Proceedings of the 3rd Edition of the International Conference
Integrated Management of Environmental Resources
Suceava, November 6‐7 th, 2015
Edited by:
Sergiu‐Andrei Horodnic Mihai‐Leonard Duduman Ciprian Palaghianu
2016 ‐ Ştefan cel Mare University of Suceava, Romania Proceedings of the 3rd International Conference Integrated Management of Environmental Resources ‐ Suceava, November 6‐7th, 2015
Editors: Sergiu‐Andrei Horodnic, Mihai‐Leonard Duduman, Ciprian Palaghianu
The contributions to this volume have been peer‐reviewed.
Reviewers: Ionel Popa ‐ “Marin Drăcea“ National Research‐Development Institute (ICAS) Marian Dragoi – “Ştefan cel Mare” University of Suceava
Responsabilitatea integrală privind conţinutul lucrărilor prezentate revine autorilor. Responsibility for the content of these papers rests fully with their authors.
Descrierea CIP a Bibliotecii Naţionale a României INTEGRATED MANAGEMENT OF ENVIRONMENTAL RESOURCES. International Conference (2015; Suceava) Proceedings of the 3rd International Conference Integrated Management of Environmental Resources ‐ Suceava, November 6‐ 7th, 2015/ ed.: Sergiu‐Andrei Horodnic, Mihai‐Leonard Duduman, Ciprian Palaghianu ‐ Suceava : Editura Universităţii "Ştefan cel Mare", 2016 ISBN 978‐973‐666‐494‐6
I. Horodnic, Sergiu Andrei (ed.) II. Duduman, Mihai‐Leonard (ed.) III. Palaghianu, Ciprian (ed.)
630(498)(063)
Table of contents
Forest areas from Forestry Administration County Suceava that 3 contain certain values that are of critical conservation priority in the forest certification process Diana Vasile, Virgil Scărlătescu
Evaluation of normality in the determination of spatio-temporal 15 autocorrelation of monthly precipitation in the central-west region of Venezuela Jesús Enrique Andrades-Grassi, Ledyz Cuesta-Herrera, Juan Ygnacio López-Hernádez, Arnaldo Goitía-Acosta
Afforestation in Romania: Realities and Perspectives 24 Ciprian Palaghianu
Evaluation of ecoprotective function intensity in stands with 32 coniferous growing outside their natural vegetation area. Case study in the Forest District Adâncata, P.U. VII Zvoriștea Cătălina Oana Barbu
Analysis of wild boar population dynamics in Suceava County 38 for the period 2004-2016 Alexandru-Mihai Corjin, Nadia-Mihaela Dănilă, Gabriel Dănilă
Considerations regarding the angular acceleration 45 influence on the vehicles movement in curves Dan Zarojanu
Spatio-temporal association among monthly precipitation and 47 relief in the central-west region of Venezuela Jesús Enrique Andrades-Grassi, Hugo Alexander Torres- Mantilla, Juan Ygnacio López-Hernádez
Proceedings of the 3rd International Conference Integrated Management of Environmental Resources, 2015
Forest areas from Forestry Administration County Suceava that contain certain values that are of critical conservation priority in the forest certification process
Diana Vasilea, Virgil Scărlătescub National Institute of Forest Research and Development ”Marin Drăcea”- Brașov National Institute of Forest Research and Development ”Marin Drăcea”- Mihăești Corresponding author e-mail:[email protected]
Abstract: Established on the FSC Principles and Criteria in 1999, the concept of High Conservation Value Forests (HCVF) has provided a useful new approach for defining and managing forest areas of critical conservation significance. This concept is flexible enough to be applied in a variety of contexts not only in the forest certification. As a result it has been applied in a range of settings, and has been used outside forest certification as well as within it. The FSC standard contains 10 conservation principles, related by several areas. Principle 9 (which is related of the HCVF identification) asks the forest manager to take a wider view, and to consider conservation issues as a high priority or significance on a national, regional or global scale. In the assessment process of HCVF from Forestry Administration County (FAC) Suceava, there was find an area of 18,543.63 ha with forest areas with high conservation values, which represent 8% of the total area with HCVF identified at national level. From this surface area, the highest percent is represented by forest areas from protected areas (31%), forests with critical seasonal use ( 23%) and of forests that are critical for erosion control (20% ). In all 24 forest districts (FD) were identified HCVF, the highest percent of the total area with HCVF from FAC Suceava, being in FD Crucea (16%), Dorna Cândreni and Stulpicani (12%). After the identification of the HCVF, there must be a decision making process that determines what form of management will be consistent with maintaining or enhancing of these values. It must be noted that the FSC standard does not require that an area of forest classified as HCVF becomes a protected area. In some cases, it may be possible to achieve commercial use of the forest while maintaining the value. Only forest areas from protected areas (National or natural parks, natural reserves, etc) will be completely protected.
Keywords: assessment, criteria, conservation values, forest areas.
1. Introduction protection. Thus in Romania are13 national parks, 14 nature parks and 382 In the last years Romania has protected areas (Ioja et al., 2010). Most implemented the EU Birds and Habitat of Romania’s protected areas and all of Directive (NATURA 2000), therefore, national and nature parks are managed an important percent of the country’s by the National Forest Administration forests are under some form of (NFA) Romsilva (Abrudan et al., 2009).
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Now, in the context of the Forestry - HCV4 - Critical ecosystem certification, the areas managed by services. Basic ecosystem services in National Forest Administration critical situations, including protection Romsilva, containing environmental of water catchments and control of and social values, such as unique erosion of vulnerable soils and slopes; biodiversity, watershed protection, soil - HCV5 - Community needs. stabilization or an archaeological site Sites and resources fundamental for will become High Conservation Value satisfying the basic necessities of local Forests (Jennings et al., 2003; Rouget communities (for livelihoods, health, et al., 2006; Schnooret al., 2010) . nutrition, water, etc.); The concept of High Conservation - HCV6 - Cultural values. Sites, Value (HCV) was used first in 1998, resources, habitats and landscapes of when the Forest Stewardship Council global or national cultural, (FSC) adopted it to define forest areas archaeological or historical of outstanding and critical importance significance, and/or of critical cultural, whose attributes should be maintained ecological, economic or or enhanced (FSC 1996; Jennings et religious/sacred importance for the al., 2003; Blackman et al., 2011; traditional cultures of local Brown et al., 2013; ). In this context, communities. High Conservation Value Forests The use of the HCVF concept is (HCVF) are evaluated by a process an important step towards better forest that identify them using a particular set management and protection and of values (Stewart et al., 2008; complements other tools for forest Tollefsonet al., 2008; FSC 2012; ): conservation such as forest - HCV1- Species diversity. certification and the designation of Concentrations of biological diversity protected areas (Van Kootenet al., including endemic species, and rare, 2004; Rietbergen-McCrackeret al., threatened orendangered species, that 2007;Kujiket al., 2009; McCormicket are significant at global, regional or al., 2009; Pena-Claros et al., 2009). national levels; This process can be considered as - HCV2- Landscape-level complementary to political planning ecosystems and mosaics. Large processes such as Natura 2000 and it is landscapelevel ecosystems and very useful for NFA Romsilva and for ecosystem mosaics that aresignificant private forest owners too, to manage at global, regional or national levels, forests to sustain and enhance the and contain viable populations of great environmental services. majority of the naturally occurring The aim of this article is to species in natural patterns of identify the HCVFs of the Forest distribution and abundance; Administration County Suceava to - HCV3 - Ecosystems and make a better forestry management habitats. Rare, threatened, or and to maintain and to enhance the endangered ecosystems, habitats or values of critical importance. refugia
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2. Materials and methods continental - moderate climate (in the western side) with cool winters and The study was conducted in the fairly warm summers with Forestry Administration County (FAC) precipitation all year round. Suceava, on the range of 24 Forest For the HCVF identification was Districts (FD), managed of National used „The High Conservation Value Forest Administration Romsilva, Forests Toolkit” (Jennings et al. 2003), respectively: Adâncata, Breaza, based on a methodology that has 4 Brodina, Broșteni, Cârlibaba, Crucea, steps: Dolhasca, Dorna, Falcău, Fălticeni, - 1. Establish a team with forest Frasin, Gura Humor, Iacobeni, engineers, scientists, communities and Marginea, Mălini, Moldovița, Pătrăuți, NGOs at a regional level; Pojorâta, Putna, Râșca, Solca, - 2.The identification of the most Stulpicani, Vama and VatraDornei. important critical values from the Suceava County covers an area of forest and the place they are located; 8,553.5 km2, representing 3.6% of the - 3. Development the best country area. It is located in the north- management measures for the eastern part of Romania, between identified HCVFs in order to maintain Pietrosul Călimani (2.022 m altitude) or enhance the identified value; and the Siret river bed (233 m). In a - 4. Monitoring the identified landscape dominated by central and HCVF to see if the management north-eastern European bioclimatic measures are properly applied. elements, which creates a unique The benefits of the HCVF landscape harmony, on the identification consist: in better and geographical coordinates 24˚57‟- more accepted forest planning 26˚40‟ east longitude and 47 ˚4‟55”- decisions; management decisions 47˚57‟31” north longitude, with an based on a precautionary principle; amphitheatric settlement. effective use of local knowledge; The main forms of relief are hills, better understanding by all slopes and plains. Overall, the county stakeholders on the range of forest has two major units of relief: the values and the costs and benefits of mountain region - 65.4% mountains protecting them; reduction of the with heights between 800 and 2.100 m conflict between competing aims of and the plateau region - 34.6% sub- resource use (Vogt et al., 2000; Carpathian plateau and hills. Nusbaum and Simula, 2005; Bartley, The heights decrease gradually 2007; Auld et al., 2008; Fujita, 2010). from west to east, highlighting thus the leveling and diversification of the 3. Results other components of the natural environment. In the 24 FD from FAC Suceava The geographical space of was identified an area of 18,543.63 ha Suceava County falls almost equally with HCVF, within the continental climate sector representing 8% from the total area (in the eastern side), having a with HCVF
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(228,636.58 ha) from the national Romsilva. forest fund managed by NFA
3000 PVRC 2500
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0 Solca Râșca Vama Putna Frasin Dorna… Mălini Falcău Breaza Crucea Brodina Pătrăuți Broșteni Fălticeni Iacobeni Pojorâta Dolhasca Cârlibaba Marginea Stulpicani Adâncata Moldovița Gura Humor Vatra Dornei Fig. 1.The 24 FD from FAC Suceava with High Conservation Value Forests
In all the 24 FD assessed, were Forest districts have on their area identified HCVF, three of them with between two and five HCVF. The the greatest area, respectively FD most categories of HCVF are in FD Crucea representing 16%, Dorna Crucea, Stulpicani, Broșteni and Cândreni and Stulpicani representing Marginea (5 categories). 12% of the total area with HCVF from FAC Suceava (Fig. 1). 4. Discussion It have been identified 8 categories of HCVF (Fig. 2) such as: 31% HCV 4.1. HCV 1.1. - Forest areas from 1.1. - Forest areas from protected protected areas areas; 24% HCV 1.3. - Forest areas with critical seasonal use; 20% HCVF The protected areas provide 4.2- Forest areas for erosion control; important habitat for native species 12% HCVF 4.1.-Forest areas for that are under pressure. All of these, watershed protection; 6% HCVF 3 - together with national, nature parks Forest areas that are in or contain rare, and nature reserves will give to native threatened or endangered ecosystems; species the best chance of survival in 5% HCVF 6 - Forest areas critical to the face of threats and of habitat loss local communities’ traditional cultural (Myers, 2000; Oszlanyi, 2004; identity; 2% HCVF 4.3 - Forest area Strimbu, 2005). with critical impact on agriculture and In FAC Suceava, from the total air quality and 0,22% HCVF 1.2. - area with HCVF 1.1., 34% are in FD Habitats for rare, threatened or Dorna Cândreni and 19% in FD endemic plant species. Stulpicani, the rest of FD have much The distribution of HCVF is smaller forest areas from protected represented on the Suceava map (Fig. areas (Fig. 4). 3) where it can be seen that all the
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338,3 958,52 5811,35 3711,7
2201,04 41,7 4412,8 1068,86
Fig. 2 and 3 The categories of High Conservation value Forests
2000 PVRC 1 1800 1600 1400 1200 1000 800 600 400 200 0 Solca Râșca Vama Putna Frasin Mălini Falcău Breaza Crucea Brodina Pătrăuți Broșteni Fălticeni Iacobeni Pojorâta Dolhasca Cârlibaba Marginea Stulpicani Adâncata Moldovița Gura Humor Vatra Dornei
Dorna Cândreni Fig. 4. Forest areas from protected areas
In FD Dorna Cândreni- 61% of reptiles, 5 species of fish (including forest area from protected areas are Hucho hucho), 6 species of represented by national park Călimani. invertebrates (including Rosalia In this region there is a significant alpina) and 8 species of plants are of concentration of flora and fauna community interest; species protected by national law and - 35% are represented by Poiana EU Directives. The percentage of Stampei Bog, representative for Dorna habitats of European interest exceeds Basin and hosts most species of 95%. According to the Handbook of oligotrophic peat, here is a compact habitats there are 13 habitats, including population of Sphagnum wulfenianum, 4 of special importance (Habitats very rare in Romania; Directive), 18 species of birds, 9 - 4% the protected area Cucureasa. species of mammals, 2 species of
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Proceedings of the 3rd International Conference Integrated Management of Environmental Resources, 2015
In FD Stulpicani – HCVF 1.1.are or endemic species are clearly more represented 100% by a secular forest – important for maintaining biodiversity Slătioara and Todirescu meadows. values than those that do not, because Here is a variety of plant species from these species are more vulnerable to different biogeographic regions, the continue habitat loss (Nussbaum, R., herbaceous species diversity creates a M. Simula. 2005; Newton, A.C. 2008). special polychromy. Here, we find The presence of the species species such as: Pulsatilla patens, requires designation of HCV 1.2 only Echium russicum, Dictamnus albus, if the concentration of the species is Adonis vernalis, Trolius europaeus, large enough to justify specific etc. management measures. In FAC Suceava only in two 4.2. HCVF 1.2. - Habitats for rare, forest districts (Fig. 5) were identified threatened or endemic plant species significant concentrations of species like in Adâncata, approximately 16 ha One of the most important (38% from the total area with HCVF aspects of biodiversity value is the 1.2.) with Fritillaria meleagris and in presence of threatened, endangered or FD Mălini 26 ha (68% from the total endemic species. Forests that contain area with HCVF 1.2.) with Taxus populations of threatened, endangered baccata.
100 PVRC 1.2 90 80 70 60 50 40 30 20 10 0 Solca Râșca Vama Putna Frasin Mălini Falcău Breaza Crucea Brodina Pătrăuți Broșteni Fălticeni Iacobeni Pojorâta Dolhasca Cârlibaba Marginea Stulpicani Adâncata Moldovița Gura Humor Vatra Dornei
Dorna Cândreni Fig. 5. Habitats for rare, threatened or endemic plant species
4.3. HCV 1.3. - Forest areas with critical breeding sites, wintering sites, critical seasonal use migration sites, migration routes or corridors. This element is designed to The species mentioned in the ensure the maintenance of important national and international legislation concentrations of species that use the ratified in Romania which, at different forest only at certain times or at certain stages of their development cycle, phases of their life cycle. It includes depending on the complex forest
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Proceedings of the 3rd International Conference Integrated Management of Environmental Resources, 2015 ecosystemwere identified in fourteen incana of montane and sub-montane FD (Fig. 6), three of them having rivers occurring on heavy soils (rich in important forest surfaces with HCVF alluvial deposits) periodically 1.3. such as Moldovița (15%), inundated by the annual rise of the Iacobeni and Pojorâta (both with river level, otherwise well-drained and 14%). Here were identified large aerated during low-water (Lazăr et al concentrations with Capercaillie 2007). The herbaceous layer includes (Tetrao urogallus) and bears (Ursus many large species (Filipendula arctos) den. An important thing is that ulmaria, Cardamine spp. Carex spp., much of the areas with critical etc) and various vernal geophytes seasonal useare included in protected (Ranunculus ficaria, Anemone areas and in national and nature parks nemorosa, Corydalis solida, etc).This and have become HCVF 1.1. type of 91E0 forest habitats, occur in all the 24 forest districts assessed. 4.4. HCVF 3 - Forest areas that are in Another type of forest habitat of or contain rare, threatened or community interest that is very wide endangered ecosystems spread across the FAC Suceava is 91D0 Bog woodland, respectively This category of HCVF was coniferous forests on a humid to wet identified in all of the 24 forest peat substrate, with the water level districts (Fig. 7), two of them having permanently high. the largest surfaces, respectively FD Even if protected areas cover a Crucea 21% and FD Marginea 14%. sufficient area, unfortunately the In both forest districts, the forest protected areas statute does not always habitats of community interest with the mean implementation of adequate largest area are 91E0 Alluvial forests measures for the protection of rare with Alnus glutinosa and Fraxinus forest ecosystems. Some of forestry excelsior (Alno-Pandion, Alnion ecosystems are naturally rare and the incanae, Salicio albe). These forest aim of this HCV 3 is to provide habitats are riparian forests of Alnus protection for these type of threatened or endangered ecosystems. PVRC 1.3 2000
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0 Gura… Solca Vatra… Râșca Vama Putna Frasin Dorna… Mălini Falcău Breaza Crucea Brodina Pătrăuți Broșteni Fălticeni Iacobeni Pojorâta Dolhasca Cârlibaba Marginea Stulpicani Adâncata Moldovița Fig. 6. Forest areas with critical seasonal use
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PVRC 3 250
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0 Gura… Solca Vatra… Râșca Vama Putna Frasin Dorna… Mălini Falcău Stulpic… Breaza Crucea Moldov… Brodina Pătrăuți Broșteni Fălticeni Iacobeni Pojorâta Dolhasca Cârlibaba Marginea Fig. 7.Forest areas that are in or contain rare, threatened or endangered ecosystems
extremely high and the consequences, 4.5. HCVF 4.1.- Forest areas for in terms of loss of productive land, watershed protection damage to ecosystems, property or loss of human life, are potentially The forests area substantial factor catastrophic, it can be considered that for maintaining the terrain stability the ecosystem service provided by the and have an important role in forest is critical, and it should be preventing flooding, controlling designated HCVF 4.2. stream flow regulation and water This category of HCVF was quality. Where the forest covers large identified in 20 forest districts (Fig. 9), areas from the water catchments, it 23% of the total area with HCVF 4.2 has a critical role in maintaining the are in FD Crucea and 17% are in FD quantity and quality of the water. Stulpicani. Forest areas for watershed In all these forest districts, the HCVF protection were identified in twelve 4.2. is a risk of serious erosion, forest districts from FAC Suceava, the because there is a slope with more than most of them being for river protection. 35 degrees and the terrain instability In FD Crucea with the largest area (44% might include damage to ecosystems. HCVF 4.1.) (Fig. 8) the forest areas are for Bistrița river protection. 4.7. HCVF 4.3 - Forest area with critical In some of forest districts, the impact on agriculture and air quality HCVF 4.1.are for protection of the mineral water springs such as in FD Where forest areas are close to Broșteni and Dorna Candreni. agricultural lands, their impact can sometimes be crucial for maintaining 4.6. HCVF 4.2- Forest areas for the resources or economic production. erosion control The forests impact will vary according to the climate and topography, spatial When the risks of severe erosion, configuration of the agricultural land landslides and avalanches are and the forest, as well as the crops types.
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PVRC 4.1 2000 1500 1000 500 0 Solca Râșca Vama Putna Frasin Mălini Falcău Breaza Crucea Brodina Pătrăuți Broșteni Fălticeni Iacobeni Pojorâta Dolhasca Cârlibaba Marginea Stulpicani Adâncata Moldovița Gura Humor Vatra Dornei
Dorna Cândreni Fig. 8. Forest areas for watershed protection
PVRC 4.2 2000 1800 1600 1400 1200 1000 800 600 400 200 0 Solca Râșca Vama Putna Frasin Mălini Falcău Breaza Crucea Brodina Pătrăuți Broșteni Fălticeni Iacobeni Pojorâta Dolhasca Cârlibaba Marginea Stulpicani Adâncata Moldovița Gura Humor Vatra Dornei
Dorna Cândreni Fig.9. Forest areas for erosion control
The crucial importance of the 4.8. HCVF 6 - Forest areas critical to local HCVF 4.3.and ameliorative influence communities’ traditional cultural identity includes stabilization of environment, the development of This type of forests includes sacred optimum regime for isolation of or religious sites, specific areas that have vehicles, accumulation of toxic been historical sites, specific areas with substances, noise insulation. remnants from the past linked to the In FAC Suceava, there were identity of the group, frequent used of identified small areas with HCVF 4.3 forest products/materials for artistic, (Fig. 10) only in two forest districts, traditional, and social status purposes, both of them being against air names for landscape features, stories pollution. In FD Stulpicani 333 ha in about the forest, historical associations, FMU V Tarnița and 5.3 ha in FD etc. Frasin around the tailing deposits from 51% of the HCVF 6 from FAC FMU IV Belțag. Suceava are in FD Mălini (Fig. 11), representing forests of religious sites,
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Proceedings of the 3rd International Conference Integrated Management of Environmental Resources, 2015 respectively forests around the Slatina required to determine whether a forest is monastery. In the rest of 7 forest districts critical to the traditional cultural identity there were identified forests from of local communities. This would cultural sites (Ion Creangă) and forest normally include: Indicators of potential around the monasteries, such as cultural significance, stories about the Sucevița, Moldovița and Putna. forest, historical associations and how The difference between having long the community has been associated some significance to cultural identity with a particular forest. This is very and being critical will often be a difficult important, because a change to a forest line to draw, therefore it will simply be can potentially cause an irreversible possible to decide this in consultation change to traditional local culture or a with the communities in question. particular forest provides a cultural value Various types of information will be that is unique or irreplaceable of a forest.
PVRC 4.3 500
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0 Gura… Solca Vatra… Râșca Vama Putna Frasin Dorna… Mălini Falcău Moldo… Stulpic… Breaza Crucea Dolhas… Cârliba… Margin… Brodina Pătrăuți Broșteni Fălticeni Iacobeni Pojorâta Fig.10. Forest area with critical impact on agriculture and air quality
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0 Gura… Solca Vatra… Râșca Vama Putna Frasin Dorna… Mălini Falcău Moldo… Stulpic… Breaza Crucea Cârliba… Margin… Brodina Pătrăuți Broșteni Fălticeni Iacobeni Pojorâta Dolhasca Fig. 11. Forest areas critical to local communities’ traditional cultural identity
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5. Conclusion acknowledge support by the National Forest Administration Romsilva and FSC requires that forest managers by FAC Suceava and all those should identify HCVs within their forest responsible for forest certification districts (FDs), respectively management from the all 24 forest districts certified. units (FMUs), to manage these to maintain or enhance the values References identified, and to monitor conservation impacts. Appropriate HCV management Abrudan, I.V., Marinescu, V., Ionescu, O., within natural forests can range from Ioras, F., Horodnic, S.A., Sestras, R., complete protection to extractive uses 2009.Developments in the Romanian such as selective logging or harvesting Forestry and its Linkages with other of natural products (Stewart, 2010). Sectors.NotulaeBotanicaeHortiAgrobotani ci Cluj-Napoca 37, 14–21. It requires public consultations and a Auld, G., Gulbrandsen, L.H. and precautionary approach to manage HCV McDermott, C.L. 2008.Certification areas. schemes and the impacts on forests and The HCV concept has been adopted forestry.Annual Review of Environment beyond its original context of forest and Resources 33. certification. Bartley, T., 2007. How certification The HCV approach may also matters: examining mechanisms of become a significant driver for land-use influence. Prepared for a working group planning and for plantation design on the assessment of sustainability (McCormick et al. 2009). standards, certification and labels, September.Indiana University, By providing a common language Bloomington, US. for industry, conservationists, Blackman, A., Rivera, J.2011. Producer- communities and financiers, this level benefits of sustainability approach needs to be much better certification. ConservBiol 25:1176–1185. understood by managers, practitioners Brown, E., Dudley, N., Lindhe, A., and auditors. Muhtaman, D.R., Stewart, C., Synnott, T. Conservation scientists need to (eds) 2013. Common guidance for the engage with the implementation of the identification of high conservation HCV requirements and to adopt this values.HCV Resource Network. concept for non-forest ecosystems such Forest Stewardship Council (FSC) 1996.FSC principles and criteria for forest as grasslands and wetlands. stewardship.FSC-STD-01-001 (version 4- By identifying and managing the 0) EN. FSC, Bonn. HCVF there are demonstrated Forest Stewardship Council (FSC) conservation benefits of the HCV 2012.FSC Principles and Criteria for approach by existing direct and Forest Stewardship Supplemented by circumstantial evidence. Explanatory Notes and Rationales.FSC- STD-01-001 V5-0 D4-9.FSC, Oaxaca, Acknowledgments Mexico. Fujita, N. 2010.Using systems concepts in evaluation. In: Fujita, N. (ed). Beyond This article was made in the Task logframe: using systems concepts in 11-24/2014 RNP and we are gratefully evaluation.Foundation for Advanced
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Studies on International Development, evaluation of forest management Tokyo, Japan. certification in the tropics. Tropical Ioja, C.I., Patroescu, M., Rozylowicz, L., Resource Management Papers 95, Popescu, V.D., Verghelet, M., Zotta, M.I., Wageningen University. Felciuc, M., 2010. The efficacy of Rietbergen-McCracker, J., Steindlegger, Romania’s protected areas network in G., Koon, S.C., 2007.High Conservation conserving biodiversity. Biological Value Forests: The concept in the theory Conservation 143, 2468–2476. and practice. WWF–for a living planet. Jennings, S., Nussbaum, R., Judd, N., Rouget, M, Cowling, RM, Vlok, JAN et Evans, T. 2003.The high conservation al.2006.Getting the biodiversity intactness value forest toolkit.ProForest, Oxford. index right: the importance of habitat Kuijk van, M., PutzF.E., Zagt, R.J. degradation data. Glob Change Biol 2009.Effects of Forest Certification on 12:2032–2036. Biodiversity.Wageningen: Tropenbos Schnoor, J.L. 2010. Sustainability: the art International. of the possible. Environ SciTech 44:7984. Lazăr, G., Stăncioiu, P.T., Tudoran, G.M., Stewart, C., George, P., Rayden, T., Șofletea, N., Bozga, Ș.B.C., Predoiu, G., Nussbaum, R. 2008. Good practice Doniță, N., Indreica, A., Mazăre, G., 2007: guidelines for high conservation value Habitateforestiere de assessments: a practical guide for interescomunitarincluseînproiectul LIFE05 practitioners and auditors. ProForest, NAT/RO/000176 ”Habitate prioritare Oxford. alpine, subalpine șiforestiere din Strimbu, B.M., Hickey, G.M., Strimbu, România” Amenințări potențiale. Ed. V.G., 2005. Forest conditions and ”Transilvania” , Brașov, 200 p. management under rapid legislation McCormick, N., Athanas, A., de Nie, D., change in Romania. Forestry Chronicle 81. Wensing, D., Heyde, J., Voss, A., Stewart, C. 2010.The HCV Dornburg, V., Nevill, A., Berenguer, P., approach.Biodiversity conservation in Stewart, C., Rayden, T., 2009.Towards a certified forests.European Tropical Forest responsible biofuels development Research Network nr. 51.Tropenbos process.www.tripleee.nl/bestanden/ International, Wageningen, the Towardsaresponsiblebiofuelsdevelopment. Netherlands. pdf. Tollefson, C., Gale, F., Haley, D. Myers, N., Mittermeier, R.A., Mittermeier, 2008.Setting the standard: certification, C.G., da Fonseca, G.A.B., Kent, J., 2000. governance, and the Forest Stewardship Biodiversity hotspots for conservation Council. UBC Press, Vancouver. priorities.Nature 403. Van Kooten, G.C., Nelson, H. W., Nussbaum, R. and M. Simula. 2005.The Vertinsky, I., 2004. Certification of Forest Certification Handbook.London: sustainable forest management practices: a Earthscan. global perspective on why countries Oszlanyi, J., Grodzinska, K., Badea, O., certify. Forest Policy and Economics 7. Shparyk, Y., 2004.Nature conservation in Vogt, K.A., B.C. Larson, J.C. Gordon, D.J. Central and Eastern Europe with a special Vogt and A. Fanzeres. 2000.Forest emphasis on the Carpathian Certification: Roots, Issues, Challenges Mountains.Environmental Pollution 130. and Benefits. Boca Raton: CRC Press, 374 Peña-Claros M.,Blommerde S., Bongers pp. F., 2009.Assessing the progress made: an
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Evaluation of normality in the determination of spatio- temporal autocorrelation of monthly precipitation in the central-west region of Venezuela
Jesús Enrique Andrades-Grassia, Ledyz Cuesta-Herrerab, Juan Ygnacio López-Hernádeza, Arnaldo Goitía-Acostab a Universidad de Los Andes, Facultad de Ciencias Forestales y Ambientales, Mérida, Venezuela b Universidad de Los Andes, Facultad de Ciencias Económicas y Sociales-Instituto de Estadística Aplicada y Computación, Mérida, Venezuela Corresponding author e-mail address: [email protected] (Jesús_EnriqueAndrades_Grassi)
Abstract: Venezuelan monthly precipitation data is geocoded, discontinuous in space and time. Missing data, summarized records, installation and removal of stations is common. Second order properties were evaluated (spatio-temporal autocorrelation), but data is not normally distributed, then a Box-Cox transformation was applied because it is known that non normal distributions tend to hide spatio-temporal autocorrelation. Intensity of spatio-temporal autocorrelation was estimated using Moran's I and its cluster variant LISA. Both with their spatio-temporal version. Cluster analysis allowed to characterize precipitation stations using k-means with five groups of monthly precipitation (low, low-medium, medium, medium-high and high). Results show an increase of the amount of clusters mapped and better representation of precipitation.
Keywords: spatial temporal autocorrelation, Box-Cox transformation, non-normality, Moran´s I, LISA, monthly data precipitation.
1. Introduction two components (spatial, temporal, or both components) is discarded (Cressie Managing spatio-temporal data is a and Winkle, 2011). common task when dealing with But, modern statistics is climate variables, in some cases it is introducing the concept that possible to obtain many observations “Stochastic data does not originate at different times from numerous from a random and independent spatial locations. This type of data process” (Cocchi and Bruno, 2010). corresponds statistically with a This idea is also present in Tobler´s structure of Pooled time series cross- First Law of Geography (1970), which section (time series with spatial cross establishes that “Everything is related section) (Gujarati and Potter, 2010), to everything else, but near things are which is the structure shown in figure more related than distant things”. 1. This type of structure requires From the univariate perspective, highly volumetric data for its this refers to the fact that a variable processing (Androva and Boland, can be correlated with itself, but in 2011). In order to perform the another position in space. modelling, it is frequent that one of the
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Proceedings of the 3rd International Conference Integrated Management of Environmental Resources, 2015
Figure 1. Structure of Pooled time series cross-section.
This will be called Spatial types of contiguity, such as “Queen”; Autocorrelation. However, if we “Bishop” and “Rook” (Figure 3). consider a spatio-temporal phenomenon, there may be Spatial The use of indexes corresponds Temporal Autocorrelation instead of with Lattice techniques (spatial Spatial Autocorrelation (Stojanova, analysis of discrete data), and may be 2012). Anselin (1988) defines three categorized into two kinds of index, as possible types of correlations: Spatial follows. (contemporaneous correlation), Temporal (time-wise correlation) and Spatial Temporal Autocorrelation (space-time correlation).
Spatial Autocorrelation and Spatial Temporal Autocorrelation are designated as Second Order Properties (Lloyd, 2010), and may be presented Figure 2. Positive Autocorrelation . b) as three types: Positive Negative Autocorrelation. c) Absence of Autocorrelation (similar values cluster Autocorrelation (Independence). together in a map), Negative Autocorrelation (dissimilar values cluster together in a map) and Absence of Autocorrelation (Independence) (Figure 2). An important element to consider is the type of neighborhood (spatial and spatio temporal) of the Figure 3. Three types of neighborhood. variables. Celemin (2009), Lloyd (2010) and Toral (2001) define many
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Proceedings of the 3rd International Conference Integrated Management of Environmental Resources, 2015
Global Measures: Moran´s I Because the observations have which is a variation of Pearson´s different spatial and temporal Linear Correlation Coefficient (Moran, locations, a variant of Moran's I and 1950) is described by several authors LISA indicator, the Spatio Temporal (Bosque, 1992; Cressie, 1993; Lloyd, Moran´s I (ESRI, 2013) was 2010; Olaya, 2011; Toral, 2001). developed. Lattice techniques assume that the data have normal distribution X ∼ N , σ , if the data is non ; normal it can introduce bias in the ∑ statistical analysis (Bivand et al, 2008). Bianco (2013), Vilar, (2006) 1 and Velilla (1991) suggest a 1 transformation in the case of non- normal data. Among the many families Where is the mean of the of transformations, one that is most variable , is the weigthed used is the Box-Cox transformation: ponderation matrix, is the expected value in the absence of 1 Autocorrelation (Independence) and si 0 is the number of observations. Local Measures: are variations of si 0 global measures and they characterize Spatial Autocorrelation and Spatial Temporal Autocorrelation, such as the The value of must be estimated, LISA test (Local Indicators of Spatial for that purpose, Log-Likehood Association). In this case, each one of Methods are used. the observations can be assigned in Venezuela is a Tropical country one of 5 types of clusters: Positive with maritime and continental Autocorrelation (H/H, High values extension. It is a country with different surrounded by High values and L/L, climates due to the Intertropical Low values surrounded by Low Convergence Zone and the orographic values); Negative Autocorrelation control. These conditions generate a (H/L, High values surrounded by Low seasonal rainfall that can be 10 times values and L/H, Low values higher in one location as compared to surrounded by High values); and another. Rainfall is unimodal at the Absence of Autocorrelation (A/A) center and east of the country and is (Anselin, 1995): bimodal at the north west of the country (with peaks in May – June and in September – November). In this ; , study we focus towards the evaluation , of the Spatial Temporal ∑ Autocorrelation of the monthly , , precipitation data that was obtained 1 after a Box-Cox transformation.
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Proceedings of the 3rd International Conference Integrated Management of Environmental Resources, 2015
2. Materials and methods Inverse distance relationship between the objects and absence of Threshold The information was obtained Distance, Temporal window of 24 from 961 climatic stations of the months (Cryerand Chan, 2008 ; monthly precipitation data from Gilgen, 2006 and Pruscha, 2013). official institutions (Figure 4), with As mentioned above, with the non-normal data. Rainfall has the LISA test, 5 types of cluster are characteristic of having only positive obtained: H/H, L/L, H/L, L/H and data and usually the probability A/A. Monthly precipitation is highly density function is decreasing and seasonal, and all the stations have a monotone (Bidegain y Díaz, 2011; combination of the 5 types of clusters. Moran, 1969). In order to avoid bias in Since Stations of high precipitation the characterization of the Spatial will have more clusters of the type Temporal Autocorrelation, a Box-Cox H/H, Stations of low precipitation will transformation was performed. have more clusters of the type L/L and In order to characterize the second Stations located at transition sites will order properties (Spatial Temporal have more clusters of the type A/A, an Autocorrelation), the following analysis of cluster dominance using hypothesis was tested with the the K-means test was performed. transformed data: The optimal number of categories H0: Monthly precipitation does not was estimated using the “Elbow” have linear null Spatial Temporal method suggested by Matthew Autocorrelation (Absence of (2011) and developed by Ketchen and Autocorrelation/Independence). Shook (1996). Using the K-means H1: Monthly precipitation has test, each station, was characterized as Spatial Temporal Autocorrelation High, Medium-High, Medium, that is different from linear null (it Medium-Low and Low precipitation. is different from Absence of Autocorrelation/Independence). 3. Results
If the null hypothesis is rejected, The estimated value of to then the Spatial Temporal perform the Box-Cox transformation Autocorrelation must be characterized was 0.34. We found an improvement as either positive or negative: if the of the normal distribution with a estimated >expected , then the skewness coefficient that approaches Spatial Temporal Autocorrelation is normality. Zero values create a positive; if the estimated < expected distortion because they determine the , then the Spatial Temporal physical barrier of the variable (Figure Autocorrelation is negative. The test 5). for Moran´s I and the LISA test for Moran´s I requires normality but Cluster categorization were performed not in a strict sense, it is affected with the following spatial temporal mainly by high skewness or by an characteristics: Significance level of excess of outlier values. 5%, Standard Space-Time Window,
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Proceedings of the 3rd International Conference Integrated Management of Environmental Resources, 2015
Figure 4. Stations selected.
(a) (b)
(c)
Figure 5. (a) Histogram of monthly precipitation, (b) Q–Q plot of monthly precipitation; (c) Log-Likehood for the estimation of in the Box-Cox transformation.
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Proceedings of the 3rd International Conference Integrated Management of Environmental Resources, 2015
Zhang et al. (2008) reported that The dominance of the AA, HH the number of clusters almost and LL is expected since the Spatial duplicates when the transformation is Temporal Autocorrelation is positive. performed. In our case, we obtained a The dominance of AA indicates that skewness of 1.63 for the data without the Moran's I is an index with low transformation and a skewness of -0.30 sensitivity to spatio-temporal changes with the Box-Cox transformation. (see Table 2). When comparing these The results in Table 1 indicate that results with the ones obtained by the null hypothesis is rejected and Andrades and Lopez (2015), we found characterize the process as positive that in the present work there is an linear Spatial Temporal increase in the number of clusters HH Autocorrelation. Because the and LL. This may indicate that the transformation decreases the risk of transformation controlled the negative bias, these results are closer to reality effect of skewness and revealed the than those presented by Andrades and occurrence of a greater number of Lopez (2015) due to the fact that the clusters that had not been previously Moran I requires that the distribution detected, increasing the number of of the data be as symmetrical as clusters by 3.2%. possible. The magnitude of the A method for choosing the Moran´s I statistic, is not comparable appropriate cluster number is to to the one previously obtained by compare the Sum of Squared Error Andrades and López (2015), in which (SSE) for a range of cluster solutions. the nature of the distribution is entirely SSE is defined as the sum of the different and the results are biased. squared distance between each The vast majority of clusters member of a cluster and its cluster obtained are typified as AA followed centroid, SSE can be seen as a global by LL and HH, characteristic of measure of error. As the number of positive Spatial Temporal groups increases, the SSE should Autocorrelation. Less frequently we decrease because pools are by obtained, the negative Spatial definition, smaller. Temporal Autocorrelation (HL and LH). Table 2. Number of clusters with Box-Cox transformation of monthly precipitation
Table 1. Spatial Temporal Moran´s I of Cluster Frequency Frequency transformed monthly precipitation. excluding AA (%) Parameter Value HH 12.8 41.3 Moran´s I 0,196942 LL 1.5 39.8 Expected I -0,000003 HL 1 11 Variance 0,000001 Z-Score 478,41 LH 2.5 7.9 p-value 0 AA 71.2 --
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Proceedings of the 3rd International Conference Integrated Management of Environmental Resources, 2015
asymmetric distribution of precipitation that otherwise distorted the allocation of clusters that were subject to transition phenomena and therefore underestimated their distribution into categories (see Figure 7). Using the Box-Cox transformation ensures a more accurate representation of reality. The main problem with autocorrelation is that a model may look better than it actually is. Autocorrelation generates several consequences: 1) Figure 6. Variation of SSE with the Estimators are linear and unbiased, but Cluster number. they are not the best, they are inefficient 2) the most serious consequence of the The appropriate number of autocorrelation is that the estimator, clusters could be defined as the turning has no minimum variance and therefore it point where the slope is stabilized, this is not efficient, and 3) the correlation produces an "elbow" appearance, coefficient R² is increased. when plotted (Matthew, 2011). All of these problems result in For this case (see Figure 6) the hypothesis tests that become invalid appropriate number of clusters is 5 (Gujarati and Potter, 2010; Bhattarai, because this is the point of inflection 2010; Ramanathan, 2013). Therefore, the of the curve. With the transformed modeling techniques must take these data, 65 stations were categorized as conditions into account. Low precipitation (7%), 289 stations as Middle-Low precipitation (30%), 143 as Medium precipitation (15%), 5. Conclusion 357 as Medium-High precipitation (37%) and 107 stations as High With the use of the Box-Cox precipitation (11%). transformation we found a more realistic and adequate characterization of the 4. Discussion Spatial Temporal Autocorrelation, obtaining a greater number of clusters
with the LISA Test, in comparison with Compared with the results of the results of Andrades and López Andrades and Lopez (2015), in this work (2015). The present results prove the we obtained 17 less stations in the Low hypothesis of the existence of Spatial category, 10 more stations in the Temporal Autocorrelation within the Medium-Low category, 17 more stations monthly rainfall data. It is important to in the Medium category, 20 stations less point out that there are alternative in the Medium-High category and 11 autocorrelation indexes, such as the more stations in the High category. Kelejian-Robinson index, that do not This difference is probably due to the need normality nor linearity and therefore use in this work of the Box-Cox they do not require transformations. transformation that controlled the
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Figure 7. Categories of precipitation stations assigned by the cluster analysis of the K- means. Acknowledgments Andrades, J, López, J. 2015. Caracterización de los procesos espaciales The authors wish to thank Prof. y temporales y sus interrelaciones en Dr. Barnoaiea Ionut for lending us his estaciones de precipitación mensual en la zona centro occidental de la República good will and time for this Bolivariana de Venezuela. presentation in the 3rd Edition of the Androva, N., Boland, S. 2011. Volume Integrated Management of variability diagnostic for 4D Environmental Resources Conference datasets.Geophysical Research Letters, 38, Suceava. Additionally, we would like 1-5. to thank Prof. Hilda Cristina Grassi Bhattarai, K. 2010. Autocorrelation. who helped with the translation of this http://www.hull.ac.uk/php/ecskrb/FCAST/ article. Autocorelation.pdf Bidegain, M., Diaz, A. 2011. Análisis References Estadístico de Datos Climáticos Distribuciones de Probabilidad. http://meteo.fisica.edu.uy/Materias/Analisi Anselin, L. 1988. Spatial econometrics: s_Estadistico_de_Datos_Climaticos/teoric methods and models. Springer. Santa o_AEDC/Distribuciones_Probabilidad_20 Barbara. 11.pdf Anselin, L. 1995. Local indicators of Bianco, M. 2013. Transformaciones de spatial association-LISA.Geographical Box y Cox. http://cms.dm.uba.ar/academ- Analysis, 27 (2), 93–115. ico/materias/1ercuat2013/modelo_lineal/te oricas/ML_Parte%2011_2013.pdf
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Bivand, R., Pebesma, E., Gómez-Rubio, Moran, P. 1969. Statistical inference with V. 2008. Applied spatial data analysis with bivariate gamma distributions. Biometrika, R. Springer.New York. p. 378. 56, (3), 627-634. Bosque, J. 1992. Sistemas de información Olaya, V. 2011. Sistemas de información geográfica. Ediciones Rialph, S.A. geográfica. Madrid. p. 451. http://wiki.osgeo.org/wiki/Libro_SIG Celemín. J. 2009. Autocorrelación espacial Pruscha, H. 2013. Statistical analysis of e indicadores locales de asociación climate series analyzing, plotting, espacial. Importancia, estructura y modeling, and predicting with R. Springer. aplicación. Revista Universitaria de New York. p. 178. Geografía, 18 (1) pp. 11-31. Ramanathan, R 2013.Introductory Cocchi, D., Bruno, F. 2010. Considering Econometrics with Applications. groups in the statistical modeling of https://en.wikibooks.org/wiki/Econometric spatio-temporal data.Statistica, 70(4), 511- _Theory 527. Stojanova, D. 2012. Considering Cressie, N. 1993.Statiscs for spatial autocorrelation in predictive models. PhD data.Wiley-Interscience. New York. p. (Thesis), Jožef Stefan International 900. Postgraduate School, Slovenia. Cressie, N., Winkle, C. 2011. Statistics for Tobler, W.1970. A computer movie spatio-temporal data.Wiley.New York.p. simulating urban growth in the Detroit 531. region.Economic Geography, 46 (2) 234- Cryer, J., Chan, K. 2008. Time series 240. analysis with applications in R. Springer. Toral, M. 2001.El factor espacial en la New York. p. 501. convergencia de las regiones de la Unión ESRI.2013. Space-Time Cluster Analysis. Europea: 1980-1996. PhD (Thesis), http://resources.arcgis.com/es/help/main/1 Centro: Facultad de Ciencias Económicas 0.1/index.html#/na/005p00000056000000/ y Empresariales. Universidad Pontificia Gilgen, H. 2006. Univariate time series in Comillas de Madrid. Madrid. geosciences theory and examples.Springer. Velilla, S.1991. Un método gráfico de Berlin. p. 738. análisis de la hipótesis de normalidad. Gujarati, D., Potter, D., 2010. Estadistica Española. 33 (127), 291-303. Econometría. McGraw-Hill. México, p. Vilar, J. 2006. Modelos Estadísticos 946. Aplicados. Ketchen, D., Shook, C. 1996. The http://dm.udc.es/asignaturas/estadistica2/ application of cluster analysis in Strategic Zhang C., Luo, L., Xu W., Ledwith ,V. Management Research: An analysis and 2008. Use of local Moran's I and GIS to critique. Strategic Management Journal, 17 identify pollution hotspots of Pb in urban (6), 441–458. soils of Galway, Ireland. Science of The Lloyd, C. 2010.Spatial data analysis an Total Environment, 398, 212-221. introduction for GIS users. Oxford University Press, New York, p. 206. Matthew, P. 2011. R Script for K-Means Cluster Analysis. http://www.mattpeeples.net/kmeans.html Moran, P. 1950.Notes on continuous stochastic phenomena.Biometrika, 37, (1), 17–23.
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Afforestation in Romania: Realities and Perspectives
Ciprian Palaghianu Stefan cel Mare University of Suceava, Forestry Faculty, Romania Corresponding author e-mail address: [email protected] (C. Palaghianu)
Abstract: Romania's forest cover is at this time below, but quite close to the European average. Despite that, recent forestry activities tend to be more oriented towards forest exploitation and not to increase the national forested area. The general public perception is that afforestation activities are limited only to NGO’s projects and media actions and numerous reports and statements made by some of these organizations are rather sensational and confusing. This paper tries to cast more light on this controversial issue, presenting accurate data and considering the whole context of afforestation - from the history of afforestation activities in Romania to the recent statements and reports submitted by the government agencies involved in these particular issues. The necessity and opportunity of afforestation is also substantiated from the perspective of reintroduction into production of marginal and degraded land. An analysis of the situation of funding through the National Rural Development Programme (2007-2013) measures is performed and possible future directions regarding afforestation programmes are discussed.
Keywords: afforestation, degraded lands, forest regeneration, funding measures.
1. Introduction the significant role of forests in mitigating climate change, as natural Indisputable, the forest represents carbon sinks and as a source of a vital global resource. Being a renewable wood that can be used as renewable resource it is continuously fuel or raw material for different harvested for quite a considerably products. Considering these particular amount of time. It is unthinkable to aspects, it is easy to understand why imagine a world without forest forest cover represents such an because of the many implications of it important indicator in many recent in our everyday lives. Forest does not statistics and reports. embody only a sum of trees, but a The distribution of forest resources complex natural system highly is uneven, both at the level of states interconnected with a lot of different and continents. Today, the global environmental elements. forest cover is approximately 4 billion Today, we all know the whole hectares, which means that almost one benefit package that comes along with third of the terrestrial area is forested the existence of forest in our life. (FAO, 2010). But nearly 8000 years Forest can deliver a wide range of ago, the forest cover was double, ecological services regarding according to World Resources Institute biodiversity, soil protection, water data. The development of agriculture quality, habitat providing, along with and the construction of the modern social and recreational services. The human society lead to massively loss new environmental realities recognize of forest cover. This phenomenon was
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Proceedings of the 3rd International Conference Integrated Management of Environmental Resources, 2015 concentrated excessively in the last afforestation initiatives (Council two centuries and the recent trends are Regulation 2080/92). Beginning with uncomforting (Palaghianu, 2009). the year 2000, the forestry sector was The global forest cover has been incorporated into the Rural drastically decreasing annually by Development Plan, according to the nearly 7 million hectares in the last Council Regulation 1257/1999. two decades (see table 1). Today we The UE aims to extend the are facing a rapid pace of deforestation forested areas and the funding and this tendency is likely to get worse mechanisms support two categories of considering the increasing trend of forest initiatives: afforestation and wood or wood products consumption other forestry measures. (Palaghianu, 2007). However, in recent years there is a 2. A review of afforestation growing interest for wood resources activities in Romania management and remarkable progress became visible considering the afforestation efforts. It's worth Romania has consistent forest mentioning the positive example of resources: about 6.5 million hectares Europe which extended its forested and the forest cover is estimated at area with nearly 1 million hectares per 27% of the country’s total area (INS, year in the last two decades. 2013). However, Romania's forest The importance of afforestation in cover is still below the European balancing the forest resources is now Union average which is currently generally accepted and every regional estimated at 42% (EC, 2013). or national forest strategy includes an It is known that Romanian forest afforestation programme (Mather, cover was superior far back in the past 1993). The European Union is actively and several well-known studies involved in the management of forest indicate that fact (Giurescu, 1975; resources using the Common Doniță et al., 1992). Agricultural Policy (CAP) and since 1992 EU is also engaged in
Table 1. Forest cover of the world (according to FAO data, 2010)
Year 1990 2000 2005 2010 million hectares Africa 749 709 691 674 Asia 576 570 584 593 Europe 989 998 1.001 1.005 North and Central America 708 705 705 705 Oceania 199 198 197 191 South America 946 904 882 864 Global 4.168 4.085 4.061 4.033
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Proceedings of the 3rd International Conference Integrated Management of Environmental Resources, 2015
During the last two centuries, (1775 and later in 1781). These first when the forest loss at global level was guidelines and procedures were at the highest level, Romania lost quickly followed by similar forest nearly 2 million hectares of forest, but protocols in Bukovina (1786), in the last century the forest cover Moldavia and Wallachia (1792). remained almost unchanged, varying However, the earliest afforestation around 6.5 million hectares (see fig. actions should be considered the 1). mobile sand fixation and afforestation Taking into account the historical that were executed in Oltenia province and current loss of the forests, in 1852 (Giurescu, 1975). Soon after afforestation and reforestation that, in 1864 the first three forest represented a key element in the effort nurseries were established, each of it of preserving the forest resources. And having an area of 50 hectares (in such efforts were made from early Brăila, Iaşi and Ismail counties). time in Romania. The promulgation of the first We could start with the first Forest Code in 1881 brought some document of Grigore Ureche, the consistent improvements in the chronicler who mentioned the early forestry sector, and later, in 1889 a ”afforestation” actions guided by new national service was founded, a Stephen the Great, voivode of service that joined afforestation and Moldavia, after the battle of the torrents control. Cosmin Forest (1497). More consistent This action demonstrates the early information regarding not only the understanding of the role of basic forest management but also afforestation in controlling torrents, afforestation was specified in the landslides and erosion. forest regulations from Transylvania
Figure 1. Changes of the forest cover in Romania during the last two centuries (unit: millions of hectares)
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Specific tasks were undertaken in stands, seed processing facilities and the effort of preventing and controlling large forest nurseries in order to boost the erosion of watershed in the the seedlings production. following years and later at the beginning of the 20th century. 3. Present state of afforestation For instance, in a rather difficult in Romania and future trends period, between 1930 and 1947, 97.000 hectares of degraded land were afforested (Crăciunescu et al, 2014). Today, the Romanian forest covers After the World War II, the 6.5 million hectares (INS, 2013) but Romanian forests were nationalized after 1990 the state was no longer the and the state took over the forest sole landowner and manager. Still, the resource management. The WWII state represents, with nearly 50% of reparations paid to Soviet Union and the forest, the most important the intense activity of the SOVROM, landowner and the legal state-owned Soviet-Romanian enterprises, created entity, RNP - National Forestry to manage the debt recovery, have left Administration, represents the largest deep scars in Romanian forests. In the administrator of Romanian forest. 50’s the forest condition was severely After 1990 the forestry sector altered - more than 700 thousand suffered remarkable changes. The hectares were deforested. Furthermore, planning and control of the state were an additional 600 thousand hectares not so strict due to the change of the were considered degraded lands. As a regime. Unfortunately, new obstacles result, a massive afforestation national appeared, considering the forest plan was developed for reforestation of fragmentation and restitution. The 1 million hectares. This notable infrastructure needed for afforestation objective was finally achieved in 1963, was also damaged because forest after more than a decade of impressive nurseries and seed orchards were afforestation efforts. The annual returned to former owners, who have average of reforested land was around neglected or destroyed it. 70 thousand hectares, with a maximum The new forestry paradigm was of 98 400 hectares in 1953. better focused on natural regeneration In the communist period, the and the afforestation effort was forestry management was quite well abruptly reduced. Moreover, illegal balanced and manifested a particular logging and forest fragmentation have interest in afforestation. In order to contributed to the negative general direct and control the field specialists public perception on forestry. there were edited Technical Guidelines Unfortunately common people in 1948, 1953, 1966, 1969, 1977 and consider many of the significant 1987. The standards for seed quality silvicultural activities to be unfamiliar assessment were updated in 1963, and incomprehensible, because of an 1973, 1983 and the afforestation improper dissemination. Furthermore, infrastructure was developed by quite few public statements clarify the creating numerous seed orchards, seed available data and information, which
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Proceedings of the 3rd International Conference Integrated Management of Environmental Resources, 2015 are generally ignored by the public hectares between 1955 and 1990, this (Palaghianu & Nichiforel, 2016). was not a constant trend. After 1990 Although significant changes in the rhythm of afforestation was clearly forest cover were not found (Dutcă & diminished. The reports on the first Abrudan, 2010; MECC, 2010; INS, decade after 1990 (Georgescu & Daia, 2013), it is quite obvious that 2002), indicated an annual average of Romanian forests have suffered at nearly 10 thousand hectares of least structural alterations in the past afforestation between 1992 and 2001 two decades. and a comparable value for the natural This observation is emphasised by regenerated areas. This trend regarding numerous media campaigns – some of afforestation was maintained at the them extremely persuasive and almost same level for the next period partially controversial. Greenpeace, (2005-2013), as shown in figure 2, WWF Romania or the local initiative considering only the afforestation “Plantăm fapte bune în România” completed by RNP. were heavily involved in such After 1990, the state was not the environmental campaigns that pointed only forest administrator and different out to the recent loss of forests. It's funding mechanisms emerged. Despite worth mentioning the Russian all the property and administration Greenpeace report on Romanian forest changes, RNP still represents the most (Greenpeace, 2012), which stated that active and visible entity involved in 3 hectares of forest per hour are the afforestation effort. RNP has ”disappearing”. Numerous media several different mechanisms of entities have cited this report and its funding afforestation: its own budget, results and many associated this loss of the Fund for the improvement of the forest with illegal logging, which in lands with forest destination, the Fund fact was not accurate. The public for forest conservation and perception was easily altered by media regeneration, as well as funds from the pressure and by misunderstanding the state budget. The output of all funding difference between authorised clear- instruments leads to the previously cutting needed in forest regeneration mentioned annual average of nearly 10 and illegal logging. In association with thousand hectares afforested. the increased wood logging and In the past two decades there were undersized afforestation plans (see additional funding mechanisms that figure 2) the general perception on have been available or used for Romanian forestry seems that it is afforestation programs: the SAPARD more oriented to logging, whatever (Special Accession Program for these actions are legit or not. Agriculture and Rural Development) However, what is the reality, Program – measure 3.5 (for the period beyond the media slogans or 2000-2006), the Environmental Fund persuaded perceptions? Although, the and Environment Fund Administration TBFRA-2000 report (TBFRA, 2000) afforestation programs and, finally, the presented a positive average annual European Union funding instruments change of forest of 14.7 thousand that have been implemented by the
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National Program for Rural bureaucracy and numerous changes in Development (PNDR) measures (in the funding guides, one single 2007-2013 and 2014-2020 programs). afforestation project (designed for an The SAPARD Program (2000- area of 40.5 hectares) was funded in 2006) was implemented by PNADR the first 7 years from the creation of (National Plan for Agriculture and this funding mechanism (RCA, 2013). Rural Development) and the measure The latest updated reports show an 3.5 was specifically designed for improvement in the past years: 2,836 forestry. hectares of afforestation were funded The 3.5 measure included more till 2014 (RCA 2014). than 7 million euros (7.445 mil. euros) Considering the partial failure of funding support for afforestation (a the previous funding mechanisms, the target of 15,000 hectares) and nearly 4 European Union funding instruments million euros (3.722 mil. euros) for were considered more adequate and forest nurseries. At the end of the better balanced. The National Program program, 3 afforestation projects and for Rural Development (PNDR) for one nursery project were funded and a 2007-2013, granted 1.2 billion euros disappointing 1.3% funding absorption for forestry measures of the total 7.5 rate was reached (MADR, 2011). billion euros. Unfortunately, the Environmental The 221 Measure – The first Fund (founded by government afforestation of agricultural land was emergency ordinance no.196/2005) designed for promoting afforestation and Environment Fund Administration projects, with a total budget of 229 were not able to produce more million euros. significant results. Due to excessive
Fig. 2 Afforestation completed by the National Forestry Administration RNP (2005-2013) (unit: hectares)
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The outcomes at the end of the The UE funding scheme is term consisted in 29 projects, funded ineffective without an adequate with a total of 185 thousand euros, support from the state. We can resulting in a shocking absorption rate mention the Mediterranean states of 0.08%. (Spain, Italy or Portugal) which have Another initiative was the Measure greatly benefited from the UE funds 122 - Improving the economic value of (Zanchi et al, 2007). Their outcomes forests, which encompasses various regarding afforestation were as solid as activities from the forestry field, their afforestation policies (Palaghianu including the creation of nurseries. & Clinovschi, 2007). From the total of 135 million euros, In Romania, the official statements only 1 million euros was used in recognize the importance of funding projects and consequently the afforestation and one important absorption rate was 0.8%. objective of the National Afforestation The new PNDR 2014-2020, Programme (2004) and the Forest granted only 300 million euros from Code (2008) was the afforestation of 2 the total 8 billion euros (excluding the million hectares of degraded lands. 10 billion euros for direct payments) Moreover, the Law no. 100 /2010 for forestry measures. The Measure regarding the afforestation of degraded 8.1 for afforestation has a budget of lands was a new reinforcement of that 105 million euros and doubles the ambitious objective, but the new standard costs for the afforestation versions of the National Afforestation activities, in order to boost the Programme from 2010 and 2013 absorption rate of funds for this altered successively the target from 2 particular field. million hectares to 422 thousand hectares, respectively to 229 thousand 4. Conclusion hectares. Although, the latest results of It is quite obvious that investments Romania in the field of afforestation in afforestation do not seem very seem inconclusive, different attractive due to the considerable time opportunities will soon arise along gap between investments and benefits, with the development of the new in this particular field. PNDR 2014-2020. However, considering the The latest Measure 8.1 for important role of forests in the afforestation appears to be limited in environmental and social paradigm as budget, but it brings new mechanisms well in the mitigation of climate of payment and the promise of change, the state should play a more reducing the bureaucracy. This might significant role. In the absence of be a fresh restart for the old Romanian private investors, the state should be afforestation engine. more active and should encourage afforestation by different funding mechanisms or attractive tax incentives.
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References Analele Universitatii Stefan cel Mare Suceava - Sectiunea Silvicultura, 9 (2), 21- Crăciunescu, A., Moatăr, M., & Stanciu, S. 32; (2014). Considerations regarding the Palaghianu, C., Clinovschi, F. (2007). afforestation fields. Journal of Analiza situatiei si tendintelor in ceea ce Horticulture, Forestry and Biotechnology, priveste impaduririle pe mari zone fizico- 18(1), 108-111. geografice. Analele Universitatii Stefan Doniţă N, Ivan D, Coldea G, Sanda V, cel Mare Suceava - Sectiunea Silvicultura, Popescu A, Chifu T, Paucă-Comeănescu 9 (2), 33-38; M, Mititelu D, Boşcaiu N. (1992). Palaghianu, C. (2009). Researches on Vegetaţia României. D. Ivan (Ed.). Ed. forests regeneration by informatical tools. Tehnicǎ Agricolǎ, 407 p.; Universitatea" Ştefan cel Mare", Suceava, Dutcă, I., & Abrudan, I. V. (2010). PhD Diss.[In Romanian]. Estimation of Forest Land Cover Change Palaghianu, C., Nichiforel, L. (2016). in Romania between 1990 and 2006. Between perceptions and precepts in the Bulletin of the Transilvania University of dialogue on Romanian forests. Bucovina Brasov, Series II-Forestry, Wood Industry, Forestiera, 16 (1), 3-8 and Agricultural Food Engineering, 52(1), RCA (Romanian Court of Auditors), 33-36. (2013). Sinteza Raportului de audit privind Food and Agriculture Organization (FAO) situația patrimonială a fondului forestier of the United Nations. (2010). Global din România, în perioada 1990-2012. forest resources assessment 2010: Main Romanian Court of Auditors Report; report. Food and Agriculture Organization RCA (Romanian Court of Auditors), of the United Nations. (2014). Sinteza Raportului de audit al Giurescu, C.C., 1975: Istoria pădurii performanței modului de administrare a românești din cele mai vechi timpuri până fondului forestier național în perioada astăzi [History of the Romanian forests 2010 – 2013. Romanian Court of Auditors from ancient times to today], Ed. Ceres, Report; Bucuresti, 388 p.; Temperate and Boreal Forest Resource Georgescu, F., Daia, M.L., (2002). Forest Assessment (TBFRA-2000). (2000). regeneration in Romania, Proceedings of Forest Resources of Europe, CIS, North the seminar”Afforestation in the context of America, Australia, Japan and New SFM”, Ennis, 15 – 19 September 2002, Zealand (industrialized Temperate/boreal 112-121; Countries): UN-ECE/FAO Contribution to Greenpeace, (2012). Evoluția suprafețelor the Global Forest Resources Assessment forestiere din România în perioada 2000 – 2000 (No. 17). United Nations 2011,(www.greenpeace.org/romania) 5p.; Publications; INS (National Institute of Statistics), Zanchi, G., Thiel, D., Green, T., Lindner, (2013). Romanian Annual Statistical M., (2007). Forest Area Change and Report for the year 2013. Afforestation in Europe: Critical Analysis Mather, A. (1993). Afforestation policies, of Available Data and the Relevance for planning and progress. Belhaven Press. International Environmental Policies, 223 p.; European Forest Institute, Technical MADR, (2011). Romanian Ministry of Report 24, pp. 45; Agriculture and Rural Development – Final report on the implementation of SAPARD Programme in Romania, 304 p.; Palaghianu, C. (2007). Aspecte privitoare la dinamica resurselor forestiere mondiale.
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Evaluation of ecoprotective function intensity in stands with coniferous growing outside their natural vegetation area. Case study in the Forest District Adâncata, P.U. VII Zvoriștea
Cătălina Oana Barbu ”Ștefan cel Mare” University of Suceava Corresponding author e-mail address: [email protected]
Abstract. The replacement of natural stands by artificial stands leds over time to important changes in the forest ecosystems characteristics. In the 1965-1970 period only, on 37,000 ha such cultures were installed by substitution works. The aim of this paper is to study the evolution of the degree of ecoprotective function intensity (Cf index) of stands part of the Production Unit VII Zvoriștea, Forest District Adâncata before and spruce and Scots pine stands were installed. For this purpose the Cf index was computed for the years: 1965, 1975, 1985 and 2005. The index takes into account diversity, stability and forest continuity. The results allow the evaluation of the intensity of the function, as follow: 100-150 – very weak, 160-250 – weak, 260-350 – medium, 360-450 – good and 450-550 – very good. The intensity of the function was: very weak - 1965- 18%; 1975- 16%; 1985-30%; 2005-31%; weak - 1965- 85%; 1975- 19%; 1985- 31%; 2005- 7%; medium - 1965- 0%; 1975- 65%; 1985-39%; 2005- 62%.
Keywords: ecoprotective functions, intensity of the function, artificial stands, natural vegetation area
1. Introduction spruce and Scots pine. The decline of industry led to the abandonment of Over time forest ecosystems have these stands in relation to the purposes undergone significant changes for which they were originally created. evidenced by the replacement of One of the important features of the natural stands by artificial stands and spruce is the shallow rooting (Șofletea the extinction of some species outside and Curtu, 2007). This feature makes of their natural area of vegetation. In spruce stands vulnerable to Romania during the 1960-1986 period, windthrows (Popa, 2002). To quantify in order to obtain wood for pulp and the structure-function relation two paper production in a short time, fast indexes were developed GEF index growing species stands were installed (Cenușă, 2000) and Cf index (a global outside their natural vegetation area. In index that quantify the ecoprotective the 1965-1970 period only, such function intensity) (Cenușă and Barbu, cultures were installed on 37,000 ha by 2004). substitution works (Marcu and The aim of this paper is to study Ionescu, 1970; Barbu, 2004). At the evolution of the degree of national level it is estimated that ecoprotective function intensity for 300,000 ha of these stand types were stands where resinous species outside created. The main used species were
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Proceedings of the 3rd International Conference Integrated Management of Environmental Resources, 2015 their natural vegetation area were installed beginning with the 70’s. (1)
2. Materials and methods Cf index has been computed for the The study was carried out in years before (1965) and after (1975, Production unit VII Zvoriștea, Forest 1985, 2005) spruce and Scots pine District Adâncata (fig. 1). Forest stands were installed. The results district Adâncata is representative for obtained allow us to assess the resinous artificial stands installed intensity of the function, as follow: between 1965 and 1975. The site is 100-150 – very weak, 160-250 – weak, situated in Suceava Plateau. The mean 260-350 – medium, 360-450 – good altitude is 400 meters a.s.l. and the and 450-550 – very good. vegetation is dominated by beech and oak stands. Annual precipitation 3. Results ranges between 600 and 800 mm and the mean annual temperature is 8°C. In the analyzed stands diversity The research methodology index (Cd) ranges between 100 and (Cenușă and Barbu, 2004) implies the 350. In 1965 this coefficient varies Cf index calculus. The Cf index takes between 150- 250 (fig. 3a) for the into account: diversity index Cd, entire study area, while in 1975 the stability index Cst and forest dominant value of Cd is between 0 and continuity index Cc (fig. 2 and 150 (fig. 3b), which indicates the equation 1). Diversity index Cd is presence of coniferous species computed as function of the diversity installed in previous years outside their of species (Cd1), diversity of forest areas of vegetation. With the increase types (Cd2) and diversity of age in the area occupied by resinous structure (Cd3). stands, the area where the values of the Cd index is in the range of 0-100 increased remarkably (fig. 3c).
Figure 1. Study area
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Figure 2. Cf index calculus
In 1965 the stability index (Cst) stability index values are in the range has values between 100 and 250 (fig. of 150-200 (fig. 4d). 4a), pointing out that forest stands that The continuity index (Cc) depends constituted Zvoriștea Cailor area were on the weight of the silvicultural more stable compared to the years treatment and percentage of forest area 1975-1985 (fig. 4b and 4c), when the managed with each treatment. The stability coefficient values were values of this index varies between between 50-100. In 2005 we can see a 100 and 450 (fig. 5). notable increase in the area where the
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a b
c d Figure 3. Diversity index (Cd) values in Zvoriștea Cailor stands, PU Zvoriștea, FD Adâncata in: 1965 (a), 1975 (b), 1985 (c) and 2005 (d).
a b a. b.
c d Figure 4. Stability index (Cst) values in Zvoriștea Cailor stands, PU Zvoriștea, FD Adâncata in: 1965 (a), 1975 (b), 1985 (c) and 2005 (d).
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a b
c d
Figure 5. Continuity index (Cc) values in Zvoriștea Cailor stands, PU Zvoriștea, FD Adâncata in: 1965 (a), 1975 (b), 1985 (c) and 2005 (d).
a b
c d
Figure 6. Cf index (degree of ecoprotective functions accomplishing) values in Zvoriștea Cailor stands, PU Zvoriștea, FD Adâncata in: 1965 (a), 1975 (b), 1985 (c) and 2005 (d).
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The lowest values are registered in funcțiilor ecoprotective în păduri. pure spruce stands where the clear Bucovina forestieră 12 (1-2): 69-74. cutting treatment was applied. The Cenușă, R., 2000. Cercetări asupra intensities of the ecoprotective dinamicii structurale a ecosistemelor de function accomplishing computed with pădure de la limita altitudinală de vegerație pentru menținerea echilibruluyi equation 1 and related to the surface ecologic. Îndrumări tehnice, ICAS. were: Marcu, G., Ionescu, A., 1970. Noi very weak: 1965 - 18%; 1975 - cercetări privind zonele și condițiile 16%; 1985 - 30%; 2005 - staționale favorabile culturii molidului în 31%; afara arealului natural. Rev. Pădurilor Nr. weak: 1965 - 85%; 1975- 3: 116-121. 19%; 1985 - 31%; 2005 - 7% Popa I., 2002. Doborâturile produse de and vânt. - factor de risc în ecosistemele forestiere montane. Anale ICAS (48): 3- medium: 1965 - 0%; 1975 - 28. Editura silvică, București. 65%; 1985 - 39%; 2005 - 62% Șofletea, N., Curtu, L., 2007. Dendrologie. (fig. 6). Editura Universității ”Transilvania”, Brașov. 4. Conclusions XXX, 1965. Amenajamentul U.P. VII Zvoriștea, O.S. Adâncata, Suceava. The Cf index was initially XXX, 1975. Amenajamentul U.P. VII developed for mountain forests but it Zvoriștea, O.S. Adâncata, Suceava. can be used successfully in other forest XXX, 1985. Amenajamentul U.P. VII Zvoriștea, O.S. Adâncata, Suceava. types. XXX, 2005. Amenajamentul U.P. VII Before resinous being installed Zvoriștea, O.S. Adâncata, Suceava. (year 1965) the intensity of ecoprotective function accomplishing was very weak on 18% of the study area and increases at 31% in 2005. The maximum value for the Cf was 350 which indicates that the highest intensity of function was medium, with the most extent in 2005 (62% of the study area). Computed for different periods, this method allows a good evaluation of the forest management quality.
References
Barbu, I., 2004. Gospodărirea culturilor de rășinoase din afara arealului. Analele Universității ”Ștefan cel Mare”, Secțiunea Silvicultură, 1:37-70. Cenușă, R., Barbu I., 2004. Metodă pentru determinarea gradului de exercitare a
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Analysis of wild boar population dynamics in Suceava County for the period 2004-2016
Alexandru-Mihai Corjin, Nadia-Mihaela Dănilă, Gabriel Dănilă ”Ștefan cel Mare” University of Suceava Corresponding author e-mail address: [email protected]
Abstract: This paper tries to capture the wild boar population dynamics in Suceava County, correlated with the change of the climatic factors and of the agricultural areas for the period 2004-2016. The current legislation has changed the wild boar hunting period, so that the male of this species may be harvested throughout the year. This regulation occurs as a consequence of the increasing population density and implicitly, of the damage caused by this species to the crops. In the last decade, we can notice a growing tendency of the monthly average temperatures for the dormant vegetation period, but also a decrease in the amount of precipitations. According to the available data, the number of wild boars in Suceava County has increased by over 50% in the past 12 years. It may be noticed an increasing tendency in the number of individuals (of population density) related to the increasing average temperatures and the decreasing precipitation, but without a statistical correlation. On the other hand, there is a significant correlation between the density of the wild boar population from the plateau area and the area under crop. There is also a higher increase in population density in the plateau area compared with that in the mountainous area.
Keywords: Sus scrofa, dynamics, population.
1. Introduction growth has economical and ecological consequences on a large scale. The wild boar (Sus scrofa) is a In Romania, throughout the years, species of major importance in the there have been some laws which Romanian and European fauna, being provided for certain hunting periods of spread all over the European continent. this species. In Romania, it is by far the most Law 103/1996 – provided for the wild adaptable species of game, being met boar hunting male and female, between from the Delta and the Danube Valley 1 August and 15 February; to the alpine zone, at altitudes of over Law 407/2007 – took the previous legal 2000 m (Șelaru, 1995). previsions regarding the hunting Since the 1980s, the wild boar period; populations have increased remarkably Law 149/2015 brought regulations on and almost simultaneously all over the hunting period; According to its Europe and implicitly Romania has provisions, the male boar can be hunt recorded significant increases. all year and the female, from 1 June to Therefore, in some European 31 January. countries, the wild boar is collected The regulations occur as a throughout the year, as it is considered consequence of the increasing a vermin species. This population’s population density, and because of the damage caused to the farming areas.
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Fig. 1. Area of study in Europe
As a result of the impact of this change of the areas and types of lands species on the agriculture, as well as under crop. the need to develop the game management practices, we were 2. Materials and Methods interested in the environmental factors, responsible for this development. The study was carried out in In the light of the above, the Suceava county, including all the 71 purpose of the study presented in this hunting areas in the county, with a paper was to determine whether the total area of 718536 ha productive for variation mode of the temperatures and hunting. In order to achieve our goals, of the multiannual monthly average the area where the measurements were precipitation for the dormant period performed, was divided into two and the change in the areas in crop and distinct zones, depending on the the type of crop, can influence the boar climate storeys and on the main use of population density, for the studied the land (forest or agricultural). period 2004-2016. This work objective The study area was divided as it is the determination of the variation follows: mode in time of the boar population in The mountain area, located in the the mountain area and the plateau area western half of the county, which of Suceava County, with the change of includes 43 hunting territories with climatic factors (temperature, a total area of 527390 ha for precipitation), as well as with the productive hunting;
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Fig. 2. The two areas of study in Suceava county
The plateau area, located in the studied period of time 2004-2016. It eastern part of the county, which was also determined the variation includes 28 hunting territories with manner of the farming lands for the a total area of 191146 ha for plateau area, aiming at the variation of productive hunting. the boar population density in relation This division aimed at the altitude, to the mentioned factors. vegetation and relief differences, which trigger climate consequence in 3. Results. Discussions. terms of shelter and food. These factors influence the wild boar spatial It was used the data on the wild distribution and population density boar populations from the 71 hunting (Comșia, 1961). territories, to determine the dynamics The data on the numbers of of the population density for the 12 boars for the period 2004-2016 used in year studied period, on two distinct this paper was taken from Suceava areas (mountain and plateau) (fig.3). It Forest Guard, the meteorological data is observed a continuous increase in used was taken from the weather the population density over the entire stations Rarău and Fălticeni, while that studied period. on agricultural land was taken from the For the mountain area, there is an national Institute of Statistics (INS) increase in density from 3.8 boars/1000 data base. The data processing was ha in 2004 to 5.2 boars/1000 ha in 2016. done in the Microsoft EXCEL, For the plateau area, it can be observed a XLSTAT – determining the variation higher increase from 1.9 boars/1000ha at mode of the climatic factors in the the beginning of the period studied, to study for the dormant period in the 4,6 boars/1000 ha in 2016.
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Fig.3. Analysis of Wild Boar Population Dynamics in period 2004-2014 for mountain area and plateau area
Fig.4. Variation of recurrent monthly average temperatures for dormant season for mountain and hill areas
For the dormant period, it was determined the variation mode of the multiannual average temperatures. One can notice an increasing tendency of them (fig. 4). In figure 5 it is presented the density variation of the wild boar populations compared with the average temperatures of the dormant period showing a rising tendency. From a statistical viewpoint, the analysis shows that there was not a strong correlation, considering the correlation coefficient R, which has a value of 0.401 for the mountain area, and 0.458 for the plateau area, with a given number of 12 freedom degree. The increase of temperature from October to March leads to more available food, it reduces juvenile death, but also it determines this species reproduction at younger age.
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Fig.5. Variation mode of wild boar population density compared to average temperature from October to March
Moreover, the wolves attack the Analysing the density variation of wild boars less, as the snow layer is the wild boar populations in relation to less thick and the boars are more the average precipitations in the vigorous. The dynamics of the wild dormant period (fig. 7.), it cannot be boar populations was also analysed in noticed a significant correlation, relation to the variation of the from a statistical viewpoint. There is multiannual monthly average. For both only a tendency in boar density studied areas, the precipitation has a increase, as the average precipitation decreasing tendency in time (fig.6.). decrease.
Fig. 6. Variation of recurrent monthly average precipitations in dormant season in mountain and plateau areas
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Fig.7. Variation mode of wild boar population density compared to average precipitations in October - March
Fig.8. Variation mode of wild boar population density in the hill area reported to cultivated area.
For the plateau area there was also development of its plots in the period considered the data on the main crops, 2004-2016. The cultures taken into as a food source and shelter for the account were those of corn, grain and wild boar in this area, as well as the feed corn. Their areas were taken from
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the National Institute of Statistics shelter for wild boars. This fact leads database. It may be noticed a to a higher growth in density of this significant correlation between the population in the plateau area, wild boar population density in the compared with the mountain area plateau area and the cultivated area (table 1). The farm land extension as a (fig.8), considering that correlation surface, has led to a greater coefficient R is 0,787. This means that development of the wild boar with the development of the cultures populations in contrast to that in the mentioned previously, there are mountainous area. provided better conditions for food and
Table 1. Density increase for mountain area and plateau area Density (boars/1000 ha) Increase Area Year 2004 Year 2016 (boars/1000 ha) Mountain 3,8 5,2 1,4 Plateau 1,4 4,5 2,6
4. Conclusions wild boar populations in the plateau area, in contrast to the mountain area. We may notice a tendency for the wild boar population density to References increase, along with the increase of the Comșia, A., M., 1961, Biologia și monthly average temperatures, for the principiile culturii vânatului, Ed. period October to March. Academiei RPR, 581p. Moreover, the wild boar Șelaru, N., 1995, Mistrețul – monografie, populations have a higher growth, due Ed. Salut 2000, 151p. to the decrease of the average ***Low 103/1996 ***Low 407/2006 precipitations for same period, in the ***Low 149/2015 studied period. http://www.mmediu.ro/categorie/vanatoare The milder climatic conditions, the /26 (database) easier access to food and better shelter http://www.mmediu.ro/articol/efective/699 facilities due to the large crop areas (database) contribute to a greater increase of the www.insse.ro
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Considerations regarding the angular acceleration influence on the vehicles movement in curves
Dan Zarojanu Stefan cel Mare University of Suceava, Forestry Faculty, Romania Corresponding author e-mail address: [email protected] (D. Zarojanu)
Abstract: This paper aims to elucidate the effects of angular acceleration on vehicles movement in curves, especially on transition curves. It is explained the nature of such effects, indicating the difficulties to be overcome. Finally, we proceed to quantify the influence of angular acceleration in crossing a transition curve.
Keywords: angular acceleration, transition curves
1. Introduction clothoid, due to its variable radius, the angular speed is also variable, and thus It is known that on vehicles generates angular acceleration. movement in curves, the angular Modifying the curvature radius acceleration is limited, in terms of generates a "run" of the circle to the comfort, to values of 0.05 rad / s2 inner - ΔR (tangent displacement) (see (Bereziuc, 1981). This article aims to Fig. 1), actually a decrease of the radius elucidate the nature of this discomfort from infinity (∞) to the finite value of and how much the angular acceleration the radius (R). Radius variation relative influence vehicle movement on a to time apparently measure angular transition curve. speed variation, and therefore, indirectly, angular acceleration. 2. Material and methods
The discomfort caused by the angular acceleration seems to be the force that angular acceleration would generate. According to the mechanics initial circulation curve second law, any acceleration applied to a mass generates a force. The problem is that the angular acceleration [rad / s2] x vehicle mass [kg] does not give Newtons. So, it would not be a force as defined in mechanics. Then, from where the discomfort comes? We observe that on crossing with constant speed – v a circular curve with Fig. 1 Radius decreasing from infinity to a radius - R, the angular speed is finite value by "running" inwards constant: v = ωxR, while on crossing a
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Angular speed changes with radius 3. Results decreasing. We believe that a force (acceleration) that would capture the We managed to discern the nature radius length variation could replace of discomfort caused by the angular angular acceleration. acceleration and we expressed the This acceleration (we will call yet effect of this acceleration by a force that pseudo angular acceleration) has the can be quantified. 2 advantage that is measured in m / s and 2 not rad / s . Replacing we spoke about 4. Discussion refers to effects, strictly speaking. It is known that ∆ ≅ , A series of approximations and where: assumptions have been made to be able R is the corner radius; to express the discomfort given by the L is the clothoid length; angular acceleration through a force. This force is not very high, about 300 Radius variation relative to time will times less than the vehicle weight, and approximately 20 times smaller than generate an average speed -v1; the maximum centrifugal force, ∆ according to the example shown. v1= 5. Conclusion Due to the clothoid shape, v1 is an average value of the speed with which The discomfort caused by the the radius is varying, from 0 in angular acceleration in transition alignment to 2v1 at the end, when curves is real. This discomfort is not entering the corner. exaggerated compared to vehicle The value in module of this weight, respectively maximum acceleration could be just the angular centrifugal force. acceleration we sought, which we have tried to avoid, because is not expressed 2 References in m/s . Thus, at the end of a clothoid Bereziuc, R., Forest Roads, Didactic and traveling along with a speed v = 40 km Pedagogic Publishing House, Bucharest, / hour, the corner radius being R = 200 1981 m, we would have a pseudo angular acceleration : , ∗ 0,03 ,
which multiplied by the vehicle mass of approximately 20 t would generate a horizontal force of about 60 kgf.
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Spatio-temporal association among monthly precipitation and relief in the central-west region of Venezuela
Jesús Enrique Andrades-Grassia, Hugo Alexander Torres-Mantillab, Juan Ygnacio López-Hernádeza aUniversidad de Los Andes, Facultad de Ciencias Forestales y Ambientales, Mérida, Venezuela bUniversidad de Los Andes, Facultad de Ciencias Económicas y Sociales-Instituto de Estadística Aplicada y Computación ,Mérida, Venezuela Corresponding author e-mail address: [email protected] (Jesús_EnriqueAndrades_Grassi)
Abstract: Venezuela is a tropical country where precipitation is affected both by global and regional conditions, such as the Intertropical Convergence Zone and relief. The aim of this research is to analyse the spatio-temporal relationship between monthly precipitation (time series spatially geocoded data) and relief at the central-west region of Venezuela. Two approaches were applied. First, we established the fit of the linear equation by Ordinary Least Squares (OLS). Second, was the implementation of modified Moran`s I (bivariate Moran`s I) between variables. Results show that there is a homoscedastic process and violation to spatial independence in disturbances. We performed a characterization of positive spatial autocorrelation of disturbances in most of the cases. Bivariate Moran`s I showed positive dominance of spatial correlation during the low precipitation months of February through June. This analysis reveals that the orographic effect is especially strong during the first semester of the year. Nevertheless, these results are conditioned by the asymmetric distribution of precipitation. It is possible that the results could be completely different after the transformation of the variable.
Keywords: Spatial dependence, linear OLS model, Moran´s I, bivariate Moran´s I, monthly data precipitation, model assumptions.
1. Introduction type of structure uses a big amount of Use and management of spatio- data (Androva and Boland, 2011). In temporal data are common tasks when order to perform the modelling, it is dealing with climatic variables, it is frequent that one of the two possible to obtain many observations components (spatial or temporal, also at different times from numerous both components) is discarded (Cressie spatial locations. This type of data and Winkle, 2011). corresponds statistically with a However, modern statistics is structure of Pooled time series cross- introducing the concept that the section (time series with spatial cross “Stochastic data does not originate section) Gujarati and Potter (2010), from a random and independent this structure is shown in figure 1. This process” (Cocchi and Bruno, 2010).
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Figure 1. Structure of Pooled time series cross-section. Variables X, Y for space and T for time measurements.
This idea is also present in (LeSage,1999). A formal statistical Tobler´s First Law of Geography definition is that the residuals are not (1970), which establishes that constant in a regression “Everything is related to everything (heteroscedasticity) or in the else, but near things are more related coefficients of a model (Anselin, than distant things”, this law can be 2001). seen from various points of view: a Spatial Heterogeneity is due to a statistical problem (Spatial real and substantive variation that Autocorrelation, SA) means evidences validity of the geographical concentration or dispersion of values context in the definition of a variable in a mapped variable (Vilalta, 2005), (O’Loughlin and Anselin, 1996). and a theoretical problem (Spatial There are many causes for the Dependency, SD), means that the Spatial Heterogeneity: 1) There is a dependent variable of a spatial unit is a statistical problem and as a partial function of the same variable in consequence, there is the neighbouring units (Flint, et al., heteroscedasticity in a regression 2000) (Figure 2). Another important model; 2) There is substantive spatial concept is the Spatial Heterogeneity, variation in a variable. defined as variation in the relationship among variables in space
Figure 2. Spatial Dependency (SD)for different spatial units.
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Spatial Heterogeneity should be studied for various reasons: 1) The structure under spatial instability may because by the geography (group wise), Figure 3. Positive Autocorrelation . b) 2) Due to Spatial structure, there is Negative Autocorrelation. c) Absence of a tendency to produce heterogeneity Autocorrelation (Independence). together with Spatial Autocorrelation and 3) In a transversal slice regression model, both effects Spatial Autocorrelation and Spatial Figure 4. Three types of neighbourhood. Heterogeneity, can be mistakenly interpreted. An important element to consider From a statistical point of view, if is the type of neighbourhood of the Spatial Autocorrelation is not variables. Celemin (2009), Lloyd considered, the coefficients of the (2010) and Toral (2001) define many regression are inefficient to show the types of contiguity, such as “Queen”, amount of relationship among “Bishop” and “Rook” (Figure 4). variables and if Spatial Heterogeneity There are three possible scenarios is not considered, statistical of Spatial Correlation. First, there significance tests can be refused could be two variables in a time slice; because the standard errors are second there could be two variables, inflated. As a consequence, Toblerś one with Spatio Temporal grouped First Law (1970) can be interpreted data, and another variable which is from a univariate point of view, in time invariant and third there could which case there is Spatial between variables of grouped Spatial Autocorrelation and Spatio Temporal Temporal data (Panel spatio temporal Autocorrelation (STA) and a bivariate data) (Anselin, 1988). point of view, in which case there is This introduces a set of relevant Spatial Correlation so that a variable in questions: How can two variables in a one place can be correlated with a slice be correlated? How can two variable in another place (SC). variables be correlated if one is in a Spatial Autocorrelation, Spatial slice and the other has grouped Spatial Dependency and Spatial correlation Temporal data? Gujarati and Potter are designated as Second Order (2010), Sridharan et al. (2011) and Properties (Lloyd, 2010), and may be Anselin (2005), proposed the presented as three types, Positive, application of a linear regression using when similar values cluster together in Ordinary Least Squares (OLS) and a map, Negative, is when dissimilar performing the diagnostics of the values cluster together in a map and assumptions of the model, such as Absence of Spatial Dependence Multicollinearity among regressive (Independence) (Figure 3). variables, Heteroscedasticity of
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Proceedings of the 3rd International Conference Integrated Management of Environmental Resources, 2015 residuals, Autocorrelation of residuals following reasons: 1) When there is and no normality of residuals. no statistical significance, it does not In order to evaluate mean that there is no autocorrelation, it Autocorrelation of residuals, it is is possible that there is a nonlinear common to use of indexes to evaluate relationship; 2) The degree of the presence or absence of spatial significance depends on the extension autocorrelation in the residuals. of the area under study and 3) The Moran´s I which is a variation of measure does not consider Spatial Pearson´s Linear Correlation Heterogeneity. Coefficient (Moran, 1950) is described Venezuela is a Tropical country by several authors (Bosque, 1992; with a maritime and continental Cressie, 1993; Lloyd, 2010; Olaya, extension, a country with different 2011; Toral, 2001) climates due to the Intertropical Convergence Zone and the orographic 1 control. These conditions generate a ; ∑ 1 seasonal rainfall that can be 10 times higher in one location as compared to Where is the mean of the another. Rainfall is unimodal at the variable , is the weigthed centre and east of the country and ponderation matrix, is the bimodal at the north west of the expected value in the absence of country (with peaks in May –June and Autocorrelation (Independence) and in September – November). The is the number of observations. orographic control generates inversion It is possible to use an index that of trade winds (1500-2500 msnm): spatially correlates two variables, like almost 0% of humidity in atmosphere, the Bivariate Moran's I (Zhu et al, restricting clouds and precipitations. A 2007; Stojanova, 2012). thorough description of the orographic control is presented by Goldbrunner ∑ ∑ w y y ∑ w y y (1984), indicating that due to the ∑ ∑ w ∑ y y ∑ y y general circulation of the atmosphere over the Venezuelan territory, it is Where is the number of possible to distinguish two periods observations, is the is the spatial commonly called "summer" (dry) and weighting matrix indicating the spatial "winter" (rainy). During the period of relationship between objects and , December through April it is observed and are the means of the variables that most of northern Venezuela is and . The range of I values is (- under the influence of the northeast 1,1), negative values indicate negative trade winds 20. with its anticyclonic Spatial Correlation, positive values fields which depend on the height of indicate positive Spatial Correlation the relief. The divergence of the and values close to 0 indicate absence resulting flow causes subsidence of air of Spatial Correlation. masses, causing strong temperature Bivariate and Univariate Moran's I inversions depending on the altitude, must be taken with caution, for the called "trade wind inversion."
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Figure 5. Monthly precipitation stations selected.
Above an altitude which ranges a spatial variable (altitude in meters) from 1500 to 2500 meters the and a variable of pooled spatial atmosphere is almost devoid of temporal data (monthly precipitation in humidity, limiting the development of millimetres). Since Spatial Correlation clouds caused by the phenomenon of between precipitation and altitude for a convection, which means they cannot particular year is expected to be originate rainfall. Therefore, the similar to previous and subsequent following hypothesis is proposed: years, and in order to avoid Monthly precipitation has linear redundancy of the data (a station that Spatial Correlation with altitude. has n number of records at constant altitude), 12 year samples were 2. Materials and methods selected with 5 years of interval from 1950 to 2005. For each year selected, We used data from 961 climatic every month was analysed. A total of stations of monthly precipitation from 144 analyses were carried out. official institutions, (Figure 5), A linear Ordinary Least Squares corresponding to Non-normally (OLS) model was fitted for the distributed data. Additionally, rainfall Monthly precipitation data has the characteristic of having only (independent variable) and the altitude positive values and usually the (dependent variable). For each OLS probability density function is fitted, Pearson’s adjusted correlation decreasing and monotone (Bidegain y coefficient was calculated, followed by Díaz, 2011; Moran, 1969). a diagnosis of the quality of the model A general scheme of the using the hypothesis tests (5% level of methodology applied is shown in significance for all the tests). The Figure 6. In this research we are Jarque-Bera (Jarque and Bera, 1980) addressing Spatial Correlation between Normality test is as follows:
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Proceedings of the 3rd International Conference Integrated Management of Environmental Resources, 2015
Ho: Normal distribution of residuals. H1: Non Normal distribution of residuals. Spatial Autocorrelation of residuals was measured with Moran's I (Moran, 1950; Anselin, 1988), the following hypothesis was tested (Moran's I was used with a relationship of the inverse of the distance without distance threshold): Ho: OLS regression residuals for monthly precipitation during month and altitude have null Spatial Autocorrelation. H1: OLS regression residuals for monthly precipitation during month and altitude have non null Spatial Autocorrelation. If the null hypothesis is rejected, then the Spatial Figure 6. General methodology. Autocorrelation must be characterized as either positive or negative. If the null hypothesis is rejected, If the estimated expected then the Spatial Correlation must be , then the Spatial Autocorrelation characterized as either positive or is positive. negative: If the estimated expected If the estimated expected , then the Spatial Autocorrelation , then the Spatial Correlation is is negative. positive. Heteroscedasticity was tested with If the estimated expected , the Koenker test (Lamoureux and then the Spatial Correlation is Lastrapes, 2012; Hafner and Herwartz, negative. 2002; Engle, 2002): Ho: Homoscedasticity of the 3. Results and Discussion residuals and H1: Heteroscedasticity of the residuals A low correlation (less than 10%) Subsequently, Bivariated Moran's was found between the variables, this I Zhu et al. (2007) among altitude and indicates that under the OLS model, precipitation, was evaluated. the results show no apparent linear Ho: Monthly precipitation during correlation. The Jarque-Bera test month and altitude have null linear rejects the null hypothesis in 99% of Spatial Correlation. the cases, this test discards the normal H1: Monthly precipitation during distribution of residuals indicating that month and altitude have non null the OLS model is incorrectly specified. linear Spatial Correlation. The significance of the Jarque-Bera
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Proceedings of the 3rd International Conference Integrated Management of Environmental Resources, 2015 test is affected by sample size, which monthly frequency of Spatial is not the case in this research (59590 Correlation shows that the relief has records were analysed which more control over precipitation in the represents 20% of the data), with the months of low rainfall and less observations ranging between 623 influence in the months of high rainfall (1980) and 135 (2005). (Figure 7). The yearly frequency of the residuals of Spatial Autocorrelation 4. Conclusions according to Moran's I, shows that positive spatial autocorrelation The results show violations to the dominates in all the years, except in OLS model. These are violations to the 1970. The monthly frequency of assumptions of normality of the Spatial Autocorrelation of the residuals residuals and independence of according to Moran's I, shows that disturbances. Precipitation represents a positive spatial autocorrelation dynamic process in time and space, dominates in all the months, with a and despite the transversal analysis of higher frequency in the months from the data, Spatial Correlation is still April to November. According to the found. This justifies the analysis of the Bivariate Moran's I, positive Spatial Spatial Correlation, especially in the Correlation dominates and the low precipitation months, in which the frequency is proportional to the influence of the relief is greater. number of observations and the
(a) (b)
(c) (d) Figure 7. (a) Number of observations analysed by cross section; (b) Frequency of occurrence of Spatial Autocorrelation types per year, according to the results obtained by Moran's I of the residuals; (c) Yearly frequency of Spatial Correlation according to Bivariate Moran's I; (d) Monthly frequency of Spatial Correlation according to Bivariate Moran's I.
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These results are conditioned by References the non-normal distribution of rainfall, therefore, it is possible that if a Anselin, L. 1988. Spatial econometrics: transformation is used, the results may methods and models. Springer. Santa be completely different. Barbara. A Spatial Correlation between a Anselin, L. 2005. Exploring Spatial Data cross-sectional space variable and a with GeoDaTM: A Workbook.Spatial pooled spatial temporal variable which Analysis Laboratory Department of Geography. is maximized in the months of https://www.google.co.ve/url?sa=t&rct=j& February to June, was tested. q=&esrc=s&source=web&cd=1&cad=rja One problem that must be taken &uact=8&ved=0ahUKEwjqpoOz2o7KAh into consideration is univariate and WCbiYKHfCdAmEQFggdMAA&url=http bivariate Moran’s I tends to s%3A%2F%2Fgeodacenter.asu.edu%2Fsy underestimate the spatial heterogeneity stem%2Ffiles%2Fgeodaworkbook.pdf&us when the working area is large, as in g=AFQjCNHYlCuxx_1mxjGSRKbZSZk this case. However, the Koenker test V4L3kIg&sig2=AF3iD5W8X2gawFi6DN suggests that the stochastic process 27yQ University of Illinois between rainfall and altitude, is Androva, N., Boland, S. 2011. Volume variability diagnostic for 4D homoscedastic, a result that must be datasets.Geophysical Research Letters, 38, studied in detail. 1-5. Furthermore, the spatial Jarque, C., Bera, A. 1980. Efficient tests relationship between the two variables for normality, homoscedasticity and serial may be underestimated and the Spatial independence of regression residuals. Heterogeneity may be "masked". EconomicsLetters, 6, 255-259. Therefore, the evaluation of the Bidegain, M., Diaz, A. 2011. Análisis spatial relationship between the two Estadístico de Datos Climáticos variables with suitable processing Distribuciones de Probabilidad. should be considered, including a http://meteo.fisica.edu.uy/Materias/Analisi s_Estadistico_de_Datos_Climaticos/teoric confirmatory analysis (setting a spatial o_AEDC/Distribuciones_Probabilidad_20 model with its own diagnosis). 11.pdf Bosque, J. 1992. Sistemas de información Acknowledgments geográfica. Ediciones Rialph, S.A. Madrid. p. 451. The authors wish to thank Prof. Cocchi, D., Bruno, F. 2010. Considering Dr. Barnoaiea Ionut for lending us his groups in the statistical modeling of good will and time for this spatio-temporal data.Statistica, 70(4), 511- 527. presentation in the 3rd Edition of the Cressie, N. 1993.Statiscs for spatial Integrated Management of data.Wiley-Interscience. New York. p. Environmental Resources Conference 900. Suceava. Additionally, we would Cressie, N., Winkle, C. 2011. Statistics for like to thank Prof. Cristina Grassi who spatio-temporal data. Wiley.New York.p. helped with the translation of this 531. article. Engle, R. 2002. Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive
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Conditional Heteroskedasticity Models. Olaya, V. 2011. Sistemas de información Journal of Business & Economic Statistics, geográfica. 20 (3) 339-350. http://wiki.osgeo.org/wiki/Libro_SIG Flint, C., Harrower M., Edsall, M. O’Loughlin, J., Anselin, L. 1996. Geo- 2000.But How Does Place Matter Using Economic competition an trade Bloc Bayesian Networks to Explore a Structural Formation: United States, German, and Definition of Place.The New Japanese Exports, 1968-1992. Economic Methodologies for the Social Sciences Geography, 72 (2), 131-160. Conference, University of Colorado, Sridharan, S., Koschinsky, J., Walker, J. Boulder. 2011. Does Context Matter for the https://www.google.co.ve/url?sa=t&rct=j& Relationship between Deprivation and All- q=&esrc=s&source=web&cd=1&cad=rja Cause Mortality? The West vs. the Rest of &uact=8&ved=0ahUKEwiTsPSJlYzKAh Scotland.https://geodacenter.asu.edu/drupa UCYyYKHQcJDYUQFggbMAA&url=htt l_files/2011-01.pdf p%3A%2F%2Fwww.colorado.edu%2Fibs Stojanova, D. 2012. Considering %2Fintdev%2Fspatial%2Fpapers%2Fflint autocorrelation in predictive models. PhD paper.doc&usg=AFQjCNE_u-I-- (Thesis), Jožef Stefan International W9bIKoLtutVztnBvk6Jpw&sig2=jqSOxl9 Postgraduate School, Slovenia. c-JNIEFJuBpwVSQ Tobler, W.1970. A computer movie Goldbrunner, A. 1984. Atlas climatológico simulating urban growth in the Detroit de Venezuela 1951-1970.Servicio de region.Economic Geography, 46 (2) 234- Meteorología de la Fuerza Aérea 240. Venezolana. Caracas. Toral, M. 2001.El factor espacial en la Gujarati, D., Potter, D., 2010. convergencia de las regiones de la Unión Econometría. McGraw-Hill. México, p. Europea: 1980-1996. PhD (Thesis), 946. Centro: Facultad de Ciencias Económicas Hafner, C., Herwartz, H. 2002. Testing for y Empresariales. Universidad Pontificia Vector Autoregressive Dynamics under Comillas de Madrid. Madrid. Heteroskedasticity.Econometric Institute Vilalta, C. 2005. Como enseñar Report EI 2002–36, 1-22. autocorrelación. Economía, Sociedad y Lamoureux, C., Lastrapes, W. 1990. Territorio. 18 (5) 323-333. Heteroskedasticity in Stock Return Data: Zhu, S., Chen, Z. y Chen, D. Volume versus GARCH Effects. The 2007.Bivariate Moran spatial correlation Journal of Finance, 45, 221–229. analysis between zonal farm-land use and Lesage, J. 1999. The Theory and Practice rural population distribution. of Spatial Econometrics. Geoinformatics 2007: Remotely Sensed http://www.spatial- Data and Information. doi: econometrics.com/html/sbook.pdf 10.1117/12.760796 Lloyd, C. 2010.Spatial data analysis an introduction for GIS users. Oxford University Press, New York, p. 206. Moran, P. 1950.Notes on continuous stochastic phenomena. Biometrika, 37, (1), 17–23. Moran, P. 1969. Statistical inference with bivariate gamma distributions. Biometrika, 56, (3), 627-634.
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Proceedings of the 3rd International Conference Integrated Management of Environmental Resources ‐ Suceava, November 6‐7th, 2015 Editori / Editors: Sergiu‐Andrei Horodnic, Mihai‐Leonard Duduman, Ciprian Palaghianu ISBN 978‐973‐666‐494‐6
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