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 1 23 Your article is protected by copyright and all rights are held exclusively by Springer Nature B.V.. This e-offprint is for personal use only and shall not be self-archived in electronic repositories. If you wish to self-archive your article, please use the accepted manuscript version for posting on your own website. You may further deposit the accepted manuscript version in any repository, provided it is only made publicly available 12 months after official publication or later and provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at link.springer.com”. 1 23 Author's personal copy 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 [email protected] Extended author information available on the last page of the article Author's personal copy S. Moreno et al. 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 Author's personal copy Statistical and Spatial Analysis of Census Data for the Study of... 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 Author's personal copy S. Moreno et al. 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). Author's personal copy Statistical and Spatial Analysis of Census Data for the Study of... 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 Author's personal copy S. Moreno et al. 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 Author's personal copy Statistical and Spatial Analysis of Census Data for the Study of... 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 Author's personal copy S. Moreno et al. 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 Author's personal copy Statistical and Spatial Analysis of Census Data for the Study of... 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 Author's personal copy S. Moreno et al. 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 Author's personal copy Statistical and Spatial Analysis of Census Data for the Study of... Fig. 3 Box Plot for the four first ACP components Author's personal copy S. Moreno et al. 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 Author's personal copy Statistical and Spatial Analysis of Census Data for the Study of... 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) Author's personal copy S. Moreno et al. 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 Author's personal copy 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) Author's personal copy 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) Author's personal copy 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. Author's personal copy 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. Author's personal copy Statistical and Spatial Analysis of Census Data for the Study of... 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. References Anselin, L. (1995). Local indicators of spatial association - LISA. Geographical Analysis, 27(2), 93–115. Anselin, L. (2005) Exploring spatial data with GeoDa: A workbook. Urbana, IL: Center for Spatially Integrated Social Science. Anselin, L., Syabri, I., Kho, Y., & Geo, D. (2006). An introduction to spatial data analysis. Geographical Analysis, 38(1), 5–22. Backer, T. (2007) La agricultura empresarial campesina y el combate a la pobreza rural andina. Retrieved from: http://orton.catie.ac.cr/repdoc/A8602e/A8602e.pdf. Accessed 28 Nov 2017. Barrientos, J. C., & Torrico, J. C. (2014). Socio-economic perspectives of family farming in South America: Cases of Bolivia, Colombia and Peru. Agronomía Colombiana, 32(2), 266–275. Caetano, C., Stege, A. (2014) Spatial differences between family and non-family farming in Brazilian agriculture. Working Paper 14, International Research Initiative on Brazil and Africa. Cambillo, E., Aguero, Y., Alvarez, M.d. P., & Riojas, A. (2016). Métodos Factoriales en el Análisis de Datos Espaciales. Una Aplicación a los Datos del Censo Agropecuario 2012 para la Caracterización de las Provincias del Perú. Pesquimat, 19(2), 50–58. Carles (2016) Características de la agricultura tradicional y moderna, Retrieved from: https://www.agroptima. com/blog/caracteristicas-de-la-agriculturatradicional-y-moderna. Accessed 23 Nov 2017. Castillo, O. (2008). Paradigmas y conceptos del desarrollo rural (Segunda edición). Bogotá D.C.: Universidad Javeriana. DANE (2016) 3er Censo Nacional Agropecuario, Tomo 1, vol. 1. DANE, Bogotá. Retrieved from: http://www.dane.gov.co/files/images/foros/foro-de-entrega-de-resultadosy-cierre-3-censo-nacionalagropecuario/CNATomo1-Memorias.pdf. Accessed 14 Dec 2017. DANE (2016) Censo Nacional Agropecuario 2014. DANE, Bogotá. Retrieved from: https://www.dane.gov. co/index.php/estadisticas-por-tema/agropecuario/censo-nacional-agropecuario-2014. Accessed 19 Feb 2018. De Smith, M., Goodchild M., Longley, P. (2018) Geospatial analysis: A comprehensive guide to principles, techniques and software tools, Sixth edition, Retrieved from: https://www.spatialanalysisonline. com/HTML/index.html. Accessed 1 Nov 2018. Department of Industry of NSW (2010) Analysis of population census and agriculture census data in Sydney statistical division: Profiles of specific agricultural commodities. Retrieved from: https://www.dpi.nsw.gov. au/data/assets/pdf file/0020/354053/Profilesof-specific-agricultural-commodities.pdf. Accessed 18 April 2018. Díaz, L., & Morales, M. (2012). Análisis Estadístico de Datos Multivariados. Universidad Nacional de Colombia. Escobal, J. (2017). Análisis espacial de la adopción de tecnologías agrarias en el Perú. Una mirada desde el Censo Nacional Agropecuario 2012. No. 4. In: IV Censo Nacional Agropecuario (Vol. 2012). Lima: Investigaciones para la toma de decisiones en políticas públicas, FAO. Gan, L., & Hu, X. (2015). Geographic distribution of livestock products in China - an application of spatial autocorrelation analysis. Indian Journal of Animal Research, 50(4), 569–579. Gras, C.S. (2013) Expansión agrícola y agricultura empresarial: el caso argentino. Retrieved from: https://www.colibri.udelar.edu.uy/bitstream/123456789/6836/1/RCSGras2013n32.pdf. Accessed 27 Nov 2017. Guillem, E., Barnes, A., Rounsevell, M., & Renwick, A. (2012). Refining perception based farmer typologies with the analysis of past census data. Journal of Environmental Management, 110, 226–235. Hernández, R., Fernández, C., & Baptista, P. (2010). Metodología de la investigación (Quinta edición). México D.F. McGraw-Hill. Author's personal copy S. Moreno et al. Lesschen, J. P., Verburg, P. H., & Staal, S. J. (2005). Statistical methods for analysing the spatial dimension of changes in land use and farming systems. International Livestock Research Institute. Maletta, H. (2011) Tendencias y perspectivas de la agricultura familiar en América Latina. Documento de Trabajo No. 1. Proyecto Conocimiento y Cambio en Pobreza Rural y Desarrollo. RIMISP - Centro Latinoamericano para el Desarrollo Rural. Miller, D., McCarthy, J., & Zakzeski, A. (2009). A fresh approach to agricultural statistics: data mining and remote sensing. In: Joint Statistical Meeting, pp. 3144-3155. Washington DC: American Statistical Association. Milovic, B., & Radojevic, V. (2015). Application of data mining in agriculture. Bulgarian Journal of Agricultural Science, 21(1), 26–34. Morrison, K., Nelson, T., & Ostry, A. (2011). Methods for mapping local food production capacity from agricultural statistics. Agricultural Systems, 104(6), 491–499 Retrieved from: https://www.sciencedirect. com/science/article/pii/S0308521X11000448. Accessed 18 Apr 2018. Niño, C. (2016) Aproximación teórica de la categoría agricultura familiar como contribución al análisis conceptual en la política pública de desarrollo rural en Colombia. In: La agricultura familiar en Colombia. Estudios de caso desde la multifuncionalidad y su aporte a la paz, pp. 47–60. Ediciones Universidad Cooperativa de Colombia. O’Donoghue, C., Grealis, E., Farrell, N., & Economy, T. (2015). Modelling the spatial distributional agricultural incomes. In anonymous (Ed.), International microsimulation association world congress. European Association of Agricultural Economists. ONU (2008) Agroindustria y Pequeña Agricultura: Vínculos, Potencialidades y Oportunidades Comerciales. Naciones Unidas, Santiago de Chile. Retrieved from: http://repositorio.cepal.org/handle/11362/2185. Accessed 28 Nov 2017. Pesti, C., & Kaposzta, J. (2008). Adaptation of statistical matching in micro-regional analysis of agricultural production (pp. 277–284). Godollo: Bulletin of the Szent Istvan University. Programa de las Naciones Unidas para el Desarrollo (PNUD) (2011) Colombia rural: Razones para la e s p e r a n z a ( E x e c u t i v e S u m m a r y ) . R e t r i e v e d f r o m : h t t p s : / / w w w. u n d p . org/content/dam/colombia/docs/DesarrolloHumano/undpco-icindh2011-parte1-2011.pdf. Accessed 16 Apr 2018. R Core team (2018) R: A language and environment for statistical computing. R Foundation for statistical computing, Vienna, Austria, https://www.Rproject.org/. Accessed 19 Feb 2018. Salcedo, S., Guzmán, L. (Eds.) (2014) Agricultura familiar en América Latina y el Caribe: Recomendaciones de política. FAO. Schejtman, A. (1998). Agroindustria y pequeña agricultura: Experiencias y opciones de transformación. In Agroindustria y pequeña agricultura: vínculos, potencialidades y oportunidades comerciales. Santiago CEPAL 1998–01. Santiago de Chile. Shahbazi, A., & Karambeygi, M. (2015). Application of data mining in rural planning. International Journal of Engineering and Innovative Technology (IJEIT), 14–18. Siabato, W., & Guzmán, J. (2019). La autocorrelación espacial y el desarrollo de la geografía cuantitativa. Cuadernos de Geografía - Revista Colombiana de Geografía, 28(1), 1–22. https://doi.org/10.15446/rcdg. v28n1.76919. Smit, M., Leeuwen, E., Uthes, S., & Zasada, I. (2011). Exploratory spatial data analysis: Why, How and What it show us. Spatial Analysis of Rural Development Measures. Soto, F., Rodríguez, M., & Falconi, C. (2007). Políticas para la Agricultura Familiar en América Latina y el Caribe [Resumen Ejecutivo]. Santiago de Chile: Oficina Regional de la FAO para América Latina y el Caribe. Tilahun, H., & Schmidt, E. (2012). Spatial analysis of livestock production patterns in Ethiopia. Working paper 44, development strategy and governance division. International Food Policy Research Institute, Ethiopia. Wye Group. (2011). Statistics on rural development and agricultural household income (2nd ed.). United Nations Publications. Yrigoyen, C. (2006). Análisis estadístico de datos geográficos en geomarketing: el programa GeoDa (pp. 34– 45). Distribución y Consumo. Yrigoyen, C., Calderón, G.F.A. (2009) Análisis de Datos Espacio-Temporales Para la Economía Y El Geomarketing. Netbiblio, S. L. Zaragozí, B. (2012) Estudio del abandono agrícola mediante el uso de minería de datos y tecnologías de la información geográfica. Ph.D. thesis, Universidad de Alicante, Alicante. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Author's personal copy Statistical and Spatial Analysis of Census Data for the Study of... Affiliations Sandra Moreno 1 & Carlos Durán 1 & Diana Galindo 1 & Cindy Torres 1 & Javier Jácome 1 & Aníbal Montero 1 Sandra Moreno [email protected] Diana Galindo [email protected] Cindy Torres [email protected] Javier Jácome [email protected] Aníbal Montero [email protected] 1 National Administrative Department of Statistics (DANE), Research and Development Group, Geostatistics Division, Bogotá D.C., South America, Colombia
