, forest plantations, orchards and

vineyards. Group I had smaller percentages of LUs as cattle, croplands, forested areas, and less

diversity in crops and livestock, whereas the sheep stocking rates were significantly larger than

for Group II.

In general, the three groups showed significant differences in 26 of the 34 variables considered

(P<0.05). Using these differences and the geographical locations of the farms it was possible to

characterize each group as follows.

Group I accounts for 80.8% of the region’s sheep farms. The majority are located in the

Coastal and Interior dry land regions, with two municipalities, Litueche and La Estrella,

accounting for 55.8% of the sheep farms (Figure 3). In general they are small to medium-sized

farms, with small areas of cereals (less than 2.3% of the area). 72% of the farms are family

owned and include very limited use of contract labor, thus indicating that they are essentially

family farms operated by their individual owners. This type of farm applies the survival strategy

I-1 defined by Bowler (1992) and Ilbery (2001), in which the challenge is to maintain a viable

agricultural enterprise, without recourse to diversification. Primary education is predominant

(61%), and only 10% of the owners have technical or university degrees. Sheep flocks are small,

and the main feed resource is native pastures. Forest plantations, orchards, perennial pastures and

annual forages areas cover 3.7, 0.7, 0.7 and 1.1% of the average arm area respectively. Lack of

irrigation (0.6% of the farm area) and very limited infrastructure were associated with little

diversity in crops and animals.

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Group II includes 17% of the sheep farms, with similar geographical location to Group I. The

municipality of Marchigue contains 29% of this group’s farms (Figure 3). Farm and flock size

are also similar to Group I, and the main differences are that cattle represent 34.1% of the LU,

and have somewhat larger areas of wheat croplands (3.4%) and forested areas (8.6%).

With regard to qualitative variables, farms in this group have better infrastructure, including

stockyards and water reservoirs. As for Group I, farms are owned by individual farmers, who also

have similar educational levels.

Group III is the smallest group, with only 2.5% of the sheep farms, and are mostly located in

the municipalities of Peralillo and Marchigue (Figure 3). Farm sizes tend to be large and have

correspondingly larger herds, including 70.4% of the LU as sheep and 25.8% as cattle. In this

sense, they are intermediate between the two previous groups. They also include larger areas of

sown pastures and annual forages, reforested areas, and irrigated sectors. Nevertheless, the

percentage areas of these land uses differ from the other groups only in the relative importance of

native pastures (41% of the total area), and larger surface areas of corn, orchards, vineyards and

sown forages, associated with larger irrigated areas.

These farms generally have better infrastructure including facilities for forage conservation

and better stockyards. 60% of the farms belong to various types of corporations, have a paid

administration (90%), and 66% of the managers have technical or university degrees.

3.2.3. Inter-census changes

The comparison between the two censuses showed significant differences (P<0.01) in 21 of

the 35 variables considered (Table 4). There are absolute and relative decreases in livestock

numbers and in cereal crops (mostly wheat crops), and native pastures (Table 4). The average

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number of crop and livestock enterprises per farm decreased between the two censuses,

suggesting an increased specialization of these farms.

Contrary to expectations, the increase in the age of farmers and managers was less than the 10

years elapsed between the two censuses but given the initial advanced age of both, the change

may have been due changes in management and decreases in number of farmers. Regarding

qualitative variables, there was a decrease in areas allocated to stockyards and water reservoirs,

with the exception of an increase in feedlot areas. There were decreases in the areas managed by

estates and de facto corporations, and an increase in farms managed by paid managers.

The classification of the 1997 sheep farms according to the groups defined for 2007

subdivided the farm population in 67.9, 30.4 and 1.7% for groups I to III respectively, showing

that Group II experienced the largest changes. Two trends were evidenced, firstly that a large

proportion of farms increased the sheep stock and decreased that of cattle, thus moving to Group

I, and secondly, a small group of farms increased farm and stock size, moving to Group III.

The largest differences between the two census data was observed in Group I. Contrary to the

overall population, Group I showed significant decreases in farm, corn, and perennial pastures

areas, with a trend towards sheep specialization relying mostly on native pastures and decreasing

areas of cereals. The limited irrigated areas were mostly dedicated to orchards. There was also a

decrease in the numbers of farms with stockyards, and little change in the remaining

infrastructure. Trends in ownership were similar to that of the whole farm population, with

increases in individual ownership and decreases in other legal modalities.

There were no changes in Group II in the relative proportions of cattle to sheep units, or in the

ratios of perennial crops and forages, and in forested areas, but there were decreases in the

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relative areas of native pastures and wheat. The social structure remained unchanged, but the

number of appointed managers decreased. There was an increase in irrigated infrastructure,

possibly due to the application of Law 18450 (MINAGRI, 1985), that stimulated private

investments in irrigation and drainage.

Group III showed the least changes over the 1997-2007 period, with decreases in wheat crops

and in the number of crops and livestock enterprises per farm. Land ownership increased and

permanent paid labor decreased, whereas the number of paid managers increased, associated with

the surface area and ownership type of these farms. The average sheep stocking rate of the region

and of the groups did not change over the 1997-2007 period, indicating the lack of intensification

and the continued dominance of extensive sheep meat production systems. The frequency of

stockyards and water reservoirs in Group III (30% each) was considerably larger than in Groups I

and II (barns 4.1 and 16.7%; water reservoirs 3.1 and 15.2% respectively). Furthermore, the

majority of farms in Group III were managed by a paid administrator.

3.3. Bio-economic performance

The simulated bio-economic performance showed between-group differences that are

essentially the consequence of three variables: farm size, farm diversification, and animal breed

respectively (Table 5). The smaller flock size of Groups I and II led to larger production costs

and less income per lamb and per kg live weight. The degree of diversification is reflected in the

relative income from sheep, the relative number of sheep LU, and of total LU/ha. Similar to data

reported by Cecchi et al., (2010) Group I farms that had little or no access to irrigation practiced

extensive grazing management, with little or no inclusion of crops so that the large majority of

16

income was derived from sheep. Differences associated with sheep breed can be observed

between Groups I and II relative to Group III since Merino predominate in the latter and it is

associated with larger flocks, lighter birth and weaning weights, and a larger number of lambs

and kg sold per ewe due to higher reproductive performance. On the other hand, reliance on

native pastures, limited sown pastures and scarce supplementation led to similar production per

hectare. Nevertheless, Groups I and II tended to have slightly larger stocking rates if expressed as

kg/ha.

The large (60%) difference between average financial and operational costs, suggests the

existence of opportunity costs ignored by small farmers and that explain the continued existence

of these systems. This finding is similar to that observed for ecological sheep farms in Castilla-La

Mancha, where unaccounted for costs of family labor explain the subsistence of family farms

(Toro-Mujica et al., 2012).

The present results indicate that sheep farms are profit-seeking, but profitability is variable. Some

costs, particularly in the smaller properties, are underestimated because farmers use family labor

exclusively and possibly also by hidden decapitalization of farm infrastructure. Simulation

modeling of prototypes farms showed that economic variables were highly variable in response

to modest variations in the stochastic variables, and to variations in the farmer-controlled

variables of stocking rate and sheep breed. This is a positive outcome since it suggests that there

are opportunities for improved farm management in Groups I and II probably, but not

exclusively, through horizontal transference of technology and knowledge. The alternative

hypothesis, that is, that differences in performance between farms within groups are due to

differences in resources endowment could not be tested with the available data. Regardless, other

forms of economic incentives, such as directed credit, and technical assistance should be

17

implemented if society values the social, cultural and environmental contribution of small

farmers.

Small and medium farms are likely to be more susceptible to climate change than larger

properties in which the scale and variability of the landscape affords more alternatives to alleviate

climate stress. It is predicted that in the next two to three decades temperatures in Chile will

increase 2-3ºC, with more pronounced increases of 2.7-3ºC in the study region

(Neuenschwander, 2010). Australian simulation studies for regions with comparable climate

indicate that a 3ºC temperature increase might decrease forage production for up to 30%

(McKeon et al., 2009). Equivalent simulation models and research approaches are available for

Chile (Castellaro et al, 2012; Vera et al, 2013), but the possible impacts of climate change have

not yet been explored. The study region has experienced a sustained rate of decrease in annual

rainfall since 1960, whose effect would be further aggravated if temperatures increase as

anticipated, with possible negative consequences on plant growth. In this context the continued

existence of small farms, such as those of Groups I and II, highly dependent on extensive grazing

of native pastures, raises a number of issues (IFPRI, 2005). Similar to the findings of Dubeuf

(2011), sheep systems in Mediterranean Chile face significant economic, social and

environmental challenges. Achieving balance between them would require government support in

terms of infrastructure and training, as well as a larger and more systematic integration with crops

in mixed-farming systems, all of which would contribute to larger profitability, and conservation

of the landscape and its biodiversity (Franco et al., 2012).

It has been repeatedly stated (e.g. Devendra, 2010) that technologies for these fragile systems

need to be socially acceptable, allow for the conservation of rural culture and traditions, enhance

systems’ resilience when faced with imminent climate changes, and conserve biodiversity.

18

European institutions and policies consider that small farms have an important role in rural areas.

Evidence from the European small farm sector indicates that they constitute a means to deal with

rural poverty, that it is advisable to promote their diversification, and that they can provide

environmental and other non-trade public goods (ENRD, 2011, 2010). As Altieri and Toledo

(2011) indicate, traditional family farms such as those in Groups I and II have the potential to

implement agro-ecological management practices based on maintenance of biodiversity,

provision of ecosystem services, allow the existence of regulatory socio-cultural institutions, and

may generate products with local identity. In that case, they may therefore contribute to local,

regional and national food security, and additionally, they may reduce local input and products

transportation costs, and reduce the agricultural ecological footprint. In this context and to the

extent possible, agricultural policies should support sheep systems of maximum resilience, self-

sufficient, economically viable, energetically efficient, socially acceptable, and able to conserve

natural resources. Regarding diversification, the European evidence also suggests that impact

may be related to household age and education, and farm location (distance from markets), as

well as agricultural policy.

The continued existence of small farms managed by an older population with little formal

education is a matter of concern. Strong sector- and group-specific, long-term policies would be

required in Chile, as well as elsewhere, if their continued existence is considered desirable from

the point of view of maintaining a rural population that can conserve typical lifestyles and

traditions, and maintain cultural identity and landscape diversity of the region. Examples of such

policies include the development of a strong story line (“relato” in Spanish), enhanced access to

market and technical information, development of growth poles, diminishing transaction costs,

promotion of continued rural education for the decreasing young to medium-age remaining

19

population, opportunities for off-farm employment, stimulus to the diversification of sheep

systems beyond just simply undifferentiated meat production, promotion and valorization of

land and water conservation and promotion of artisanal leather, wool and other byproducts of

sheep production, strengthening technical and financial assistance, and linking small farms to

supply and marketing chains, among others but it is clear that no single policy or action would be

sufficient to achieve these aims (Ilbery, 1991, McNally, 2001, Morgan-Davies et al., 2012). The

role of women, rural schools, and other local institutions in this scenario remains to be

established but it would appear to be a major priority (Kazakopoulos and Gidarakou, 2003).

4. CONCLUSIONS

The area of native pastures in the study region decreased 30% over 10 years, reflecting a

partial abandonment of sheep raising in exchange for higher value agricultural activities. The

smaller number of paid labor, the larger surface area per laborer and larger own areas over time

indicate modest changes in the farm portfolio, as well as rural migration to urban centers. In

general, sheep farms did not show improvements in irrigation or farmer education, indicating the

limited dynamism of these farms. The number of sheep-only farms in Group I increased from

1997 to 2007, and was accompanied by increases in flock size and area sown to improved

pastures. These changes were interpreted as a need to diminish cost and increase farm outputs,

but with continued importance of sheep production as the main source of income for family

farms. On the other hand the sheep-cattle farms of group II decreased in number. These farms

were better endowed, including limited irrigation, which allowed for more profitable land uses

that represented 50% of farm income.

20

The much larger surface areas of Group III farms allowed the coexistence of sheep farming

with other agricultural activities, where sheep graze cereal stubbles, forested areas, and irrigated

crops. Simulated economic performance of these farms showed lower costs per lamb produced

due to dilution of labor costs over a large number of agricultural activities. The use of Merino in

these farms resulted in a larger number of lambs produced per ewe, leading to larger farm

profitability. Lastly, large simulated bioeconomic differences in performance between farms

within groups suggests that there exists opportunities for improvements in technical and

economic performance among the least profitable small farms.

5. ACKNOLEDGEMENTS

The senior author acknowledges support of Comisión Nacional de Ciencia y Tecnología

(CONICYT), project FONDECYT 3130346 for her postdoctoral studies. The comments of two

anonymous reviewers are appreciated.

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Figure captions:

Figure 1: Geographical location of sheep farms

Figure 2: Distribution of the sheep farms according to the scores obtained for Factor 1 and

Factor 2

Figure 3: Geographical location of sheep farms of Groups I, II and III in 2007

31

Table 1: Distribution of sheep farms according to flock size reported in the 1997 and 2007

agricultural surveys, expressed in livestock units (LU).

Class No of LU

1997 2007 Change, %

Nº % Nº %

T0 Less than 5 LU 612 63,5 471 42,4 -23,0

T1 5-30 LU 277 28,7 333 48,5 20,2

T2 30-100 LU 40 4,1 39 5,7 -2,5

T3 >100 LU 35 3,6 23 3,4 -34,3

Commercial sheep farms Over 5 LU 352 36,5 395 57,6 12,2

Total no. of farms

964 100 866 100 -10,2

32

Table 2: Factors selected in the factor analysis, eigenvalues, partial and cumulative variances and

correlation coefficients of the variables with the corresponding factor (as per Hairs et al., 2009 ).

Factor

Nº.

Eigenvalue

Observed Variables Correlation of variables

with the respective factor

% explained variance

(% cumulative

variance)

1

5,1

36,5

(36,5)

Surface area, ha 0,90

Sheep LU 0,86

Cereals, ha 0,72

Cattle LU 0,75

Irrigated area, ha 0,79

Total workers, nº 0,86

Forested area, ha 0,90

2

1,9 Cattle, %LU 0,90

13,5 Sheep, %LU -0,88

(50,0) No. of crops 0,45

3

1,6 Orchards, ha

Vineyards, ha

0,84

0,86 11,3

(61,3)

4

1,4 Sheep stocking rate, LU/ha

Perennial forages , % area

0,84

0,85 10,3

(71,6)

33

33

Table 3: Statistical descriptors of three farm groups identified through hierarchical cluster

analysis of the 2007 survey data, based on sheep livestock units.

Total

Groups

p Dimensional variables I II III

Number of farms --- 319 66 10 --

Surface area, ha 160.1±444.5 137.3±345.2ª 100.6±87.8ª 1281.6±1715b 0.01

Livestock units, sheep 26.6±58.4 24.4±49.2ª 15.7±13.2ª 165.9±199.2b 0.01

Livestock units, cattle 4.5±20.6 1.1±3.9ª 9.4±7.1b 78.2±105.3c 0.01

Total LU 33.9±73.8 28.3±53.0ª 28.6±20.8ª 249.0±288.4b 0.01

Cereals, ha 2.7±9.3 1.8±6.7ª 4.3±6.3ª 19.2±39.4b 0.01

Maize, ha 0.6±6.5 0.1±0.7ª 0.5±1.6ª 16.7±38.9b 0.01

Wheat, ha 1.9±6.6 1.5±6.6ª 3.6±6.2b 2.5±5.4ªb 0.05

Native pastures, ha 64.4±30.4 102.5±308.3ª 64.4±73.7b 604.1±1097.3ªb 0.01

Improved pastures, ha 1.5±19.9 1.8±22.2 0.4±3.0 1.7±5.4 0.88

Sown perennial forages, ha 0.3±2.2 0.1±0.7ª 0.5±2.6ª 5.3±10.3b 0.01

Sown anual forages, ha 2.9±38.4 0.7±3.0ª 1.0±1.6ª 85.5±237.3b 0.01

Forested area, ha 12.2±107.4 3.6±14.0ª 11.1±27.2ª 294.8±633.2b 0.01

Orchards, ha 7.2±0.1 0.3±1.3ª 0.2±0.5ª 13.8±11.7b 0.01

Vineyards, ha 0.5±4.0 0.1±1.0ª 0.05±0.2ª 16.2±19.1b 0.01

Irrigated area, ha 1.1±11.9 0.2±1.9ª 0.4±1.6ª 34.8±69.3b 0.01

Variables related to intensification

Sheep stocking rate, LU/ha 0.28±0.33 0.31±0.36b 0.17±0.07ª 0.15±0.04ª 0.01

Sheep, %LU 80.4±19.7 86.1±16.3c 53.8±9.9ª 70.4±18.4b 0.01

Cattle, %LU 9.1±15.1 3.4±8.5ª 34.1±12.3c 25.8±18.7b 0.01

Native pastures, % surface area 64.4±30.4 65.5±30.7b 63.2±28.0b 41.0±28.9ª 0.04

Improved pastures, % surface area 1.6±10.2 1.8±11.1 0.6±4.5 0.5±1.5 0.63

Sown perennial forages, % surface

area 0.7±4.3 0.7±4.7 0.4±1.3 1.3±2.1 0.48

Sown annual forages, % surface area 1.2±3.8 1.1±3.7 1.3±2.7 2.9±8.7 0.32

Forested area, % surface area 4.6±10.3 3.7±9.4ª 8.6±12.7b 8.2±15.0b 0.01

Orchards, % surface area 0.7±2.6 0.7±2.7ª 0.3±1.1ª 3.5±4.2b 0.01

Cereals, % surface area 2.7±7.2 2.3±7.2ª 4.6±6.3b 3.5±10.6ª 0.05

Maize grain, % surface area 0.7±5.4 0.6±5.7 0.8±3.1 3.0±8.7 0.39

Wheat, % surface area 1.6±4.2 1.3±3.8ª 3.4±5.7b 0.7±1.9ª 0.01

Irrigated area, % surface area 0.7±5.5 0.6±5.7ª 0.7±3.2ª 4.6±10.6b 0.08

Livestock classes, nº 2.9±1.3 2.6±1.2ª 4.0±1.0b 2.9±0.9ª 0.01

Crops, nº 1.2±1.5 1.0±1.3ª 2.4±1.8b 2.2±2.1b 0.01

Social variables

Age, years 61.9±13.3 61.4±13.2 63.9±13.7 70.6±10.1 0.19

Ownland, % 87.2±30.3 71.5±42.7ª 77.2±38.3ªb 99.1±2.9b 0.08

Total workers, nº 1.7±2.7 1.3±1.4ª 1.5±1.1ª 13.0±9.81b 0.01

Permanent workers, nº 0.8±2.6 0.47±0.7ª 0.42±0.7ª 12.8±10.0b 0.01

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Table 4: Farm structural changes between 1997 and 2007.

Groups

Total I II III

Dimensional Variables 1997 2007 p 1997 2007 p 1997 2007 p 1997 2007 p

Number of farms 239 319 --- 107 66 --- 6 10 --- 352 395 ---

Surface area, ha 204.6b 137.3a 0.04 166.9 100.6 0.96 1281.4 1281.6 0.99 211.5 160.1 0.11 Animal units, sheep 37.6 24.5 0.39 23.4 15.8 0.86 211.8 165.9 0.66 36.2 26.6 0.59

Animal units, cattle 3.2b 1.1a 0.001 16.3 9.4 0.42 130.6 78.2 0.42 9.3b 4.5a 0.01

Total animal units 45.3 28.3 0.25 44.5 28.6 0.59 474.2 249 0.22 52.3b 34a 0.02 Cereals, ha 11.5b 1.8a 0.001 18.9b 4.3a 0.001 82.7 19.2 0.11 17.6b 2.7a 0.01

Wheat, ha 5.5b 1.5a 0.001 8.5 3.6 0.001 36.1b 2.5a 0.04 6.9b 1.9a 0.01

Native pastures, ha 163.4b 102.5a 0.03 115.2b 64.4a 0.05 443 604.1 0.74 153.5b 108.8a 0.06 Improved pastures, ha 0.7 1.8 0.48 11.8 0.4 0.19 317.4 1.7 0.15 9.5 1.5 0.11

Sown perennial forages, ha 1.2b 0.1a 0.02 3 0.5 0.25 282 5.3 0.13 6.5 0.3 0.11

Forested area, ha 8.0 3.6 0.31 7.4 11.1 0.39 267.2 294.8 0.93 12.3 12.2 0.99 Orchards, ha 0.1a 0.3b 0.05 0.4 0.2 0.47 13.3 13.8 0.95 0.4 0.6 0.48

Irrigated area, ha 1.3b 0.2a 0.001 5.6 0.4 0.22 25.8 34.8 0.79 3.0 1.1 0.13

Variables related to intensification Sheep stocking rate, LU/ha 0.29 0.31 0.46 0.19 0.17 0.15 0.16 0.15 0.59 0.3 0.3 0.27

Sheep, %LU 84.4 86.2 0.23 51.7 53.8 0.22 60.8 70.4 0.33 74.1ª 80.4b 0.01

Cattle, %LU 5.0b 3.4a 0.03 35.2 34.1 0.70 21.9 25.8 0.66 14.4b 9.1b 0.01 Native pastures, % surface area 74.7b 65.4a 0.001 73.8a 63.2b 0.001 58.8 41 0.31 74.1b 64.4a 0.01

Improved pastures, % surface area 0.5a 1.8b 0.07 2.0 0.6 0.34 15 0.5 0.17 1.2 1.6 0.56

Sown perennial forages, % surface area 1.5 0.7 0.17 0.9 0.4 0.21 10.9 2.1 0.21 1.5b 0.7a 0.08

Forested area, % surface area 2.1 3.7 0.97 3.6a 8.6b 0.00 10.1 8.2 0.82 2.7 4.6 0.79

Orchards, % surface area 0.2a 0.7b 0.02 0.3 0.3 0.82 0.8 3.5 0.15 0.2a 0.7b 0.01 Cereals, % surface area 10.7b 2.3a 0.01 14.4b 4.6a 0.00 5.4 3.7 0.63 14.2b 2.7a 0.01

Wheat, % surface area 5.0 1.3 0.001 7b 3.4a 0.01 2.5 0.7 0.13 5.5b 1.6a 0.01

Irrigated area, % surface area 2.2 0.6 0.001 1.2 0.7 0.54 1.4 4.6 0.49 1.9b 0.7a 0.06 Livestock classes, nº 3.2 2.6 0.001 4.8 4.0 0.001 4.2 2.9 0.01 3.7b 2.9a 0.01

Crops, nº 2.0 1.0 0.001 3.4 2.4 0.001 5.3 2.2 0.04 2.5b 1.2a 0.01

Social variables

Age, years 59.2a 61.4b 0.06 60.4a 64b 0.10 60 70.7 0.4 59.5a 61.9b 0.02

Land own, % 62.9a 71.5b 0.02 61.3a 77.2b 0.05 50a 99.1b 0.05 62.2a 73.2b 0.01

Total workers, nº 2.4b 0.4a 0.001 2.8 0.3 0.001 30 12.6 0.22 3.0b 0.7a 0.01 Permanent workers, nº 1.8b 0.1a 0.001 1.9 0.2 0.001 22.3b 2.5a 0.06 2.2b 0.2a 0.01

Surface area per paid worker, ha 233.0b 92.4a 0.08 83.5a 190.1b 0.02 108.6a 303.2b 0.04 90a 229.2b 0.01

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Table 5: Bio-economic results of simulation runs for prototypes of three farm groups (monetary

values in Chilean $ of 2013 (1 US=$ 610)).

Variable Group I Group II Group III p

Yearly lamb production per farm, nº 59.4±26.8a 101±58

a 2,820±2,917.5b 0.01

Lambs per ewe per year, nº 0.8±0.1a 0.8±0.1

a 1±0.1b 0.05

Sheep-derived income, % of total 79.5±23.4b 54.3±20.8

ab 44.9±11.4a 0.07

Stocking rate, LU/ha pasture 0.35±0.13 0.39±0.31 0.29±0.09 0.85

Stocking rate, LU/ha total 0.22±0.05b 0.19±0.07

ab 0.11±0.01a 0.10

Net income, $/lamb 10,300±8,009a 8,721±6,055

a 17,985±3,224b 0.07

Operational income*, $/lamb 27,724±3,811a 30,003±1,808

ab 33,598±5,015b 0.09

Average cost, $/lamb 29,311±7,413b 33,062±6,154

b 23,964±3,169a 0.08

Mean operational cost, $/lamb 11,887±2,724b 11,779±1,819

ab 8,351±4,961a 0.10

Sheep LU, % of total 95.5±8.7b 59.1±5.6

a 70.2±10.1a ≤0.01

*Does not consider the opportunity costs of pastures and animals.

Highlights

Sheep systems in the semiarid zone of Central Chile are classified into three farms types

Meat- and- wool sheep are mainly run by family farms at close to subsistence level.

Economic subsistence of family farms occurs at the expense of not valuing some production

resources

Social and environmental sustainability require support of public policies

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Figure 2: Geographical location of sheep farms

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Figure 2: Distribution of the sheep farms according to the scores obtained for Factor 1 and

Factor 2

-4

-3

-2

-1

0

1

2

3

4

5

6

-4 -2 0 2 4 6 8 10

Fact

or

2

Factor 1

Group I Group II Group III

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Figure 3: Geographical location of sheep farms of Groups I, II and III in 2007