© The Authors, 2021, Published by the Universidad del Zulia*Corresponding author: yormazabal@utalca.cl
Identication of productive clusters in the blueberry cultivation (Vaccinium corymbosum) in
central Chile
Identicación de clústeres productivos en el cultivo de arándanos (Vaccinium corymbosum) en Chile
central
Identicação de clusters produtivos no cultivo de mirtilo (Vaccinium corymbosum) no
Chile central
Carlos Mena
1
Yony Ormazábal
1*
Juan Carlos Cantillana
2
Lisandro Roco
3
Rev. Fac. Agron. (LUZ). 2022, 39(1): e223902
ISSN 2477-9407
DOI: https://doi.org/10.47280/RevFacAgron(LUZ).v39.n1.02
Socieconomy
Associate editor: Dra. Fatima Urdaneta
1
Center of Geomatics, Universidad de Talca, Talca 3460000,
Chile.
2
Faculty of Administration and Economics, Universidad
Tecnológica Metropolitana, Santiago 8320000, Chile.
3
Institute of Agricultural Economics, Faculty of Agricultural
and Food Sciences, Universidad Austral de Chile, Valdivia
5090000, Chile.
Received: 06-05-2020
Accepted: 31-08-2021
Published: 16-12-2021
Abstract
The understanding of the productive characteristics and tendencies of
fruit producer’s territorial concentration can be explained starting from
the singularities of the territory, the production techniques or the market
conditions. This article analyzes the formation of clusters of blueberry
producers in the Maule region in central Chile, based on the productive
characteristics of the crops that include the technological levels, the age of
the plantations and the size of the farms. For this, a two-step cluster analysis
was performed to obtain homogeneous groups or conglomerates. The results
obtained were analyzed in the ArcGIS software, using the Ripley K function,
to determine their spatial concentration and to relate the spatial location of
the orchards belonging to each cluster and their geographical distribution.
The analysis indicates the existence of four clusters in the region, differing
preferably by the sizes of the farms. The predominant technological level
between the clusters identied is the intermediate, followed by the advanced.
The clusters tend to generate spatial and geographic concentrations related
with communication facilities and agroecological conditions (climate, soil,
relief features). Results founded can improve focus of public efforts and
private investments in the productive activity of blueberry cultivation.
Keywords:
Agroindustry
K function
Geographical distribution
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| Rev. Fac. Agron. (LUZ). 2022, 39(1): e223902. January - March. ISSN 2477-9407.
Resumen
La comprensión de las características productivas y las tendencias
de concentración territorial de los productores frutícolas se puede
explicar a partir de las particularidades del territorio, las técnicas de
producción o las condiciones del mercado. Este artículo analiza la
formación de conglomerados de productores de arándano en la Región
del Maule en el centro de Chile, basándose en las características
productivas de los cultivos que incluyen el nivel tecnológico, la
antigüedad de las plantaciones y el tamaño de los huertos. Para esto,
se realizó un análisis de conglomerados de dos pasos para obtener
grupos homogéneos. Los resultados obtenidos se analizaron en el
software ArcGIS, utilizando la función K de Ripley, para determinar
la concentración espacial y relacionar la ubicación espacial de los
huertos pertenecientes a cada grupo y su distribución geográca. Los
análisis indican la existencia de cuatro conglomerados en la región,
que se diferencian preferentemente por el tamaño de los huertos.
El nivel tecnológico predominante entre los clústeres identicados
es el intermedio, seguido del avanzado. Los clústeres tienden a
generar concentraciones espaciales y geográcas relacionadas con la
infraestructura de comunicación y con las condiciones agroecológicas
(clima, suelo, características del relieve). Los resultados encontrados
pueden mejorar el enfoque de los esfuerzos públicos y las inversiones
privadas en la actividad productiva del cultivo del arándano.
Palabras clave: agroindustria; función K; distribución geográca.
Resumo
A compreensão das características produtivas e tendências de
concentração territorial dos fruticultores pode ser explicada a partir
das particularidades do território, das técnicas de produção e das
condições de mercado. Este artigo analisa a formação de clusters
de produtores de mirtilo na região de Maule, no Chile central, com
base nas características produtivas das lavouras que incluem o nível
tecnológico, a idade das plantações e o tamanho das propriedades.
Para isso, foi realizada uma análise de cluster em duas etapas para
obter grupos ou conglomerados homogêneos. Os resultados obtidos
foram analisados no software ArcGIS, utilizando a função Ripley K,
para determinar a concentração e localização espacial dos pomares
pertencentes a cada cluster, assim como a sua distribuição geográca.
As análises indicam a existência de quatro clusters na região,
diferenciando-se preferencialmente pelo tamanho das propriedades.
O nível tecnológico predominante entre os clusters identicados
é intermediário, seguido do avançado. Os clusters tendem a
gerar concentrações espaciais e geográcas relacionadas com as
infraestruturas de comunicação e as condições agroecológicas (clima,
solo, características de relevo). Os resultados encontrados podem
melhorar o foco de esforços públicos e investimentos privados da
atividade produtiva da cultura do mirtilo.
Palavras-chave: agroindústrias; função K; distribuição geográca.
Introduction
The Maule region has a Mediterranean climate zone that allows
the development of an important agro-industrial and forestry activity,
with the fruit sector being one of the most relevant (Maturana et al.,
2019; Soza-Amigo, 2011). The blueberry cultivation has increased
rapidly in central Chile in the last years, reaching the 5,942.8 ha
planted in the region (ODEPA and CIREN, 2019) which correspond
to 32.3% of the national total, most of it (79.8 %) is being destined
preferably for export as fresh fruit shipped to the northern hemisphere.
Currently, Chile is among the main producing countries of blueberries,
positioning itself as the third producer and the second exporter in the
world (Almonacid, 2018). As for fresh blueberries, it is the world’s
leading exporter and the main supplier of this fruit in the winter of the
northern hemisphere. The harvest in that hemisphere ends in October,
allowing the entry of the Chilean production from the counter-season
harvest in response to the growing demand for fresh blueberry.
Chilean blueberry exports are mainly destined for the United
States and Canada (51.236 t), followed by Europe (16.570 t) and Asia
(6.456 t), according to statistics of the Chilean Blueberry Committee,
2020. This situation is consistent with a Chilean productive sector
that has based its insertion in the world economy on its comparative
advantages and an important processes of land transformation for
generating new productive and territorial dynamics (Riffo, 2018).
On the other hand, the blueberry cultivation in central Chile at the
Maule region has been studied preferably from specic productive
approaches, with an emphasis on the cultivars planted, the production
characteristics or the technology used; however, no specic studies
have been carried out on the conguration of clusters or on the
agglomeration conditions of productive farms, which makes it
necessary to review the spatial and geographical characteristics of the
productive units that make up this fruit sector.
Studies focused on the analysis of the distribution of agricultural
activities in the territory have shown that there are local socio-
economic factors that explain the patterns of land use conversion,
in addition to the physical conditions of the territory where the
location factors themselves described alone most of the diversity of
the farms studied (Van Doorn & Baker, 2007; Van de Steeg et al.,
2009). However, the complexity of the economy in rural areas and its
structure have not been sufciently considered. One way to improve
the study of this situation is the cluster approach, a methodology that
can be applied to complex territorial scenarios where small producers
dominate (Tapia et al., 2015).
The concept of cluster involves the idea of a set of companies
in a given industry that follows the same or similar strategy (Lobos,
2006), where the close location of companies with each other can
allow the generation of economies of scale (Ortega-Colomer et al.,
2016). In this way, the formation of clusters allows companies to
take advantage of agglomeration economies, obtaining multiple
benets (Vidal-Suñé & Pezoa-Fuentes, 2012). Therefore, different
studies have incorporated distance-based methods mainly in the
evaluation of the concentrations of industries and economic activities.
Duranton & Overman (2005) used tests to study the location patterns
of industries, based on distance. In Italy, Dominicis et al. (2007)
considered geographical concentration to study both manufacturing
and service industries. Marcon & Puech (2003) incorporated distance-
based methods in studies of companies’ clusters, in addition to other
methods derived from the Ripley K function.
The spatial analysis of multiple distance based on the Ripley
K function is a good way to study the pattern of point data related
to spatial elements. One of the outstanding characteristics of this
analysis is that it summarizes the spatial dependency in a determined
range of distances, also allowing to illustrate the changes in the
spatial clustering or the dispersion of the entity centroids, when
there is a change in the neighborhood size. In this way, authors from
different countries have applied conglomerate analyzes to the study
and classication of agricultural producers, management practices
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Mena et al. Rev. Fac. Agron. (LUZ). 2022, 39(1): e223901
and ecosystem impacts (Acosta et al., 2014; Borja-Bravo et al., 2016;
González et al., 2018; Carpio & Urbano, 2019).
In Chile, Carmona & Nahuehual (2009) analyzed the strategies
of agricultural producers by comparing the size of production and
their production systems, while Carrillo et al. (2011) characterized
typologies of dairy production systems, using variables related to
the technological level of producers. In the Maule region, Guerrero
et al. (2015) studied plantations of European hazelnuts, recognizing
the inuence of environmental conditions among other factors, in
the formation of conglomerates. Likewise, Lobos (2006) studied
agricultural clusters (wine, fruit and wood) of the region from an
economic perspective; and Fernández et al. (2019) studied groups of
annual crop producers.
As observed, research on productive conglomerates has been
a topic of interest in various elds in the last decade, covering
academic, public and private interests (Duque et al., 2009), since
the geographical concentration of companies and their economic
implications is a key element, even more considering the conditions
of agricultural competitiveness warned in the recent decade, together
with the conditions generated by globalization and market instability
(Castellanos et al., 2012). Thus, the possible existence of clusters in
the different productive areas of a region constitutes a eld of study
that is directly related to the trend of concentration of economic
activities in the territory (Garrocho et al., 2012).
The present study aimed to determine the characteristics of
blueberry producing farms in the Maule region in terms of production
conditions and their spatial distribution. In this way, the distinctive
types of blueberry producing orchards in the region are analyzed,
considering the criteria used in the studies reviewed, especially
those related to productive characteristics, farm size and plantations
age, also reviewing the spatial concentration of each of the clusters
identied in the study.
Materials and methods
Description of the study area
The study area corresponds to the Maule region, where the
highest concentration of blueberry orchards is found in the sectors of
the Central Valley and to a lesser extent, in the marginal basins of the
Coastal Cordillera and bottoms of Andean and Coastal valleys. These
sectors present favorable conditions for the growth of blueberry
with preferably acid soils, with a pH between 4.0-5.0, and textures
sandy, sandy loam or clayey; characteristic situation of the area
located between 35° and 37° South latitude, where also the physical
conditions of the terrain are enhanced by a temperate Mediterranean
climate with a prolonged dry season, mild winters and temperate
springs (Sarricolea et al., 2017).
Information collection techniques and population sample
The blueberry producing orchards were geo-referenced by means
of a eld survey supported by GPS technology (Global Positioning
System). Then, these places were located on a SPOTMaps color
mosaic, used as a cartographic base cover. This mosaic was
produced by Spot Image (https://www.intelligence-airbusds.com)
from orthorectied SPOT-5 satellite images with spatial resolution
of 2.5 m and with a geometric precision level that allowed to work
on a cartographic base of scale 1:10,000. The production units were
determined on the basis of the production orchards, identifying
202 validated producers by eld work made at the end of the 2016
harvest season. A technical form to collect data referring to technical
production variables was designed by a panel of experts including both
professionals related to the cultivation of blueberries (agronomists
and agricultural technicians) as well as geomatics specialists. The
selection of variables considered aspects as the size of the producer
and the type or variety of crops, as well as the production, irrigation
and harvesting systems. In this way, the form included productive
technical variables such as: types of plantations, yield, irrigation,
mechanization of the harvest process, age of plantations, type of plant
material, weed control mechanisms, plantation design and production
systems; and was completed in the eld by the interviewers in
conjunction with each producer. This survey considered 93 % of
blueberry producers at the regional level with orchards over 1 ha of
plantation. Thus, each one of the orchards was spatially represented
as a point in the cartographical base according to its geographical
coordinates. In addition, each one was linked with all its respective
technical data collected with the eld form, forming a GIS thematic
layer used later to carry out the cluster analyses.
Systematization of variables
The orchards were characterized according to three parameters
that represent and group the productive technical variables previously
outlined. In this way, the following were considered as relevant
factors:
a. Production size: expressed as the total hectares allocated by
each producer to blueberry plantations and reects the producer’s
investment capacity as well as the weight that can exert in the local
market. It is a relevant variable given the dispersion of the cultivation
areas, ranging for example from 1 to 140 ha of continuous crops
(including various cultivars) for a same producer.
b. Age of the orchard: taken from the year of planting, it reects
the phase in which the production is within the yield curve presented
by this fruit understanding that blueberry plants show their highest
yield between 5 to 9 years of age (Retamales & Hancock, 2012
).
Thus, this parameter allows identifying productive differences
between orchards.
c. Technological Level (TL) of production: calculated based on
the proposal made by Ormazábal et al. (2020), it allowed establishing
differences about the incorporation of technology in the blueberries
production for the orchards studied. This TL was estimated from
technical parameters, considering the plant material used, the origin
of this plant material, use of mulch, irrigation system, plantation
design, use of ridges, type of weed control, technique of production
and harvesting system. For each variable, alternatives ranked in
ascending order were considered; then, to each variable a weight or
weight was assigned according to its importance. The determination
of hierarchies and weights was made by the panel of experts. Finally,
the TL was determined using the weighted linear combination (WLC)
technique by mean of the ArcGIS software. The nal score gave rise
to 3 categories, with TL1 being the high technology level, TL2 being
the medium level and TL3 being the low level.
Data analysis techniques
A two-step cluster analysis was performed to obtain groups or
conglomerates. One advantage of cluster analysis is that it can
minimizes research bias by not specifying classes according to
prespecied conceptions (Rosenberg & Turvey, 1991). Two-step
cluster analysis is an exploration tool to discover the natural groupings
of a data set (Satish & Bharadhwaj, 2010). Its unique characteristics,
compared to other traditional clustering methods, are the following:
an automatic procedure for the optimal number of clusters, the
possibility of creating cluster models with both categorical and
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continuous variables, and the option of working with large data les
(Pérez, 2011). The procedure is based on an algorithm that produces
optimal results if all variables are independent, the continuous
variables are normally distributed, and the categorical variables are
multinomial; but it is a procedure that works reasonably well in the
absence of these assumptions in larger samples (Hair et al., 2010).
So, the conditions of the data were checked before the application
of this tool (
Rubio-Hurtado & Vil-Baños, 2017). The described
procedure was performed using the SPSS Software.
The conglomerates or clusters obtained from the described
analysis, were then analyzed using the Ripley K function to
characterize their spatial distribution and identify trends in the
concentration or dispersion of the orchards belonging to each one
of them. Ripley K function is dened as:
(1)
where N (r) is the mean number of neighboring points within
a circle of radius r around any typical point of the pattern, and λ is
the intensity of the pattern (in most cases equivalent to the density
of points). The function K is often dened by saying that the
product λ K(r) is the average number of neighbors within a circle
of radius r around a typical individual of the pattern (De la Cruz,
2008). Therefore, it is expected that, if there is a regular pattern, the
probability of nding one point near another is greater.
Finally, the clusters were visualized on a cartographical
background based on the geomorphic units present in the region, to
establish relationships between the position of each conglomerate
and the environmental conditions where they are located.
Results and discussion
From the 202 blueberry producers interviewed and geo-
referenced in the Maule region, we obtained 4 groups or clusters
with 84, 12, 53 and 53 producers respectively (table 1).
Table 1. Number of producers (n) and proportion of regional
total by cluster
Cluster Main characteristics
Number
of
producers
% of the
planted area
in the region
A Orchards of mayor size with a
preferably intermediate TL (TL2)
84 65.7
B Orchards of intermediate size
with an advanced or intermediate
TL (TL1, TL2)
12 6.3
C Orchards of small size with
a preferably intermediate TL
(TL2)
53 15.7
D Orchards of small size with a
diverse TL
53 12.3
Total 202 100.0
According to the graphic quality of the analysis, which is
equivalent to a silhouette measure of the cohesion and separation of
the clusters, the result is classied as good, equivalent to 0.7. The
clusters found show a clear tendency to differentiate based on the
size of the orchards, a situation also noted in the work of Carmona
& Nahuelhual (2009).
Table 2 shows that cluster A has more than 50 % of producers
with TL2, which added to those corresponding to TL3 represent 84.4
% of this cluster wich on average, has orchards with 22.12 ha and
plantations from 1982 to 2012. Cluster B concentrates preferably
producers with TL1 and TL2 (91.6 %), an average area of 14.05 ha
per orchard and plantations planted between 2002 and 2011. Cluster
C concentrates 73.5 % of producers with TL1 and TL2, an average
area of 8.19 ha and plantations from 2001 to 2010. Finally, cluster
D presents a more homogeneous distribution in terms of technology
implementation with 35.8 % in TL1, 37.7 % in TL2 and 26.4 %
in TL3, the average area of these orchards reaches 6.37 ha with
plantation years between 1987 and 2011.
Table 2. Characterization of identied clusters
Cluster TL ( %) Years of the
plantations
(since –
until)
Mean age
of the
orchards
(years)
Size of the
orchards
(ha)
TL1 TL2 TL3
A 15.4 52.3 32.1 1982 - 2012 12.5
a
22.12
a
B 41.6 50.0 8.3 2002 - 2011 18.5
b
14.05
b
C 32.0 41.5 26.4 2001 - 2010 12.2
a
8.19
c
D 35.8 37.7 26.4 1987 - 2011 12.6
a
6.37
c
a, b, c
Means with different literals are signicantly different (p<0.01).
There is a clear differentiation in the size of the orchards, where
the average of cluster A almost quadruples that of cluster D; this
predominance of larger orchards has been maintained over time
considering that the plantation ages of cluster A are the widest,
covering a time range of 20 years. It is possible that the differences
regarding the productive life of different blueberry species may
explain the tendency to maintain plantations of more than 20
years (based on more widespread yielding cultivars), in addition
to the management applied by companies with greater investment
capacity.
Clusters B and C have similarity in the plantation ages, but
they are clearly differentiated by the orchards’ size, appreciating
a concentration in TL of higher hierarchy. Cluster D, on the other
hand, would be representing smaller producers and possibly with a
less capacity of reaction to market changes, which would explain,
in this case, the wide range of plantation ages, slightly lower than
that of cluster A, but apparently inuenced by the lower capacity
for plant renewal.
This latter group includes mostly producers attached to INDAP
(Institute of Agricultural Development) or who receive productive
support from PRODESALES (Local Development Programs), and
their dispersion in terms of TL is consistent with the characterization
carried out by González et al. (2016) regarding to variability in
aspects such as the plantation framework, application of fertilizers,
among others management aspects. Although a signicant number
of these producers receive this technical advice, the variability
in the technological implementation for production and the wide
dispersion in the ages of the orchards seem to indicate the need to
reinforce such actions, especially in the producers of this group. A
relevant aspect to consider is the fact that many of these producers
are obtaining a current yield of just over a third of the potential for
the cultivars that they possess, being the main reason for this the
lack of technological implementation.
To review the geographical distribution of the four conglomerates,
the delimitation of geomorphic units proposed by Börgel (Cortez
et al., 2021) was used, nding blueberry orchards in the Coastal
Plains, the Coastal Cordillera, the Plains of Sedimentation (terraces
of the two exorheic Andean rivers of the region), the Central Plain
and the Precordillera ( gure 1).
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Figure 1. Geographical distribution of clusters of blueberry orchards.
The orchards corresponding to the Coastal Plains represent
0.49 % of the regional total, those of the Coastal Cordillera 2.47
%, in the Plains of Sedimentation there is 11.88 %, the Intermediate
Depression concentrates the highest percentage with 83.16 %, while
the remaining 1.98 % is in the Precordillera. This concentration of
producers in sectors that offer the most appropriate edaphic and
climatic conditions for agricultural development is in line with what
was proposed by De la Cruz (2008), in relation to the underlying
mechanisms in the construction of a structure and the functioning
of the dynamics resulting from the distributions of the productive
activities in the territory. It is also interesting to note the trend of
concentration of producing orchards at distances less than 20 km
from the axis of the Pan-American highway (Route CH5), this trend
is related to the advantage offered by the proximity to the most
important road used for marketing of the fruits.
Figure 2 shows the geographical distribution by geomorphic
units and the number of producers by TL in each of them. In the
Intermediate Depression, there is a predominance of orchards
corresponding to cluster A (37.5 %), a situation that is also
registered in the Plains of Sedimentation (66.6 %), in the Coastal
Plains (100 %) and in the Cordillera Coastal (60 %). For its part,
the Precordillera has orchards mainly belonging to cluster D (50
%), where there is a predominance of smaller productions that use
the increasingly scarce at areas in the narrow plains forming the
rivers, prior to entering the Intermediate Depression.
Figure 2. Distribution of blueberry orchards by cluster and
relief units.
The majority group (cluster A) is present in the various
territorial spaces, marking a strong predominance in the areas of
inuence of the Coastal Cordillera, especially in the Cauquenes
basin. The existence of these larger orchards in one of the most
economically depressed areas in the region is highlighted, since
it is part of the sector called Secano Maule Sur included in the
Development Plan for Lagging Areas, whose territory also includes
the province of Cauquenes as well as the county of Empedrado.
According to the data used in the preparation of this Development
Plan, it is interesting that, among the 10 main occupations in the area,
activities related to agriculture represent 23 %. Given the size of the
orchards that comprise this group, it is possible to consider them as
large companies, which, according to the same document, represent
only 0.6 % of the companies in the region, but employ 34.9 % of the
workers (GORE Maule, 2019). This reects the strong differences
between the production conditions of the orchards studied and the
need to focus resources and efforts on those producers who require it.
From analysis of the different clusters obtained by means of the
Ripley K function applied to all the blueberry-producing orchards
in the region ( gure 3), a clear trend towards concentration appears,
mainly in the northern and southern sectors of the region, in addition
to a singular concentration in the Cauquenes basin. In this way, the
arrangement of most clusters along the Pan-American highway and
other major communication routes reafrms that structuring routes
are an anthropic location factor that encourages the concentration of
blueberry-producing orchards.
The identication of the tendency to the formation of
conglomerates with certain related elements can facilitates the
elaboration of policies and strategies, both for the public and private
sectors, interested in strengthening agricultural productive activity,
especially in a region closely linked to the rural areas (GORE Maule,
2019). When analyzing the impact of Chile’s free trade agreements
on exports of fresh fruit, it has been found that these agreements
have been important instruments to provide greater market access
for Chilean products (Fulponi & Engler, 2013).
During the last decades, Chilean agri-food exports have achieved
an important presence in the main world markets, increasing their
volume and value signicantly. However, not only Chile has
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| Rev. Fac. Agron. (LUZ). 2022, 39(1): e223902. January - March. ISSN 2477-9407.
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increased its shipments and the total value of exports to destinations
such as the United States, the European Union, and China, but other
countries in the region and from other parts of the world compete with
Chile in these markets, the growth of exports from these countries
is affecting Chile’s competitiveness and participation in the world
market (Pérez & Valdés, 2019).
Therefore, Fulponi & Engler (2013) indicate that both the Chilean
animal and plant health authority (SAG) and the Chilean export
promotion agency (PROCHILE) are essential to promote Chile’s
reputation as exporter of quality products. However, they nd that real
market access did not always benet all the sectors studied equally.
These results highlight the importance of the design of territorial
public policies. Considering that in Chile, according to the OECD
(2019), producer support is mainly provided to small farmers, in the
form of input subsidies and support for the formation of xed capital,
such as investments in irrigation at farm level and the provision of
public goods; the results obtained in the present study can help in the
development of support mechanisms at the territorial scale.
Figure 3. Spatial correlation between orchards for the identied clusters.
Conclusions
Blueberry producers in the Maule region can be grouped into 4
clearly differentiable conglomerates (A, B, C and D) based on the
studied parameters of production size, age and technological level
(TL) of the orchards, the rst of which appears as the most decisive at
the time of make up the groupings.
The TL2 is clearly the predominant technological level between
all the clusters identied, followed by TL1 (in clusters B, C and D).
TL3 is the technological level less predominant (excepting in cluster
A).
The spatial distribution of the producing orchards is not a matter of
variables as size or age or TL, it’s a matter of communication facilities,
for inputs and outputs management and also of agroecological
conditions (climate, soil, relief) for blueberries cultivation among
others aspects.
Spatial agglomeration identication and measurement are high-
value inputs for public policies design and implementation. In this
sense, inputs mentioned can improve focus of public efforts and
private investments in the area.
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