Competitiveness of beef farming in Rio Grande do Sul

Agricultural Systems 104 (2011) 689–693
Contents lists available at SciVerse ScienceDirect
Agricultural Systems
journal homepage: www.elsevier.com/locate/agsy
Competitiveness of beef farming in Rio Grande do Sul State, Brazil
P.R. Marques a, J.O.J. Barcellos a,b,⇑, C. McManus a,b, R.P. Oaigen c, F.C. Collares a,
M.E.A. Canozzi a, V.N. Lampert d
a
Núcleo de Estudos em Sistemas de Produção de Bovinos de Corte e Cadeia Produtiva-NESPRO, Departamento de Zootecnia, Faculdade de Agronomia, UFRGS,
Avenida Bento Gonçalves, no. 7712, 91.540-000, Porto Alegre, Rio Grande do Sul, Brazil
b
Bolsistas de Produtividade do CNPq
c
Faculdade de Medicina Veterinária – Universidade Federal do Pará (UFPA), Brazil
d
Universidade Estadual do Mato Grosso do Sul (UEMS), Brazil
a r t i c l e
i n f o
Article history:
Received 17 March 2011
Received in revised form 20 July 2011
Accepted 17 August 2011
Available online 20 September 2011
Keywords:
Level
Institutional arrangements
Management
Market access
Technology adoption
a b s t r a c t
The aim of this study was to typify competitiveness on beef cattle farms from the western border region
of the state of Rio Grande do Sul, Brazil. Sixty-three farmers, each with an individual farm area exceeding
900 ha, were interviewed using a semi-structured questionnaire, divided in four sections: technology
(TEC), management (MAN), market relationships (MR) and institutional environment (IE). Data were analysed using Cluster and Discriminant analyses. Beef cattle producers were divided into three levels of
competitiveness: low (LCL), medium (MCL) and high (HCL). Comparing LCL MCL, the former group of
farmers showed lower levels of pasture and reproduction management than the latter (subfactors within
TEC). When LCL HCL were compared, the main differences were the lower access to technological innovation and low investment with herd genetics of LCL compared with HCL. The lower level of management
activities (performance recording, animal handling and calculation of financial parameters) of MCL compared with HCL were the main variables that differ between these farms. Cattle producers interviewed
here were, in general, competitive, mainly due to the use of technologies on farm. However, there were
limitations in the variables related to management.
Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction
Brazilian beef cattle production and exports have shown significant growth since 2000. From 2000 to 2006, meat exports grew in
volume and value by 371% and 480.08%, respectively (Neves and
Castro, 2007). Production areas within the country are showing a
shift from the southern and southeastern regions (which have more
expensive land and a smaller scale of production) to the north and
center-west regions, where these factors are more favorable (IBGE,
2006). This has led to a loss in competiveness of the former, more traditional, regions, especially in the South. This translocation has also
been seen within regions. For example, in Rio Grande do Sul State
(RS) beef cattle production is becoming concentrated in regions with
less crop production (soybean and corn) such as the Western Frontier (ANUALPEC, 2010).
Bio-economic indicators in RS State are low, with farmers frequently operating at a loss for this type of production (Andreatta,
2009). A wide range of breeding and management systems are used
⇑ Corresponding author at: Núcleo de Estudos em Sistemas de Produção de
Bovinos de Corte e Cadeia Produtiva-NESPRO, Departamento de Zootecnia, Faculdade de Agronomia, UFRGS, Avenida Bento Gonçalves, no. 7712, 91.540-000, Porto
Alegre, Rio Grande do Sul, Brazil. Tel.: +55 51 33 08 60 42; fax: +55 51 33 02 60 48.
E-mail address: [email protected] (J.O.J. Barcellos).
0308-521X/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved.
doi:10.1016/j.agsy.2011.08.002
to produce cattle on farms in this region. Technology used on these
farms varies widely, with a resulting variation in socioeconomic
indicators (SEBRAE/SENAR/FARSUL, 2005).
Competitiveness of beef cattle farming has a direct relationship
with the economics of the production system. To be more competitive, as well as more profitable, the production system must be sustainable, this being a more widespread definition of economic
efficiency (ANUALPEC, 2009). It is important, therefore, to carry
out a detailed study of the region where most beef production is concentrated in RS (Western Frontier) so that strategies can be identified to assure competitiveness and maintain its importance within
the region. Results of this diagnosis should be used to aid in understanding the structure and function of production systems, after
characterizing and grouping the farmers, as well as typifying them
using the characteristics that these groups have in common (Aguinaga, 2009). The aim of the present study was to typify farmer competitiveness in the Western Frontier of Rio Grande do Sul State, Brazil,
based on directional indicators (limiting and/or stimulating).
2. Material and methods
This study was carried out with farmers in the Western Frontier
region of Rio Grande do Sul State, Brazil. The farmers were from an
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P.R. Marques et al. / Agricultural Systems 104 (2011) 689–693
intentional non-probabilistic sample. Only large farms were chosen,
with an individual farm area greater than 900 ha. The farmers were
interviewed in the municipalities of Alegrete, Santana do
Livramento, São Gabriel, Rosário do Sul, Uruguaiana, Quaraí, Itaqui
and São Borja (Fig. 1) which makes up 90.1% of the regional herd
(IBGE/SIDRA, 2010). Eight specialists aided in the elaboration of
the questionnaire which was then applied to 63 farmers, varying
from 5 to 10 per municipality. These were private consultants working with beef cattle production in the region. They included veterinarians, agronomists, administrators and economists working
with technology, management, market relations and institutional
environment of the beef cattle production chain.
This is the major rice producing region of Rio Grande do Sul
State, as well as having the largest beef herd (3,285,590 head)
which is about 22.2% of the total cattle herd for the State (IBGE,
2006). Cattle are farmed extensively, based on unimproved native
pasture and high stocking rates. Mean productivity indices are usually low. Farms with an area above 500 ha occupy more than 72% of
the agricultural land in the region, but represent less than 11% of
the total number of farms. These large farms were responsible
for most of the meat production in the region, as they produced
on a scale which could influence the local market for beef in Rio
Grande do Sul State.
The rapid assessment or quick appraisal method was used, taking
into consideration the study objectives, their scope and the time
period. This objective method combines different methods of information collection with flexibility and preserves statistical rigour
(USAID, 2007). These studies are classified as a mixture of quantitative and qualitative research (Oliveira, 2008).
The present study used earlier studies on regional competitiveness (Meister and Moura, 2007; Oaigen, 2010) or typification of
profiles of beef cattle farmers (Andreatta, 2009; Aguinaga, 2009)
as a basis.
Competiveness was evaluated by considering that the efficiency
of a production system is determined by various factors, which
may be controlled by businesses or government. Each element
was first classified as controllable, almost-controllable or not controllable by the eight specialists who helped to structure the base
questionnaire. These factors, classified as vectors, were grouped
into four large blocks: technology (TEC), management (MAN), market relations (MR) and institutional environment (IE). These four
blocks were divided into sub-factors which were identified and
analysed as to the intensity with which they contributed (favorably
or not) to the efficiency of the sector.
After interviewing specialists and carrying out preliminary
research (literature revision) each sub-factor received a score
(Table 1). The subfactors of each vector summed 1.00. The score
for each vector was defined by the level of influence the farmer
had over it. The vectors which a farmer had greater ability to alter
had higher weights. The weights for the sub-factors took into
Table 1
Variables (vectors and subfactors) with their respective weights.
Variable
Weight
Technology (TEC)
Type of production system (TYP)
Pasture quality, management and grass species (PAST)
Animal supplementation (SUP)
Integration crops and animal production (ICAP)
Reproductive management (REP)
Herd genetics (GEN)
Herd health (HH)
Production indices control (PIC)
Regular technical assistance (RTA)
Routine management of animals (ROT)
Management (MAN)
Workforce training (WT)
Patrimonial (PAT)
Finance and cash flow (FCF)
Strategic planning (EP)
Control of production costs (CPC)
Calculate financial indicators (CFI)
Herd identification (HI)
Commercialization (COM)
Use of Information Technology on-farm (IT)
Scale of production (SP)
Market relationships (MR)
Relationship farmer–supplier (RFS)
Relationship farmer–abattoir (RFA)
Formation of prices (FP)
Product differentiation (PD)
Institutional environment (IE)
Access to technological innovations (ATI)
Tax and workforce policies (TWP)
Environmental policies and fiscalization (EPF)
Agricultural credit policies (ACP)
Health policies and fiscalization (HPF)
Official legislation and farm ownership (OLFO)
Farmer organization (FO)
3.50
0.10
0.15
0.15
0.10
0.10
0.05
0.15
0.05
0.10
0.05
3.50
0.15
0.05
0.10
0.05
0.15
0.10
0.10
0.10
0.05
0.15
2.00
0.35
0.35
0.15
0.15
1.00
0.15
0.15
0.15
0.10
0.15
0.10
0.20
consideration the degree of importance of that sub-factor for the
competitiveness of a beef cattle production system (Table 1).
There were four questions per sub-factor and answers were positive or negative. A greater number of positive answers meant a more
favorable participation of the sub-factor in the competitiveness of
the business. After applying the questionnaire to the farmers, each
sub-factor received a status varying from highly unfavorable (HU)
to highly favorable (HF). The criteria used to qualify the answer
and determine the percentage of acceptance (PA) was HF – highly
favorable: 4 (four) positive answers (100%); F – favorable: 3 (three)
positive answers (75%); N – neutral: 2 (two) positive answers (50%);
U –unfavorable: 1 (one) positive answer (25%); HU – highly unfavorable: no positive answers (0%).
A competitiveness index (CI) was created from the scores for each
sub-factor. This was composed of scores and weights (values)
between competitiveness vectors and subfactors. The vectors
Fig. 1. Localization of West Frontier in Brazil. Source: www.mapas.fee.tche.br.
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P.R. Marques et al. / Agricultural Systems 104 (2011) 689–693
technology, management, market relations and institutional
environment were evaluated.
The sub-factor values (SV) were obtained from the answers
given by the farmers, using the percentage of acceptance (PA) of
each reply and weight (WS) as in:
SV ¼ PA WS
ð1Þ
The vector value (VV) was obtained from the sum of values for
subfactors and vector weights (VW).
"
VV ¼
n
X
!,
NFn
n¼1
n
X
!#
RFn
VW
ð2Þ
Table 3
Descriptive statistics of farms evaluated.
Trait
Unit
Mean
SD
Area
Cattle herd
Sheep flock
Crops
ha
Head
Head
ha
3737.79
2708.85
750.42
658.20
±3177.55
±2100.17
±1650.50
±1126.02
SD = standard deviation.
Table 4
Production system of farmers in the Western Frontier of Rio Grande do Sul State.
n¼1
The competiveness index (CI) was obtained by adding the values for the vectors
IC ¼ NVTechnology þ NVManagement þ NVMarket relations
þ NVInstitutional environment
ð3Þ
The final classification depended on the predefined criteria
(Table 2). Statistical Analyses were carried out using SASÒ. The original variables with less than 10% or greater than 90% positive replies were removed as these were not discriminatory. Therefore, of
the 71 variables in the original questionnaire, only 29 were analysed. A multiple correspondence analysis (MCA) was carried out
to identify the relationship between farmers and variables (vectors
and subfactors).
A cluster analysis was then carried out with individual information from farmers. According to Hair (2005), the clusters formed
have internal homogeneity within groups and high heterogeneity
between groups. In the present study, a hierarchical cluster analysis
and an analysis using Ward’s method of Quadratic Euclidean
Distance were used. Three clusters were formed and defined as competitiveness levels: low (LCL), medium (MCL) and high (HCL). A discriminant analysis was used to define which factors were important
in separating the clusters.
3. Results and discussion
The mean farm area was 3737 ha with on average 2708.8 cattle
(Table 3). The main economic activity in the region was crop production (88.8% of the farmers interviewed), showing that family
revenue is directly linked to the competitiveness of the production
system. The farm size in this study was above the average for the
State of Rio Grande do Sul, where the mean farm size was found
to be 948.8 ha (SEBRAE/SENAR/FARSUL, 2005).
Six types of agricultural activity were found on these farms. Cattle production integrated with rice growing was the predominant
system (Table 4). High levels of integration with crop production
(82.5%) and sheep farming (60.3%) on these farms should be noted.
Farmers in this region of Brazil show a high level of technology
use (Table 5). The famers use technologies without intensive use of
management practices. This can be seen as the vector MAN had
lower scores than TEC. In Brazil, farmers use technology, as they
believe it works or because they see others using it, but have difficulty evaluating the economic efficiency of technologies used (Bar-
Table 2
Score and competitive status of farms.
Status
Score
Highly unfavorable
Unfavorable
Neutral
favorable
Highly favorable
0–0.2
0.21–0.4
0.41–0.6
0.61–0.8
0.81–1.0
Production System
N
%
Cattle + Sheep
Cattle + Sheep + Horses
Crops + Cattle + Sheep + Horses
Crops + Cattle + Horses
Crops + Cattle
Crops + Cattle + Sheep
Total
9
2
9
2
23
18
63
14.28
3.17
14.28
3.17
36.50
28.57
100.00
Table 5
Values and status of competiveness directions presented by farmers in the Western
Frontier region of Rio Grande do Sul State.
Direction
N
Mean
Status
SD
Technology
Management
Market relations
Environment
C.I
63
63
63
63
63
0.74
0.56
0.59
0.56
0.63
F
N
N
N
F
±0.14
±0.2
±0.16
±0.13
0.13
SD = standard deviation.
cellos et al., 2011). MR and IE were classified as neutral for
competitiveness of farms in the region.
TEC favorably influenced competitiveness of the farmers. Adequate use of the production system (TYP), health management
(HH) and investments in superior breeding stock (GEN) helped reach
this status. This shows that the farms had an adequate production
system based on these subfactors. Nevertheless, these farmers still
need to improve other factors within this vector, mainly in terms
of crop-animal integration (ICP), regular use of technical assistance
(RTA) and pasture management (PAST).
In an analysis of competitiveness with farmers in Mato Grosso
State, technology was seen to be unfavorable for competitiveness
in the beef cattle production chain (Moura et al., 2009), as in their
study nutritional and reproductive management were deficient. It
should be noted that profit in animal production is obtained by
adapting the production system to the production environment
and optimizing to the use of available resources associated with
efficient management (Gaspar et al., 2009). The present study
was carried out when beef had a high market price leading to higher investment in technology on the farms, while the study in Mato
Grosso was carried out when domestic cattle prices were low.
The farmers showed an overall neutral status for MAN, as they
were generally deficient in the management of their farms. The most
important subfactors were control of production costs (CPC), calculation of financial indicators (CFI), strategic planning (EP) and herd
identification (HI). This indicates that, although the main activity
for most farmers was beef cattle production, they do not control production costs and do not calculate financial indicators on their farms.
It is important to note that these subfactors are directly related to the
collection, registering and processing of data. Ramsey et al. (2005)
found that the interdependent factors which affected economic performance of a beef farm included production costs, productivity and
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P.R. Marques et al. / Agricultural Systems 104 (2011) 689–693
profit of the production system. Many rural properties are still managed empirically, without knowing or controlling production costs
and other subfactors related to farm management or using these
in making decisions (Oaigen and Barcellos, 2008; Oaigen et al.,
2008). This lack of efficient management generates a loss of competitivity, as the farmers do not see their farm as a business and
therefore do not evaluate investment plans, profit margins and other
important economic indicators.
MR here was also neutral, as seen in other studies (Ferreira and
Padula, 2002), which indicates a conflict or lack of confidence in
the relationship between farmers and abattoirs. The beef production chain in this case loses competiveness as there are high transaction costs due to opportunism, which in turn generates a loss of
confidence and asymmetry of information between the links in the
chain (Storper and Harrison, 1991; Almeida, 1997; Barcellos, 2004;
Malafaia et al., 2005). As there is a weak relationship between
these two links in the chain, farmers tend to sell to whoever pays
most at that moment, thereby not creating any firm relationship
with any abattoir in particular. This means the slaughterhouses
do not have a clear idea of how many animals they will slaughter
in any specific period of time. This generates insecurity and lack of
trust within the system.
Evaluating the institutional environment, farmers classified IE
as neutral. The sub-factors with the lowest scores contributing to
this were ATI and FO. Access to innovative technology (ATI) is limiting in the Western Frontier of Rio Grande do Sul State as there are
no rural extension services for diffusion of new technologies, leading to loss of competitiveness. The farmers lack organization and
interest which makes the exchange of ideas and experiences difficult. The individualism of agents within the sector is a barrier to
access and introduction of new production processes.
The farmers were divided into three clusters (Fig. 2): low (LCL)
(23.8%), medium (MCL) (34.9%) and high (HCL) (41.2%) competitiveness. These clusters differed in terms of technology level
(TEC), reproductive management (REP), cost control (CPC), competitiveness index (CI), workforce training (WT), control of patrimony
(PAT) and formation of prices (FP). LCL showed lower scores than
MCL which in turn were lower than in HCL.
Depending on which clusters were compared, the variables
which were important varied. LCL differed from MCL for the variables PAST, REP, IT, EPF and CI (Table 6) for example. The farmers
in cluster LCL were less efficient than those in MCL, especially in
pasture and reproductive management, both of which are essential
for internal competitiveness of a beef production system. Beretta
et al. (2002) stated that the increase in competitiveness through increased productivity on farm is fundamentally due to an increase
Fig. 2. Clusters of the properties interviewed.
Table 6
Variables which distinguished clusters of farmers by discriminant analysis.
Cluster
MCL
HCL
LCL
MCL
CI, EPF, TI, REP, PAST, SP, ROT
–
ATI, EN, CPC, OLFO, TEC, CFI, IT, EPF
CFI, REP, PIC, OLFO, ROT
Block letters were highly significant variables (P < 0.01) – see Table 1 for
abbreviations.
Competiveness level: MCL – medium ; LCL – low; HCL – high.
in reproductive efficiency. For pasture management, one of the
main problems identified in LCL was the presence of weeds in
the fields, degraded pastures and high stocking rates. Andreatta
(2009) also found in Rio Grande do Sul that farmers in the lowest
competitiveness group had problems with pasture management
due to high stocking rates. This type of management is usually carried out by farmers who are tied to the business as a family tradition and not as a generator of profit, which was seen to be critical
for investment in pasture management. There has to be a change in
mentality on the part of these farmers, to see their farm as a business and therefore look to correcting faults in the system with an
aim of elevating competitivity and profitability.
Reproductive efficiency is the variable with highest impact on
the system and is directly related to pasture management, influencing production per area and production costs (Pötter and Lobato, 2004). The use of high stocking rates during pre and post
calving makes body condition recovery of lactating or weaned
animals difficult (Rovira, 1996), thereby compromising their reproductive performance and reducing competitiveness of the system.
Farmers in the LCL group have less access to innovative technology (AIT) than in the HCL group and invest less in genetic
improvement of the herd (GEN). AIT impacts directly on the competitiveness of the system (Gonçalves et al., 2006), as it contributes
to cost reduction or differentiation of business practices (Ghemawat, 2000). It would therefore create more competitiveness in
the on-farm organization and result in optimization of productivity
and product quality, as well as methods of control and planning
(Sachuck et al., 2008).
Genetic quality of the herd is strategic for productivity, as it is one
of the requirements for better use of natural resources on the farm.
Animal improvement through crossbreeding with adapted breeds
allows for intensification of the production system at low cost, producing animals with better feed conversion than those that are less
well genetically adapted to their environment (Rubiano et al., 2009).
The LCL group did not use this strategic resource well. This was also
seen in the ‘‘Diagnosis of Beef Cattle Production in RS’’ where 34% of
the farmers raised cattle without a defined breed (SEBRAE/SENAR/
FARSUL, 2005).
Comparing MCL and HCL, MCL had lower scores for reproductive management (REP), production indices (PIC), routine management of the animals (ROT), calculation of financial indicators (CIF)
as well as official legislation and regularization of farm deeds
(OLFO). Although they belong to the technological vector, the subfactors ROT and PIC are more related to technology and not inputs,
having a direct relationship with management of the production
system.
Crepaldi (1998) showed that generating managerial information,
which allows for making decisions based on consistent and real data,
is a constant challenge for farmers. Rosado Junior and Lobato (2010)
mentioned that, to guarantee competitiveness, farmers and technicians need to look for managerial solutions and alternatives. These
authors state that production systems which do not use production
data and indicators tend to show low competitiveness. For farmers
with MCL to become HCL, investments should be made in terms of
management of farm activities and the use of financial indicators
within the production system.
P.R. Marques et al. / Agricultural Systems 104 (2011) 689–693
4. Conclusion
Most farmers interviewed in the present study showed a favorable status for competitiveness due to use of adequate production
technologies. Improvements would be possible mainly in terms of
management strategies.
For farms with a low level of competiveness to reach a medium
level, there should be investments in pasture management to optimize reproductive efficiency. The lower access to innovative technology and low investments in herd genetics characterizes the
main differences between farmers of low and high competiveness
levels.
Farmers at the medium level should invest in management of
the production system through the improved use of control of
reproduction, performance recording and routine management of
the animals to become more highly competitive.
The high group invest more in management practices, with a
more business type approach to farming. The use of technology
is therefore integrated into the production system and evaluated
on a regular basis, leading to higher competitiveness.
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