10.1007 s12061-019-09324-4

Statistical and Spatial Analysis of Census
Data for the Study of Family and Industrial
Farming in Colombia
Sandra Moreno, Carlos Durán, Diana
Galindo, Cindy Torres, Javier Jácome &
Aníbal Montero
Applied Spatial Analysis and Policy
ISSN 1874-463X
Appl. Spatial Analysis
DOI 10.1007/s12061-019-09324-4
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Applied Spatial Analysis and Policy
https://doi.org/10.1007/s12061-019-09324-4
Statistical and Spatial Analysis of Census Data
for the Study of Family and Industrial
Farming in Colombia
Sandra Moreno 1 & Carlos Durán 1 & Diana Galindo 1 & Cindy Torres 1 &
Javier Jácome 1 & Aníbal Montero 1
Received: 26 November 2018 / Accepted: 5 September 2019/
# Springer Nature B.V. 2019
Abstract
This article presents a study of the Third National Agricultural and Livestock Census
data to examine the characteristics of family and industrial farming in Colombia using
together, statistical and spatial analysis techniques. From the available anonymized
census microdata, which was added up to a level of rural relevance called vereda, a
selection of variables was made according to properties found out in the state of art as
related to both agriculture types and to the established theoretical framework. From
there on, a Principal Component Analysis allowed distinguishing association between
the attributes of family and industrial agriculture, while an Exploratory Spatial Data
Analysis lets identifying polarized geographical distributions of these two phenomena
in the country. In this way, this study seeks to become a starting point and a benchmark
for analyzing the Colombian agricultural sector, as well as demonstrating the importance of census information, data reduction and statistical and spatial exploration
methods.
Keywords Family farming . Industrial farming . Principal Components Analysis - PCA .
Exploratory Data Analysis - ESDA . Census data . Agricultural and livestock censuses
Introduction
The Colombia's National Administrative Department of Statistics (DANE1) conducted
between 2013 and 2014, after 45 years from the previous one, the Third National
1
For its acronym in Spanish
* Carlos Durán
cadurang@dane.gov.co
Extended author information available on the last page of the article
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Agricultural and Livestock Census (3rd CNA). This operation presents relevant results
for territorial and sectoral socioeconomic public policy design. From a statistical point
of view, agricultural and livestock censuses are the reference for defining sampling
frames required for surveys, which are carried out more frequently and whose purpose
is to update their results (Wye Group 2011).
In order to promote the use of this information, DANE opened the access to the
Census data, anonymizing them previously. To complement the dissemination strategy,
it also developed products aimed at a non-specialist audience in data analysis. These
include bulletins, presentations, departmental and municipal annexes of the main
variables, as well as a census book presenting memories, results and, maps. Both the
microdata and the products are available through the DANE institutional website2
(DANE 2016).
To respond to the local need to exploit census data, this study proposes a structured
analysis focused to characterize family farming and industrial farming. There is an
interest to prioritize this issue built on the premise that poverty is significantly higher in
the countryside than in the city (Programa de las Naciones Unidas para el Desarrollo
(PNUD) 2011) and it is related to family farming. In contrast, industrial farming has
characteristics promoting agricultural modernization. In this way, as a support to the
focused decision making associated to improve conditions of the most vulnerable
population in the sector, it is necessary to establish both the characteristics and the
location of both types of agriculture.
A statistical and spatial study of census data may contribute to this matter, though it
is necessary to go beyond univariate analysis, since there are many variables interacting
in space and given the complex nature of some phenomena. The presented analyses
seeking to explore how variables are related to each other to identify structures or
patterns underlying family and industrial farming.
In the next section we review the state of the art, and develop a conceptual
framework to determine the main characteristics of the family and the industrial
farming activities. Subsequently, the proposed methodology is exposed, which includes: census data pre-processing; the Principal Component Analysis (PCA); and
the Exploratory Spatial Data Analysis (ESDA). Finally, the results and conclusion of
this study are presented.
Theoretical Bases
State of the Art
Wye Group (Wye Group 2011) affirms that to obtain structural information on the
sector at detailed scales is only one of the benefits of conducting agriculture and
livestock censuses, which, together with other sources of information, constitute the
agriculture and livestock statistics systems. According to the authors, these information
sources include: sample surveys conducted periodically; remote sensors for large-scale
data collection; administrative records collected by specific themes and the geo-
2
https://www.dane.gov.co/index.php/estadisticas-portema/agropecuario/censonacional-agropecuario-2014
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referenced data, which are very useful for the calculation of indicators through spatial
analysis and the targeting of public policies.
In this context, studies conducted from the sources mentioned are notable for
covering a wide range of topics and objectives, as well as potential users of information. For example, it has been sought to determine profiles of specific agricultural
products in the state of New South Wales, Australia (Department of Industry of NSW
2010), and methods for mapping local food production in the province of British
Columbia, Canada (Morrison et al. 2011). Also, spatial models and analyses have been
developed in order to evaluate, among others: livestock production patterns in Ethiopia
(Tilahun and Schmidt 2012); the characterization of the provinces and adoption of
agricultural technologies in Peru (Cambillo et al. 2016; Escobal 2017); the distribution
of agriculture and livestock income in Ireland (O’Donoghue et al. 2015); the differences between family and non-family farming in Brazil (Caetano and Stege 2014); and
regional agricultural production in Hungary (Pesti and Kaposzta 2008).
Within the statistical methodologies applied to land use or agriculture studies stands
out data exploration and analysis techniques (Lesschen et al. 2005). Particularly, the
factorial analysis for the study of structures in observed variables and their covariance
relationships (Cambillo et al. 2016; Caetano and Stege 2014), and the principal
components analysis for the reduction of numerous data and the identification of
underlying structures (Guillem et al. 2012). Furthermore, data mining techniques, has
been used to extract knowledge reflecting trends in the agriculture and livestock sector
(Miller et al. 2009; Milovic and Radojevic 2015; Shahbazi and Karambeygi 2015;
Zaragozí 2012).
From the geospatial perspective, studies are characterized by the use of spatial
statistics, explicitly incorporating the location of the data for the calculation of their
results. The use of global and local indicators of spatial dependence was observed in
order to identify different spatial patterns in topics such as: agglomerations of crop
groups (Morrison et al. 2011); agriculture and livestock activity, marketing of products
and sources of water for irrigation (Cambillo et al. 2016); education, infrastructure and
structure in rural farms (Smit et al. 2011); productivity, degree of mechanization,
intensity of labor and investments in family and non-family farming (Caetano and
Stege 2014); livestock production and by-products (Gan and Hu 2015); and agricultural
innovation indicators (Escobal 2017).
Therefore, studies related to agriculture go beyond numerical evaluation or conventional statistical analyses on data obtained in censuses and surveys. The spatial
component has become a fundamental part of the research of the agricultural sector,
representing and visualizing different phenomena and providing descriptive, relational
or causal spatial analysis, whose findings support the focus on decision making
processes, seeking greater efficiency and effectiveness to solve the problems that affect
the sector.
Theoretical Framework
Most of the Colombian rural population is organized around family farming, while
participating in unprofitable productive dynamics. Consequently, social indicators
show worse living conditions and higher levels of poverty in rural areas than in urban
areas (Programa de las Naciones Unidas para el Desarrollo (PNUD) 2011). On the
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other hand, there are activities linked to industrial farming that even being more
profitable and are better integrated to commercial circuits, they tend to employ a lesser
amount of the peasant population. In general, family and industrial farming have weak
linkages.
One of the factors that hinder the orientation of public policies in the agriculture and
livestock sector is the absence of concepts and methods allowing its characterization,
due to the high structural heterogeneity of agriculture and livestock activity (Niño
2016). However, it is common to define a bimodal structure of agriculture: industrial
farming and peasant or family farming, which have marked differences in agricultural
activity in relation to certain fundamental criteria for production, such as: credit; risks in
crops; access to information; specialized technology and supplies; the labor force; the
land market, among others (Schejtman 1998).
The concept of family farming (FF) depends, either, on the region where it is carried
out or on the point of view of particular authors (Barrientos and Torrico 2014). In
accordance with Maletta (Maletta 2011), a brief description of the FF corresponds to a
small-scale farm production. In the framework of the International Year of Family
Farming (IYFF), for Latin America and the Caribbean, the definition of FF emphasizes
that it is managed, operated and worked exclusively by a family, generating particular
links of a social, economic, cultural and environmental nature (Salcedo and Guzmán
2014).
According to Soto et al. (Soto et al. 2007), the FF is classified into three types: 1.
Family Subsistence Farming (FSF), oriented to self-consumption, with available land,
but insufficient production income and low agriculture and livestock potential; 2.
Family Farming in Transition (FFT), with greater dependence on own production for
self-consumption or sale, access to better resources that allow meeting the family needs,
but with difficulties to generate surpluses; 3. Consolidated Family Farming (CFF), with
sufficient sustenance in own production, greater exploitation of the land, access to
markets and generation of surpluses. In the geospatial context, these typologies can be
manifested through geographic concentrations (Maletta 2011), which facilitates the
identification of the differentiating characteristics between family farming and industrial farming, as well as the targeting of the actions to take in order to achieve
homogeneity in the agriculture and livestock sector.
In the mid-twentieth century, the modernization policy with respect to the rural
sector began to take relevance through agriculture and livestock industrialization
processes with a view to global markets, thus forming the Industrial Farming (IF). Its
main characteristic is the application of techniques and the use of machinery, which
means that the harvesting process is done in an automated, technified and highly
efficient manner, using irrigation systems, fertilizers, crop monitoring and pest control,
which results in higher production, at lower cost and time (Carles 2016), as well as the
growth of monoculture systems (Castillo 2008).
The UN (ONU 2008) acknowledges that the IF is of great importance for the
progress of the agriculture and livestock sector, by increasing the added value of the
production of land that has not been fully used through the aforementioned processes.
In addition, its high productive capacity is designed to meet the needs of the markets
and to commercialize large quantities of products, both internally and externally (Carles
2016), where the agricultural intensification and the expansion of the agricultural
frontier affects the smaller farms causing them to decrease in area (Gras 2013).
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Table 1 Main characteristics of industrial farming and family farming
Criterion
Industrial farming
Family farming
Production logic
i) To maximize profit rate; ii) High yields.
i) To ensure the survival of the
family; ii) Low yields.
Production organization
i) Salaried work; ii) High division of labor. i) Family labor; ii) Scarce division
of labor.
Productive and commercialization
systems
i) Monoculture or little
diversification; ii) Large scale; iii)
Modern technology; iv)
Oriented to the domestic or export
market; v) Storage,
packaging and commercialization systems.
i) Polyculture and breeding of
domestic animals; ii) Small scale
iii) Traditional, 1 oriented to
self-consumption and small
commercialization;
v) Access to local markets or
intermediate sale.
Quantity and quality of resources
i) Abundant land and water, of good
quality; ii) Adequate
facilities, machinery and
equipment; iii) Access to credit; iv)
Own technical assistance.
i) Scarce land and water, of low
quality; ii) No facilities,
rudimentary machinery and
equipment; iii) Difficult access to
credit; iv) Without technical
assistance.
Socioeconomic and agro-economic i) Easy access to public utilities; ii)
location
Availability of access roads and
communications;
iii) Favorable location with respect
to urban centers.
i) Inadequate or no access to
public utilities; ii) Far from access
roads and communications; iii)
Located in areas far from urban
centers.
In line with the above, it can be said that the duality between the FF and the IF
presents fully differentiable conditions, although the reality may be more complex due
to the multiplicity of present situations depending on the countries, regions or productive chains at local scales. According to Backer (Backer 2007), in the rural sector, there
are clear divergences between the two types of agriculture based on criteria as production, marketing, resources used and location, framed more precisely, in the Table 1.
Methodology
For the development of this study, it was used the data obtained in the Third National
Agriculture and Livestock Census 2014 (3rd CNA3). The Agriculture and Livestock
Production Unit (UPA4) was defined as observation and analysis unit, collecting data
on three main topics: social, economic and environmental (DANE 2016). For the
present analysis, the information was grouped in a rural zoning known as vereda, of
geographical and cultural connotation, which groups the dispersed population of the
municipalities, it contains one or more agriculture and livestock production units. That
is, the two sources of information used for this analysis were the microdata base of the
3rd CNA and the geographical coverage of the country veredas.
As described by Hernández (Hernández et al. 2010), from a quantitative methodology perspective, this study is descriptive and correlational. From a descriptive statistical
3
4
For its acronym in Spanish
Idem
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operation as the CNA, a set of variables was defined to determine its properties in the
exploratory data analysis, and the degree of association between them in a statisticalspatial context. This study has no explanatory purpose, because its scope did not seek to
determine causalities of a dependent variable from independent ones.
This study included three stages: 1. Pre-processing of information; 2. Multivariate
analysis; and 3. Spatial statistical analysis. The process as a whole can be seen in Fig. 1.
Data Pre-Processing
The information pre-processing stage began with a literature review focused on
identifying the differential features of the FF and the IF. Subsequently, the census form
was examined to identify the information associated with these features, and it was
extracted from the database. Thus, 30 variables were obtained and the indicators
required to carry out the analysis were calculated (see Table 2), these were linked to
28,837 veredas distributed throughout the country. As a result, a data matrix was
created, where the rows are the veredas, and the columns, each of the indicators
generated from the variables.
Multivariate Analysis: Principal Component Analysis (PCA)
The second stage involved the Principal Components Analysis (PCA) with the aim of
reducing the dimensionality of the data. As exposed by Díaz and Morales (Díaz and
Morales 2012), the PCA is a methodology that aims to construct linear combinations,
not correlated to each other, of the original variables, in such a way that they contain the
highest proportion of original total variability.
A preliminary statistical analysis was performed, during which missing data were
removed, histograms of variables for the evaluation of data distribution were generated
Fig. 1 Methodological process for the analysis of the CNA information
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Table 2 Indicators generated by vereda
Characterization
Variable
Indicator (per vereda)
General variables on land use
Sown area
Agricultural area
Pastures area
Stubble area
Total area planted(ar semb)
Percentage of agricultural (p ar agricola)
Percentage of area in pasture (p ar pastos)
Percentage of area in stubble (p ar rastrojo)
Variables that affect the
agricultural production
Credit or funding
Infrastructure
Machinery
Labor force
Percentage of UPA that requested credit(p s cred)
Percentage of area with infrastructure (p ar InfAgric)
Percentage of UPA with machinery (p maq)
Total permanent workers (t tr perm)
Percentage of workers belonging to the household
(p trper hogp)
Percentage of UPA with collective work (p _tr col)
Total resident producers (ProductResi)
Average number of dwellings per UPA(m vivxupa)
Production orientation
Percentage of UPA with self-consumption (p UAutoc)
Percentage of UPA with barter or sale (p UTruequeVL)
Percentage of UPA with sale to local market
(p UVentaM)
Percentage of UPA with sale to large market (p UGranM)
Irrigation
Technical assistance
Land tenure
Percentage of UPA using irrigation systems (p usoSRiego)
Percentage of UPA that received assistance (p asist)
Percentage of UPA with own tenure (p t prop)
Percentage of UPA with lease (p t arr)
Percentage of UPA with collective ownership (p ten col)
Percentage of UPA with another type of tenure
(p t otros)
Services access
Percentage of UPA with access to water(p AcH2O)
Percentage of UPA with electricity dedicated to
agriculture and livestock activities (p en agro)
Cultivation system
Percentage of area in agro-industrial crops(p cagroind)
Percentage of area in monocultures (p monoc)
Access to health
Literacy
Schooling
Participation
Percentage of persons affiliated to health care (p afsalud)
Percentage of illiterate persons over 15 years of age
(p analf)
Average years of schooling (m escolar)
Proportion of women (p muj)
and the correlation matrix between variables was calculated. This procedure was
performed to identify the behavior of the variables and to prepare the interpretation
of the PCA. All the variables identified during the pre-processing stage were included
within the PCA given the thematic weight of each variable in the characterization of FF
and IF according to the literature review. The PCA was generated using the
FactoMineR library of R statistical software (R Core team 2018).
During this process, Conventional and Robust PCA were carried out in order to
establish if it was possible to minimize the effect of the magnitude of the variables and
the presence of outliers. In each case, the analysis was evaluated by reviewing the
variance retained by component, the variables quality of representation and the information completeness. The conventional method was chosen because the robust one
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requires that the data be on the same scale, which meant leaving out some variables that
are deemed to be important for the analysis.
Finally, in order to choose the number of components to analyze, it was established
as a criterion a balance between the variance retained, the data reduction and the
variables representation in each of the components.
Exploratory Spatial Data Analysis (ESDA)
The third stage encompasses the Exploratory Spatial Data Analysis (ESDA), through
the GeoDa software (Anselin et al. 2006), for the first four components and a series of
prioritized variables as established from the literature review and the PCA results. By
means of graphs, maps, and techniques of spatial statistics, this analysis pursued the
following objectives: 1. To visualize the trend and spatial distribution of the variables;
2. To establish the existence of atypical data; 3. To identify the existence of spatial
dependence, both globally and locally; 4. To identify the existence of spatial outliers; 5.
To describe possible conditional relationships between variables (Yrigoyen 2006).
The visualization of the spatial distribution of the variables aimed to distinguish the
tendencies of gradual change and places where sudden changes occur in the values of a
variable (Yrigoyen and Calderón 2009). In order to detect them, the present study made
choropleth maps using the natural breaks partitioning method, which through varianceminimization separate and classify values where large changes occur (De Smith et al.
2018). As well, were used box maps, as a cartographic representation of a box plot, in
order to clearly identify both the quartiles and the outliers.
Subsequently, it was established if the components and variables had spatial dependence or if the represented phenomena are rather random. This association or spatial
dependence occurs when the values observed in a geographical entity depend on the
values observed in neighboring regions, (Yrigoyen 2006). The degree of association
related to the space of analysis is measured through of Spatial Autocorrelation (Siabato
and Guzmán 2019). One of the best-known global spatial statistics to quantify spatial
autocorrelation is the Moran’s I, which indicates the presence of a conglomerate-type
spatial pattern when takes a positive and significant value.
On the other hand, Local Indicators of Spatial Association (LISA) (Anselin 1995)
are used with the purpose of decomposing the global indicators of spatial autocorrelation. These indicators measure the statistical significance of the degree of concentration
of high / low values of a variable in the geographical environment of each of the
observations in the sample (Yrigoyen 2006), to identify the presence of clusters of high
values surrounded by other high values and / or clusters of low values surrounded by
other low values, as well as the spatial outliers (Siabato and Guzmán 2019), which
indicate high values of the variable surrounded by low values or vice versa. In this
sense, Local Moran’s Index was used to produce spatial association maps for each
prioritized component and variable.
Since the veredas are lattice geographical entities that belong to a level of local
organization within the municipalities, the relations between them occur from a
neighborhood context. For this study, the Moran’s global and local spatial statistics
were calculated from a matrix of spatial weights defined by the contiguity type queen of
order 1. Likewise, the parameters for LISA maps were a randomization with defined
seed of 999 permutations and a filter for the significance level equal to 0.01. To define
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whether a component or a variable evidenced positive spatial autocorrelation, the value
generally recommended in practice was adopted, which states that Moran’s I must be
greater than 0.35 (Siabato and Guzmán 2019).
From the results obtained with the LISA maps, conditional maps were included, to
evaluate possible regional and zonal relations between variables that have similar
spatial concentration patterns (Anselin, 2005). Therefore, the LISA of a variable is
related to two other variables (one on the vertical axis and one on the horizontal axis)
classified by natural breaks method to assess whether certain zonal spatial conglomerates have conditions of high or low values associated to other variables.
Results
Principal Component Analysis (PCA)
After the preliminary analysis of the census variables, a PCA was done; it converted 30
variables into 15 principal components. The study focused only on the first four
principal components, which retain the 33.095% of the variance of the initial data.
Taking as reference the measurement of quality of representation of each variable
(squared cosine), in this PCA the variables best represented were: total of permanent
workers; percentage of area in agriculture; resident producers; percentage of area in
pasture; percentage of UPA with sale to local market; each one with a quality measure
above 60%. Following, with a percentage higher than 50% were: percentage of UPA
with sale to large market; with self-consumption; with machinery; with own tenure; and
total area planted. Variables with the lowest quality of representation were: proportion
of women, percentage of persons affiliated to health care and percentage of area with
infrastructure, with a percentage lower than 8%.
Figure 2 shows, on the left side, the correlation circle of the first factorial plane. This
represents information about the two first components which retains 19% of the
variance of the initial data. Although this percentage is not high, the first main
component made clear some associations between the characteristics of the FF and
the IF in Colombia. On the right side, the figure illustrates the correlation matrix of the
variables with each of the selected components.
The correlation circle presents associations in the positive axes of the two first
principal components between the variables: percentage of UPA that requested credit,
using machinery, with electricity dedicated to agriculture and livestock activities, and
percentage of the area in agro-industrial crops. In contrast, in the negative axis, there is
evidence of relations between the percentage of illiterate persons over 15 years of age,
the percentage of UPA with self-consumption and the percentage of workers belonging
to the household, which according to the theoretical framework are related to FF.
In the second principal component, there were represented at one extreme: percentage of UPA that received assistance, requested credit, using machinery and percentage
of the area in agro-industrial crops, while at the opposite end, veredas with similar
behavior in the percentage of the area in pasture and UPA using irrigation systems. In
the third component are represented variables of number of permanent workers,
resident producers, planted area and the percentage of illiterate persons over 15 years
of age; and on the opposite side: percentage of UPA using irrigation systems, with sale
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Fig. 2 Correlation circle of the first factorial plane (left), and correlation matrix of the variables in the chosen
components (right)
to large market, along with average years of schooling. Finally, the fourth component,
presented the veredas with similar values in terms of percentage of UPA using
machinery, with access to water, the percentage of the area in pasture and percentage
of UPA with collective work distant from the veredas with related values in percentage
of the area in monocultures and agro-industrial crops.The results obtained through the
PCA identified and confirm connections of characteristics responding to the dynamics
of the FF and IF in Colombia. Following, it was checked if these statistical relations
present particular patterns or trends from the geospatial perspective.
Exploratory Spatial Data Analysis – ESDA
The first four components selected of the PCA present atypical values according to the
Box Plot diagrams explored (see Fig. 3) where Component 1 has a significant amount
of lower outliers (negative values) compared to the other components, which stand out
for having a greater number of atypical superiors.
Moran’s Indicator was calculated for the first four components (see Table 3), the
values reflect associative behavior at a global level in the four components.
In order to achieve an understanding of the spatial conglomerates found in the
components, relations were established between variables and veredas according to the
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Fig. 3 Box Plot for the four first ACP components
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Table 3 Moran’s I for first four
components of the PCA
Component Moran’s I
1 0,74
2 0,65
3 0,46
4 0,62
results obtained from the PCA and in accordance with the theoretical framework. For
the interpretation of the LISA maps presented below, the high value concentration
patterns are shown in red and the low value patterns in blue.
It stands out the clusters of the first component, shown in Fig. 4, which clearly
highlight possible differences in the agricultural sector between the interior and the
periphery of the country. In the bordering areas of the country, scenarios within the
framework of FF are manifested, such as: forms of collective or informal land tenure;
self-employment or family labor; production for self-consumption or barter; and
illiteracy. This pattern coincides with regions with little accessibility and where the
greatest number of ethnic territories are located, which would highlight the necessary
conditions for the presence of FSF, either because of a productive weakness due to less
access to resources and improvements, or because of traditional and ancestral practices
Fig. 4 LISA - Component 1
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that are the basis for local knowledge in production, added to unfavorable socioeconomic conditions.
On the other hand, in specific inner areas of the country are present
conditions related to IF, including formal land tenure; access to public services; production for sale to markets; access to technical assistance, machinery
and credit; and prevalence of agro-industrial crops. These areas coincide with
those of higher agro-industrial productivity in the country, as shown in Fig. 4
of 1) coffee and sugar cane; 2) oil palm and, 3) banana export type; and on
the other hand, extensive livestock activity, such as 4) cattle ranching.
The variables that characterize land use do not present extraordinary atypical
values, but rather positive spatial autocorrelation and local association conglomerates. In this way, the highest percentage of area in agriculture (Moran’s I =
0.61) is concentrated mainly in the west of the country, in the so-called Colombian Pacific Region, as well as in veredas located between the western and
central mountain ranges (Fig. 5 - left). On the other hand, the proportion of
pasture area (Moran’s I = 0.57), which is related to livestock activity, is predominantly grouped in the plains east of the country in the Orinoquia region, and in
specific savannah areas to the north (Fig. 5 - center). The areas of stubble
(Moran’s I = 0.52) present more specific associative behaviors, where the conglomerate located to the north of the country which involves part of the Guajira
peninsula and the adjacent zone to the Sierra Nevada de Santa Marta stands out
(Fig. 5 - right).According to the Box Maps, the variables affecting agricultural
production are highlighted in red, these present a significant amount of veredas
whose values are extraordinary high in: collective tenure (4606) (Fig. 6 - left);
production for barter or sale (3764) (Fig. 6 - center); monoculture (3408) (Fig. 6
- right); collective work (3321); permanent workers (2393); and resident producers (2293). The spatial similarities are clear between the first two variables,
contrasting with the third.Table 4 shows Moran’s I values demonstrating positive
spatial autocorrelation to variables affecting agricultural production.
From the obtained results through the Local Moran’s I for each variable, it can be
observed that clusters of high values (in red) are mainly located on the periphery of the
country, in the variables: dwellings per producing unit; collective tenure; collective
Fig. 5 LISA: Agricultural area (left); area in pasture (center); Stubble area (right)
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Fig. 6 Box Maps: Collective ownership (left); UPA with barter (center); area in monocultures (right)
work; permanent household workers (Fig. 7 - left); production for self-consumption
(Fig. 7 - right); and resident producers. According to the theoretical framework
established for this study, the greater participation of these variables is related to FF.
Table 4 Variables affecting agricultural production with positive spatial autocorrelation
Variable
Moran’s I
Area in monocultures
0,352
Resident producers
0,354
Average years of schooling
0,355
Permanent workers
0,370
% UPA that requested credit
0,374
% of UPA with lease
0,379
% of UPA with barter or sale
0,446
% of UPA with sale to local market
0,453
Workers belonging to the household
0,484
% of UPA with collective work
0,490
% of UPA with sale to large market
0,490
% of UPA with own tenure
0,492
% of UPA with self-consumption
0,510
Average number of dwellings per UPA
0,518
% of UPA with access to water
0,519
% of UPA using irrigation systems
0,549
% of UPA with another type of tenure
0,574
% of UPA with machinery
0,574
% of UPA with electricity dedicated to agriculture and livestock activities
0,605
% of area in agro-industrial crops
0,630
% of UPA with collective ownership
0,632
% of UPA that received assistance
0,643
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Statistical and Spatial Analysis of Census Data for the Study of...
Fig. 7 LISA: workers belonging to the household (left) and UPA with self-consumption (right)
As well, that high values of following variables: technical assistance (Fig. 8 - left);
agro-industrial crops (Fig. 8 - right); electricity dedicated to agriculture and livestock
activities; requested credit; sale to market; and large markets, are located in the inner
Fig. 8 LISA: technical assistance (left) and area in agro-industrial crops (right)
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S. Moreno et al.
areas of the country. Increased participation of these variables is related to IF in
agreement to the theoretical framework established.
According to the conditional map in Fig. 9 (top right), in the spatial conglomerates
of veredas located in the west and east border areas of the country, where there is
concentration of high percentages of workers belonging to the household (red color),
there are also conditions for a greater participation of veredas that allocate production to
self-consumption (vertical axis - higher than 69%), and a predominance in the form of
collective land tenure (horizontal axis - higher than 60%).
The conditional map in Fig. 10 (bottom left) shows spatial conglomerates of
veredas with a low percentage of agro-industrial crops (blue color) are conditioned to a low participation of credit (vertical axis - lower than 10%) and
technical assistance (horizontal axis - lower than 17%). In contrast, in the top
right position conditional map, clusters of high percentage of agro-industrial
crops are located in the inner areas of Colombia (red color), corresponding to
Fig. 9 Conditional maps: LISA workers belonging to the household versus selfconsumption UPA classification (vertical axis) and collective ownership classification (horizontal axis)
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Statistical and Spatial Analysis of Census Data for the Study of...
Fig. 10 Conditional maps: LISA agro-industrial crops versus credit request classification (vertical axis) and
technical assistance classification (horizontal axis)
higher percentages of technical assistance (higher to 48%) and credit requests
(higher than 29%).
Conclusions
The selected variables formed a group of attributes that allowed characterizing the
prevailing conditions in both FF and IF in Colombia, which coincided with the theoretical
framework. In general terms, during the methodological development, which its scope
was descriptive and correlational, the PCA confirmed the relationships between variables
defined in the literature, while the ESDA was able to show the polarized spatial behavior
between FF and IF.
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S. Moreno et al.
In the PCA, it was found that the characteristics related to FF were largely associated
with the negative axis of the first component: collective forms of land tenure and
resident producers with a permanent work for self-consumption. On the other hand, in
the positive axis, properties that define IF were grouped together: land tenure; access to
credit, public services and technical assistance; use of machinery; production for sale to
markets; and prevalence of agro-industrial crops.
The implementation of the PCA made it possible to summarize only one third of the
information in the original data. This can be explained by the local dynamics of
variables associated with FF and IF, taking into account that data were analyzed at
the national level. In other words, the variance of the data is high and not homogeneous, so the information summary method can only identify the most marked
differences.
The ESDA confirmed that the component coordinates show spatial concentration
patterns, both at low (negative) and high (positive) values. LISA indicators reflected for
the first component that spatial conglomerates of negative values are located in the
outskirts of the country. Concentrations of high values in the interior of the country
coincide with specific areas where IF is manifested, with high percentages of agroindustrial production and extensive cattle ranching.
The three variables associated to land use (percentage of agriculture, pastures and
stubble area) presented positive spatial autocorrelation as well as the 22 variables
affecting agriculture production. In the outskirts of Colombia, there is a convergence
of properties, as those established in the theoretical framework, related to FF as
collective ownership, permanent workers, and production to self-consumption. In inner
areas, exist side by side attributes referred to IF as access to public services, agroindustrial crops and sale to market.
In specific spatial conglomerates in eastern and western areas of the country
where prevails a high number of permanent workers in the household, there is a
greater percentage of units that allocate production to self-consumption. In
addition, where agro-industry clusters predominates, there is a greater probability to find UPA with technical assistance and access to credit.
It must be taken into account that the Census was based on declarative information.
In this context, the measurements correspond to those answered by the census respondents. This implies that, although the methodological development was rigorous and
the results obtained had statistical significance, there is a margin of uncertainty inherent
to the data. In any case the results reflected cluster patterns with statistical significance
which rule out random behaviors in most components and variables analyzed.
In the same way, although the 3rd CNA comprised information to describe a wide
variety of phenomena related to the subject of the study, it did not count with specific
information of interest to characterize the FF and the IF, according to the theoretical
framework, as crop risk insurance and quality control in production processes.
Finally, as future work, it is proposed to perform this type of analyzes at more
detailed scales such as regions, departments or municipalities, as well as comparative
studies involving other relevant conditions for the agricultural and livestock sector as
location respecting road infrastructure, connectivity to urban centers, access to communications and the land potential for production. As well, to develop studies with
explanatory scope to determine possible causalities which could explain the occurrence
and conditions in which AF and AS are manifested in the country.
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Compliance with Ethical Standards
Disclaimer The views expressed in this article are those of the authors and do not necessarily represent those
of The Administrative Department of National Statistics of Colombia (DANE).
Conflict of Interest
The authors declare that they have no conflict of interest.
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Affiliations
Sandra Moreno 1 & Carlos Durán 1 & Diana Galindo 1 & Cindy Torres 1 & Javier
Jácome 1 & Aníbal Montero 1
Sandra Moreno
slmorenom@dane.gov.co
Diana Galindo
drgalindog@dane.gov.co
Cindy Torres
catorresm@dane.gov.co
Javier Jácome
jmjacomem@dane.gov.co
Aníbal Montero
amonterol@dane.gov.co
1
National Administrative Department of Statistics (DANE), Research and Development Group,
Geostatistics Division, Bogotá D.C., South America, Colombia