Climate change vulnerability in Serbia An assessment of exposure, susceptibility and capacity at the municipal level

Climate change vulnerability in Serbia An assessment of exposure, susceptibility and capacity at the municipal level

Food and Agriculture Organization of the United Nations Budapest, 2021 Required citation: FAO. 2021. Climate change vulnerability in Serbia – An assessment of exposure, susceptibility and capacity at municipal level. Budapest. https://doi.org/10.4060/cb4916en

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Cover photograph: ©Unsplash/Filip Zrnzević iii

Contents Acknowledgements ...... iv Abbreviations and acronyms ...... v Introduction ...... 1 Approach ...... 2 Defining vulnerability: schools of thought ...... 2 International Panel on Climate Change (IPCC) definition ...... 3 Relevance to this assessment ...... 4 Agricultural sector in Serbia ...... 6 Institutions and key stakeholders ...... 7 Methodology ...... 9 Indicators ...... 10 Exposure ...... 11 Susceptibility ...... 11 Capacity ...... 12 Data collection and analysis ...... 14 Data sources ...... 14 Normalisation ...... 14 Categorical ranking ...... 15 Expert input ...... 15 Weighting ...... 16 Aggregation of indicators ...... 16 Key findings ...... 19 Agricultural vulnerability at the municipal level...... 19 Municipality rankings ...... 20 Comparison and analysis ...... 22 Vulnerability at the regional and district levels ...... 24 Regional ...... 24 Districts ...... 25 Thematic trends...... 27 Exposure ...... 27 Susceptibility ...... 28 Capacity ...... 30 Spotlight: Agricultural trends ...... 31 Spotlight: Economic trends ...... 33 Challenges and lessons learned...... 35 Recommendations ...... 39 Recommendations: Sub-national and community level actions ...... 40 Recommendations: Policy level ...... 41 Recommendations: Further research ...... 43 Conclusion ...... 45 Bibliography ...... 46 Annexes ...... 48 Annex 1 – 90 Key municipalities ...... 48 Annex 2 – Indicators for measuring social and economic vulnerability to climate change ...... 49 Annex 3 – Municipalities’ individual vulnerability scores ...... 53 Annex 4 – Most and least vulnerable by theme ...... 54 Annex 5 – FAO 2020 municipality questionnaire questions ...... 56 Annex 6 – Full final results of 90 key municipalities ...... 63 iv

Acknowledgements Under the technical guidance of Carolina Starr, Agricultural Officer – Regional Office for Europe and Central Asia, FAO and Aleksandar Mentov, National Program Coordinator for FAO – Serbia, this document was created and authored by Paul Conrad with invaluable contributions from Ljiljana Isic, Danijela Bozanic and the professional copy-editing support of Jenny Conrad. Feedback and contributions by representatives from the Ministry of Agriculture Forestry and Water Management (MAFWM), Statistical Office of the Republic of Serbia (SORS), Standing Conference of Towns and Municipalities (SKGO), Republic Hydrometeorological Service of Serbia (RHMSS), and other institutions have been crucial throughout this process. v

Abbreviations and acronyms

COVID-19 ...... Novel Coronavirus Disease 2019 DMCSEE ...... Drought Management Centre for Southeastern Europe E-OBS ...... High-resolution gridded mean/max./min. temperature, precipitation and sea level pressure for Europe and Northern Africa FAO ...... Food and Agriculture Organization of the United Nations GPCP ...... Global Precipitation Climatology Project from National Oceanic and Atmospheric Administration IPCC ...... International Panel on Climate Change MAFWM ...... Ministry of Agriculture Forestry and Water Management MoI ...... Ministry of Interior NAP ...... National Action Plan NOAA ...... National Oceanic and Atmospheric Administration RHMSS ...... Republic Hydrometeorological Service of Serbia SKGO ...... Standing Conference of Towns and Municipalities SORS ...... Statistical Office of the Republic of Serbia UNDRR ...... United Nations Office for Disaster Risk Reduction UNICEF ...... United Nations Children’s Fund vi

©Unsplash/Kamil Feczko 1

Introduction

In Serbia, agriculture, as one of the most important productive sectors, accounts for about 10 percent of the national gross domestic product (GDP) and contributes to 20 percent of exports, employing over 20 percent of the working-age population. Serbia is highly exposed and vulnerable to natural hazards, ranked at 87 on the world vulnerability list, with evidently the highest score in the region (European Commission, 2015). Agriculture is one of the sectors most affected by natural hazards, which impact a large proportion of the population and cause significant economic losses.

In the Intended Nationally Determined Contribution of Serbia, it is noted that extreme weather events are already having considerable impacts on the agricultural sector. It is also estimated that climate change will have considerable impacts on future production, with 22-52 percent reduction in corn yields expected by 2071–2100 in non-irrigated areas. Further increases in occurrence and intensity of extreme events (heat waves, drought and floods) are also expected to cause additional significant material and financial losses, loss of human lives and could devastate the agriculture sector and the farming-based livelihoods of thousands of people. This will have a direct impact on the food security of the population due to the impact on food production and food value chains. Moreover, since poverty and vulnerability to disasters are closely linked to each other, the small-scale family farms living in high-risk areas are particularly vulnerable due to the lack of resources to mitigate the adverse impact of natural hazards through risk insurance or technical measures.

An action plan for the implementation of the National Disaster Risk Management Programme (2016– 2020) was developed and adopted in March 2017 by the Government of Serbia. The Food and Agriculture Organization of the United Nations (FAO) supported development of this action plan and continue to provide support to the government for its implementation. One element of this support includes assessing the level of climate change vulnerability of the municipalities, ranking the most socially and economically vulnerable ones.

This report documents the process undertaken by FAO to analyse vulnerability at the municipal level in Serbia, ranks municipal vulnerability and highlights key findings. A series of recommendations based on assessment insights presents suggested actions to be taken at national and sub-national levels to reduce the vulnerability of smallholder farmers in Serbia to climate change. 2 Approach

The main purpose of understanding vulnerability and, therefore, of undertaking a vulnerability assessment is to improve the targeting and effectiveness of adaptation actions. Through a vulnerability assessment, one seeks to answer the basic question of “who (or what) is vulnerable to what?” (Downing, 2017, p. iii).

Defining vulnerability: schools of thought

Vulnerability is a complex and subjective topic (Brugère and De Young, 2015). Historically, there have been a number of schools of thought on defining vulnerability (see Box 1). These interpretations of vulnerability tend to be derived from risk/hazard, political economy or ecology, or resilience concepts.

Box 1

For each of these schools of thought, there are key concepts which influence how each perceives vulnerability (see Table 1).

Table 1 3

More recently, a number of different perspectives, which often combine or encompass aspects of these, have emerged.

Two important groupings of different perspectives include whether vulnerability is considered as an outcome of a given change (outcome vulnerability) or is determined by current contextual underpinnings (contextual vulnerability) (O’Brien et al., 2004a) (see Table 2). Outcome-based vulnerability relates to the effects of future climate/ environmental-related changes as the main driver of climate change vulnerability, while contextual vulnerability looks at how the current contextual situation of a system may affect its vulnerability to current and future climate change (Brugère and De Young, 2015).

Table 2

International Panel on Climate Change (IPCC) definition

With the understanding that vulnerability is extremely complex and subjective, the International Panel on Climate Change (IPCC) in 2001 developed, under its Third Assessment Report (AR3), a very broad definition of vulnerability:Vulnerability being a function of a system’s exposure to change, its sensitivity to such change and its capacity to adapt to it (see Figure 1). In 2014, the IPCC under its Fifth Assessment Report (AR5), updated its definition in order to emphasize the importance of contextual vulnerability (see Figure 2). Under AR5, the Intergovernmental Panel on Climate Change defines vulnerability as “the propensity or predisposition to be adversely affected”, explaining further that vulnerability “encompasses a variety of concepts and elements including sensitivity or susceptibility to harm and lack of capacity to cope and adapt” (Intergovernmental Panel on Climate Change, n.d., sec. Vulnerability). Vulnerability is a function of a system’s exposure to change, its sensitivity or susceptibility to such change, and its capacity to adapt.

• Exposure is the presence of people, ecosystems, infrastructure or a species in an area expected to be exposed to changes in climate or to an extreme event, either under present conditions or in the future.

• Susceptibility or Sensitivity is the magnitude of the direct or indirect effects of climate or extreme weather, either adverse or beneficial, relative to the climatic event.

• Adaptive Capacity is the ability of a system or a species to respond to climate change or a climatic event in a way that reduces harmful impacts.

IPCC 2014b also went on to define risk as a combination of the likelihood or probability that an event will happen and the consequences if that event were to happen. Exposure, susceptibility and adaptive capacity are not only components of the vulnerability of the system; they also contribute to determining risk. In other words, risk only exists in relation to a system where it might occur. 4

Figure 1: IPCC original model of vulnerability.

Figure 2: . IPCC updated model of vulnerability.

Relevance to this assessment

This assessment will approach vulnerability from the perspective of contextual vulnerability.

Contextual vulnerability (also known as the “starting point” interpretation) is a concept which considers vulnerability as the present inability of a system to cope with changing conditions, whereby vulnerability is seen to be influenced by changing biophysical conditions as well as dynamic social, economic, political, institutional and technological structures and processes. With the contextual approach, vulnerability is seen as a characteristic of ecological and social systems that is determined by multiple factors and processes (Adger, 2006; O’Brien et al., 2004b). 5

When considering agricultural-based vulnerability, the contextual approach has been singled out as more relevant as it looks to understand the whole multifaceted system. It builds on the duel consideration of socioeconomic and biophysical aspects (O’Brien et al., 2004a; Polsky, Neff and Yarnal, 2007; Turneret al., 2003) and often identifies underlying or even unknown factors which can contribute to agricultural vulnerability.

Practical examples where this approach has been used include:

• In 2019, the Committee on Forestry (COFO), FAO’s global statutory body for forestry, took an in-depth look at vulnerability as related to forest-dependent people. In order to understand vulnerability, it looks at biophysical, economic, climatic, socioeconomic and several other key factors as related to forest dwellers using this to understand the main drivers of vulnerability (Meybeck, Rose and Gitz, 2019).

• The Benguela region (Angola, Namibia, and South Africa) context-driven, socioeconomic assessments were used to understand climate change vulnerability as related to the systems around marine fisheries (Raemaekers and Sowman, 2015).

• In 2008, a study in China correlated the socioeconomic factors that make grain harvests in China sensitive to rainfall anomalies (Fraser et al., 2008).

• A 2007 study looking at vulnerability among rural people in the United States of America and Brazil found that only 39 percent of the variations in average crop failure rates across the United States of America can be explained by soils and climactic factors. The other 61 percent must be based on other factors, such as management skills, socioeconomic, institutional and political conditions (Mendelsohn et al., 2007).

Moving beyond agriculture, recent global events have also shown how contextual vulnerability is more relevant than outcome vulnerability for the purposes of this assessment. Outcome vulnerability is dependent on anticipation of likely impacts; as climate change accelerates, specific impacts become more and more difficult to predict. The Novel Coronavirus (2019-nCoV) (COVID-19) pandemic is an example of an issue none had predicted but has had far-reaching impacts on many aspects of people’s lives and livelihoods across the globe. Similar to COVID-19, future climate change events may not be able to be predicted, so assessing context and capacity beforehand is important for identifying potential vulnerability.

The ongoing impacts of COVID-19 on health systems, economies, social interactions and global supply chains are still emerging, yet these will unavoidably influence the overall vulnerability of communities, including to climate change. While sufficient research and data is not yet available to factor these into this assessment, taking a contextual vulnerability approach ensures areas already vulnerable due to existing socioeconomic factors are identified, as these issues are likely to be exacerbated by COVID-19. 6 Agricultural sector in Serbia

Agriculture is at the heart of the Serbian economy and is an engine for development of rural areas (Maslac, 2015). Agriculture contributes around 11.9 percent to the GDP of Serbia (U.S. Department of Commerce, 2017), a percentage which is significantly higher than the rest of Europe at 1.1 percent of GDP (Eurostat, 2019). An estimated 40 percent of the total population lives in rural areas, while approximately one-fifth of the working population is employed in the agricultural sector (FAO, 2018).

This assessment takes an in-depth look at the level of vulnerability which currently exists within and around the agricultural sector within Serbia. Geographically, it focuses on the whole of Serbia with the goal of trying to understand the level of climate change vulnerability as it applies to individual municipalities. 7 Institutions and key stakeholders

Throughout the development of this vulnerability assessment, a number of key stakeholders have played significant roles:

Name Role In partnership with the Ministry of Agriculture, Forestry and Water Management (MAFWM), FAO is the lead Food and Agriculture organization conducting and analysing the data for this Organization of the climate change vulnerability assessment. It has brought United Nations (FAO) together a team of national and international experts from the fields of agriculture production, climate change and disaster risk reduction. In direct partnership with FAO, MAFWM is the main technical collaborator. MAFWM has brought together a diverse team of individuals from the departments of Rural Ministry of Agriculture, Development Sector, Republic Water Directorate, Agrarian Forestry and Water Payments, and Sector for Agricultural Policy. Throughout Management every stage of this project – indicator development, data (MAFWM) collection, indicator weighting and data analysis – MAFWM has provided valued insight and critical counterpoints which have allowed for a balanced assessment. Throughout the process, SORS data was used to develop Statistical Office of indicators in the Susceptibility and Capacity sections of the Republic of Serbia the assessment. Data was sourced from the SORS online (SORS) database, annual Municipalities and Regions reports, other key SORS reports and directly from SORS. Throughout the process, RHMSS data was used to Republic examine and understand the climatic trends within Hydrometeorological Serbia, both historic (1981–2015) and projected climate Service of Serbia (2021–2050). This data was incorporated into the Exposure (RHMSS) section of the assessment. RHMSS provided data on precipitation, heatwaves, frost, hail and temperature trends. SKGO played a vital role in linking the project directly with municipalities. In partnership with SKGO, FAO launched the in-depth Assessment of Social and Economic Vulnerability Standing Conference to Climate Change which sought to gain key information of Towns and directly from municipalities regarding their vulnerability Municipalities (SKGO) to climate change. Additionally, SKGO and the team coordinating civil protection fed into the discussion around damage and loss data. MoI provided a wealth of knowledge on a range of different topics used throughout this assessment. Initially, they provided base level details pertaining to the levels of disaster preparedness of individual municipalities used in Ministry of Interior of the Capacity section. Based on MoI’s Assessment of Disaster the Republic of Serbia Risk in the Republic of Serbia, trends regarding precipitation, (MoI) drought, heat waves and hail were able to be included in the Exposure section. Damage and loss trends used in the Exposure section were based on MoI data taken from DesInventar.

United Nations UNICEF provided information on child well-being for the Children’s Fund Susceptibility section through the Child Well Being Index (UNICEF) available to Serbia DevInfo.

The Institute for Nature Conservation provided access to The Institute for the size of registered protected lands within Serbia for the Nature Conservation Capacity section of the assessment.

The Republic Secretariat for Public Policies provided The Republic access to data regarding local government budgeting and Secretariat for Public degree of development for the Capacity section of the Policies Table 3 assessment. 8

©Unsplash/sasa damjanovic 9

Methodology

This assessment analyses the contextual vulnerability of municipalities across Serbia and ranks these according to their vulnerability to climate change.

Based on the Intergovernmental Panel on Climate Change (IPCC) AR5 Working Group II’s updated definition of vulnerability as “the propensity or predisposition to be adversely affected” (Intergovernmental Panel on Climate Change, n.d.) and with an understanding that vulnerability is a complex concept of many dimensions, this assessment aims to analyse the underlying root causes which affect vulnerability. It uses commonly available sources of data relating to local municipalities throughout Serbia to assess and identify trends which make certain municipalities more or less vulnerable than others. Based on data analysis with input from subject-matter experts and validation with relevant government ministries, it provides a ranking of municipalities with regard to their individual vulnerability level (see Annex 3) along with recommendations for future steps which can be taken. 10 Indicators

The methodology consists of the development of a two-tier system of indicators with the overall purpose of creating a list of the most agriculturally sensitive municipalities to target for future interventions. An initial review prioritized areas which are agriculturally sensitive, with the second level of analysis focusing specifically on drivers of vulnerability.

Branch one: Analysis of the share of agricultural land This refines Share of as compared to the total area of land in the the list of 168 agricultural municipality. municipalities land as Municipalities with less than 25 percent (2019) to 140. compared to of their land focused on agriculture municipality removed. Data Source: SORS, Farm Structure Survey FSS 2018 Branch two: Determination of whether each This refines Agricultural municipality has a major focus on the list of households agriculture by reviewing the number 140 to 120 in each of agricultural households in each municipalities. municipality municipality. The total number of households in a municipality is compared to the total number of agricultural holdings. This is set at a ratio of per-1000 households. Municipalities with less than 150 per-1000 removed. Data Source: SORS, Census 2011, Agriculture Census 2012, Farm Structure Survey FSS 2018 Branch Analysis of the average size of total This refines three: agricultural holdings available in the the list of Average municipality. The total available agricultural 120 to 90 size of total area in a municipality is divided by the municipalities. agricultural total number of agricultural holdings in a holdings municipality. Only municipalities whose average holding was less than 10 ha were selected. This aligns closely with the national average farm size of the country and omits regions where large-scale industrial agriculture is taking place. Data Source: SORS, Census 2011, Agriculture Census 2012, Farm Structure Survey FSS 2018 Table 4

Tier 1 looks at a series of factors relating to the agricultural sector with the goal of identifying a list of municipalities which would be particularly sensitive to climatic changes relating to the agricultural sector. It used a three-branch approach as described in Table 4 to identify these municipalities. Based on this methodology, the total number of key targeted municipalities was refined from 168 to 90 (see Annex 1 for a full list of these municipalities).

Tier 2 takes a more in-depth look at vulnerability. As stated before, the Intergovernmental Panel on Climate Change (IPCC) defines vulnerability as “the propensity or predisposition to be adversely affected. Vulnerability encompasses a 1 The IPCC used both terms sensitivity and variety of concepts including sensitivity or susceptibility1 to harm and lack of capacity susceptibility interchangeably within the definition of vulnerability. For the purpose of this assessment, to cope and adapt” (Intergovernmental Panel on Climate Change, n.d.). As mentioned, we will commonly use the term susceptibility to vulnerability is related to the impacts of climate change and can be seen through reference both sensitivity and susceptibility. 11

a multitude of different contextual factors. Tier 2 of this assessment was developed based on the IPCC AR5 revised definition of vulnerability and based on tools developed by Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH and their Vulnerability Scorebook. It examines vulnerability through the three different lenses of Exposure, Susceptibility, and Adaptive Capacity. Its goal is to examine in depth a significant portion of the smaller aspects, including socioeconomic elements, which, when linked together, make a municipality vulnerable to the impacts of climate change. In doing so, the Tier 2 analysis looks to understand how the system at a municipal level might be more or less vulnerable.

Exposure

The Exposure section of this assessment focuses specifically on climatic issues – rain, drought, temperature trends and hail – and exposure to these events. These climatic factors ranked as some of the topmost frequented hazards which faced Serbia in the FAO’s 2018 Comprehensive Analysis of the Disaster Risk Reduction and Management System for the Agriculture Sector in Serbia (FAO, 2018).

For each topic, data was identified which could be used to measure how one municipality might experience different impacts from climatic issues than other municipalities. See Table 5 for explanation of the topic selection, data used and approach.

Topic Types of data used and approach Flooding Historic trends relating to damage and loss, flood risk trends, and long-term precipitation trends. This allowed identification of municipalities which tend to be more impacted by precipitation and flooding events. Drought Historic trends relating to damage and loss, drought frequency data, and agricultural holdings data overlaid with areas which have been identified as having been particularly impacted by past drought. This allowed identification of areas which historically have been the hardest impacted by drought. Temperature Historic average temperature trends were examined and used to compare to temperature trends for more recent periods. We looked at seasonal temperature trends, specifically the main growing season (April–September) and historic trends from 1961 to 1990 compared to 2014–2019 to understand larger-scale temperature shifts within areas. We also looked at trends for both heat waves and frost days and used these to look at areas which are commonly more affected by extreme events. Hail Historic data (1949–2012 and 1980–2015) relating to the frequency of hail events within Serbia was used to identify those areas where hail is a more common occurrence. This allowed identification of agricultural holdings which tend to be Table 5 more exposed to hail events.

Susceptibility

This section of the assessment takes an in-depth look at some of the social and economic factors – the indirect factors – which drive overall vulnerability. There are numerous definitions of social vulnerability; two commonly used definitions are: “the differential capacity of groups and individuals to deal with hazards, based on their positions within physical and social worlds” (Dow, 1992) and “the inability to take effective measures to insure against losses” (Bogard, 1989).

In the context of this survey, social vulnerability has been defined as a measure of the factors which make a community system more vulnerable to external stresses. This simplifies and combines the key elements of these two definitions. 12

This assessment takes an in-depth look at indicators illustrating some of the social trends within the different municipalities in Serbia which make these more or less vulnerable to the external climatic stresses they might face.

In addition to social trends, a range of direct economic effects and agrarian development trends which could be used to identify a community system as more vulnerable were considered. These include the level of economic reliance on certain crops and economic trends in the municipality.

Topic Types of data used and approach Demographic Different groupings of people who might be considered trends more physically vulnerable to the impacts of climate change, including children under the age of 5, people over the age of 65, and those classed as being more vulnerable, were analysed. This offered greater understanding of municipalities where a larger concentration of vulnerable people lives. Education levels When facing natural hazards or climate risks, households and societies which have populations with a higher level of education tend to be more empowered. These also tend to be more adaptive in their response to, preparation for, and recovery from disasters, making them less at risk (Muttarak and Lutz, 2014; Striessnig, Wolfgang and Patt, 2013). As the opposite is also true, identification of communities with lower education levels allowed identification of those which may be more vulnerable. Access to water Access to water was examined to understand a municipality's level of basic service provision. Lack of clean water in a dwelling means other sanitation facilities will likely be limited. When faced with both drought and/or floods, households which do not have access to clean water would be more vulnerable to insufficient nutrition, vector-borne disease and other issues as they arise. Employment Trends of high unemployment, higher dependency ratios, and and income lower net annual salaries as compared to the regional mean salaries would indicate a community that tends to be more vulnerable. Additionally, employment which is solely reliant on one sector, such as the agriculture sector, would also be more vulnerable in the long run. Bio-physical Several reports have identified the impact climate change will (levels of have on different agricultural-based crops throughout Serbia. agricultural This portion of the assessment seeks to understand the levels production) of reliance that a community has on a given crop in order to understand its level of vulnerability should changes in climate impact that crop. Health Climate change affects health directly. It undermines social determinants of health and threatens the viability of a number of environmental services provided by natural systems. It can present multiple hazards, which interact with pre- existing vulnerabilities, to cause substantially worse health outcomes. Most importantly, almost all of the health impacts are moderated by the strength of the health system, and its capacity to manage and adapt to climate-sensitive health risks (Watts et al., 2015). Table 6

Capacity

This section assesses the current state of local systems and whether these are actively preparing to reduce their vulnerability to climate change. This reviews concepts such as natural resource protection, local government tools which allow 13

for long-term community development and innovation, social cohesiveness and whether, at the local level, those who work in the agricultural sector have already adopted techniques which could mitigate the risks associated with climate change.

Topic Types of data used and approach Protected land This relates to sustainable land management in relation to climate change adaptation. It reviews the official land set aside as protected, including but not limited to: nature reserves, special nature reserves, national parks, protected habitats, landscapes of outstanding features, and nature parks. Protected areas maintain essential ecosystem services and pathways which can increase resistance and resilience and reduce the vulnerability of livelihoods against climate change. Protected areas can also contribute to alleviating the causes and effects of climate change through mitigation and adaptation measures included in their management (Hoegh-Guldberg et al., 2018; IUCN Caucasus Cooperation Centre, 2012). Local This considers local government’s capacity for forward thinking government and anticipatory actions – their ability to implement actions and plans which will allow the societies living within that municipality to mitigate the impacts of climate change. This includes examining local government trends related to expenses and revenue, ability to put in place longer term development plans, ability to set up and update emergency plans and risk reduction plans, and the level of investment they are putting toward the innovation within the agricultural sector. Social The system is only as strong as all its parts. In order for a system cohesiveness or local government to promote positive change, it will need buy-in, support and engagement from the community. This section attempts to measure this by looking at trends related to key measurable factors around social cohesiveness: involvement in the voting process and the number of locally operating civil society organizations per capita. Local climate In order to adapt to the impacts of climate change, there are change certain underlying techniques and upgrades which, when adaptation implemented, can have long-lasting impacts. This includes focusing on the ability of the agricultural sector to fully adopt and implement new and improved climate resilient agricultural Table 7 practices and considering access to improved irrigation systems. 14 Data collection and analysis

Data sources

The data used in this assessment was a mix of primary and secondary data collected through a series of interviews, questionnaires and an extensive literature review. Whenever possible, the sources of the data were derived based on government counterparts and then cross-checked with other sources.

• Exposure data: The main sources of data on exposure were the Hydrometeorological Service of Serbia, the Ministry of Interior for Serbia Disaster Risk Assessment, FAO Agriculture Stress Index and data taken directly from questionnaires sent to municipalities. Other sources such as E-OBS, National Oceanic and Atmospheric Administration (NOAA), Global Precipitation Climatology Project (GPCP), Drought Management Centre for Southeastern Europe (DMCSEE), were used as cross-references. Damage and loss data was based on United Nations Office for Disaster Risk Reduction (UNDRR)’s DesInventar data shared through the Republic of Serbia Ministry of Interior. The percentage of the population exposed to the risk associated with an event was based on questionnaires sent directly to municipalities.

• Susceptibility data: The main source of data was the Statistical Office of the Republic of Serbia (SORS), while additional data was provided by the Ministry of Agriculture, Forestry and Water Management; DevInfo Serbia; World Bank and UNICEF.

• Capacity data: This was based on both primary and secondary data sources. A questionnaire regarding social and economic vulnerability to climate change was sent to all municipalities through the Standing Conference of Towns and Municipalities. Further data was obtained through SORS, Government of the Republic of Serbia Secretariat for Public Policies, and the Institute for Nature Conservation.

A list of indicators and sources is included in Annex 2 and the questionnaire is in Annex 5.

Once data was collected, it was reviewed, analysed and loaded directly to the Data Matrix. This overall table was used to compile all data into one unified format, including references and links to the original data source, where available.

See Annex 2 for a brief explanation of how each indicator was examined and the format used to transfer it into the Data Matrix.

Normalisation

Once data was acquired, it went through a normalisation process. The process of ‘normalisation’ refers to the transformation of indicator values measured on different scales and in different units into unitless values on a common scale (Organization for Economic Co-Operation and Development and European Commission, 2008).

In this assessment, several different forms of measurement were used, from Serbian dinar (RSD)/household, to number of doctors, temperatures, and crop types. All these different measurement forms mean indicators follow different scales; therefore, a process was needed in order to standardise these into one unified, measurable scale.

This was achieved by setting up a standard value range from 0 to 1 for all data and then determining levels of optimal and critical, with optimal (or 0) meaning the system is working well with no issues; and critical (or 1) being that essentially the system is not currently functioning.

For example, an annual precipitation rate of 600 mm/year, which is optimal, receives a score of 0, while a precipitation rate of 200 mm/year might be critical and receive a score of 1. 15

= x! − x(!) X!,# %& ' x(*+ − x(!) is the individual data point to be transformed is the lowest value of the indicator ! x is the highest value of the indicator "!# x is the normalised value, i.e. the individual data point transformed to fall within the range "$% fromx 0 to 1 Equation 1 X!,' )* +

The most common data sets used during this assessment were based on metric data. In order to determine the normalisation value using a metric scale, min-max normalisation was used (see Equation 1). This transforms values and scores into a range from 0 to 1 by subtracting the minimum score and dividing it by the indicator values’ range.

One obstacle experienced when using this system arose from negative numbers. An example of this could be seen when reviewing average net salaries as compared to regional average net salaries where several figures were negative, while others were positive. Instead of using the actual negative number, these were given a ranking of 1–5, 1 being the optimal or closest to the norm and 5 being the critical and furthest from the norm. This was similar to the process used under the categorical ranking (see Table 8). This made the data compatible with the data matrix.

Other obstacles included the direction of the value range, with a need to check whether the indicator values increase in the right direction. That is, lower values should reflect positive conditions in terms of vulnerability and higher values more negative conditions. For example, with percentage of female school enrolment, having higher or more enrolment equated to more of an optimal situation than a critical situation. In this case, inverse values were applied, making the highest value equate to 0 while the lowest value equated to 1. Categorical ranking

Not all data collected was able to be aligned with the metric system. For example, when examining capacity, the types of development plans which had been adopted by a municipality over the last 10 years were assessed. Those who adopted on average over 1.5 plans per year were given a score of 1, while those who had none were given a score of 5. In these situations, categorical values were applied in order to normalise the data. Categorical values applied can be viewed in Table 8. When possible, categorical values were determined through discussion with experts.

Score Description 1 Optimal (no improvements necessary or possible) 2 Rather positive 3 Neutral 4 Rather negative Table 8 5 Critical (system not functioning)

Expert input

To complete the normalisation process, definitions of the min-max thresholds were finalized. With the standard min-max system, the value of 0 is automatically considered for the lowest value in the dataset and the highest value always given a score of 1. However, this might not always be the case; all scores in the dataset may be near the min or the max in reality.

Critical expertise with local, context-specific knowledge was consulted in order to determine true min-max thresholds and define these appropriately. 16

Weighting

Once data was collected and normalised into a single common scale, data went through a process of weighting. Not all indicators have equal influence over respective vulnerability, with some more important than others for determining whether a particular area is more or less vulnerable. For example, in the case of a community that is more susceptible to flooding, it might be more important to give the flood and precipitation indicator a higher weighting than indicators relating to drought. In order to determine proper weighting, a participatory workshop was conducted with representatives holding a wide range of knowledge from different departments of MAFWM and FAO. In this workshop, individuals were given a mock budget based on the indicators from the Susceptibility and Capacity sections of the assessment with “points” they could assign to each indicator. Individuals developed their own unique budget assignments and then the average score of this assessment was applied to the weighting.

For the Exposure section, weighting was based on the actual level of vulnerability a municipality might have to a particular climatic event. For example, if a municipality was more susceptible to flooding, the weighting given to the indicators on flooding would be higher. To determine weighting for this section, data was taken from MoI Assessment of Disaster Risk in the Republic of Serbia along with data taken from indicators pertaining to flooding, drought, hail and frost.

This assessment prioritized the impacts of particular climatic events on municipalities, so an additional weighting was used in which potential impact (exposure + susceptibility) was given a slightly higher weighting score over adaptive capacity.

Aggregation of indicators

The Organisation for Economic Co-operation and Development (OECD) Handbook states policymakers and the general public often find it easier to comprehend a composite indicator than numerous discrete indicators. Once all data was collected in the data matrix, it was aggregated using a weighted arithmetic aggregation (see Equation 2). In this, individual indicators are multiplied by their assigned weights, summed, and subsequently divided by the sum of their weights to calculate a composite indicator (CI). A composite indicator is defined as a quantitative or a qualitative measure derived from a series of observed facts that can reveal relative positions (e.g. of a country) in a given area. “When evaluated at regular intervals, an indicator can point out the direction of change across different units and through time” (Organization for Economic Co-Operation and Development and European Commission, 2008, p. 13).

(I! ∗ w! + I" ∗ w" + ⋯ + I# ∗ w#) CI = # ∑! w CI is the composite indicator, e.g. sensitivity; is an individual indicator of a vulnerability component, e.g. land use; and is the weight assigned to the indicator. I$ $ Equation 2 w

18

©Unsplash/Milica Spasojevic 19

Key findings

Agricultural vulnerability at the municipal level The primary purpose of this assessment is to analyse the contextual vulnerability of agricultural municipalities across Serbia and rank these according to their vulnerability to climate change. The following rankings identify municipalities which are most at risk of experiencing negative impacts as a result of climate change, highlighting that there are clear pockets of vulnerability which are compounded by a number of factors. While no one, clear driver of increased vulnerability is evident, this assessment shows that those most at risk are those facing numerous challenges, which together decrease their overall resilience. 20

Municipality rankings

Priority municipalities

Given the focus of this assessment on agricultural vulnerability, the initial review prioritized areas which are agriculturally sensitive. The 90 municipalities identified as Tier 1 (listed in (Organization for Economic Co-Operation and Development and European Commission, 2008)) are primarily clustered towards the centre of Serbia, as shown on the map (see Figure 3). These are all municipalities which have at least 25 percent of their land dedicated to agriculture and a considerable proportion of households reliant on smallholder agriculture with small–average sized holdings.

These include a range of topographies and common characteristics, including hilly and mountainous regions of the Dinaric Alps in the west, Rhodope and Balkan Mountains in the south-east, and Carpathian Mountains in the east of the country. The territory is interspersed with fertile valleys along multiple tributaries (Great Morava, West Morava, South Morava) of the Danube River Basin.

Figure 3: Priority municipalities following Source: Developed by the author using QGIS, with the base layer derived from Serbia’s national application of Tier 1 criteria. GeoPortal (https://geosrbija.rs/) data.

Most vulnerable municipalities

The overall results of this assessment (see Annex 6) are based on the IPCC approach in which exposure and susceptibility are combined together to formulate potential impact, and then potential impact is analysed against capacity. The final result of the combination of these factors is an overall vulnerability score for each individual municipality, all to the same scale and comparable to each other. 21

Based on this scoring, the 15 municipalities in Serbia which are most vulnerable to climate change are:

Total Total Vulnerability Territory Total capacity exposure susceptibility score Žabari 0.699384 0.619687184 0.753308 0.682978578 Merošina 0.712203 0.510216886 0.823041 0.664167888 Malo Crniće 0.642248 0.668888281 0.667174 0.658469419 Rekovac 0.626481 0.609243851 0.778148 0.657933704 Žagubica 0.74375 0.488051786 0.756347 0.651012496 Vladimirci 0.652104 0.701018702 0.561769 0.647863363 Bogatić 0.472316 0.684024712 0.804486 0.634749169 Preševo 0.68093 0.49000976 0.768451 0.631215117 Brus 0.743444 0.474095063 0.697901 0.631052558 Varvarin 0.6506 0.635225111 0.573915 0.625663079 Koceljeva 0.603651 0.611984446 0.675664 0.624779393 Žitorađa 0.634149 0.594580188 0.655639 0.624683085 Tutin 0.717123 0.389015419 0.829829 0.622259253 Table 9 Ražanj 0.673658 0.432262434 0.546771 0.618616517 Veliko Gradište 0.672222 0.579506372 0.593364 0.617738985

All of the most vulnerable municipalities are located in Šumadija and West Serbia Region and South and East Serbia Regions. They tend to be fairly sparsely populated with an average population density of around 48 people per km2. A significant portion of these municipalities scored high vulnerability rankings related to their exposure to both heatwaves and drought, along with reporting that farmers have relatively low levels of access to recently improved irrigation systems. Additionally, the majority report that on average they have relatively low levels of investment within the agricultural sector, with the majority officially reporting having spent 0 RSD on agrarian investment over the last three years.

Least vulnerable municipalities

Within these 90 agriculturally-sensitive municipalities, the 15 municipalities in Serbia which are least vulnerable to climate change are:

Total Total Total Vulnerability Territory exposure susceptibility capacity score Zaječar 0.482159 0.349024153 0.445084 0.422964719 Pirot 0.445532 0.432262434 0.705459 0.453156548 Bor 0.571854 0.324016742 0.511556 0.463840665 Vranje 0.500065 0.323666064 0.671526 0.47678063 Sremska Mitrovica 0.456749 0.44700749 0.586975 0.485652438 Šabac 0.475721 0.492130126 0.49981 0.48789683 Požega 0.624642 0.349403136 0.503438 0.491126236 Pećinci 0.63792 0.354058861 0.514537 0.500626479 Loznica 0.484929 0.504633666 0.570188 0.513632896 Aranđelovac 0.635743 0.345655779 0.59189 0.515997174 Topola 0.542837 0.515241591 0.481443 0.517140248 Požarevac 0.637327 0.458023638 0.426849 0.51746856 Crveni krst 0.669994 0.493316485 0.325013 0.517494498 Bačka Palanka 0.612666 0.457121145 0.47062 0.518825149 Table 10 Čačak 0.639228 0.353035608 0.587794 0.519047099

The 15 least vulnerable municipalities are more widely spread throughout all four regions of Serbia. In general, these tend to be slightly more densely populated, having an average population density of around 99 people per km2. A significant number of these municipalities report a larger proportion of their population completing high school, being employed across multiple sectors (agricultural, financial, civil service, hospitality, extractive, etc.), having relatively low levels of social dependence, and reporting that the majority of their homes have access to clean water. 22

Comparison and analysis There is no single defining characteristic which results in a Examining the average of the results for the top 15 most vulnerable municipalities municipality being at a significantly and comparing these to the results of the 15 least vulnerable municipalities provides higher level of vulnerability than insight into factors which drive vulnerability at the municipal level. another.

Overall, there is a noteworthy difference between municipalities with the highest level This is the culmination of all three areas of vulnerability and those with the lowest level. As discussed in the Methodology of focus: exposure; susceptibility; and section, a common scale for scoring vulnerability was used which ranked all scores capacity. between 0 and 1. When looking at the most vulnerable score (Žabari: 0.6829) and least vulnerable (Zaječar: 0.4229), there is a difference in vulnerability scores of 0.2600, or one-quarter of the overall score. This suggests that there are some fairly significant factors which differentiate the most and the least vulnerable of municipalities.

High levels of vulnerability are the culmination of all three areas of focus: exposure, susceptibility, and capacity. In general, there does not appear to be any Vulnerability is the result of one defining characteristic which results in a municipality being at a significantly compounded issues across all three higher level of vulnerability than another. areas.

• Exposure scores vary across all municipalities, whether classed as vulnerable or For example, increased exposure to not, so there is no significantly higher incidence of extreme climatic events in those drought, combined with increased classed as the most vulnerable when compared to the least. The difference in the susceptibility through lack of water average exposure scores of the most and least vulnerable is just 0.1004. access, and compounded by lack of capacity to invest in irrigation • The difference between the averageadaptive capacity scores for the most and improvements at municipal level. least vulnerable municipalities is greater, at 0.1729. This suggests that the presence or lack of capacity is a consistent driving factor of vulnerability. Where high levels of capacity exist, these can offset any particular susceptibilities experienced by a municipality.

In those with the highest rankings, vulnerability is often the result of compounded issues across all three areas. Where high levels of capacity exist, these can offset any particular An example is increased exposure to drought combined with increased susceptibility susceptibilities experienced by a through lack of water access and lack of capacity to invest in irrigation improvements municipality. at municipal level.

While no one defining factor is evident, examining thematically similar indicators highlights some specific areas of note and emphasizes where particular issues are compounded.

1. High temperatures and drought are the biggest challenges for all municipalities. Whether most or least Compare least and most exposure vulnerable, all show a similar trend of Least vuln. Most vuln. high temperature and drought being experienced more frequently than Flood floods or hailstorms. 0.8 0.6 2. The most vulnerable municipalities are 0.4 disproportionately likely to be affected 0.2 by drought. While levels of exposure Temp. 0 Drought to other climatic hazards remain similar across the most and least vulnerable areas, drought is the exception. All 15 of the most vulnerable municipalities scored as being extremely impacted by Hail drought over a 34-year period (1981– Figure 4 2015). 23

3. In the most vulnerable municipalities, Compare least and most susceptibility vulnerability as a result of exposure to drought is compounded by higher Least vuln. Most vuln. susceptibility as a result of poor Demographic water access. Those in the most 0.8 vulnerable municipalities are more 0.6 likely to experience water scarcity, with Health 0.4 Education a significant gap between the ability 0.2 of farmers to access water in the most 0 vulnerable (average score: 0.4618) and least vulnerable (average score: 0.1421) Agri Water municipalities. This further limits their Figure 5 ability to reduce the impacts of drought on their agricultural production. Employment

4. Lack of investment in local adaptation measures to mitigate the impacts Compare least and most capacity of drought is evident in the most Least vuln. Most vuln. vulnerable municipalities. All those Protected land ranked as most vulnerable scored 0.8 poorly on the percentage of farmers 0.6 with access to recently improved 0.4 CC adaptation Local gov. plan irrigation systems. Without the 0.2 investment in specific adaptation 0 measures, municipalities will continue to be susceptible to the impacts of drought and temperature change and Figure 6 lack the capacity to make meaningful Social Local gov. fin improvements.

5. All municipalities are vulnerable to some extent due to unsustainable agricultural practices. These include lack of crop diversification or tillage methods without a focus on soil conservation. Regardless of where municipalities appeared in the overall ranking, all scored relatively highly on the need to improve some aspects of their land management practices.

6. High reliance on agriculture for employment in the most vulnerable municipalities compounds the overall risk. With high exposure scores, these municipalities are at greater risk of experiencing the impacts of climate change; a greater dependence on agriculture for employment means their local economy is more susceptible to shocks resulting from the impact of climate change on the agriculture sector.

7. Health is the highest driver of susceptibility in the most vulnerable municipalities. These showed a marked increase in scores relating to health – such as the number of doctors per capita – when compared to less vulnerable municipalities. It would be interesting to explore further what this means in practice for smallholder farmers in their day-to-day work.

8. Almost all of the most vulnerable municipalities failed to make any investments in agriculture in recent years. This severely limits their capacity to prepare for potential challenges and to respond to the impacts of climate change in the future. It also indicates a lack of ability to plan for longer term systematic development; developing these forward-thinking approaches and making them systemic within local government culture are crucial for increasing local capacity. 24 Vulnerability at the regional and district levels Looking at country-wide data through the same indicators for exposure, susceptibility and capacity enables Serbia’s regions and districts to be ranked according to their overall vulnerability. This allows for broader insight. Regional Unsurprisingly, the regions with larger proportions of municipalities ranking highest for vulnerability are most vulnerable overall, whereas more urban areas are less so.

Regional Regional Regional Score Score average high low Šumadija 0.560686 Rekovac 0.657934 Šabac 0.487897 Most and West vulnerable Serbia region South and 0.547906 Žabari 0.682979 Zaječar 0.422965 East Serbia region

Vojvodina 0.527724 Žitište 0.639488 Vršac 0.443567 region

Least Belgrade 0.523328 Grocka 0.583111 Zvezdara 0.45671 vulnerable region Table 11

As can be seen in Figure 7, the Šumadija and West Serbia and South and East Serbia regions follow similar paths in their scores for exposure, susceptibility and capacity. Šumadija and West Serbia ranks as the most vulnerable region as its scores for both exposure and capacity are slightly higher than the respective scores for South and East Serbia.

Regional comparison

Šumadija and West Serbia region South and East Serbia region Vojvodina region Belgrade region

Total exposure 0.65

0.6

0.55

0.5

0.45

0.4

0.35

Total capacity Total susceptibility Figure 7

All regions show relatively similar levels of exposure, indicating that differing levels of susceptibility and capacity are what set apart the less vulnerable areas. It is clear the Belgrade Region is least likely to feel the socioeconomic impacts of climate change. This is likely due to higher levels of urbanization and, therefore, less dependence on agriculture.

One interesting point to note is that the Vojvodina Region, though having the highest levels of socioeconomic vulnerability, seems to have the highest level of capacity. By having higher levels of capacity in place, the region’s overall level of vulnerability drops significantly. This illustrates how having higher levels of capacity appears to significantly decrease an area’s overall level of vulnerability. 25

Districts At the district level, Braničevo ranks as having the highest vulnerability score, closely followed by Rasina and Pomoravlje. Zaječar, Srem and Pirot rank as the least vulnerable, with Pirot receiving the lowest overall vulnerably score.

District District high Score District low Score average Braničevo area 0.60365 Žabari 0.682979 Požarevac 0.517469 Rasina area 0.602595 Brus 0.631053 Kruševac 0.572031 Pomoravlje 0.581749 Rekovac 0.657934 Ćuprija 0.53276 area Mačva area 0.576707 Vladimirci 0.647863 Šabac 0.487897 Podunavlje 0.571541 Velika Plana 0.602202 Smederevo 0.541604 area Kolubara area 0.56603 Lajkovac 0.588223 Valjevo 0.535697 Central Banat 0.553275 Žitište 0.639488 Zrenjanin 0.490798 area Nišava area 0.551715 Merošina 0.664168 Medijana 0.459833 Toplica area 0.548528 Žitorađa 0.624683 Kuršumlija 0.489227 Raška area 0.546979 Tutin 0.622259 Raška 0.500334 Šumadija area 0.541065 Rača 0.59079 Kragujevac 0.490663 Pčinja area 0.540017 Preševo 0.631215 Vranje 0.476781 Jablanica area 0.539372 Bojnik 0.58698 Crna Trava 0.490666 Zlatibor area 0.539045 Arilje 0.582826 Požega 0.491126 Moravica area 0.53715 Ivanjica 0.568319 Čačak 0.519047 South Banat 0.530963 Alibunar 0.590839 Vršac 0.443567 area South Bačka 0.530558 Bač 0.59984 Petrovaradin 0.451539 area North Bačka 0.527363 Mali Iđoš 0.535487 Bačka Topola 0.52149 area North Banat 0.526195 Čoka 0.557717 Novi 0.493003 area Kneževac Belgrade 0.523328 Grocka 0.583111 Zvezdara 0.45671 area (City of Belgrade) West Bačka 0.522579 Odžaci 0.576786 Apatin 0.490049 area Bor area 0.517754 Kladovo 0.566175 Bor 0.463841 Zaječar area 0.515341 Boljevac 0.55879 Zaječar 0.422965 Srem area 0.504916 Irig 0.558952 Sremska 0.485652 Mitrovica Pirot area 0.484957 Bela 0.531254 Dimitrovgrad 0.442668 Table 12 Palanka 26

Mapping vulnerability across Serbia districts emphasizes some clear pockets of vulnerability. Figure 8 highlights specific districts where addressing the impacts of climate change is particularly important.

Rather Rather Optimal Neutral Critical positive negative

Source: Developed by the author using QGIS, with the base layer derived from Serbia’s national Figure 8 GeoPortal (https://geosrbija.rs/) data.

Susceptibility Exposure Capacity

Most Least Most Least Most Least

Demographic Flood Protected land 0.8 0.8 0.8 0.6 0.6 0.6 Health Education 0.4 0.4 0.4 CC adaptation Local gov. plan 0.2 0.2 0.2 0 Temp. 0 Drought 0

Agri Water

Social Local gov. fin Employment Hail Figure 9

The district-level analysis includes all municipalities, not just those with a priority focus on agriculture. It is important to note that despite this, similar results are evident, with similar drivers of vulnerability affecting a municipality’s ability to cope with the impacts of climate change. 27

Thematic trends

The overall vulnerability scores in this assessment rank each of the 90 municipalities identified as agriculturally sensitive from most to least vulnerable. The process for obtaining these scores can also be used to rank municipalities according to the three major focus areas: exposure, susceptibility and adaptive capacity. In doing so, thematic trends can be identified and specific indicators considered in more depth. While these often mirror the overall findings, where differences are apparent, this helps identify specific areas for action which may benefit all agricultural municipalities across the country. Full lists of the municipalities scoring highest and lowest for each focus area can be found in Annex 4. Exposure With climate change, it can be expected that all municipalities in Serbia will be Exposure is the presence of people, impacted at some level by a shift in climatic events or exposure to extreme weather. ecosystems, infrastructure or a species The exposure scores provide some insight into how municipalities experience this in an area expected to be exposed change. to changes in climate or to extreme weather, either under present conditions Indicators for this section include: the average number of days with precipitation or in the future. greater than 30 mm (1981–2015); total number of occurrences of moderate, severe and extreme drought during the summer (1981–2015); and average annual number of days of hail (1981–2015). A full list can be found in Annex 2. These indicators can be summarized under four distinct types of hazard: flooding, drought, hail, and temperature change.

Theme Driving factors Temperature This is the highest scoring climatic event experienced by farmers, change with the most and least exposed municipalities all facing this as an issue for agriculture. Flooding Flooding is particularly relevant in those areas near the many larger rivers and drainage basins within Serbia. Hail Hail seems to be particularly relevant to more mountainous/ hilly regions in Sothern Serbia and in many of the western municipalities along the border with Bosnia and Herzegovina and Montenegro. Drought Drought has high exposure scores overall, indicating this is likely Table 13 to have a negative impact on farmers throughout Serbia.

Key insight

Average most exposure 1.01 Comparing most and least exposed 1.15 1 1.02 0.8 1.14 1.03 0.6 Least Most 0.4 1.13 1.04 0.2 Flood 0 1.12 1.05 1

1.11 1.06 0.8

1.1 1.07 0.6 1.09 1.08 0.4

Average least exposure 0.2 1.01 Temp. change 0 Drought 1.15 1 1.02 0.8 1.14 1.03 0.6 0.4 1.13 1.04 0.2 0 1.12 1.05

1.11 1.06

Figure 10 1.1 1.07 Hail 1.09 1.08 28

Addressing the impact of temperature extremes on farmers is potentially an issue needing widescale attention. Temperature change contributed to high scores across the board, suggesting this is having a real impact on farmers across the whole country.

Addressing the impact of drought is an effective way of supporting the most exposed municipalities. It has already been noted that the 15 municipalities with the highest overall vulnerability are disproportionately likely to be affected by drought; this is also a key factor when looking at the municipalities with the highest overall levels of exposure, as these appear to experience drought more often.

Hail increases exposure but this does not always equate to increased overall vulnerability. The most exposed municipalities are more impacted by hail; however, this has less of an overall impact when examining which municipalities are most vulnerable overall. This could indicate that some of these municipalities have greater capacity and less overall susceptibility, or that the effects of hail are limited to smaller areas. Looking geographically, many of the municipalities who reported the largest issues with hail were either located in southern or western Serbia in and around the District of Sabac; however, challenges in data mean that this issue could be explored further.

Reduced capacity affects the ability to accurately assess local exposure. In reviewing exposure data, a set of indicators (1.12–1.15) regarding reported damage and loss data appeared to be an anomaly, with the least vulnerable scoring higher overall than the most vulnerable. The team spent some time exploring this anomaly by contacting several municipalities who had scored at the higher risk regarding climatic threats but who had reported relatively low figures regarding damage and loss. It was unanimously found that it was not that these municipalities had not incurred any damage or loss; rather, the municipality itself did not have adequate resources to systematically record damage and loss. This often aligned with the findings (3.03–3.07) in the Capacities section with regard to local government, with these municipalities having lower capacity scores.

Susceptibility Susceptibility scores highlight which municipalities are most or least likely to feel the impacts of climate change in terms of socioeconomic factors. Susceptibility is the magnitude of the direct or indirect effects of climate or Thematic topics considered include demographic trends, education, access to basic extreme weather, either adverse or services such as water, employment and income trends, bio-physical (levels of beneficial, relative to the climatic event. agricultural production), and health. These were measured through indicators such as the percentage of population in vulnerable group live with disability; percentage of households with no water supply in dwelling; child wellbeing index scores; or bio- physical indicators such as crop diversification. A full list can be found in Annex 2.

Theme Driving factors Demographics In almost all cases, the most vulnerable municipalities have higher percentages of vulnerable people – such as people under 5, over 65 or living with a disability – living within them when compared to the least vulnerable municipalities. Education As would be expected, municipalities with higher levels of education tend to be less susceptible than those with lower levels of education. However, when compared to other indicators, education levels are not one of the main drivers of vulnerability. Table 14 29

Theme Driving factors Access to water Access to water is the most significant disparity between the most and least susceptible areas. On average, the most vulnerable municipalities receive a vulnerability score of 0.46, while the least vulnerable receive a score of 0.14. This is 0.32, or over one-third difference in score, indicating this is a significant driver of vulnerability. Employment and income While some disparity exists between the levels of employment and income in municipalities which are more or less susceptible to the impacts of climate change, this plays less of a role in contributing to overall susceptibility. Bio-physical (levels of Receiving the highest scores overall, agricultural agricultural production) production ranks as one of the leading factors when defining a municipality’s overall level of vulnerability to the impacts of climate change. Where municipalities lack sustainable agricultural production practices, their vulnerability increases regardless of whether municipalities scored high or low overall. Health Health plays a significant role in increasing the susceptibility of a municipality. The most vulnerable municipalities score highly on this, and there is a significant difference between the most and least vulnerable areas. Those with better scores on the child wellbeing index and better ratios of doctors to inhabitants have decreased susceptibility to the Table 14 impacts of climate change.

Key insight

Average most susceptibility 2.01 Comparing least and most susceptibility 2.14 1 2.02 0.8 2.13 0.6 2.03 Least Most 0.4 2.12 0.2 2.04 0 Demographic 2.11 2.05 0.8

2.1 2.06 0.6 2.09 2.07 2.08 Health 0.4 Education

Average least susceptibility 0.2 2.01 2.14 1 2.02 0 0.8 2.13 0.6 2.03 0.4 2.12 0.2 2.04 Agri Water 0 2.11 2.05

2.1 2.06

Figure 11 2.09 2.07 Employment 2.08

Water access is the greatest difference between the most and least susceptible municipalities. Not just among the most vulnerable areas overall but across all agricultural municipalities, it is clear that where a greater proportion of households have water supply to their dwelling, there is more resilience to the impacts of climate change. This suggests a need to focus on water availability as a priority for particularly vulnerable municipalities. 30

All municipalities scored poorly for their use of conservative tillage methods regardless of how they fared across other susceptibility indicators. This indicates the likelihood of high levels of soil nutrient and moisture wastage and suggests newer, more innovative agricultural approaches might not have taken root within much of Serbia’s agricultural sector. Introducing more sustainable agricultural approaches could be an appropriate action across all areas.

Employment and education trends compound vulnerability by making municipalities more susceptible to economic shocks. This is particularly relevant in locations where all four indicators (2.03–4, 2.07–8) relevant to this, such as the percentage of people without educational attainment and the percentage of the population employed in agriculture, forest and fishing, are highlighted as an issue. This includes municipalities such as Vladimirci, Bogatić, and Malo Crniće; it could be expected that, should a larger climatic event take place within these municipalities, the compounding impact of such an event would likely be severe. Capacity Adaptive capacity scores indicate the ability of a municipality to cope with a changing climate. The regional analysis showed that, even in areas with high susceptibility Adaptive Capacity is the ability of to the impacts of climate change, if this is combined with high levels of adaptive a system or a species to respond to capacity, their overall vulnerability is lower. Similarly, municipalities with the most climate change or a climatic event in a ability to respond to climate change and reduce harmful impacts have significantly way that reduces harmful impacts. lower overall vulnerability scores, while the most vulnerable have some clear areas for improvement. Analysing these allows for the identification of positive or negative trends which can be addressed to increase the ability of all areas to respond well to the challenges they face.

Thematic topics considered include protected land, local government planning, local government financing resource and allocation, social cohesiveness, and local climate change adaptation actions.

These were measured through indicators such as local government ability to produce and implement local development strategies, local government amount of investments in agriculture (agrarian budget) 2016–2018, or the percentage of farmers practicing improved climate-resilient agricultural practices (e.g. hybrid seeds, hail/ shade nets, improved tillage methods, multi-purpose water supply). A full list can be found in Annex 2.

Theme Driving factors Protected land Protected land does not seem to factor into vulnerability scores when comparing the most and least vulnerable in Serbia. Local government Local governments lacking up-to-date, long-term planning development plans or contingency plans consistently scored poorly for adaptive capacity. Local government Municipalities with good systems in place for collecting and resource allocation allocating financial resources appear to have lower levels of and finances vulnerability on average. These municipalities also tend to routinely invest in their agrarian budgets. Social cohesiveness Indicators of social cohesiveness – such as participation in sub-national elections and the presence of civil society organizations – do not appear to have a substantial impact on vulnerability scores overall, and there is not a significant difference between the most and least vulnerable areas. Local climate Local adaptation practices play a major role in increasing change adaptation capacity and, therefore, reducing vulnerability. This includes efforts such as improved irrigation systems and experience applying climate-resilient agricultural practices like hybrid seeds, hail/shade nets, improved tillage methods and multi-purpose water supplies. Table 15 31

Key insight

Average highest capacity 3.01 1 3.11 3.02 0.8 0.6 Comparing least and most capacity 3.1 0.4 3.03 0.2 0 Least vuln. Most vuln. 3.09 3.04 Protected land

3.08 3.05 0.8

3.07 3.06 0.6 0.4 Average least capacity CC adaptation Local gov. plan 3.01 0.2 0.7 3.11 0.6 3.02 0 0.5 0.4 3.1 0.3 3.03 0.2 0.1 0 3.09 3.04 Social Local gov. fin Figure 12 3.08 3.05 3.07 3.06

The ability of local governments to plan ahead is crucial for improving their capacity to respond to the impacts of climate change. Some local areas have good policies in place, such as regularly developing and updating longer term development plans or developing and updating contingency/emergency plans annually. Where municipalities lack these or fail to keep them current, there is a clear effect on their adaptive capacity and their overall vulnerability.

Poor financial management systems at the municipal level increases vulnerability. One of the largest gaps between municipalities with the most and least capacity is in local government finance. Where municipalities lack available resources or the ability to properly manage their budgets, investment in forward planning is less likely to happen and funds to support smallholder farmers in adapting to a changing climate are less likely to be available.

Investment in agrarian budgets improves local adaptive capacity. Many of the municipalities with good policies and contingency plans in place also allocate resources to support farmers; these often help smallholders invest in improved and innovative agricultural practices, removing a level of risk for them to try implementing new climate change-focused agricultural techniques, such as improved tillage methods, using hybrid seeds, or investing in multipurpose irrigation systems. Many of these municipalities have recently invested in improvements to agricultural irrigation systems; this is particularly important given the dual roles of drought and lack of access to water in increasing overall vulnerability. All of these support local areas to deal with the challenges of a changing climate, no matter how exposed or susceptible they are.

Members of vulnerable communities in Serbia are engaged with political processes. One interesting note which can be observed is that, on average, municipalities which are more vulnerable seem to have a higher proportion of citizens engaged in the voting process. This is in contrast to other regions around the world, where more vulnerable populations may be disillusioned and not take part.

Spotlight: Agricultural trends As this assessment had a focus on agricultural trends, there were five indicators specific to the agricultural sector used in the Susceptibility and Capacity sections to examine vulnerability. In separating these out and examining them in the context of the most and least vulnerable municipalities, we gain further insight relevant to agriculture. 32

Indicator Description number Agricultural indicators

2.06 Percentage of people employed in Most Least agriculture, forest and fishing 2.06 2.10 Percentage of holdings not practicing 1 improved land management through 0.8 Table 16 conservative tillage methods 0.6 0.4 3.04 2.1 2.11 Crop Diversification Risk Score 0.2 0 2.12 Percentage of total cropland dedicated to corn/maize 3.04 Amount of local government investments in agriculture (agrarian 2.12 2.11 budget) 2016–2018 Figure 13

New approaches to land management education could affect sustainable farming methods: Consistent with previous findings, unsustainable tillage methods (indicator 2.10) are evident across almost all municipalities.

This can be used as a proxy for understanding levels of innovation regarding agricultural approaches within Serbia. While there has been a long history of innovative agricultural approaches being rolled out through agricultural development programming, it can be assumed that newer techniques have not taken root within much of the country.

This suggests there may be benefits in reviewing the approaches used for this training, potentially incorporating modern behaviour change communication and market system development approaches into future agriculture-based development initiatives.

Opportunities exist to learn from agrarian budget expenditure: Lack of investment in agriculture at the municipal level (indicator 3.04) has been identified as a clear driver of vulnerability.

Overall scores were based on data provided by The Republic Secretariat for Public Policies. In examining these, there are two municipalities that scored as having the highest level of investments in agrarian budget: Požarevac and Crveni krst. Perhaps unsurprisingly, these scored as having the highest levels of capacity across all 90 agricultural municipalities.

It might be worth further exploring with these municipalities the reason why they invest so much within their agrarian budget and if there is any learning from their approach which could be used and shared with other municipalities.

Climate change adaptation is imperative for reducing economic risk: When specifically considering the percentage of people employed in agriculture, forest and fishing (indicator 2.06), it is clear this is the largest gap between the most and least vulnerable municipalities.

This is of some concern, as the risk to livelihoods from climate change is compounded by high overall vulnerability in conjunction with a high level of reliance on agriculture for income.

These municipalities which have such a huge reliance solely on one sector – agriculture – will likely be some of the hardest economically impacted under future negative climate change scenarios. 33

Spotlight: Economic trends Four key economic indicators were used to assess vulnerability. By merging these and examining from an economic standpoint, we can start to understand some trends with regard to economic vulnerability.

Indicator Description Economic indicators number Most Least

2.06 Percentage of people employed in 2.06 agriculture, forest and fishing 0.8 Table 17 0.6 2.07 Average net salary deviation score 0.4 3.05 Local government average per 0.2 capita revenue for last eight years 3.06 0 2.07 vulnerability score 3.06 Local government surplus and deficit Figure 14 over last eight years vulnerability score 3.05

Dependency on agriculture risks local economies in particularly vulnerable areas: It has already been noted that high dependency on agriculture for employment (indicator 2.06) is a particular risk to the local economy when municipalities have a high level of overall vulnerability to the effects of climate change.

Where there is a lack of diversity of employment options in a specific location, the potential for a large proportion of the population to see reduced employment opportunities will also have a knock-on effect on the other industries within that location, as the base of their businesses are often also reliant on a particular sector.

Municipalities with such a high level of reliance on agriculture will likely be some of the hardest economically impacted under future negative climate change scenarios.

Lack of economic resilience is evident amongst the most vulnerable farmers: The average net salary deviation score (indicator 2.07) compares the recorded net salary at municipal level to the regional average net salary. This highlights discrepancies between different municipalities with regard to wages and, assuming costs of living would be similar across regions, discrepancies with regard to costs of living.

This signals locations which would likely have a higher percentage of households with disposable income and, therefore, individual funds or resources set aside against the potential of a major climatic event impacting them, which should enable them to bounce back quicker economically.

It is evident there is a huge gap between the most and least vulnerable municipalities regarding this, indicating that farmers in the areas most vulnerable to the impacts of climate change will be the least able to recover from disaster or climatic shocks.

Insufficient funding may affect the ability of the most vulnerable municipalities to make improvements: The average amount of local government per capita revenue (indicator 3.05) implies that a high percentage of the most at-risk municipalities do not have adequate systems in place to raise revenue.

This often correlates directly to an overall lack of resources at the institutional level. This could explain why many of the damage and loss scores for these municipalities do not seem to correlate to the exposure trends, as in they do not have the means to properly record this information.

It is also less likely that these municipalities will have funds available to make needed improvements with regard to investment in new or improved climate change- sensitive agricultural practices. 34

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Challenges and lessons learned

Throughout the different stages of this assessment, a number of challenges were faced. These primarily resulted in two key issues: data access and data quality.

Although the team was largely able to overcome these challenges, it is useful to note them so that future assessments can take these into account. 36

COVID-19 pandemic By far the biggest challenge throughout this assessment was the unprecedented occurrence of the COVID-19 global pandemic. The assessment officially started in April 2020, a time when Serbia – along with much of the world – was enacting lockdown measures to try and suppress the spread of the virus. This meant that a lot of the anticipated initial meetings, preparation work, and relationship building was either postponed or held remotely. These constraints in the initial stages, along with the uncertainty resulting from COVID-19, meant delays in activities and collecting data, with the team constantly needing to adjust their original plans to try and creatively access the data needed to inform the assessment. Parliamentary election The Parliamentary election, which took place in June 2020, was another factor which affected engagement with national ministries and local government officials. In the run up to the election, several officials stated that they did not have the time to feed information into the assessment. Following the election, there were several weeks when the new government was not officially formed, which meant officials were unclear what their mandates entitled them to do. This became very evident when running the municipality survey, as several municipalities never responded. Data quality at the municipal level Obtaining accurate data was the largest challenge, particularly data specific to municipalities. Statistical Office of the Republic of Serbia (SORS) and in particular the annual Municipalities and Regions reports were an invaluable source of information. However, while the information from SORS was generally correct, it was noticed that this did at times seem to have misinformation or random outliers in the data which did not always mirror the situation on the ground. To overcome this, when discrepancies were noted, the team would try and cross-reference the data with data collected through the FOA Climate Change Municipality Survey or reach out to municipalities directly. Responses often stated the SORS data was the official data, although it might not be 100 percent reflective of the situation on the ground due to an overall lack of resources to collect the data in full detail. This was especially true for many of the smaller municipalities. Information gaps in DesInventar Damage and Loss data A key source of information for the vulnerability assessment was DesInventar Damage and Loss data. However, analysis revealed large gaps in information. For example, from 2011 to the present, DesInventar noted only 2 municipalities had incurred damage and loss related to drought; however, there were reports of drought damage throughout the Danube River Basin, including Serbia in 2015 (Hödl and Mair, 2015), along with wider scale drought observed in Serbia in 2018 (Djurdjevic, 2020). The lack of damage and loss data would suggest that overall data within DesInventar is incomplete. When following up with municipalities directly regarding this, they would often state that damage and loss had occurred, but they did not have the necessary resources to collect the information needed to accurately feed into the damage and loss reports. Unavailable data on specific topics There were several indicators which the team had wished to include in the assessment but which had to be discarded as it became apparent that the relevant data did not exist at the level of detail needed. This included indicators relating to land tenure, insurance penetration at the municipal level, gender-disaggregated education statistics, maternal mortality rates, and the percentage of household income available for investment into new crop types/innovative agricultural practice. Being able to include data regarding these indicators could improve the overall quality of the assessment. 37

Unavailable data for relevant timescales Timescales of data were also a challenge. The team always tried to use the most up-to-date datasets available. However, despite using data which was the most up to date, for several indicators this might not reflect the current situation on the ground. For example, some social indicators – such as access to clean water, education, and health – were based on 2011 census data, which the team were informed is the most current and official data. Much of the data regarding agricultural practices came from agricultural surveys done in 2012 and 2018. Limiting comparison to two time periods made it difficult to analyse longer term trends regarding agricultural practices.

Challenges obtaining hydrometrical data The team had hoped that by working through MAFWM it would be able to access data for individual municipalities based on previous vulnerability related work conducted by RHMSS, such as the MoI Disaster Risk Assessment. Initially, the team was informed that this data could not be shared as RHMSS was not a direct partner of the project. This stance was later amended through formal channels between MAFWM and RHMSS, although RHMSS stated they could only provide access to regional data, not municipal data such as that in the Disaster Risk Assessment. In the end, the team used a combination of data provided directly by RHMSS along with manually extracting the data from MoI’s Disaster Risk Assessment to inform several of the exposure indicators. The difficulties the team experienced when trying to access data between the two ministries is indicative of barriers to the sharing of basic data which could either support or hinder efforts to address the impacts of climate change. 38

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Recommendations

Based on analysis of the data acquired throughout this assessment, this report proposes a series of recommendations for reducing vulnerability to climate change in Serbia.

These include sub-national and local level actions for reducing vulnerability to the effects of climate change within communities, opportunities to improve how climate change vulnerability is addressed in proposed and existing policies at the national level, and suggested areas for further research. 40 Recommendations: Sub-national and community level actions Based on the results of this assessment, a number of concrete actions by local authorities and communities could help reduce their vulnerability to the effects of climate change.

• Focus agricultural trainings on identifying the most effective ways of supporting smallholder farmers to adapt to drought and higher temperatures. Temperature change is a key driver of vulnerability for all agriculture-based municipalities throughout all of Serbia and drought is also significant, particularly for the most vulnerable municipalities. All future initiatives should take this into account when planning climate change adaptation activities. At the local level, the impact of this on local farmers should be explored further and existing good practices identified. Nationally-led, locally-based trainings, workshops, and support activities should build on this to provide practical support to smallholder farmers so they have the knowledge and resources to adapt their farming practices. • Explore new approaches to promoting improved agricultural practices. Newer, more climate-sensitive practices do not seem to have taken root throughout Serbia, particularly regarding conservation in tillage methods. It would be beneficial to explore incorporating behaviour change communication and market system development approaches into agricultural trainings and campaigns to ensure understanding and effective adoption at local level. • Address water access issues in the most vulnerable municipalities. Access to water and basic services disproportionately affects the most vulnerable municipalities. When we compound this with the climate change possibilities regarding temperature change and drought, this issue has potential to really drive up levels of vulnerability with all current climate change scenarios. Decisive actions now have the potential to significantly reduce their vulnerability, particularly given their potential exposure to drought and high temperatures. Investment in improved access to drinking water and modernized irrigation systems which ensure universal access to water could help bridge the gap between the most and least vulnerable areas and increase farmers’ resilience to climate change. • Strengthen local government capacity in strategic planning and resource allocation. Wide variations in the capacity of local government to plan for the impacts of climate change offer opportunities for sharing best practices. Learnings from municipalities with strong contingency plans, updated development plans, and robust financial management systems can be used to develop standardised guidance for municipalities to support the development of strategic plans, ensure the resources to fund these and ensure they are regularly updated. Initially, this could focus on development of systems for collecting and allocating resources, then move to helping develop the systems for assuring that development and emergency/DRR plans are routinely developed and updated and that damage and loss data is accurately collected. This whole process should be based on a sustainable resource allocation lens. As these systems are developed, there would also be a lot of potential for cross-learning and sharing between municipalities with the highest and least capacity so that good practices regarding these processes could be shared and contextualized, as appropriate, for a particular municipality. • Increase investment in climate change adaptation practices at the municipal level. Wide variations in investment in climate change adaptation practices clearly contribute to increased vulnerability; this was particularly evident in municipalities which are very reliant on agricultural production but had not invested much in agrarian budgets in recent years. Ensuring a regular agrarian budget is set aside to promote innovations and improvements while undercutting some of the risks associated with innovations could help increase adaptation and reduce the impact of climate change over the long run. • Explore alternative livelihood options in municipalities with high reliance on agriculture. Several of the smaller, more vulnerable municipalities – such as Ražanj, Žabari and Veliko Gradište – rely heavily on the agricultural sector for employment and are also in the higher exposure risk category. It is worth examining the other industries in these municipalities to ascertain to what extent these are also reliant on agriculture. Exploring options for developing alternative livelihoods which are not so dependent on the agricultural sector would create local economies which are less vulnerable to climate change. 41 Recommendations: Policy level

There are a number of key policies and plans to which the findings of this assessment are relevant.

• Draft Law on Climate Change: At the national level, there are no set standards for collecting information regarding the impacts of temperature change and drought, and there are no standards or regulations which will support overcoming these challenges. The Draft Law on Climate Change presents an opportunity to address this. • The new law should have a focus on both climate change mitigation (reduction of CO2) and adaptation (reduction of impacts) actions, and it should include processes for setting clear plans for how this will be rolled out and for reporting on implementation at both the national and local level. • The law should set up a systemized process for the tracking of temperature change and the impacts of drought, clearly defining and linking the roles of different key Ministries (MAFWM, RHMSS, MoI) with regard to information sharing. • The law should be sure to incorporate and define how it will be linked with financial resources going forward. Resources should be earmarked so that information can be collected concisely from the ground level. A component of the Draft Law on Climate Change will require local communities to report on adaptation implementation actions. • Municipalities should review the Recommendations section of this assessment to identify key actions which may decrease their overall level of vulnerability. As this assessment is a multisectoral socioeconomic assessment and looked to quantify information on each municipality, it may be helpful in suggesting broader actions that municipalities could take. • Components of this assessment could be adapted into a baseline benchmark which municipalities could use to determine the impact their adaptation actions have on decreasing their overall climate change vulnerability. • Law on Disaster Risk Reduction and Emergency Management: This law requires all local municipalities to have Disaster Management plans in place. Best practice examples should be developed and shared which take into account learning from this assessment, specifically: • Local Disaster Management plans should include climate change monitoring and data collection as well as climate projections. • Local Disaster Management plans should include actions to support community adaptation. • Drought Strategy and National Drought Plan: Temperature change and drought are key drivers of vulnerability for agriculture-based municipalities throughout Serbia. • Currently, the development of a plan is still only a concept. Based on the findings of this report, which highlights that drought and temperature change will likely have a major impact on the agricultural sector, moving this concept forward and preparing a plan should be prioritized over the next year. • The National Drought Plan should be driven by community inputs and needs with regard to temperature change and drought. It should be developed in such a way as to take a strong look at the impacts of drought at the community level. In February 2020, a series of recommendations for development of the National Drought Plan of the Republic of Serbia was published by the United Nations Convention to Combat Desertification (UNCCD) and RHMSS.. There is also a rule book for making, issuing and delivering extraordinary meteorological and hydrological information and warnings (Kocic, 2013). Recommendations for the National Drought Plan define this as meteorological drought, hydrological drought, agricultural drought and socioeconomic drought, whereas the Official Gazette only seems to define in relation to meteorological and hydrological information. • Any new Drought Warning System should approach drought from these four perspectives (meteorological, hydrological, agricultural and socioeconomic), not only from meteorological and hydrological. To do this, it will need to incorporate information regarding agricultural drought. FAO has been tracking agricultural- based stress and droughts through its Global Information and Early Warning System (GIEWS). 42

As the National Drought Plan is developed, information from GIEWS, including its tools for monitoring, tracking and issuing warnings regarding agricultural stress, should be explored to feed into this. • Revision of Serbia’s National Adaptation Plan (NAP): Among ongoing activities, development of NAP identification of specific adaptation actions at the level of local communities is planned. Such actions rely on local level vulnerability assessments. • Key information from this assessment could be used to specifically incorporate climate change vulnerability into the NAP and highlight areas which should be prioritized for improvements regarding climate change. • National Platform for Disaster Risk Reduction: Officially recognized in January 2013, The National Platform for Disaster Risk Reduction has the mandate of coordination of the activities in the field of emergency management on national, regional and local level, as well as implementation of disaster risk reduction concepts into national and local policies, sustainable development strategies, and protection and rescue strategies. • Given the growing impacts of climate change, it could be beneficial to update this mandate with a broader focus in order to incorporate concepts related to adaptation to climate change. • Inter-Ministry Discussions on Climate Change: There are several ongoing discussions between key ministries in Serbia regarding climate change and its potential impacts. Once such discussion between Republic Hydrometeorological Service of Serbia (RHMSS) and Ministry of Interior (MoI) is focused on identifying municipalities which will be the most at risk from climate change based on specific climate scenarios. • Data from this assessment relating to exposure should be shared with key ministry counterparts for review as part of these ongoing discussions. • Overall findings of this assessment should be shared with key ministry counterparts for review as part of these discussions to support understanding of some of the wider ranging socioeconomic vulnerabilities which contribute to a location being more or less vulnerable. • MAFWM Flood Susceptibility Project: Within the Ministry of Agriculture, Forestry and Water Management (MAFWM), there is an ongoing project aiming to map locations which are particularly at risk of flooding. • Data from this assessment could be adjusted to only focus on flood-related data sets and additional flood-related datasets could be added, if necessary. This would support MAFWM to understand overall vulnerability, incorporating susceptibility and adaptive capacity as well as direct exposure to flood risk. By doing this, MAFWM would gain a stronger understanding of how a location could be impacted by flooding events. • Laws Relevant to Land Use and Soil Protection: One of the key findings related to indicator 2.10 was that much of Serbia has not incorporated conservation-based tillage practices throughout its agricultural sector. The possibility of climate change bringing more intense periods of precipitation to much of Serbia, compounded by unsustainable agricultural land use practices, could be seen as a growing risk in which arable land turns unfertile. • The Law on Agricultural Land (2006), Law on Environmental Protection (2009), Law on Spatial Planning and Construction (2009), Law on Forests (2010) and Law on Land Protection (2015) all have components which explicitly or implicitly relate to land use and soil protection. A review of these laws for ways to incorporate and amend them to include sustainable land use practices, specifically around tillage practices, could alleviate a portion of this risk going forward. 43 Recommendations: Further research

• Examine the role of local health systems in increasing vulnerability. This assessment identified that health factors seem to play a role in increasing susceptibility for the most vulnerable municipalities. Further research examining this in greater detail to link climate change, the agricultural sector and health trends could shed further light on these preliminary findings, particularly in the context of COVID-19. • Identify underlying drivers of vulnerability and resilience at the regional level. There seem to be clearly defined pockets of vulnerability that exist. These may be based on factors such as drainage basins or specific types of crop; efforts could focus on these thematic localizations and examine the underlying causes which drive vulnerability while examining why some municipalities in these local areas do not seem to be at such high levels of risk. Identifying these factors could lead to interesting cross-sharing and learning initiatives, along with the development of similar systems, which could in the long run drive climate change resilience. • Access to clear climatological data at the local level. Throughout this assessment, it became clear that data regarding climatological events and climate projections is only available at the national and regional levels. However, this level of data is not precise enough for farmers and municipal planners to accurately project how climate change will affect them. Further research on how best to make local level climatic data freely available and easy to understand for all would support future climate change adaptation initiatives at the local level. 44

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Conclusion

It is clear there is no single factor driving vulnerability for municipalities in Serbia. Instead, the more factors affecting a municipality in relation to the issues arising from a changing climate, the more their vulnerability is compounded.

Given the importance of agriculture to Serbia’s economy and food security, it is imperative that these issues are addressed. If at-risk municipalities do not take action to prepare for the impacts of climate change, the social and economic shocks will be much more severe, and they will be ill-equipped to respond to the resulting crises.

The positive finding is that where capacity is strong, vulnerability is reduced. This offers hope for areas most likely to feel the effects of climate change as there is meaningful action they can take to reduce their risk of experiencing considerable negative impacts from climate change in the future.

There are opportunities for local governments to learn from each other, identifying best practice systems to improve their planning. A renewed emphasis on the need for improved agricultural practices and adaptive actions can spur the sector into action, employing new and innovative strategies to influence change at the community, sub-national and national level. Investment in local infrastructure to support climate change preparedness can lead to wider benefits for communities.

With leadership at the national level and targeted support – both technical and financial – at the sub-national level, significant progress could be made in preparing Serbia’s smallholder farmers to withstand the impacts of climate change. 46 Bibliography

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Annex 2 – Indicators for measuring social and economic vulnerability to climate change Exposure

Number Indicator What Source 1.01 The average number of Measures the history of extreme rain events. MoI, Disaster Risk Assessment days with precipitation Analysis shows that when looking at climate (p. 339) greater than 30 mm, change, these events will become more prevalent 1981–2015 going forward. 1.02 Frequency of severe Measures the history of drought events. Analysis FAO, Earth Observation, Serbia drought, >30 percent of shows that when looking at climate change, these cropland affected for one events will become more prevalent going forward. complete season, 1984– 2018

1.03 Total number of Measures the history of drought events over the MoI, Disaster Risk Assessment occurrences of moderate, summertime in Serbia. (p. 360) severe and extreme drought during the summer in the analysed period, 1981–2015 1.04 Projected Heat Wave Extended heatwaves will have impacts on crops Data provided by RHMSS (WSI13) 2021–2050 (yields) and people and are one of the predicted directly in line with RHMSS Heat impacts of climate change going forward. Wave Index (WSI13) 2021–2050 1.05 Average annual number of Occurrences of hail can have huge financial impacts MoI, Disaster Risk Assessment days of hail, 1981–2015 with regard to agriculture. Predictive models have (p. 320) shown that one of the possible impacts of climate change on this region of the world will be increased frequency and intensity of hail events. 1.06 Mean hail frequency for Measures the history of extreme hail events. Data was taken from “Hail April–September, 1949–2012 Analysis shows that when looking at climate climatology in Serbia” (Ćurić change, these events will become more prevalent and Janc, 2015) reports (Figure going forward. 2: The mean hail frequency for the April–September period) and based on data provided by RHMSS. 1.07 Average value of the total Extended heat waves will have impacts on crops Data provided by RHMSS annual number of days of (yields) and people and are one of the predicted directly heat waves in the territory impacts of climate change going forward. of the Republic of Serbia, 1981–2015 1.08 2015–2019 Average This will show areas during the main growing Based on historic weather Temperature April to season which are annually already experiencing station data provided by RHMSS September as compared to temperature changes compared to historic data. and modelled in GIS using 1961–1990 Distance Weighted Interpolation 1.09 Average number days frost Increased levels of frost during the initial growing Data provided by RHMSS was reported in March– season could damage early season crops and stunt directly June, 1981–2015 crop annual yields. 1.10 Percentage of population This shows the population that would likely be the Data was based on information living in drought prone area most impacted by a drought going forward. collected through the FAO Municipality Questionnaire 2020 and the answers municipalities provided. 50

Number Indicator What Source 1.11 Percentage of population This shows the population that would likely be the Data was based on information living in flood prone area most impacted by flood events going forward. collected through the FAO Municipality Questionnaire 2020 and the answers municipalities provided. 1.12 Percentage of population This shows the population that would likely be the Data was based on information living in hail prone area most impacted by hail events going forward. collected through the FAO Municipality Questionnaire 2020 and the answers municipalities provided. 1.13 Hail damage and loss These are regions that historically have reported UNDRR DesInventar damage and loss from hail. 1.14 Drought damage and loss These are regions that historically have reported UNDRR DesInventar damage and loss from droughts. 1.15 Flood damage and loss These are regions that historically have reported UNDRR DesInventar damage and loss from flooding.

Sensitivity

Number Indicator What Source 2.01 Percentage of vulnerable Shows the age groups of those individuals who could be the SORS data, 2019 age groups (under 5, over most impacted by climate change. A larger number could 65) mean a larger societal impact. 2.02 Percentage of population in Shows the groups of those individuals who have some level SORS, 2016 Persons with vulnerable group living with of disability which could make them more susceptible to the Disability Report disability effects of climate change. A larger number could mean a larger social impact when faced with a changing climate. 2.03 Percentage without When facing natural hazards or climate risks, households World Banks Stats 2016, educational attainment and societies which have populations with a higher level based on 2011 census of education tend to be more empowered. These also data tend to be more adaptive in their response to, preparation for, and recovery from disasters, making them less at risk (Muttarak and Lutz, 2014; Striessnig, Wolfgang and Patt, 2013). As the opposite is also true, identification of communities with lower education levels allowed identification of those which may be more vulnerable. 2.04 Percentage of female (Gender) Measuring gender sensitivity. Where there is a SORS, Municipalities students not enrolled in larger level of gender equality, there is a larger chance that and Regions Report tertiary education, 2014– women will be affected by climate change. 2014–2019 2019 2.05 Percentage of household It can be assumed that if there is no water supply in the World Banks Stats 2016, with no water supply in dwelling, then other sanitation facilities will likely be limited. based on 2011 census dwelling When faced with both drought and/or floods, such as the data case with climate change, those households that do not already have access to water and sewer are more vulnerable to nutrition, vector borne disease and other issues as they arise. 2.06 Percentage of people Agriculture, forest, and fisheries will be the sector which will SORS, Municipalities and employed in agriculture, likely be the most impacted with regard to climate change. Regions Report 2019 (pp. forest and fishing 160–167) 51

Number Indicator What Source 2.07 Average annual net salaries Shows the discrepancies between different municipalities SORS, Municipalities and and wages with regard to wages. With climate change, this is an Regions Report 2019 (pp. indication that those who get a high wage will be able to 180–183) save money which they can use when faced with a crisis in the future. 2.08 Dependency ratio per Ratio of the non-working vs. working population (non- SORS, Municipalities and 1 000 people workers <15 years old and >65 years old vs. age 15–65 who Regions Report 2019 (pp. are employed), measuring the economic burden for social 156–159) policy and care but also intrapersonal networks 2.09 Unemployment level 2019 This ratio shows the rate of unemployment in 2019. Those SORS, Municipalities and areas with already large unemployment will likely have Regions Report 2019 (pp. growing issues as climate change starts to take place. 176–179) 2.10 Percentage of holdings A proxy to measure the current level of land deterioration SORS, 2012 UAA Survey not practicing improved and how local populations sustain soil. With climate change, land management through it will be vital that improved tilling and improved land conservative tillage management methods are put into place. methods 2.11 Crop Diversification United Nations Development Programme (UNDP) and Data provided by Ministry of Agriculture and Environment Protection (MoAEP) MAFWM directly report indicates that maize yields will be hardest hit by climate change and that there will be potential decline in wheat yields, sugar producing crops and fruit production. This score looks at crops across wheat, maize, sugar beet, soy, fruit and vegetable production, orchard and nuts, and tries to determine the level of reliance a municipality has on crops. 2.12 Percentage of total UNDP, MAFWM report indicates that maize yields will be the Data provided by cropland dedicated to hardest impacted crop in Serbia when faced with climate MAFWM directly corn/maize change. This indicator looks at those municipalities who currently rely on maize as their top cash crop. 2.13 Child Wellbeing Index Children are a more vulnerable group. This demonstrates UNICEF Child Wellbeing the municipalities’ emphasis on the health and wellbeing of Report the children. 2.14 Number of inhabitants per The ratio of medical professionals per capita can serve as SORS, Municipalities and one physician a proxy indicator which demonstrates the municipalities' Regions Report 2019 (pp. emphasis on the overall health and wellbeing. 332–335)

Capacity

Number Indicator What Source 3.01 Percentage protected land Demonstrates a municipality is willing to Based on data from The Institute for (forests, wetlands, grasslands, conserve a level of land and is actively Nature Conservation of Serbia etc.) working to mitigate climate change MoAEP and cross-refenced for FAO Municipality Survey 3.02 Local government ability to Demonstrates the level of local government FAO Municipality Questionnaire 2020 produce and implement local ability to implement new plans. With development strategies climate change, it will be important that local governments are able to do this themselves going forward. 52

Number Indicator What Source 3.03 Local government has a Demonstrates the level of local government Received data from MoI and cross- level of risk reduction/ ability to foresee risks and develop plans to refenced with data from FAO contingency/ mitigate these risks Municipality Questionnaire 2020 emergency plan which has been updated and signed off on in the last two years 3.04 Local government amount Demonstrates overall drive of local SORS, Municipality and Region report of investments in agriculture government to improve its agricultural sector 2016–2019 (agrarian budget) 3.05 Local government per capita With access to funds (taxation, municipal Based on data provided by The revenue for last eight years bonds, etc.), local government actually has Republic Secretariat for Public vulnerability score effective systems in place to raise funds, Policies when needed. 3.06 Local government surplus This demonstrates that local government can Based on data provided by The and deficit over last eight spend funds within the community. We are Republic Secretariat for Public years vulnerability score looking for a budget as close as possible to Policies balanced. 3.07 Degree of development of The score of development that the Serbian Based on data provided by The local self-government units Government has assigned to a local Republic Secretariat for Public government Policies 3.08 Difference of percentage of This shows the level of overall civil All data was based on SORS voters who voted on sub- involvement of a community. The more information regarding voting during national 2016 elections as involvement, the greater the likelihood that the 2016 sub-national elections. compared to national average the community will be able to pull together and support itself when faced with problems. 3.09 Number of locally operating The more active civil society is at a Based on data from the Open Data civil society organizations per community level, the better chances Portal Serbia, combined with data capita there are that adaptive practices could be provided by MAFWM regarding implemented. Also, should a larger scale agricultural associations climate-related event take place in the future, local government and civil society will be the first able to respond. 3.10 Percentage of farmers who Improved irrigation systems have been Based on data from FAO Municipality have access to recently proven to mitigate both the effects of drought Questionnaire 2020 improved irrigation systems in and flooding events. With climate change, the last ten years this will be important and shows that a community is working ahead to prepare. 3.11 Percentage of farmers Shows the level of a community to prepare Based on data from FAO Municipality practicing improved climate- for and adapt to a changing climate Questionnaire 2020 resilient agricultural practices (hybrid seeds, hail/shade nets, improved tillage methods, multi-purpose water supply, etc.) 53

Annex 3 – Municipalities’ individual vulnerability scores

Municipality Vuln. score Municipality Vuln. score Municipality Vuln. score 1 Ada 0.537704 39 Gadžin Han 0.574891 77 Medijana 0.459833 2 Aleksandrovac 0.610885 40 Golubac 0.604121 78 Medveđa 0.521692 3 Aleksinac 0.57567 41 Gornji Milanovac 0.533818 79 Merošina 0.664168 4 Alibunar 0.590839 42 Grocka 0.583111 80 Mionica 0.587366 5 Apatin 0.490049 43 Inđija 0.49632 81 Mladenovac 0.555964 6 Aranđelovac 0.515997 44 Irig 0.558952 82 Negotin 0.52064 7 Arilje 0.582826 45 Ivanjica 0.568319 83 Niška Banja 0.49928 8 Babušnica 0.51275 46 Jagodina 0.540546 84 Nova Crnja 0.611682 9 Bač 0.59984 47 Kanjiža 0.509146 85 Nova Varoš 0.57557 10 Bačka Palanka 0.518825 48 Kikinda 0.523448 86 Novi Bečej 0.520129 11 Bačka Topola 0.52149 49 Kladovo 0.566175 87 Novi Beograd 0.524477 12 Bački Petrovac 0.53955 50 Knić 0.540659 88 Novi Kneževac 0.493003 13 Bajina Bašta 0.525692 51 Knjaževac 0.525505 89 Novi Pazar 0.525808 14 Barajevo 0.567189 52 Koceljeva 0.624779 90 Novi Sad 0.480112 15 Batočina 0.560305 53 Kosjerić 0.535542 91 Obrenovac 0.559531 16 Bečej 0.580881 54 Kostolac 0.537411 92 Odžaci 0.576786 17 Bela Crkva 0.516182 55 Kovačica 0.570197 93 Opovo 0.543592 18 Bela Palanka 0.531254 56 Kovin 0.589476 94 Osečina 0.577288 19 Beočin 0.483165 57 Kragujevac 0.490663 95 Palilula 0.505945 20 Blace 0.542668 58 Kraljevo 0.561762 96 Palilula+ 0.477506 21 Bogatić 0.634749 59 Krupanj 0.598171 97 Pančevo 0.491108 22 Bojnik 0.58698 60 Kruševac 0.572031 98 Pantelej 0.501333 23 Boljevac 0.55879 61 Kučevo 0.566358 99 Paraćin 0.617203 24 Bor 0.463841 62 Kula 0.518038 100 Pećinci 0.500626 25 Bosilegrad 0.549894 63 Kuršumlija 0.489227 101 Petrovac na Mlavi 0.597296 26 Brus 0.631053 64 Lajkovac 0.588223 102 Petrovaradin 0.451539 27 Bujanovac 0.562841 65 Lapovo 0.571901 103 Pirot 0.453157 28 Čačak 0.519047 66 Lazarevac 0.580722 104 Plandište 0.502739 29 Čajetina 0.540861 67 Lebane 0.545413 105 Požarevac 0.517469 30 Ćićevac 0.589476 68 Leskovac 0.550358 106 Požega 0.491126 31 Čoka 0.557717 69 Ljig 0.549896 107 Preševo 0.631215 32 Crna Trava 0.490666 70 Ljubovija 0.544504 108 Priboj 0.519465 33 Crveni krst 0.517494 71 Loznica 0.513633 109 Prijepolje 0.569112 34 Čukarica 0.491986 72 Lučani 0.527415 110 Prokuplje 0.537534 35 Ćuprija 0.53276 73 Majdanpek 0.520359 111 Rača 0.59079 36 Despotovac 0.567764 74 Mali Iđoš 0.535487 112 Rakovica 0.487525 37 Dimitrovgrad 0.442668 75 Mali Zvornik 0.562061 113 Raška 0.500334 38 Doljevac 0.567746 76 Malo Crniće 0.658469 114 Ražanj 0.618617 54

Municipality Vuln. score Municipality Vuln. score Municipality Vuln. score 115 Rekovac 0.657934 133 Stari grad 0.480565 152 Vladimirci 0.647863 116 Ruma 0.493893 134 Subotica 0.525112 153 Vlasotince 0.541121 117 Šabac 0.487897 135 Surčin 0.553421 154 Voždovac 0.476411 118 Savski venac 0.483181 136 Surdulica 0.497238 155 Vračar 0.476704 119 Sečanj 0.504278 137 Svilajnac 0.574286 156 Vranje 0.476781 120 Senta 0.536154 138 Svrljig 0.612323 157 Vranjska Banja 0.500576 121 Sevojno 0.533724 139 Temerin 0.55434 158 Vrbas 0.530743 122 Šid 0.503459 140 Titel 0.566654 159 Vrnjačka Banja 0.524731 123 Sjenica 0.519516 141 Topola 0.51714 160 Vršac 0.443567 124 Smederevo 0.541604 142 Trgovište 0.545084 161 Žabalj 0.557763 125 Smederevska 0.570817 143 Trstenik 0.586466 162 Žabari 0.682979 Palanka 144 Tutin 0.622259 163 Žagubica 0.651012 126 Sokobanja 0.554104 145 Ub 0.557714 164 Zaječar 0.422965 127 Sombor 0.505441 146 Užice 0.536056 165 Zemun 0.532977 128 Sopot 0.580147 147 Valjevo 0.535697 166 Žitište 0.639488 129 Srbobran 0.538621 148 Varvarin 0.625663 167 Žitorađa 0.624683 130 Sremska 0.485652 149 Velika Plana 0.602202 Mitrovica 168 Zrenjanin 0.490798 150 Veliko Gradište 0.617739 131 Sremski Karlovci 0.495216 169 Zvezdara 0.45671 151 Vladičin Han 0.556507 132 Stara Pazova 0.495508

Annex 4 – Most and least vulnerable by theme Most Least

Total Vulnerability Total Vulnerability Territory Territory exposure score exposure score

Nova Varoš 0.771491454 0.575569886 Pirot 0.445531973 0.453156548 Aleksandrovac 0.758039344 0.610884946 Sremska Mitrovica 0.456748962 0.485652438 Žagubica 0.74375006 0.651012496 Bogatić 0.472315618 0.634749169 Brus 0.743444345 0.631052558 Šabac 0.47572136 0.48789683 Ivanjica 0.730173564 0.568319213 Zaječar 0.482159208 0.422964719 Čajetina 0.729937685 0.54086067 Loznica 0.484928541 0.513632896 Arilje 0.720334262 0.582825702 Prokuplje 0.488703375 0.537533782 Tutin 0.717123382 0.622259253 Vranje 0.500064728 0.47678063 Merošina 0.712203255 0.664167888 Aleksinac 0.535487281 0.575669604 Žabari 0.699383669 0.682978578 Doljevac 0.53912266 0.5677457 Užice 0.698252343 0.53605598 Blace 0.539576955 0.542668131 Ljubovija 0.687394909 0.54450419 Topola 0.542837051 0.517140248 Velika Plana 0.681443172 0.602201954 Vrnjačka Banja 0.546918949 0.524731382 Preševo 0.680929878 0.631215117 Titel 0.548527262 0.566654449 Gornji Milanovac 0.680672198 0.533818139 Mladenovac 0.550577615 0.555964445 55

Most Least

Total Vulnerability Total Vulnerability Territory Territory susceptibility score susceptibility score

Vladimirci 0.701018702 0.647863363 Užice 0.283621709 0.53605598 Bogatić 0.684024712 0.634749169 Vranje 0.323666064 0.47678063 Malo Crniće 0.668888281 0.658469419 Bor 0.324016742 0.463840665 Varvarin 0.635225111 0.625663079 Novi Pazar 0.325844222 0.525808449 Žabari 0.619687184 0.682978578 Valjevo 0.339900654 0.535696722 Koceljeva 0.611984446 0.624779393 Gornji Milanovac 0.341665842 0.533818139 Rekovac 0.609243851 0.657933704 Aranđelovac 0.345655779 0.515997174 Petrovac na Mlavi 0.605861043 0.597295785 Zaječar 0.349024153 0.422964719 Žitorađa 0.594580188 0.624683085 Požega 0.349403136 0.491126236 Veliko Gradište 0.579506372 0.617738985 Čajetina 0.352791891 0.54086067 Ub 0.552266434 0.557713708 Čačak 0.353035608 0.519047099 Bojnik 0.550451328 0.586980149 Prijepolje 0.354036433 0.569111764 Titel 0.548938454 0.566654449 Pećinci 0.354058861 0.500626479 Svilajnac 0.545590082 0.574285548 Nova Varoš 0.356041425 0.575569886 Doljevac 0.545127461 0.5677457 Bajina Bašta 0.357010804 0.525692127

Most Least

Total Vulnerability Total Vulnerability Territory Territory capacity score capacity score Prijepolje 0.878619438 0.569111764 Crveni krst 0.32501254 0.517494498 Tutin 0.829828809 0.622259253 Požarevac 0.426848934 0.51746856 Merošina 0.823041339 0.664167888 Zaječar 0.445083835 0.422964719 Bogatić 0.80448618 0.634749169 Bačka Palanka 0.47061963 0.518825149 Prokuplje 0.800621738 0.537533782 Topola 0.481443028 0.517140248 Rekovac 0.778147583 0.657933704 Ćuprija 0.487709383 0.532760355 Preševo 0.768451013 0.631215117 Šabac 0.49981009 0.48789683 Žagubica 0.756347216 0.651012496 Požega 0.503437987 0.491126236 Žabari 0.753308034 0.682978578 Bor 0.511556408 0.463840665 Lazarevac 0.751404832 0.580721849 Pećinci 0.514537068 0.500626479 Aleksinac 0.748286723 0.575669604 Lapovo 0.516705512 0.571900822 Kruševac 0.73786154 0.572030851 Čajetina 0.539348313 0.54086067 Valjevo 0.733343722 0.535696722 Ub 0.542902978 0.557713708 Vladičin Han 0.728776841 0.556507291 Negotin 0.543254066 0.520639675 Svrljig 0.721459129 0.612322788 Ražanj 0.546771009 0.618616517 56

Annex 5 – FAO 2020 municipality questionnaire questions 57

Упитник за локалне самоуправе о рањивости на промене климе Local government climate change vulnerability questionnaire

Овај упитник израђен је за потребе пројекта „Изградња отпорности сектора пољопривредe на природне катастрофе и утицаје климатских промена“. Пројекат спроводе Министарство пољопривреде, шумарства и водопривреде, Канцеларија за управљање јавним улагањима и Организација Уједињених нација за храну и пољопривреду (ФАО). Питања у анкети биће коришћена за процену, рангирање и анализу различитих нивоа угрожености од климатских промена са којима се општине широм Србије суочавају. The following questionnaire has been developed as a part of the project Building Resilience of Agricultural Sector to Natural Disasters and Climate Change Impacts. This project is being implemented by the Ministry of Agriculture, Forestry and Water Management, Public Investment Management Office and The Food and Agriculture Organization of the United Nations (FAO). Questions in the survey will be used to assess, rank and analyse the different levels of vulnerability to climate change which municipalities throughout Serbia face going forward.

Попуњени упитници неће бити појединачно објављивани. Completed questionnaires will not be published individually.

Молимо Вас да на крају сваког одељка наведете извор података који сте користили (да ли су званични подаци или Ваша/ експертска процена). Please indicate at the end of each section the source of the data (whether it is official data or your/еxpert estimate).

Уколико имате додатних питања, молимо Вас да контактирате ФАО сараднице, Љиљану Исић на [email protected] или телефоном на 064/4047176, или Данијелу Божанић на [email protected] или телефоном на 064/1847477. Should you have additional questions, please contact FAO Consultants, Ljiljana Isić at [email protected] or by phone at 064/4047176, or Danijela Božanić at [email protected] or by phone at 064/1847477.

Email адреса* Обавезно поље Email address* Required

1.1 Назив града/општине Name of the City/Municipality

1.2. Датум уноса података Date information was entered

1.3. Име и презиме особе која попуњава упитник Person entering information

1.4. Званична позиција особе која попуњава упитник Official Position

1.5. Телефон за контакт Contact Phone Number

2. Основни подаци Basic information 2.1. Наведите тренутни укупан број становника града/општине. Please state the total current population of the city/ municipality.

2.2. Наведите укупан број становника града/општине 2011. године. Please state the total population of the municipality in 2011. 58

2.3. Наведите укупан број становника града/општине 2002. године. Please state the total population of the municipality in 2002.

2.4. Наведите укупну површину територијe града/општине (ha). Please state the total area (ha) of the municipality.

2.5. Наведите укупан број регистрованих пољопривредних газдинстава на територији општине. Please state the total number of registered agricultural holdings within the municipality.

2.6. Наведите укупан процењен број нерегистрованих пољопривредних газдинстава на територији општине. Please state the total estimated number of non-registered agricultural holdings within the municipality.

2.7. Извор података: Data source:

3. Земљиште и сточни фонд Land and livestock 3.1. Наведите површину коришћеног пољопривредног земљишта, у просеку по газдинству (ha), за последњих 10 година, по години. Please state utilized agricultural area (ha) on average per holding, for the last ten years, by year. Одговор унесите за период 2010–2019, наводећи годину, а затим површину. Користите посебан ред за сваку годину. Enter the answer for the period 2010–2019, stating the year and then the area. Use a separate line for each year. Пример:

2010: 7,82 2015: 6,12 2011: 7,30 2016: 6,08 2012: 6,60 2017: 5,51 2013: 6,74 2018: 5,85 2014: 6,93 2019: 5,77

3.2. Наведите број условних грла стоке, у просеку по газдинству, за последњих 10 година, по години. Please state livestock unit on average per holding, for the last ten years, by year. Условно грло је прописана јединица за поређење међу врстама и категоријама домаћих животиња. То је животиња или скуп животиња тежине 500 kg, рачунајући највећу тежину производне категорије. Livestock Unit is an official unit for comparison between species and categories of domestic animals. It is an animal or group of animals weighing 500 kg, counting the maximum weight of the production category. Одговор унесите за период 2010–2019, наводећи годину, а затим број условних грла. Користите посебан ред за сваку годину. Enter the answer for the period 2010–2019, stating the year and then the number of the Livestock Unit. Use a separate line for each year. Пример:

2010: 3,8 2015: 4,1 2011: 3,4 2016: 4,0 2012: 3,4 2017: 3,8 2013: 3,7 2018: 3,8 2014: 3,6 2019: 3,7

3.3. Извор података: Data source: 59

4. Становништво у ризику од елементарних непогода Population at risk of natural disasters

4.1. Наведите проценат становништва за који сматрате да је у највећем ризику од будућих појава суше. Please indicate the percentage of the population you would consider to be the most at risk of being impacted by future drought events. Могу бити и домаћинства или подручја код којих је већ уочена појава недостатка расположиве воде. There may also be households or areas where a shortage of available water has already been observed.

4.2. Наведите проценат становништва за који сматрате да је у највећем ризику од будућих појава поплаве. Please indicate the percentage of the population you would consider to be the most at risk of being impacted by future flood events.

4.3. Наведите проценат становништва за који сматрате да је у највећем ризику од будућих појава града. Please indicate the percentage of the population you would consider to be the most at risk of being impacted by future hail events.

4.4. Извор података: Data source:

5. Планирање Planning 5.1. Наведите датуме, током последњих 10 година, када сте развили или ажурирали локалне развојне планове/ програме или стратегије о кључним темама. Please list the dates over the last ten years you have updated your local development plans or strategies on key topics. Користите посебан ред за сваку ставку. Use a separate line for each item.

Пример: 14.03.2011. Стратегија развоја пољопривреде и села општине... 28.04.2016. Стратегија развоја социјалне заштите града .... за период од 2016. до 2020. године 02.06.2018. ревизија Стратегије ордживог развоја општине .... за период од 2014. до 2020. године 20.06.2019. усвојен Програм електрификације сеоских подручја ......

5.2. Наведите датуме и кључне теме, током последњих 10 година, када сте развили или ажурирали локалне планове за смањење ризика од катастрофа, за ванредне ситуације итд. Please list the dates and key topics over the last ten years when you have developed or updated your local disaster risk reduction, emergency, contingency plans, etc. Ако ажурирања укључују уочене или пројектоване климатске промене, молимо Вас да на крају описа додате „**” (види пример). Користите посебан ред за сваку ставку. If updates include concepts around observed or projected climate change, please mark with “**” at the end of the description (see example). Use a separate line for each item.

Пример: 07.11.2015. усвојена Процена угрожености од елементарних непогода и других несрећа 20.06.2019. усвојен План адаптације на измењене климатске услове** 20.11.2019. Ажурирана листа контаката за хитне случајеве ......

5.3. Извор података: Data source: 60

6. Финансије града/општине City/municipality finances 6.1. Наведите укупнe годишњe расходе на нивоу града/општине, за последњих 6 година, по годинама, у динарима. State the total annual expenditures (RSD) at the city/municipality level, for the last six years, by year. Одговор унесите за период 2014–2019, наводећи годину, а затим износ. Користите посебан ред за сваку годину. Enter the answer for the period 2014–2019, stating the year and then the amount. Use a separate line for each year.

ПрПример: 2014: 4222111000 2017: 3888777666 2015: 4123654789 2018: 4999888777 2016: 4222333444 2019: 5111222333

6.2. Наведите износ годишњих градских/општинских финансијских средстава која су пласирана у рурални развој (водовод, канализација, асфалтирање путева), појединачно за последњих 6 година, у динарима. Please list the amount (RSD) of city/municipal funding which has been put towards rural development (water supply system, sewerage, asphalting of roads), annually over the last six years. Одговор унесите за период 2014–2019, наводећи годину, а затим износ. Користите посебан ред за сваку годину. Enter the answer for the period 2014–2019, stating the year and then the amount. Use a separate line for each year. Пример: 2014: 43111000 2017: 66777666 2015: 34654789 2018: 77888777 2016: 33333444 2019: 22222333

6.3. Наведите износ годишњих градских/општинских финансијских средстава која су пласирана директно у пољопривредну производњу (улагања у основна средства), појединачно за последњих 6 година, у динарима. Please list the amount (RSD) of city/municipal funding which has been put towards agricultural production (investment in fixed assets), annually over the last six years. Одговор унесите за период 2014–2019, наводећи годину, а затим износ. Користите посебан ред за сваку годину. Enter the answer for the period 2014–2019, stating the year and then the amount. Use a separate line for each year. Пример: 2014: 33111000 2017: 44777666 2015: 33654789 2018: 66888777 2016: 22333444 2019: 55222333

6.4. Извор података: Data source:

7. Пољопривредно осигурање Agricultural insurance 7.1. Да ли ви, као градскa/општинска управа, дајете субвенције за пољопривредно осигурање? Do you, as the city/municipal government, provide subsidies for agricultural insurance? Да / Не Yes/No

7.2. Ако је то случај, наведите процењени годишњи износ (РСД) који сте обезбедили у последњих 10 година, као и број газдинстава који су добили ову подршку, по години. If so, please list the estimated amount per year you have provided over the last ten years, along with the number of holdings receiving this support. Одговор унесите за период 2010–2019, наводећи годину, а затим износ и број газдинстава. Користите посебан ред за сваку годину. Enter the answer for the period 2014–2019, stating the year and then the amount and number of AH. Use a separate line for each year. Пример: 2010: 400000 РСД, 40 газдинстава 2015: 580000 РСД, 45 газдинстава 2011: 360000 РСД, 32 газдинства 2016: 600000 РСД, 38 газдинстава 2012: 340000 РСД, 30 газдинстава 2017: - 2013: - 2018: 380000 РСД, 30 газдинстава 2014: - 2019: 750000 РСД, 60 газдинстава 61

7.3. По Вашим сазнањима, који проценат становништва Вашег града/општине има додатно приватно осигурање (осигурање усева, осигурање од одговорности, животно осигурање итд.). Please estimate the percentage of the population living in your municipality that has additional private insurance (crop insurance, liability insurance, life insurance, etc.).

7.4. Извор података: Data source:

8. Системи за наводњавање Irrigation systems 8.1. Наведите проценат пољопривредних газдинстава која су у последњих 10 година добила приступ унапређеним системима за наводњавање, укључујући и она која ће имати приступ системима који су тренутно у процесу унапређења. Please indicate the percentage of agricultural holdings that have had access to recently improved or currently improving irrigation systems over the last ten years.

8.2. Да ли имате обезбеђена финансијска средства за побољшање или проширење система за наводњавање у наредних 5 година? Do you have any finances secured to improve or extend irrigation systems over the next five years? Да / Не Yes/No

8.3. Наведите укупан износ обезбеђених финансијских средстава за побољшање или проширење система за наводњавање у наредних 5 година, у динарима. Indicate the total amount (RSD) of funds provided for the improvement or extension of irrigation systems in the next five years.

8.4. Наведите очекивани ниво побољшања (број додатних газдинстава која ће имати приступ систему, побољшање ефикасности итд.). Indicate the expected level of improvement (number of additional farms that will have access to the system, efficiency improvement, etc.)

8.5. Извор података: Data source:

9. Побољшане пољопривредне праксе Improved agricultural practices 9.1. Наведите укупан проценат пољопривредних газдинстава за која сматрате да примењују напредне пољопривредне праксе (сетва хибридних семена, употреба противградних мрежа/ мрежа за засену, побољшане методе обраде земље, развој вишенаменског водоснабдевања, добровољно осигурање итд.) Please indicate an estimate of the percentage of total holdings that have taken part in improved agricultural practices (planting hybrid seeds, using hail/shade nets, improved tillage methods, developing multi-purpose water supply, voluntary insurance, etc.).

9.2. Да ли планирате да у наредних 5 година обезбедите финансијска средства за подстицање примене напредних пољопривредних пракси у циљу смањења утицаја климатских промена? Do you plan to provide funding in the next five years to encourage the application of advanced agricultural practices in order to reduce the impact of climate change? Да / Не Yes / No

9.3. Извор података: Data source: 62

10. Заштићена подручја Protected areas 10.1. Наведите укупну површину земљишта у Вашој општини које је заштићено званичним програмима (ha). То може укључивати, али није ограничено на: општи резерват природе, специјални резерват природе, национални парк, заштићено станиште, предео изузетних одлика, парк природе итд. Please state the total amount of land in hectares designated as protected through official programs in your municipality. (This can include but is not limited to: nature reserve, special nature reserve, national park, protected habitats, landscapes of outstanding features, nature park, etc.)

10.2. Извор података: Data source:

11. Елементарне непогоде Natural disasters 11.1. Наведите елементарне непогоде које су се догодиле на територији Вашег града/ општине у последњих 10 година, заједно са вредношћу укупне процењене штете, у динарима. Over the last ten years, can you please list the natural disasters which have occurred in your city/municipality, along with a financial estimation of the amount of loss (RSD). Наведите датум и тип непогоде, а затим укупан износ процењене штете. Користите посебан ред за сваку непогоду. Indicate the date and type of disaster and then the total amount of estimated damage. Use a separate line for each event. Пример: Догађај: 24.05.2014., поплава Финансијска штета: 1.250.648.000 Example: Event: 24.05.2014., flood Financial Damage: 1.250.648.000 Догађај: 13.06.2018., град, олуја Финансијска штета: 822.100.000 Догађај: Финансијска штета: Догађај: Финансијска штета: … …

11.2. Извор података: Data source:

Хвала!!! Thank you!!! 63 Annex 6 – Full final results of 90 key municipalities

Total Territory Total population Vulnerability score Total suscepti Total 1.01 1.02 1.03 1.04 1.05 1.06 1.07 1.08 1.09 1.1 1.11 1.12 1.13 1.14 1.15 exposure 2.01 2.02 2.03 2.04 2.05 2.06 2.07 2.08 2.09 2.1 2.11 2.12 2.13 2.14 bility 3.01 3.02 3.03 3.04 3.05 3.06 3.07 3.08 3.09 3.1 3.11 capacity Žabari 9585 0.682978578 0.333333 0.642857 0.9 0.4 0.4 0.934418 0.840472 0.521819 0.77219 0.95 0.15 0.3 0.2 0.2 0.2 0.699384 0.769544 0.821082 0.262455 0.694524 0.335414 0.714784 1 0.131453 0.138017 0.958048 0.6 0.56948 0.91755 0.676768 0.619687 0 1 1 1 0.6 0.2 0.8 0.8 0.108975 1 0.6 0.753308 Merošina 12963 0.664167888 0.333333 0.714286 0.9 0.2 0.4 0.92895 0.853994 0.459442 0.798521 0.8 0.22 0.43 0.6 1 0.4 0.712203 0.695098 0.580354 0.665217 0.563494 0.407744 0.191432 0.8 0.069777 0.180861 0.995156 0.4 0.599616 0.886573 0.719503 0.510217 0.025181 1 0.733333 1 0.8 0.2 1 0.2 0.07466 1 1 0.823041 Malo Crniće 9903 0.658469419 0.5 0.642857 1 0.4 0.35 0.934418 0.840472 0.356248 0.77219 0.9 0.4 0.3 0.6 0.2 1 0.642248 0.693301 0.705384 0.223351 0.694524 0.437071 0.970932 0.8 0.10336 0.155617 0.866666 0.8 0.647495 0.994357 0.641026 0.668888 0 0.6 0.2 1 0.6 0.6 0.8 1 0.120533 1 0.6 0.667174 Rekovac 9270 0.657933704 0.5 0.642857 1 0.4 0.35 0.914335 0.820016 0.431386 0.793514 0.5 0.3 0.8 0.6 0.2 1 0.626481 0.853958 0.905771 0.505005 0.22697 0.833013 0.717154 0.8 0.134648 0.151166 0.951351 0.2 0.483668 0.897513 0.480186 0.609244 5.9E-05 1 0.933333 1 0.6 0.6 0.8 0.2 0.081585 1 0.6 0.778148 Žagubica 11348 0.651012496 0.5 0.642857 1 0.2 0.3 0.934418 0.840472 0.761106 0.77219 0.9 0.8 0.01 0.2 0.2 1 0.74375 0.778548 0.790583 0.55198 0.694524 0.459957 0.578533 0.2 0.100345 0.156798 0.982457 0.2 0.271072 0.819668 0.48951 0.488052 0.001504 0.8 0.8 1 0.4 0.2 0.8 0.2 0.130558 1 1 0.756347 Vladimirci 15818 0.647863363 0.5 0.214286 1 0.2 0.5 0.901906 0.801652 0.608787 0.702901 0.89 0.6 0.9 0.6 0.6 0.6 0.652104 0.653006 0.610611 0.494056 0.427187 0.767812 0.837831 0.8 0.046829 0.198879 0.985209 0.8 0.80195 0.854215 0.878011 0.701019 0 0.2 0.4 0.4 1 0.2 0.6 0.2 0.05165 0.8 1 0.561769 Bogatić 26630 0.634749169 0.5 0.214286 0.9 0.2 0.45 0.901906 0.801652 0.507437 0.702901 0.3 0.2 0.15 0.2 0.8 0.8 0.472316 0.581515 0.498187 0.384083 0.427187 0.588124 1 0.8 0.027363 0.185292 0.931853 0.8 0.844817 0.87287 0.667444 0.684025 0.003624 0.8 0.8 1 1 0.8 0.6 0.2 0.078067 1 0.8 0.804486 Preševo 29989 0.631215117 0.333333 0.785714 1 0.6 0.7 0.973202 1 -0.40954 0.952964 0.8 0.1 0.6 0.2 0.2 0.4 0.68093 0.518326 0.03905 0.479839 0.435259 0.665103 0.028081 0.8 0.004536 1 1 0.4 0.379186 0.848111 0.54157 0.49001 0 1 1 1 0.8 0.2 1 1 0.622266 1 0.4 0.768451 Brus 14814 0.631052558 0.5 0.785714 0.9 1 0.45 0.952736 0.827112 1 0.850891 0.05 0.1 0.9 0.6 0.2 0.2 0.743444 0.6543 0.73215 0.629989 0.653547 0.164929 0.449524 0.8 0.044799 0.217738 0.977386 0.4 0.190485 0.811147 0.547786 0.474095 0.076901 1 1 1 0.6 0.4 0.8 0.2 0.088094 1 0.2 0.697901 Varvarin 16340 0.625663079 0.333333 0.785714 1 0.4 0.35 0.952736 0.827112 0.485975 0.850891 0.2 0.1 0.4 0.6 0.2 0.6 0.6506 0.672709 0.804233 0.378356 0.653547 0.461672 0.831649 1 0.054878 0.169666 0.999254 0.4 0.639704 0.864233 0.705517 0.635225 0 1 0.866667 1 1 0.2 0.8 0.2 0.076871 0.2 0.2 0.573915 Koceljeva 11844 0.624779393 0.5 0.285714 0.9 0.6 0.5 0.901906 0.801652 0.629936 0.702901 0.7 0.4 0.9 0.8 0.8 1 0.603651 0.603891 0.486122 0.553738 0.427187 0.621454 0.617899 0.8 0.055501 0.214298 0.996042 0.6 0.713349 0.881506 0.707848 0.611984 0 0.8 0.8 1 0.6 0.8 0.6 0.2 0.064403 0.8 0.4 0.675664 Žitorađa 14980 0.624683085 0.333333 0.214286 1 0.4 0.35 0.966888 0.871088 0.439433 0.875621 0.8 0.3 0.4 0.6 0.2 0.6 0.634149 0.683366 0.659091 0.616803 0.684213 0.381635 0.493588 0.8 0.060652 0.178803 0.958486 0.8 0.594364 0.891525 0.684538 0.59458 0 1 0.8 0.8 1 0.2 1 0.2 0.053381 0.6 0.4 0.655639 Tutin 31670 0.622259253 0.333333 0.071429 1 0.2 0.65 0.960726 0.937572 0.683422 0.950689 1 0.7 0.7 0.2 0.2 0.2 0.717123 0.440648 0.338575 0.326638 0.555573 0.235201 0.393169 0.8 0.033619 0.135521 0.998622 0.2 0.006256 1 0.57265 0.389015 0.006883 1 0.733333 1 0.8 0.2 1 0.8 0.06785 1 1 0.829829 Ražanj 7998 0.618616517 0.333333 0.785714 1 0.2 0.35 0.92895 0.853994 0.488289 0.798521 0.35 0.25 0.4 0.2 0.6 0.6 0.673658 0.806106 0.749131 0.434521 0.563494 0.423061 0.717181 1 0.146414 0.156271 0.983354 0.4 0.604578 0.949217 0.51826 0.432262 7.47E-07 0.6 0.333333 1 0.4 0.2 0.8 0.2 0.110892 1 0.2 0.546771 Veliko Gradište 15966 0.617738985 0.333333 0.642857 0.9 0.2 0.3 0.934418 0.840472 0.224954 0.77219 0.95 0.15 0.01 0.4 0.2 0.4 0.672222 0.680933 0.493246 0.183354 0.694524 0.145149 0.680639 0.8 0.054322 0.17803 0.92313 0.8 0.755097 0.824044 0.516706 0.579506 2.2E-06 0.6 0.2 1 0.4 0.2 0.6 1 0.131242 1 0.6 0.593364 Paraćin 50798 0.617203026 0.333333 0.642857 0.9 0.2 0.3 0.914335 0.820016 0.574226 0.793514 0.95 0.55 0.98 0.6 0.2 1 0.675691 0.600665 0.548961 0.250368 0.22697 0.198793 0.130286 0.6 0.01501 0.178756 0.994135 0.8 0.825945 0.835905 0.328671 0.490398 0.000376 1 0.933333 1 1 0.6 0.6 0.2 0.113276 1 0.2 0.719679 Svrljig 12557 0.612322788 0.333333 0.785714 0.9 0.4 0.4 0.92895 0.853994 0.457899 0.798521 0.5 0.2 0.4 0.8 1 0.4 0.67044 0.8929 0.830725 0.255822 0.563494 0.509692 0.057564 1 0.086754 0.183075 0.995221 0.2 0.396169 0.789383 0.487956 0.432262 0.002391 0.8 0.2 1 0.4 0.6 1 0.2 0.116704 1 0.8 0.721459 Aleksandrovac 24223 0.610884946 0.5 0.785714 1 0.6 0.4 0.952736 0.827112 0.771247 0.850891 0.7 0.2 0.77 0.6 0.2 0.4 0.758039 0.627108 0.743267 0.419105 0.653547 0.171747 0.418943 0.8 0.027994 0.208882 0.99593 0.2 0.44306 0.778443 0.62704 0.476251 0.000545 0.6 1 0.8 1 0.2 0.4 0.2 0.079703 1 0.2 0.592105 Velika Plana 38000 0.602201954 0.333333 1 0.9 0.4 0.4 0.905466 0.805952 0.527098 0.659837 0.8 0.53 0.3 0.2 0.2 0.6 0.681443 0.58071 0.628236 0.197612 0.054834 0.361382 0.172247 0.4 0.018731 0.188781 0.936827 0.8 0.688728 0.845233 0.703186 0.432262 0.003442 0.6 0.933333 0.8 1 0.2 0.6 0.8 0.095094 0.8 0.6 0.652639 Krupanj 15602 0.598171059 0.833333 0.214286 0.9 0.6 0.55 0.901906 0.801652 0.55742 0.702901 0.4 0.6 0.3 1 0.8 1 0.618919 0.549846 0.613584 0.55513 0.427187 0.355421 0.546469 0.8 0.045987 0.173884 0.991561 0.4 0.522894 0.934477 0.932401 0.538966 0.000244 0.4 0.4 1 0.8 0.4 0.8 0.2 0.061927 0.6 1 0.655856 Petrovac na Mlavi 28157 0.597295785 0.5 0.642857 0.9 0.2 0.35 0.934418 0.840472 0.519812 0.77219 0.3 0.2 0.2 1 0.2 0.4 0.574944 0.706407 0.824992 0.273609 0.694524 0.31172 0.714476 0.8 0.029351 0.19129 0.938475 0.6 0.718846 0.818402 0.299922 0.605861 2.31E-06 0.6 0.533333 0.8 0.8 0.6 0.8 0.8 0.094674 1 0.2 0.617976 Rača 10467 0.590789625 0.333333 0.642857 0.9 0.4 0.35 0.887022 0.843738 0.517311 0.747376 0.4 0.2 0.2 0.4 0.2 1 0.578012 0.637877 0.662782 0.189507 0.436242 0.471534 0.405191 0.6 0.066046 0.20347 0.985474 0.6 0.501274 0.857439 0.542347 0.529292 0.001991 0.8 0.8 1 0.4 0.4 0.4 0.2 0.137213 1 0.8 0.702203 Ćićevac 8606 0.589475541 0.333333 0.785714 1 0.2 0.35 0.952736 0.827112 0.46341 0.850891 0.342873 0.234358 0.376395 0.6 0.2 0.8 0.655899 0.618811 0.631642 0.274269 0.653547 0.279 0.129965 0.8 0.077507 0.208929 0.995154 1 0.443718 0.924228 0.55711 0.525855 0 0.6 0.666667 1 0.6 0.2 0.6 0.2 0.13682 0.6 0.6 0.585271 Lajkovac 14721 0.588222565 0.666667 0.428571 1 0.2 0.5 0.871622 0.780821 0.641534 0.767085 0.95 0.3 0.8 1 0.2 1 0.654917 0.557806 0.43873 0.336694 0.363565 0.407275 0.114531 0.2 0.038671 0.22483 0.996268 0.6 0.670172 0.89544 0.602176 0.46102 0.001853 0.8 0.533333 1 0.2 0.8 0.2 0.2 0.063746 1 0.8 0.678986 Mionica 13080 0.587366095 0.833333 0.428571 1 0.4 0.55 0.871622 0.780821 0.631351 0.767085 0.7 0.3 1 0.2 0.2 1 0.65698 0.665345 0.484807 0.413584 0.363565 0.346886 0.533674 0.6 0.057812 0.202732 0.989388 0.2 0.456264 0.917895 0.677545 0.481612 0.001283 0.6 0.6 1 0.4 0.2 0.8 0.2 0.082579 1 0.6 0.641577 Bojnik 10176 0.586980149 0.333333 0.428571 1 0.4 0.35 0.936888 0.940219 0.406903 0.842395 0.57446 0.26523 0.431193 0.2 0.2 0.8 0.584239 0.722776 0.761949 0.838749 0.474277 0.815217 0.228342 1 0.134128 0.133013 0.994561 0.2 0.502449 0.831184 0.465423 0.550451 0.290168 0.6 0.666667 0.6 0.4 1 1 0.2 0.101531 0.6 0.6 0.645886 Trstenik 38915 0.586465663 0.5 0.785714 0.9 0.6 0.35 0.952736 0.827112 0.382627 0.850891 0.4 0.3 0.8 0.4 0.2 0.8 0.6219 0.627126 0.530101 0.325746 1 0.227239 0.375452 0.8 0.018987 0.184158 0.967411 0.4 0.550105 0.797328 0.604507 0.500667 0.003201 0.8 0.8 0.8 0.8 0.8 0.6 0.2 0.108237 1 0.2 0.662011 Arilje 17947 0.582825702 0.666667 0.214286 0.9 0.8 0.9 0.942753 0.849751 0.463819 1 0.3 0.4 0.6 1 0.2 0.6 0.720334 0.553286 0.608012 0.273017 0.456247 0.145213 0.198879 0.8 0.030446 0.236536 0.948402 0.6 0.111949 0.75403 0.664336 0.415727 7.58E-05 0.8 0.6 1 0.8 0.2 0.4 0.2 0.101989 0.8 0.6 0.627211 Lazarevac 56865 0.580721849 0.5 0.785714 0.9 0.4 0.5 0.880585 0.801815 0.593174 0.635257 0.35 0.45 0.8 0.4 0.2 0.2 0.668021 0.499007 0.48364 0.175716 0.534187 0.110832 0.021912 0.2 0.007011 0.28811 0.986864 0.4 0.612405 0.710963 0.465423 0.379634 0 1 0.933333 1 0.2 0.8 0.2 0.8 0.098562 1 0.8 0.751405 Sopot 19819 0.580147236 0.5 0.785714 0.9 0.2 0.45 0.880585 0.801815 0.542993 0.635257 0.364735 0.162986 0.290372 0.2 0.2 0.2 0.643763 0.625186 0.677301 0.180391 0.534187 0.292776 0.108943 1 0.03054 0.232625 0.972081 0.4 0.55059 0.761515 0.669775 0.477307 0.076341 1 0.733333 1 0.2 0.8 0.2 0.8 0.069193 0.6 0.6 0.638984 Osečina 11113 0.577288086 0.833333 0.428571 0.7 0.8 0.6 0.871622 0.780821 0.610932 0.767085 0.5 0.4 0.5 0.4 0.2 1 0.628522 0.667356 0.475531 0.596092 0.363565 0.500154 0.696264 0.8 0.070922 0.189248 0.997691 0.2 0.412151 0.929986 0.664336 0.533033 0 0.8 0.4 0.8 0.8 0.2 0.6 0.6 0.056208 1 0.2 0.56682 Odžaci 27407 0.576786423 0.5 0.785714 0.7 0.4 0.35 0.977858 0.844556 0.515828 0.677125 0.1 0.512966 0.2 0.2 0.2 0.2 0.585889 0.566422 0.62282 0.307572 0.541912 0.040532 0.301287 0.8 0.021463 0.207641 0.862978 0.8 0.800059 0.801819 0.575758 0.517494 0.000792 1 0.733333 0.4 0.6 0.8 0.6 0.2 0.174789 1 0.4 0.652072 Aleksinac 47562 0.575669604 0.333333 0.785714 0.9 0.2 0.35 0.92895 0.853994 0.464047 0.798521 0.2 0.07 0.08 0.6 1 1 0.535487 0.622791 0.761756 0.285789 0.104795 0.217903 0.222878 0.6 0.016359 0.180092 0.958386 0.8 0.704009 0.802165 0.365967 0.500774 0 1 0.333333 0.8 1 0.8 0.8 0.6 0.072279 1 0.6 0.748287 Nova Varoš 14595 0.575569886 0.666667 0.214286 0.9 0.8 0.95 0.942753 0.849751 0.565957 1 0.6 0.1 0.8 0.2 0.2 0.2 0.771491 0.608462 0.61498 0.411466 0.456247 0.191111 0.160049 0.6 0.052054 0.175395 0.978712 0.2 0.02863 0.789843 0.472416 0.356041 0.199561 0.8 0.733333 1 0.2 0.6 0.8 0.2 0.111805 0.8 0.2 0.61098 Svilajnac 21414 0.574285548 0.333333 0.642857 1 0.4 0.35 0.914335 0.820016 0.540082 0.793514 0.5 0.243 0.5 0.4 0.2 1 0.583283 0.697637 0.58431 0.259811 0.22697 0.24328 0.347215 0.8 0.036924 0.202558 0.98093 0.8 0.802557 0.794334 0.536908 0.54559 0 0.6 0.866667 1 0.2 0.2 0.6 0.6 0.088561 1 0.4 0.603833 Kruševac 121293 0.572030851 0.333333 0.785714 1 0.4 0.35 0.952736 0.827112 0.460939 0.850891 0.3 0.27 0.03 0.6 1 0.6 0.600697 0.564092 0.638441 0.242403 0.410182 0.126795 0.178997 0.2 0.005147 0.207406 0.973138 0.6 0.669219 0.754376 0.271173 0.432811 0.004366 0.8 0.6 1 1 0.8 0.4 0.8 0.079026 1 0.6 0.737862 Lapovo 7210 0.571900822 0.333333 0.642857 0.9 0.2 0.35 0.887022 0.843738 0.519239 0.747376 0.95 0.1 0.1 0.2 0.2 0.6 0.679539 0.589572 0.702927 0.139562 0.436242 0.113646 0.060945 0.6 0.090463 0.202105 0.933758 0.8 0.833535 0.969023 0.508936 0.50106 0 0.2 0.2 0.6 0.6 0.2 0.4 0.2 0.226573 1 0.8 0.516706 Smederevska Palanka 45823 0.570817152 0.333333 0.928571 0.9 0.2 0.45 0.905466 0.805952 0.527689 0.659837 0.6 0.2 0.6 0.6 0.2 1 0.631193 0.58633 0.575912 0.200389 0.054834 0.361382 0.28203 0.2 0.015725 0.184781 0.930512 0.4 0.612088 0.765431 0.24087 0.424623 0.005131 1 0.533333 1 1 0.4 0.6 0.6 0.093046 1 0.4 0.699543 Prijepolje 34810 0.569111764 0.833333 0.214286 0.9 0.4 0.9 0.942753 0.849751 0.620177 1 0.05 0.1 0.2 0.8 0.2 1 0.577849 0.536202 0.562912 0.354588 0.456247 0.263052 0.077118 0.8 0.021394 0.173846 0.984944 0.2 0.030954 0.813795 0.28749 0.354036 0.260249 1 0.733333 1 1 0.8 1 0.6 0.090505 0.8 1 0.878619 Ivanjica 29832 0.568319213 0.666667 0.428571 0.9 0.8 0.7 0.924889 0.816558 0.605457 0.914402 0.5 0.7 0.8 1 0.2 0.6 0.730174 0.575266 0.43947 0.374167 0.787012 0.109615 0.219112 0.8 0.020273 0.214204 0.952375 0.2 0.088259 0.767388 0.515152 0.370036 0.409341 0.8 0.533333 1 0.8 0.2 0.6 0.4 0.087298 0.6 0.6 0.622962 Despotovac 20629 0.567764444 0.333333 0.642857 1 0.2 0.35 0.914335 0.820016 0.594092 0.793514 0.5 0.6 0.4 0.2 0.2 1 0.663527 0.709791 0.482067 0.401843 0.22697 0.227332 0.323319 0.2 0.043294 0.178251 0.909943 0.2 0.499614 0.762667 0.471639 0.392551 0.178362 1 0.933333 0.2 0.4 0.2 0.6 0.8 0.109381 1 1 0.68694 Doljevac 17991 0.5677457 0.333333 0.785714 0.9 0.2 0.4 0.92895 0.853994 0.455703 0.798521 0.1 0.12 0.7 0.2 1 1 0.539123 0.640882 0.686097 0.396942 0.563494 0.365842 0.030358 0.8 0.040814 0.202431 0.96635 0.8 0.819237 0.863657 0.635587 0.545127 0 0.6 0.333333 1 0.8 0.2 0.8 0.2 0.073275 1 0.6 0.644608 Barajevo 26855 0.567188944 0.5 0.785714 0.7 0.2 0.45 0.880585 0.801815 0.561217 0.635257 0.5 0.2 0.2 0.6 0.2 1 0.60157 0.591424 0.69713 0.153273 0.534187 0.145513 0.068826 1 0.02131 0.236015 0.981359 0.4 0.602216 0.781783 0.613831 0.432262 0.00441 0.8 0.933333 1 0.2 0.8 0.2 0.4 0.092529 1 1 0.718008 Titel 15031 0.566654449 0.333333 0.642857 0.7 0.6 0.35 0.951975 0.867149 0.572532 0.632806 0.1 0.25 0.08 0.6 0.2 0.2 0.548527 0.526701 0.655598 0.333384 0.55751 0.216698 0.491384 0.8 0.04132 0.197284 0.842289 0.6 0.72585 0.936089 0.778555 0.548938 0.062217 0.8 1 0.4 0.8 0.2 0.6 0.2 0.164762 0.6 0.8 0.620419 Bujanovac 37735 0.562840787 0.333333 0.785714 1 1 0.65 0.973202 1 -0.45071 0.952964 0.495437 0.074792 0.564721 0.2 0.2 1 0.643051 0.499446 0.23016 0.563228 0.435259 0.335285 0.08682 0.8 0.013757 0.264596 0.974619 0.2 0.454121 0.798135 0.553225 0.400589 0.061052 0.6 0.466667 1 1 0.6 1 1 0.151323 0.6 0.6 0.685902 Kraljevo 118363 0.561761749 0.5 0.071429 0.9 0.4 0.55 0.960726 0.937572 0.001868 0.950689 0.4 1 0.2 0.2 0.2 1 0.657441 0.580782 0.650308 0.226414 0.940868 0.102462 0.108469 0.2 0.005474 0.210257 0.991199 0.2 0.435035 0.818632 0.268065 0.379919 0.100193 0.4 0.6 0.8 0.8 1 0.6 0.8 0.085574 1 0.6 0.691007 Batočina 10977 0.560305032 0.5 0.642857 0.9 0.2 0.35 0.887022 0.843738 0.516632 0.747376 0.825609 0.130885 0.148731 0.2 0.2 0.8 0.644597 0.563893 0.686339 0.215952 0.436242 0.335511 0.102901 0.8 0.06069 0.195049 0.993908 0.6 0.460946 0.949908 0.533023 0.482412 0.024302 0.6 0.466667 1 0.4 0.2 0.6 0.2 0.112644 0.6 0.6 0.550706 Obrenovac 72124 0.559530863 0.333333 0.785714 0.9 0.2 0.45 0.880585 0.801815 0.592671 0.635257 0.37 0.7 0.5 0.6 0.2 1 0.632811 0.532889 0.539738 0.195088 0.534187 0.129428 0.082191 1 0.006636 0.256996 0.967092 0.4 0.622576 0.733303 0.709402 0.44789 0.001263 0.8 0.8 1 0.2 0.8 0.2 0.6 0.090162 0.6 0.6 0.617072 Boljevac 11358 0.558789858 0.333333 0.642857 0.9 0.2 0.35 0.932482 0.816604 0.567815 0.859134 0.6 0.1 0.3 0.2 0.2 0.6 0.668515 0.739129 0.192399 0.270067 0.544751 0.549626 0.641133 0.2 0.08242 0.173979 0.927053 0.2 0.235685 0.838784 0.464646 0.439144 0.0339 0.6 0.8 1 0.4 0.2 0.6 0.2 0.099778 1 0.2 0.573671 Žabalj 25156 0.55776313 0.333333 0.642857 0.7 0.6 0.3 0.951975 0.867149 0.537397 0.632806 0.399368 0.156559 0.141004 0.2 0.2 0.6 0.553961 0.492904 0.661317 0.372362 0.55751 0.054466 0.365161 0.8 0.023841 0.208565 0.835838 0.6 0.668888 0.901889 0.781663 0.502547 0.054 0.6 0.8 0.6 1 0.2 0.6 0.4 0.12079 0.8 0.8 0.646291 Ub 27407 0.557713708 0.5 0.428571 0.9 0.4 0.5 0.871622 0.780821 0.642223 0.767085 0.65 0.25 0.65 0.4 0.2 1 0.573035 0.59061 0.516653 0.405566 0.363565 0.470183 0.825576 0.2 0.023731 0.211336 0.997846 0.4 0.651668 0.857784 0.665113 0.552266 0 0.6 0.333333 0.4 0.4 0.4 0.6 0.4 0.054237 1 0.6 0.542903 Vladičin Han 18967 0.556507291 0.5 0.785714 0.9 0.6 0.55 0.973202 1 -0.34706 0.952964 0.8 0.15 0.01 0.2 0.2 0.4 0.623656 0.582018 0.579287 0.588094 0.435259 0.28515 0.047292 0.4 0.037259 0.189815 0.960039 0.2 0.311453 0.841893 0.475524 0.374512 0.003803 0.6 0.733333 1 0.6 0.6 1 0.2 0.078325 1 0.6 0.728777 Mladenovac 51889 0.555964445 0.333333 0.785714 0.9 0.4 0.5 0.880585 0.801815 0.526342 0.635257 0.15 0.03 0.25 0.4 0.2 1 0.550578 0.55494 0.6537 0.157056 0.804135 0.216811 0.187931 1 0.010331 0.23162 0.945467 0.4 0.568427 0.745394 0.452991 0.478703 0.050938 1 0.8 0.8 0.2 0.8 0.2 0.8 0.086525 1 0.6 0.679937 Sokobanja 14201 0.554103639 0.333333 0.642857 1 0.4 0.4 0.932482 0.816604 0.465115 0.859134 0.4 0.35 0.4 0.2 0.2 0.2 0.658068 0.738249 0.725002 0.175096 0.544751 0.117212 0.282158 0.2 0.058511 0.19405 0.965141 0.2 0.420026 0.779364 0.177933 0.394189 0.087223 0.6 0.333333 1 0.4 0.6 0.6 0.2 0.119629 1 0.6 0.638029 Leskovac 135591 0.550357851 0.5 0.428571 0.9 0.4 0.35 0.936888 0.940219 0.417853 0.842395 0.5 0.2 0.3 1 1 1 0.566604 0.559188 0.587488 0.375332 0.474277 0.175822 0.123548 0.6 0.004971 0.193372 0.98188 0.4 0.637592 0.781437 0.251748 0.432891 0.0022 0.8 0.466667 1 1 0.8 0.6 0.2 0.069775 1 0.4 0.702189 Ljig 11374 0.549895708 0.666667 0.428571 1 0.6 0.55 0.871622 0.780821 0.571514 0.767085 0.7 0.3 0.2 0.4 0.2 0.6 0.605558 0.671596 0.541768 0.234878 0.363565 0.180618 0.29737 0.2 0.07225 0.184096 0.976434 0.2 0.448985 0.91018 0.736597 0.403705 0.046453 0.4 0.733333 1 0.6 0.6 0.8 0.2 0.090606 1 0.6 0.685689 Lebane 19730 0.545413313 0.333333 0.428571 1 0.6 0.4 0.936888 0.940219 0.279723 0.842395 0.2 0.3 0.4 0.2 0.2 0.6 0.560612 0.603783 0.609503 0.620439 0.474277 0.442975 0.327268 0.8 0.048727 0.149198 0.981092 0.2 0.477095 0.888531 0.464646 0.483494 0.154469 1 0.6 1 0.8 0.2 1 0.2 0.073025 0.6 0.2 0.615494 Ljubovija 12805 0.54450419 1 0.214286 1 0.8 0.65 0.901906 0.801652 0.574403 0.702901 0.75 0.55 0.4 0.4 0.8 1 0.687395 0.542482 0.404731 0.538528 0.427187 0.23751 0.297921 0.2 0.050473 0.192262 0.990464 0.2 0.367522 0.907877 0.432789 0.388014 0.018421 0.6 0.466667 1 0.2 0.6 0.6 0.2 0.093502 1 0.2 0.564903 Blace 10509 0.542668131 0.333333 0.214286 0.9 0.6 0.4 0.966888 0.871088 0.587124 0.875621 0.2 0.1 0.2 0.2 0.2 0.2 0.539577 0.746581 0.728548 0.664991 0.672941 0.173222 0.145629 0.8 0.087821 0.182752 0.99911 0.4 0.300814 0.766352 0.480186 0.447284 3.46E-05 1 0.733333 1 0.6 0.2 0.8 0.2 0.127089 0.8 0.6 0.690382 Smederevo 103180 0.54160391 0.333333 1 0.7 0.4 0.5 0.905466 0.805952 0.518145 0.659837 0.4 0.2 0.5 1 1 1 0.569486 0.501991 0.668888 0.194835 0.054834 0.149305 0.110137 0.2 0.005386 0.214474 0.904601 0.6 0.655673 0.767158 0.355089 0.432262 0.000432 0.8 0.466667 1 0.6 0.8 0.4 1 0.077359 0.8 0.8 0.702332 Vlasotince 27718 0.541121066 0.5 0.428571 0.9 0.6 0.4 0.936888 0.940219 0.407376 0.842395 0.2 0.42 0.55 0.2 0.2 1 0.562292 0.568115 0.522597 0.528226 0.474277 0.256053 0.134315 1 0.024175 0.198762 0.983817 0.2 0.543742 0.825426 0.633256 0.446612 1.75E-06 0.4 0.6 1 1 0.4 0.8 0.2 0.070715 0.8 0.6 0.651129 Čajetina 14564 0.54086067 0.666667 0.214286 0.9 0.4 1 0.942753 0.849751 0.447689 1 0.7 0.2 0.9 0.2 0.2 0.2 0.729938 0.660552 0.565741 0.45111 0.456247 0.15588 0.111326 0.8 0.040662 0.24451 0.95227 0.2 0.012274 0.721211 0.471639 0.352792 0.67838 0.4 0.933333 0.4 0.2 0.8 0.4 0.2 0.131893 0.6 0.6 0.539348 Knić 12919 0.540659004 0.5 0.642857 0.9 0.4 0.4 0.887022 0.843738 0.239143 0.747376 0.3 0.6 0.4 0.2 0.2 0.2 0.58908 0.736594 0.647559 0.341662 0.436242 0.267015 0.503848 0.4 0.068761 0.185245 0.980701 0.2 0.493807 0.837748 0.557887 0.468837 0.001784 0.8 0.333333 0.8 0.8 0.4 0.6 0.2 0.083147 1 0.2 0.57576 Jagodina 69384 0.540546494 0.333333 0.642857 1 0.4 0.35 0.914335 0.820016 0.556116 0.793514 0.4 0.2 0.5 0.6 1 1 0.619051 0.584054 0.680224 0.209792 0.144571 0.182466 0.093114 0.6 0.009096 0.211274 0.954649 0.6 0.70104 0.73169 0.296037 0.446275 0.001306 0.6 0.466667 1 0.4 0.2 0.4 0.6 0.081983 0.8 0.6 0.564196 Prokuplje 41218 0.537533782 0.333333 0.214286 0.9 0.4 0.4 0.966888 0.871088 0.465583 0.875621 0.616731 0.213483 0.431889 0.2 0.2 0.4 0.488703 0.592673 0.634903 0.381672 0.713609 0.141744 0.060782 0.4 0.015415 0.220604 0.974737 0.6 0.376251 0.757255 0.197358 0.410972 0.131905 1 1 1 0.6 0.8 0.6 0.4 0.093275 0.6 1 0.800622 Užice 66996 0.53605598 0.666667 0.214286 0.9 0.6 0.9 0.942753 0.849751 0.451028 1 0.513835 0.224668 0.734724 0.6 1 0.6 0.698252 0.537153 0.412949 0.215186 0.456247 0.052011 0.044299 0.2 0.007201 0.246444 0.971349 0.2 0.16935 0.680101 0.16317 0.283622 0.206986 1 1 1 0.4 0.8 0.2 0.6 0.085234 1 0.2 0.671413 Valjevo 85962 0.535696722 0.833333 0.428571 0.9 0.6 0.55 0.871622 0.780821 0.643825 0.767085 0.3 0.3 0.4 0.6 0.6 1 0.599728 0.565522 0.451352 0.243602 0.363565 0.141776 0.179313 0.2 0.005523 0.258391 0.952593 0.2 0.354082 0.726509 0.222999 0.339901 0.016158 0.6 1 1 0.6 0.4 0.2 0.8 0.084263 1 1 0.733344 Kosjerić 10814 0.53554154 0.666667 0.214286 1 0.6 0.75 0.942753 0.849751 0.50108 1 0.6 0.3 0.8 0.6 0.2 0.4 0.672368 0.671237 0.557827 0.482209 0.456247 0.108454 0.29178 0.2 0.063553 0.215219 0.992457 0.2 0.209372 0.771879 0.560218 0.367031 1.93E-05 0.8 0.8 1 0.4 0.2 0.4 0.2 0.114232 0.8 0.4 0.583068 Gornji Milanovac 41492 0.533818139 0.666667 0.428571 1 1 0.55 0.924889 0.816558 0.473874 0.914402 0.5 0.125 0.68 1 0.2 0.6 0.680672 0.596765 0.509069 0.19056 0.787012 0.086021 0.123475 0.2 0.013109 0.238718 0.959489 0.2 0.293701 0.732842 0.328671 0.341666 0.000415 0.2 0.6 1 0.6 0.2 0.4 0.2 0.071088 1 0.8 0.601765 Ćuprija 28203 0.532760355 0.333333 0.642857 0.9 0.2 0.3 0.914335 0.820016 0.577 0.793514 0.3 0.6 0.7 0.6 0.2 0.8 0.635669 0.618875 0.672453 0.296098 0.297402 0.128756 0.136592 0.6 0.025222 0.192607 0.779486 0.8 0.744598 0.709351 0.112665 0.459885 0.002788 0.2 0.466667 1 1 0.2 0.6 0.8 0.082775 0.4 0.4 0.487709 Lučani 18602 0.527414845 0.666667 0.428571 1 0.4 0.7 0.924889 0.816558 0.419594 0.914402 0.184 0.316 0.263 0.2 0.2 1 0.572171 0.673115 0.519173 0.432084 0.787012 0.247525 0.203362 0.2 0.038638 0.206916 0.979879 0.2 0.393 0.797789 0.657343 0.403418 0.015787 0.2 0.466667 1 0.6 0.2 0.6 0.4 0.091716 1 1 0.646276 Novi Pazar 106261 0.525808449 0.333333 0.071429 0.9 0.4 0.6 0.960726 0.937572 0.858905 0.950689 1 0.3 1 1 0.2 0.8 0.619585 0.436872 0.362273 0.262921 0.508442 0.104904 0.074728 0.8 0.008097 0.161132 0.991827 0.2 0.123102 0.822202 0.303807 0.325844 0.085745 0.8 0.466667 1 1 0.2 0.6 0.6 0.093753 1 0.6 0.68509 Bajina Bašta 24345 0.525692127 0.666667 0.214286 0.9 0.4 0.85 0.942753 0.849751 0.47259 1 0.3 0.2 0.6 0.2 0.2 1 0.602871 0.585575 0.450492 0.474711 0.456247 0.078714 0.196635 0.6 0.025586 0.211397 0.946752 0.2 0.196373 0.789843 0.609946 0.357011 0.401189 0.6 0.533333 1 0.4 0.2 0.6 0.4 0.114811 1 0.8 0.662946 Vrnjačka Banja 26141 0.524731382 0.666667 0.071429 0.9 0.4 0.4 0.960726 0.937572 0.039825 0.950689 0.1 0.08 0.2 0.2 0.2 0.2 0.546919 0.604399 0.659582 0.228818 0.403768 0.0525 0.072433 0.8 0.024077 0.219385 0.993816 0.4 0.628009 0.763934 0.250971 0.426346 0.06187 0.6 0.4 0.8 0.4 1 0.4 0.2 0.118773 1 0.6 0.639028 Negotin 32654 0.520639675 0.5 0.785714 0.9 0.4 0.25 0.945629 0.754254 0.370076 0.801041 0.35 0.45 0.5 0.2 0.2 1 0.616605 0.748617 0.6604 0.342827 0.595189 0.227385 0.313546 0.2 0.028268 0.178646 0.827859 0.4 0.34381 0.748964 0.25641 0.409598 0.00215 0.6 0.6 0.8 0.4 0.2 0.6 1 0.099639 0.8 0.4 0.543254 Čačak 110279 0.519047099 0.666667 0.428571 1 0.6 0.55 0.924889 0.816558 0.221056 0.914402 0.6 0.4 0.8 0.4 0.2 1 0.639228 0.576687 0.479552 0.155558 0.787012 0.095115 0.060009 0.2 0.004773 0.247375 0.990414 0.2 0.461705 0.740673 0.306915 0.353036 0.028908 0.8 0.6 0.8 0.8 0.2 0.2 0.6 0.076732 0.8 0.6 0.587794 Bačka Palanka 52263 0.518825149 0.5 0.714286 0.7 0.4 0.35 0.951975 0.867149 0.49229 0.632806 0.5 0.7 0.323688 0.6 0.2 0.6 0.612666 0.550401 0.553709 0.226834 0.55751 0.029089 0.236468 0.2 0.01001 0.238976 0.916891 0.8 0.68199 0.777752 0.700078 0.457121 0.019495 0.8 1 0.2 0.8 0.4 0.2 0.2 0.1406 0.6 0.2 0.47062 Crveni krst 30939 0.517494498 0.333333 0.785714 0.9 0.4 0.4 0.92895 0.853994 0.460888 0.798521 0.6 0.15 0.3 0.2 0.2 0.2 0.669994 0.562435 0.735774 0.193277 0.85258 0.082402 0.011705 0.6 0.017692 0.238694 0.986166 0.8 0.746688 0.828089 0.423854 0.493316 0.16022 0.6 0.866667 0.2 0 0 0 0.8 0.136121 0.6 0.2 0.325013 Požarevac 59131 0.51746856 0.5 0.714286 0.9 0.2 0.4 0.934418 0.840472 0.329841 0.77219 0.849883 0.259814 0.197911 0.2 0.2 1 0.637327 0.550104 0.586346 0.1407 0.766301 0.102991 0.142733 0.2 0.008953 0.239643 0.8745 0.8 0.778417 0.734224 0.21756 0.458024 0.001011 0.6 0.733333 0.6 0 0 0 0.8 0.153038 0.6 0.6 0.426849 Topola 20445 0.517140248 0.5 0.642857 0.7 0.6 0.45 0.887022 0.843738 0.513885 0.747376 0.3 0.05 0.5 0.6 0.2 0.8 0.542837 0.630062 0.603056 0.18424 0.436242 0.34395 0.79433 0.6 0.033434 0.206817 0.927673 0.2 0.42712 0.943574 0.610723 0.515242 0.002511 0.6 0.2 1 1 0.2 0.4 0.4 0.095932 0.6 0.2 0.481443 Aranđelovac 43834 0.515997174 0.5 0.642857 0.9 0.8 0.5 0.887022 0.843738 0.521638 0.747376 0.367505 0.171239 0.462362 0.2 0.2 0.2 0.635743 0.542675 0.542715 0.139195 0.5606 0.104257 0.078633 0.2 0.01297 0.219873 0.980464 0.2 0.426577 0.760594 0.321678 0.345656 0.002046 1 0.4 1 0.8 0.2 0.4 0.2 0.06951 1 0.2 0.59189 Loznica 75286 0.513632896 0.666667 0.214286 0.7 0.4 0.45 0.901906 0.801652 0.554999 0.702901 0.15 0.25 0.3 0.6 0.8 1 0.484929 0.527793 0.54496 0.345897 0.427187 0.184682 0.156828 0.8 0.008534 0.191052 0.99685 0.6 0.910684 0.897973 0.263403 0.504634 0.031868 0.4 0.6 1 1 0.4 0.6 0.8 0.060757 0.8 0.2 0.570188 Pećinci 19222 0.500626479 0.333333 0.642857 1 0.4 0.5 0.919208 0.807714 0.550744 0.646739 0.401112 0.299048 0.27218 0.2 0.2 0.2 0.63792 0.52686 0.437604 0.217044 0.158916 0.091205 0.175162 0.2 0.02497 0.254301 0.633993 0.4 0.598711 0.740327 0.574204 0.354059 0.169178 0.6 0.666667 0.8 0.2 0.4 0.2 0.2 0.102907 0.6 0.6 0.514537 Požega 27554 0.491126236 0.5 0.214286 1 0.4 0.8 0.942753 0.849751 0.464574 1 0.4 0.35 0.8 0.2 0.2 0.6 0.624642 0.610035 0.520156 0.270333 0.456247 0.113064 0.121756 0.2 0.021336 0.23097 0.996338 0.2 0.308561 0.794565 0.668998 0.349403 0.000798 0.8 0.8 0.6 0.8 0.2 0.4 0.6 0.088441 0.6 0.2 0.503438 Šabac 110918 0.48789683 0.5 0.214286 0.9 0.6 0.45 0.901906 0.801652 0.551195 0.702901 0.5 0.1 0.5 0.6 1 1 0.475721 0.521244 0.563932 0.268848 0.427187 0.332345 0.242971 0.2 0.00471 0.228952 0.918854 0.8 0.784727 0.764049 0.289044 0.49213 0.000731 0.2 0.866667 0.8 0.6 0.8 0.4 0.2 0.074911 0.6 0.2 0.49981 Sremska Mitrovica 75873 0.485652438 0.333333 0.642857 0.7 0.8 0.45 0.919208 0.807714 0.475074 0.646739 0.1 0.2 0.02 0.6 0.2 0.4 0.456749 0.537764 0.597397 0.254497 0.158916 0.09374 0.195132 0.2 0.007085 0.228356 0.841258 0.8 0.850856 0.719714 0.227661 0.447007 0.067614 0.6 0.866667 0.6 0.6 0.8 0.4 0.2 0.13821 1 0.2 0.586975 Vranje 71551 0.47678063 0.5 0.785714 0.9 0.6 0.6 0.973202 1 -0.44642 0.952964 0.001 0.01 0.105025 1 0.2 1 0.500065 0.485323 0.440839 0.271771 0.435259 0.170881 0.029547 0.2 0.00715 0.231316 0.983014 0.2 0.377011 0.70555 0.224553 0.323666 0 0.8 0.6 1 0.6 0.8 0.4 0.2 0.096816 0.8 0.6 0.671526 Bor 45266 0.463840665 0.5 0.785714 0.9 0.4 0.3 0.945629 0.754254 0.782929 0.801041 0.15 0.01 0.1 0.4 0.2 1 0.571854 0.520048 0.58812 0.173458 0.595189 0.114899 0.032362 0.2 0.012249 0.213212 0.961142 0.2 0.273457 0.779134 0.228438 0.324017 0.014837 0.2 0.6 1 0.2 0.8 0.2 0.4 0.103197 1 0.2 0.511556 Pirot 54342 0.453156548 0.333333 0.214286 1 0.2 0.5 0.873752 0.827664 0.485317 0.911812 0.2 0.05 0.6 0.2 0.2 1 0.445532 0.610937 0.475489 0.165327 0.088395 0.072471 0.043225 0.2 0.010766 0.227585 0.98292 0.2 0.248595 0.747351 0.228438 0.432262 0.483037 1 1 1 0.6 0.4 0.4 0.4 0.088066 1 0.4 0.705459 Zaječar 54355 0.422964719 0.333333 0.642857 0.7 0.4 0.3 0.932482 0.816604 0.314068 0.859134 0.28 0.16 0.2 0.2 0.2 1 0.482159 0.653961 0.34595 0.166712 0.544751 0.201106 0.177836 0.2 0.014179 0.182665 0.943648 0.2 0.412321 0.744818 0.20979 0.349024 0.065375 0.6 0.466667 0.2 0.6 0.2 0.4 0.8 0.093854 1 0.2 0.445084 Total Total Territory Total population Vulnerability score Territory Total populationTotal Vulnerability score suscepti Total Total suscepti Total 1.01 1.02 1.03 1.04 1.05 1.06 1.07 1.08 1.09 1.1 1.11 1.12 1.13 1.14 1.15 exposure 2.01 2.02 2.03 1.01 2.04 1.02 2.05 1.03 2.06 1.04 2.07 1.05 2.08 1.06 2.09 1.07 2.1 1.08 2.11 1.09 2.12 1.1 2.13 1.11 2.14 1.12 bility 1.13 3.01 1.14 3.02 1.15 3.03exposure3.04 2.01 3.05 2.02 3.06 2.03 3.07 2.04 3.08 2.05 3.09 2.06 3.1 2.07 3.11 2.08capacity2.09 2.1 2.11 2.12 2.13 2.14 bility 3.01 3.02 3.03 3.04 3.05 3.06 3.07 3.08 3.09 3.1 3.11 capacity Žabari 9585 0.682978578 0.333333 0.642857 0.9 0.4 0.4 0.934418 0.840472 0.521819 0.77219 0.95 0.15 0.3Žabari 0.2 0.2 0.2 0.6993849585 0.769544 0.8210820.6829785780.2624550.3333330.6945240.6428570.335414 0.7147840.9 0.4 1 0.1314530.4 0.9344180.1380170.8404720.9580480.521819 0.60.772190.56948 0.950.91755 0.150.676768 0.6196870.3 0.2 0 0.2 1 0.2 0.6993841 0.7695441 0.8210820.6 0.2624550.2 0.6945240.8 0.3354140.8 0.1089750.714784 1 1 0.1314530.6 0.7533080.138017 0.958048 0.6 0.56948 0.91755 0.676768 0.619687 0 1 1 1 0.6 0.2 0.8 0.8 0.108975 1 0.6 0.753308 Merošina 12963 0.664167888 0.333333 0.714286 0.9 0.2 0.4 0.92895 0.853994 0.459442 0.798521 0.8 0.22 0.43Merošina0.6 1 0.4 0.71220312963 0.695098 0.5803540.6641678880.6652170.3333330.5634940.7142860.407744 0.1914320.9 0.2 0.8 0.0697770.4 0.928950.1808610.8539940.9951560.459442 0.7985210.4 0.599616 0.8865730.8 0.220.719503 0.430.510217 0.0251810.6 1 1 0.7333330.4 0.712203 0.6950981 0.5803540.8 0.6652170.2 0.5634941 0.4077440.2 0.074660.191432 1 0.8 0.0697771 0.8230410.180861 0.995156 0.4 0.599616 0.886573 0.719503 0.510217 0.025181 1 0.733333 1 0.8 0.2 1 0.2 0.07466 1 1 0.823041 Malo Crniće 9903 0.658469419 0.5 0.642857 1 0.4 0.35 0.934418 0.840472 0.356248 0.77219 0.9 0.4 0.3Malo Crniće0.6 0.2 1 0.6422489903 0.693301 0.7053840.6584694190.223351 0.6945240.5 0.6428570.437071 0.9709321 0.4 0.8 0.350.103360.9344180.1556170.8404720.8666660.356248 0.80.772190.647495 0.9943570.9 0.6410260.4 0.6688880.3 0.6 0 0.2 0.6 1 0.6422480.2 0.6933011 0.7053840.6 0.2233510.6 0.6945240.8 0.4370711 0.1205330.970932 1 0.8 0.60.103360.6671740.155617 0.866666 0.8 0.647495 0.994357 0.641026 0.668888 0 0.6 0.2 1 0.6 0.6 0.8 1 0.120533 1 0.6 0.667174 Rekovac 9270 0.657933704 0.5 0.642857 1 0.4 0.35 0.914335 0.820016 0.431386 0.793514 0.5 0.3 0.8Rekovac 0.6 0.2 1 0.6264819270 0.853958 0.9057710.6579337040.505005 0.50.226970.6428570.833013 0.7171541 0.4 0.8 0.350.1346480.9143350.1511660.8200160.9513510.431386 0.7935140.2 0.483668 0.8975130.5 0.4801860.3 0.6092440.8 0.65.9E-05 0.2 1 0.9333331 0.626481 0.8539581 0.9057710.6 0.5050050.6 0.80.22697 0.8330130.2 0.0815850.717154 1 0.8 0.1346480.6 0.7781480.151166 0.951351 0.2 0.483668 0.897513 0.480186 0.609244 5.9E-05 1 0.933333 1 0.6 0.6 0.8 0.2 0.081585 1 0.6 0.778148 Žagubica 11348 0.651012496 0.5 0.642857 1 0.2 0.3 0.934418 0.840472 0.761106 0.77219 0.9 0.8 0.01Žagubica 0.2 0.2 1 0.7437511348 0.778548 0.7905830.6510124960.55198 0.6945240.5 0.6428570.459957 0.5785331 0.2 0.2 0.1003450.3 0.9344180.1567980.8404720.9824570.761106 0.20.772190.271072 0.8196680.9 0.80.48951 0.010.488052 0.0015040.2 0.2 0.8 1 0.80.74375 0.7785481 0.7905830.4 0.20.55198 0.6945240.8 0.4599570.2 0.1305580.578533 1 0.2 0.1003451 0.7563470.156798 0.982457 0.2 0.271072 0.819668 0.48951 0.488052 0.001504 0.8 0.8 1 0.4 0.2 0.8 0.2 0.130558 1 1 0.756347 Vladimirci 15818 0.647863363 0.5 0.214286 1 0.2 0.5 0.901906 0.801652 0.608787 0.702901 0.89 0.6 0.9Vladimirci0.6 0.6 0.6 0.65210415818 0.653006 0.6106110.6478633630.494056 0.4271870.5 0.2142860.767812 0.8378311 0.2 0.8 0.0468290.5 0.9019060.1988790.8016520.9852090.608787 0.7029010.8 0.80195 0.890.854215 0.8780110.6 0.7010190.9 0.6 0 0.6 0.2 0.6 0.6521040.4 0.6530060.4 0.6106111 0.4940560.2 0.4271870.6 0.7678120.2 0.051650.837831 0.8 0.8 0.0468291 0.5617690.198879 0.985209 0.8 0.80195 0.854215 0.878011 0.701019 0 0.2 0.4 0.4 1 0.2 0.6 0.2 0.05165 0.8 1 0.561769 Bogatić 26630 0.634749169 0.5 0.214286 0.9 0.2 0.45 0.901906 0.801652 0.507437 0.702901 0.3 0.2 0.15Bogatić 0.2 0.8 0.8 0.47231626630 0.581515 0.4981870.6347491690.384083 0.4271870.5 0.2142860.588124 0.9 1 0.2 0.8 0.450.0273630.9019060.1852920.8016520.9318530.507437 0.7029010.8 0.844817 0.30.87287 0.6674440.2 0.150.684025 0.0036240.2 0.8 0.8 0.8 0.4723160.8 0.5815151 0.4981871 0.3840830.8 0.4271870.6 0.5881240.2 0.078067 1 1 0.8 0.0273630.8 0.8044860.185292 0.931853 0.8 0.844817 0.87287 0.667444 0.684025 0.003624 0.8 0.8 1 1 0.8 0.6 0.2 0.078067 1 0.8 0.804486 Preševo 29989 0.631215117 0.333333 0.785714 1 0.6 0.7 0.973202 1 -0.40954 0.952964 0.8 0.1 0.6Preševo 0.2 0.2 0.4 0.6809329989 0.518326 0.039050.6312151170.4798390.3333330.4352590.7857140.665103 0.0280811 0.6 0.8 0.0045360.7 0.973202 1 1 -0.409541 0.9529640.4 0.379186 0.8481110.8 0.10.54157 0.60.49001 0.2 0 0.2 1 0.4 0.680931 0.5183261 0.80.03905 0.4798390.2 0.4352591 0.6651031 0.6222660.028081 1 0.8 0.0045360.4 0.768451 1 1 0.4 0.379186 0.848111 0.54157 0.49001 0 1 1 1 0.8 0.2 1 1 0.622266 1 0.4 0.768451 Brus 14814 0.631052558 0.5 0.785714 0.9 1 0.45 0.952736 0.827112 1 0.850891 0.05 0.1 0.9Brus 0.6 0.2 0.2 0.74344414814 0.6543 0.732150.6310525580.629989 0.6535470.5 0.7857140.164929 0.4495240.9 1 0.8 0.450.0447990.9527360.2177380.8271120.977386 1 0.8508910.4 0.190485 0.050.811147 0.5477860.1 0.4740950.9 0.0769010.6 0.2 1 0.2 0.7434441 10.6543 0.60.73215 0.6299890.4 0.6535470.8 0.1649290.2 0.0880940.449524 1 0.8 0.0447990.2 0.6979010.217738 0.977386 0.4 0.190485 0.811147 0.547786 0.474095 0.076901 1 1 1 0.6 0.4 0.8 0.2 0.088094 1 0.2 0.697901 Varvarin 16340 0.625663079 0.333333 0.785714 1 0.4 0.35 0.952736 0.827112 0.485975 0.850891 0.2 0.1 0.4Varvarin 0.6 0.2 0.6 0.650616340 0.672709 0.8042330.6256630790.3783560.3333330.6535470.7857140.461672 0.8316491 0.4 1 0.350.0548780.9527360.1696660.8271120.9992540.485975 0.8508910.4 0.639704 0.8642330.2 0.7055170.1 0.6352250.4 0.6 0 0.2 1 0.8666670.6 0.6506 0.6727091 0.8042331 0.3783560.2 0.6535470.8 0.4616720.2 0.0768710.831649 0.2 1 0.0548780.2 0.5739150.169666 0.999254 0.4 0.639704 0.864233 0.705517 0.635225 0 1 0.866667 1 1 0.2 0.8 0.2 0.076871 0.2 0.2 0.573915 Koceljeva 11844 0.624779393 0.5 0.285714 0.9 0.6 0.5 0.901906 0.801652 0.629936 0.702901 0.7 0.4 0.9Koceljeva0.8 0.8 1 0.60365111844 0.603891 0.4861220.6247793930.553738 0.4271870.5 0.2857140.621454 0.6178990.9 0.6 0.8 0.0555010.5 0.9019060.2142980.8016520.9960420.629936 0.7029010.6 0.713349 0.8815060.7 0.7078480.4 0.6119840.9 0.8 0 0.8 0.8 1 0.6036510.8 0.6038911 0.4861220.6 0.5537380.8 0.4271870.6 0.6214540.2 0.0644030.617899 0.8 0.8 0.0555010.4 0.6756640.214298 0.996042 0.6 0.713349 0.881506 0.707848 0.611984 0 0.8 0.8 1 0.6 0.8 0.6 0.2 0.064403 0.8 0.4 0.675664 Žitorađa 14980 0.624683085 0.333333 0.214286 1 0.4 0.35 0.966888 0.871088 0.439433 0.875621 0.8 0.3 0.4Žitorađa 0.6 0.2 0.6 0.63414914980 0.683366 0.6590910.6246830850.6168030.3333330.6842130.2142860.381635 0.4935881 0.4 0.8 0.350.0606520.9668880.1788030.8710880.9584860.439433 0.8756210.8 0.594364 0.8915250.8 0.6845380.3 0.40.59458 0.6 0 0.2 1 0.6 0.6341490.8 0.6833660.8 0.6590911 0.6168030.2 0.6842131 0.3816350.2 0.0533810.493588 0.6 0.8 0.0606520.4 0.6556390.178803 0.958486 0.8 0.594364 0.891525 0.684538 0.59458 0 1 0.8 0.8 1 0.2 1 0.2 0.053381 0.6 0.4 0.655639 Tutin 31670 0.622259253 0.333333 0.071429 1 0.2 0.65 0.960726 0.937572 0.683422 0.950689 1 0.7 0.7Tutin 0.2 0.2 0.2 0.71712331670 0.440648 0.3385750.6222592530.3266380.3333330.5555730.0714290.235201 0.3931691 0.2 0.8 0.650.0336190.9607260.1355210.9375720.9986220.683422 0.9506890.2 0.006256 1 1 0.70.57265 0.3890150.7 0.0068830.2 0.2 1 0.7333330.2 0.717123 0.4406481 0.3385750.8 0.3266380.2 0.5555731 0.2352010.8 0.067850.393169 1 0.8 0.0336191 0.8298290.135521 0.998622 0.2 0.006256 1 0.57265 0.389015 0.006883 1 0.733333 1 0.8 0.2 1 0.8 0.06785 1 1 0.829829 Ražanj 7998 0.618616517 0.333333 0.785714 1 0.2 0.35 0.92895 0.853994 0.488289 0.798521 0.35 0.25 0.4Ražanj 0.2 0.6 0.6 0.6736587998 0.806106 0.7491310.6186165170.4345210.3333330.5634940.7857140.423061 0.7171811 0.2 1 0.350.1464140.928950.1562710.8539940.9833540.488289 0.7985210.4 0.604578 0.350.949217 0.250.51826 0.4322620.4 7.47E-070.2 0.6 0.6 0.3333330.6 0.673658 0.8061061 0.7491310.4 0.4345210.2 0.5634940.8 0.4230610.2 0.1108920.717181 1 1 0.1464140.2 0.5467710.156271 0.983354 0.4 0.604578 0.949217 0.51826 0.432262 7.47E-07 0.6 0.333333 1 0.4 0.2 0.8 0.2 0.110892 1 0.2 0.546771 Veliko Gradište 15966 0.617738985 0.333333 0.642857 0.9 0.2 0.3 0.934418 0.840472 0.224954 0.77219 0.95 0.15 0.01Veliko Gradište0.4 0.2 0.4 0.67222215966 0.680933 0.4932460.6177389850.1833540.3333330.6945240.6428570.145149 0.6806390.9 0.2 0.8 0.0543220.3 0.9344180.178030.8404720.923130.224954 0.80.772190.755097 0.950.824044 0.150.516706 0.010.579506 0.42.2E-06 0.2 0.6 0.4 0.6722220.2 0.6809331 0.4932460.4 0.1833540.2 0.6945240.6 0.1451491 0.1312420.680639 1 0.8 0.0543220.6 0.5933640.17803 0.92313 0.8 0.755097 0.824044 0.516706 0.579506 2.2E-06 0.6 0.2 1 0.4 0.2 0.6 1 0.131242 1 0.6 0.593364 Paraćin 50798 0.617203026 0.333333 0.642857 0.9 0.2 0.3 0.914335 0.820016 0.574226 0.793514 0.95 0.55 0.98Paraćin 0.6 0.2 1 0.67569150798 0.600665 0.5489610.6172030260.2503680.3333330.226970.6428570.198793 0.1302860.9 0.2 0.6 0.30.015010.9143350.1787560.8200160.9941350.574226 0.7935140.8 0.825945 0.950.835905 0.550.328671 0.980.490398 0.0003760.6 0.2 1 0.9333331 0.675691 0.6006651 0.5489611 0.2503680.6 0.60.22697 0.1987930.2 0.1132760.130286 1 0.6 0.20.015010.7196790.178756 0.994135 0.8 0.825945 0.835905 0.328671 0.490398 0.000376 1 0.933333 1 1 0.6 0.6 0.2 0.113276 1 0.2 0.719679 Svrljig 12557 0.612322788 0.333333 0.785714 0.9 0.4 0.4 0.92895 0.853994 0.457899 0.798521 0.5 0.2 0.4Svrljig 0.8 1 0.4 0.6704412557 0.8929 0.8307250.6123227880.2558220.3333330.5634940.7857140.509692 0.0575640.9 0.4 1 0.0867540.4 0.928950.1830750.8539940.9952210.457899 0.7985210.2 0.396169 0.7893830.5 0.4879560.2 0.4322620.4 0.0023910.8 1 0.8 0.4 0.20.67044 10.8929 0.8307250.4 0.2558220.6 0.5634941 0.5096920.2 0.1167040.057564 1 1 0.0867540.8 0.7214590.183075 0.995221 0.2 0.396169 0.789383 0.487956 0.432262 0.002391 0.8 0.2 1 0.4 0.6 1 0.2 0.116704 1 0.8 0.721459 Aleksandrovac 24223 0.610884946 0.5 0.785714 1 0.6 0.4 0.952736 0.827112 0.771247 0.850891 0.7 0.2 0.77Aleksandrovac0.6 0.2 0.4 0.75803924223 0.627108 0.7432670.6108849460.419105 0.6535470.5 0.7857140.171747 0.4189431 0.6 0.8 0.0279940.4 0.9527360.2088820.8271120.995930.771247 0.8508910.2 0.44306 0.7784430.7 0.20.62704 0.770.476251 0.0005450.6 0.2 0.6 0.4 0.7580391 0.6271080.8 0.7432671 0.4191050.2 0.6535470.4 0.1717470.2 0.0797030.418943 1 0.8 0.0279940.2 0.5921050.208882 0.99593 0.2 0.44306 0.778443 0.62704 0.476251 0.000545 0.6 1 0.8 1 0.2 0.4 0.2 0.079703 1 0.2 0.592105 Velika Plana 38000 0.602201954 0.333333 1 0.9 0.4 0.4 0.905466 0.805952 0.527098 0.659837 0.8 0.53 0.3Velika Plana0.2 0.2 0.6 0.68144338000 0.58071 0.6282360.6022019540.1976120.3333330.054834 0.3613821 0.1722470.9 0.4 0.4 0.0187310.4 0.9054660.1887810.8059520.9368270.527098 0.6598370.8 0.688728 0.8452330.8 0.530.703186 0.4322620.3 0.0034420.2 0.2 0.6 0.9333330.6 0.681443 0.80.58071 0.6282361 0.1976120.2 0.0548340.6 0.3613820.8 0.0950940.172247 0.8 0.4 0.0187310.6 0.6526390.188781 0.936827 0.8 0.688728 0.845233 0.703186 0.432262 0.003442 0.6 0.933333 0.8 1 0.2 0.6 0.8 0.095094 0.8 0.6 0.652639 Krupanj 15602 0.598171059 0.833333 0.214286 0.9 0.6 0.55 0.901906 0.801652 0.55742 0.702901 0.4 0.6 0.3Krupanj 1 0.8 1 0.61891915602 0.549846 0.6135840.5981710590.555130.8333330.4271870.2142860.355421 0.5464690.9 0.6 0.8 0.550.0459870.9019060.1738840.8016520.9915610.55742 0.7029010.4 0.522894 0.9344770.4 0.9324010.6 0.5389660.3 0.0002441 0.8 0.4 1 0.6189190.4 0.5498461 0.6135840.8 0.40.55513 0.4271870.8 0.3554210.2 0.0619270.546469 0.6 0.8 0.0459871 0.6558560.173884 0.991561 0.4 0.522894 0.934477 0.932401 0.538966 0.000244 0.4 0.4 1 0.8 0.4 0.8 0.2 0.061927 0.6 1 0.655856 Petrovac na Mlavi 28157 0.597295785 0.5 0.642857 0.9 0.2 0.35 0.934418 0.840472 0.519812 0.77219 0.3 0.2 0.2Petrovac na1 Mlavi 0.2 0.4 0.57494428157 0.706407 0.8249920.5972957850.273609 0.6945240.5 0.6428570.31172 0.7144760.9 0.2 0.8 0.350.0293510.9344180.191290.8404720.9384750.519812 0.60.772190.718846 0.8184020.3 0.2999220.2 0.6058610.2 2.31E-061 0.2 0.6 0.5333330.4 0.574944 0.7064070.8 0.8249920.8 0.2736090.6 0.6945240.8 0.80.311720.0946740.714476 1 0.8 0.0293510.2 0.6179760.19129 0.938475 0.6 0.718846 0.818402 0.299922 0.605861 2.31E-06 0.6 0.533333 0.8 0.8 0.6 0.8 0.8 0.094674 1 0.2 0.617976 Rača 10467 0.590789625 0.333333 0.642857 0.9 0.4 0.35 0.887022 0.843738 0.517311 0.747376 0.4 0.2 0.2Rača 0.4 0.2 1 0.57801210467 0.637877 0.6627820.5907896250.1895070.3333330.4362420.6428570.471534 0.4051910.9 0.4 0.6 0.350.0660460.8870220.203470.8437380.9854740.517311 0.7473760.6 0.501274 0.8574390.4 0.5423470.2 0.5292920.2 0.0019910.4 0.2 0.8 1 0.5780120.8 0.6378771 0.6627820.4 0.1895070.4 0.4362420.4 0.4715340.2 0.1372130.405191 1 0.6 0.0660460.8 0.7022030.20347 0.985474 0.6 0.501274 0.857439 0.542347 0.529292 0.001991 0.8 0.8 1 0.4 0.4 0.4 0.2 0.137213 1 0.8 0.702203 Ćićevac 8606 0.589475541 0.333333 0.785714 1 0.2 0.35 0.952736 0.827112 0.46341 0.850891 0.342873 0.234358 0.376395Ćićevac 0.6 0.2 0.8 0.6558998606 0.618811 0.6316420.5894755410.2742690.3333330.6535470.7857140.279 0.1299651 0.2 0.8 0.350.0775070.9527360.2089290.8271120.9951540.46341 0.8508911 0.4437180.3428730.9242280.2343580.557110.3763950.525855 0.6 0 0.2 0.6 0.6666670.8 0.655899 0.6188111 0.6316420.6 0.2742690.2 0.6535470.6 0.20.2790.136820.129965 0.6 0.8 0.0775070.6 0.5852710.208929 0.995154 1 0.443718 0.924228 0.55711 0.525855 0 0.6 0.666667 1 0.6 0.2 0.6 0.2 0.13682 0.6 0.6 0.585271 Lajkovac 14721 0.588222565 0.666667 0.428571 1 0.2 0.5 0.871622 0.780821 0.641534 0.767085 0.95 0.3 0.8Lajkovac 1 0.2 1 0.65491714721 0.557806 0.438730.5882225650.3366940.6666670.3635650.4285710.407275 0.1145311 0.2 0.2 0.0386710.5 0.8716220.224830.7808210.9962680.641534 0.7670850.6 0.670172 0.950.89544 0.6021760.3 0.80.46102 0.0018531 0.2 0.8 0.5333331 0.654917 0.5578061 0.20.43873 0.3366940.8 0.3635650.2 0.4072750.2 0.0637460.114531 1 0.2 0.0386710.8 0.6789860.22483 0.996268 0.6 0.670172 0.89544 0.602176 0.46102 0.001853 0.8 0.533333 1 0.2 0.8 0.2 0.2 0.063746 1 0.8 0.678986 Mionica 13080 0.587366095 0.833333 0.428571 1 0.4 0.55 0.871622 0.780821 0.631351 0.767085 0.7 0.3 Mionica1 0.2 0.2 1 0.6569813080 0.665345 0.4848070.5873660950.4135840.8333330.3635650.4285710.346886 0.5336741 0.4 0.6 0.550.0578120.8716220.2027320.7808210.9893880.631351 0.7670850.2 0.456264 0.9178950.7 0.6775450.3 0.4816121 0.0012830.2 0.2 0.6 1 0.60.65698 0.6653451 0.4848070.4 0.4135840.2 0.3635650.8 0.3468860.2 0.0825790.533674 1 0.6 0.0578120.6 0.6415770.202732 0.989388 0.2 0.456264 0.917895 0.677545 0.481612 0.001283 0.6 0.6 1 0.4 0.2 0.8 0.2 0.082579 1 0.6 0.641577 Bojnik 10176 0.586980149 0.333333 0.428571 1 0.4 0.35 0.936888 0.940219 0.406903 0.842395 0.57446 0.26523 0.431193Bojnik 0.2 0.2 0.8 0.58423910176 0.722776 0.7619490.5869801490.8387490.3333330.4742770.4285710.815217 0.2283421 0.4 1 0.350.1341280.9368880.1330130.9402190.9945610.406903 0.8423950.2 0.5024490.574460.8311840.265230.4654230.4311930.550451 0.2901680.2 0.2 0.6 0.6666670.8 0.584239 0.7227760.6 0.7619490.4 0.8387491 0.4742771 0.8152170.2 0.1015310.228342 0.6 1 0.1341280.6 0.6458860.133013 0.994561 0.2 0.502449 0.831184 0.465423 0.550451 0.290168 0.6 0.666667 0.6 0.4 1 1 0.2 0.101531 0.6 0.6 0.645886 Trstenik 38915 0.586465663 0.5 0.785714 0.9 0.6 0.35 0.952736 0.827112 0.382627 0.850891 0.4 0.3 0.8Trstenik 0.4 0.2 0.8 0.621938915 0.627126 0.5301010.5864656630.325746 0.5 0.7857141 0.227239 0.3754520.9 0.6 0.8 0.350.0189870.9527360.1841580.8271120.9674110.382627 0.8508910.4 0.550105 0.7973280.4 0.6045070.3 0.5006670.8 0.0032010.4 0.2 0.8 0.8 0.80.6219 0.6271260.8 0.5301010.8 0.3257460.8 0.6 1 0.2272390.2 0.1082370.375452 1 0.8 0.0189870.2 0.6620110.184158 0.967411 0.4 0.550105 0.797328 0.604507 0.500667 0.003201 0.8 0.8 0.8 0.8 0.8 0.6 0.2 0.108237 1 0.2 0.662011 Arilje 17947 0.582825702 0.666667 0.214286 0.9 0.8 0.9 0.942753 0.849751 0.463819 1 0.3 0.4 0.6Arilje 1 0.2 0.6 0.72033417947 0.553286 0.6080120.5828257020.2730170.6666670.4562470.2142860.145213 0.1988790.9 0.8 0.8 0.0304460.9 0.9427530.2365360.8497510.9484020.463819 0.6 0.1119491 0.30.75403 0.6643360.4 0.4157270.6 7.58E-051 0.2 0.8 0.6 0.7203340.6 0.5532861 0.6080120.8 0.2730170.2 0.4562470.4 0.1452130.2 0.1019890.198879 0.8 0.8 0.0304460.6 0.6272110.236536 0.948402 0.6 0.111949 0.75403 0.664336 0.415727 7.58E-05 0.8 0.6 1 0.8 0.2 0.4 0.2 0.101989 0.8 0.6 0.627211 Lazarevac 56865 0.580721849 0.5 0.785714 0.9 0.4 0.5 0.880585 0.801815 0.593174 0.635257 0.35 0.45 0.8Lazarevac0.4 0.2 0.2 0.66802156865 0.499007 0.483640.5807218490.175716 0.5341870.5 0.7857140.110832 0.0219120.9 0.4 0.2 0.0070110.5 0.8805850.288110.8018150.9868640.593174 0.6352570.4 0.612405 0.350.710963 0.450.465423 0.3796340.8 0.4 0 0.2 1 0.9333330.2 0.668021 0.4990071 0.20.48364 0.1757160.8 0.5341870.2 0.1108320.8 0.0985620.021912 1 0.2 0.0070110.8 0.7514050.28811 0.986864 0.4 0.612405 0.710963 0.465423 0.379634 0 1 0.933333 1 0.2 0.8 0.2 0.8 0.098562 1 0.8 0.751405 Sopot 19819 0.580147236 0.5 0.785714 0.9 0.2 0.45 0.880585 0.801815 0.542993 0.635257 0.364735 0.162986 0.290372Sopot 0.2 0.2 0.2 0.64376319819 0.625186 0.6773010.5801472360.180391 0.5341870.5 0.7857140.292776 0.1089430.9 0.2 1 0.450.030540.8805850.2326250.8018150.9720810.542993 0.6352570.4 0.550590.3647350.7615150.1629860.6697750.2903720.477307 0.0763410.2 0.2 1 0.7333330.2 0.643763 0.6251861 0.6773010.2 0.1803910.8 0.5341870.2 0.2927760.8 0.0691930.108943 0.6 1 0.60.030540.6389840.232625 0.972081 0.4 0.55059 0.761515 0.669775 0.477307 0.076341 1 0.733333 1 0.2 0.8 0.2 0.8 0.069193 0.6 0.6 0.638984 Osečina 11113 0.577288086 0.833333 0.428571 0.7 0.8 0.6 0.871622 0.780821 0.610932 0.767085 0.5 0.4 0.5Osečina 0.4 0.2 1 0.62852211113 0.667356 0.4755310.5772880860.5960920.8333330.3635650.4285710.500154 0.6962640.7 0.8 0.8 0.0709220.6 0.8716220.1892480.7808210.9976910.610932 0.7670850.2 0.412151 0.9299860.5 0.6643360.4 0.5330330.5 0.4 0 0.2 0.8 1 0.6285220.4 0.6673560.8 0.4755310.8 0.5960920.2 0.3635650.6 0.5001540.6 0.0562080.696264 1 0.8 0.0709220.2 0.566820.189248 0.997691 0.2 0.412151 0.929986 0.664336 0.533033 0 0.8 0.4 0.8 0.8 0.2 0.6 0.6 0.056208 1 0.2 0.56682 Odžaci 27407 0.576786423 0.5 0.785714 0.7 0.4 0.35 0.977858 0.844556 0.515828 0.677125 0.1 0.512966 0.2Odžaci 0.2 0.2 0.2 0.58588927407 0.566422 0.622820.5767864230.307572 0.5419120.5 0.7857140.040532 0.3012870.7 0.4 0.8 0.350.0214630.9778580.2076410.8445560.8629780.515828 0.6771250.8 0.800059 0.8018190.1 0.5129660.575758 0.5174940.2 0.0007920.2 0.2 1 0.7333330.2 0.585889 0.5664220.4 0.60.62282 0.3075720.8 0.5419120.6 0.0405320.2 0.1747890.301287 1 0.8 0.0214630.4 0.6520720.207641 0.862978 0.8 0.800059 0.801819 0.575758 0.517494 0.000792 1 0.733333 0.4 0.6 0.8 0.6 0.2 0.174789 1 0.4 0.652072 Aleksinac 47562 0.575669604 0.333333 0.785714 0.9 0.2 0.35 0.92895 0.853994 0.464047 0.798521 0.2 0.07 0.08Aleksinac0.6 1 1 0.53548747562 0.622791 0.7617560.5756696040.2857890.3333330.1047950.7857140.217903 0.2228780.9 0.2 0.6 0.350.0163590.928950.1800920.8539940.9583860.464047 0.7985210.8 0.704009 0.8021650.2 0.070.365967 0.080.500774 0.6 0 1 1 0.3333331 0.535487 0.6227910.8 0.7617561 0.2857890.8 0.1047950.8 0.2179030.6 0.0722790.222878 1 0.6 0.0163590.6 0.7482870.180092 0.958386 0.8 0.704009 0.802165 0.365967 0.500774 0 1 0.333333 0.8 1 0.8 0.8 0.6 0.072279 1 0.6 0.748287 Nova Varoš 14595 0.575569886 0.666667 0.214286 0.9 0.8 0.95 0.942753 0.849751 0.565957 1 0.6 0.1 0.8Nova Varoš0.2 0.2 0.2 0.77149114595 0.608462 0.614980.5755698860.4114660.6666670.4562470.2142860.191111 0.1600490.9 0.8 0.6 0.950.0520540.9427530.1753950.8497510.9787120.565957 0.2 0.028631 0.7898430.6 0.4724160.1 0.3560410.8 0.1995610.2 0.2 0.8 0.7333330.2 0.771491 0.6084621 0.20.61498 0.4114660.6 0.4562470.8 0.1911110.2 0.1118050.160049 0.8 0.6 0.0520540.2 0.610980.175395 0.978712 0.2 0.02863 0.789843 0.472416 0.356041 0.199561 0.8 0.733333 1 0.2 0.6 0.8 0.2 0.111805 0.8 0.2 0.61098 Svilajnac 21414 0.574285548 0.333333 0.642857 1 0.4 0.35 0.914335 0.820016 0.540082 0.793514 0.5 0.243 0.5Svilajnac 0.4 0.2 1 0.58328321414 0.697637 0.584310.5742855480.2598110.3333330.226970.6428570.24328 0.3472151 0.4 0.8 0.350.0369240.9143350.2025580.8200160.980930.540082 0.7935140.8 0.802557 0.7943340.5 0.2430.536908 0.50.54559 0.4 0 0.2 0.6 0.8666671 0.583283 0.6976371 0.20.58431 0.2598110.2 0.60.22697 0.60.243280.0885610.347215 1 0.8 0.0369240.4 0.6038330.202558 0.98093 0.8 0.802557 0.794334 0.536908 0.54559 0 0.6 0.866667 1 0.2 0.2 0.6 0.6 0.088561 1 0.4 0.603833 Kruševac 121293 0.572030851 0.333333 0.785714 1 0.4 0.35 0.952736 0.827112 0.460939 0.850891 0.3 0.27 0.03Kruševac 0.6 1 0.6 0.600697121293 0.564092 0.6384410.5720308510.2424030.3333330.4101820.7857140.126795 0.1789971 0.4 0.2 0.350.0051470.9527360.2074060.8271120.9731380.460939 0.8508910.6 0.669219 0.7543760.3 0.270.271173 0.030.432811 0.0043660.6 1 0.8 0.6 0.6006970.6 0.5640921 0.6384411 0.2424030.8 0.4101820.4 0.1267950.8 0.0790260.178997 1 0.2 0.0051470.6 0.7378620.207406 0.973138 0.6 0.669219 0.754376 0.271173 0.432811 0.004366 0.8 0.6 1 1 0.8 0.4 0.8 0.079026 1 0.6 0.737862 Lapovo 7210 0.571900822 0.333333 0.642857 0.9 0.2 0.35 0.887022 0.843738 0.519239 0.747376 0.95 0.1 0.1Lapovo 0.2 0.2 0.6 0.6795397210 0.589572 0.7029270.5719008220.1395620.3333330.4362420.6428570.113646 0.0609450.9 0.2 0.6 0.350.0904630.8870220.2021050.8437380.9337580.519239 0.7473760.8 0.833535 0.950.969023 0.5089360.1 0.10.50106 0.2 0 0.2 0.2 0.6 0.6795390.2 0.5895720.6 0.7029270.6 0.1395620.2 0.4362420.4 0.1136460.2 0.2265730.060945 1 0.6 0.0904630.8 0.5167060.202105 0.933758 0.8 0.833535 0.969023 0.508936 0.50106 0 0.2 0.2 0.6 0.6 0.2 0.4 0.2 0.226573 1 0.8 0.516706 Smederevska Palanka 45823 0.570817152 0.333333 0.928571 0.9 0.2 0.45 0.905466 0.805952 0.527689 0.659837 0.6 0.2 0.6Smederevska0.6 Palanka0.2 1 0.63119345823 0.58633 0.5759120.5708171520.2003890.3333330.0548340.9285710.361382 0.90.28203 0.2 0.2 0.450.0157250.9054660.1847810.8059520.9305120.527689 0.6598370.4 0.612088 0.7654310.6 0.20.24087 0.4246230.6 0.0051310.6 0.2 1 0.5333331 0.631193 0.586331 0.5759121 0.2003890.4 0.0548340.6 0.3613820.6 0.0930460.28203 1 0.2 0.0157250.4 0.6995430.184781 0.930512 0.4 0.612088 0.765431 0.24087 0.424623 0.005131 1 0.533333 1 1 0.4 0.6 0.6 0.093046 1 0.4 0.699543 Prijepolje 34810 0.569111764 0.833333 0.214286 0.9 0.4 0.9 0.942753 0.849751 0.620177 1 0.05 0.1 0.2Prijepolje0.8 0.2 1 0.57784934810 0.536202 0.5629120.5691117640.3545880.8333330.4562470.2142860.263052 0.0771180.9 0.4 0.8 0.0213940.9 0.9427530.1738460.8497510.9849440.620177 0.2 0.0309541 0.050.813795 0.10.28749 0.3540360.2 0.2602490.8 0.2 1 0.7333331 0.577849 0.5362021 0.5629121 0.3545880.8 0.4562471 0.2630520.6 0.0905050.077118 0.8 0.8 0.0213941 0.8786190.173846 0.984944 0.2 0.030954 0.813795 0.28749 0.354036 0.260249 1 0.733333 1 1 0.8 1 0.6 0.090505 0.8 1 0.878619 Ivanjica 29832 0.568319213 0.666667 0.428571 0.9 0.8 0.7 0.924889 0.816558 0.605457 0.914402 0.5 0.7 0.8Ivanjica 1 0.2 0.6 0.73017429832 0.575266 0.439470.5683192130.3741670.6666670.7870120.4285710.109615 0.2191120.9 0.8 0.8 0.0202730.7 0.9248890.2142040.8165580.9523750.605457 0.9144020.2 0.088259 0.7673880.5 0.5151520.7 0.3700360.8 0.4093411 0.2 0.8 0.5333330.6 0.730174 0.5752661 0.80.43947 0.3741670.2 0.7870120.6 0.1096150.4 0.0872980.219112 0.6 0.8 0.0202730.6 0.6229620.214204 0.952375 0.2 0.088259 0.767388 0.515152 0.370036 0.409341 0.8 0.533333 1 0.8 0.2 0.6 0.4 0.087298 0.6 0.6 0.622962 Despotovac 20629 0.567764444 0.333333 0.642857 1 0.2 0.35 0.914335 0.820016 0.594092 0.793514 0.5 0.6 0.4Despotovac0.2 0.2 1 0.66352720629 0.709791 0.4820670.5677644440.4018430.3333330.226970.6428570.227332 0.3233191 0.2 0.2 0.350.0432940.9143350.1782510.8200160.9099430.594092 0.7935140.2 0.499614 0.7626670.5 0.4716390.6 0.3925510.4 0.1783620.2 0.2 1 0.9333331 0.663527 0.7097910.2 0.4820670.4 0.4018430.2 0.60.22697 0.2273320.8 0.1093810.323319 1 0.2 0.0432941 0.686940.178251 0.909943 0.2 0.499614 0.762667 0.471639 0.392551 0.178362 1 0.933333 0.2 0.4 0.2 0.6 0.8 0.109381 1 1 0.68694 Doljevac 17991 0.5677457 0.333333 0.785714 0.9 0.2 0.4 0.92895 0.853994 0.455703 0.798521 0.1 0.12 0.7Doljevac 0.2 1 1 0.53912317991 0.640882 0.6860970.56774570.3969420.3333330.5634940.7857140.365842 0.0303580.9 0.2 0.8 0.0408140.4 0.928950.2024310.8539940.966350.455703 0.7985210.8 0.819237 0.8636570.1 0.120.635587 0.5451270.7 0.2 0 1 0.6 0.3333331 0.539123 0.6408821 0.6860970.8 0.3969420.2 0.5634940.8 0.3658420.2 0.0732750.030358 1 0.8 0.0408140.6 0.6446080.202431 0.96635 0.8 0.819237 0.863657 0.635587 0.545127 0 0.6 0.333333 1 0.8 0.2 0.8 0.2 0.073275 1 0.6 0.644608 Barajevo 26855 0.567188944 0.5 0.785714 0.7 0.2 0.45 0.880585 0.801815 0.561217 0.635257 0.5 0.2 0.2Barajevo 0.6 0.2 1 0.6015726855 0.591424 0.697130.5671889440.153273 0.5341870.5 0.7857140.145513 0.0688260.7 0.2 1 0.450.021310.8805850.2360150.8018150.9813590.561217 0.6352570.4 0.602216 0.7817830.5 0.6138310.2 0.4322620.2 0.60.00441 0.2 0.8 0.9333331 0.60157 0.5914241 0.20.69713 0.1532730.8 0.5341870.2 0.1455130.4 0.0925290.068826 1 1 0.021311 0.7180080.236015 0.981359 0.4 0.602216 0.781783 0.613831 0.432262 0.00441 0.8 0.933333 1 0.2 0.8 0.2 0.4 0.092529 1 1 0.718008 Titel 15031 0.566654449 0.333333 0.642857 0.7 0.6 0.35 0.951975 0.867149 0.572532 0.632806 0.1 0.25 0.08Titel 0.6 0.2 0.2 0.54852715031 0.526701 0.6555980.5666544490.3333840.3333330.557510.6428570.216698 0.4913840.7 0.6 0.8 0.350.041320.9519750.1972840.8671490.8422890.572532 0.6328060.6 0.72585 0.9360890.1 0.250.778555 0.080.548938 0.0622170.6 0.2 0.8 0.2 0.5485271 0.5267010.4 0.6555980.8 0.3333840.2 0.60.55751 0.2166980.2 0.1647620.491384 0.6 0.8 0.80.041320.6204190.197284 0.842289 0.6 0.72585 0.936089 0.778555 0.548938 0.062217 0.8 1 0.4 0.8 0.2 0.6 0.2 0.164762 0.6 0.8 0.620419 Bujanovac 37735 0.562840787 0.333333 0.785714 1 1 0.65 0.973202 1 -0.45071 0.952964 0.495437 0.074792 0.564721Bujanovac0.2 0.2 1 0.64305137735 0.499446 0.230160.5628407870.5632280.3333330.4352590.7857140.335285 0.086821 1 0.8 0.650.0137570.9732020.264596 0.9746191 -0.45071 0.9529640.2 0.4541210.4954370.7981350.0747920.5532250.5647210.400589 0.0610520.2 0.2 0.6 0.4666671 0.643051 0.4994461 0.230161 0.5632280.6 0.4352591 0.3352851 0.1513230.08682 0.6 0.8 0.0137570.6 0.6859020.264596 0.974619 0.2 0.454121 0.798135 0.553225 0.400589 0.061052 0.6 0.466667 1 1 0.6 1 1 0.151323 0.6 0.6 0.685902 Kraljevo 118363 0.561761749 0.5 0.071429 0.9 0.4 0.55 0.960726 0.937572 0.001868 0.950689 0.4 1 0.2Kraljevo 0.2 0.2 1 0.657441118363 0.580782 0.6503080.5617617490.226414 0.9408680.5 0.0714290.102462 0.1084690.9 0.4 0.2 0.550.0054740.9607260.2102570.9375720.9911990.001868 0.9506890.2 0.435035 0.8186320.4 0.2680651 0.3799190.2 0.1001930.2 0.2 0.4 1 0.6574410.6 0.5807820.8 0.6503080.8 0.2264141 0.9408680.6 0.1024620.8 0.0855740.108469 1 0.2 0.0054740.6 0.6910070.210257 0.991199 0.2 0.435035 0.818632 0.268065 0.379919 0.100193 0.4 0.6 0.8 0.8 1 0.6 0.8 0.085574 1 0.6 0.691007 Batočina 10977 0.560305032 0.5 0.642857 0.9 0.2 0.35 0.887022 0.843738 0.516632 0.747376 0.825609 0.130885 0.148731Batočina 0.2 0.2 0.8 0.64459710977 0.563893 0.6863390.5603050320.215952 0.4362420.5 0.6428570.335511 0.1029010.9 0.2 0.8 0.350.060690.8870220.1950490.8437380.9939080.516632 0.7473760.6 0.4609460.8256090.9499080.1308850.5330230.1487310.482412 0.0243020.2 0.2 0.6 0.4666670.8 0.644597 0.5638931 0.6863390.4 0.2159520.2 0.4362420.6 0.3355110.2 0.1126440.102901 0.6 0.8 0.60.060690.5507060.195049 0.993908 0.6 0.460946 0.949908 0.533023 0.482412 0.024302 0.6 0.466667 1 0.4 0.2 0.6 0.2 0.112644 0.6 0.6 0.550706 Obrenovac 72124 0.559530863 0.333333 0.785714 0.9 0.2 0.45 0.880585 0.801815 0.592671 0.635257 0.37 0.7 0.5Obrenovac0.6 0.2 1 0.63281172124 0.532889 0.5397380.5595308630.1950880.3333330.5341870.7857140.129428 0.0821910.9 0.2 1 0.450.0066360.8805850.2569960.8018150.9670920.592671 0.6352570.4 0.622576 0.370.733303 0.7094020.7 0.50.44789 0.0012630.6 0.2 0.8 1 0.6328110.8 0.5328891 0.5397380.2 0.1950880.8 0.5341870.2 0.1294280.6 0.0901620.082191 0.6 1 0.0066360.6 0.6170720.256996 0.967092 0.4 0.622576 0.733303 0.709402 0.44789 0.001263 0.8 0.8 1 0.2 0.8 0.2 0.6 0.090162 0.6 0.6 0.617072 Boljevac 11358 0.558789858 0.333333 0.642857 0.9 0.2 0.35 0.932482 0.816604 0.567815 0.859134 0.6 0.1 0.3Boljevac 0.2 0.2 0.6 0.66851511358 0.739129 0.1923990.5587898580.2700670.3333330.5447510.6428570.549626 0.6411330.9 0.2 0.2 0.350.082420.9324820.1739790.8166040.9270530.567815 0.8591340.2 0.235685 0.8387840.6 0.4646460.1 0.4391440.3 0.20.0339 0.2 0.6 0.6 0.6685150.8 0.7391291 0.1923990.4 0.2700670.2 0.5447510.6 0.5496260.2 0.0997780.641133 1 0.2 0.20.082420.5736710.173979 0.927053 0.2 0.235685 0.838784 0.464646 0.439144 0.0339 0.6 0.8 1 0.4 0.2 0.6 0.2 0.099778 1 0.2 0.573671 Žabalj 25156 0.55776313 0.333333 0.642857 0.7 0.6 0.3 0.951975 0.867149 0.537397 0.632806 0.399368 0.156559 0.141004Žabalj 0.2 0.2 0.6 0.55396125156 0.492904 0.6613170.557763130.3723620.3333330.557510.6428570.054466 0.3651610.7 0.6 0.8 0.0238410.3 0.9519750.2085650.8671490.8358380.537397 0.6328060.6 0.6688880.3993680.9018890.1565590.7816630.1410040.502547 0.20.054 0.2 0.6 0.6 0.5539610.8 0.4929040.6 0.6613171 0.3723620.2 0.60.55751 0.0544660.4 0.120790.365161 0.8 0.8 0.0238410.8 0.6462910.208565 0.835838 0.6 0.668888 0.901889 0.781663 0.502547 0.054 0.6 0.8 0.6 1 0.2 0.6 0.4 0.12079 0.8 0.8 0.646291 Ub 27407 0.557713708 0.5 0.428571 0.9 0.4 0.5 0.871622 0.780821 0.642223 0.767085 0.65 0.25 0.65Ub 0.4 0.2 1 0.57303527407 0.59061 0.5166530.5577137080.405566 0.3635650.5 0.4285710.470183 0.8255760.9 0.4 0.2 0.0237310.5 0.8716220.2113360.7808210.9978460.642223 0.7670850.4 0.651668 0.650.857784 0.250.665113 0.650.552266 0.4 0 0.2 0.6 0.3333331 0.573035 0.40.59061 0.5166530.4 0.4055660.4 0.3635650.6 0.4701830.4 0.0542370.825576 1 0.2 0.0237310.6 0.5429030.211336 0.997846 0.4 0.651668 0.857784 0.665113 0.552266 0 0.6 0.333333 0.4 0.4 0.4 0.6 0.4 0.054237 1 0.6 0.542903 Vladičin Han 18967 0.556507291 0.5 0.785714 0.9 0.6 0.55 0.973202 1 -0.34706 0.952964 0.8 0.15 0.01Vladičin Han0.2 0.2 0.4 0.62365618967 0.582018 0.5792870.5565072910.588094 0.4352590.5 0.7857140.28515 0.0472920.9 0.6 0.4 0.550.0372590.9732020.189815 0.9600391 -0.34706 0.9529640.2 0.311453 0.8418930.8 0.150.475524 0.010.374512 0.0038030.2 0.2 0.6 0.7333330.4 0.623656 0.5820181 0.5792870.6 0.5880940.6 0.4352591 0.20.285150.0783250.047292 1 0.4 0.0372590.6 0.7287770.189815 0.960039 0.2 0.311453 0.841893 0.475524 0.374512 0.003803 0.6 0.733333 1 0.6 0.6 1 0.2 0.078325 1 0.6 0.728777 Mladenovac 51889 0.555964445 0.333333 0.785714 0.9 0.4 0.5 0.880585 0.801815 0.526342 0.635257 0.15 0.03 0.25Mladenovac0.4 0.2 1 0.55057851889 0.55494 0.5559644450.6537 0.1570560.3333330.8041350.7857140.216811 0.1879310.9 0.4 1 0.0103310.5 0.8805850.231620.8018150.9454670.526342 0.6352570.4 0.568427 0.150.745394 0.030.452991 0.250.478703 0.0509380.4 0.2 1 1 0.5505780.8 0.80.55494 0.20.6537 0.1570560.8 0.8041350.2 0.2168110.8 0.0865250.187931 1 1 0.0103310.6 0.6799370.23162 0.945467 0.4 0.568427 0.745394 0.452991 0.478703 0.050938 1 0.8 0.8 0.2 0.8 0.2 0.8 0.086525 1 0.6 0.679937 Sokobanja 14201 0.554103639 0.333333 0.642857 1 0.4 0.4 0.932482 0.816604 0.465115 0.859134 0.4 0.35 0.4Sokobanja0.2 0.2 0.2 0.65806814201 0.738249 0.7250020.5541036390.1750960.3333330.5447510.6428570.117212 0.2821581 0.4 0.2 0.0585110.4 0.9324820.194050.8166040.9651410.465115 0.8591340.2 0.420026 0.7793640.4 0.350.177933 0.3941890.4 0.0872230.2 0.2 0.6 0.3333330.2 0.658068 0.7382491 0.7250020.4 0.1750960.6 0.5447510.6 0.1172120.2 0.1196290.282158 1 0.2 0.0585110.6 0.6380290.19405 0.965141 0.2 0.420026 0.779364 0.177933 0.394189 0.087223 0.6 0.333333 1 0.4 0.6 0.6 0.2 0.119629 1 0.6 0.638029 Leskovac 135591 0.550357851 0.5 0.428571 0.9 0.4 0.35 0.936888 0.940219 0.417853 0.842395 0.5 0.2 0.3Leskovac 1 1 1 0.566604135591 0.559188 0.5874880.5503578510.375332 0.4742770.5 0.4285710.175822 0.1235480.9 0.4 0.6 0.350.0049710.9368880.1933720.9402190.981880.417853 0.8423950.4 0.637592 0.7814370.5 0.2517480.2 0.4328910.3 0.00221 1 0.8 0.4666671 0.566604 0.5591881 0.5874881 0.3753320.8 0.4742770.6 0.1758220.2 0.0697750.123548 1 0.6 0.0049710.4 0.7021890.193372 0.98188 0.4 0.637592 0.781437 0.251748 0.432891 0.0022 0.8 0.466667 1 1 0.8 0.6 0.2 0.069775 1 0.4 0.702189 Ljig 11374 0.549895708 0.666667 0.428571 1 0.6 0.55 0.871622 0.780821 0.571514 0.767085 0.7 0.3 0.2Ljig 0.4 0.2 0.6 0.60555811374 0.671596 0.5417680.5498957080.2348780.6666670.3635650.4285710.180618 0.297371 0.6 0.2 0.550.072250.8716220.1840960.7808210.9764340.571514 0.7670850.2 0.448985 0.70.91018 0.7365970.3 0.4037050.2 0.0464530.4 0.2 0.4 0.7333330.6 0.605558 0.6715961 0.5417680.6 0.2348780.6 0.3635650.8 0.1806180.2 0.0906060.29737 1 0.2 0.60.072250.6856890.184096 0.976434 0.2 0.448985 0.91018 0.736597 0.403705 0.046453 0.4 0.733333 1 0.6 0.6 0.8 0.2 0.090606 1 0.6 0.685689 Lebane 19730 0.545413313 0.333333 0.428571 1 0.6 0.4 0.936888 0.940219 0.279723 0.842395 0.2 0.3 0.4Lebane 0.2 0.2 0.6 0.56061219730 0.603783 0.6095030.5454133130.6204390.3333330.4742770.4285710.442975 0.3272681 0.6 0.8 0.0487270.4 0.9368880.1491980.9402190.9810920.279723 0.8423950.2 0.477095 0.8885310.2 0.4646460.3 0.4834940.4 0.1544690.2 0.2 1 0.6 0.5606120.6 0.6037831 0.6095030.8 0.6204390.2 0.4742771 0.4429750.2 0.0730250.327268 0.6 0.8 0.0487270.2 0.6154940.149198 0.981092 0.2 0.477095 0.888531 0.464646 0.483494 0.154469 1 0.6 1 0.8 0.2 1 0.2 0.073025 0.6 0.2 0.615494 Ljubovija 12805 0.54450419 1 0.214286 1 0.8 0.65 0.901906 0.801652 0.574403 0.702901 0.75 0.55 0.4Ljubovija0.4 0.8 1 0.68739512805 0.542482 0.4047310.544504190.538528 0.4271871 0.2142860.23751 0.2979211 0.8 0.2 0.650.0504730.9019060.1922620.8016520.9904640.574403 0.7029010.2 0.367522 0.750.907877 0.550.432789 0.3880140.4 0.0184210.4 0.8 0.6 0.4666671 0.687395 0.5424821 0.4047310.2 0.5385280.6 0.4271870.6 0.20.237510.0935020.297921 1 0.2 0.0504730.2 0.5649030.192262 0.990464 0.2 0.367522 0.907877 0.432789 0.388014 0.018421 0.6 0.466667 1 0.2 0.6 0.6 0.2 0.093502 1 0.2 0.564903 Blace 10509 0.542668131 0.333333 0.214286 0.9 0.6 0.4 0.966888 0.871088 0.587124 0.875621 0.2 0.1 0.2Blace 0.2 0.2 0.2 0.53957710509 0.746581 0.7285480.5426681310.6649910.3333330.6729410.2142860.173222 0.1456290.9 0.6 0.8 0.0878210.4 0.9668880.1827520.8710880.999110.587124 0.8756210.4 0.300814 0.7663520.2 0.4801860.1 0.4472840.2 3.46E-050.2 0.2 1 0.7333330.2 0.539577 0.7465811 0.7285480.6 0.6649910.2 0.6729410.8 0.1732220.2 0.1270890.145629 0.8 0.8 0.0878210.6 0.6903820.182752 0.99911 0.4 0.300814 0.766352 0.480186 0.447284 3.46E-05 1 0.733333 1 0.6 0.2 0.8 0.2 0.127089 0.8 0.6 0.690382 Smederevo 103180 0.54160391 0.333333 1 0.7 0.4 0.5 0.905466 0.805952 0.518145 0.659837 0.4 0.2 0.5Smederevo1 1 1 0.569486103180 0.501991 0.6688880.541603910.1948350.3333330.054834 0.1493051 0.1101370.7 0.4 0.2 0.0053860.5 0.9054660.2144740.8059520.9046010.518145 0.6598370.6 0.655673 0.7671580.4 0.3550890.2 0.4322620.5 0.0004321 1 0.8 0.4666671 0.569486 0.5019911 0.6688880.6 0.1948350.8 0.0548340.4 0.1493051 0.0773590.110137 0.8 0.2 0.0053860.8 0.7023320.214474 0.904601 0.6 0.655673 0.767158 0.355089 0.432262 0.000432 0.8 0.466667 1 0.6 0.8 0.4 1 0.077359 0.8 0.8 0.702332 Vlasotince 27718 0.541121066 0.5 0.428571 0.9 0.6 0.4 0.936888 0.940219 0.407376 0.842395 0.2 0.42 0.55Vlasotince0.2 0.2 1 0.56229227718 0.568115 0.5225970.5411210660.528226 0.4742770.5 0.4285710.256053 0.1343150.9 0.6 1 0.0241750.4 0.9368880.1987620.9402190.9838170.407376 0.8423950.2 0.543742 0.8254260.2 0.420.633256 0.550.446612 1.75E-060.2 0.2 0.4 1 0.5622920.6 0.5681151 0.5225971 0.5282260.4 0.4742770.8 0.2560530.2 0.0707150.134315 0.8 1 0.0241750.6 0.6511290.198762 0.983817 0.2 0.543742 0.825426 0.633256 0.446612 1.75E-06 0.4 0.6 1 1 0.4 0.8 0.2 0.070715 0.8 0.6 0.651129 Čajetina 14564 0.54086067 0.666667 0.214286 0.9 0.4 1 0.942753 0.849751 0.447689 1 0.7 0.2 0.9Čajetina 0.2 0.2 0.2 0.72993814564 0.660552 0.5657410.540860670.451110.6666670.4562470.2142860.15588 0.1113260.9 0.4 0.8 0.0406621 0.9427530.244510.8497510.952270.447689 0.2 0.0122741 0.7212110.7 0.4716390.2 0.3527920.9 0.20.67838 0.2 0.4 0.9333330.2 0.729938 0.6605520.4 0.5657410.2 0.80.45111 0.4562470.4 0.20.155880.1318930.111326 0.6 0.8 0.0406620.6 0.5393480.24451 0.95227 0.2 0.012274 0.721211 0.471639 0.352792 0.67838 0.4 0.933333 0.4 0.2 0.8 0.4 0.2 0.131893 0.6 0.6 0.539348 Knić 12919 0.540659004 0.5 0.642857 0.9 0.4 0.4 0.887022 0.843738 0.239143 0.747376 0.3 0.6 0.4Knić 0.2 0.2 0.2 0.5890812919 0.736594 0.6475590.5406590040.341662 0.4362420.5 0.6428570.267015 0.5038480.9 0.4 0.4 0.0687610.4 0.8870220.1852450.8437380.9807010.239143 0.7473760.2 0.493807 0.8377480.3 0.5578870.6 0.4688370.4 0.0017840.2 0.2 0.8 0.3333330.2 0.58908 0.7365940.8 0.6475590.8 0.3416620.4 0.4362420.6 0.2670150.2 0.0831470.503848 1 0.4 0.0687610.2 0.575760.185245 0.980701 0.2 0.493807 0.837748 0.557887 0.468837 0.001784 0.8 0.333333 0.8 0.8 0.4 0.6 0.2 0.083147 1 0.2 0.57576 Jagodina 69384 0.540546494 0.333333 0.642857 1 0.4 0.35 0.914335 0.820016 0.556116 0.793514 0.4 0.2 0.5Jagodina 0.6 1 1 0.61905169384 0.584054 0.6802240.5405464940.2097920.3333330.1445710.6428570.182466 0.0931141 0.4 0.6 0.350.0090960.9143350.2112740.8200160.9546490.556116 0.7935140.6 0.70104 0.40.73169 0.2960370.2 0.4462750.5 0.0013060.6 1 0.6 0.4666671 0.619051 0.5840541 0.6802240.4 0.2097920.2 0.1445710.4 0.1824660.6 0.0819830.093114 0.8 0.6 0.0090960.6 0.5641960.211274 0.954649 0.6 0.70104 0.73169 0.296037 0.446275 0.001306 0.6 0.466667 1 0.4 0.2 0.4 0.6 0.081983 0.8 0.6 0.564196 Prokuplje 41218 0.537533782 0.333333 0.214286 0.9 0.4 0.4 0.966888 0.871088 0.465583 0.875621 0.616731 0.213483 0.431889Prokuplje0.2 0.2 0.4 0.48870341218 0.592673 0.6349030.5375337820.3816720.3333330.7136090.2142860.141744 0.0607820.9 0.4 0.4 0.0154150.4 0.9668880.2206040.8710880.9747370.465583 0.8756210.6 0.3762510.6167310.7572550.2134830.1973580.4318890.410972 0.1319050.2 0.2 1 0.4 0.4887031 0.5926731 0.6349030.6 0.3816720.8 0.7136090.6 0.1417440.4 0.0932750.060782 0.6 0.4 0.0154151 0.8006220.220604 0.974737 0.6 0.376251 0.757255 0.197358 0.410972 0.131905 1 1 1 0.6 0.8 0.6 0.4 0.093275 0.6 1 0.800622 Užice 66996 0.53605598 0.666667 0.214286 0.9 0.6 0.9 0.942753 0.849751 0.451028 1 0.513835 0.224668 0.734724Užice 0.6 1 0.6 0.69825266996 0.537153 0.4129490.536055980.2151860.6666670.4562470.2142860.052011 0.0442990.9 0.6 0.2 0.0072010.9 0.9427530.2464440.8497510.9713490.451028 0.2 0.169351 0.5138350.6801010.2246680.163170.7347240.283622 0.2069860.6 1 1 0.6 0.6982521 0.5371531 0.4129490.4 0.2151860.8 0.4562470.2 0.0520110.6 0.0852340.044299 1 0.2 0.0072010.2 0.6714130.246444 0.971349 0.2 0.16935 0.680101 0.16317 0.283622 0.206986 1 1 1 0.4 0.8 0.2 0.6 0.085234 1 0.2 0.671413 Valjevo 85962 0.535696722 0.833333 0.428571 0.9 0.6 0.55 0.871622 0.780821 0.643825 0.767085 0.3 0.3 0.4Valjevo 0.6 0.6 1 0.59972885962 0.565522 0.4513520.5356967220.2436020.8333330.3635650.4285710.141776 0.1793130.9 0.6 0.2 0.550.0055230.8716220.2583910.7808210.9525930.643825 0.7670850.2 0.354082 0.7265090.3 0.2229990.3 0.3399010.4 0.0161580.6 0.6 0.6 1 0.5997281 0.5655221 0.4513520.6 0.2436020.4 0.3635650.2 0.1417760.8 0.0842630.179313 1 0.2 0.0055231 0.7333440.258391 0.952593 0.2 0.354082 0.726509 0.222999 0.339901 0.016158 0.6 1 1 0.6 0.4 0.2 0.8 0.084263 1 1 0.733344 Kosjerić 10814 0.53554154 0.666667 0.214286 1 0.6 0.75 0.942753 0.849751 0.50108 1 0.6 0.3 0.8Kosjerić 0.6 0.2 0.4 0.67236810814 0.671237 0.5578270.535541540.4822090.6666670.4562470.2142860.108454 0.291781 0.6 0.2 0.750.0635530.9427530.2152190.8497510.9924570.50108 0.2 0.2093721 0.7718790.6 0.5602180.3 0.3670310.8 1.93E-050.6 0.2 0.8 0.4 0.6723680.8 0.6712371 0.5578270.4 0.4822090.2 0.4562470.4 0.1084540.2 0.1142320.29178 0.8 0.2 0.0635530.4 0.5830680.215219 0.992457 0.2 0.209372 0.771879 0.560218 0.367031 1.93E-05 0.8 0.8 1 0.4 0.2 0.4 0.2 0.114232 0.8 0.4 0.583068 Gornji Milanovac 41492 0.533818139 0.666667 0.428571 1 1 0.55 0.924889 0.816558 0.473874 0.914402 0.5 0.125 0.68Gornji Milanovac1 0.2 0.6 0.68067241492 0.596765 0.5090690.5338181390.190560.6666670.7870120.4285710.086021 0.1234751 1 0.2 0.550.0131090.9248890.2387180.8165580.9594890.473874 0.9144020.2 0.293701 0.7328420.5 0.1250.328671 0.680.341666 0.0004151 0.2 0.2 0.6 0.6806720.6 0.5967651 0.5090690.6 0.20.19056 0.7870120.4 0.0860210.2 0.0710880.123475 1 0.2 0.0131090.8 0.6017650.238718 0.959489 0.2 0.293701 0.732842 0.328671 0.341666 0.000415 0.2 0.6 1 0.6 0.2 0.4 0.2 0.071088 1 0.8 0.601765 Ćuprija 28203 0.532760355 0.333333 0.642857 0.9 0.2 0.3 0.914335 0.820016 0.577 0.793514 0.3 0.6 0.7Ćuprija 0.6 0.2 0.8 0.63566928203 0.618875 0.6724530.5327603550.2960980.3333330.2974020.6428570.128756 0.1365920.9 0.2 0.6 0.0252220.3 0.9143350.1926070.8200160.7794860.577 0.7935140.8 0.744598 0.7093510.3 0.1126650.6 0.4598850.7 0.0027880.6 0.2 0.2 0.4666670.8 0.635669 0.6188751 0.6724531 0.2960980.2 0.2974020.6 0.1287560.8 0.0827750.136592 0.4 0.6 0.0252220.4 0.4877090.192607 0.779486 0.8 0.744598 0.709351 0.112665 0.459885 0.002788 0.2 0.466667 1 1 0.2 0.6 0.8 0.082775 0.4 0.4 0.487709 Lučani 18602 0.527414845 0.666667 0.428571 1 0.4 0.7 0.924889 0.816558 0.419594 0.914402 0.184 0.316 0.263Lučani 0.2 0.2 1 0.57217118602 0.673115 0.5191730.5274148450.4320840.6666670.7870120.4285710.247525 0.2033621 0.4 0.2 0.0386380.7 0.9248890.2069160.8165580.9798790.419594 0.9144020.2 0.3930.1840.7977890.3160.6573430.2630.403418 0.0157870.2 0.2 0.2 0.4666671 0.572171 0.6731151 0.5191730.6 0.4320840.2 0.7870120.6 0.2475250.4 0.0917160.203362 1 0.2 0.0386381 0.6462760.206916 0.979879 0.2 0.393 0.797789 0.657343 0.403418 0.015787 0.2 0.466667 1 0.6 0.2 0.6 0.4 0.091716 1 1 0.646276 Novi Pazar 106261 0.525808449 0.333333 0.071429 0.9 0.4 0.6 0.960726 0.937572 0.858905 0.950689 1 0.3 Novi1 Pazar 1 0.2 0.8 0.619585106261 0.436872 0.3622730.5258084490.2629210.3333330.5084420.0714290.104904 0.0747280.9 0.4 0.8 0.0080970.6 0.9607260.1611320.9375720.9918270.858905 0.9506890.2 0.123102 0.8222021 0.3038070.3 0.3258441 0.0857451 0.2 0.8 0.4666670.8 0.619585 0.4368721 0.3622731 0.2629210.2 0.5084420.6 0.1049040.6 0.0937530.074728 1 0.8 0.0080970.6 0.685090.161132 0.991827 0.2 0.123102 0.822202 0.303807 0.325844 0.085745 0.8 0.466667 1 1 0.2 0.6 0.6 0.093753 1 0.6 0.68509 Bajina Bašta 24345 0.525692127 0.666667 0.214286 0.9 0.4 0.85 0.942753 0.849751 0.47259 1 0.3 0.2 0.6Bajina Bašta0.2 0.2 1 0.60287124345 0.585575 0.4504920.5256921270.4747110.6666670.4562470.2142860.078714 0.1966350.9 0.4 0.6 0.850.0255860.9427530.2113970.8497510.9467520.47259 0.2 0.1963731 0.7898430.3 0.6099460.2 0.3570110.6 0.4011890.2 0.2 0.6 0.5333331 0.602871 0.5855751 0.4504920.4 0.4747110.2 0.4562470.6 0.0787140.4 0.1148110.196635 1 0.6 0.0255860.8 0.6629460.211397 0.946752 0.2 0.196373 0.789843 0.609946 0.357011 0.401189 0.6 0.533333 1 0.4 0.2 0.6 0.4 0.114811 1 0.8 0.662946 Vrnjačka Banja 26141 0.524731382 0.666667 0.071429 0.9 0.4 0.4 0.960726 0.937572 0.039825 0.950689 0.1 0.08 0.2Vrnjačka Banja0.2 0.2 0.2 0.54691926141 0.604399 0.6595820.5247313820.2288180.6666670.4037680.0714290.0525 0.0724330.9 0.4 0.8 0.0240770.4 0.9607260.2193850.9375720.9938160.039825 0.9506890.4 0.628009 0.7639340.1 0.080.250971 0.4263460.2 0.20.06187 0.2 0.6 0.2 0.5469190.4 0.6043990.8 0.6595820.4 0.2288181 0.4037680.4 0.20.05250.1187730.072433 1 0.8 0.0240770.6 0.6390280.219385 0.993816 0.4 0.628009 0.763934 0.250971 0.426346 0.06187 0.6 0.4 0.8 0.4 1 0.4 0.2 0.118773 1 0.6 0.639028 Negotin 32654 0.520639675 0.5 0.785714 0.9 0.4 0.25 0.945629 0.754254 0.370076 0.801041 0.35 0.45 0.5Negotin 0.2 0.2 1 0.61660532654 0.748617 0.5206396750.6604 0.342827 0.5951890.5 0.7857140.227385 0.3135460.9 0.4 0.2 0.250.0282680.9456290.1786460.7542540.8278590.370076 0.8010410.4 0.34381 0.350.748964 0.450.25641 0.4095980.5 0.20.00215 0.2 0.6 1 0.6166050.6 0.7486170.8 0.40.6604 0.3428270.2 0.5951890.6 0.2273851 0.0996390.313546 0.8 0.2 0.0282680.4 0.5432540.178646 0.827859 0.4 0.34381 0.748964 0.25641 0.409598 0.00215 0.6 0.6 0.8 0.4 0.2 0.6 1 0.099639 0.8 0.4 0.543254 Čačak 110279 0.519047099 0.666667 0.428571 1 0.6 0.55 0.924889 0.816558 0.221056 0.914402 0.6 0.4 0.8Čačak 0.4 0.2 1 0.639228110279 0.576687 0.4795520.5190470990.1555580.6666670.7870120.4285710.095115 0.0600091 0.6 0.2 0.550.0047730.9248890.2473750.8165580.9904140.221056 0.9144020.2 0.461705 0.7406730.6 0.3069150.4 0.3530360.8 0.0289080.4 0.2 0.8 1 0.6392280.6 0.5766870.8 0.4795520.8 0.1555580.2 0.7870120.2 0.0951150.6 0.0767320.060009 0.8 0.2 0.0047730.6 0.5877940.247375 0.990414 0.2 0.461705 0.740673 0.306915 0.353036 0.028908 0.8 0.6 0.8 0.8 0.2 0.2 0.6 0.076732 0.8 0.6 0.587794 Bačka Palanka 52263 0.518825149 0.5 0.714286 0.7 0.4 0.35 0.951975 0.867149 0.49229 0.632806 0.5 0.7 0.323688Bačka Palanka0.6 0.2 0.6 0.61266652263 0.550401 0.5537090.5188251490.226834 0.50.557510.7142860.029089 0.2364680.7 0.4 0.2 0.350.010010.9519750.2389760.8671490.9168910.49229 0.6328060.8 0.68199 0.7777520.5 0.7000780.7 0.3236880.457121 0.0194950.6 0.2 0.8 0.6 0.6126661 0.5504010.2 0.5537090.8 0.2268340.4 0.20.55751 0.0290890.2 0.14060.236468 0.6 0.2 0.20.010010.470620.238976 0.916891 0.8 0.68199 0.777752 0.700078 0.457121 0.019495 0.8 1 0.2 0.8 0.4 0.2 0.2 0.1406 0.6 0.2 0.47062 Crveni krst 30939 0.517494498 0.333333 0.785714 0.9 0.4 0.4 0.92895 0.853994 0.460888 0.798521 0.6 0.15 0.3Crveni krst0.2 0.2 0.2 0.66999430939 0.562435 0.7357740.5174944980.1932770.3333330.852580.7857140.082402 0.0117050.9 0.4 0.6 0.0176920.4 0.928950.2386940.8539940.9861660.460888 0.7985210.8 0.746688 0.8280890.6 0.150.423854 0.4933160.3 0.20.16022 0.2 0.6 0.8666670.2 0.669994 0.5624350.2 0.7357740 0.1932770 0.852580 0.0824020.8 0.1361210.011705 0.6 0.6 0.0176920.2 0.3250130.238694 0.986166 0.8 0.746688 0.828089 0.423854 0.493316 0.16022 0.6 0.866667 0.2 0 0 0 0.8 0.136121 0.6 0.2 0.325013 Požarevac 59131 0.51746856 0.5 0.714286 0.9 0.2 0.4 0.934418 0.840472 0.329841 0.77219 0.849883 0.259814 0.197911Požarevac0.2 0.2 1 0.63732759131 0.550104 0.5863460.517468560.1407 0.7663010.5 0.7142860.102991 0.1427330.9 0.2 0.2 0.0089530.4 0.9344180.2396430.8404720.87450.329841 0.80.772190.7784170.8498830.7342240.2598140.217560.1979110.458024 0.0010110.2 0.2 0.6 0.7333331 0.637327 0.5501040.6 0.5863460 00.1407 0.7663010 0.1029910.8 0.1530380.142733 0.6 0.2 0.0089530.6 0.4268490.239643 0.8745 0.8 0.778417 0.734224 0.21756 0.458024 0.001011 0.6 0.733333 0.6 0 0 0 0.8 0.153038 0.6 0.6 0.426849 Topola 20445 0.517140248 0.5 0.642857 0.7 0.6 0.45 0.887022 0.843738 0.513885 0.747376 0.3 0.05 0.5Topola 0.6 0.2 0.8 0.54283720445 0.630062 0.6030560.5171402480.18424 0.4362420.5 0.6428570.34395 0.70.79433 0.6 0.6 0.450.0334340.8870220.2068170.8437380.9276730.513885 0.7473760.2 0.42712 0.9435740.3 0.050.610723 0.5152420.5 0.0025110.6 0.2 0.6 0.8 0.5428370.2 0.6300621 0.6030561 0.20.18424 0.4362420.4 0.40.343950.0959320.79433 0.6 0.6 0.0334340.2 0.4814430.206817 0.927673 0.2 0.42712 0.943574 0.610723 0.515242 0.002511 0.6 0.2 1 1 0.2 0.4 0.4 0.095932 0.6 0.2 0.481443 Aranđelovac 43834 0.515997174 0.5 0.642857 0.9 0.8 0.5 0.887022 0.843738 0.521638 0.747376 0.367505 0.171239 0.462362Aranđelovac0.2 0.2 0.2 0.63574343834 0.542675 0.5427150.5159971740.139195 0.50.56060.6428570.104257 0.0786330.9 0.8 0.2 0.50.012970.8870220.2198730.8437380.9804640.521638 0.7473760.2 0.4265770.3675050.7605940.1712390.3216780.4623620.345656 0.0020460.2 0.2 1 0.2 0.6357430.4 0.5426751 0.5427150.8 0.1391950.2 0.40.5606 0.1042570.2 0.069510.078633 1 0.2 0.20.012970.591890.219873 0.980464 0.2 0.426577 0.760594 0.321678 0.345656 0.002046 1 0.4 1 0.8 0.2 0.4 0.2 0.06951 1 0.2 0.59189 Loznica 75286 0.513632896 0.666667 0.214286 0.7 0.4 0.45 0.901906 0.801652 0.554999 0.702901 0.15 0.25 0.3Loznica 0.6 0.8 1 0.48492975286 0.527793 0.544960.5136328960.3458970.6666670.4271870.2142860.184682 0.1568280.7 0.4 0.8 0.450.0085340.9019060.1910520.8016520.996850.554999 0.7029010.6 0.910684 0.150.897973 0.250.263403 0.5046340.3 0.0318680.6 0.8 0.4 1 0.4849290.6 0.5277931 0.544961 0.3458970.4 0.4271870.6 0.1846820.8 0.0607570.156828 0.8 0.8 0.0085340.2 0.5701880.191052 0.99685 0.6 0.910684 0.897973 0.263403 0.504634 0.031868 0.4 0.6 1 1 0.4 0.6 0.8 0.060757 0.8 0.2 0.570188 Pećinci 19222 0.500626479 0.333333 0.642857 1 0.4 0.5 0.919208 0.807714 0.550744 0.646739 0.401112 0.299048 0.27218Pećinci 0.2 0.2 0.2 0.6379219222 0.52686 0.4376040.5006264790.2170440.3333330.1589160.6428570.091205 0.1751621 0.4 0.2 0.50.024970.9192080.2543010.8077140.6339930.550744 0.6467390.4 0.5987110.4011120.7403270.2990480.5742040.272180.354059 0.1691780.2 0.2 0.6 0.6666670.2 0.63792 0.80.52686 0.4376040.2 0.2170440.4 0.1589160.2 0.0912050.2 0.1029070.175162 0.6 0.2 0.60.024970.5145370.254301 0.633993 0.4 0.598711 0.740327 0.574204 0.354059 0.169178 0.6 0.666667 0.8 0.2 0.4 0.2 0.2 0.102907 0.6 0.6 0.514537 Požega 27554 0.491126236 0.5 0.214286 1 0.4 0.8 0.942753 0.849751 0.464574 1 0.4 0.35 0.8Požega 0.2 0.2 0.6 0.62464227554 0.610035 0.5201560.4911262360.270333 0.4562470.5 0.2142860.113064 0.1217561 0.4 0.2 0.0213360.8 0.9427530.230970.8497510.9963380.464574 0.2 0.3085611 0.7945650.4 0.350.668998 0.3494030.8 0.0007980.2 0.2 0.8 0.6 0.6246420.8 0.6100350.6 0.5201560.8 0.2703330.2 0.4562470.4 0.1130640.6 0.0884410.121756 0.6 0.2 0.0213360.2 0.5034380.23097 0.996338 0.2 0.308561 0.794565 0.668998 0.349403 0.000798 0.8 0.8 0.6 0.8 0.2 0.4 0.6 0.088441 0.6 0.2 0.503438 Šabac 110918 0.48789683 0.5 0.214286 0.9 0.6 0.45 0.901906 0.801652 0.551195 0.702901 0.5 0.1 0.5Šabac 0.6 1 1 0.475721110918 0.521244 0.5639320.487896830.268848 0.4271870.5 0.2142860.332345 0.2429710.9 0.6 0.2 0.450.004710.9019060.2289520.8016520.9188540.551195 0.7029010.8 0.784727 0.7640490.5 0.2890440.1 0.50.49213 0.0007310.6 1 0.2 0.8666671 0.475721 0.5212440.8 0.5639320.6 0.2688480.8 0.4271870.4 0.3323450.2 0.0749110.242971 0.6 0.2 0.20.004710.499810.228952 0.918854 0.8 0.784727 0.764049 0.289044 0.49213 0.000731 0.2 0.866667 0.8 0.6 0.8 0.4 0.2 0.074911 0.6 0.2 0.49981 Sremska Mitrovica 75873 0.485652438 0.333333 0.642857 0.7 0.8 0.45 0.919208 0.807714 0.475074 0.646739 0.1 0.2 0.02Sremska Mitrovica0.6 0.2 0.4 0.45674975873 0.537764 0.5973970.4856524380.2544970.3333330.1589160.6428570.09374 0.1951320.7 0.8 0.2 0.450.0070850.9192080.2283560.8077140.8412580.475074 0.6467390.8 0.850856 0.7197140.1 0.2276610.2 0.020.447007 0.0676140.6 0.2 0.6 0.8666670.4 0.456749 0.5377640.6 0.5973970.6 0.2544970.8 0.1589160.4 0.20.093740.138210.195132 1 0.2 0.0070850.2 0.5869750.228356 0.841258 0.8 0.850856 0.719714 0.227661 0.447007 0.067614 0.6 0.866667 0.6 0.6 0.8 0.4 0.2 0.13821 1 0.2 0.586975 Vranje 71551 0.47678063 0.5 0.785714 0.9 0.6 0.6 0.973202 1 -0.44642 0.952964 0.001 0.01 0.105025Vranje 1 0.2 1 0.50006571551 0.485323 0.4408390.476780630.271771 0.4352590.5 0.7857140.170881 0.0295470.9 0.6 0.2 0.60.007150.9732020.231316 0.9830141 -0.44642 0.9529640.2 0.3770110.0010.70555 0.010.2245530.1050250.323666 1 0 0.2 0.8 1 0.5000650.6 0.4853231 0.4408390.6 0.2717710.8 0.4352590.4 0.1708810.2 0.0968160.029547 0.8 0.2 0.60.007150.6715260.231316 0.983014 0.2 0.377011 0.70555 0.224553 0.323666 0 0.8 0.6 1 0.6 0.8 0.4 0.2 0.096816 0.8 0.6 0.671526 Bor 45266 0.463840665 0.5 0.785714 0.9 0.4 0.3 0.945629 0.754254 0.782929 0.801041 0.15 0.01 0.1Bor 0.4 0.2 1 0.57185445266 0.520048 0.588120.4638406650.173458 0.5951890.5 0.7857140.114899 0.0323620.9 0.4 0.2 0.0122490.3 0.9456290.2132120.7542540.9611420.782929 0.8010410.2 0.273457 0.150.779134 0.010.228438 0.3240170.1 0.0148370.4 0.2 0.2 1 0.5718540.6 0.5200481 0.20.58812 0.1734580.8 0.5951890.2 0.1148990.4 0.1031970.032362 1 0.2 0.0122490.2 0.5115560.213212 0.961142 0.2 0.273457 0.779134 0.228438 0.324017 0.014837 0.2 0.6 1 0.2 0.8 0.2 0.4 0.103197 1 0.2 0.511556 Pirot 54342 0.453156548 0.333333 0.214286 1 0.2 0.5 0.873752 0.827664 0.485317 0.911812 0.2 0.05 0.6Pirot 0.2 0.2 1 0.44553254342 0.610937 0.4754890.4531565480.1653270.3333330.0883950.2142860.072471 0.0432251 0.2 0.2 0.0107660.5 0.8737520.2275850.8276640.982920.485317 0.9118120.2 0.248595 0.7473510.2 0.050.228438 0.4322620.6 0.4830370.2 0.2 1 1 0.4455321 0.6109371 0.4754890.6 0.1653270.4 0.0883950.4 0.0724710.4 0.0880660.043225 1 0.2 0.0107660.4 0.7054590.227585 0.98292 0.2 0.248595 0.747351 0.228438 0.432262 0.483037 1 1 1 0.6 0.4 0.4 0.4 0.088066 1 0.4 0.705459 Zaječar 54355 0.422964719 0.333333 0.642857 0.7 0.4 0.3 0.932482 0.816604 0.314068 0.859134 0.28 0.16 0.2Zaječar 0.2 0.2 1 0.48215954355 0.653961 0.345950.4229647190.1667120.3333330.5447510.6428570.201106 0.1778360.7 0.4 0.2 0.0141790.3 0.9324820.1826650.8166040.9436480.314068 0.8591340.2 0.412321 0.280.744818 0.160.20979 0.3490240.2 0.0653750.2 0.2 0.6 0.4666671 0.482159 0.6539610.2 0.60.34595 0.1667120.2 0.5447510.4 0.2011060.8 0.0938540.177836 1 0.2 0.0141790.2 0.4450840.182665 0.943648 0.2 0.412321 0.744818 0.20979 0.349024 0.065375 0.6 0.466667 0.2 0.6 0.2 0.4 0.8 0.093854 1 0.2 0.445084 Total Total Territory Total population Vulnerability score Total Territory Total populationsuscepti Vulnerability score Total Total suscepti Total 1.01 1.02 1.03 1.04 1.05 1.06 1.07 1.08 1.09 1.1 1.11 1.12 1.13 1.14 1.15 exposure 2.01 2.02 2.03 2.04 2.05 2.06 2.07 2.08 2.09 2.1 2.11 2.12 2.13 2.14 bility 3.01 3.02 3.03 1.01 3.04 1.02 3.05 1.03 3.06 1.04 3.07 1.05 3.08 1.06 3.09 1.07 3.1 1.08 3.11 1.09capacity1.1 1.11 1.12 1.13 1.14 1.15 exposure 2.01 2.02 2.03 2.04 2.05 2.06 2.07 2.08 2.09 2.1 2.11 2.12 2.13 2.14 bility 3.01 3.02 3.03 3.04 3.05 3.06 3.07 3.08 3.09 3.1 3.11 capacity Žabari 9585 0.682978578 0.333333 0.642857 0.9 0.4 0.4 0.934418 0.840472 0.521819 0.77219 0.95 0.15 0.3 0.2 0.2 0.2 0.699384 0.769544 0.821082 0.262455 0.694524 0.335414 0.714784 1 0.131453 0.138017 0.958048 0.6Žabari0.56948 0.91755 0.676768 0.6196879585 0 0.6829785781 0.3333331 0.6428571 0.6 0.9 0.2 0.4 0.8 0.4 0.9344180.8 0.1089750.840472 0.5218191 0.60.772190.753308 0.95 0.15 0.3 0.2 0.2 0.2 0.699384 0.769544 0.821082 0.262455 0.694524 0.335414 0.714784 1 0.131453 0.138017 0.958048 0.6 0.56948 0.91755 0.676768 0.619687 0 1 1 1 0.6 0.2 0.8 0.8 0.108975 1 0.6 0.753308 Merošina 12963 0.664167888 0.333333 0.714286 0.9 0.2 0.4 0.92895 0.853994 0.459442 0.798521 0.8 0.22 0.43 0.6 1 0.4 0.712203 0.695098 0.580354 0.665217 0.563494 0.407744 0.191432 0.8 0.069777 0.180861 0.995156 0.4Merošina0.599616 0.886573 0.719503 0.51021712963 0.025181 0.6641678881 0.7333330.333333 0.7142861 0.8 0.9 0.2 0.2 1 0.4 0.20.928950.074660.853994 0.4594421 0.7985211 0.823041 0.8 0.22 0.43 0.6 1 0.4 0.712203 0.695098 0.580354 0.665217 0.563494 0.407744 0.191432 0.8 0.069777 0.180861 0.995156 0.4 0.599616 0.886573 0.719503 0.510217 0.025181 1 0.733333 1 0.8 0.2 1 0.2 0.07466 1 1 0.823041 Malo Crniće 9903 0.658469419 0.5 0.642857 1 0.4 0.35 0.934418 0.840472 0.356248 0.77219 0.9 0.4 0.3 0.6 0.2 1 0.642248 0.693301 0.705384 0.223351 0.694524 0.437071 0.970932 0.8 0.10336 0.155617 0.866666 0.8Malo0.647495 Crniće 0.994357 0.641026 0.6688889903 0 0.6584694190.6 0.2 0.5 0.6428571 0.6 1 0.6 0.4 0.8 0.35 0.9344181 0.1205330.840472 0.3562481 0.60.772190.667174 0.9 0.4 0.3 0.6 0.2 1 0.642248 0.693301 0.705384 0.223351 0.694524 0.437071 0.970932 0.8 0.10336 0.155617 0.866666 0.8 0.647495 0.994357 0.641026 0.668888 0 0.6 0.2 1 0.6 0.6 0.8 1 0.120533 1 0.6 0.667174 Rekovac 9270 0.657933704 0.5 0.642857 1 0.4 0.35 0.914335 0.820016 0.431386 0.793514 0.5 0.3 0.8 0.6 0.2 1 0.626481 0.853958 0.905771 0.505005 0.22697 0.833013 0.717154 0.8 0.134648 0.151166 0.951351 0.2Rekovac0.483668 0.897513 0.480186 0.6092449270 5.9E-05 0.6579337041 0.933333 0.5 0.6428571 0.6 1 0.6 0.4 0.8 0.35 0.9143350.2 0.0815850.820016 0.4313861 0.7935140.6 0.778148 0.5 0.3 0.8 0.6 0.2 1 0.626481 0.853958 0.905771 0.505005 0.22697 0.833013 0.717154 0.8 0.134648 0.151166 0.951351 0.2 0.483668 0.897513 0.480186 0.609244 5.9E-05 1 0.933333 1 0.6 0.6 0.8 0.2 0.081585 1 0.6 0.778148 Žagubica 11348 0.651012496 0.5 0.642857 1 0.2 0.3 0.934418 0.840472 0.761106 0.77219 0.9 0.8 0.01 0.2 0.2 1 0.74375 0.778548 0.790583 0.55198 0.694524 0.459957 0.578533 0.2 0.100345 0.156798 0.982457 0.2Žagubica0.271072 0.819668 0.48951 0.48805211348 0.001504 0.6510124960.8 0.8 0.5 0.6428571 0.4 1 0.2 0.2 0.8 0.3 0.9344180.2 0.1305580.840472 0.7611061 0.772191 0.756347 0.9 0.8 0.01 0.2 0.2 1 0.74375 0.778548 0.790583 0.55198 0.694524 0.459957 0.578533 0.2 0.100345 0.156798 0.982457 0.2 0.271072 0.819668 0.48951 0.488052 0.001504 0.8 0.8 1 0.4 0.2 0.8 0.2 0.130558 1 1 0.756347 Vladimirci 15818 0.647863363 0.5 0.214286 1 0.2 0.5 0.901906 0.801652 0.608787 0.702901 0.89 0.6 0.9 0.6 0.6 0.6 0.652104 0.653006 0.610611 0.494056 0.427187 0.767812 0.837831 0.8 0.046829 0.198879 0.985209 0.8Vladimirci0.80195 0.854215 0.878011 0.70101915818 0 0.6478633630.2 0.4 0.5 0.2142860.4 1 1 0.2 0.2 0.6 0.5 0.9019060.2 0.051650.801652 0.6087870.8 0.7029011 0.561769 0.89 0.6 0.9 0.6 0.6 0.6 0.652104 0.653006 0.610611 0.494056 0.427187 0.767812 0.837831 0.8 0.046829 0.198879 0.985209 0.8 0.80195 0.854215 0.878011 0.701019 0 0.2 0.4 0.4 1 0.2 0.6 0.2 0.05165 0.8 1 0.561769 Bogatić 26630 0.634749169 0.5 0.214286 0.9 0.2 0.45 0.901906 0.801652 0.507437 0.702901 0.3 0.2 0.15 0.2 0.8 0.8 0.472316 0.581515 0.498187 0.384083 0.427187 0.588124 1 0.8 0.027363 0.185292 0.931853 0.8Bogatić0.844817 0.87287 0.667444 0.68402526630 0.003624 0.6347491690.8 0.8 0.5 0.2142861 1 0.9 0.8 0.2 0.6 0.45 0.9019060.2 0.0780670.801652 0.5074371 0.7029010.8 0.804486 0.3 0.2 0.15 0.2 0.8 0.8 0.472316 0.581515 0.498187 0.384083 0.427187 0.588124 1 0.8 0.027363 0.185292 0.931853 0.8 0.844817 0.87287 0.667444 0.684025 0.003624 0.8 0.8 1 1 0.8 0.6 0.2 0.078067 1 0.8 0.804486 Preševo 29989 0.631215117 0.333333 0.785714 1 0.6 0.7 0.973202 1 -0.40954 0.952964 0.8 0.1 0.6 0.2 0.2 0.4 0.68093 0.518326 0.03905 0.479839 0.435259 0.665103 0.028081 0.8 0.004536 1 1 0.4Preševo0.379186 0.848111 0.54157 0.4900129989 0 0.6312151171 0.3333331 0.7857141 0.8 1 0.2 0.6 1 0.7 0.9732021 0.622266 1 -0.409541 0.9529640.4 0.768451 0.8 0.1 0.6 0.2 0.2 0.4 0.68093 0.518326 0.03905 0.479839 0.435259 0.665103 0.028081 0.8 0.004536 1 1 0.4 0.379186 0.848111 0.54157 0.49001 0 1 1 1 0.8 0.2 1 1 0.622266 1 0.4 0.768451 Brus 14814 0.631052558 0.5 0.785714 0.9 1 0.45 0.952736 0.827112 1 0.850891 0.05 0.1 0.9 0.6 0.2 0.2 0.743444 0.6543 0.73215 0.629989 0.653547 0.164929 0.449524 0.8 0.044799 0.217738 0.977386 0.4Brus0.190485 0.811147 0.547786 0.47409514814 0.076901 0.6310525581 1 0.5 0.7857141 0.6 0.9 0.4 1 0.8 0.45 0.9527360.2 0.0880940.827112 1 1 0.8508910.2 0.697901 0.05 0.1 0.9 0.6 0.2 0.2 0.743444 0.6543 0.73215 0.629989 0.653547 0.164929 0.449524 0.8 0.044799 0.217738 0.977386 0.4 0.190485 0.811147 0.547786 0.474095 0.076901 1 1 1 0.6 0.4 0.8 0.2 0.088094 1 0.2 0.697901 Varvarin 16340 0.625663079 0.333333 0.785714 1 0.4 0.35 0.952736 0.827112 0.485975 0.850891 0.2 0.1 0.4 0.6 0.2 0.6 0.6506 0.672709 0.804233 0.378356 0.653547 0.461672 0.831649 1 0.054878 0.169666 0.999254 0.4Varvarin0.639704 0.864233 0.705517 0.63522516340 0 0.6256630791 0.8666670.333333 0.7857141 1 1 0.2 0.4 0.8 0.35 0.9527360.2 0.0768710.827112 0.4859750.2 0.8508910.2 0.573915 0.2 0.1 0.4 0.6 0.2 0.6 0.6506 0.672709 0.804233 0.378356 0.653547 0.461672 0.831649 1 0.054878 0.169666 0.999254 0.4 0.639704 0.864233 0.705517 0.635225 0 1 0.866667 1 1 0.2 0.8 0.2 0.076871 0.2 0.2 0.573915 Koceljeva 11844 0.624779393 0.5 0.285714 0.9 0.6 0.5 0.901906 0.801652 0.629936 0.702901 0.7 0.4 0.9 0.8 0.8 1 0.603651 0.603891 0.486122 0.553738 0.427187 0.621454 0.617899 0.8 0.055501 0.214298 0.996042 0.6Koceljeva0.713349 0.881506 0.707848 0.61198411844 0 0.6247793930.8 0.8 0.5 0.2857141 0.6 0.9 0.8 0.6 0.6 0.5 0.9019060.2 0.0644030.801652 0.6299360.8 0.7029010.4 0.675664 0.7 0.4 0.9 0.8 0.8 1 0.603651 0.603891 0.486122 0.553738 0.427187 0.621454 0.617899 0.8 0.055501 0.214298 0.996042 0.6 0.713349 0.881506 0.707848 0.611984 0 0.8 0.8 1 0.6 0.8 0.6 0.2 0.064403 0.8 0.4 0.675664 Žitorađa 14980 0.624683085 0.333333 0.214286 1 0.4 0.35 0.966888 0.871088 0.439433 0.875621 0.8 0.3 0.4 0.6 0.2 0.6 0.634149 0.683366 0.659091 0.616803 0.684213 0.381635 0.493588 0.8 0.060652 0.178803 0.958486 0.8Žitorađa0.594364 0.891525 0.684538 0.5945814980 0 0.6246830851 0.3333330.8 0.2142860.8 1 1 0.2 0.4 1 0.35 0.9668880.2 0.0533810.871088 0.4394330.6 0.8756210.4 0.655639 0.8 0.3 0.4 0.6 0.2 0.6 0.634149 0.683366 0.659091 0.616803 0.684213 0.381635 0.493588 0.8 0.060652 0.178803 0.958486 0.8 0.594364 0.891525 0.684538 0.59458 0 1 0.8 0.8 1 0.2 1 0.2 0.053381 0.6 0.4 0.655639 Tutin 31670 0.622259253 0.333333 0.071429 1 0.2 0.65 0.960726 0.937572 0.683422 0.950689 1 0.7 0.7 0.2 0.2 0.2 0.717123 0.440648 0.338575 0.326638 0.555573 0.235201 0.393169 0.8 0.033619 0.135521 0.998622 0.2Tutin0.006256 1 0.57265 0.38901531670 0.006883 0.6222592531 0.7333330.333333 0.0714291 0.8 1 0.2 0.2 1 0.65 0.9607260.8 0.067850.937572 0.6834221 0.9506891 0.829829 1 0.7 0.7 0.2 0.2 0.2 0.717123 0.440648 0.338575 0.326638 0.555573 0.235201 0.393169 0.8 0.033619 0.135521 0.998622 0.2 0.006256 1 0.57265 0.389015 0.006883 1 0.733333 1 0.8 0.2 1 0.8 0.06785 1 1 0.829829 Ražanj 7998 0.618616517 0.333333 0.785714 1 0.2 0.35 0.92895 0.853994 0.488289 0.798521 0.35 0.25 0.4 0.2 0.6 0.6 0.673658 0.806106 0.749131 0.434521 0.563494 0.423061 0.717181 1 0.146414 0.156271 0.983354 0.4Ražanj0.604578 0.949217 0.51826 0.4322627998 7.47E-07 0.6186165170.6 0.3333330.333333 0.7857141 0.4 1 0.2 0.2 0.8 0.35 0.20.928950.1108920.853994 0.4882891 0.7985210.2 0.546771 0.35 0.25 0.4 0.2 0.6 0.6 0.673658 0.806106 0.749131 0.434521 0.563494 0.423061 0.717181 1 0.146414 0.156271 0.983354 0.4 0.604578 0.949217 0.51826 0.432262 7.47E-07 0.6 0.333333 1 0.4 0.2 0.8 0.2 0.110892 1 0.2 0.546771 Veliko Gradište 15966 0.617738985 0.333333 0.642857 0.9 0.2 0.3 0.934418 0.840472 0.224954 0.77219 0.95 0.15 0.01 0.4 0.2 0.4 0.672222 0.680933 0.493246 0.183354 0.694524 0.145149 0.680639 0.8 0.054322 0.17803 0.92313 0.8Veliko0.755097 Gradište0.824044 0.516706 0.57950615966 2.2E-06 0.6177389850.6 0.3333330.2 0.6428571 0.4 0.9 0.2 0.2 0.6 0.3 0.9344181 0.1312420.840472 0.2249541 0.60.772190.593364 0.95 0.15 0.01 0.4 0.2 0.4 0.672222 0.680933 0.493246 0.183354 0.694524 0.145149 0.680639 0.8 0.054322 0.17803 0.92313 0.8 0.755097 0.824044 0.516706 0.579506 2.2E-06 0.6 0.2 1 0.4 0.2 0.6 1 0.131242 1 0.6 0.593364 Paraćin 50798 0.617203026 0.333333 0.642857 0.9 0.2 0.3 0.914335 0.820016 0.574226 0.793514 0.95 0.55 0.98 0.6 0.2 1 0.675691 0.600665 0.548961 0.250368 0.22697 0.198793 0.130286 0.6 0.01501 0.178756 0.994135 0.8Paraćin0.825945 0.835905 0.328671 0.49039850798 0.000376 0.6172030261 0.9333330.333333 0.6428571 1 0.9 0.6 0.2 0.6 0.3 0.9143350.2 0.1132760.820016 0.5742261 0.7935140.2 0.719679 0.95 0.55 0.98 0.6 0.2 1 0.675691 0.600665 0.548961 0.250368 0.22697 0.198793 0.130286 0.6 0.01501 0.178756 0.994135 0.8 0.825945 0.835905 0.328671 0.490398 0.000376 1 0.933333 1 1 0.6 0.6 0.2 0.113276 1 0.2 0.719679 Svrljig 12557 0.612322788 0.333333 0.785714 0.9 0.4 0.4 0.92895 0.853994 0.457899 0.798521 0.5 0.2 0.4 0.8 1 0.4 0.67044 0.8929 0.830725 0.255822 0.563494 0.509692 0.057564 1 0.086754 0.183075 0.995221 0.2Svrljig0.396169 0.789383 0.487956 0.43226212557 0.002391 0.6123227880.8 0.3333330.2 0.7857141 0.4 0.9 0.6 0.4 1 0.4 0.20.928950.1167040.853994 0.4578991 0.7985210.8 0.721459 0.5 0.2 0.4 0.8 1 0.4 0.67044 0.8929 0.830725 0.255822 0.563494 0.509692 0.057564 1 0.086754 0.183075 0.995221 0.2 0.396169 0.789383 0.487956 0.432262 0.002391 0.8 0.2 1 0.4 0.6 1 0.2 0.116704 1 0.8 0.721459 Aleksandrovac 24223 0.610884946 0.5 0.785714 1 0.6 0.4 0.952736 0.827112 0.771247 0.850891 0.7 0.2 0.77 0.6 0.2 0.4 0.758039 0.627108 0.743267 0.419105 0.653547 0.171747 0.418943 0.8 0.027994 0.208882 0.99593 0.2Aleksandrovac0.44306 0.778443 0.62704 0.47625124223 0.000545 0.6108849460.6 1 0.5 0.7857140.8 1 1 0.2 0.6 0.4 0.4 0.9527360.2 0.0797030.827112 0.7712471 0.8508910.2 0.592105 0.7 0.2 0.77 0.6 0.2 0.4 0.758039 0.627108 0.743267 0.419105 0.653547 0.171747 0.418943 0.8 0.027994 0.208882 0.99593 0.2 0.44306 0.778443 0.62704 0.476251 0.000545 0.6 1 0.8 1 0.2 0.4 0.2 0.079703 1 0.2 0.592105 Velika Plana 38000 0.602201954 0.333333 1 0.9 0.4 0.4 0.905466 0.805952 0.527098 0.659837 0.8 0.53 0.3 0.2 0.2 0.6 0.681443 0.58071 0.628236 0.197612 0.054834 0.361382 0.172247 0.4 0.018731 0.188781 0.936827 0.8Velika0.688728 Plana 0.845233 0.703186 0.43226238000 0.003442 0.6022019540.6 0.9333330.333333 0.8 1 1 0.9 0.2 0.4 0.6 0.4 0.9054660.8 0.0950940.805952 0.5270980.8 0.6598370.6 0.652639 0.8 0.53 0.3 0.2 0.2 0.6 0.681443 0.58071 0.628236 0.197612 0.054834 0.361382 0.172247 0.4 0.018731 0.188781 0.936827 0.8 0.688728 0.845233 0.703186 0.432262 0.003442 0.6 0.933333 0.8 1 0.2 0.6 0.8 0.095094 0.8 0.6 0.652639 Krupanj 15602 0.598171059 0.833333 0.214286 0.9 0.6 0.55 0.901906 0.801652 0.55742 0.702901 0.4 0.6 0.3 1 0.8 1 0.618919 0.549846 0.613584 0.55513 0.427187 0.355421 0.546469 0.8 0.045987 0.173884 0.991561 0.4Krupanj0.522894 0.934477 0.932401 0.53896615602 0.000244 0.5981710590.4 0.8333330.4 0.2142861 0.8 0.9 0.4 0.6 0.8 0.55 0.9019060.2 0.0619270.801652 0.60.55742 0.7029011 0.655856 0.4 0.6 0.3 1 0.8 1 0.618919 0.549846 0.613584 0.55513 0.427187 0.355421 0.546469 0.8 0.045987 0.173884 0.991561 0.4 0.522894 0.934477 0.932401 0.538966 0.000244 0.4 0.4 1 0.8 0.4 0.8 0.2 0.061927 0.6 1 0.655856 Petrovac na Mlavi 28157 0.597295785 0.5 0.642857 0.9 0.2 0.35 0.934418 0.840472 0.519812 0.77219 0.3 0.2 0.2 1 0.2 0.4 0.574944 0.706407 0.824992 0.273609 0.694524 0.31172 0.714476 0.8 0.029351 0.19129 0.938475 0.6Petrovac0.718846 na Mlavi0.818402 0.299922 0.60586128157 2.31E-06 0.5972957850.6 0.533333 0.5 0.6428570.8 0.8 0.9 0.6 0.2 0.8 0.35 0.9344180.8 0.0946740.840472 0.5198121 0.20.772190.617976 0.3 0.2 0.2 1 0.2 0.4 0.574944 0.706407 0.824992 0.273609 0.694524 0.31172 0.714476 0.8 0.029351 0.19129 0.938475 0.6 0.718846 0.818402 0.299922 0.605861 2.31E-06 0.6 0.533333 0.8 0.8 0.6 0.8 0.8 0.094674 1 0.2 0.617976 Rača 10467 0.590789625 0.333333 0.642857 0.9 0.4 0.35 0.887022 0.843738 0.517311 0.747376 0.4 0.2 0.2 0.4 0.2 1 0.578012 0.637877 0.662782 0.189507 0.436242 0.471534 0.405191 0.6 0.066046 0.20347 0.985474 0.6Rača0.501274 0.857439 0.542347 0.52929210467 0.001991 0.5907896250.8 0.3333330.8 0.6428571 0.4 0.9 0.4 0.4 0.4 0.35 0.8870220.2 0.1372130.843738 0.5173111 0.7473760.8 0.702203 0.4 0.2 0.2 0.4 0.2 1 0.578012 0.637877 0.662782 0.189507 0.436242 0.471534 0.405191 0.6 0.066046 0.20347 0.985474 0.6 0.501274 0.857439 0.542347 0.529292 0.001991 0.8 0.8 1 0.4 0.4 0.4 0.2 0.137213 1 0.8 0.702203 Ćićevac 8606 0.589475541 0.333333 0.785714 1 0.2 0.35 0.952736 0.827112 0.46341 0.850891 0.342873 0.234358 0.376395 0.6 0.2 0.8 0.655899 0.618811 0.631642 0.274269 0.653547 0.279 0.129965 0.8 0.077507 0.208929 0.995154 Ćićevac1 0.443718 0.924228 0.55711 0.5258558606 0 0.5894755410.6 0.6666670.333333 0.7857141 0.6 1 0.2 0.2 0.6 0.35 0.9527360.2 0.136820.827112 0.60.46341 0.8508910.6 0.5852710.342873 0.234358 0.376395 0.6 0.2 0.8 0.655899 0.618811 0.631642 0.274269 0.653547 0.279 0.129965 0.8 0.077507 0.208929 0.995154 1 0.443718 0.924228 0.55711 0.525855 0 0.6 0.666667 1 0.6 0.2 0.6 0.2 0.13682 0.6 0.6 0.585271 Lajkovac 14721 0.588222565 0.666667 0.428571 1 0.2 0.5 0.871622 0.780821 0.641534 0.767085 0.95 0.3 0.8 1 0.2 1 0.654917 0.557806 0.43873 0.336694 0.363565 0.407275 0.114531 0.2 0.038671 0.22483 0.996268 0.6Lajkovac0.670172 0.89544 0.602176 0.4610214721 0.001853 0.5882225650.8 0.5333330.666667 0.4285711 0.2 1 0.8 0.2 0.2 0.5 0.8716220.2 0.0637460.780821 0.6415341 0.7670850.8 0.678986 0.95 0.3 0.8 1 0.2 1 0.654917 0.557806 0.43873 0.336694 0.363565 0.407275 0.114531 0.2 0.038671 0.22483 0.996268 0.6 0.670172 0.89544 0.602176 0.46102 0.001853 0.8 0.533333 1 0.2 0.8 0.2 0.2 0.063746 1 0.8 0.678986 Mionica 13080 0.587366095 0.833333 0.428571 1 0.4 0.55 0.871622 0.780821 0.631351 0.767085 0.7 0.3 1 0.2 0.2 1 0.65698 0.665345 0.484807 0.413584 0.363565 0.346886 0.533674 0.6 0.057812 0.202732 0.989388 0.2Mionica0.456264 0.917895 0.677545 0.48161213080 0.001283 0.5873660950.6 0.8333330.6 0.4285711 0.4 1 0.2 0.4 0.8 0.55 0.8716220.2 0.0825790.780821 0.6313511 0.7670850.6 0.641577 0.7 0.3 1 0.2 0.2 1 0.65698 0.665345 0.484807 0.413584 0.363565 0.346886 0.533674 0.6 0.057812 0.202732 0.989388 0.2 0.456264 0.917895 0.677545 0.481612 0.001283 0.6 0.6 1 0.4 0.2 0.8 0.2 0.082579 1 0.6 0.641577 Bojnik 10176 0.586980149 0.333333 0.428571 1 0.4 0.35 0.936888 0.940219 0.406903 0.842395 0.57446 0.26523 0.431193 0.2 0.2 0.8 0.584239 0.722776 0.761949 0.838749 0.474277 0.815217 0.228342 1 0.134128 0.133013 0.994561 0.2Bojnik0.502449 0.831184 0.465423 0.55045110176 0.290168 0.5869801490.6 0.6666670.333333 0.4285710.6 0.4 1 1 0.4 1 0.35 0.9368880.2 0.1015310.940219 0.4069030.6 0.8423950.6 0.6458860.57446 0.26523 0.431193 0.2 0.2 0.8 0.584239 0.722776 0.761949 0.838749 0.474277 0.815217 0.228342 1 0.134128 0.133013 0.994561 0.2 0.502449 0.831184 0.465423 0.550451 0.290168 0.6 0.666667 0.6 0.4 1 1 0.2 0.101531 0.6 0.6 0.645886 Trstenik 38915 0.586465663 0.5 0.785714 0.9 0.6 0.35 0.952736 0.827112 0.382627 0.850891 0.4 0.3 0.8 0.4 0.2 0.8 0.6219 0.627126 0.530101 0.325746 1 0.227239 0.375452 0.8 0.018987 0.184158 0.967411 0.4Trstenik0.550105 0.797328 0.604507 0.50066738915 0.003201 0.5864656630.8 0.8 0.5 0.7857140.8 0.8 0.9 0.8 0.6 0.6 0.35 0.9527360.2 0.1082370.827112 0.3826271 0.8508910.2 0.662011 0.4 0.3 0.8 0.4 0.2 0.8 0.6219 0.627126 0.530101 0.325746 1 0.227239 0.375452 0.8 0.018987 0.184158 0.967411 0.4 0.550105 0.797328 0.604507 0.500667 0.003201 0.8 0.8 0.8 0.8 0.8 0.6 0.2 0.108237 1 0.2 0.662011 Arilje 17947 0.582825702 0.666667 0.214286 0.9 0.8 0.9 0.942753 0.849751 0.463819 1 0.3 0.4 0.6 1 0.2 0.6 0.720334 0.553286 0.608012 0.273017 0.456247 0.145213 0.198879 0.8 0.030446 0.236536 0.948402 0.6Arilje0.111949 0.75403 0.664336 0.41572717947 7.58E-05 0.5828257020.8 0.6666670.6 0.2142861 0.8 0.9 0.2 0.8 0.4 0.9 0.9427530.2 0.1019890.849751 0.4638190.8 0.6 0.6272111 0.3 0.4 0.6 1 0.2 0.6 0.720334 0.553286 0.608012 0.273017 0.456247 0.145213 0.198879 0.8 0.030446 0.236536 0.948402 0.6 0.111949 0.75403 0.664336 0.415727 7.58E-05 0.8 0.6 1 0.8 0.2 0.4 0.2 0.101989 0.8 0.6 0.627211 Lazarevac 56865 0.580721849 0.5 0.785714 0.9 0.4 0.5 0.880585 0.801815 0.593174 0.635257 0.35 0.45 0.8 0.4 0.2 0.2 0.668021 0.499007 0.48364 0.175716 0.534187 0.110832 0.021912 0.2 0.007011 0.28811 0.986864 0.4Lazarevac0.612405 0.710963 0.465423 0.37963456865 0 0.5807218491 0.933333 0.5 0.7857141 0.2 0.9 0.8 0.4 0.2 0.5 0.8805850.8 0.0985620.801815 0.5931741 0.6352570.8 0.751405 0.35 0.45 0.8 0.4 0.2 0.2 0.668021 0.499007 0.48364 0.175716 0.534187 0.110832 0.021912 0.2 0.007011 0.28811 0.986864 0.4 0.612405 0.710963 0.465423 0.379634 0 1 0.933333 1 0.2 0.8 0.2 0.8 0.098562 1 0.8 0.751405 Sopot 19819 0.580147236 0.5 0.785714 0.9 0.2 0.45 0.880585 0.801815 0.542993 0.635257 0.364735 0.162986 0.290372 0.2 0.2 0.2 0.643763 0.625186 0.677301 0.180391 0.534187 0.292776 0.108943 1 0.03054 0.232625 0.972081 0.4Sopot0.55059 0.761515 0.669775 0.47730719819 0.076341 0.5801472361 0.733333 0.5 0.7857141 0.2 0.9 0.8 0.2 0.2 0.45 0.8805850.8 0.0691930.801815 0.5429930.6 0.6352570.6 0.6389840.364735 0.162986 0.290372 0.2 0.2 0.2 0.643763 0.625186 0.677301 0.180391 0.534187 0.292776 0.108943 1 0.03054 0.232625 0.972081 0.4 0.55059 0.761515 0.669775 0.477307 0.076341 1 0.733333 1 0.2 0.8 0.2 0.8 0.069193 0.6 0.6 0.638984 Osečina 11113 0.577288086 0.833333 0.428571 0.7 0.8 0.6 0.871622 0.780821 0.610932 0.767085 0.5 0.4 0.5 0.4 0.2 1 0.628522 0.667356 0.475531 0.596092 0.363565 0.500154 0.696264 0.8 0.070922 0.189248 0.997691 0.2Osečina0.412151 0.929986 0.664336 0.53303311113 0 0.5772880860.8 0.8333330.4 0.4285710.8 0.8 0.7 0.2 0.8 0.6 0.6 0.8716220.6 0.0562080.780821 0.6109321 0.7670850.2 0.56682 0.5 0.4 0.5 0.4 0.2 1 0.628522 0.667356 0.475531 0.596092 0.363565 0.500154 0.696264 0.8 0.070922 0.189248 0.997691 0.2 0.412151 0.929986 0.664336 0.533033 0 0.8 0.4 0.8 0.8 0.2 0.6 0.6 0.056208 1 0.2 0.56682 Odžaci 27407 0.576786423 0.5 0.785714 0.7 0.4 0.35 0.977858 0.844556 0.515828 0.677125 0.1 0.512966 0.2 0.2 0.2 0.2 0.585889 0.566422 0.62282 0.307572 0.541912 0.040532 0.301287 0.8 0.021463 0.207641 0.862978 0.8Odžaci0.800059 0.801819 0.575758 0.51749427407 0.000792 0.5767864231 0.733333 0.5 0.7857140.4 0.6 0.7 0.8 0.4 0.6 0.35 0.9778580.2 0.1747890.844556 0.5158281 0.6771250.4 0.652072 0.1 0.512966 0.2 0.2 0.2 0.2 0.585889 0.566422 0.62282 0.307572 0.541912 0.040532 0.301287 0.8 0.021463 0.207641 0.862978 0.8 0.800059 0.801819 0.575758 0.517494 0.000792 1 0.733333 0.4 0.6 0.8 0.6 0.2 0.174789 1 0.4 0.652072 Aleksinac 47562 0.575669604 0.333333 0.785714 0.9 0.2 0.35 0.92895 0.853994 0.464047 0.798521 0.2 0.07 0.08 0.6 1 1 0.535487 0.622791 0.761756 0.285789 0.104795 0.217903 0.222878 0.6 0.016359 0.180092 0.958386 0.8Aleksinac0.704009 0.802165 0.365967 0.50077447562 0 0.5756696041 0.3333330.333333 0.7857140.8 1 0.9 0.8 0.2 0.8 0.35 0.60.928950.0722790.853994 0.4640471 0.7985210.6 0.748287 0.2 0.07 0.08 0.6 1 1 0.535487 0.622791 0.761756 0.285789 0.104795 0.217903 0.222878 0.6 0.016359 0.180092 0.958386 0.8 0.704009 0.802165 0.365967 0.500774 0 1 0.333333 0.8 1 0.8 0.8 0.6 0.072279 1 0.6 0.748287 Nova Varoš 14595 0.575569886 0.666667 0.214286 0.9 0.8 0.95 0.942753 0.849751 0.565957 1 0.6 0.1 0.8 0.2 0.2 0.2 0.771491 0.608462 0.61498 0.411466 0.456247 0.191111 0.160049 0.6 0.052054 0.175395 0.978712 0.2Nova 0.02863Varoš 0.789843 0.472416 0.35604114595 0.199561 0.5755698860.8 0.7333330.666667 0.2142861 0.2 0.9 0.6 0.8 0.8 0.95 0.9427530.2 0.1118050.849751 0.5659570.8 0.2 0.610981 0.6 0.1 0.8 0.2 0.2 0.2 0.771491 0.608462 0.61498 0.411466 0.456247 0.191111 0.160049 0.6 0.052054 0.175395 0.978712 0.2 0.02863 0.789843 0.472416 0.356041 0.199561 0.8 0.733333 1 0.2 0.6 0.8 0.2 0.111805 0.8 0.2 0.61098 Svilajnac 21414 0.574285548 0.333333 0.642857 1 0.4 0.35 0.914335 0.820016 0.540082 0.793514 0.5 0.243 0.5 0.4 0.2 1 0.583283 0.697637 0.58431 0.259811 0.22697 0.24328 0.347215 0.8 0.036924 0.202558 0.98093 0.8Svilajnac0.802557 0.794334 0.536908 0.5455921414 0 0.5742855480.6 0.8666670.333333 0.6428571 0.2 1 0.2 0.4 0.6 0.35 0.9143350.6 0.0885610.820016 0.5400821 0.7935140.4 0.603833 0.5 0.243 0.5 0.4 0.2 1 0.583283 0.697637 0.58431 0.259811 0.22697 0.24328 0.347215 0.8 0.036924 0.202558 0.98093 0.8 0.802557 0.794334 0.536908 0.54559 0 0.6 0.866667 1 0.2 0.2 0.6 0.6 0.088561 1 0.4 0.603833 Kruševac 121293 0.572030851 0.333333 0.785714 1 0.4 0.35 0.952736 0.827112 0.460939 0.850891 0.3 0.27 0.03 0.6 1 0.6 0.600697 0.564092 0.638441 0.242403 0.410182 0.126795 0.178997 0.2 0.005147 0.207406 0.973138 0.6Kruševac0.669219 0.754376 0.271173 0.432811121293 0.004366 0.5720308510.8 0.3333330.6 0.7857141 1 1 0.8 0.4 0.4 0.35 0.9527360.8 0.0790260.827112 0.4609391 0.8508910.6 0.737862 0.3 0.27 0.03 0.6 1 0.6 0.600697 0.564092 0.638441 0.242403 0.410182 0.126795 0.178997 0.2 0.005147 0.207406 0.973138 0.6 0.669219 0.754376 0.271173 0.432811 0.004366 0.8 0.6 1 1 0.8 0.4 0.8 0.079026 1 0.6 0.737862 Lapovo 7210 0.571900822 0.333333 0.642857 0.9 0.2 0.35 0.887022 0.843738 0.519239 0.747376 0.95 0.1 0.1 0.2 0.2 0.6 0.679539 0.589572 0.702927 0.139562 0.436242 0.113646 0.060945 0.6 0.090463 0.202105 0.933758 0.8Lapovo0.833535 0.969023 0.508936 0.501067210 0 0.5719008220.2 0.3333330.2 0.6428570.6 0.6 0.9 0.2 0.2 0.4 0.35 0.8870220.2 0.2265730.843738 0.5192391 0.7473760.8 0.516706 0.95 0.1 0.1 0.2 0.2 0.6 0.679539 0.589572 0.702927 0.139562 0.436242 0.113646 0.060945 0.6 0.090463 0.202105 0.933758 0.8 0.833535 0.969023 0.508936 0.50106 0 0.2 0.2 0.6 0.6 0.2 0.4 0.2 0.226573 1 0.8 0.516706 Smederevska Palanka 45823 0.570817152 0.333333 0.928571 0.9 0.2 0.45 0.905466 0.805952 0.527689 0.659837 0.6 0.2 0.6 0.6 0.2 1 0.631193 0.58633 0.575912 0.200389 0.054834 0.361382 0.28203 0.2 0.015725 0.184781 0.930512 0.4Smederevska0.612088 Palanka0.765431 0.24087 0.42462345823 0.005131 0.5708171521 0.5333330.333333 0.9285711 1 0.9 0.4 0.2 0.6 0.45 0.9054660.6 0.0930460.805952 0.5276891 0.6598370.4 0.699543 0.6 0.2 0.6 0.6 0.2 1 0.631193 0.58633 0.575912 0.200389 0.054834 0.361382 0.28203 0.2 0.015725 0.184781 0.930512 0.4 0.612088 0.765431 0.24087 0.424623 0.005131 1 0.533333 1 1 0.4 0.6 0.6 0.093046 1 0.4 0.699543 Prijepolje 34810 0.569111764 0.833333 0.214286 0.9 0.4 0.9 0.942753 0.849751 0.620177 1 0.05 0.1 0.2 0.8 0.2 1 0.577849 0.536202 0.562912 0.354588 0.456247 0.263052 0.077118 0.8 0.021394 0.173846 0.984944 0.2Prijepolje0.030954 0.813795 0.28749 0.35403634810 0.260249 0.5691117641 0.7333330.833333 0.2142861 1 0.9 0.8 0.4 1 0.9 0.9427530.6 0.0905050.849751 0.6201770.8 1 0.8786191 0.05 0.1 0.2 0.8 0.2 1 0.577849 0.536202 0.562912 0.354588 0.456247 0.263052 0.077118 0.8 0.021394 0.173846 0.984944 0.2 0.030954 0.813795 0.28749 0.354036 0.260249 1 0.733333 1 1 0.8 1 0.6 0.090505 0.8 1 0.878619 Ivanjica 29832 0.568319213 0.666667 0.428571 0.9 0.8 0.7 0.924889 0.816558 0.605457 0.914402 0.5 0.7 0.8 1 0.2 0.6 0.730174 0.575266 0.43947 0.374167 0.787012 0.109615 0.219112 0.8 0.020273 0.214204 0.952375 0.2Ivanjica0.088259 0.767388 0.515152 0.37003629832 0.409341 0.5683192130.8 0.5333330.666667 0.4285711 0.8 0.9 0.2 0.8 0.6 0.7 0.9248890.4 0.0872980.816558 0.6054570.6 0.9144020.6 0.622962 0.5 0.7 0.8 1 0.2 0.6 0.730174 0.575266 0.43947 0.374167 0.787012 0.109615 0.219112 0.8 0.020273 0.214204 0.952375 0.2 0.088259 0.767388 0.515152 0.370036 0.409341 0.8 0.533333 1 0.8 0.2 0.6 0.4 0.087298 0.6 0.6 0.622962 Despotovac 20629 0.567764444 0.333333 0.642857 1 0.2 0.35 0.914335 0.820016 0.594092 0.793514 0.5 0.6 0.4 0.2 0.2 1 0.663527 0.709791 0.482067 0.401843 0.22697 0.227332 0.323319 0.2 0.043294 0.178251 0.909943 0.2Despotovac0.499614 0.762667 0.471639 0.39255120629 0.178362 0.5677644441 0.9333330.333333 0.6428570.2 0.4 1 0.2 0.2 0.6 0.35 0.9143350.8 0.1093810.820016 0.5940921 0.7935141 0.68694 0.5 0.6 0.4 0.2 0.2 1 0.663527 0.709791 0.482067 0.401843 0.22697 0.227332 0.323319 0.2 0.043294 0.178251 0.909943 0.2 0.499614 0.762667 0.471639 0.392551 0.178362 1 0.933333 0.2 0.4 0.2 0.6 0.8 0.109381 1 1 0.68694 Doljevac 17991 0.5677457 0.333333 0.785714 0.9 0.2 0.4 0.92895 0.853994 0.455703 0.798521 0.1 0.12 0.7 0.2 1 1 0.539123 0.640882 0.686097 0.396942 0.563494 0.365842 0.030358 0.8 0.040814 0.202431 0.96635 0.8Doljevac0.819237 0.863657 0.635587 0.54512717991 0 0.56774570.6 0.3333330.333333 0.7857141 0.8 0.9 0.2 0.2 0.8 0.4 0.20.928950.0732750.853994 0.4557031 0.7985210.6 0.644608 0.1 0.12 0.7 0.2 1 1 0.539123 0.640882 0.686097 0.396942 0.563494 0.365842 0.030358 0.8 0.040814 0.202431 0.96635 0.8 0.819237 0.863657 0.635587 0.545127 0 0.6 0.333333 1 0.8 0.2 0.8 0.2 0.073275 1 0.6 0.644608 Barajevo 26855 0.567188944 0.5 0.785714 0.7 0.2 0.45 0.880585 0.801815 0.561217 0.635257 0.5 0.2 0.2 0.6 0.2 1 0.60157 0.591424 0.69713 0.153273 0.534187 0.145513 0.068826 1 0.02131 0.236015 0.981359 0.4Barajevo0.602216 0.781783 0.613831 0.43226226855 0.00441 0.5671889440.8 0.933333 0.5 0.7857141 0.2 0.7 0.8 0.2 0.2 0.45 0.8805850.4 0.0925290.801815 0.5612171 0.6352571 0.718008 0.5 0.2 0.2 0.6 0.2 1 0.60157 0.591424 0.69713 0.153273 0.534187 0.145513 0.068826 1 0.02131 0.236015 0.981359 0.4 0.602216 0.781783 0.613831 0.432262 0.00441 0.8 0.933333 1 0.2 0.8 0.2 0.4 0.092529 1 1 0.718008 Titel 15031 0.566654449 0.333333 0.642857 0.7 0.6 0.35 0.951975 0.867149 0.572532 0.632806 0.1 0.25 0.08 0.6 0.2 0.2 0.548527 0.526701 0.655598 0.333384 0.55751 0.216698 0.491384 0.8 0.04132 0.197284 0.842289 0.6Titel 0.72585 0.936089 0.778555 0.54893815031 0.062217 0.5666544490.8 0.3333331 0.6428570.4 0.8 0.7 0.2 0.6 0.6 0.35 0.9519750.2 0.1647620.867149 0.5725320.6 0.6328060.8 0.620419 0.1 0.25 0.08 0.6 0.2 0.2 0.548527 0.526701 0.655598 0.333384 0.55751 0.216698 0.491384 0.8 0.04132 0.197284 0.842289 0.6 0.72585 0.936089 0.778555 0.548938 0.062217 0.8 1 0.4 0.8 0.2 0.6 0.2 0.164762 0.6 0.8 0.620419 Bujanovac 37735 0.562840787 0.333333 0.785714 1 1 0.65 0.973202 1 -0.45071 0.952964 0.495437 0.074792 0.564721 0.2 0.2 1 0.643051 0.499446 0.23016 0.563228 0.435259 0.335285 0.08682 0.8 0.013757 0.264596 0.974619 0.2Bujanovac0.454121 0.798135 0.553225 0.40058937735 0.061052 0.5628407870.6 0.4666670.333333 0.7857141 1 1 0.6 1 1 0.65 0.9732021 0.151323 1 -0.450710.6 0.9529640.6 0.6859020.495437 0.074792 0.564721 0.2 0.2 1 0.643051 0.499446 0.23016 0.563228 0.435259 0.335285 0.08682 0.8 0.013757 0.264596 0.974619 0.2 0.454121 0.798135 0.553225 0.400589 0.061052 0.6 0.466667 1 1 0.6 1 1 0.151323 0.6 0.6 0.685902 Kraljevo 118363 0.561761749 0.5 0.071429 0.9 0.4 0.55 0.960726 0.937572 0.001868 0.950689 0.4 1 0.2 0.2 0.2 1 0.657441 0.580782 0.650308 0.226414 0.940868 0.102462 0.108469 0.2 0.005474 0.210257 0.991199 0.2Kraljevo0.435035 0.818632 0.268065 0.379919118363 0.100193 0.5617617490.4 0.6 0.5 0.0714290.8 0.8 0.9 1 0.4 0.6 0.55 0.9607260.8 0.0855740.937572 0.0018681 0.9506890.6 0.691007 0.4 1 0.2 0.2 0.2 1 0.657441 0.580782 0.650308 0.226414 0.940868 0.102462 0.108469 0.2 0.005474 0.210257 0.991199 0.2 0.435035 0.818632 0.268065 0.379919 0.100193 0.4 0.6 0.8 0.8 1 0.6 0.8 0.085574 1 0.6 0.691007 Batočina 10977 0.560305032 0.5 0.642857 0.9 0.2 0.35 0.887022 0.843738 0.516632 0.747376 0.825609 0.130885 0.148731 0.2 0.2 0.8 0.644597 0.563893 0.686339 0.215952 0.436242 0.335511 0.102901 0.8 0.06069 0.195049 0.993908 0.6Batočina0.460946 0.949908 0.533023 0.48241210977 0.024302 0.5603050320.6 0.466667 0.5 0.6428571 0.4 0.9 0.2 0.2 0.6 0.35 0.8870220.2 0.1126440.843738 0.5166320.6 0.7473760.6 0.5507060.825609 0.130885 0.148731 0.2 0.2 0.8 0.644597 0.563893 0.686339 0.215952 0.436242 0.335511 0.102901 0.8 0.06069 0.195049 0.993908 0.6 0.460946 0.949908 0.533023 0.482412 0.024302 0.6 0.466667 1 0.4 0.2 0.6 0.2 0.112644 0.6 0.6 0.550706 Obrenovac 72124 0.559530863 0.333333 0.785714 0.9 0.2 0.45 0.880585 0.801815 0.592671 0.635257 0.37 0.7 0.5 0.6 0.2 1 0.632811 0.532889 0.539738 0.195088 0.534187 0.129428 0.082191 1 0.006636 0.256996 0.967092 0.4Obrenovac0.622576 0.733303 0.709402 0.4478972124 0.001263 0.5595308630.8 0.3333330.8 0.7857141 0.2 0.9 0.8 0.2 0.2 0.45 0.8805850.6 0.0901620.801815 0.5926710.6 0.6352570.6 0.617072 0.37 0.7 0.5 0.6 0.2 1 0.632811 0.532889 0.539738 0.195088 0.534187 0.129428 0.082191 1 0.006636 0.256996 0.967092 0.4 0.622576 0.733303 0.709402 0.44789 0.001263 0.8 0.8 1 0.2 0.8 0.2 0.6 0.090162 0.6 0.6 0.617072 Boljevac 11358 0.558789858 0.333333 0.642857 0.9 0.2 0.35 0.932482 0.816604 0.567815 0.859134 0.6 0.1 0.3 0.2 0.2 0.6 0.668515 0.739129 0.192399 0.270067 0.544751 0.549626 0.641133 0.2 0.08242 0.173979 0.927053 0.2Boljevac0.235685 0.838784 0.464646 0.43914411358 0.0339 0.5587898580.6 0.3333330.8 0.6428571 0.4 0.9 0.2 0.2 0.6 0.35 0.9324820.2 0.0997780.816604 0.5678151 0.8591340.2 0.573671 0.6 0.1 0.3 0.2 0.2 0.6 0.668515 0.739129 0.192399 0.270067 0.544751 0.549626 0.641133 0.2 0.08242 0.173979 0.927053 0.2 0.235685 0.838784 0.464646 0.439144 0.0339 0.6 0.8 1 0.4 0.2 0.6 0.2 0.099778 1 0.2 0.573671 Žabalj 25156 0.55776313 0.333333 0.642857 0.7 0.6 0.3 0.951975 0.867149 0.537397 0.632806 0.399368 0.156559 0.141004 0.2 0.2 0.6 0.553961 0.492904 0.661317 0.372362 0.55751 0.054466 0.365161 0.8 0.023841 0.208565 0.835838 0.6Žabalj0.668888 0.901889 0.781663 0.50254725156 0.054 0.557763130.6 0.3333330.8 0.6428570.6 1 0.7 0.2 0.6 0.6 0.3 0.9519750.4 0.120790.867149 0.5373970.8 0.6328060.8 0.6462910.399368 0.156559 0.141004 0.2 0.2 0.6 0.553961 0.492904 0.661317 0.372362 0.55751 0.054466 0.365161 0.8 0.023841 0.208565 0.835838 0.6 0.668888 0.901889 0.781663 0.502547 0.054 0.6 0.8 0.6 1 0.2 0.6 0.4 0.12079 0.8 0.8 0.646291 Ub 27407 0.557713708 0.5 0.428571 0.9 0.4 0.5 0.871622 0.780821 0.642223 0.767085 0.65 0.25 0.65 0.4 0.2 1 0.573035 0.59061 0.516653 0.405566 0.363565 0.470183 0.825576 0.2 0.023731 0.211336 0.997846 0.4Ub 0.651668 0.857784 0.665113 0.55226627407 0 0.5577137080.6 0.333333 0.5 0.4285710.4 0.4 0.9 0.4 0.4 0.6 0.5 0.8716220.4 0.0542370.780821 0.6422231 0.7670850.6 0.542903 0.65 0.25 0.65 0.4 0.2 1 0.573035 0.59061 0.516653 0.405566 0.363565 0.470183 0.825576 0.2 0.023731 0.211336 0.997846 0.4 0.651668 0.857784 0.665113 0.552266 0 0.6 0.333333 0.4 0.4 0.4 0.6 0.4 0.054237 1 0.6 0.542903 Vladičin Han 18967 0.556507291 0.5 0.785714 0.9 0.6 0.55 0.973202 1 -0.34706 0.952964 0.8 0.15 0.01 0.2 0.2 0.4 0.623656 0.582018 0.579287 0.588094 0.435259 0.28515 0.047292 0.4 0.037259 0.189815 0.960039 0.2Vladičin0.311453 Han 0.841893 0.475524 0.37451218967 0.003803 0.5565072910.6 0.733333 0.5 0.7857141 0.6 0.9 0.6 0.6 1 0.55 0.9732020.2 0.078325 1 -0.347061 0.9529640.6 0.728777 0.8 0.15 0.01 0.2 0.2 0.4 0.623656 0.582018 0.579287 0.588094 0.435259 0.28515 0.047292 0.4 0.037259 0.189815 0.960039 0.2 0.311453 0.841893 0.475524 0.374512 0.003803 0.6 0.733333 1 0.6 0.6 1 0.2 0.078325 1 0.6 0.728777 Mladenovac 51889 0.555964445 0.333333 0.785714 0.9 0.4 0.5 0.880585 0.801815 0.526342 0.635257 0.15 0.03 0.25 0.4 0.2 1 0.550578 0.55494 0.6537 0.157056 0.804135 0.216811 0.187931 1 0.010331 0.23162 0.945467 0.4Mladenovac0.568427 0.745394 0.452991 0.47870351889 0.050938 0.5559644451 0.3333330.8 0.7857140.8 0.2 0.9 0.8 0.4 0.2 0.5 0.8805850.8 0.0865250.801815 0.5263421 0.6352570.6 0.679937 0.15 0.03 0.25 0.4 0.2 1 0.550578 0.55494 0.6537 0.157056 0.804135 0.216811 0.187931 1 0.010331 0.23162 0.945467 0.4 0.568427 0.745394 0.452991 0.478703 0.050938 1 0.8 0.8 0.2 0.8 0.2 0.8 0.086525 1 0.6 0.679937 Sokobanja 14201 0.554103639 0.333333 0.642857 1 0.4 0.4 0.932482 0.816604 0.465115 0.859134 0.4 0.35 0.4 0.2 0.2 0.2 0.658068 0.738249 0.725002 0.175096 0.544751 0.117212 0.282158 0.2 0.058511 0.19405 0.965141 0.2Sokobanja0.420026 0.779364 0.177933 0.39418914201 0.087223 0.5541036390.6 0.3333330.333333 0.6428571 0.4 1 0.6 0.4 0.6 0.4 0.9324820.2 0.1196290.816604 0.4651151 0.8591340.6 0.638029 0.4 0.35 0.4 0.2 0.2 0.2 0.658068 0.738249 0.725002 0.175096 0.544751 0.117212 0.282158 0.2 0.058511 0.19405 0.965141 0.2 0.420026 0.779364 0.177933 0.394189 0.087223 0.6 0.333333 1 0.4 0.6 0.6 0.2 0.119629 1 0.6 0.638029 Leskovac 135591 0.550357851 0.5 0.428571 0.9 0.4 0.35 0.936888 0.940219 0.417853 0.842395 0.5 0.2 0.3 1 1 1 0.566604 0.559188 0.587488 0.375332 0.474277 0.175822 0.123548 0.6 0.004971 0.193372 0.98188 0.4Leskovac0.637592 0.781437 0.251748 0.432891135591 0.0022 0.5503578510.8 0.466667 0.5 0.4285711 1 0.9 0.8 0.4 0.6 0.35 0.9368880.2 0.0697750.940219 0.4178531 0.8423950.4 0.702189 0.5 0.2 0.3 1 1 1 0.566604 0.559188 0.587488 0.375332 0.474277 0.175822 0.123548 0.6 0.004971 0.193372 0.98188 0.4 0.637592 0.781437 0.251748 0.432891 0.0022 0.8 0.466667 1 1 0.8 0.6 0.2 0.069775 1 0.4 0.702189 Ljig 11374 0.549895708 0.666667 0.428571 1 0.6 0.55 0.871622 0.780821 0.571514 0.767085 0.7 0.3 0.2 0.4 0.2 0.6 0.605558 0.671596 0.541768 0.234878 0.363565 0.180618 0.29737 0.2 0.07225 0.184096 0.976434 0.2Ljig 0.448985 0.91018 0.736597 0.40370511374 0.046453 0.5498957080.4 0.7333330.666667 0.4285711 0.6 1 0.6 0.6 0.8 0.55 0.8716220.2 0.0906060.780821 0.5715141 0.7670850.6 0.685689 0.7 0.3 0.2 0.4 0.2 0.6 0.605558 0.671596 0.541768 0.234878 0.363565 0.180618 0.29737 0.2 0.07225 0.184096 0.976434 0.2 0.448985 0.91018 0.736597 0.403705 0.046453 0.4 0.733333 1 0.6 0.6 0.8 0.2 0.090606 1 0.6 0.685689 Lebane 19730 0.545413313 0.333333 0.428571 1 0.6 0.4 0.936888 0.940219 0.279723 0.842395 0.2 0.3 0.4 0.2 0.2 0.6 0.560612 0.603783 0.609503 0.620439 0.474277 0.442975 0.327268 0.8 0.048727 0.149198 0.981092 0.2Lebane0.477095 0.888531 0.464646 0.48349419730 0.154469 0.5454133131 0.3333330.6 0.4285711 0.8 1 0.2 0.6 1 0.4 0.9368880.2 0.0730250.940219 0.2797230.6 0.8423950.2 0.615494 0.2 0.3 0.4 0.2 0.2 0.6 0.560612 0.603783 0.609503 0.620439 0.474277 0.442975 0.327268 0.8 0.048727 0.149198 0.981092 0.2 0.477095 0.888531 0.464646 0.483494 0.154469 1 0.6 1 0.8 0.2 1 0.2 0.073025 0.6 0.2 0.615494 Ljubovija 12805 0.54450419 1 0.214286 1 0.8 0.65 0.901906 0.801652 0.574403 0.702901 0.75 0.55 0.4 0.4 0.8 1 0.687395 0.542482 0.404731 0.538528 0.427187 0.23751 0.297921 0.2 0.050473 0.192262 0.990464 0.2Ljubovija0.367522 0.907877 0.432789 0.38801412805 0.018421 0.544504190.6 0.466667 1 0.2142861 0.2 1 0.6 0.8 0.6 0.65 0.9019060.2 0.0935020.801652 0.5744031 0.7029010.2 0.564903 0.75 0.55 0.4 0.4 0.8 1 0.687395 0.542482 0.404731 0.538528 0.427187 0.23751 0.297921 0.2 0.050473 0.192262 0.990464 0.2 0.367522 0.907877 0.432789 0.388014 0.018421 0.6 0.466667 1 0.2 0.6 0.6 0.2 0.093502 1 0.2 0.564903 Blace 10509 0.542668131 0.333333 0.214286 0.9 0.6 0.4 0.966888 0.871088 0.587124 0.875621 0.2 0.1 0.2 0.2 0.2 0.2 0.539577 0.746581 0.728548 0.664991 0.672941 0.173222 0.145629 0.8 0.087821 0.182752 0.99911 0.4Blace0.300814 0.766352 0.480186 0.44728410509 3.46E-05 0.5426681311 0.7333330.333333 0.2142861 0.6 0.9 0.2 0.6 0.8 0.4 0.9668880.2 0.1270890.871088 0.5871240.8 0.8756210.6 0.690382 0.2 0.1 0.2 0.2 0.2 0.2 0.539577 0.746581 0.728548 0.664991 0.672941 0.173222 0.145629 0.8 0.087821 0.182752 0.99911 0.4 0.300814 0.766352 0.480186 0.447284 3.46E-05 1 0.733333 1 0.6 0.2 0.8 0.2 0.127089 0.8 0.6 0.690382 Smederevo 103180 0.54160391 0.333333 1 0.7 0.4 0.5 0.905466 0.805952 0.518145 0.659837 0.4 0.2 0.5 1 1 1 0.569486 0.501991 0.668888 0.194835 0.054834 0.149305 0.110137 0.2 0.005386 0.214474 0.904601 0.6Smederevo0.655673 0.767158 0.355089 0.432262103180 0.000432 0.541603910.8 0.4666670.333333 1 1 0.6 0.7 0.8 0.4 0.4 0.5 0.9054661 0.0773590.805952 0.5181450.8 0.6598370.8 0.702332 0.4 0.2 0.5 1 1 1 0.569486 0.501991 0.668888 0.194835 0.054834 0.149305 0.110137 0.2 0.005386 0.214474 0.904601 0.6 0.655673 0.767158 0.355089 0.432262 0.000432 0.8 0.466667 1 0.6 0.8 0.4 1 0.077359 0.8 0.8 0.702332 Vlasotince 27718 0.541121066 0.5 0.428571 0.9 0.6 0.4 0.936888 0.940219 0.407376 0.842395 0.2 0.42 0.55 0.2 0.2 1 0.562292 0.568115 0.522597 0.528226 0.474277 0.256053 0.134315 1 0.024175 0.198762 0.983817 0.2Vlasotince0.543742 0.825426 0.633256 0.44661227718 1.75E-06 0.5411210660.4 0.6 0.5 0.4285711 1 0.9 0.4 0.6 0.8 0.4 0.9368880.2 0.0707150.940219 0.4073760.8 0.8423950.6 0.651129 0.2 0.42 0.55 0.2 0.2 1 0.562292 0.568115 0.522597 0.528226 0.474277 0.256053 0.134315 1 0.024175 0.198762 0.983817 0.2 0.543742 0.825426 0.633256 0.446612 1.75E-06 0.4 0.6 1 1 0.4 0.8 0.2 0.070715 0.8 0.6 0.651129 Čajetina 14564 0.54086067 0.666667 0.214286 0.9 0.4 1 0.942753 0.849751 0.447689 1 0.7 0.2 0.9 0.2 0.2 0.2 0.729938 0.660552 0.565741 0.45111 0.456247 0.15588 0.111326 0.8 0.040662 0.24451 0.95227 0.2Čajetina0.012274 0.721211 0.471639 0.35279214564 0.67838 0.540860670.4 0.9333330.666667 0.2142860.4 0.2 0.9 0.8 0.4 0.4 1 0.9427530.2 0.1318930.849751 0.4476890.6 0.6 0.5393481 0.7 0.2 0.9 0.2 0.2 0.2 0.729938 0.660552 0.565741 0.45111 0.456247 0.15588 0.111326 0.8 0.040662 0.24451 0.95227 0.2 0.012274 0.721211 0.471639 0.352792 0.67838 0.4 0.933333 0.4 0.2 0.8 0.4 0.2 0.131893 0.6 0.6 0.539348 Knić 12919 0.540659004 0.5 0.642857 0.9 0.4 0.4 0.887022 0.843738 0.239143 0.747376 0.3 0.6 0.4 0.2 0.2 0.2 0.58908 0.736594 0.647559 0.341662 0.436242 0.267015 0.503848 0.4 0.068761 0.185245 0.980701 0.2Knić0.493807 0.837748 0.557887 0.46883712919 0.001784 0.5406590040.8 0.333333 0.5 0.6428570.8 0.8 0.9 0.4 0.4 0.6 0.4 0.8870220.2 0.0831470.843738 0.2391431 0.7473760.2 0.57576 0.3 0.6 0.4 0.2 0.2 0.2 0.58908 0.736594 0.647559 0.341662 0.436242 0.267015 0.503848 0.4 0.068761 0.185245 0.980701 0.2 0.493807 0.837748 0.557887 0.468837 0.001784 0.8 0.333333 0.8 0.8 0.4 0.6 0.2 0.083147 1 0.2 0.57576 Jagodina 69384 0.540546494 0.333333 0.642857 1 0.4 0.35 0.914335 0.820016 0.556116 0.793514 0.4 0.2 0.5 0.6 1 1 0.619051 0.584054 0.680224 0.209792 0.144571 0.182466 0.093114 0.6 0.009096 0.211274 0.954649 0.6Jagodina0.70104 0.73169 0.296037 0.44627569384 0.001306 0.5405464940.6 0.4666670.333333 0.6428571 0.4 1 0.2 0.4 0.4 0.35 0.9143350.6 0.0819830.820016 0.5561160.8 0.7935140.6 0.564196 0.4 0.2 0.5 0.6 1 1 0.619051 0.584054 0.680224 0.209792 0.144571 0.182466 0.093114 0.6 0.009096 0.211274 0.954649 0.6 0.70104 0.73169 0.296037 0.446275 0.001306 0.6 0.466667 1 0.4 0.2 0.4 0.6 0.081983 0.8 0.6 0.564196 Prokuplje 41218 0.537533782 0.333333 0.214286 0.9 0.4 0.4 0.966888 0.871088 0.465583 0.875621 0.616731 0.213483 0.431889 0.2 0.2 0.4 0.488703 0.592673 0.634903 0.381672 0.713609 0.141744 0.060782 0.4 0.015415 0.220604 0.974737 0.6Prokuplje0.376251 0.757255 0.197358 0.41097241218 0.131905 0.5375337821 0.3333331 0.2142861 0.6 0.9 0.8 0.4 0.6 0.4 0.9668880.4 0.0932750.871088 0.4655830.6 0.8756211 0.8006220.616731 0.213483 0.431889 0.2 0.2 0.4 0.488703 0.592673 0.634903 0.381672 0.713609 0.141744 0.060782 0.4 0.015415 0.220604 0.974737 0.6 0.376251 0.757255 0.197358 0.410972 0.131905 1 1 1 0.6 0.8 0.6 0.4 0.093275 0.6 1 0.800622 Užice 66996 0.53605598 0.666667 0.214286 0.9 0.6 0.9 0.942753 0.849751 0.451028 1 0.513835 0.224668 0.734724 0.6 1 0.6 0.698252 0.537153 0.412949 0.215186 0.456247 0.052011 0.044299 0.2 0.007201 0.246444 0.971349 0.2Užice0.16935 0.680101 0.16317 0.28362266996 0.206986 0.536055981 0.6666671 0.2142861 0.4 0.9 0.8 0.6 0.2 0.9 0.9427530.6 0.0852340.849751 0.4510281 0.2 0.6714131 0.513835 0.224668 0.734724 0.6 1 0.6 0.698252 0.537153 0.412949 0.215186 0.456247 0.052011 0.044299 0.2 0.007201 0.246444 0.971349 0.2 0.16935 0.680101 0.16317 0.283622 0.206986 1 1 1 0.4 0.8 0.2 0.6 0.085234 1 0.2 0.671413 Valjevo 85962 0.535696722 0.833333 0.428571 0.9 0.6 0.55 0.871622 0.780821 0.643825 0.767085 0.3 0.3 0.4 0.6 0.6 1 0.599728 0.565522 0.451352 0.243602 0.363565 0.141776 0.179313 0.2 0.005523 0.258391 0.952593 0.2Valjevo0.354082 0.726509 0.222999 0.33990185962 0.016158 0.5356967220.6 0.8333331 0.4285711 0.6 0.9 0.4 0.6 0.2 0.55 0.8716220.8 0.0842630.780821 0.6438251 0.7670851 0.733344 0.3 0.3 0.4 0.6 0.6 1 0.599728 0.565522 0.451352 0.243602 0.363565 0.141776 0.179313 0.2 0.005523 0.258391 0.952593 0.2 0.354082 0.726509 0.222999 0.339901 0.016158 0.6 1 1 0.6 0.4 0.2 0.8 0.084263 1 1 0.733344 Kosjerić 10814 0.53554154 0.666667 0.214286 1 0.6 0.75 0.942753 0.849751 0.50108 1 0.6 0.3 0.8 0.6 0.2 0.4 0.672368 0.671237 0.557827 0.482209 0.456247 0.108454 0.29178 0.2 0.063553 0.215219 0.992457 0.2Kosjerić0.209372 0.771879 0.560218 0.36703110814 1.93E-05 0.535541540.8 0.6666670.8 0.2142861 0.4 1 0.2 0.6 0.4 0.75 0.9427530.2 0.1142320.849751 0.80.50108 0.4 0.5830681 0.6 0.3 0.8 0.6 0.2 0.4 0.672368 0.671237 0.557827 0.482209 0.456247 0.108454 0.29178 0.2 0.063553 0.215219 0.992457 0.2 0.209372 0.771879 0.560218 0.367031 1.93E-05 0.8 0.8 1 0.4 0.2 0.4 0.2 0.114232 0.8 0.4 0.583068 Gornji Milanovac 41492 0.533818139 0.666667 0.428571 1 1 0.55 0.924889 0.816558 0.473874 0.914402 0.5 0.125 0.68 1 0.2 0.6 0.680672 0.596765 0.509069 0.19056 0.787012 0.086021 0.123475 0.2 0.013109 0.238718 0.959489 0.2Gornji0.293701 Milanovac0.732842 0.328671 0.34166641492 0.000415 0.5338181390.2 0.6666670.6 0.4285711 0.6 1 0.2 1 0.4 0.55 0.9248890.2 0.0710880.816558 0.4738741 0.9144020.8 0.601765 0.5 0.125 0.68 1 0.2 0.6 0.680672 0.596765 0.509069 0.19056 0.787012 0.086021 0.123475 0.2 0.013109 0.238718 0.959489 0.2 0.293701 0.732842 0.328671 0.341666 0.000415 0.2 0.6 1 0.6 0.2 0.4 0.2 0.071088 1 0.8 0.601765 Ćuprija 28203 0.532760355 0.333333 0.642857 0.9 0.2 0.3 0.914335 0.820016 0.577 0.793514 0.3 0.6 0.7 0.6 0.2 0.8 0.635669 0.618875 0.672453 0.296098 0.297402 0.128756 0.136592 0.6 0.025222 0.192607 0.779486 0.8Ćuprija0.744598 0.709351 0.112665 0.45988528203 0.002788 0.5327603550.2 0.4666670.333333 0.6428571 1 0.9 0.2 0.2 0.6 0.3 0.9143350.8 0.0827750.820016 0.40.577 0.7935140.4 0.487709 0.3 0.6 0.7 0.6 0.2 0.8 0.635669 0.618875 0.672453 0.296098 0.297402 0.128756 0.136592 0.6 0.025222 0.192607 0.779486 0.8 0.744598 0.709351 0.112665 0.459885 0.002788 0.2 0.466667 1 1 0.2 0.6 0.8 0.082775 0.4 0.4 0.487709 Lučani 18602 0.527414845 0.666667 0.428571 1 0.4 0.7 0.924889 0.816558 0.419594 0.914402 0.184 0.316 0.263 0.2 0.2 1 0.572171 0.673115 0.519173 0.432084 0.787012 0.247525 0.203362 0.2 0.038638 0.206916 0.979879 0.2Lučani 0.393 0.797789 0.657343 0.40341818602 0.015787 0.5274148450.2 0.4666670.666667 0.4285711 0.6 1 0.2 0.4 0.6 0.7 0.9248890.4 0.0917160.816558 0.4195941 0.9144021 0.6462760.184 0.316 0.263 0.2 0.2 1 0.572171 0.673115 0.519173 0.432084 0.787012 0.247525 0.203362 0.2 0.038638 0.206916 0.979879 0.2 0.393 0.797789 0.657343 0.403418 0.015787 0.2 0.466667 1 0.6 0.2 0.6 0.4 0.091716 1 1 0.646276 Novi Pazar 106261 0.525808449 0.333333 0.071429 0.9 0.4 0.6 0.960726 0.937572 0.858905 0.950689 1 0.3 1 1 0.2 0.8 0.619585 0.436872 0.362273 0.262921 0.508442 0.104904 0.074728 0.8 0.008097 0.161132 0.991827 0.2Novi0.123102 Pazar 0.822202 0.303807 0.325844106261 0.085745 0.5258084490.8 0.4666670.333333 0.0714291 1 0.9 0.2 0.4 0.6 0.6 0.9607260.6 0.0937530.937572 0.8589051 0.9506890.6 0.68509 1 0.3 1 1 0.2 0.8 0.619585 0.436872 0.362273 0.262921 0.508442 0.104904 0.074728 0.8 0.008097 0.161132 0.991827 0.2 0.123102 0.822202 0.303807 0.325844 0.085745 0.8 0.466667 1 1 0.2 0.6 0.6 0.093753 1 0.6 0.68509 Bajina Bašta 24345 0.525692127 0.666667 0.214286 0.9 0.4 0.85 0.942753 0.849751 0.47259 1 0.3 0.2 0.6 0.2 0.2 1 0.602871 0.585575 0.450492 0.474711 0.456247 0.078714 0.196635 0.6 0.025586 0.211397 0.946752 0.2Bajina0.196373 Bašta 0.789843 0.609946 0.35701124345 0.401189 0.5256921270.6 0.5333330.666667 0.2142861 0.4 0.9 0.2 0.4 0.6 0.85 0.9427530.4 0.1148110.849751 0.472591 0.8 0.6629461 0.3 0.2 0.6 0.2 0.2 1 0.602871 0.585575 0.450492 0.474711 0.456247 0.078714 0.196635 0.6 0.025586 0.211397 0.946752 0.2 0.196373 0.789843 0.609946 0.357011 0.401189 0.6 0.533333 1 0.4 0.2 0.6 0.4 0.114811 1 0.8 0.662946 Vrnjačka Banja 26141 0.524731382 0.666667 0.071429 0.9 0.4 0.4 0.960726 0.937572 0.039825 0.950689 0.1 0.08 0.2 0.2 0.2 0.2 0.546919 0.604399 0.659582 0.228818 0.403768 0.0525 0.072433 0.8 0.024077 0.219385 0.993816 0.4Vrnjačka0.628009 Banja0.763934 0.250971 0.42634626141 0.06187 0.5247313820.6 0.6666670.4 0.0714290.8 0.4 0.9 1 0.4 0.4 0.4 0.9607260.2 0.1187730.937572 0.0398251 0.9506890.6 0.639028 0.1 0.08 0.2 0.2 0.2 0.2 0.546919 0.604399 0.659582 0.228818 0.403768 0.0525 0.072433 0.8 0.024077 0.219385 0.993816 0.4 0.628009 0.763934 0.250971 0.426346 0.06187 0.6 0.4 0.8 0.4 1 0.4 0.2 0.118773 1 0.6 0.639028 Negotin 32654 0.520639675 0.5 0.785714 0.9 0.4 0.25 0.945629 0.754254 0.370076 0.801041 0.35 0.45 0.5 0.2 0.2 1 0.616605 0.748617 0.6604 0.342827 0.595189 0.227385 0.313546 0.2 0.028268 0.178646 0.827859 0.4Negotin0.34381 0.748964 0.25641 0.40959832654 0.00215 0.5206396750.6 0.6 0.5 0.7857140.8 0.4 0.9 0.2 0.4 0.6 0.25 0.9456291 0.0996390.754254 0.3700760.8 0.8010410.4 0.543254 0.35 0.45 0.5 0.2 0.2 1 0.616605 0.748617 0.6604 0.342827 0.595189 0.227385 0.313546 0.2 0.028268 0.178646 0.827859 0.4 0.34381 0.748964 0.25641 0.409598 0.00215 0.6 0.6 0.8 0.4 0.2 0.6 1 0.099639 0.8 0.4 0.543254 Čačak 110279 0.519047099 0.666667 0.428571 1 0.6 0.55 0.924889 0.816558 0.221056 0.914402 0.6 0.4 0.8 0.4 0.2 1 0.639228 0.576687 0.479552 0.155558 0.787012 0.095115 0.060009 0.2 0.004773 0.247375 0.990414 0.2Čačak0.461705 0.740673 0.306915 0.353036110279 0.028908 0.5190470990.8 0.6666670.6 0.4285710.8 0.8 1 0.2 0.6 0.2 0.55 0.9248890.6 0.0767320.816558 0.2210560.8 0.9144020.6 0.587794 0.6 0.4 0.8 0.4 0.2 1 0.639228 0.576687 0.479552 0.155558 0.787012 0.095115 0.060009 0.2 0.004773 0.247375 0.990414 0.2 0.461705 0.740673 0.306915 0.353036 0.028908 0.8 0.6 0.8 0.8 0.2 0.2 0.6 0.076732 0.8 0.6 0.587794 Bačka Palanka 52263 0.518825149 0.5 0.714286 0.7 0.4 0.35 0.951975 0.867149 0.49229 0.632806 0.5 0.7 0.323688 0.6 0.2 0.6 0.612666 0.550401 0.553709 0.226834 0.55751 0.029089 0.236468 0.2 0.01001 0.238976 0.916891 0.8Bačka0.68199 Palanka 0.777752 0.700078 0.45712152263 0.019495 0.5188251490.8 1 0.5 0.7142860.2 0.8 0.7 0.4 0.4 0.2 0.35 0.9519750.2 0.14060.867149 0.60.49229 0.6328060.2 0.47062 0.5 0.7 0.323688 0.6 0.2 0.6 0.612666 0.550401 0.553709 0.226834 0.55751 0.029089 0.236468 0.2 0.01001 0.238976 0.916891 0.8 0.68199 0.777752 0.700078 0.457121 0.019495 0.8 1 0.2 0.8 0.4 0.2 0.2 0.1406 0.6 0.2 0.47062 Crveni krst 30939 0.517494498 0.333333 0.785714 0.9 0.4 0.4 0.92895 0.853994 0.460888 0.798521 0.6 0.15 0.3 0.2 0.2 0.2 0.669994 0.562435 0.735774 0.193277 0.85258 0.082402 0.011705 0.6 0.017692 0.238694 0.986166 0.8Crveni0.746688 krst 0.828089 0.423854 0.49331630939 0.16022 0.5174944980.6 0.8666670.333333 0.7857140.2 0 0.9 0 0.4 0 0.4 0.80.928950.1361210.853994 0.4608880.6 0.7985210.2 0.325013 0.6 0.15 0.3 0.2 0.2 0.2 0.669994 0.562435 0.735774 0.193277 0.85258 0.082402 0.011705 0.6 0.017692 0.238694 0.986166 0.8 0.746688 0.828089 0.423854 0.493316 0.16022 0.6 0.866667 0.2 0 0 0 0.8 0.136121 0.6 0.2 0.325013 Požarevac 59131 0.51746856 0.5 0.714286 0.9 0.2 0.4 0.934418 0.840472 0.329841 0.77219 0.849883 0.259814 0.197911 0.2 0.2 1 0.637327 0.550104 0.586346 0.1407 0.766301 0.102991 0.142733 0.2 0.008953 0.239643 0.8745 0.8Požarevac0.778417 0.734224 0.21756 0.45802459131 0.001011 0.517468560.6 0.733333 0.5 0.7142860.6 0 0.9 0 0.2 0 0.4 0.9344180.8 0.1530380.840472 0.3298410.6 0.60.772190.4268490.849883 0.259814 0.197911 0.2 0.2 1 0.637327 0.550104 0.586346 0.1407 0.766301 0.102991 0.142733 0.2 0.008953 0.239643 0.8745 0.8 0.778417 0.734224 0.21756 0.458024 0.001011 0.6 0.733333 0.6 0 0 0 0.8 0.153038 0.6 0.6 0.426849 Topola 20445 0.517140248 0.5 0.642857 0.7 0.6 0.45 0.887022 0.843738 0.513885 0.747376 0.3 0.05 0.5 0.6 0.2 0.8 0.542837 0.630062 0.603056 0.18424 0.436242 0.34395 0.79433 0.6 0.033434 0.206817 0.927673 0.2Topola0.42712 0.943574 0.610723 0.51524220445 0.002511 0.5171402480.6 0.2 0.5 0.6428571 1 0.7 0.2 0.6 0.4 0.45 0.8870220.4 0.0959320.843738 0.5138850.6 0.7473760.2 0.481443 0.3 0.05 0.5 0.6 0.2 0.8 0.542837 0.630062 0.603056 0.18424 0.436242 0.34395 0.79433 0.6 0.033434 0.206817 0.927673 0.2 0.42712 0.943574 0.610723 0.515242 0.002511 0.6 0.2 1 1 0.2 0.4 0.4 0.095932 0.6 0.2 0.481443 Aranđelovac 43834 0.515997174 0.5 0.642857 0.9 0.8 0.5 0.887022 0.843738 0.521638 0.747376 0.367505 0.171239 0.462362 0.2 0.2 0.2 0.635743 0.542675 0.542715 0.139195 0.5606 0.104257 0.078633 0.2 0.01297 0.219873 0.980464 0.2Aranđelovac0.426577 0.760594 0.321678 0.34565643834 0.002046 0.5159971741 0.4 0.5 0.6428571 0.8 0.9 0.2 0.8 0.4 0.5 0.8870220.2 0.069510.843738 0.5216381 0.7473760.2 0.591890.367505 0.171239 0.462362 0.2 0.2 0.2 0.635743 0.542675 0.542715 0.139195 0.5606 0.104257 0.078633 0.2 0.01297 0.219873 0.980464 0.2 0.426577 0.760594 0.321678 0.345656 0.002046 1 0.4 1 0.8 0.2 0.4 0.2 0.06951 1 0.2 0.59189 Loznica 75286 0.513632896 0.666667 0.214286 0.7 0.4 0.45 0.901906 0.801652 0.554999 0.702901 0.15 0.25 0.3 0.6 0.8 1 0.484929 0.527793 0.54496 0.345897 0.427187 0.184682 0.156828 0.8 0.008534 0.191052 0.99685 0.6Loznica0.910684 0.897973 0.263403 0.50463475286 0.031868 0.5136328960.4 0.6666670.6 0.2142861 1 0.7 0.4 0.4 0.6 0.45 0.9019060.8 0.0607570.801652 0.5549990.8 0.7029010.2 0.570188 0.15 0.25 0.3 0.6 0.8 1 0.484929 0.527793 0.54496 0.345897 0.427187 0.184682 0.156828 0.8 0.008534 0.191052 0.99685 0.6 0.910684 0.897973 0.263403 0.504634 0.031868 0.4 0.6 1 1 0.4 0.6 0.8 0.060757 0.8 0.2 0.570188 Pećinci 19222 0.500626479 0.333333 0.642857 1 0.4 0.5 0.919208 0.807714 0.550744 0.646739 0.401112 0.299048 0.27218 0.2 0.2 0.2 0.63792 0.52686 0.437604 0.217044 0.158916 0.091205 0.175162 0.2 0.02497 0.254301 0.633993 0.4Pećinci0.598711 0.740327 0.574204 0.35405919222 0.169178 0.5006264790.6 0.6666670.333333 0.6428570.8 0.2 1 0.4 0.4 0.2 0.5 0.9192080.2 0.1029070.807714 0.5507440.6 0.6467390.6 0.5145370.401112 0.299048 0.27218 0.2 0.2 0.2 0.63792 0.52686 0.437604 0.217044 0.158916 0.091205 0.175162 0.2 0.02497 0.254301 0.633993 0.4 0.598711 0.740327 0.574204 0.354059 0.169178 0.6 0.666667 0.8 0.2 0.4 0.2 0.2 0.102907 0.6 0.6 0.514537 Požega 27554 0.491126236 0.5 0.214286 1 0.4 0.8 0.942753 0.849751 0.464574 1 0.4 0.35 0.8 0.2 0.2 0.6 0.624642 0.610035 0.520156 0.270333 0.456247 0.113064 0.121756 0.2 0.021336 0.23097 0.996338 0.2Požega0.308561 0.794565 0.668998 0.34940327554 0.000798 0.4911262360.8 0.8 0.5 0.2142860.6 0.8 1 0.2 0.4 0.4 0.8 0.9427530.6 0.0884410.849751 0.4645740.6 0.2 0.5034381 0.4 0.35 0.8 0.2 0.2 0.6 0.624642 0.610035 0.520156 0.270333 0.456247 0.113064 0.121756 0.2 0.021336 0.23097 0.996338 0.2 0.308561 0.794565 0.668998 0.349403 0.000798 0.8 0.8 0.6 0.8 0.2 0.4 0.6 0.088441 0.6 0.2 0.503438 Šabac 110918 0.48789683 0.5 0.214286 0.9 0.6 0.45 0.901906 0.801652 0.551195 0.702901 0.5 0.1 0.5 0.6 1 1 0.475721 0.521244 0.563932 0.268848 0.427187 0.332345 0.242971 0.2 0.00471 0.228952 0.918854 0.8Šabac0.784727 0.764049 0.289044 0.49213110918 0.000731 0.487896830.2 0.866667 0.5 0.2142860.8 0.6 0.9 0.8 0.6 0.4 0.45 0.9019060.2 0.0749110.801652 0.5511950.6 0.7029010.2 0.49981 0.5 0.1 0.5 0.6 1 1 0.475721 0.521244 0.563932 0.268848 0.427187 0.332345 0.242971 0.2 0.00471 0.228952 0.918854 0.8 0.784727 0.764049 0.289044 0.49213 0.000731 0.2 0.866667 0.8 0.6 0.8 0.4 0.2 0.074911 0.6 0.2 0.49981 Sremska Mitrovica 75873 0.485652438 0.333333 0.642857 0.7 0.8 0.45 0.919208 0.807714 0.475074 0.646739 0.1 0.2 0.02 0.6 0.2 0.4 0.456749 0.537764 0.597397 0.254497 0.158916 0.09374 0.195132 0.2 0.007085 0.228356 0.841258 0.8Sremska0.850856 Mitrovica0.719714 0.227661 0.44700775873 0.067614 0.4856524380.6 0.8666670.333333 0.6428570.6 0.6 0.7 0.8 0.8 0.4 0.45 0.9192080.2 0.138210.807714 0.4750741 0.6467390.2 0.586975 0.1 0.2 0.02 0.6 0.2 0.4 0.456749 0.537764 0.597397 0.254497 0.158916 0.09374 0.195132 0.2 0.007085 0.228356 0.841258 0.8 0.850856 0.719714 0.227661 0.447007 0.067614 0.6 0.866667 0.6 0.6 0.8 0.4 0.2 0.13821 1 0.2 0.586975 Vranje 71551 0.47678063 0.5 0.785714 0.9 0.6 0.6 0.973202 1 -0.44642 0.952964 0.001 0.01 0.105025 1 0.2 1 0.500065 0.485323 0.440839 0.271771 0.435259 0.170881 0.029547 0.2 0.00715 0.231316 0.983014 0.2Vranje0.377011 0.70555 0.224553 0.32366671551 0 0.476780630.8 0.6 0.5 0.7857141 0.6 0.9 0.8 0.6 0.4 0.6 0.9732020.2 0.096816 1 -0.446420.8 0.9529640.6 0.6715260.001 0.01 0.105025 1 0.2 1 0.500065 0.485323 0.440839 0.271771 0.435259 0.170881 0.029547 0.2 0.00715 0.231316 0.983014 0.2 0.377011 0.70555 0.224553 0.323666 0 0.8 0.6 1 0.6 0.8 0.4 0.2 0.096816 0.8 0.6 0.671526 Bor 45266 0.463840665 0.5 0.785714 0.9 0.4 0.3 0.945629 0.754254 0.782929 0.801041 0.15 0.01 0.1 0.4 0.2 1 0.571854 0.520048 0.58812 0.173458 0.595189 0.114899 0.032362 0.2 0.012249 0.213212 0.961142 0.2Bor 0.273457 0.779134 0.228438 0.32401745266 0.014837 0.4638406650.2 0.6 0.5 0.7857141 0.2 0.9 0.8 0.4 0.2 0.3 0.9456290.4 0.1031970.754254 0.7829291 0.8010410.2 0.511556 0.15 0.01 0.1 0.4 0.2 1 0.571854 0.520048 0.58812 0.173458 0.595189 0.114899 0.032362 0.2 0.012249 0.213212 0.961142 0.2 0.273457 0.779134 0.228438 0.324017 0.014837 0.2 0.6 1 0.2 0.8 0.2 0.4 0.103197 1 0.2 0.511556 Pirot 54342 0.453156548 0.333333 0.214286 1 0.2 0.5 0.873752 0.827664 0.485317 0.911812 0.2 0.05 0.6 0.2 0.2 1 0.445532 0.610937 0.475489 0.165327 0.088395 0.072471 0.043225 0.2 0.010766 0.227585 0.98292 0.2Pirot0.248595 0.747351 0.228438 0.43226254342 0.483037 0.4531565481 0.3333331 0.2142861 0.6 1 0.4 0.2 0.4 0.5 0.8737520.4 0.0880660.827664 0.4853171 0.9118120.4 0.705459 0.2 0.05 0.6 0.2 0.2 1 0.445532 0.610937 0.475489 0.165327 0.088395 0.072471 0.043225 0.2 0.010766 0.227585 0.98292 0.2 0.248595 0.747351 0.228438 0.432262 0.483037 1 1 1 0.6 0.4 0.4 0.4 0.088066 1 0.4 0.705459 Zaječar 54355 0.422964719 0.333333 0.642857 0.7 0.4 0.3 0.932482 0.816604 0.314068 0.859134 0.28 0.16 0.2 0.2 0.2 1 0.482159 0.653961 0.34595 0.166712 0.544751 0.201106 0.177836 0.2 0.014179 0.182665 0.943648 0.2Zaječar0.412321 0.744818 0.20979 0.34902454355 0.065375 0.4229647190.6 0.4666670.333333 0.6428570.2 0.6 0.7 0.2 0.4 0.4 0.3 0.9324820.8 0.0938540.816604 0.3140681 0.8591340.2 0.445084 0.28 0.16 0.2 0.2 0.2 1 0.482159 0.653961 0.34595 0.166712 0.544751 0.201106 0.177836 0.2 0.014179 0.182665 0.943648 0.2 0.412321 0.744818 0.20979 0.349024 0.065375 0.6 0.466667 0.2 0.6 0.2 0.4 0.8 0.093854 1 0.2 0.445084

ISBN 978-92-5-134477-4

FAO Regional Office for Europe and Central Asia [email protected]

Food and Agriculture Organization of the United Nations 9 789251 344774 Budapest, Hungary CB4916EN/1/06.21