Spatial Analysis of Sustainable and Agricultural Development in Southern Brazil

Spatial Analysis of Sustainable and Agricultural Development in Southern Brazil | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatial Analysis of Sustainable and Agricultural Development in Southern Brazil Fernanda Cigainski Lisbinski, Felipe André Oliveira Freitas This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4939731/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This work sought to create and analyze a Sustainable and Agricultural Development Index (SADI) for the municipalities that make up the Southern Region of the country. For this, the methodology used was the factor analysis through the analysis of principal components and, subsequently, the exploratory analysis of spatial data (ESDA) was performed, verifying the distribution of the indicator. The main results revealed that the values found for the indicators showed a medium-low degree, considering that the highest value found was 0.5885 and the others presented values below 0.5. The municipalities with the highest indicators, with the highest degree of sustainable and agricultural development, were Curitiba (PR) (0.5885); Capivari do Sul (RS) (0.4541); and Paranapoema (PR) (0.4536). While the lowest indicators, with a lower degree of sustainable and agricultural development, were: Bombinhas (SC), (0.1843); Governador Celso Ramos (SC) (0.2170); and Matinhos (PR) (0.2172). Regarding the ESDA, it showed strong evidence of a positive and significant spatial correlation between the municipalities analyzed, in addition, there was a high disparity in the sustainable and agricultural development of the municipalities that make up these states. Agricultural Economics & Policy Agronomy Environmental Economics Environmental Policy Agriculture Sustainability Factor Analysis Exploratory Spatial Data Analysis (ESDA) Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction One of the great challenges today is to align the increase in agricultural production while promoting sustainable development, reducing impacts on the use of natural resources, and assisting in social development. The motivation stems from international debates and social pressure that plead for a new development model that can reconcile economic growth, social development, and environmental preservation (Sambuichi et al., 2012 ). For Froehlich ( 2014 ), the theme of sustainability is increasingly in evidence, however, it is a concept that is in the construction phase and needs to be studied in greater detail for its better understanding. Despite this, the most widely used concept of sustainable development is the one developed by the Brundtland report of the World Commission for Economic Development – WCED (1987), which states that sustainable development is a process of change where the exploitation of resources, the application of investments, technological development and institutional changes are carried out consciously and according to current and future needs. Sustainable development is defined based on a few dimensions, the three dimensions most indicated by the literature are: economic, social, and environmental (Werbach, 2010 ; Pawlowski, 2008 ; Spangerber & Bonniot, 1998; Sachs, 1993 ; Organisation for Economic Cooperation and Development – OECD, 1993). The economic dimension refers to the macroeconomic plan, i.e., the more efficient allocation and management of resources and a constant flow of public and private investments of endogenous origin, aiming at expanding access to these resources and economic and social development. The social dimension refers to the search for a standard of living adequate to the present and the future, aligned with an improvement in the quality of life of the population, with greater equity in income distribution, improvement in health, education, job creation, etc. And the environmental dimension is the one that aims at: the rational use of natural resources, the consumption of fossil fuels, the use of renewable and non-renewable resources in general; reducing the volume of waste and pollutants; increased research for the development of new low-waste technologies that use resources that promote urban, rural, and industrial development; and the definition of standards for environmental protection. This dimension requires solutions to production processes that use large amounts of natural resources, reducing degradation and producing with a view to meeting the demand of the world population without degrading the environment (Sachs, 1993 ). In this context, it should be noted that Brazil is an essentially agricultural country and, therefore, this sector is extremely important for the generation of wealth and income in the country. However, the development of this sector has not occurred in a homogeneous manner across regions and product specialization. According to Melo ( 2006 ), this unbalanced development occurred due to the evolution of the agricultural sector, which was based on the formation of agro-industrial complexes. The formation of these agro-industrial complexes led to the generation of wealth, accompanied by an increase in the concentration of income and land, rural exodus, unemployment, imbalances in fauna and flora, and exclusion of the less privileged, since the capital invested over the years benefited the employer producers more than the small family producers (Pereira et al ., 2004). Thus, agricultural establishments occupy a total area of 351.3 million hectares, corresponding to about 41% of the Brazilian territory, with large discrepancies in the percentage of occupied area between Brazilian regions (Brazilian Institute of Geography and Statistics - IBGE, 2017). Thus, because it mainly uses land and natural resources in its production processes, the impacts on the environment caused by the agricultural sector are considerable, which ends up directly or indirectly affecting the climate, the hydrological cycle, and the quality of the natural resources available in the country (Sambuichi et al. 2012 ). Thus, a major concern arises: the search for growth and development patterns in the agricultural sector aligned with sustainable development. In this context, the following questions arise: What is the contribution of agricultural development to economic, social, and environmental sustainability? How to reconcile this? And what is the current situation in Brazil? Thus, the objective of this work is to create and analyze an index of sustainable and agricultural development for the municipalities that make up the Southern Region of the country. The specific objectives were to create an indicator based on the dimensions of sustainability and the current agricultural situation in the region; and, to analyze its spatial distribution, discussing the results found. The methodology used was factor analysis through principal component analysis and later the Exploratory Analysis of Spatial Data (ESDA) was performed, verifying the distribution of the indicator and the formation of clusters. The justification for the choice of the theme is due to the constant academic and social debate on the subject and the great difficulty in reconciling sustainable and agricultural development. Thus, this study sought to verify the construction of an indicator based on these two areas, which go hand in hand, verifying the relationship of the variables in the construction of the indicator. The choice of the analyzed region is due to the fact that the South Region had the second largest planted area in the country (22,320,494 hectares), behind only the Midwest Region (31,803,034), is the largest producer of wheat in the country (85% of production) and rice (82%), and is the second largest producer of corn (22%) and soybeans (28.26%) (Municipal Agricultural Survey – Brazilian Institute of Geography and Statistics - PAM-IBGE, 2021). In addition, the agricultural sector has great prominence in the region, it is a great generator of employment and income, and due to its geographical location, favorable climate, and available natural resources, the region has great potential for agricultural production. Finally, it is highlighted that studying agriculture in the Southern Region of Brazil, aligned with sustainable development, can help in understanding how agricultural production can be carried out in a sustainable way, preserving the natural resources and biodiversity of this region. The present study is divided into four sections, the first of which consists of this introduction. Section two presents the methodology used; Section three analyzes and discusses the results and finally, the conclusions of the study are presented in Section four. Methodology 2.1 Statistical Foundations of the SADI To capture the sustainable and agricultural development of the municipalities belonging to the Southern Region, a wide range of variables was used. Therefore, to reduce this broad set, the method of factor analysis was used, which transforms this set into a reduced number of factors. As a result, there is a more direct and parsimonious reduction of data, which facilitates the interpretation and analysis of data. Thus, factor analysis is a multivariate technique that allows the union of variables that follow the same behavior about correlation, thus, from a group of variables chosen to compose the model, the union of information occurs through factors or components (Hair Jr. et al. , 2009). Therefore, to explain the factor analysis model, this study uses the principal components method, which extracts uncorrelated factors, maximizing their contribution to the common variance (Fávero & Belfiore, 2017 ). A factor analysis model is given by the following equation (Mingoti, 2005 ): $$\:{X}_{i}={\alpha\:}_{ij}{F}_{j}+{\epsilon\:}_{i}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(1\right)$$ where, \(\:{X}_{i}\) = \(\:{({X}_{1},\:{X}_{2},\:{X}_{3}\dots\:,{X}_{p})}^{t}\:\) = a transposed vector of observable random variables; \(\:{\alpha\:}_{ij}\) = a matrix \(\:\left(p\:x\:m\right)\:\) of fixed coefficients called factor loadings, which describe the linear relationship of \(\:{X}_{i};\:e\) \(\:{F}_{j}\) ; \(\:{F}_{j}\) = ( \(\:{F}_{1}{,\:F}_{2}\) ,…, \(\:\:{F}_{p}\) ) is a transposed vector \(\:(m\:<\:p)\) is a transposed vector of latent variables, describing the unobservable elements of the sample used; \(\:{\epsilon\:}_{i}\) = ( \(\:{\epsilon\:}_{1}\) , \(\:{\epsilon\:}_{2}\) ,…, \(\:\:{\epsilon\:}_{p}\) ) represents a transposed vector of random errors, corresponding to measurement and variance errors not explained by common factors. Considering that the variables composing the indicator have different scales and principal component analysis aims to maximize variance, it may be sensitive to these different scales, potentially affecting the results. To address this issue, it is necessary to standardize the variables, expressing the data in comparable units (Lattin, 2011). This procedure can be achieved by the following equation: $$\:Z=\frac{({X}_{i}-\stackrel{-}{X})}{S}\:,\:i=1,\:2,\:3\:...,n\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(2\right)$$ where, Z = the standardized variable; \(\:{X}_{i}\) = the variable to be standardized; \(\:\stackrel{-}{X}\) = the arithmetic mean of the variable \(\:X\) ; S = to the sample standard deviation of the variable \(\:X\) . With the standardization of the observable variables \(\:{X}_{i}\) , these can be replaced by a standardized variable vector \(\:{Z}_{i}\) , which will solve the problem of difference in scale units. The next step is to identify the number of factors suitable to compose the model, for this the measure called eigenvalue or commonly known as characteristic roots, which express the total variance explained by each observed factor. The determination of the number of factors required and that will represent the data set is defined by Kaiser's Rule, which recommends the use of those principal components whose characteristic roots have values greater than one (Lattin, 2011). To facilitate the interpretation of these factors, orthogonal rotation is performed using the varimax method , which builds a simple structure based on the structure of the columns of the matrix of factor loadings, promoting the maximization of the variance of the factor (HAIR et al., 2009 ). According to Mingoti ( 2005 ), the factorial scores are those values referring to each observation of the sample and situate them in the space of common factors, given by: \(\:{F}_{j\:}\) = \(\:\sum\:_{j=1}^{k}{b}_{i}{X}_{ij}\) , com i = 1, 2,..., p (3) where, \(\:{F}_{j\:}\) = to the factorial scores; \(\:{b}_{i}=\:\) the regression coefficients that represent the weighting weights of the variables \(\:{X}_{ij}\) in the factor \(\:{F}_{j\:}\) ; \(\:{X}_{ij}\) = to the values of the variables for the k-th element that makes up the sample. Thus, the Sustainable and Agricultural Development Index ( \(\:{SADI}_{m}\) ) can be given, according to Mingoti ( 2005 ), by the following expression: $$\:{SADI}_{m}\:{=\:{\sum\:}_{i=1}^{p}\:\left(\frac{{\sigma\:i}^{2}}{{\sum\:}_{i=1}^{p}{\sigma\:}^{2}i}\:{F}_{im}\right)}_{\:}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(4\right)$$ Where \(\:{\sigma\:}^{2}\:\) is the variance explained by the fator \(\:i\) ; \(\:p\) is the number of factors chosen; \(\:{\sum\:}_{i=1}^{p}{\sigma\:}^{2}i\) is the sum of the explained variances of \(\:p\) and \(\:{F}_{ie}\) is the factorial score of the m-th municipality in the Southern Region of Brazil, of the factor \(\:i\) . The results are standardized in the form: $$\:{SADI}_{m}=\frac{{SADI}_{m\left(before\right)}-{SADI}_{min}}{{SADI}_{m\text{á}x}-{SADI}_{min}}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(5\right)$$ in which the individual values of \(\:{SADI}_{m},\:\) as well as the maximum and minimum values are used to adjust the results between 0 and1. \(\:\:\) According to Fávero and Belfiore ( 2017 ), the results achieved can be evaluated through the Kaiser-Meyer-Olkin (KMO) test and the Bartlett sphericity test , the first is given by the following equation: $$\:KMO=\frac{\sum\:_{l=1}^{k}\sum\:_{c=1}^{k}{\rho\:}_{lc}^{2}}{\sum\:_{l=1}^{k}\sum\:_{c=1}^{k}{\rho\:}_{lc}^{2}+\sum\:_{l=1}^{k}\sum\:_{c=1}^{k}{\phi\:}_{lc}^{2}}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(6\right)$$ where, KMO values between 0.6 and 0.7 are considered reasonable, however, it should be noted that the closer to 1, the better the overall adequacy of the model. The Bartlett test is given by: $$\:{\text{{\rm\:X}}}_{Bartlett}^{2}=\left[(n-1)-\left(\frac{2k+5}{6}\right)\right]\text{l}\text{n}\left|\text{D}\right|\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(7\right)$$ where, the degrees of freedom are obtained by \(\:\frac{k\:(k-1)}{2}\) , being \(\:n\) the sample size, \(\:\:k\) the number of variables, and \(\:D\:\) the determinant of the correlation matrix \(\:\rho\:\) . When the value of the test is greater than the critical value, the identity matrix hypothesis is rejected. Thus, in the next topic, the second methodology used in this work will be described, which is the exploratory analysis of spatial data (ESDA). After the presentation of the econometric procedure adopted for the development of the SADI proposed in this research, as well as its form of spatial distribution, the description of the variables used is presented, along with the source of the data used to compose the SADI of the municipalities belonging to the Southern Region of Brazil. 2.2 A Synthesis of Exploratory Spatial Data Analysis (ESDA) The Exploratory Analysis of Spatial Data (ESDA) is given through the interaction of agents in space, that is, the value of a certain variable of interest in each region \(\:i\) depends on the value of this variable in neighboring regions \(\:j\) . The inclusion of location in the study makes the results more consistent, according to Anselin ( 1999 ) spatial econometrics is a subfield of econometrics that deals with spatial interaction (spatial autocorrelation), in addition to spatial structure (spatial heterogeneity) in regression models. Thus, to obtain more consistent results, the ESDA is used, verifying whether there is spatial autocorrelation between the municipalities and their neighbors, and whether they influence the sustainable and agricultural development of the latter. Autocorrelation is obtained by Moran's statistics \(\:I\) , which indicate the degree of linear association between the vectors of the values observed over time, in addition to the weighted average of the values of the neighbors (Parré, 2014 ; Almeida, 2012 ). Moran ( apud Almeida ( 2012 )), states that the formula for this statistic can be expressed by the following equation: $$\:I=\frac{n}{\sum\:\sum\:{\omega\:}_{i}}\frac{\sum\:\sum\:{\omega\:}_{ij}\:\left({y}_{i}-\stackrel{-}{y}\right)({y}_{i}-\stackrel{-}{y})}{{(\sum\:{y}_{i}-\stackrel{-}{y})}^{2}}$$ 8 where, \(\:n\) = the number of spatial units, \(\:{y}_{i}\) = the variable of interest; \(\:{\omega\:}_{ij}\) = the spatial weight for the pair of spatial units \(\:i\) and \(\:\:j\) measure the level of interaction between them. Considering this, spatial dependence is one of the main characteristics of spatial data. However, to determine the degree of spatial autocorrelation, one must consider the degree of neighborhood in which one intends to analyze the spatial dependence. By adopting the neighborhood criterion, it is possible to construct the matrix of space weights (Sabater, Tur & Azorín, 2011 ). These matrices are based on contiguity, and can be determined according to neighborhood, geographic or socioeconomic distance, or even by a combination of both (Almeida, 2012 ). The most common forms of spatial weight matrices used are the queen convention and the tower as shown in Fig. 1 , where the neighbors of regions A and B are the highlighted regions. About the queen contiguity convention, it considers beyond the boundaries with non-zero extension, the vertices (nodes), in the presentation of a map, as contiguous. The Torre contiguity, on the other hand, considers only the physical borders with non-zero extension between the regions (Almeida, 2012 ). The values obtained for Moran's \(\:I\) can vary between − 1 and 1. Thus, negative values indicate negative spatial autocorrelation, demonstrating that there are dissimilarities between the values of the studied attribute and its spatial location; positive values, on the other hand, indicate positive spatial autocorrelation, demonstrating similarities between the values of the studied attribute and its spatial location (Lins et al., 2015 ). In view of this, Fig. 2 presents in a generic way the scatter plot of Moran's \(\:I\) statistic with the possible types of clusters of spatial linear association. It is noteworthy that this dispersion analysis is valid for both the univariate and the multivariate Moran's \(\:I\:\) statistics \(\:.\) On the horizontal axis, the variable of interest ( \(\:X\) ) is placed, and on the vertical axis, the spatial lag of the variable of interest ( \(\:W\_X\) ) is presented. Thus, it is possible to verify the concentration pattern of the data, which are divided into four types of associations: Q1 - high-high (HH), Q2 - high-low (LH), Q3 - low-low (LL), Q4 - high-low (HL). If the spatial association is positive, the regression line will be increasing and the municipalities will tend to be grouped in the first and third quadrants, however, if the relationship is negative, the line will be decreasing, and the units will be grouped predominantly in the second and fourth quadrants (ALMEIDA, 2012 ). 2.3 Variables and Data Source The data used in the multivariate model were extracted from the database of the Brazilian Institute of Geography and Statistics (IBGE), the Department of Informatics of the Brazilian Unified Health System (DATASUS) and the transparency portal of the Office of the Comptroller General of the Union. The sample comprises 1,191 municipalities belonging to the Southern Region of Brazil. The list of variables used is shown in Table 1 . Table 1 Variables used to calculate the Sustainable and Agricultural Development Index Dimension Variable Description Source Year Sustainable and social development x1 Geometric Growth Rate IBGE 2021 x2 Birth Rate Datasus and IBGE 2021 x3 Number of hospital beds/thousand inhabitants Datasus 2022 x4 Illiteracy rate IBGE 2017 x5 Number of households with access to mains water supply/TF Datasus 2015 x6 Number of households with access to Garbage/TF collection Datasus 2015 x7 Number of households with other garbage disposal/TF Datasus 2015 x8 Number of households with access to the sewer/TF network Datasus 2015 x9 Number of households that do not have access to the sewer/TF network Datasus 2015 Environmental x10 Number of agricultural establishments with Springs or Streams - protected by forests/NE IBGE 2017 x11 Number of agricultural establishments with silviculture species/NE IBGE 2017 x12 Forestry production - planted forests/TA IBGE 2017 x13 Forest production - native forests/TA IBGE 2017 x14 Number of agricultural establishments that use chemical fertilization/NE IBGE 2017 x15 Number of establishments that use pesticides/NE IBGE 2017 Economic Dimension x16 Value of resources allocated to municipalities (Union and states) Office of the Comptroller General of the Union 2022 x17 GDP per capita IBGE 2020 Agricultural x18 Production of temporary/HV crops IBGE 2017 x19 Production of permanent crops/HV IBGE 2017 x20 Livestock and other animal husbandry/TA IBGE 2017 x21 Number of agricultural establishments that make use of Irrigation/NE IBGE 2017 x22 Number of tractors in agricultural establishments/NE IBGE 2017 x23 Number of agricultural establishments with employed staff/NE IBGE 2017 x24 Amount of expenses incurred by agricultural establishments with Medicines for animals/NE IBGE 2017 x25 Amount of expenses incurred by agricultural establishments with fuels and lubricants/NE IBGE 2017 x26 GVA of agriculture/TA IBGE 2020 Source: Prepared by the authors, 2023. Note: TF = Total Families; AT = Total Area; and NE = Number of Agricultural Establishments. Finally, after the presentation of the methodology used, the results are presented, analyzed, and discussed. Analysis and Discussion of Results 3.1 Presentation and Interpretation of Factors When analyzing the adequacy of the sample used, it was found that in the Kaiser-Meyer Olkim (KMO) test the value obtained was 0.6568, indicating the good adequacy of the sample used, and the Bartlett sphericity test was significant and with a statistical of 26528.302, rejecting the null hypothesis, which indicates that the correlation matrix is an identity matrix. Thus, based on the values obtained through the tests performed, it was possible to conclude that the sample used is adequate to the factor analysis procedure and, therefore, allows the continuation of the study. By performing the factor analysis using the principal components method, it was possible to extract seven factors with characteristic roots greater than one, synthesizing the information contained in the twenty-six variables analyzed, as shown in Table 1 . The contribution of the seven factors that explain the total variance of the indicators used is significant, representing 67.09% of the total variance of the data set. For Hair et al. ( 2009 ), the use of an accumulated variance of 60% is satisfactory in social sciences. Table 1 Characteristic root, percentage explained by each factor and accumulated variance Factor Characteristic Root Variance explained by the factor Cumulative variance Factor 1 4.66137 18.65% 18.65% Factor 2 3.26799 13.07% 31.72% Factor 3 2.87585 11.50% 43.22% Factor 4 1.86501 7.46% 50.68% Factor 5 1.54686 6.19% 56.87% Factor 6 1.4244 5.70% 62.57% Factor 7 1.132 4.53% 67.09% Source: Survey results, 2023. After presenting the factors that will compose the constructed indicator, the orthogonal rotation of these factors is carried out using the varimax method. Thus, Table 2 shows the factor loadings and commonalities for the factors considered in this research. In its interpretation, only factor loadings with values above 0.5 (bold italics) were considered, to indicate the variables that are most strongly associated with a given factor. Table 2 Factor loadings and commonality after orthogonal rotation of factors Variable Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7 Commonalities x1 0.0975 0.212 0.0128 0.5292 0.2588 0.0318 0.235 0.5422 x2 0.0521 0.1921 0.0853 0.0001 0.0303 0.7754 0.0482 0.3486 x3 0.1024 0.0161 0.0053 0.12 0.0414 0.7737 0.0537 0.3716 x4 -0.0248 0.0644 -0.0943 0.0572 -0.1544 -0.1544 -0.6473 0.5164 x5 0.9901 0.0312 0.0073 0.0217 0.0092 0.0324 0.0238 0.0165 x6 0.99 0.0383 0.0031 0.0314 0.0075 0.0401 0.0282 0.015 x7 0.5477 0.167 0.1686 0.3016 0.0799 0.0499 0.0302 0.6225 x8 0.9321 0.0058 0.0241 0.0092 0.0121 0.0752 0.0129 0.1246 x9 0.6208 0.0999 0.0645 0.0305 0.0618 0.3252 0.109 0.4781 x10 0.0465 0.3635 0.0403 0.5714 0.3192 0.1822 0.0316 0.4015 x11 0.0685 0.0494 0.1979 0.0004 0.8918 0.0064 0.0397 0.1568 x12 0.0952 0.274 0.7204 0.1874 0.1595 0.0106 0.1243 0.3207 x13 0.1093 0.7879 0.2081 0.2014 0.1083 0.029 0.2314 0.2173 x14 0.0371 0.7983 0.1175 0.0104 0.1548 0.0069 0.0591 0.3199 x15 0.0131 0.2874 0.0778 0.018 0.8338 0.0713 0.0922 0.202 x16 0.7973 0.1396 0.0426 0.2857 0.0476 0.1643 0.0729 0.2267 x17 0.0414 0.0999 0.2114 0.0701 0.0194 0.0224 0.6688 0.4906 x18 0.131 0.0428 0.0742 0.7251 0.0206 0.0648 0.0207 0.4447 x19 0.1485 0.6159 0.0451 0.3753 0.0587 0.0945 0.0687 0.4386 x20 0.0493 0.7899 0.0034 0.1803 0.2619 0.1031 0.2057 0.2196 x21 0.0071 0.6469 0.2533 0.3827 0.2416 0.012 0.2591 0.2452 x22 0.0074 0.1812 0.7751 0.1034 0.0288 0.0191 0.039 0.3529 x23 0.03 0.5336 0.3481 0.4139 0.0487 0.0132 0.007 0.4193 x24 0.0491 0.1259 0.6215 0.2062 0.0458 0.0115 0.2508 0.4878 x25 0.0106 0.0403 0.7932 0.0879 0.08 0.0253 0.0404 0.3528 x26 0.113 0.1057 0.1788 0.2122 0.1581 0.0144 0.6803 0.4111 Source: Survey results, 2023. The values found for the commonalities point to how much of the seven factors explain each variable, and it is also observed that there is a positive relationship between the variables that make up the factors. Commonality is presented next to each factor loading. In view of this, it is possible to observe that Factor 1 is strongly related to the following variables: Number of families with access to public water supply/TF (x5); Number of households with access to garbage/TF collection (x6); Number of households with other garbage disposal/TF (x7); Number of households with access to the sewer/TF network (8); Number of households that do not have access to the sewer/TF network (x9); and, Transfers of public resources (x16). Factor 1 presents the highest explained variance, corresponding to 18.65% of the total accumulated variance. This factor is related to public investment, infrastructure, and basic sanitation. Factor 2 is related to the following variables: Forest production - native forests/TA (x13); number of agricultural establishments that use chemical fertilization/NE (x14); Production of permanent crops/TA (x19); Livestock and other animal husbandry/TA (x20); Number of agricultural establishments that make use of Irrigation/NE (x21); Number of agricultural establishments with employed personnel/ NE (x23). This factor is associated with technology and land use, and has the second highest explained variance, corresponding to 13.07% of the total accumulated variance. Factor 3 is related to the following variables: Forest production - planted forests/TA (x12); Number of tractors in agricultural establishments/NE (x22); Amount of expenses incurred by agricultural establishments with Medicines for animals/NE (x24); Value of expenses incurred by agricultural establishments with fuels and lubricants/NE (x25). This factor is associated with environmental degradation, and has the third highest explained variance, corresponding to 11.50% of the total accumulated variance. Factor 4 is related to the following variables: Geometric growth rate (x1); Number of agricultural establishments with springs or streams - protected by forests/NE (x10); Production of temporary crops/TA (x18). This factor is associated with the increase in agricultural production, and has the fourth highest explained variance, corresponding to 7.46% of the total accumulated variance. Factor 5 is related to the following variables: Number of agricultural establishments with silviculture/NE species (x11); and Number of establishments that use pesticides/NE (x15). This factor is associated with pest control and combat and has the fifth highest explained variance, corresponding to 6.19% of the total accumulated variance. Factor 6 is related to the following variables: Birth Rate (x2); and Number of hospital beds/thousand inhabitants (x3). This factor is associated with Hospital and Health Structure and has the sixth highest explained variance, corresponding to 5.70% of the total accumulated variance. Factor 7 is related to the following variables: Illiteracy rate (x4); GDP per Capita (x17); and GVA of agriculture/TA (x26). It is noteworthy that the variable x4 has a negative impact on the composition of the factor, i.e., on both agricultural and per capita income. This factor is associated with income and has the seventh highest explained variance, corresponding to 4.53% of the total accumulated variance. Thus, the twenty-six variables used were synthesized into seven factors, namely: Factor 1, Public Investment and Basic Sanitation; Factor 2: Technology and Land Use; Factor 3, Environmental Degradation; Factor 4, Agricultural Production; Factor 5, Pest Fighting and Control; Factor 6, Hospital Structure and Health; and Factor 7, Income, which together explain 67.09% of the total variance of the indicators analyzed. 3.2 Analyzing and Discussing the SADI From the factorial scores, it was possible to construct the Sustainable and Agricultural Development Index (SADI) for the municipalities of the Southern Region of Brazil. This index varies between 0 and 1, and the closer it is to 1, the greater the degree of sustainable and agricultural development of the municipality that makes up the sample. The values found for the indicators showed a medium-low degree, considering that the highest value was 0.5885 and the others had values below 0.5. Thus, the municipalities with the highest indicators, with the highest degree of sustainable and agricultural development, were Curitiba (PR) (0.5885); Capivari do Sul (RS) (0.4541); and Paranapoema (PR) (0.4536). While the lowest indicators, with a lower degree of sustainable and agricultural development, were: Bombinhas (SC), (0.1843); Governador Celso Ramos (SC) (0.2170); and Matinhos (PR) (0.2172), as shown in Table 3 . Table 3 Calculated SADI Result Major Indicators Lower Indicators Municipality SADI GDP (thousand USD) GDP Agriculture (thousand USD) Municipality SADI GDP (thousand USD) GDP Agriculture (thousand USD) Curitiba (PR) 0.5885 17602600.86 3277.99 Adrianópolis (PR) 0.2371 56.98 4.46 Capivari do Sul (RS) 0.4541 52.23 13962.88 Itaperuçu (PR) 0.2305 114.35 5.69 Paranapoema (PR) 0.4536 15.48 6873.11 Garopaba (SC) 0.2277 136.53 3.70 Turvo (SC) 0.4348 128.48 22505.38 Gravatal (SC) 0.2274 57.66 3.66 Ituporanga (SC) 0.4333 221231.86 51600.62 Pontal do Paraná (PR) 0.2256 118.68 0.81 Imbuia (SC) 0.4296 42.40 17631.16 Altamira do Paraná (PR) 0.2222 16.70 8.53 Faxinal do Soturno (RS) 0.4287 45.19 3520.97 Pescaria Brava (SC) 0.2173 21.46 1.06 Carazinho (RS) 0.4262 618106.56 25205.71 Matinhos (PR) 0.2172 163.97 0.38 Mafra (SC) 0.4240 419950.17 54590.97 Governador Celso Ramos (SC) 0.2170 75.70 9.94 Vacaria (RS) 0.4221 506691.12 95583.64 Bombinhas (SC) 0.1843 156.99 5.06 Source: Survey results, 2023. Figure 3 shows the spatial distribution of the SADI proposed in this study. Therefore, the areas were colored according to the values presented in this index, so that the lighter shades refer to the municipalities in the South Region of Brazil that had the lowest SADIs, while the darker shades represent the states with the highest SADIs. Data from IBGE (2020) indicate that the Northwest Mesoregion of Rio Grande do Sul is responsible for 41.89% of the GVA of agriculture, being the region with the highest agricultural production in the state. In view of this, Lisbinski et al. ( 2020 ) analyzed the degree of rural development of this Mesoregion and found that the municipalities belonging to the South and Midwest of this region had a higher concentration of high and medium degrees of rural development. On the other hand, the municipalities in the northern part of this Mesoregion showed a low degree of development. These findings agree with the results found in this research, because when observing the map, it is possible to verify that the highest concentration of high SADIs in the state of Rio Grande do Sul are found in the Northwest Mesoregion. It was also observed that the municipality in the state of Rio Grande do Sul with the highest SADI was Capivari do Sul (0.4541). The result corroborates the work of Fujimoto et al. ( 2006 ) who, when developing an indicator of socioeconomic performance of the northern coast of the state of Rio Grande do Sul, identifying environmental problems, found that the municipality with the highest degree of development in this region was Capivari do Sul. In addition, the authors found that the municipality's GVA has a predominance in agriculture and the population is predominantly urban, however, according to IBGE data (2022), there was a decrease of 1.36% in the population between the years 2020 and 2021. Regarding the state of Santa Catarina, the highest indicator was found in the municipality of Turvo located on the southern coast of the state, while the other high-performance indicators were found in the Central Region, in the Alto Vale do Itajaí. Silva and Rosa ( 2020 ) analyzed the sustainable development indicators of the Santa Catarina mesoregions, the results pointed out that the state has a sustainable performance considered medium-low in its five mesoregions, and one mesoregion presented a median sustainable performance, which is the Itajaí Valley, with an IDMS of 0.625, which corroborates the results found in this work. The authors justify this by the fact of the low levels of environmental management, economic dynamism, and distribution of wealth, pointing to the need for greater attention to the Economic and Environmental dimensions by municipal public managers. About the state of Paraná, it was possible to verify that the largest indicator of the observed sample belongs to Curitiba, in addition, it can be observed that the highest indicators of SADIs are in the east of Paraná, in the municipalities close to Curitiba. Eberhardt and Lima ( 2012 ) analyzed the profile and stage of economic development of the regions of the state of Paraná and found similar results. The authors were able to verify that of the regions analyzed, the micro-region of Curitiba was the one that presented the best performance, since its indicator was higher than the others, in addition, the region was considered the most dynamic in economic terms, when compared to the other micro-regions of the state. Turra and Lima ( 2018 ) created a Sustainable Development Index (SDI) for the micro-regions of the state of Paraná, the results pointed out that the Micro-region of Curitiba occupied the first place, having its performance associated with the economic and institutional dimensions, a characteristic present in places that have already undergone an intense urbanization process. While the worst indicators were concentrated in micro-regions located in the Center-South Mesoregion of the state, which is characterized by low temperatures and rugged terrain, inhibiting the development of certain grain crops (soybeans and corn, for example), favoring livestock activities and reforestation. In view of this, it was possible to verify the high degree of heterogeneity in the distribution of the SADI among the municipalities of the Southern Region. According to Belik ( 2015 ), in view of this existing heterogeneity, especially in the rural sector, policies must be differentiated according to the type of clientele, making it impossible to adopt a single agricultural and sustainable development policy. It is noteworthy that it is not only a question of the technological gap, but also of the gap in relation to access to services, transportation and commercialization, and income. Thus, if the objective is to promote sustainable development and Brazilian agribusiness jointly, it is necessary to work on some policies capable of facilitating the insertion of more appropriate and sustainable technologies, as well as the construction of markets for these products and services offered by rural producers. 3.3 Spatial Distribution of Data From now on, we move on to the analysis of the ESDA, which occurs from the interaction of the municipalities in space, that is, whether the value of a variable observed in one municipality \(\:i\) depends on the value of this variable in the other neighboring municipalities. To perform the Moran's \(\:I\:\) autocorrelation test, the Queen and Tower spatial weight matrices were tested, the highest result was achieved by using a Queen-type spatial weight matrix, therefore, the analyses were made using this spatial configuration, considering that it better represents the interaction between the regions. Thus, Moran's \(\:I\) autocorrelation test presented a statistic of 0.870, demonstrating strong evidence of positive spatial correlation, presenting high statistical significance, rejecting the null hypothesis, demonstrating the existence of spatial correlation between the municipalities that make up the sample. Cluster analysis grouped the indicator into five groups: High-High, Low-High, High-Low, Low-Low and non-significant. Thus, it is possible to observe that the municipalities that presented high indicators and that have neighbors with high SADI indicators (High-High) are mainly concentrated in the state of Rio Grande do Sul and the South and Center-South of Santa Catarina. The aspects that lead to the highest degree of sustainable and agricultural development in these regions are the climate and soil favorable to the production of various crops such as soybeans, corn and wheat, for example; the high degree of technology and innovation used in these regions, which indicated high indicators, since in the Southern Region of Brazil there are several institutions for research and development of agricultural technology, which contribute to the improvement of productivity and sustainability of agriculture in these areas; the organization of farmers in these regions into cooperatives, allowing greater bargaining capacity and access to broader and more lucrative markets; and, invested in public policies aimed at agriculture, such as tax incentives, rural credit, technical assistance and rural extension, which has contributed to the sustainable development of the agricultural sector (Tiecher, 2016 ; Tomazzoni & Schneider, 2017; Lisbinski et al., 2020 ). The Low-Low clusters, those with low indicators, surrounded by low indicators, are mainly found in the Central and Western regions of Paraná. According to Leiva ( 2018 ) and Gioia, Barros, and Barros ( 2017 ), the Central and Western regions of Paraná still face problems related to basic sanitation in the region, where most of them do not have satisfactory data on access to sewage networks, which can negatively affect the quality of life of rural producers and limit the potential for development in the region. In addition, about solid waste collection, it was evidenced that the cities in this region still have dumps that do not comply with specific legislation. Finally, it should be noted that some parts of these regions have soils with low fertility, which can hinder the cultivation of some agricultural crops, in addition to limiting productivity, and the region is also affected by droughts and intense heat, which affects agricultural production and increases the risk of loss of some crops (Tiecher, 2016 ). The Low-High clusters, with low indicators, but surrounded by high indicators, are found in the states of Rio Grande do Sul and Santa Catarina; while the High-Low clusters, with high indicators, but surrounded by low indicators, are mainly found in the state of Paraná, which demonstrates the high disparity in the sustainable and agricultural development of the municipalities that make up these states. Among the factors that lead to this, we can mention the diversity in terms of geographical characteristics, such as relief, climate, soil, and water availability, which affects soil fertility, agricultural productivity, and sustainable development (Medeiros, 2011 ). In addition, investment in infrastructure among municipalities is skyrocketing, so that some receive more investments than others, impacting the availability of roads, storage, and transportation (Gazolla & Schneider, 2013; Manica, 2017). The municipalities of the Southern Region of Brazil have different economic activities, so that some may be more prone to sustainable development than others (Rossato, File & Lily, 2010; Fochezatto & Tartaruga, 2016). And finally, there is a difference in the implementation of public policies among municipalities, so that those that implement public policies aimed at access to health, education and sanitation services, sustainable agriculture and invest in agricultural research and technology will present a more advanced level of development (Schneider & Waquil, 2001; Smile, Gazolla & Schneider, 2010). Therefore, it is possible to observe that the regional inequalities between the analyzed states and within the analyzed states are large, so that in the same region there are the highest and lowest indicators, and this is due to the various factors mentioned above. Thus, to face these challenges, a joint and coordinated effort between producers, government and civil society is necessary, aiming at the implementation of sustainable agricultural practices, improving infrastructure and investments in research and technology, in addition to promoting public policies aimed at regional and social development. Conclusion The objective of this study was to create and analyze an index of sustainable and agricultural development for the municipalities that make up the Southern Region of the country. For this, factor analysis was used as a methodology through principal component analysis and later the exploratory analysis of spatial data (ESDA) was performed, verifying the distribution of the indicator and the formation of clusters. The main results revealed that the values found for the indicators showed a medium-low degree, considering that the highest value found was 0.5885 and the others presented values below 0.5. The municipalities with the highest indicators, with the highest degree of sustainable and agricultural development, were Curitiba (PR) (0.5885); Capivari do Sul (RS) (0.4541); and Paranapoema (PR) (0.4536). While the lowest indicators, with a lower degree of sustainable and agricultural development, were: Bombinhas (SC), (0.1843); Governador Celso Ramos (SC) (0.2170); and Matinhos (PR) (0.2172). Regarding the ESDA, it showed strong evidence of a positive and significant spatial correlation between the municipalities analyzed. It was observed that the municipalities with clusters of the High-High type are mainly concentrated in the state of Rio Grande do Sul and the South and Center-South of Santa Catarina. The Low-Low clusters are mainly found in the Central and Western Regions of Paraná. The Low-High clusters are found in the states of Rio Grande do Sul and Santa Catarina. And, finally, the High-Low clusters are mainly concentrated in the state of Paraná, which demonstrates the high disparity in the sustainable and agricultural development of the municipalities that make up these states. Therefore, from this work, it was possible to verify which municipalities present problems of sustainable and agricultural development, as well as in which regions these problems are concentrated, suggesting the adoption of more efficient and effective strategies by the public authorities, with the objective of leveraging sustainable and agricultural development, aligning both purposes. Bearing in mind that this is the great challenge of Brazilian agriculture. With this, it will be possible to promote the improvement of the infrastructure of these municipalities, as well as the quality of life of the population residing there. Finally, in view of the complexity and breadth of the theme of the present work, as there is still no consensus on the concept and dimensions of sustainable development, in addition to the difficulty in defining variables that can capture effects related to both agricultural and sustainable development, further research is suggested with a view to deepening the theme of agricultural and sustainable development. either through an increase in the number of analysis variables, or through regional, state, and national analysis. References Almeida E (2012) Econometria espacial. Alínea, Campinas–SP, p 31 Anselin L (1999) The future of spatial analysis in the social sciences. Geographic Inform Sci 5(2):67–76 Belik W (2015) A heterogeneidade e suas implicações para as políticas públicas no rural brasileiro. Revista de Economia e Sociologia Rural 53:9–30 Controladoria-Geral da União (2022) Portal da Transparência, Detalhamento de Recursos Transferidos por UF e Município. 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REVISTA AMBIENTE CONTÁBIL-Universidade Federal do Rio Grande do Norte, 12(1), 273–294 Spangenberg J, Bonniot O (1998) Sustainability indicators: a compasso the road towards sustainability. Wuppertal Institute, p 81 Tiecher T (2016) Manejo e conservação do solo e da água em pequenas propriedades rurais no sul do Brasil: práticas alternativas de manejo visando a conservação do solo e da água. UFRGS, Porto Alegre Tomazzoni GC, Schneider S (eds) (2022) A presença do cooperativismo na agricultura do Sul do Brasil: uma breve caracterização a partir dos dados do censo agropecuário 2017. DRd-Desenvolvimento Regional Em Debate, 12(ed. esp. Dossie), 65–88 Turra S, Lima AC (2018) Desenvolvimento Sustentável das Microrregiões do Estado do Paraná: uma análise pelo método Dashboard of Sustentability, vol 14. Revista Brasileira de Gestão e Desenvolvimento Regional, 5 Werbach A (2010) Estratégia para sustentabilidade: uma nova forma de planejar sua estratégia empresarial. Elsevier, Rio de Janeiro World Commission on Environment and Development, WCED (1987) OUR COMMON FUTURE. Retrieved from < https: //sustainabledevelopment.un.org/content/documents/5987our-common-future.pdf accessed on: April 12, 2024 Additional Declarations The authors declare no competing interests. Supplementary Files DeclarationofConflictofInterest1.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4939731","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":342236364,"identity":"844eb5c3-2204-4b7a-a983-7bd8dff2cd66","order_by":0,"name":"Fernanda Cigainski 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77).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4939731/v1/da155d8857b47717b072b192.png"},{"id":62854342,"identity":"474d1d2a-f190-4bc5-a22e-d146aaf2063b","added_by":"auto","created_at":"2024-08-20 09:10:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":15721,"visible":true,"origin":"","legend":"\u003cp\u003eMoran's Scatter Plot\u003c/p\u003e\n\u003cp\u003eSource: Prepared by the authors based on Almeida (2012).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4939731/v1/48d9a34e35f1253ae8a90d89.png"},{"id":62854344,"identity":"b498f338-4168-4e05-be88-96f396254f06","added_by":"auto","created_at":"2024-08-20 09:10:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":453958,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of the Sustainable and Agricultural Development Index in the municipalities of the Southern Region of Brazil.\u003c/p\u003e\n\u003cp\u003eSource: Survey results, 2023.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4939731/v1/b8d9e2bfaaa0e99323cb653d.png"},{"id":62854340,"identity":"cd7ed556-e058-4823-978f-4ea7f6356e75","added_by":"auto","created_at":"2024-08-20 09:10:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":441567,"visible":true,"origin":"","legend":"\u003cp\u003eMap of clusters and their spatial correlations for municipalities in the Southern Region of Brazil\u003c/p\u003e\n\u003cp\u003eSource: Survey results, 2023.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4939731/v1/6facbd11970c01d4013b0ff2.png"},{"id":62855773,"identity":"3429898b-31fd-40f2-8657-41e97dfac0cf","added_by":"auto","created_at":"2024-08-20 09:26:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1665834,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4939731/v1/acdf5c5b-0fa0-49b0-9042-7c140e2d0c2b.pdf"},{"id":62855024,"identity":"826da2ee-c383-4eef-a8a6-1ac6c1a52115","added_by":"auto","created_at":"2024-08-20 09:18:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":213724,"visible":true,"origin":"","legend":"","description":"","filename":"DeclarationofConflictofInterest1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4939731/v1/90e90697fbd759ad2e0d1593.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eSpatial Analysis of Sustainable and Agricultural Development in Southern Brazil\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":" Introduction","content":"\u003cp\u003eOne of the great challenges today is to align the increase in agricultural production while promoting sustainable development, reducing impacts on the use of natural resources, and assisting in social development. The motivation stems from international debates and social pressure that plead for a new development model that can reconcile economic growth, social development, and environmental preservation (Sambuichi et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor Froehlich (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), the theme of sustainability is increasingly in evidence, however, it is a concept that is in the construction phase and needs to be studied in greater detail for its better understanding. Despite this, the most widely used concept of sustainable development is the one developed by the Brundtland report of the World Commission for Economic Development \u0026ndash; WCED (1987), which states that sustainable development is a process of change where the exploitation of resources, the application of investments, technological development and institutional changes are carried out consciously and according to current and future needs. Sustainable development is defined based on a few dimensions, the three dimensions most indicated by the literature are: economic, social, and environmental (Werbach, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Pawlowski, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Spangerber \u0026amp; Bonniot, 1998; Sachs, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Organisation for Economic Cooperation and Development \u0026ndash; OECD, 1993).\u003c/p\u003e \u003cp\u003eThe economic dimension refers to the macroeconomic plan, i.e., the more efficient allocation and management of resources and a constant flow of public and private investments of endogenous origin, aiming at expanding access to these resources and economic and social development. The social dimension refers to the search for a standard of living adequate to the present and the future, aligned with an improvement in the quality of life of the population, with greater equity in income distribution, improvement in health, education, job creation, etc. And the environmental dimension is the one that aims at: the rational use of natural resources, the consumption of fossil fuels, the use of renewable and non-renewable resources in general; reducing the volume of waste and pollutants; increased research for the development of new low-waste technologies that use resources that promote urban, rural, and industrial development; and the definition of standards for environmental protection. This dimension requires solutions to production processes that use large amounts of natural resources, reducing degradation and producing with a view to meeting the demand of the world population without degrading the environment (Sachs, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1993\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this context, it should be noted that Brazil is an essentially agricultural country and, therefore, this sector is extremely important for the generation of wealth and income in the country. However, the development of this sector has not occurred in a homogeneous manner across regions and product specialization. According to Melo (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), this unbalanced development occurred due to the evolution of the agricultural sector, which was based on the formation of agro-industrial complexes. The formation of these agro-industrial complexes led to the generation of wealth, accompanied by an increase in the concentration of income and land, rural exodus, unemployment, imbalances in fauna and flora, and exclusion of the less privileged, since the capital invested over the years benefited the employer producers more than the small family producers (Pereira \u003cem\u003eet al\u003c/em\u003e., 2004).\u003c/p\u003e \u003cp\u003eThus, agricultural establishments occupy a total area of 351.3\u0026nbsp;million hectares, corresponding to about 41% of the Brazilian territory, with large discrepancies in the percentage of occupied area between Brazilian regions (Brazilian Institute of Geography and Statistics - IBGE, 2017). Thus, because it mainly uses land and natural resources in its production processes, the impacts on the environment caused by the agricultural sector are considerable, which ends up directly or indirectly affecting the climate, the hydrological cycle, and the quality of the natural resources available in the country (Sambuichi et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Thus, a major concern arises: the search for growth and development patterns in the agricultural sector aligned with sustainable development.\u003c/p\u003e \u003cp\u003eIn this context, the following questions arise: What is the contribution of agricultural development to economic, social, and environmental sustainability? How to reconcile this? And what is the current situation in Brazil? Thus, the objective of this work is to create and analyze an index of sustainable and agricultural development for the municipalities that make up the Southern Region of the country. The specific objectives were to create an indicator based on the dimensions of sustainability and the current agricultural situation in the region; and, to analyze its spatial distribution, discussing the results found. The methodology used was factor analysis through principal component analysis and later the Exploratory Analysis of Spatial Data (ESDA) was performed, verifying the distribution of the indicator and the formation of clusters.\u003c/p\u003e \u003cp\u003eThe justification for the choice of the theme is due to the constant academic and social debate on the subject and the great difficulty in reconciling sustainable and agricultural development. Thus, this study sought to verify the construction of an indicator based on these two areas, which go hand in hand, verifying the relationship of the variables in the construction of the indicator. The choice of the analyzed region is due to the fact that the South Region had the second largest planted area in the country (22,320,494 hectares), behind only the Midwest Region (31,803,034), is the largest producer of wheat in the country (85% of production) and rice (82%), and is the second largest producer of corn (22%) and soybeans (28.26%) (Municipal Agricultural Survey \u0026ndash; Brazilian Institute of Geography and Statistics - PAM-IBGE, 2021). In addition, the agricultural sector has great prominence in the region, it is a great generator of employment and income, and due to its geographical location, favorable climate, and available natural resources, the region has great potential for agricultural production. Finally, it is highlighted that studying agriculture in the Southern Region of Brazil, aligned with sustainable development, can help in understanding how agricultural production can be carried out in a sustainable way, preserving the natural resources and biodiversity of this region.\u003c/p\u003e \u003cp\u003eThe present study is divided into four sections, the first of which consists of this introduction. Section two presents the methodology used; Section three analyzes and discusses the results and finally, the conclusions of the study are presented in Section four.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e2.1 Statistical Foundations of the SADI\u003c/h2\u003e\n\u003cp\u003eTo capture the sustainable and agricultural development of the municipalities belonging to the Southern Region, a wide range of variables was used. Therefore, to reduce this broad set, the method of factor analysis was used, which transforms this set into a reduced number of factors. As a result, there is a more direct and parsimonious reduction of data, which facilitates the interpretation and analysis of data. Thus, factor analysis is a multivariate technique that allows the union of variables that follow the same behavior about correlation, thus, from a group of variables chosen to compose the model, the union of information occurs through factors or components (Hair Jr. \u003cem\u003eet al.\u003c/em\u003e, 2009). Therefore, to explain the factor analysis model, this study uses the principal components method, which extracts uncorrelated factors, maximizing their contribution to the common variance (F\u0026aacute;vero \u0026amp; Belfiore, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eA factor analysis model is given by the following equation (Mingoti, \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e):\u003c/p\u003e\n\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equa\" class=\"mathdisplay\"\u003e$$\\:{X}_{i}={\\alpha\\:}_{ij}{F}_{j}+{\\epsilon\\:}_{i}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(1\\right)$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{i}\\)\u003c/span\u003e\u003c/span\u003e = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{({X}_{1},\\:{X}_{2},\\:{X}_{3}\\dots\\:,{X}_{p})}^{t}\\:\\)\u003c/span\u003e\u003c/span\u003e= a transposed vector of observable random variables; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{ij}\\)\u003c/span\u003e\u003c/span\u003e = a matrix \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(p\\:x\\:m\\right)\\:\\)\u003c/span\u003e\u003c/span\u003eof fixed coefficients called factor loadings, which describe the linear relationship of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{i};\\:e\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{F}_{j}\\)\u003c/span\u003e\u003c/span\u003e ; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{F}_{j}\\)\u003c/span\u003e\u003c/span\u003e = (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{F}_{1}{,\\:F}_{2}\\)\u003c/span\u003e\u003c/span\u003e,\u0026hellip;,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{F}_{p}\\)\u003c/span\u003e\u003c/span\u003e) is a transposed vector \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:(m\\:\u0026lt;\\:p)\\)\u003c/span\u003e\u003c/span\u003e is a transposed vector of latent variables, describing the unobservable elements of the sample used; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e = (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{2}\\)\u003c/span\u003e\u003c/span\u003e,\u0026hellip;,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{\\epsilon\\:}_{p}\\)\u003c/span\u003e\u003c/span\u003e) represents a transposed vector of random errors, corresponding to measurement and variance errors not explained by common factors. Considering that the variables composing the indicator have different scales and principal component analysis aims to maximize variance, it may be sensitive to these different scales, potentially affecting the results. To address this issue, it is necessary to standardize the variables, expressing the data in comparable units (Lattin, 2011). This procedure can be achieved by the following equation:\u003c/p\u003e\n\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equb\" class=\"mathdisplay\"\u003e$$\\:Z=\\frac{({X}_{i}-\\stackrel{-}{X})}{S}\\:,\\:i=1,\\:2,\\:3\\:...,n\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(2\\right)$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere, Z\u0026thinsp;=\u0026thinsp;the standardized variable; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{i}\\)\u003c/span\u003e\u003c/span\u003e = the variable to be standardized; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{X}\\)\u003c/span\u003e\u003c/span\u003e = the arithmetic mean of the variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:X\\)\u003c/span\u003e\u003c/span\u003e; S = to the sample standard deviation of the variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:X\\)\u003c/span\u003e\u003c/span\u003e. With the standardization of the observable variables \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{i}\\)\u003c/span\u003e\u003c/span\u003e, these can be replaced by a standardized variable vector \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Z}_{i}\\)\u003c/span\u003e\u003c/span\u003e, which will solve the problem of difference in scale units.\u003c/p\u003e\n\u003cp\u003eThe next step is to identify the number of factors suitable to compose the model, for this the measure called \u003cem\u003eeigenvalue\u003c/em\u003e or commonly known as characteristic roots, which express the total variance explained by each observed factor. The determination of the number of factors required and that will represent the data set is defined by Kaiser's Rule, which recommends the use of those principal components whose characteristic roots have values greater than one (Lattin, 2011).\u003c/p\u003e\n\u003cp\u003eTo facilitate the interpretation of these factors, orthogonal rotation is performed using the \u003cem\u003evarimax method\u003c/em\u003e, which builds a simple structure based on the structure of the columns of the matrix of factor loadings, promoting the maximization of the variance of the factor (HAIR et al., \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eAccording to Mingoti (\u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e), the factorial scores are those values referring to each observation of the sample and situate them in the space of common factors, given by:\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{F}_{j\\:}\\)\u003c/span\u003e \u003c/span\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{j=1}^{k}{b}_{i}{X}_{ij}\\)\u003c/span\u003e\u003c/span\u003e, com i\u0026thinsp;=\u0026thinsp;1, 2,..., p (3)\u003c/p\u003e\n\u003cp\u003ewhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{F}_{j\\:}\\)\u003c/span\u003e\u003c/span\u003e= to the factorial scores; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{b}_{i}=\\:\\)\u003c/span\u003e\u003c/span\u003e the regression coefficients that represent the weighting weights of the variables \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{ij}\\)\u003c/span\u003e\u003c/span\u003e in the factor \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{F}_{j\\:}\\)\u003c/span\u003e\u003c/span\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{ij}\\)\u003c/span\u003e\u003c/span\u003e = to the values of the variables for the k-th element that makes up the sample.\u003c/p\u003e\n\u003cp\u003eThus, the Sustainable and Agricultural Development Index (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{SADI}_{m}\\)\u003c/span\u003e\u003c/span\u003e) can be given, according to Mingoti (\u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e), by the following expression:\u003c/p\u003e\n\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equc\" class=\"mathdisplay\"\u003e$$\\:{SADI}_{m}\\:{=\\:{\\sum\\:}_{i=1}^{p}\\:\\left(\\frac{{\\sigma\\:i}^{2}}{{\\sum\\:}_{i=1}^{p}{\\sigma\\:}^{2}i}\\:{F}_{im}\\right)}_{\\:}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(4\\right)$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}^{2}\\:\\)\u003c/span\u003e\u003c/span\u003eis the variance explained by the fator \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\)\u003c/span\u003e\u003c/span\u003e is the number of factors chosen; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sum\\:}_{i=1}^{p}{\\sigma\\:}^{2}i\\)\u003c/span\u003e\u003c/span\u003e is the sum of the explained variances of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{F}_{ie}\\)\u003c/span\u003e\u003c/span\u003e is the factorial score of the m-th municipality in the Southern Region of Brazil, of the factor \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e. The results are standardized in the form:\u003c/p\u003e\n\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equd\" class=\"mathdisplay\"\u003e$$\\:{SADI}_{m}=\\frac{{SADI}_{m\\left(before\\right)}-{SADI}_{min}}{{SADI}_{m\\text{\u0026aacute;}x}-{SADI}_{min}}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(5\\right)$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ein which the individual values of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{SADI}_{m},\\:\\)\u003c/span\u003e\u003c/span\u003eas well as the maximum and minimum values are used to adjust the results between 0 and1.\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eAccording to F\u0026aacute;vero and Belfiore (\u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e), the results achieved can be evaluated through the \u003cem\u003eKaiser-Meyer-Olkin\u003c/em\u003e (KMO) test and the \u003cem\u003eBartlett sphericity test\u003c/em\u003e, the first is given by the following equation:\u003c/p\u003e\n\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Eque\" class=\"mathdisplay\"\u003e$$\\:KMO=\\frac{\\sum\\:_{l=1}^{k}\\sum\\:_{c=1}^{k}{\\rho\\:}_{lc}^{2}}{\\sum\\:_{l=1}^{k}\\sum\\:_{c=1}^{k}{\\rho\\:}_{lc}^{2}+\\sum\\:_{l=1}^{k}\\sum\\:_{c=1}^{k}{\\phi\\:}_{lc}^{2}}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(6\\right)$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere, KMO values between 0.6 and 0.7 are considered reasonable, however, it should be noted that the closer to 1, the better the overall adequacy of the model. The \u003cem\u003eBartlett\u003c/em\u003e test is given by:\u003c/p\u003e\n\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equf\" class=\"mathdisplay\"\u003e$$\\:{\\text{{\\rm\\:X}}}_{Bartlett}^{2}=\\left[(n-1)-\\left(\\frac{2k+5}{6}\\right)\\right]\\text{l}\\text{n}\\left|\\text{D}\\right|\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(7\\right)$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere, the degrees of freedom are obtained by \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{k\\:(k-1)}{2}\\)\u003c/span\u003e\u003c/span\u003e, being \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:n\\)\u003c/span\u003e\u003c/span\u003e the sample size,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:k\\)\u003c/span\u003e\u003c/span\u003e the number of variables, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:D\\:\\)\u003c/span\u003e\u003c/span\u003ethe determinant of the correlation matrix \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\rho\\:\\)\u003c/span\u003e\u003c/span\u003e. When the value of the test is greater than the critical value, the identity matrix hypothesis is rejected.\u003c/p\u003e\n\u003cp\u003eThus, in the next topic, the second methodology used in this work will be described, which is the exploratory analysis of spatial data (ESDA). After the presentation of the econometric procedure adopted for the development of the SADI proposed in this research, as well as its form of spatial distribution, the description of the variables used is presented, along with the source of the data used to compose the SADI of the municipalities belonging to the Southern Region of Brazil.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e2.2 A Synthesis of Exploratory Spatial Data Analysis (ESDA)\u003c/h2\u003e\n\u003cp\u003eThe Exploratory Analysis of Spatial Data (ESDA) is given through the interaction of agents in space, that is, the value of a certain variable of interest in each region \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e depends on the value of this variable in neighboring regions \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\)\u003c/span\u003e\u003c/span\u003e. The inclusion of location in the study makes the results more consistent, according to Anselin (\u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e) spatial econometrics is a subfield of econometrics that deals with spatial interaction (spatial autocorrelation), in addition to spatial structure (spatial heterogeneity) in regression models.\u003c/p\u003e\n\u003cp\u003eThus, to obtain more consistent results, the ESDA is used, verifying whether there is spatial autocorrelation between the municipalities and their neighbors, and whether they influence the sustainable and agricultural development of the latter. Autocorrelation is obtained by Moran's statistics \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:I\\)\u003c/span\u003e\u003c/span\u003e, which indicate the degree of linear association between the vectors of the values observed over time, in addition to the weighted average of the values of the neighbors (Parr\u0026eacute;, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Almeida, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). Moran (\u003cem\u003eapud\u003c/em\u003e Almeida (\u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e)), states that the formula for this statistic can be expressed by the following equation:\u003c/p\u003e\n\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ1\" class=\"mathdisplay\"\u003e$$\\:I=\\frac{n}{\\sum\\:\\sum\\:{\\omega\\:}_{i}}\\frac{\\sum\\:\\sum\\:{\\omega\\:}_{ij}\\:\\left({y}_{i}-\\stackrel{-}{y}\\right)({y}_{i}-\\stackrel{-}{y})}{{(\\sum\\:{y}_{i}-\\stackrel{-}{y})}^{2}}$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:n\\)\u003c/span\u003e\u003c/span\u003e = the number of spatial units, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{i}\\)\u003c/span\u003e\u003c/span\u003e = the variable of interest; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\omega\\:}_{ij}\\)\u003c/span\u003e\u003c/span\u003e = the spatial weight for the pair of spatial units \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:j\\)\u003c/span\u003e\u003c/span\u003e measure the level of interaction between them.\u003c/p\u003e\n\u003cp\u003eConsidering this, spatial dependence is one of the main characteristics of spatial data. However, to determine the degree of spatial autocorrelation, one must consider the degree of neighborhood in which one intends to analyze the spatial dependence. By adopting the neighborhood criterion, it is possible to construct the matrix of space weights (Sabater, Tur \u0026amp; Azor\u0026iacute;n, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). These matrices are based on contiguity, and can be determined according to neighborhood, geographic or socioeconomic distance, or even by a combination of both (Almeida, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). The most common forms of spatial weight matrices used are the queen convention and the tower as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, where the neighbors of regions A and B are the highlighted regions. About the queen contiguity convention, it considers beyond the boundaries with non-zero extension, the vertices (nodes), in the presentation of a map, as contiguous. The Torre contiguity, on the other hand, considers only the physical borders with non-zero extension between the regions (Almeida, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe values obtained for Moran's \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:I\\)\u003c/span\u003e\u003c/span\u003e can vary between \u0026minus;\u0026thinsp;1 and 1. Thus, negative values indicate negative spatial autocorrelation, demonstrating that there are dissimilarities between the values of the studied attribute and its spatial location; positive values, on the other hand, indicate positive spatial autocorrelation, demonstrating similarities between the values of the studied attribute and its spatial location (Lins et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). In view of this, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents in a generic way the scatter plot of Moran's \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:I\\)\u003c/span\u003e\u003c/span\u003e statistic with the possible types of clusters of spatial linear association. It is noteworthy that this dispersion analysis is valid for both the univariate and the multivariate Moran's \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:I\\:\\)\u003c/span\u003e\u003c/span\u003estatistics\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:.\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eOn the horizontal axis, the variable of interest (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:X\\)\u003c/span\u003e\u003c/span\u003e) is placed, and on the vertical axis, the spatial lag of the variable of interest (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:W\\_X\\)\u003c/span\u003e\u003c/span\u003e) is presented. Thus, it is possible to verify the concentration pattern of the data, which are divided into four types of associations: Q1 - high-high (HH), Q2 - high-low (LH), Q3 - low-low (LL), Q4 - high-low (HL). If the spatial association is positive, the regression line will be increasing and the municipalities will tend to be grouped in the first and third quadrants, however, if the relationship is negative, the line will be decreasing, and the units will be grouped predominantly in the second and fourth quadrants (ALMEIDA, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003e2.3 Variables and Data Source\u003c/h2\u003e\n\u003cp\u003eThe data used in the multivariate model were extracted from the database of the Brazilian Institute of Geography and Statistics (IBGE), the Department of Informatics of the Brazilian Unified Health System (DATASUS) and the transparency portal of the Office of the Comptroller General of the Union. The sample comprises 1,191 municipalities belonging to the Southern Region of Brazil. The list of variables used is shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eVariables used to calculate the Sustainable and Agricultural Development Index\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDimension\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDescription\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSource\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eYear\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"9\" align=\"left\"\u003e\n\u003cp\u003eSustainable and social development\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGeometric Growth Rate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIBGE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2021\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBirth Rate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDatasus and IBGE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2021\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNumber of hospital beds/thousand inhabitants\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDatasus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2022\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIlliteracy rate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIBGE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2017\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNumber of households with access to mains water supply/TF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDatasus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2015\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNumber of households with access to Garbage/TF collection\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDatasus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2015\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNumber of households with other garbage disposal/TF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDatasus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2015\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNumber of households with access to the sewer/TF network\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDatasus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2015\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNumber of households that do not have access to the sewer/TF network\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDatasus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2015\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eEnvironmental\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNumber of agricultural establishments with Springs or Streams - protected by forests/NE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIBGE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2017\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNumber of agricultural establishments with silviculture species/NE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIBGE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2017\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eForestry production - planted forests/TA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIBGE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2017\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eForest production - native forests/TA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIBGE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2017\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNumber of agricultural establishments that use chemical fertilization/NE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIBGE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2017\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNumber of establishments that use pesticides/NE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIBGE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2017\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eEconomic Dimension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eValue of resources allocated to municipalities (Union and states)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOffice of the Comptroller General of the Union\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2022\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGDP per capita\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIBGE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2020\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"9\" align=\"left\"\u003e\n\u003cp\u003eAgricultural\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eProduction of temporary/HV crops\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIBGE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2017\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eProduction of permanent crops/HV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIBGE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2017\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLivestock and other animal husbandry/TA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIBGE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2017\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNumber of agricultural establishments that make use of Irrigation/NE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIBGE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2017\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNumber of tractors in agricultural establishments/NE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIBGE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2017\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNumber of agricultural establishments with employed staff/NE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIBGE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2017\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAmount of expenses incurred by agricultural establishments with Medicines for animals/NE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIBGE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2017\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAmount of expenses incurred by agricultural establishments with fuels and lubricants/NE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIBGE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2017\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGVA of agriculture/TA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIBGE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2020\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSource: Prepared by the authors, 2023.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u0026nbsp;\u003c/strong\u003eTF\u0026thinsp;=\u0026thinsp;Total Families; AT\u0026thinsp;=\u0026thinsp;Total Area; and NE\u0026thinsp;=\u0026thinsp;Number of Agricultural Establishments.\u003c/p\u003e\n\u003cp\u003eFinally, after the presentation of the methodology used, the results are presented, analyzed, and discussed.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Analysis and Discussion of Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1 Presentation and Interpretation of Factors\u003c/h2\u003e\n\u003cp\u003eWhen analyzing the adequacy of the sample used, it was found that in the Kaiser-Meyer Olkim (KMO) test the value obtained was 0.6568, indicating the good adequacy of the sample used, and the Bartlett sphericity test was significant and with a statistical of 26528.302, rejecting the null hypothesis, which indicates that the correlation matrix is an identity matrix. Thus, based on the values obtained through the tests performed, it was possible to conclude that the sample used is adequate to the factor analysis procedure and, therefore, allows the continuation of the study.\u003c/p\u003e\n\u003cp\u003eBy performing the factor analysis using the principal components method, it was possible to extract seven factors with characteristic roots greater than one, synthesizing the information contained in the twenty-six variables analyzed, as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The contribution of the seven factors that explain the total variance of the indicators used is significant, representing 67.09% of the total variance of the data set. For Hair et al. (\u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e), the use of an accumulated variance of 60% is satisfactory in social sciences.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCharacteristic root, percentage explained by each factor and accumulated variance\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFactor\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCharacteristic Root\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariance explained by the factor\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCumulative variance\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFactor 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.66137\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e18.65%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e18.65%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFactor 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.26799\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13.07%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e31.72%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFactor 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.87585\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.50%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e43.22%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFactor 4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.86501\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.46%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e50.68%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFactor 5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.54686\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.19%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e56.87%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFactor 6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.4244\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.70%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e62.57%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFactor 7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.132\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.53%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e67.09%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSource: Survey results, 2023.\u003c/p\u003e\n\u003cp\u003eAfter presenting the factors that will compose the constructed indicator, the orthogonal rotation of these factors is carried out using the \u003cem\u003evarimax\u003c/em\u003e method. Thus, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows the factor loadings and commonalities for the factors considered in this research. In its interpretation, only factor loadings with values above 0.5 (bold italics) were considered, to indicate the variables that are most strongly associated with a given factor.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eFactor loadings and commonality after orthogonal rotation of factors\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFactor 1\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFactor 2\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFactor 3\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFactor 4\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFactor 5\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFactor 6\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eFactor 7\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCommonalities\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0975\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.212\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0128\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.5292\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2588\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0318\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.235\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.5422\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0521\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1921\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0853\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0303\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.7754\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0482\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.3486\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1024\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0161\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0053\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0414\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.7737\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0537\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.3716\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0248\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0644\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0943\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0572\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.1544\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.1544\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e-0.6473\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.5164\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.9901\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0312\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0073\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0217\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0092\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0324\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0238\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.0165\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.99\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0383\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0031\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0314\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0075\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0401\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0282\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.5477\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.167\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1686\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0799\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0499\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0302\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.6225\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.9321\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0058\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0241\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0092\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0121\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0752\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0129\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.1246\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.6208\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0999\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0645\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0305\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0618\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3252\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.109\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.4781\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0465\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3635\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0403\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.5714\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3192\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1822\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0316\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.4015\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0685\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0494\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1979\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.8918\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0064\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0397\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.1568\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0952\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.274\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.7204\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1874\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1595\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0106\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1243\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.3207\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1093\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.7879\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2081\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1083\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.029\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2314\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.2173\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0371\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.7983\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1175\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0104\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1548\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0069\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0591\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.3199\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0131\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2874\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0778\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.8338\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0713\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0922\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.202\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.7973\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1396\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0426\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2857\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0476\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1643\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0729\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.2267\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0414\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0999\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2114\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0701\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0194\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0224\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.6688\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.4906\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.131\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0428\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0742\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.7251\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0206\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0648\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0207\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.4447\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1485\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.6159\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0451\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3753\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0587\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0945\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0687\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.4386\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0493\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.7899\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0034\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1803\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2619\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1031\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2057\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.2196\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0071\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.6469\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2533\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3827\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2416\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2591\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.2452\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0074\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1812\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.7751\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1034\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0288\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0191\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.039\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.3529\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.5336\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3481\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4139\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0487\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0132\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.4193\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0491\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1259\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.6215\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2062\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0458\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0115\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2508\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.4878\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0106\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0403\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.7932\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0879\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0253\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0404\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.3528\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ex26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.113\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1057\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1788\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2122\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1581\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0144\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.6803\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.4111\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSource: Survey results, 2023.\u003c/p\u003e\n\u003cp\u003eThe values found for the commonalities point to how much of the seven factors explain each variable, and it is also observed that there is a positive relationship between the variables that make up the factors. Commonality is presented next to each factor loading. In view of this, it is possible to observe that Factor 1 is strongly related to the following variables: Number of families with access to public water supply/TF (x5); Number of households with access to garbage/TF collection (x6); Number of households with other garbage disposal/TF (x7); Number of households with access to the sewer/TF network (8); Number of households that do not have access to the sewer/TF network (x9); and, Transfers of public resources (x16). Factor 1 presents the highest explained variance, corresponding to 18.65% of the total accumulated variance. This factor is related to public investment, infrastructure, and basic sanitation.\u003c/p\u003e\n\u003cp\u003eFactor 2 is related to the following variables: Forest production - native forests/TA (x13); number of agricultural establishments that use chemical fertilization/NE (x14); Production of permanent crops/TA (x19); Livestock and other animal husbandry/TA (x20); Number of agricultural establishments that make use of Irrigation/NE (x21); Number of agricultural establishments with employed personnel/ NE (x23). This factor is associated with technology and land use, and has the second highest explained variance, corresponding to 13.07% of the total accumulated variance.\u003c/p\u003e\n\u003cp\u003eFactor 3 is related to the following variables: Forest production - planted forests/TA (x12); Number of tractors in agricultural establishments/NE (x22); Amount of expenses incurred by agricultural establishments with Medicines for animals/NE (x24); Value of expenses incurred by agricultural establishments with fuels and lubricants/NE (x25). This factor is associated with environmental degradation, and has the third highest explained variance, corresponding to 11.50% of the total accumulated variance.\u003c/p\u003e\n\u003cp\u003eFactor 4 is related to the following variables: Geometric growth rate (x1); Number of agricultural establishments with springs or streams - protected by forests/NE (x10); Production of temporary crops/TA (x18). This factor is associated with the increase in agricultural production, and has the fourth highest explained variance, corresponding to 7.46% of the total accumulated variance.\u003c/p\u003e\n\u003cp\u003eFactor 5 is related to the following variables: Number of agricultural establishments with silviculture/NE species (x11); and Number of establishments that use pesticides/NE (x15). This factor is associated with pest control and combat and has the fifth highest explained variance, corresponding to 6.19% of the total accumulated variance.\u003c/p\u003e\n\u003cp\u003eFactor 6 is related to the following variables: Birth Rate (x2); and Number of hospital beds/thousand inhabitants (x3). This factor is associated with Hospital and Health Structure and has the sixth highest explained variance, corresponding to 5.70% of the total accumulated variance.\u003c/p\u003e\n\u003cp\u003eFactor 7 is related to the following variables: Illiteracy rate (x4); GDP per Capita (x17); and GVA of agriculture/TA (x26). It is noteworthy that the variable x4 has a negative impact on the composition of the factor, i.e., on both agricultural and per capita income. This factor is associated with income and has the seventh highest explained variance, corresponding to 4.53% of the total accumulated variance.\u003c/p\u003e\n\u003cp\u003eThus, the twenty-six variables used were synthesized into seven factors, namely: Factor 1, Public Investment and Basic Sanitation; Factor 2: Technology and Land Use; Factor 3, Environmental Degradation; Factor 4, Agricultural Production; Factor 5, Pest Fighting and Control; Factor 6, Hospital Structure and Health; and Factor 7, Income, which together explain 67.09% of the total variance of the indicators analyzed.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2 Analyzing and Discussing the SADI\u003c/h2\u003e\n\u003cp\u003eFrom the factorial scores, it was possible to construct the Sustainable and Agricultural Development Index (SADI) for the municipalities of the Southern Region of Brazil. This index varies between 0 and 1, and the closer it is to 1, the greater the degree of sustainable and agricultural development of the municipality that makes up the sample. The values found for the indicators showed a medium-low degree, considering that the highest value was 0.5885 and the others had values below 0.5. Thus, the municipalities with the highest indicators, with the highest degree of sustainable and agricultural development, were Curitiba (PR) (0.5885); Capivari do Sul (RS) (0.4541); and Paranapoema (PR) (0.4536). While the lowest indicators, with a lower degree of sustainable and agricultural development, were: Bombinhas (SC), (0.1843); Governador Celso Ramos (SC) (0.2170); and Matinhos (PR) (0.2172), as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCalculated SADI Result\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eMajor Indicators\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eLower Indicators\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMunicipality\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSADI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGDP (thousand USD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGDP Agriculture (thousand USD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMunicipality\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSADI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGDP (thousand USD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGDP Agriculture (thousand USD)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCuritiba (PR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5885\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17602600.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3277.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAdrian\u0026oacute;polis (PR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2371\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.46\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCapivari do Sul (RS)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4541\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13962.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eItaperu\u0026ccedil;u (PR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2305\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e114.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.69\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eParanapoema (PR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4536\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6873.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGaropaba (SC)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2277\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e136.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.70\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTurvo (SC)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4348\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e128.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22505.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGravatal (SC)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2274\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.66\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eItuporanga (SC)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4333\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e221231.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51600.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePontal do Paran\u0026aacute; (PR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2256\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e118.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.81\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eImbuia (SC)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4296\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17631.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAltamira do Paran\u0026aacute; (PR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2222\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.53\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFaxinal do Soturno (RS)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4287\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3520.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePescaria Brava (SC)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2173\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.06\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCarazinho (RS)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4262\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e618106.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25205.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMatinhos (PR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2172\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e163.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.38\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMafra (SC)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4240\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e419950.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54590.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGovernador Celso Ramos (SC)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2170\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e75.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.94\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVacaria (RS)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4221\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e506691.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e95583.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBombinhas (SC)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1843\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e156.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.06\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSource: Survey results, 2023.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the spatial distribution of the SADI proposed in this study. Therefore, the areas were colored according to the values presented in this index, so that the lighter shades refer to the municipalities in the South Region of Brazil that had the lowest SADIs, while the darker shades represent the states with the highest SADIs.\u003c/p\u003e\n\u003cp\u003eData from IBGE (2020) indicate that the Northwest Mesoregion of Rio Grande do Sul is responsible for 41.89% of the GVA of agriculture, being the region with the highest agricultural production in the state. In view of this, Lisbinski et al. (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) analyzed the degree of rural development of this Mesoregion and found that the municipalities belonging to the South and Midwest of this region had a higher concentration of high and medium degrees of rural development. On the other hand, the municipalities in the northern part of this Mesoregion showed a low degree of development. These findings agree with the results found in this research, because when observing the map, it is possible to verify that the highest concentration of high SADIs in the state of Rio Grande do Sul are found in the Northwest Mesoregion.\u003c/p\u003e\n\u003cp\u003eIt was also observed that the municipality in the state of Rio Grande do Sul with the highest SADI was Capivari do Sul (0.4541). The result corroborates the work of Fujimoto et al. (\u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e) who, when developing an indicator of socioeconomic performance of the northern coast of the state of Rio Grande do Sul, identifying environmental problems, found that the municipality with the highest degree of development in this region was Capivari do Sul. In addition, the authors found that the municipality's GVA has a predominance in agriculture and the population is predominantly urban, however, according to IBGE data (2022), there was a decrease of 1.36% in the population between the years 2020 and 2021.\u003c/p\u003e\n\u003cp\u003eRegarding the state of Santa Catarina, the highest indicator was found in the municipality of Turvo located on the southern coast of the state, while the other high-performance indicators were found in the Central Region, in the Alto Vale do Itaja\u0026iacute;. Silva and Rosa (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) analyzed the sustainable development indicators of the Santa Catarina mesoregions, the results pointed out that the state has a sustainable performance considered medium-low in its five mesoregions, and one mesoregion presented a median sustainable performance, which is the Itaja\u0026iacute; Valley, with an IDMS of 0.625, which corroborates the results found in this work. The authors justify this by the fact of the low levels of environmental management, economic dynamism, and distribution of wealth, pointing to the need for greater attention to the Economic and Environmental dimensions by municipal public managers.\u003c/p\u003e\n\u003cp\u003eAbout the state of Paran\u0026aacute;, it was possible to verify that the largest indicator of the observed sample belongs to Curitiba, in addition, it can be observed that the highest indicators of SADIs are in the east of Paran\u0026aacute;, in the municipalities close to Curitiba. Eberhardt and Lima (\u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e) analyzed the profile and stage of economic development of the regions of the state of Paran\u0026aacute; and found similar results. The authors were able to verify that of the regions analyzed, the micro-region of Curitiba was the one that presented the best performance, since its indicator was higher than the others, in addition, the region was considered the most dynamic in economic terms, when compared to the other micro-regions of the state.\u003c/p\u003e\n\u003cp\u003eTurra and Lima (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) created a Sustainable Development Index (SDI) for the micro-regions of the state of Paran\u0026aacute;, the results pointed out that the Micro-region of Curitiba occupied the first place, having its performance associated with the economic and institutional dimensions, a characteristic present in places that have already undergone an intense urbanization process. While the worst indicators were concentrated in micro-regions located in the Center-South Mesoregion of the state, which is characterized by low temperatures and rugged terrain, inhibiting the development of certain grain crops (soybeans and corn, for example), favoring livestock activities and reforestation.\u003c/p\u003e\n\u003cp\u003eIn view of this, it was possible to verify the high degree of heterogeneity in the distribution of the SADI among the municipalities of the Southern Region. According to Belik (\u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e), in view of this existing heterogeneity, especially in the rural sector, policies must be differentiated according to the type of clientele, making it impossible to adopt a single agricultural and sustainable development policy. It is noteworthy that it is not only a question of the technological gap, but also of the gap in relation to access to services, transportation and commercialization, and income. Thus, if the objective is to promote sustainable development and Brazilian agribusiness jointly, it is necessary to work on some policies capable of facilitating the insertion of more appropriate and sustainable technologies, as well as the construction of markets for these products and services offered by rural producers.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3 Spatial Distribution of Data\u003c/h2\u003e\n\u003cp\u003eFrom now on, we move on to the analysis of the ESDA, which occurs from the interaction of the municipalities in space, that is, whether the value of a variable observed in one municipality \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e depends on the value of this variable in the other neighboring municipalities.\u003c/p\u003e\n\u003cp\u003eTo perform the Moran's \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:I\\:\\)\u003c/span\u003e\u003c/span\u003eautocorrelation test, the Queen and Tower spatial weight matrices were tested, the highest result was achieved by using a Queen-type spatial weight matrix, therefore, the analyses were made using this spatial configuration, considering that it better represents the interaction between the regions. Thus, Moran's \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:I\\)\u003c/span\u003e\u003c/span\u003e autocorrelation test presented a statistic of 0.870, demonstrating strong evidence of positive spatial correlation, presenting high statistical significance, rejecting the null hypothesis, demonstrating the existence of spatial correlation between the municipalities that make up the sample. Cluster analysis grouped the indicator into five groups: High-High, Low-High, High-Low, Low-Low and non-significant. Thus, it is possible to observe that the municipalities that presented high indicators and that have neighbors with high SADI indicators (High-High) are mainly concentrated in the state of Rio Grande do Sul and the South and Center-South of Santa Catarina.\u003c/p\u003e\n\u003cp\u003eThe aspects that lead to the highest degree of sustainable and agricultural development in these regions are the climate and soil favorable to the production of various crops such as soybeans, corn and wheat, for example; the high degree of technology and innovation used in these regions, which indicated high indicators, since in the Southern Region of Brazil there are several institutions for research and development of agricultural technology, which contribute to the improvement of productivity and sustainability of agriculture in these areas; the organization of farmers in these regions into cooperatives, allowing greater bargaining capacity and access to broader and more lucrative markets; and, invested in public policies aimed at agriculture, such as tax incentives, rural credit, technical assistance and rural extension, which has contributed to the sustainable development of the agricultural sector (Tiecher, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Tomazzoni \u0026amp; Schneider, 2017; Lisbinski et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Low-Low clusters, those with low indicators, surrounded by low indicators, are mainly found in the Central and Western regions of Paran\u0026aacute;. According to Leiva (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Gioia, Barros, and Barros (\u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e), the Central and Western regions of Paran\u0026aacute; still face problems related to basic sanitation in the region, where most of them do not have satisfactory data on access to sewage networks, which can negatively affect the quality of life of rural producers and limit the potential for development in the region. In addition, about solid waste collection, it was evidenced that the cities in this region still have dumps that do not comply with specific legislation. Finally, it should be noted that some parts of these regions have soils with low fertility, which can hinder the cultivation of some agricultural crops, in addition to limiting productivity, and the region is also affected by droughts and intense heat, which affects agricultural production and increases the risk of loss of some crops (Tiecher, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe Low-High clusters, with low indicators, but surrounded by high indicators, are found in the states of Rio Grande do Sul and Santa Catarina; while the High-Low clusters, with high indicators, but surrounded by low indicators, are mainly found in the state of Paran\u0026aacute;, which demonstrates the high disparity in the sustainable and agricultural development of the municipalities that make up these states. Among the factors that lead to this, we can mention the diversity in terms of geographical characteristics, such as relief, climate, soil, and water availability, which affects soil fertility, agricultural productivity, and sustainable development (Medeiros, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). In addition, investment in infrastructure among municipalities is skyrocketing, so that some receive more investments than others, impacting the availability of roads, storage, and transportation (Gazolla \u0026amp; Schneider, 2013; Manica, 2017). The municipalities of the Southern Region of Brazil have different economic activities, so that some may be more prone to sustainable development than others (Rossato, File \u0026amp; Lily, 2010; Fochezatto \u0026amp; Tartaruga, 2016). And finally, there is a difference in the implementation of public policies among municipalities, so that those that implement public policies aimed at access to health, education and sanitation services, sustainable agriculture and invest in agricultural research and technology will present a more advanced level of development (Schneider \u0026amp; Waquil, 2001; Smile, Gazolla \u0026amp; Schneider, 2010).\u003c/p\u003e\n\u003cp\u003eTherefore, it is possible to observe that the regional inequalities between the analyzed states and within the analyzed states are large, so that in the same region there are the highest and lowest indicators, and this is due to the various factors mentioned above. Thus, to face these challenges, a joint and coordinated effort between producers, government and civil society is necessary, aiming at the implementation of sustainable agricultural practices, improving infrastructure and investments in research and technology, in addition to promoting public policies aimed at regional and social development.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe objective of this study was to create and analyze an index of sustainable and agricultural development for the municipalities that make up the Southern Region of the country. For this, factor analysis was used as a methodology through principal component analysis and later the exploratory analysis of spatial data (ESDA) was performed, verifying the distribution of the indicator and the formation of clusters.\u003c/p\u003e \u003cp\u003eThe main results revealed that the values found for the indicators showed a medium-low degree, considering that the highest value found was 0.5885 and the others presented values below 0.5. The municipalities with the highest indicators, with the highest degree of sustainable and agricultural development, were Curitiba (PR) (0.5885); Capivari do Sul (RS) (0.4541); and Paranapoema (PR) (0.4536). While the lowest indicators, with a lower degree of sustainable and agricultural development, were: Bombinhas (SC), (0.1843); Governador Celso Ramos (SC) (0.2170); and Matinhos (PR) (0.2172).\u003c/p\u003e \u003cp\u003eRegarding the ESDA, it showed strong evidence of a positive and significant spatial correlation between the municipalities analyzed. It was observed that the municipalities with clusters of the High-High type are mainly concentrated in the state of Rio Grande do Sul and the South and Center-South of Santa Catarina. The Low-Low clusters are mainly found in the Central and Western Regions of Paran\u0026aacute;. The Low-High clusters are found in the states of Rio Grande do Sul and Santa Catarina. And, finally, the High-Low clusters are mainly concentrated in the state of Paran\u0026aacute;, which demonstrates the high disparity in the sustainable and agricultural development of the municipalities that make up these states.\u003c/p\u003e \u003cp\u003eTherefore, from this work, it was possible to verify which municipalities present problems of sustainable and agricultural development, as well as in which regions these problems are concentrated, suggesting the adoption of more efficient and effective strategies by the public authorities, with the objective of leveraging sustainable and agricultural development, aligning both purposes. Bearing in mind that this is the great challenge of Brazilian agriculture. With this, it will be possible to promote the improvement of the infrastructure of these municipalities, as well as the quality of life of the population residing there.\u003c/p\u003e \u003cp\u003eFinally, in view of the complexity and breadth of the theme of the present work, as there is still no consensus on the concept and dimensions of sustainable development, in addition to the difficulty in defining variables that can capture effects related to both agricultural and sustainable development, further research is suggested with a view to deepening the theme of agricultural and sustainable development. either through an increase in the number of analysis variables, or through regional, state, and national analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlmeida E (2012) Econometria espacial. 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Retrieved from \u0026lt;\u0026thinsp;https:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e//sustainabledevelopment.un.org/content/documents/5987our-common-future.pdf\u003c/span\u003e\u003cspan address=\"http:////sustainabledevelopment.un.org/content/documents/5987our-common-future.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e accessed on: April 12, 2024\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"593393c9-6ca7-40e0-8886-7ab8f74e8407","identifier":"10.13039/501100002322","name":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","awardNumber":"001","order_by":0},{"identity":"6119223c-b289-4edd-990d-f316eef2182a","identifier":"10.13039/501100003593","name":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","awardNumber":"9884374438299747","order_by":1}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Universidade de São Paulo","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Agriculture, Sustainability, Factor Analysis, Exploratory Spatial Data Analysis (ESDA)","lastPublishedDoi":"10.21203/rs.3.rs-4939731/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4939731/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis work sought to create and analyze a Sustainable and Agricultural Development Index (SADI) for the municipalities that make up the Southern Region of the country. For this, the methodology used was the factor analysis through the analysis of principal components and, subsequently, the exploratory analysis of spatial data (ESDA) was performed, verifying the distribution of the indicator. The main results revealed that the values found for the indicators showed a medium-low degree, considering that the highest value found was 0.5885 and the others presented values below 0.5. The municipalities with the highest indicators, with the highest degree of sustainable and agricultural development, were Curitiba (PR) (0.5885); Capivari do Sul (RS) (0.4541); and Paranapoema (PR) (0.4536). While the lowest indicators, with a lower degree of sustainable and agricultural development, were: Bombinhas (SC), (0.1843); Governador Celso Ramos (SC) (0.2170); and Matinhos (PR) (0.2172). Regarding the ESDA, it showed strong evidence of a positive and significant spatial correlation between the municipalities analyzed, in addition, there was a high disparity in the sustainable and agricultural development of the municipalities that make up these states.\u003c/p\u003e","manuscriptTitle":"Spatial Analysis of Sustainable and Agricultural Development in Southern Brazil","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-20 09:10:27","doi":"10.21203/rs.3.rs-4939731/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1b082fb0-2967-490e-b6fe-efcbf6aa8e37","owner":[],"postedDate":"August 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":36289660,"name":"Agricultural Economics \u0026 Policy"},{"id":36289661,"name":"Agronomy"},{"id":36289662,"name":"Environmental Economics"},{"id":36289663,"name":"Environmental Policy"}],"tags":[],"updatedAt":"2024-08-20T09:10:27+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-20 09:10:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4939731","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4939731","identity":"rs-4939731","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}