Spatial distribution of coffee crop productivity in Minas Gerais over time
Renan Serenini
1
, Patrícia de Siqueira Ramos
2
, Lincoln Frias
3
DOI: https://doi.org/10.35699/2447-6218.2020.26171
Abstract
Brazil is the world’s largest coffee producer and the state of Minas Gerais is responsible for half of the Brazilian pro- duction.
However, productivity is unevenly distributed throughout the state. Therefore, the purpose of this study is to analyze the spatial
distribution of coffee productivity in Minas Gerais from 2002 to 2017, a valuable information to identify regions where coffee
production may be more promising in the future. This paper investigates the existence of spatial dependence of productivity
between regions (using Moran’s I), its dynamics throughout the period and the presence of clusters of high and low productivity
(using local Moran’s I). The results show that the spatial dependence of productivity was stronger from 2002 to 2009 than
between 2010 and 2017. Some regions with small coffee areas but high productivity have stopped producing the crop whereas
some of those with large areas but low productivity increased their productivity levels. Therefore, there is a tendency of
homogenization of productivity in Minas Gerais, with values close to 30 bags per hectare.
Key-words
Moran’s I. Spatial autocorrelation. Spatial Statistics.
Distribuição espacial da produtividade de café em Minas Gerais ao longo do tempo
Resumo
O Brasil é o maior produtor mundial de café e o estado de Minas Gerais é responsável por metade da produção brasi- leira.
Entretanto, a produtividade é distribuída de forma desigual no estado. Assim, o objetivo deste estudo é analisar a distribuição
espacial da produtividade de café em Minas Gerais entre 2002 e 2017, uma informação valiosa para identificar regiões em
que a produção de café possa ser mais promissora no futuro. O artigo em questão investiga a existência de dependência
espacial na produtividade entre regiões (usando o I de Moran), a dinâmica ao longo do período estudado e a presença de
agrupamentos de alta e baixa produtividade (usando o I de Moran local). Os resultados mostram que a dependência espacial
era mais forte de 2002 a 2009 do que entre 2010 e 2017. Algumas regiões com pequenas áreas de plantio de café mas com
alta produtividade deixaram de produzir enquanto regiões com grandes áreas mas baixa produtividade aumentaram seus
níveis de produtividade. Assim, existe uma tendência de homogeneização da produtividade em Minas Gerais, com valores em
torno de 30 sacas por hectare.
Palavras-chave:
Autocorrelação espacial. Estatística Espacial. I de Moran
.
Recebido para publicação em 10 de novembro de 2020. Aceito para publicação em 11 de dezembro de 2020.
e-ISSN: 2447-6218 /
ISSN: 2447-6218. Atribuição CC BY.
CADERNO DE CIÊNCIAS AGRÁRIAS
Agrarian Sciences Journal
2
Serenini, R. et al.
Introduction
In 2017, Brazil produced 32% of all the world’s
coffee, thus being the largest producer of this crop ac-
cording to the International Coffee Organization (ICO,
2018). In that year, the state of Minas Gerais was res-
ponsible for 53% of the Brazilian production (CONAB,
2018). Therefore, the study of coffee production in this state
is fundamental to understand coffee production in general.
ductivity in the 558 Brazilian microregions in 1991, 1997
and
2003. The values of Moran’s I were 0.30 in 1991,
0.34 in 1997 and again 0.34 in 2003, always statistically
significant. Therefore, the authors confirmed the existence
of
positive spatial correlation, persisting throughout the studied
period.
Almeida et al. (2006) examined the spatial dis-
tribution of the coffee productivity in Minas Gerais for the
years 2000 and 2004 taking as units the 66 microre-
gions of
the state. Productivity was measured as the ratio
between the
sum of the production of all the cities in the microregion and
its total harvested area. The study not only found spatial
autocorrelation in coffee productivity in both years but also
that it increased between them, as the Moran’s I went from
0.24 in 2000 to 0.32 in 2004.
The clusters with higher
productivity were located in the northwestern region of the
state. According to the authors, the rising concentration suggests
that higher productivity
could be influencing the emergence
of regions of equal performance in their neighborhood.
Neither production nor productivity are evenly
distributed throughout the state. This is a recurring phe-
nomenon in agriculture, given that the development of
agricultural crops relies on factors that vary spatially. These
include both natural factors (soil fertility, tem- perature,
rainfall etc.) as well as logistic factors (roads,
warehouses,
access to technical assistance, cost of inputs,
competition with
other crops etc.) (Teixeira; Bertella, 2015; Souza; Perobelli,
2007).
In view of this, the purpose of this paper is to
analyze the dynamics of the spatial distribution of coffee
productivity in Minas Gerais from 2002 to 2017. The aim is
to verify whether coffee productivity is randomly distributed
in the state or whether there are patterns of spatial
distribution of regions with similar levels of pro-
ductivity.
For example, the most efficient regions may tend
to be
concentrated in the south of the state. The paper also
describes how this spatial distribution changed over time,
identifying regions which increased or decreased their
productivity levels.
The remainder of this paper is organized as follo- ws.
Section 2 presents the dataset, the kind of geographic units
under study and the central concepts in exploratory
spatial
data analysis. Section 3 contains the results and
discussion of
both the descriptive and the spatial analysis.
Finally, some
concluding remarks are given in Section 4.
Materials and methods
This kind of information is useful in identifying the
most promising areas for coffee production. Also, it could
help in the improvement of the official harvest forecasts
(Neves; Luiz, 2006). The Companhia Nacional
de
Abastecimento (CONAB) monitors the Brazilian coffee crops,
publishing regular forecasts of harvested area, pro-
duction
and productivity, using, among other methods, satellite
images of statistically selected regions (CONAB, 2018). The
results of the spatial analysis are helpful in the sampling
design of those regions.
This study uses data from the Municipal Agri-
cultural Production (Produção Agrícola Municipal, PAM)
survey (IBGE, 2019), ranging from 2002 to 2017. The data
begins in 2002 because this is the year when coffee
measuring unit changed from dry cherry coffee (café em
côco) to processed coffee or coffee beans (café beneficiado ou
em grão). The data is available through IBGE System of
Automatic Recovery System (SIDRA), table 1613 (“Area to be
harvested, harvested area, quantity produced, average
productivity and production value of permanent crops”).
In this study, productivity is specified as the ratio
between the total production volume (in 60kg bags) and
the
total area harvested (in hectares) in a certain year. High
productivity regions are those that produce more than 25
bags per hectare (1,500 kg), the median value among the
immediate geographic regions 2017.
The innovative contribution of this work is two-
fold. In contrast to previous studies, to be summarized in
the next section, this paper examines all the years from
2002 to 2017, not only two or three points in time.
Secondly,
it employs the new Brazilian regional division,
published by
the Brazilian Institute of Geography and Statistics (Instituto
Brasileiro de Geografia e Estatística, IBGE) in 2017. This
revised division improves the old micro and mesoregions
division by taking into account the urban network and the
management flows. The in-
corporation of this new and more
rigorous division in the
spatial analysis may reveal patterns
previously masked.
In 2017, from the 853 Minas Gerais munici-
palities, 354 (42%) did not produce coffee. A variable with
a great number of zeros creates some problems for the spatial
analysis. Thus, instead of municipalities, the adopted unit of
analysis are the immediate geographic regions, the updated
equivalent of microregions. Consi-
dering the 70 immediate
geographic regions in the state,
only 7 did not produce coffee.
Spatial analysis has been widely used in studies of
crop production and productivity. Souza and Perobelli (2007)
examined the spatial distribution of coffee pro-
Cad. Ciênc. Agrá., v. 12, p. 01–10, https://doi.org/10.35699/2447-6218.2020.26171
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Spatial distribution of coffee crop productivity in Minas Gerais over time
The division of Brazilian states into mesoregions
and
microregions was used by IBGE from 1989 to 2017.
IBGE
published in 2017 a new framework for the regional
division of
Brazil, rearranging the former mesoregions and
microregions into intermediate and immediate geo-
graphical
regions, respectively (IBGE, 2017). Comparing
the two
divisions, Minas Gerais has 12 mesoregions, but 13
intermediate regions and, at the lower level, to the 66
microregions correspond 70 immediate regions.
criterion of contiguity, which defines as contiguous all
spatial units sharing a boundary, in any direction, with the
region in focus (Almeida, 2012).
Results and discussion
In 2002, 21.68 million bags of coffee were pro-
duced on 1.09 million hectares, resulting in 19.9 bags per
hectare. In 2017, 24.25 million bags were produced in 925
thousand hectares, resulting in a productivity of
26.2 bags per hectare. The increase in production (11.9%
from
2002 to 2017) was, in relative terms, slightly less
relevant for
the increase in productivity than the reduction
of the area
(15.1% from 2002 to 2017).
Exploratory spatial data analysis is the set of
techniques used to describe and examine spatial data
(Anselin, 1996). The central problem in this kind of study is to
check whether or not the data is randomly distributed
in space.
In this context, the opposite of randomness is spatial
dependence (or spatial autocorrelation) between the
observations, meaning that neighboring regions are more
closely associated with each other regarding the
value of a
determined variable than with distant regions,
either positively
or negatively.
Coffee productivity in the state has increased over
time, representing an improvement in efficiency in coffee
production, measured as the division of the production in
the
state by the area harvested in each year. In 2002, the average
production was 20 bags per hectare, whereas in
2016, the last
year with positive biannuality, 29.4 bags/ha
were produced (in
2017, 26.2 bags/ha). The increase in
productivity was not only
due to a reduction in harvested
area (keeping production
constant) or an increase in
production (keeping harvested
area constant), but rather
a consequence of both factors:
increased production and reduced harvested area throughout
the state of Minas Gerais.
The fundamental statistics in an exploratory
spatial
data analysis are global and local autocorrelation. The first
requirement in their calculation is the definition
of a spatial
weights matrix (W) (Almeida, 2012). This is a n-square
matrix whose elements denote the spatial connection
(neighborhood) between the regions accor- ding to some
criterion of proximity, such as contiguity or distance,
representing for each i region its spatial relationship to each
other region j. The present paper uses contiguity as the
proximity criterion. In this case,
the matrix element w
ij
has
value 1 when the regions share
a border and 0 otherwise. This
paper adopts the queen
Moving from the entire state of Minas Gerais to its
subdivisions, Table 1 gives an overview of the distri-
bution
of the harvested area, production and productivity
among the
70 immediate geographic regions of Minas Gerais in 2002,
2010 and 2017.
Table 1 – Descriptive statistics of harvested area, production and productivity of the immediate geographic regions of Minas
Gerais (2002, 2010 e 2017)
year
mean
standard deviation
minimum
maximum
harvested area (ha)
22,694.1
21,564.9
21,693.1
production (bags)
483,054.0
560,156.9
548,525.4
productivity (bags/ha)
9.0
11.6
10.6
2002
2010
2017
15,522.4
14,665.9
13,215.8
0.0
0.0
0.0
115,949.0
117,440.0
121,277.0
2002
2010
2017
309,768.8
358,140.0
346,376.0
0.0
0.0
0.0
2,419,200.0
2,756,133.3
2,648,583.3
2002
2010
2017
17.1
21.8
23.0
0.0
0.0
0.0
47.8
64.5
42.5
Source: Prepared by the authors based on data from the Produção Agrícola Municipal (IBGE, 2019).
Cad. Ciênc. Agrá., v. 12, p. 01–10, https://doi.org/10.35699/2447-6218.2020.26171
4
Serenini, R. et al.
The average harvested area decreased 15% from 2002
to 2017. On the other hand, the average production
increased
12% in the same period. The minimum value of both
harvested area and production has remained at zero since at
least one region did not produce coffee in each one of the
years. The maximum value of harvested
area had a slight
increase. However, production decreased
from 2010 to 2017.
of the 13 intermediate regions, only one (Ipatinga) did
not
contain any immediate region with high productivity.
Table 3 shows in greater detail how the number of im-
mediate regions in each productivity interval (the same
used
in Figure 2) changed by year. An impressive increase
was
observed in the higher intervals (fourth, fifth and sixth), that
together compose what this paper has been calling high
productivity. From 2002 to 2017, the num- ber of regions in
the fourth interval (between 25 and 29 bags/ha) went from 2
to 13, while in the fifth interval
(between 29 and 33 bags/ha)
it went from 4 to 9. Finally,
the number of regions producing
over 33 bags/ha went from 5 in 2002 to 12 in 2017.
Despite this recent fall in production, average
productivity shown a steady increase. In 2010, a redu- ced
harvested area and a larger production improved
productivity from 17.1 to 21.8 bags per hectare. A new
increase, although smaller, was seen in 2017 as average
productivity reached 23 bags per hectare. So, whereas the
first period saw a 27% improvement, it represented only
5,5% in the second period.
Therefore, there was a steady spreading of pro-
ductivity throughout the state. In addition, this occurred
by
increasing the productivity level of regions with lower
levels
while generally maintaining the levels of those regions that
were already at the higher levels. In this way, coffee
productivity became more homogeneous in the state of
Minas Gerais.
In 2017, the distribution of productivity among
the
immediate regions was as follows: 25% of the regions
had
productivity of up to 18 bags/ha, 50% up to 24.7 bags/ha
and 75% up to 29.7 bags per hectare.
To begin to understand the spatial dimension of
the data, Figure 2 includes choropleth maps of coffee
productivity by immediate region in Minas Gerais from
2002 to 2017. These kind of maps, also called thema- tic
maps, color the region according to the level of the variable
under study. In the present case, the threshold values of
productivity was based on 2017 data, the last of the series,
selecting the values which divided the ob-
servations into six
quantiles of approximately equal sizes
(containing 11 or 12
observations each).
It is possible that this spreading process has as one
of its main determining factors the contiguity (the sharing
of a border) to a region that already had high levels of
productivity. As can be seen in Figure 2, each
year many of
the regions that showed higher productivity for the first time
were the neighbors of those that already
were in that
condition, generating a spreading effect of high productivity
throughout the state.
The following paragraphs describe how high
coffee productivity spread among the immediate regions
of
Minas Gerais from 2002 to 2017, stressing the role of
contiguity. This movement is also described on the upper
geographic level, that of intermediate regions.
According to Figure 1, the spatial pattern of
productivity per region has changed over time. In the early
years, few regions had average productivity above 25 bags
per hectare and they were predominantly loca- ted in the
north and northwest of Minas Gerais, more specifically in
the regions of Montes Claros and Patos de Minas. In the
following years, other regions began also to show higher
levels of productivity, resulting in a more dispersed
distribution of regions with high productivity throughout the
territory.
In 2002, of the 70 immediate regions of Minas
Gerais (the old microregions), only 8 showed high pro-
ductivity. These regions were distributed in only 4 inter-
mediate regions of the 13 that compose the state (the old
mesoregions). Four immediate regions were in the
intermediate region of Montes Claros (Salinas, Pirapora,
Espinosa and São Francisco), two in the region of Patos de
Minas (Patos de Minas and Unaí), one in Uberlândia
(Uberlândia) and one in Uberaba (Araxá).
Table 2 shows the evolution of the number of
immediate regions in the high productivity group in each
intermediate region in the years under consideration,
whereas Table 3 shows the number of immediate regions
in
each reference interval by year.
In 2006, the number of immediate regions with
high
productivity increased from 8 to 15. Of the 8 regions
that were
at that level in 2002, six remained, and two left, Espinosa
and Araxá. In addition to these six, 9 other
regions joined the
group. Of the nine, five were neighbors
of regions that already
had high productivity: Janaúba
(neighbor of Salinas and
Espinosa), Curvelo (neighbor of
Pirapora), Dores do Indaiá
(neighbor of Patos de Minas and Araxá), Abaeté (neighbor
of Patos de Minas) and Monte Carmelo (neighbor of
Uberlândia and Araxá).
The spread of high productivity among the inter-
mediate regions is evident in Table 2. In 2002, of the 13
intermediate regions, only 4 contained any immediate
regions of high productivity. In the following years, a
steady
increase was observed: 8 in 2006, 9 in 2010, 10 in 2013, and
12 in 2017. Thus, in the final year of the series,
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5
Spatial distribution of coffee crop productivity in Minas Gerais over time
Figure 1 – Coffee productivity in the immediate geographic regions of Minas Gerais, 2002 to 2017
Source: Prepared by the authors based on data from the Produção Agrícola Municipal (IBGE, 2019).
Concerning the upper geographical level (the
intermediate regions, the equivalent of mesorregions), if
in
2002 only four of them contained immediate regions of high
productivity, in 2006 this number had doubled. The
intermediate region of Montes Claros had four im-
mediate
regions with high productivity, while each of the
regions of
Belo Horizonte, Patos de Minas, Uberlândia,
and Divinópolis
comprised two immediate regions in this
situation and the
regions of Varginha, Pouso Alegre and Juiz de Fora
contained only one each.
Returning to the level of the immediate regions, in
2010, once again there was an increase in the number of
regions with high coffee productivity (fourth, fifth and sixth
reference ranges shown on the maps), from 15 to 21. Of the
fifteen immediate regions of 2006 in this situation, ten
remained. Of the eleven that became
highly productive
(Araxá, which was already in the group
in 2002, returned),
eight were neighbors of regions that already had high
productivity in 2006 and/or 2002. Ex- ceptions are the
immediate regions of Poços de Caldas, Alfenas and São João
del Rei, which were not neighbors of highly productive
regions.
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6
Serenini, R. et al.
Table 2 – Number of immediate geographic regions with high productivity (above 25 bags/ha) by intermediate region
Intermediate Region
2002
2006
2010
2013
2017
Barbacena Belo
Horizonte
Divinópolis
Governador Valadares
Ipatinga
Juiz de Fora
Montes Claros
Patos de Minas
Pouso Alegre
Teófilo Otoni
Uberaba
Uberlândia
Varginha
0
0
0
0
0
0
4
2
0
0
1
1
0
0
2
2
0
0
1
3
3
1
0
0
2
1
1
0
2
0
0
1
3
3
1
0
2
3
5
0
1
3
0
0
6
3
3
1
1
3
3
2
2
2
3
1
0
3
4
2
3
1
2
3
8
Total
8
15
21
26
34
Source: Prepared by the authors based on data from the Produção Agrícola Municipal (IBGE, 2019).
Table 3 – Number of immediate geographic regions in each reference interval per year
0-13
bags/ha
13-21
bags/ha
21-25
bags/ha
25-29
bags/ha
29-33
bags/ha
> 33
bags/ha
Highly productive ( >
25 bags/ha)
Year
2002
2006
2010
2013
2017
24
13
9
7
13
32
29
33
14
10
6
13
7
23
13
2
5
9
12
13
1
5
1
7
9
5
5
11
7
12
8
15
21
26
34
Source: Prepared by the authors based on data from the Produção Agrícola Municipal (IBGE, 2019).
In 2010, the number of intermediate regions with
immediate regions of high productivity increased to 9,
with
the inclusion of Barbacena. The 21 highly productive
immediate regions were distributed as follows: five in the
intermediate region of Varginha, three in the regions of
Uberlândia, Patos de Minas and Montes Claros, two in the
regions of Uberaba and Divinópolis and one in the regions
of Pouso Alegre, Juiz de Fora and Barbacena.
with regions that already had high productivity, they are
neighbors to each other and belong to the intermediate
region of Juiz de Fora, that until then only had the im-
mediate region of Além Paraíba in the high productivity
group.
Finally, in 2017, the number of immediate re-
gions in the high productivity group went from 26 to 34, a
remarkable increase when compared to the eight observed
in 2002. Among the 34, 17 were in the group in 2013 and
six had already been present in one of the other previous
years under study, although not in 2013.
Of the remaining
eleven, seven were neighbors of regions
that belonged to the
high productivity group in 2013.
In 2013, the number of immediate regions with high
productivity increased again, from 21 to 26. Of
these, fifteen
were already in the group in 2010 and one,
the immediate
region of Abaeté, had already appeared in 2006. Of the
other ten, six were neighbors of imme- diate regions that
were in the high productivity group in previous years.
Although the other four (Viçosa, Ponte Nova, Manhuaçu
and Muriaé) did not share a border
Therefore, the increase in productivity tended to
occur in the neighbors of those regions that were already
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Spatial distribution of coffee crop productivity in Minas Gerais over time
highly productive, pointing to the presence of spatial
autocorrelation. As mentioned in the previous section,
Moran’s I is a statistical measure that quantifies this kind of
spatial association. Table 4 shows its values for each year in
the period under consideration.
the last eight years, from 2010 to 2017. In the first period,
Moran’s I was statistically significant at a significance level
of 5% in all years, with a positive sign, indicating the
presence of positive autocorrelation between the im-
mediate
regions. In other words, there was a tendency of highly
productive regions to share some border with other
regions in
the same situation, the same being applied to those with low
productivity.
It is possible to divide the data on Table 4 into
two
periods: the first eight years, from 2002 to 2009, and
Table 4 – Spatial autocorrelation (Moran’s I) of coffee productivity in immediate geographic regions in Minas Gerais by year
Year
Moran’s I
p-value
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
0.347*
0.370*
0.304*
0.308*
0.380*
0.202*
0.168*
0.182*
0.097
0.132*
0.106
0.009
0.056
-0.043
0.099
0.068
0.001
0.001
0.001
0.001
0.001
0.010
0.007
0.015
0.086
0.031
0.061
0.377
0.158
0.370
0.065
0.153
Source: Prepared by the authors based on data from the Produção Agrícola Municipal (IBGE, 2019). Pseudo p-value obtained from 999 random
permutations.
In the second period, only one year had statis-
tically significant Moran’s I, 2011, and even in this case the
magnitude was smaller than those observed in the previous
period. This means that the spatial dependence of productivity
has been reduced over the years due to the spread of
productivity throughout the state, with the ensuing greater
homogeneity of the regions. This can be
checked using
Moran’s scatterplot for each year, shown in Figure 3. In this
kind of plot, with each point representing
a region, the
horizontal axis represents the value of the
variable in that
region and the vertical axis represents the
spatially lagged
variable, that is, its average value in the
contiguous regions.
The adjustment line slope coefficient
corresponds to the value
of Moran’s I.
quadrants. On the other hand, in the last years the dis-
tribution is more homogeneous between the quadrants, with
the adjustment line slope becoming flatter.
To determine the presence of spatial clusters and
outliers, local Moran’s I for each region were calculated.
These are local indicators of spatial autocorrelation (LISA)
and
are the building blocks for the LISA maps, such as those
shown in Figure 4. The maps show the names of the
immediate regions with significant LISA values at a 5%
level of significance.
Confirming the descriptive and the global analy-
sis,
the LISA maps in Figure 3 show a reduction in the
significant clusters and outliers over the period under
consideration. In the first eight years, large clusters are
noticeable, mainly the cluster of high productivity in the
Northwest region of Minas Gerais. However, over the
following years, this cluster gradually disappeared
The scatterplots of the early years show the dis-
tinctive pattern of positive autocorrelation, in which
most
regions are in the high-high and low-low quadrants.
In 2002,
57 of the 70 regions were in one of these two
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8
Serenini, R. et al.
in response to two movements: the increase in the pro-
ductivity of other regions turned the difference between them
not statistically significant and the abandonment of coffee
production in some regions.
regions: Santa Bárbara - Ouro Preto, Guanhães, Ipatinga,
Itabira and João Monlevade. All of them had productivity
below 13 bags/ha. In 2017, of the five regions, three had
increased appreciably their productivity levels. Santa
Bárbara-Ouro Preto increased productivity by approxi-
mately 334%, Ipatinga, 210% and Guanhães, 128%. The
other
two regions also increased their productivity, but at lower
levels, 4 and 15%.
On one hand, some regions with low productivity
increased their productivity levels, as exemplified by the
largest cluster of low productivity seen over the period. In
2002, there was a low-low cluster consisting of five
Figure 2 – Moran’s scatterplots for productivity, 2002 to 2017
Source: Prepared by the authors based on data from the Produção Agrícola Municipal (IBGE, 2019).
In 2006, a low-low cluster comprised almost all of
the eastern area of the state, composed by 10 imme- diate
regions: Governador Valadares, Guanhães, Ponte Nova,
Capelinha, Ipatinga, Almenara, Manhuaçu, Ubá, João
Monlevade and Teófilo Otoni. In that year, of these
regions
only two had productivity above 16 bags/ha. No-
netheless, in
2017, the situation was reversed: only two of the same ten
regions had productivity below 16 bags per hectare. Nine of
them (the exception being Teófilo
Otoni) increased their
average coffee productivity. Seven
regions had an increase above 20%, of which three had an
increase above 70%.
In addition to the regions already mentioned,
others that were not included in low-low clusters also had
significant increases in productivity. Conselheiro Lafaiete,
Além Paraíba, Janaúba, São João del Rei, Montes Claros and
São João Nepomuceno-Bicas increased productivity by more
than 100% from 2002 to 2017.
Cad. Ciênc. Agrá., v. 12, p. 01–10, https://doi.org/10.35699/2447-6218.2020.26171
9
Spatial distribution of coffee crop productivity in Minas Gerais over time
On the other hand, in addition to this increase of
productivity in many regions, there was a second kind of
movement: some regions with small production but
high
productivity stopped their production. Regions who-
se
production was concentrated in a few cities help to
understand the difference between the scenarios before and
after 2010. The immediate region of São Francisco was
characterized as a region of high productivity from 2002 to
2009, sharing borders with the regions of Pira-
pora and
Unaí, which fit into the high productivity group
in every year.
However, in the region of San Francisco, only one of its six
cities was a producer and from 2010
on its productivity became low and it stopped producing
coffee altogether after 2012. In this manner, a high-high
spatial pattern turned into a high-low one.
A similar case is that of the immediate region of
Pirapora, which was in the high productivity group in 12 of the
16 years. One of its neighboring regions, Januária,
had only
three municipalities producing coffee in 2002,
reducing to
two between 2003 and 2005, one from 2005 to 2009 and
stopping coffee production completely from 2010 on. Thus, in
a few years, a neighborhood pattern that
was classified as high-
high became high-low thereafter.
Figure 3 – LISA maps to productivity of immediate geographic regions, 2002 to 2017
Source: Prepared by the authors based on data from the Produção Agrícola Municipal (IBGE, 2019).
Cad. Ciênc. Agrá., v. 12, p. 01–10, https://doi.org/10.35699/2447-6218.2020.26171
10
Serenini, R. et al.
In the immediate region of Curvelo, in 2002, three
municipalities produced coffee. Nonetheless, as of 2003,
only one was still producing and what stopped from 2012
onwards. The region shares borders with two regions that
showed high productivity in almost every year (Divinópolis
and Pirapora) and had above average productivity in the
years 2006, 2008 and 2009. There- fore, this is another
example of high-high spatial pattern becoming a high-low
one.
Permeating these trends is the spreading of high
productivity through contiguity mentioned in the begin-
ning
of this section. The regions with higher productivity in the first
years influenced positively their neighbors with
lower
productivity, a movement that has repeated itself
over the
years, increasing the average productivity in the state. This
result was also found by Almeida et al. (2006) studying the
microregions of Minas Gerais between 2000 and 2004 and
Teixeira and Bertella (2015) analyzing the
same microregions
but between 1997 and 2006. Given the influence of regions
with higher productivity over those with lower productivity,
the result was a tendency of homogenization of productivity
throughout the state.
A common feature of the mentioned municipa-
lities that stopped producing coffee is a small harves- ted
area throughout the period, generally less than 100 hectares.
This suggests that the previously seen overall
tendency to
reduce the harvested area in the state comes
from the
extinction of production in municipalities (and consequently
regions) whose total output was already small, leading to the
concentration in municipalities and regions with greater
harvested area.
Concluding remarks
This paper examined the spatial distribution of
coffee productivity in Minas Gerais between 2002 and
2017, including the presence of spatial patterns (spatial
clusters and outliers). Positive spatial autocorrelation,
measured by Moran’s I, was observed in 9 of the 16 years
under consideration. However, it was largely res- tricted to
the first half of those years, when the measure was
statistically significant in all of them. The maps of the local
Moran’s I for the immediate regions show the existence of
local clusters of high productivity, but also its declining
throughout the years.
A third movement was the sustainment or increa- se in
productivity in regions with large coffee production and that
were already highly productive, such as Alfenas,
Manhuaçu,
Passos and Poços de Caldas.
In summary, three trends were observed from 2002
to 2017: (a) increased productivity in regions with large
harvested area but low productivity, such as Patro- cínio,
Caratinga and Campo Belo; (b) abandonment of (or great
reduction in) production in regions with small harvested
area, even in some of those which had high productivity in
some years, such as the regions of San
Francisco, Espinosa
and Curvelo; and (c) sustainment or
increase in some regions
with large harvested areas and already high productivity.
In general, the regions with small harvested areas
of
coffee tended to leave the market, even when they
have high
productivity. Also, regions with large harvested
areas tended to
increase their productivity, even those
with historically low
productivity levels. Therefore, from 2002 to 2017, it was
observed a homogenization of coffee
productivity in Minas
Gerais, converging to values close to 30 bags/ha.
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